cAMP Signaling: Methods and Protocols (Methods in Molecular Biology, 2483) 1071622447, 9781071622445

Table of contents :
Preface
Contents
Contributors
Chapter 1: Real-Time Measurements of Intracellular cAMP Gradients Using FRET-Based cAMP Nanorulers
1 Introduction
2 Materials
2.1 Cell Culture, Seeding, and Transfection
2.2 cDNA
2.3 Ligand Solution and Buffer Preparation
2.4 Single-Cell FRET Experiment
3 Methods
3.1 Cell Culture, Seeding, and Transfection
3.2 Mounting on Microscope
3.3 Focusing
3.4 ROI Selection
3.5 FRET Experiment
3.6 Data Analysis
4 Notes
References
Chapter 2: Assaying Protein Kinase A Activity Using a FRET-Based Sensor Purified from Mammalian Cells
1 Introduction
2 Materials
2.1 Mammalian Cell Expression
2.2 Reporter Purification
2.3 Ratiometric Plate Reader Assays
3 Methods
3.1 Optimizing Reporter Expression by PEI Transfection
3.2 Reporter Purification Following Scaled up Expression in HEK293T Cells
3.3 Calibrating the Reporter for Plate Reader Activity Assays
3.4 Determining the IC50 of a PKA Inhibitor
4 Notes
References
Chapter 3: MultiFRET: A Detailed Protocol for High-Throughput Multiplexed Ratiometric FRET
1 Introduction
2 Materials
3 Methods
3.1 Icy Setup
3.2 Initial Setup of MultiFRET
3.2.1 Active Contours
3.2.2 Excel Output Template (Optional)
3.2.3 Custom Calculations (Optional)
3.2.4 Sample Preparation (Example)
3.3 Running MultiFRET
4 Notes
References
Chapter 4: Photoactivated Adenylyl Cyclases as Optogenetic Modulators of Neuronal Activity
1 Introduction
2 Materials
2.1 Common Materials
2.2 C. elegans Strains
2.3 C. elegans Cultivation
2.4 Analyses of Behavior in Liquid
2.5 Analyses of Behavior on Solid Substrate
2.5.1 Body Length Measurements
2.5.2 Analyses of Crawling Behavior
2.6 cNMP Measurements
2.6.1 General Equipment and Materials
2.6.2 cAMP Measurements
2.6.3 cGMP Measurements
3 Methods
3.1 Optogenetic Tool Expression
3.2 C. elegans Cultivation
3.3 Analyses of Behavior in Liquid
3.4 Analyses of Behavior on Solid Substrate
3.4.1 Body Length Measurement
3.4.2 Analysis of Crawling Behavior
3.5 cNMP Measurements
3.5.1 C. elegans Extract Preparation
3.5.2 cAMP Measurements
3.5.3 The Subsequent Steps Should Be Carried out in the Dark
3.5.4 cGMP Measurements
4 Notes
References
Chapter 5: Imaging the cAMP Signaling Microdomain of the Primary Cilium Using Targeted FRET-Based Biosensors
1 Introduction
2 Materials
2.1 Stock Solutions
2.2 Materials
2.3 Working Solutions
2.4 Cell Culture Media
2.5 Microscope
3 Methods
3.1 Transfection: ``Drop Method´´
3.2 Serum Starving
3.3 FRET Measurements
3.4 Data Analysis
4 Caveats and Conclusions
5 Notes
References
Chapter 6: Methods to Assess Phosphodiesterase and/or Adenylyl Cyclase Activity Via Heterologous Expression in Fission Yeast
1 Introduction
2 Materials
2.1 Media
2.2 Chemicals for Mass Spectrometry Studies
2.2.1 Chemicals and Standards
2.2.2 Chromatography Mobile Phases
2.3 Equipment
2.3.1 Equipment for 5FOA Assays
2.3.2 Equipment for LC-MS/MS Measurements
3 Methods
3.1 5FOA Assays to Assess PDE Inhibitors
3.1.1 Strains that Lack AC Activity
3.1.2 Strains that Express a Functional AC Gene
3.2 Direct Measurement of Cyclic Nucleotide Levels for Assessing AC Inhibitors
3.2.1 Preparation of cAMP Samples
3.2.2 Measure Cyclic Nucleotide Levels Via Mass Spectrometry
4 Notes
References
Chapter 7: Time-Domain Fluorescence Lifetime Imaging of cAMP Levels with EPAC-Based FRET Sensors
1 Introduction
2 Materials
2.1 Stock Solutions
2.2 Disposables
2.3 Working Solutions
3 Methods
3.1 Microscope
3.2 Cell Culture
3.3 Transfection
3.4 Imaging
3.5 TSCPC FLIM
4 Notes
References
Chapter 8: Disruptors of AKAP-Dependent Protein-Protein Interactions
1 Introduction
2 Material
2.1 HTRF
2.2 AlphaScreen
2.3 Human Induced Pluripotent Stem Cells and Differentiation to Cardiac Myocytes
2.3.1 hiPSC Culture
2.3.2 hiPSC-CM Differentiation and Culture
2.3.3 Quality Control
2.3.4 Thawing of hiPSC-CMs
2.3.5 Calcium Imaging
2.3.6 Data Analysis
3 Methods
3.1 HTRF for Screening for Small Molecule Inhibitors of AKAP-PKA Interactions
3.1.1 Data Analysis
3.2 AlphaScreen Assay
3.2.1 Prepare Test Compound Dilution
3.2.2 Data Analysis
3.3 hiPSC Differentiation to Cardiac Myocytes
3.3.1 Geltrex Coating of 6-Well Cell culture Plates
3.3.2 hiPSC Culture
3.3.3 Cardiac Myocyte Differentiation
3.3.4 Quality Control
3.3.5 Calcium Imaging
3.3.6 Thawing of hiPSC-CMs
3.3.7 Calcium Imaging
3.3.8 Data Analysis
4 Notes
References
Chapter 9: Micro-2D Cell Culture for cAMP Measurements Using FRET Reporters in Human iPSC-Derived Cardiomyocytes
1 Introduction
2 Materials
2.1 Cell Culture Equipment
2.2 Consumable Supplies
2.3 FRET Equipment
2.4 Consumable Supplies for FRET Measurements
2.5 Analysis Equipment
3 Methods
3.1 Cell Seeding Numbers and Media Requirements for Different Cell Culture Vessel Formats
3.2 Preparation of Rings
3.3 Testing the Ring-Coverslip Assemblies for Leakage
3.4 Sterilizing the Ring-Coverslip Assemblies
3.5 Coating the Bottom Area Within the Rings
3.6 Seeding hIPS-Derived Cardiomyocytes into Rings
3.7 Maintaining hIPS-Derived Cardiomyocytes
3.8 Infection of Human IPS-Derived Cardiomyocytes for cAMP FRET Measurements
3.9 Transfection of hIPS-Derived Cardiomyocytes for cAMP FRET Measurements
3.10 cAMP FRET Measurements of hIPS-Derived Cardiomyocytes (See Note 36)
3.11 Analysis of cAMP FRET Measurements of hIPS-Derived Cardiomyocytes
4 Notes
References
Chapter 10: Automated Image Analysis of FRET Signals for Subcellular cAMP Quantification
1 Introduction
2 Materials
2.1 FRET-Based cAMP Measurement Components
2.2 Image Analysis Components
3 Methods
3.1 Image Acquisition: Spectral Imaging Fluorescence Microscopy of FRET-Based cAMP Reporters
3.2 Image Segmentation: Automated Whole-Cell cAMP Measurements
3.3 Feature Extraction: Quantitative Measurement of Whole-Cell Data
3.4 Automated Subcellular cAMP Measurements
3.5 Feature Extraction: Quantitative Subcellular Measurements
4 Notes
References
Chapter 11: In Vivo cAMP Dynamics in Drosophila Larval Neurons
1 Introduction
1.1 Epac 1-Camps Based FRET Biosensor
1.2 Anatomy of a Third Instar Larva
1.3 Generation of Transgenic Flies Using the GAL4-UAS System
1.4 Power and Caveats
2 Materials
2.1 Dissection
2.2 FRET Measurements
3 Methods
3.1 Generation of Transgenic Larvae Expressing Epac1-Camps Sensor in Neurons
3.2 Preparing the Dissecting Surface
3.3 Select Male Wandering Third Instar Larva
3.4 Dissection of Larva
3.5 FRET Measurements
3.6 Data Analysis
4 Notes
References
Chapter 12: Live Cell Imaging of Cyclic Nucleotides in Human Cardiomyocytes
1 Introduction
2 Materials
2.1 Isolation and Culture
2.2 Biosensors
2.3 Myocytes Isolation Solutions
2.4 Myocytes Culture and Transduction
2.5 FRET System
3 Methods
3.1 Obtaining and Transporting Human Tissue
3.2 Myocytes Isolation Prearrangements
3.3 Myocytes Isolation
3.4 Myocytes Culture
3.5 FRET Experiments
4 Notes
References
Chapter 13: Optogenetic Control of Heart Rhythm: Lightly Guiding the Cardiac Pace
1 Introduction
1.1 Overview
1.2 Physiology of Heart Rhythm Automaticity
1.3 Neurogenic Modulation of Heart Rate: Role of the Autonomic Nervous System
1.3.1 Cardiac Autonomic Innervation
1.3.2 Neurogenic Modulation of Cardiomyocyte Activity
1.4 Fundamentals of Optogenetics
1.5 Theoretical Bases of Cardiac Optogenetics
1.5.1 Advantages and Differences of Optogenetic vs. Conventional Cardiac Pacing Studies
1.5.2 Limitations of Cardiac Optogenetics
1.6 Model Systems for Cardiac Optogenetics
1.7 Optogenetic Control of Heart Rhythm
1.7.1 In Vitro Testing of the Function of the Chosen Opsin
1.7.2 Cell Selective Expression of ChR2 in the Mouse
2 Materials: Hardware Required to Perform In Vivo Cardiac Optogenetics
3 Methods
3.1 Preparation of the Mouse for In Vivo SN Optogenetics and Protocol of Photostimulation
3.2 Preparation of the Mouse and Optogenetic Protocol to Study Cardiac Electrophysiology
3.3 Data Collection and Analysis
4 Notes
References
Chapter 14: Live Imaging of cAMP Signaling in D. discoideum Based on a Bioluminescent Indicator, Nano-lantern (cAMP)
1 Introduction
2 Materials
2.1 CTZ Stock
2.2 D. discoideum Cells
2.3 CTZ for Bioluminescence Observation
3 Methods
3.1 Imaging of Cellular cAMP Dynamics in Buffer-Submerged Condition
3.2 Bioluminescence Imaging of the Spontaneous cAMP Signaling in Developing D. discoideum Cells on Agar Plate
3.2.1 Preparation of Agar Plate
3.2.2 Mounting Cells on Agar Plate
3.2.3 Bioluminescence Imaging of the Spontaneous cAMP Signaling
3.3 Spectroscopic Measurement
4 Notes
References
Chapter 15: Generation of Transgenic Mice Expressing Cytosolic and Targeted FRET Biosensors for cAMP and cGMP
1 Introduction
2 Materials
2.1 Equipment
2.2 Mice
2.3 Vectors
2.4 Cells
2.5 Substances and Solutions
2.6 Kits
2.7 Primers
2.8 LoxP Stop Sequence
2.9 Restriction Enzymes
2.10 Other Materials for DNA Purification
3 Methods
3.1 Cloning Strategy
3.2 PCR and DNA Extraction
3.3 Restriction Digest and Ligation
3.4 Transformation
3.5 Control Digest
3.6 Amplification of the Constructed Vector
3.7 Preparation of the DNA for the Microinjection
3.8 Genotyping
4 Notes
References
Chapter 16: How to Make the CUTiest Sensor in Three Simple Steps for Computational Pedestrians
1 Introduction
1.1 Beauty Is in the Eye of the Beholder
2 Methodology: Designing the CUTiest Sensor in Three Simple Steps
3 Experimental Validation
4 Conclusions
5 Notes
References
Chapter 17: Ion Channel-Based Reporters for cAMP Detection
1 Introduction
2 Materials
2.1 Fluorometric cAMP Measurements
2.2 Reagents
2.3 Electrophysiological cAMP Measurements
2.4 Data Analysis Software
3 Methods
3.1 cAMP Measurements Using a Spectrofluorometer
3.2 cAMP Measurements Using Electrophysiological Approaches
4 Notes
References
Chapter 18: Quantitative Phosphoproteomics to Study cAMP Signaling
1 Introduction
2 Materials and Reagents
2.1 Materials and Equipment
2.2 Working Solutions
2.2.1 For Cell Lysis
2.2.2 For Filter-Aided Sample Preparation (FASP)
2.2.3 For On-Column Stable Isotope Dimethyl Labeling
2.2.4 For Titanium Dioxide (TiO2)-Mediated Phosphopeptide Enrichment
2.2.5 For Tandem Mass Spectrometry (LC-MS/MS) Analysis
3 Methods
3.1 Sample Collection
3.2 Cell Lysis
3.3 FASP Digest
3.4 On-column Stable Isotope Dimethyl Labeling
3.5 Titanium Dioxide Phosphopeptide Enrichment
3.6 Mass Spectrometry Analysis
3.7 Quantitative Data Analysis
3.7.1 Peptide Searches
3.7.2 Downstream Data Processing and Statistical Analysis
4 Notes
4.1 Sample Collection
4.2 Cell Lysis
4.3 FASP Digest
4.4 Dimethyl Labeling
4.5 Phosphopeptide Enrichment
4.6 Mass Spectrometry
4.7 Quantitative Data Analysis
References
Chapter 19: Biochemical Analysis of AKAP-Anchored PKA Signaling Complexes
1 Introduction
1.1 RII-Selective Disruptors of AKAP Complexes
1.2 cAMP-Modulatory Effectors
1.3 PKA Inhibitors: From Proteins to Small Molecules
1.4 Chemical Inhibitors of PKA-C
2 Materials
2.1 Expression and Purification of AKAP-79, PKA-C, and PKA-RII Proteins
2.1.1 List of Required Chemicals and Reagents
2.2 In Vitro AKAP-RII-C Binding Assay
2.2.1 List of Reagents
3 Methods
3.1 Expression and Purification of Recombinant RII, C and AKAP Proteins in BL21 (DE3) pLysS E. coli
3.2 Lysis of the Bacterial Cell Pellet
3.3 Protein Purification
3.4 AKAP-RII Binding Assay (Fig. 3a)
3.5 SDS-PAGE and Coomassie Blue Staining
3.6 Biophysical Analysis of Purified Recombinant PKA Signaling Components
4 Notes
References
Chapter 20: Fluorescent Translocation Reporters for Sub-plasma Membrane cAMP Imaging
1 Introduction
2 Materials
2.1 Cell Culture and Transfection
2.2 Fluorescence Microscopy
3 Methods
3.1 Preparation of Poly-l-lysine-Coated Coverslips
3.2 Transfection of Cells
3.3 Time-Lapse Imaging of Sub-plasma Membrane cAMP Dynamics
3.4 Simultaneous Imaging of Sub-plasma Membrane Ca2+ and cAMP
3.5 Simultaneous Imaging of cAMP and PKA Activity
3.6 Simultaneous Imaging of cAMP and Subcellular Localization of Epac2
4 Notes
References
Chapter 21: A Live-Cell Imaging Assay for Nuclear Entry of cAMP-Dependent Protein Kinase Catalytic Subunits Stimulated by Endo...
1 Introduction
2 Materials
2.1 Cell Culture
2.2 Imaging System
3 Methods
3.1 Cell Culture and Transient Transfection
3.2 Imaging
3.3 Data Analysis
4 Notes
References
Chapter 22: Measuring Spatiotemporal cAMP Dynamics Within an Endogenous Signaling Compartment Using FluoSTEP-ICUE
1 Introduction
1.1 cAMP/PKA Signaling
1.2 FRET-Based cAMP Reporters
1.3 Visualizing Local cAMP Signaling Dynamics
2 Materials
2.1 Stock Solutions (See Note 1)
2.2 Cell Culture and Transfection
2.3 Epifluorescence Microscope
2.4 Image Analysis
3 Methods
3.1 Cell Culture
3.2 PolyJet Transfection (See Note 1)
3.3 Attaching GFP11 to a POI via CRISPR-Cas9
3.4 Measuring Spatiotemporal cAMP Dynamics at an Endogenous POI
3.5 Analyzing FluoSTEP-ICUE FRET Data
4 Notes
References
Index

Citation preview

Methods in Molecular Biology 2483

Manuela Zaccolo Editor

cAMP Signaling Methods and Protocols Second Edition

METHODS

IN

MOLECULAR BIOLOGY

Series Editor John M. Walker School of Life and Medical Sciences University of Hertfordshire Hatfield, Hertfordshire, UK

For further volumes: http://www.springer.com/series/7651

For over 35 years, biological scientists have come to rely on the research protocols and methodologies in the critically acclaimed Methods in Molecular Biology series. The series was the first to introduce the step-by-step protocols approach that has become the standard in all biomedical protocol publishing. Each protocol is provided in readily-reproducible step-by step fashion, opening with an introductory overview, a list of the materials and reagents needed to complete the experiment, and followed by a detailed procedure that is supported with a helpful notes section offering tips and tricks of the trade as well as troubleshooting advice. These hallmark features were introduced by series editor Dr. John Walker and constitute the key ingredient in each and every volume of the Methods in Molecular Biology series. Tested and trusted, comprehensive and reliable, all protocols from the series are indexed in PubMed.

cAMP Signaling Methods and Protocols Second Edition

Edited by

Manuela Zaccolo Department of Physiology, Anatomy and Genetics, Oxford University, Oxford, UK

Editor Manuela Zaccolo Department of Physiology Anatomy and Genetics Oxford University Oxford, UK

ISSN 1064-3745 ISSN 1940-6029 (electronic) Methods in Molecular Biology ISBN 978-1-0716-2244-5 ISBN 978-1-0716-2245-2 (eBook) https://doi.org/10.1007/978-1-0716-2245-2 © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Science+Business Media, LLC, part of Springer Nature 2022 Chapter 7 is licensed under the terms of the Creative Commons Attribution 4.0 International License (http:// creativecommons.org/licenses/by/4.0/). For further details see license information in the chapter. This work is subject to copyright. All rights are solely and exclusively licensed by the Publisher, whether the whole or part of the material is concerned, specifically the rights of translation, reprinting, reuse of illustrations, recitation, broadcasting, reproduction on microfilms or in any other physical way, and transmission or information storage and retrieval, electronic adaptation, computer software, or by similar or dissimilar methodology now known or hereafter developed. The use of general descriptive names, registered names, trademarks, service marks, etc. in this publication does not imply, even in the absence of a specific statement, that such names are exempt from the relevant protective laws and regulations and therefore free for general use. The publisher, the authors and the editors are safe to assume that the advice and information in this book are believed to be true and accurate at the date of publication. Neither the publisher nor the authors or the editors give a warranty, expressed or implied, with respect to the material contained herein or for any errors or omissions that may have been made. The publisher remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. Cover Caption: Cells with primary cilium decorated in yellow showing the localization of a cAMP reporter. This Humana imprint is published by the registered company Springer Science+Business Media, LLC part of Springer Nature. The registered company address is: 1 New York Plaza, New York, NY 10004, U.S.A.

Preface Adenosine 30 ,50 -monophosphate (cAMP), the prototypical intracellular second messenger, regulates a large variety of cellular functions and biological processes, including gene transcription, cell metabolism, proliferation, development, as well as more specialized functions depending on the cell type. In its simpler formulation, the cAMP signaling pathway involves a hormone (the “first” messenger) that binds and activates a specific G proteincoupled receptor which, in turn, activates adenylyl cyclases to synthesize cAMP. The intracellular (or “second”) messenger cAMP then binds to a limited number of intracellular effectors, including protein kinase A (PKA), the exchange factor activated by cAMP (EPAC), hyperpolarization channels activated by cyclic nucleotides (HCN), and the Popeye domain containing (POPDC) proteins, each exerting a variety of different functions. PKA phosphorylates multiple downstream targets, each leading to a specific functional outcome. Signal termination is mediated by phosphodiesterases (that hydrolyze cAMP) and phosphatases (that dephosphorylate PKA targets), enzymes that are modulated by complex regulatory mechanisms. In the last 20 years, the field of cAMP signaling has witnessed an exciting development with accumulating evidence demonstrating that cAMP is compartmentalized and that spatial regulation of cAMP signals is critical for faithful signal propagation and for specificity of response. This realization has changed our understanding of cAMP signaling, from a model where a linear pathway connects the receptor located at the plasma membrane with an effector and its function, to a model where signal propagation occurs within a complex network of cAMP-dependent signaling pathways simultaneously operating within the same cell. The pathway or pathways the cAMP signal travels along are dictated by the overall state of the cell at the time the cAMP signal is generated, depending on the activity of on/off signals that operate on individual routes at that particular time. Based on this new model, the functional outcome of a signal mediated by cAMP depends strictly on local and temporal regulation. The hormonal specificity of cAMP action results from the generation of distinct pools of the second messenger which in turn mediate different functional outcomes via activation of different subsets of the cAMP effectors. PKA is largely localized to different subcellular compartments via binding to a family of scaffolding proteins known as A Kinase Anchoring Proteins (AKAPs). Apart from their common ability to anchor PKA, AKAPs show a high degree of structural variability which allows for different subcellular localization and binding to a variety of other signaling components. As a result, AKAPs serve as signaling centers, where elements of the cAMP signaling pathway and other regulatory molecules are organized for a particular task. Recently, PKA regulatory subunits have been shown to undergo liquid-liquid phase separation (LLPS) in the cytosol to form biomolecular condensates, or liquid droplets. These condensates contain a concentration of cAMP that is significantly higher than in the surrounding cytosol, thereby acting as a local buffer for the second messenger which slows down diffusion of cAMP and aids control of local cAMP levels by PDEs. The realization of this extremely complex spatial organization and local regulation is providing novel mechanistic insight into cell physiology and is producing a novel framework for the identification of disease mechanisms. This new model also offers the potential to establish original avenues for the treatment of disease. New approaches have been developed

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that allow researchers to gain information that goes beyond a measure of cAMP activity at the whole cell or cell population level. In preparing this updated volume, I have tried to encompass new technological developments that specifically address questions related to cAMP compartmentalization, that probe relevant protein–protein interactions that increase the spatial and temporal resolution of cAMP signals detection, and that can facilitate integration of the mounting complexity of the information that is becoming available on this signaling system. I am extremely grateful to all authors for providing excellent and comprehensive methods and extensive notes with essential “tricks of the trade” that are so precious when troubleshooting a new technique. Finally, I thank the Senior Editor, John Walker, for giving me the opportunity to compile this second edition of the volume in the excellent series Methods in Molecular Biology. I hope the selection of methods will prove appealing and will be a real resource to researchers in the field. Oxford, UK

Manuela Zaccolo

Contents Preface . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Contributors. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

1 Real-Time Measurements of Intracellular cAMP Gradients Using FRET-Based cAMP Nanorulers . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Charlotte Kayser, Martin J. Lohse, and Andreas Bock 2 Assaying Protein Kinase A Activity Using a FRET-Based Sensor Purified from Mammalian Cells . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Ashton J. Curtis, Ryan S. Dowsell, and Matthew G. Gold 3 MultiFRET: A Detailed Protocol for High-Throughput Multiplexed Ratiometric FRET . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Masoud Ramuz, Ivan Diakonov, Chris Dunsby, and Julia Gorelik 4 Photoactivated Adenylyl Cyclases as Optogenetic Modulators of Neuronal Activity. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Thilo Henss, Martin Schneider, Dennis Vettko¨tter, Wagner Steuer Costa, Jana F. Liewald, and Alexander Gottschalk 5 Imaging the cAMP Signaling Microdomain of the Primary Cilium Using Targeted FRET-Based Biosensors . . . . . . . . . . . . . . . . . . . . . . . . . . . . Danielle T. Arena and Aldebaran M. Hofer 6 Methods to Assess Phosphodiesterase and/or Adenylyl Cyclase Activity Via Heterologous Expression in Fission Yeast . . . . . . . . . . . . . . . . . . . . . . . Marek Domin and Charles S. Hoffman 7 Time-Domain Fluorescence Lifetime Imaging of cAMP Levels with EPAC-Based FRET Sensors. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Olga Kukk, Jeffrey Klarenbeek, and Kees Jalink 8 Disruptors of AKAP-Dependent Protein–Protein Interactions . . . . . . . . . . . . . . . . Ryan Walker-Gray, Tamara Pallien, Duncan C. Miller, Andreas Oder, Martin Neuenschwander, Jens Peter von Kries, Sebastian Diecke, and Enno Klussmann 9 Micro-2D Cell Culture for cAMP Measurements Using FRET Reporters in Human iPSC-Derived Cardiomyocytes . . . . . . . . . . . . . . . . . . . . . . . . Andreas Koschinski and Manuela Zaccolo 10 Automated Image Analysis of FRET Signals for Subcellular cAMP Quantification. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Silas J. Leavesley, Naga Annamdevula, Santina Johnson, D. J. Pleshinger, and Thomas C. Rich 11 In Vivo cAMP Dynamics in Drosophila Larval Neurons . . . . . . . . . . . . . . . . . . . . . . Isabella Maiellaro 12 Live Cell Imaging of Cyclic Nucleotides in Human Cardiomyocytes . . . . . . . . . . Kira Beneke and Cristina E. Molina

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Optogenetic Control of Heart Rhythm: Lightly Guiding the Cardiac Pace . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Lolita Dokshokova, Nicola Pianca, Tania Zaglia, and Marco Mongillo Live Imaging of cAMP Signaling in D. discoideum Based on a Bioluminescent Indicator, Nano-lantern (cAMP). . . . . . . . . . . . . . . . . . . . . . . . . . Kazuki Horikawa and Takeharu Nagai Generation of Transgenic Mice Expressing Cytosolic and Targeted FRET Biosensors for cAMP and cGMP. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Roberta Kurelic´ and Viacheslav O. Nikolaev How to Make the CUTiest Sensor in Three Simple Steps for Computational Pedestrians. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Florencia Klein, Cecilia Abreu, and Sergio Pantano Ion Channel–Based Reporters for cAMP Detection . . . . . . . . . . . . . . . . . . . . . . . . . Thomas C. Rich, Wenkuan Xin, Silas J. Leavesley, C. Michael Francis, and Mark Taylor Quantitative Phosphoproteomics to Study cAMP Signaling . . . . . . . . . . . . . . . . . . Katharina Schleicher, Svenja Hester, Monika Stegmann, and Manuela Zaccolo Biochemical Analysis of AKAP-Anchored PKA Signaling Complexes . . . . . . . . . . Dominic P. Byrne, Mitchell H. Omar, Eileen J. Kennedy, Patrick A. Eyers, and John D. Scott Fluorescent Translocation Reporters for Sub–plasma Membrane cAMP Imaging . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Oleg Dyachok, Yunjian Xu, Olof Idevall-Hagren, and Anders Tengholm A Live-Cell Imaging Assay for Nuclear Entry of cAMP-Dependent Protein Kinase Catalytic Subunits Stimulated by Endogenous GPCR Activation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Grace E. Peng and Mark von Zastrow Measuring Spatiotemporal cAMP Dynamics Within an Endogenous Signaling Compartment Using FluoSTEP-ICUE . . . . . . . . . . . . . . . Julia C. Hardy, Sohum Mehta, and Jin Zhang

Index . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

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Contributors CECILIA ABREU • Institut Pasteur de Montevideo, Montevideo, Uruguay NAGA ANNAMDEVULA • Department of Pharmacology, University of South Alabama, Mobile, AL, USA; Center for Lung Biology, University of South Alabama, Mobile, AL, USA; Department of Physiology, University of South Alabama, Mobile, AL, USA DANIELLE T. ARENA • VA Boston Healthcare System and the Department of Surgery, Brigham & Women’s Hospital, Harvard Medical School, Boston, MA, USA KIRA BENEKE • Institute of Experimental Cardiovascular Research, University Medical Center Hamburg –Eppendorf (UKE), Hamburg, Germany; Germany DZHK (German Centre for Cardiovascular Research), partner site Hamburg/Kiel/Lu¨beck, Hamburg, Germany ANDREAS BOCK • Max Delbru¨ck Center for Molecular Medicine in the Helmholtz Association, Receptor Signaling Lab, Robert-Roessle-Strasse 10, Berlin, Germany; Rudolf-BoehmInstitute for Pharmacology and Toxicology, University of Leipzig, Haertelstrasse, Leipzig, Germany DOMINIC P. BYRNE • Department of Biochemistry and Systems Biology, ISMIB, University of Liverpool, Liverpool, UK WAGNER STEUER COSTA • Institute of Biophysical Chemistry and Buchmann Institute for Molecular Life Sciences, Johann Wolfgang Goethe-University, Frankfurt, Germany ASHTON J. CURTIS • Department of Neuroscience, Physiology & Pharmacology, University College London, London, UK IVAN DIAKONOV • Imperial College London, National Heart and Lung Institute, London, UK SEBASTIAN DIECKE • Max-Delbru¨ck-Center for Molecular Medicine in the Helmholtz Association (MDC), Berlin, Germany; DZHK (German Centre for Cardiovascular Research), Berlin, Germany LOLITA DOKSHOKOVA • Department of Biomedical Sciences, University of Padova, Padova, Italy; Veneto Institute of Molecular Medicine, Padova, Italy MAREK DOMIN • Mass Spectrometry Center, Chemistry Department, Boston College, Chestnut Hill, MA, USA RYAN S. DOWSELL • Department of Neuroscience, Physiology & Pharmacology, University College London, London, UK CHRIS DUNSBY • Photonics Group, Department of Physics, and Centre for Pathology, Imperial College London, London, UK; Department of Medicine, Imperial College London, London, UK OLEG DYACHOK • Department of Medical Cell Biology, Uppsala University, Biomedical Centre, Uppsala, Sweden PATRICK A. EYERS • Department of Biochemistry and Systems Biology, ISMIB, University of Liverpool, Liverpool, UK C. MICHAEL FRANCIS • Center for Lung Biology, University of South Alabama, Mobile, AL, USA; Department of Physiology and Cell Biology, University of South Alabama, Mobile, AL, USA MATTHEW G. GOLD • Department of Neuroscience, Physiology & Pharmacology, University College London, London, UK

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Contributors

JULIA GORELIK • Imperial College London, National Heart and Lung Institute, London, UK ALEXANDER GOTTSCHALK • Institute of Biophysical Chemistry and Buchmann Institute for Molecular Life Sciences, Johann Wolfgang Goethe-University, Frankfurt, Germany JULIA C. HARDY • Department of Bioengineering, University of California San Diego, La Jolla, CA, USA THILO HENSS • Institute of Biophysical Chemistry and Buchmann Institute for Molecular Life Sciences, Johann Wolfgang Goethe-University, Frankfurt, Germany SVENJA HESTER • Nuffield Department of Medicine, Target Discovery Institute, University of Oxford, Oxford, UK; Department of Biochemistry, University of Oxford, Oxford, UK ALDEBARAN M. HOFER • VA Boston Healthcare System and the Department of Surgery, Brigham & Women’s Hospital, Harvard Medical School, Boston, MA, USA CHARLES S. HOFFMAN • Biology Department, Boston College, Chestnut Hill, MA, USA KAZUKI HORIKAWA • Department of Optical Imaging, Advanced Research Promotion Center, Tokushima University, Tokushima City, Tokushima, Japan OLOF IDEVALL-HAGREN • Department of Medical Cell Biology, Uppsala University, Biomedical Centre, Uppsala, Sweden KEES JALINK • The Netherlands Cancer Institute, Amsterdam, The Netherlands SANTINA JOHNSON • Department of Pharmacology, University of South Alabama, Mobile, AL, USA; Center for Lung Biology, University of South Alabama, Mobile, AL, USA CHARLOTTE KAYSER • Max Delbru¨ck Center for Molecular Medicine in the Helmholtz Association, Receptor Signaling Lab, Robert-Roessle-Strasse 10, Berlin, Germany EILEEN J. KENNEDY • Department of Pharmaceutical and Biomedical Sciences, College of Pharmacy, University of Georgia, Athens, GA, USA JEFFREY KLARENBEEK • The Netherlands Cancer Institute, Amsterdam, The Netherlands FLORENCIA KLEIN • Institut Pasteur de Montevideo, Montevideo, Uruguay ENNO KLUSSMANN • Max-Delbru¨ck-Center for Molecular Medicine in the Helmholtz Association (MDC), Berlin, Germany; DZHK (German Centre for Cardiovascular Research), Berlin, Germany ANDREAS KOSCHINSKI • Department of Physiology, Anatomy and Genetics, University of Oxford, Oxford, UK OLGA KUKK • The Netherlands Cancer Institute, Amsterdam, The Netherlands ROBERTA KURELIC´ • Institute of Experimental Cardiovascular Research, University Medical Center Hamburg-Eppendorf, Hamburg, Germany SILAS J. LEAVESLEY • Department of Chemical and Biomolecular Engineering, University of South Alabama, Mobile, AL, USA; Department of Pharmacology, University of South Alabama, Mobile, AL, USA; Center for Lung Biology, University of South Alabama, Mobile, AL, USA JANA F. LIEWALD • Institute of Biophysical Chemistry and Buchmann Institute for Molecular Life Sciences, Johann Wolfgang Goethe-University, Frankfurt, Germany MARTIN J. LOHSE • ISAR Bioscience Institute, Semmelweisstraße 5, Munich, Germany ISABELLA MAIELLARO • School of Life Sciences, Queens Medical Centre, University of Nottingham, Nottingham, UK SOHUM MEHTA • Department of Pharmacology, University of California San Diego, La Jolla, CA, USA DUNCAN C. MILLER • Max-Delbru¨ck-Center for Molecular Medicine in the Helmholtz Association (MDC), Berlin, Germany; DZHK (German Centre for Cardiovascular Research), Berlin, Germany

Contributors

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CRISTINA E. MOLINA • Institute of Experimental Cardiovascular Research, University Medical Center Hamburg –Eppendorf (UKE), Hamburg, Germany; Germany DZHK (German Centre for Cardiovascular Research), partner site Hamburg/Kiel/Lu¨beck, Hamburg, Germany MARCO MONGILLO • Department of Biomedical Sciences, University of Padova, Padova, Italy; Veneto Institute of Molecular Medicine, Padova, Italy TAKEHARU NAGAI • Department of Biomolecular Science and Engineering, SANKEN (The Institute of Scientific and Industrial Research), Osaka University, Ibaraki, Osaka, Japan MARTIN NEUENSCHWANDER • Leibniz-Forschungsinstitut fu¨r Molekulare Pharmakologie (FMP), Berlin, Germany VIACHESLAV O. NIKOLAEV • Institute of Experimental Cardiovascular Research, University Medical Center Hamburg-Eppendorf, Hamburg, Germany; German Center for Cardiovascular Research (DZHK), Partner Site Hamburg/Kiel/Lu¨beck, Hamburg, Germany ANDREAS ODER • Leibniz-Forschungsinstitut fu¨r Molekulare Pharmakologie (FMP), Berlin, Germany MITCHELL H. OMAR • Department of Pharmacology, University of Washington, Seattle, WA, USA TAMARA PALLIEN • Max-Delbru¨ck-Center for Molecular Medicine in the Helmholtz Association (MDC), Berlin, Germany SERGIO PANTANO • Institut Pasteur de Montevideo, Montevideo, Uruguay GRACE E. PENG • Program in Cell Biology, University of California, San Francisco, San Francisco, CA, USA; Department of Psychiatry and Behavioral Sciences, University of California, San Francisco, San Francisco, CA, USA NICOLA PIANCA • Department of Biomedical Sciences, University of Padova, Padova, Italy; Veneto Institute of Molecular Medicine, Padova, Italy D. J. PLESHINGER • Department of Pharmacology, University of South Alabama, Mobile, AL, USA; Center for Lung Biology, University of South Alabama, Mobile, AL, USA MASOUD RAMUZ • Imperial College London, National Heart and Lung Institute, London, UK THOMAS C. RICH • Department of Pharmacology, University of South Alabama, Mobile, AL, USA; Center for Lung Biology, University of South Alabama, Mobile, AL, USA; Department of Physiology, University of South Alabama, Mobile, AL, USA; College of Engineering, University of South Alabama, Mobile, AL, USA KATHARINA SCHLEICHER • Department of Physiology, Anatomy and Genetics, University of Oxford, Oxford, UK MARTIN SCHNEIDER • Institute of Biophysical Chemistry and Buchmann Institute for Molecular Life Sciences, Johann Wolfgang Goethe-University, Frankfurt, Germany JOHN D. SCOTT • Department of Pharmacology, University of Washington, Seattle, WA, USA MONIKA STEGMANN • Sir William Dunn School of Pathology, University of Oxford, Oxford, UK MARK TAYLOR • Department of Physiology and Cell Biology, University of South Alabama, Mobile, AL, USA ANDERS TENGHOLM • Department of Medical Cell Biology, Uppsala University, Biomedical Centre, Uppsala, Sweden DENNIS VETTKO¨TTER • Institute of Biophysical Chemistry and Buchmann Institute for Molecular Life Sciences, Johann Wolfgang Goethe-University, Frankfurt, Germany

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Contributors

JENS PETER VON KRIES • Leibniz-Forschungsinstitut fu¨r Molekulare Pharmakologie (FMP), Berlin, Germany MARK VON ZASTROW • Department of Psychiatry and Behavioral Sciences, University of California, San Francisco, San Francisco, CA, USA; Department of Cellular and Molecular Pharmacology, University of California, San Francisco, San Francisco, CA, USA; Quantitative Biology Institute, University of California, San Francisco, San Francisco, CA, USA RYAN WALKER-GRAY • Max-Delbru¨ck-Center for Molecular Medicine in the Helmholtz Association (MDC), Berlin, Germany WENKUAN XIN • Department of Pharmaceutical Sciences, College of Pharmacy, University of Tennessee Health Science Center, Memphis, TN, USA YUNJIAN XU • Department of Medical Cell Biology, Uppsala University, Biomedical Centre, Uppsala, Sweden MANUELA ZACCOLO • Department of Physiology, Anatomy and Genetics, University of Oxford, Oxford, UK TANIA ZAGLIA • Department of Biomedical Sciences, University of Padova, Padova, Italy; Veneto Institute of Molecular Medicine, Padova, Italy JIN ZHANG • Department of Bioengineering, University of California San Diego, La Jolla, CA, USA; Department of Pharmacology, University of California San Diego, La Jolla, CA, USA; Department of Chemistry and Biochemistry, University of California San Diego, La Jolla, CA, USA

Chapter 1 Real-Time Measurements of Intracellular cAMP Gradients Using FRET-Based cAMP Nanorulers Charlotte Kayser, Martin J. Lohse, and Andreas Bock Abstract 30 ,50 -cyclic adenosine monophosphate (cAMP) is one of the most important and ubiquitous second messengers in cells downstream of G protein-coupled receptors (GPCRs). In a single cell, cAMP can exert innumerous specific cell functions in response to more than one hundred different GPCRs. Cells achieve this extraordinary functional specificity of cAMP signaling by limiting the spread of these signals in space and time. To do so, cells establish nanometer-size cAMP gradients by immobilizing cAMP via cAMP binding proteins and via targeted activity of cAMP-degrading phosphodiesterases (PDEs). As cAMP gradients appear to be essential for cell function, new technologies are needed to accurately measure cAMP gradients in intact cells with nanometer-resolution. Here we describe FRET-based cAMP nanorulers to measure local, nanometer-size cAMP gradients in intact cells in the direct vicinity of PDEs. Key words cAMP, Phosphodiesterase, Compartmentation, Compartmentalized signaling, cAMP nanodomains, Biosensors, Nanoruler, FRET, Cell signaling, GPCR

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Introduction 30 ,50 -cyclic adenosine monophosphate (cAMP) is an essential second messenger that relays extracellular information, resulting from activation of G protein-coupled receptors (GPCRs) by hormones and neurotransmitters, to highly specific cellular functions [1]. Upon agonist stimulation of GPCRs, cAMP is enzymatically synthesized by membrane-bound adenylyl cyclases (ACs), and it is enzymatically degraded by phosphodiesterases (PDEs). cAMP activates four classes of downstream effectors, i.e., cAMP-dependent protein kinase (PKA) [2–4], exchange factor directly activated by cAMP (EPAC) [5, 6], (hyperpolarization-activated) cyclic nucleotide-gated channels (HCN and CNG) [7–9], and Popeye domain containing proteins (POPDC) [10]. These cAMP effectors further modulate the function of numerous downstream signaling proteins allowing cAMP to exert control of a myriad of

Manuela Zaccolo (ed.), cAMP Signaling: Methods and Protocols, Methods in Molecular Biology, vol. 2483, https://doi.org/10.1007/978-1-0716-2245-2_1, © The Author(s), under exclusive license to Springer Science+Business Media, LLC, part of Springer Nature 2022

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physiological functions in humans. Most prominently, cAMP controls contractility of the heart, relaxation of smooth muscle, metabolism of glycogen and lipids, cell proliferation, and gene transcription. The ability of cAMP to elicit this large variety of highly specific cellular functions is especially remarkable given the fact that the cAMP concentration in a single cell is regulated by more than one hundred different GPCRs [11]. Since its discovery by Earl W. Sutherland [12], cAMP has been regarded to be freely diffusible in intact cells [13–15], a property that would impose significant challenges on a cell to orchestrate signaling specificity in space and time. In contrast to this view, to explain receptor-specific cellular functions mediated by cAMP, it was proposed already 40 years ago that cAMP signaling might be compartmentalized, i.e., that intracellular cAMP gradients exist and that specific receptors modulate only some local cAMP pools while leaving other compartments unchanged [16–18]. This hypothesis is based on classical experiments by Buxton and Brunton where they showed that activation of cardiac β-adrenergic receptors increased the contractility of the heart and stimulated glycogen metabolism, whereas stimulation of prostaglandin receptors did not. Strikingly, both receptors increased total cAMP levels to the same extent, leaving spatial compartmentation of cAMP as a likely explanation for receptorspecific cellular responses [16–18]. The development and use of Fo¨rster resonance energy transfer (FRET)-based biosensors allowed measuring cAMP levels in intact cells [14, 19–24] and demonstrated that cAMP concentrations at distinct cellular locations can be different, providing the first direct evidence for cAMP compartmentation at the micrometer scale. It became also clear that inhibition of PDEs abolishes cAMP gradients, suggesting that PDE activity may be important in shaping cAMP compartmentation [25–30]. However, the apparently high diffusivity of cAMP and the slow turnover rates of PDEs would suggest that cAMP compartmentation is virtually impossible, and, thus, the molecular basis for cAMP compartmentation has remained enigmatic for decades. Recently, we have shown that cAMP, in contrast to long-held tenets, is not freely diffusible in cells but, under basal conditions, is largely bound to cAMP binding proteins, resulting in “buffered diffusion” as the main mechanism of cAMP dynamics unless cAMP levels are increased upon GPCR activation [31]. In line with this, it has been demonstrated that the regulatory subunit RIα of PKA undergoes liquid-liquid phase separation forming liquid-like cAMP droplets that further sequester cAMP in cells [32]. Both mechanisms result in a free concentration of cAMP that is exceedingly low, which in turn allows PDEs to create areas of even lower cAMP in their immediate vicinity. By mapping cAMP gradients around PDEs with engineered FRET-based cAMP nanorulers, we have identified

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such low-cAMP nanodomains, i.e., areas in cells with low cAMP levels and a diameter of up to tens of nanometers. Importantly, cAMP effectors that are located inside these low-cAMP nanodomains remain protected from cAMP waves originating from activated GPCRs at the cell membrane. Thus, controlling local cAMP pools with PDEs allows the cell, with nanometer precision, to exert spatial control of which cAMP effectors will be activated and which will not. As it is becoming increasingly clear that local cAMP pools are physiologically important [33, 34] and that disruption of cAMP compartmentation likely leads to disease, investigating cAMP nanodomains in intact cells will become of utmost importance. In this chapter we describe in detail how cAMP gradients around PDEs can be identified and mapped in intact cells using FRET-based cAMP nanorulers. These nanorulers comprise a set of fusion proteins consisting of the well-characterized FRET-based cAMP biosensor Epac1-camps fused to a PDE, either directly or by single-alpha helical (SAH) linkers of defined nanometer length [35]. Specifically, we present two different sets of cAMP nanorulers that are based on two different PDEs (PDE4A1 and PDE2A), and that we have validated extensively. In principle, cAMP nanorulers can be designed for any PDE; however, new constructs require rigorous and laborious validation which will not be covered in this chapter. The validated cAMP nanorulers presented here allow measuring local cAMP levels in direct vicinity of PDE4A1 and PDE2A and at 10 and 30 nm distance from PDE4A1 and PDE2A, respectively (Fig. 1).

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Materials

2.1 Cell Culture, Seeding, and Transfection

1. HEK-tsA201 cells. 2. Cell culture medium: Dulbecco’s modified Eagle Medium (DMEM) with 4.5 g/L glucose. Add 10% fetal bovine serum, add 100 U/mL penicillin + 100 μg/mL streptomycin and 2 mM L-glutamine. Store at 4  C. 3. DPBS (Dulbecco’s phosphate-buffered saline). 4. 24 mm glass coverslips. 5. 6-well TC-plates. 6. Trypsin/EDTA. 7. Effectene Transfection Reagent. 8. Pure Ethanol.

2.2

cDNA

1. Epac1-camps-PDE4A1 [36]. 2. Epac1-camps-SAH10-PDE4A1 [31] (see Note 1). 3. Epac1-camps-IRES-PDE4A1 [31] (see Note 2).

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Fig. 1 FRET-based nanorulers and experimental procedure to measure cAMP gradients. (a) Schematic structure of FRET-based biosensors and nanorulers used in the set of experiments. Epac1-camps-IRESPDE: The equimolar expression of Epac1-camps and the indicated PDE allows to measure the PDE’s influence on global (i.e., bulk) cAMP levels and serves as control. Epac1-camps-PDE: The direct fusion of Epac1-camps and the indicated PDE senses local cAMP levels in direct vicinity of a single PDE molecule. Epac1-campsSAH10/30-PDE: Inserting a genetically encoded, nanometer-size linker between Epac1-camps and the indicated PDE detects cAMP levels at a defined distance from a single PDE. (b) Concept of real-time measurement of cAMP gradients: Left: If the PDE creates a cAMP gradient, the sensor will recognize lower cAMP levels when being fused directly to the PDE than in the bulk cytosol. Right: a linker can shift the sensor outside of the PDE nanodomain, depending on the linker’s length. Therefore, the radius of the cAMP gradient can be measured directly. CNBD: cyclic nucleotide binding domain

4. Epac1-camps-PDE2cat [31]. 5. Epac1-camps-SAH30-PDE2cat [31] (see Note 1). 6. Epac1-camps-IRES-PDE2cat [31] (see Note 2). All constructs are freely available from the authors upon request.

FRET-Based Nanorulers Reveal cAMP Gradients in Intact Cells

2.3 Ligand Solution and Buffer Preparation

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All ligands need to be prepared in 1000 stock solutions for the experiments. 1. ()-Isoproterenol hydrochloride: Prepare a 100 mM isoproterenol (Iso) solution freshly at every experimental day in double distilled water. Store at 4  C in the fridge throughout experiments. Immediately before starting the measurements, dilute tenfold to yield the desired 1000 ligand solution (10 mM) for the experiment. Keep the 10 mM Iso on ice throughout the experiment. 2. 3-Isobutyl-1-methylxanthine (IBMX): Prepare a 100 mM IBMX stock solution in DMSO and store 10 μL aliquots at 20  C until use. 3. BAY 60-7550: Prepare a 100 μM BAY 60-7550 stock solution in DMSO and store aliquots at 20  C until use. 4. Forskolin: Prepare a 10 mM forskolin (fsk) stock solution in DMSO and store 10 μL aliquots at 20  C until use. 5. Roflumilast: Prepare a 300 μM roflumilast stock solution in DMSO and store aliquots at 20  C until use. 6. 5 FRET imaging buffer: 720 mM NaCl, 27 mM KCl, 10 mM CaCl2, 5 mM MgCl2, 50 mM HEPES. The buffer can be stored at 4  C for 6 months. 7. Before starting an experiment, prepare 50 mL of 1 FRET imaging buffer from 5 stock (final concentration: 144 mM NaCl, 5.4 mM KCl, 2 mM CaCl2, 1 mM MgCl2, 10 mM HEPES) and adjust to pH 7.3 with NaOH. Keep 1 FRET imaging buffer at room temperature for the measurement. 1 FRET imaging buffer should be prepared freshly for every imaging day.

2.4 Single-Cell FRET Experiment

1. Immersion oil. 2. Epifluorescence microscope (Leica DMi8 inverted microscope, Leica Microsystems, Wetzlar, Germany) equipped with an oil immersion objective (HC PL APO 40/1.30, or HC PL APO 63/1.40–0.60, both Leica Microsystems, Wetzlar, Germany), a dichroic beam splitter (T505lpxr, Visitron Systems, Puchheim, Germany), a high-speed polychromator (VisiChrome, Visitron Systems), a Xe-Lamp (75 W, 5.7 A, Hamamatsu Photonics, Hamamatsu City, Japan), a camera system (Photometrics Prime 95B CMOS camera, Visitron systems) with an Optosplit II dual emission image splitter (Cairn, Edinburgh, Scotland, UK) with CFP 470/24 and YFP 535/30 emission filters (Chroma Technology, Bellows Falls, VT, USA). 3. Imaging chamber. 4. VisiView 4.0 imaging software, Visitron Systems, https:// www.visitron.de/products/visiviewr-software.html.

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5. GraphPad Prism software 7.0, GraphPad Software Inc., https://www.graphpad.com/. 6. Microsoft Excel, Microsoft Corporation, https://office. microsoft.com/excel. 7. ImageJ, https://imagej.net/ImageJ ([37, 38)].

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Methods

3.1 Cell Culture, Seeding, and Transfection

1. Wash 24 mm glass coverslips with pure ethanol. Let them air-dry. 2. Wash coverslips with DPBS. 3. Aspirate cell culture medium from cells (at 80–90% confluency in a T-75-flask) and wash them with 10 mL DPBS. Carefully aspirate DPBS. 4. Detach cells from the T-75-flask with Trypsin/EDTA, resuspend them in 2–5 mL of cell culture medium. 5. Count cells and seed at a density of 2  105 cells/mL on cleaned coverslips in 6-well plates in 1.6 mL cell culture medium. 6. Let cells adhere at 37  C and 5% CO2. 7. Six hours later, prepare transfection reagent according to manufacturer’s instructions. In our lab we use Effectene Transfection Reagent and 500–600 ng cDNA of FRET-based nanorulers and biosensors per coverslip. 8. Incubate for 24–48 h depending on expression level at 37  C and 5% CO2. When expression is longer than 24 h, the medium should be exchanged.

3.2 Mounting on Microscope

1. Carefully take the coverslip out of the 6-well plate with a scalpel or tweezers. 2. Remove residual medium by placing one edge of the coverslip on a tissue paper. 3. Place coverslip into the center of the imaging chamber. 4. Close the imaging chamber carefully (see Note 3). 5. Add 900 μL 1  FRET imaging buffer to the imaging chamber to wash the cells (see Notes 4–6). 6. Carefully remove the buffer. 7. Add 900 μL 1  FRET imaging buffer to the cells. 8. Mount imaging chamber onto the microscope.

3.3

Focusing

1. Select your objective of choice (we commonly use 40 and 63 objectives) and carefully put some immersion oil onto it.

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2. Adjust the microscope stage carefully until the objective and coverslip are in close proximity. 3. Bring the cells into focus in brightfield mode. This can be done easily by using the ocular and not the camera. 4. Select both CFP and FRET emission channels as readouts. Excite CFP (excitation wavelength: 436 nm) in live mode to readjust the focus until the fluorescent cell appears in focus on the camera (Fig. 2a–c). (In the VisiView software use the “Show Live” button to excite your donor fluorophore and to focus.) 3.4

ROI Selection

1. Choose an area of cells in the live mode with nicely shaped cells that slightly touch each other and grow attached in a flat monolayer. If all selected cells can be brought into focus at one time, the selection is good (see Notes 7 and 8). 2. In your imaging software (we use VisiView), create a ROI for the background (cell free area/untransfected cell) and ROIs according to the shape of your selected cells to track FRET changes live during the experiment.

3.5

FRET Experiment

1. Set up the experiment in your imaging software. Select both CFP and FRET emission channels (in VisiView you can find this in the “Acquire” panel) and excite CFP at 436 nm. In our lab we use a Xe-Lamp (75 W, 5.7 A, Hamamatsu Photonics, Hamamatsu City, Japan) as a light source and set the time interval to 5 s, and the illumination time to 100 ms in the “Acquire” panel in the VisiView software. 2. When cells are in focus and ROIs are selected, start the experiment (“Sequence” button in VisiView). 3. Observe the FRET ratio of your selected ROIs in real time in your imaging software (in VisiView open the “Online Ratio” panel and select the CFP and FRET emission channel as source images to see FRET ratio changes). 4. Wait about 300 s until the basal FRET ratio is absolutely stable before first ligand application. 5. Add 10 μM Iso (final concentration) directly to the cells (bath application). To do so, take 1 μL of the 10 mM Iso stock, add 99 μL of FRET imaging buffer, and mix by pipetting up and down. Carefully add the 100 μL solution into the imaging chamber (see Notes 4, 9 and 10). 6. Immediately after application, press the event button to record the time of ligand addition (see Note 11). 7. Record ligand-induced FRET changes and wait until signal is in plateau.

Fig. 2 Representative experiment of mapping nanometer-size cAMP gradients in intact cells. (a-c) Epifluorescence images of HEK cells expressing FRET-based biosensors of PDE4A1. Sensor constructs are expressed evenly in the cytosol. Scale bars are 10 μm. (a) Epac1-camps-IRES-PDE4A1, (b) Epac1-camps-PDE4A1, (c) Epac1-camps-SAH10-PDE4A1. (d–f) Representative normalized FRET ratios of single cells overexpressing FRET-based sensors and nanorulers of PDE4A1. Cells were treated consecutively with Iso (10 μM), roflumilast (300 nM), and fsk/IBMX (10 μM/100 μM). (d) When Epac1-camps and PDE4A1 are expressed at equimolar levels, Iso stimulation leads to a significant and robust increase in cAMP levels. (e) When fused directly to the PDE, Epac1-camps does not detect Iso-mediated increases in cAMP levels. (f) When inserting a 10 nm linker between sensor and PDE4A1 the Iso stimulus leads to the same amplitude as measured with the equimolar overexpression in (d). Thus, PDE4A1 generates a cAMP nanodomains with a radius of smaller than 10 nm. (g–i) Representative normalized FRET ratios of single cells overexpressing FRET-based nanorulers of PDE2cat. Cells were treated consecutively with Iso (10 μM), BAY (BAY 60-7550, 100 nM) and fsk/IBMX (10 μM/100 μM). (g) When Epac1-camps and PDE2cat are expressed at equimolar levels, Iso stimulation leads to a significant and robust increase in cAMP levels. (h) When fused directly to the PDE, Epac1-camps does again not detect Iso-mediated increases in cAMP levels. (i) When inserting a 30 nm linker between sensor and PDE2cat, almost no FRET change is induced upon Iso stimulation. Thus, PDE2cat generates a cAMP nanodomain with a radius of larger than 30 nm. (d–i) FRET traces are normalized to basal (set to 0%) and saturating stimulation upon fsk/IBMX treatment (set to 100%)

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8. For adding a specific PDE inhibitor (final concentrations: 300 nM roflumilast/100 nM BAY 60-7550), take 1.1 μL of its stock solution, add 108.9 μL of FRET imaging buffer and mix with the pipette. 9. Add 110 μL (to account for the volume addition of the first stimulation) to bath. 10. Mark the event in your imaging software according to 6. 11. For maximal stimulation add 10 μM fsk/100 μM IBMX (final concentrations). Therefore, take 1.2 μL of each stock solution, add 117.6 μL FRET imaging buffer and mix with the pipette. Add 120 μL to bath. 12. After each measurement, acquire 5–10 pictures with direct YFP excitation (excitation wavelength: 505 nm) to get information about the sensor expression levels. 3.6

Data Analysis

1. Data can be directly exported to excel files in the VisiView software. Be aware that only the data of your chosen ROIs will be exported. Alternatively, you can use ImageJ for data export which allows you to work with all the emission intensities measured in the entire field of view. 2. After exporting the data, the CFP and FRET channels need to be corrected for background. Therefore, subtract the CFP and FRET channel intensities of your background region from the respective emission channels of the selected ROIs. 3. Correct for bleedthrough, i.e., the spectral cross-talk between CFP and FRET channels, using the experimentally determined correction factors for the microscopy setup, as described previously [39]. 4. The background-corrected CFP emission divided by the background and bleedthrough-corrected FRET channel will give the FRET ratio: FRET ratio ¼

background corrected CFP background and bleedthrough corrected FRET

5. To compare traces and have a defined baseline, normalize the FRET ratio to the average of 10 data points directly before the first stimulation. 6. For data visualization, GraphPad Prism can be used. 7. To compare the different cAMP nanorulers and biosensors, normalize the traces to baseline (set to 0%) and the maximum stimulus fsk/IBMX (set to 100%). 8. After normalization, the Iso response gives information about the local cAMP levels measured in direct vicinity of the PDE (when the PDE is tethered, Fig. 2e, h) and at the distance of 10/30 nm (linker construct, Fig. 2f, i) or global cAMP levels (equimolar overexpression, Fig. 2d, g).

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Fig. 3 Stability of the normalized FRET ratios upon Iso stimulation over various expression levels. It is crucial to choose the right expression level of FRET-based biosensors and nanorulers, so that the FRET response induced by a stimulus (here: Iso) is nearly independent of the expression level in all three sensor constructs. This area is highlighted in gray for (a) FRET-based biosensors and nanorulers of PDE4A1 and for (b) FRETbased biosensors and nanorulers of PDE2cat. Note that the sensor expression levels chosen for experiments depend on the catalytic activity of the indicated PDE: Sensors and nanorulers need to be expressed at much lower levels for PDE2cat because of its high catalytic activity in comparison to PDE4A1

9. In general, it is important to select a range of sensor expression in which the observed FRET responses are independent of sensor expression (Fig. 3). 10. To accurately compare cAMP levels measured with different biosensors and nanorulers (e.g., in direct vicinity of and at different distances from the PDE), it is crucial that all sensors are expressed at the same level. 11. Finally, to identify and map local cAMP gradients it is of utmost importance that all measurements are performed at sensor expression levels that provide a window to robustly distinguish between global (stoichiometric overexpression of Epac1-camps

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and PDE, i.e., untethered) and local (Epac1-camps-PDE, i.e., tethered). If sensor expression is too high, the overexpressed PDE activity may blunt all Iso-induced cAMP increases.

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Notes 1. Amino acid sequences of single-alpha helical (SAH) linkers: SAH linkers are proteins based on ER/K repeats. SAH domains exclusively form single-alpha helices and do not further arrange into coiled-coil motives. SAH linkers have a rod-like shape with a defined length that makes them ideally suited to function as molecular rulers. SAH10 (length: 10 nm): EEEEKKKQQEEEAERLRRI QEEMEKERKRREEDEQRRRKEEEERRMKLEMEAKRK QEEEERKKREDDEKRKKK. SAH30 (length: 30 nm): EEEEKKKEEEEKKQKEEQER LAKEEAERKQKEEQERLAKEEAERKQKEEEERKQ KEEEERKQKEEEERKLKEEQERKAAEEKKAKEEAERKA KEEQERKAEEERKKKEEEERLERERKEREEQEKKA KEEAERIAKLEAEKKAEEERKAKEEEERKAKEEEERKK KEEQERLAKEKEEAERKAAEEKKAKEEQERKE KEEAERKQR. 2. Epac1-camps-IRES-PDE4A1/PDE2cat: To have better control on expression patterns and stoichiometry, we chose to generate biscistronic plasmids comprising an IRES sequence situated between Epac1-camps and the PDE. We prefer using an IRES sequence over an alternative 2A peptide because the latter is not cleaved quantitatively leaving behind Epac1camps-PDE fusion proteins that would interfere with the measurements. 3. Take care that the coverslip does not move or break. 4. If cells detach from surface while washing or ligand application, consider coating the coverslips, e.g., with poly-D-lysine. 5. You might try to pipette not directly on the cells, but rather to the wall of the imaging chamber. 6. During the washing step you might check that the imaging chamber is properly closed, and that no buffer leaks. You can easily check this by carefully wiping a tissue paper at the bottom of the coverslip and imaging chamber. If there is any leakage, you will see it immediately. 7. Use cells with homogenous sensor expression (be careful: some linker constructs may tend to form aggregates). 8. Do not select rounded cells. Do not select overly bright cells that could influence the signal of a neighboring cell.

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9. When adding the ligand, be careful not to wash away the cells. 10. Consider preparing a new 10 mM Iso solution from your 100 mM stock solution after 3 h of imaging. 11. Ideally you want to stimulate directly after the camera took a picture, which allows you to do the application and to mark the event in between two pictures. You might consider programming in your ligand name and final concentration as an event in your imaging software. This makes it easier to follow the procedure during the experiment and while data analysis. References 1. Rosenbaum DM, Rasmussen SG, Kobilka BK (2009) The structure and function of Gprotein-coupled receptors. Nature 459: 3 5 6 – 3 6 3 . h t t p s : // d o i . o r g / 1 0 . 1 0 3 8 / nature08144 2. Taylor SS, Buechler JA, Yonemoto W (1990) cAMP-dependent protein kinase: framework for a diverse family of regulatory enzymes. Annu Rev Biochem 59:971–1005. https:// doi.org/10.1146/annurev.bi.59.070190. 004543 3. Johnson DA, Akamine P, Radzio-Andzelm E et al (2001) Dynamics of cAMP-dependent protein kinase. Chem Rev 101:2243–2270. https://doi.org/10.1021/cr000226k 4. Taylor SS, Ilouz R, Zhang P et al (2012) Assembly of allosteric macromolecular switches: lessons from PKA. Nat Rev Mol Cell Biol 13:646–658. https://doi.org/10.1038/ nrm3432 5. Bos JL (2006) Epac proteins: multi-purpose cAMP targets. Trends Biochem Sci 31: 680–686. https://doi.org/10.1016/j.tibs. 2006.10.002 6. Gloerich M, Bos JL (2010) Epac: defining a new mechanism for cAMP action. Annu Rev Pharmacol Toxicol 50:355–375. https://doi. org/10.1146/annurev.pharmtox.010909. 105714 7. Biel M, Wahl-Schott C, Michalakis S et al (2009) Hyperpolarization-activated cation channels: from genes to function. Physiol Rev 89:847–885. https://doi.org/10.1152/ physrev.00029.2008 8. Postea O, Biel M (2011) Exploring HCN channels as novel drug targets. Nat Rev Drug Discov 10:903–914. https://doi.org/10. 1038/nrd3576 9. Craven KB, Zagotta WN (2006) CNG and HCN channels: two peas, one pod. Annu Rev

Physiol 68:375–401. https://doi.org/10. 1146/annurev.physiol.68.040104.134728 10. Brand T, Schindler R (2017) New kids on the block: the Popeye domain containing (POPDC) protein family acting as a novel class of cAMP effector proteins in striated muscle. Cell Signal 40:156–165. https://doi.org/ 10.1016/j.cellsig.2017.09.015 11. Sriram K, Insel PA (2018) G protein-coupled receptors as targets for approved drugs: how many targets and how many drugs? Mol Pharmacol 93:251–258. https://doi.org/10. 1124/mol.117.111062 12. Rall TW, Sutherland EW (1958) Formation of a cyclic adenine ribonucleotide by tissue particles. J Biol Chem 232:1065–1076 13. Bacskai BJ, Hochner B, Mahaut-Smith M et al (1993) Spatially resolved dynamics of cAMP and protein kinase A subunits in Aplysia sensory neurons. Science 260:222–226. https:// doi.org/10.1126/science.7682336 14. Nikolaev VO, Bunemann M, Hein L et al (2004) Novel single chain cAMP sensors for receptor-induced signal propagation. J Biol Chem 279:37215–37218. https://doi.org/ 10.1074/jbc.C400302200 15. Nikolaev VO, Bunemann M, Schmitteckert E et al (2006) Cyclic AMP imaging in adult cardiac myocytes reveals far-reaching beta1-adrenergic but locally confined beta2-adrenergic receptor-mediated signaling. Circ Res 99: 1084–1091. https://doi.org/10.1161/01. RES.0000250046.69918.d5 16. Brunton LL, Hayes JS, Mayer SE (1979) Hormonally specific phosphorylation of cardiac troponin I and activation of glycogen phosphorylase. Nature 280:78–80. https://doi. org/10.1038/280078a0 17. Hayes JS, Brunton LL, Mayer SE (1980) Selective activation of particulate cAMP-dependent

FRET-Based Nanorulers Reveal cAMP Gradients in Intact Cells protein kinase by isoproterenol and prostaglandin E1. J Biol Chem 255:5113–5119 18. Buxton IL, Brunton LL (1983) Compartments of cyclic AMP and protein kinase in mammalian cardiomyocytes. J Biol Chem 258:10233–10239 19. Zaccolo M, Pozzan T (2002) Discrete microdomains with high concentration of cAMP in stimulated rat neonatal cardiac myocytes. Science 295:1711–1715. https://doi.org/10. 1126/science.1069982 20. DiPilato LM, Cheng X, Zhang J (2004) Fluorescent indicators of cAMP and Epac activation reveal differential dynamics of cAMP signaling within discrete subcellular compartments. Proc Natl Acad Sci U S A 101:16513–16518. https://doi.org/10.1073/pnas.0405973101 21. Ponsioen B, Zhao J, Riedl J et al (2004) Detecting cAMP-induced Epac activation by fluorescence resonance energy transfer: Epac as a novel cAMP indicator. EMBO Rep 5: 1176–1180. https://doi.org/10.1038/sj. embor.7400290 22. Zhang JF, Mehta S, Zhang J (2021) Signaling microdomains in the spotlight: visualizing compartmentalized Signaling using genetically encoded fluorescent biosensors. Annu Rev Pharmacol Toxicol 61:587–608. https://doi. org/10.1146/annurev-pharmtox010617-053137 23. Surdo NC, Berrera M, Koschinski A et al (2017) FRET biosensor uncovers cAMP nano-domains at beta-adrenergic targets that dictate precise tuning of cardiac contractility. Nat Commun 8:15031. https://doi.org/10. 1038/ncomms15031 24. Zaccolo M, De Giorgi F, Cho CY et al (2000) A genetically encoded, fluorescent indicator for cyclic AMP in living cells. Nat Cell Biol 2: 25–29. https://doi.org/10.1038/71345 25. Bender AT, Beavo JA (2006) Cyclic nucleotide phosphodiesterases: molecular regulation to clinical use. Pharmacol Rev 58:488–520. https://doi.org/10.1124/pr.58.3.5 26. Conti M, Beavo J (2007) Biochemistry and physiology of cyclic nucleotide phosphodiesterases: essential components in cyclic nucleotide signaling. Annu Rev Biochem 76: 481–511. https://doi.org/10.1146/annurev. biochem.76.060305.150444 27. Francis SH, Blount MA, Corbin JD (2011) Mammalian cyclic nucleotide phosphodiesterases: molecular mechanisms and physiological functions. Physiol Rev 91:651–690. https://doi.org/10.1152/physrev.00030. 2010 28. Houslay MD (2010) Underpinning compartmentalised cAMP signalling through targeted

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cAMP breakdown. Trends Biochem Sci 35: 91–100. https://doi.org/10.1016/j.tibs. 2009.09.007 29. Kokkonen K, Kass DA (2017) Nanodomain regulation of cardiac cyclic nucleotide Signaling by Phosphodiesterases. Annu Rev Pharmacol Toxicol 57:455–479. https://doi.org/10. 1146/annurev-pharmtox-010716-104756 30. Mongillo M, McSorley T, Evellin S et al (2004) Fluorescence resonance energy transfer-based analysis of cAMP dynamics in live neonatal rat cardiac myocytes reveals distinct functions of compartmentalized phosphodiesterases. Circ Res 95:67–75. https://doi.org/10.1161/01. RES.0000134629.84732.11 31. Bock A, Annibale P, Konrad C et al (2020) Optical mapping of cAMP Signaling at the Nanometer scale. Cell 182:1519–1530. e1517. https://doi.org/10.1016/j.cell.2020. 07.035 32. Zhang JZ, Lu TW, Stolerman LM et al (2020) Phase separation of a PKA regulatory subunit controls cAMP compartmentation and oncogenic Signaling. Cell 182:1531–1544. e1515. https://doi.org/10.1016/j.cell.2020.07.043 33. Bers DM, Xiang YK, Zaccolo M (2019) Whole-cell cAMP and PKA activity are epiphenomena, nanodomain signaling matters. Physiology (Bethesda) 34:240–249. https://doi. org/10.1152/physiol.00002.2019 34. Zaccolo M, Zerio A, Lobo MJ (2021) Subcellular organization of the cAMP Signaling pathway. Pharmacol Rev 73:278–309. https://doi. org/10.1124/pharmrev.120.000086 35. Sivaramakrishnan S, Spudich JA (2011) Systematic control of protein interaction using a modular ER/K alpha-helix linker. Proc Natl Acad Sci U S A 108:20467–20472. https:// doi.org/10.1073/pnas.1116066108 36. Herget S, Lohse MJ, Nikolaev VO (2008) Real-time monitoring of phosphodiesterase inhibition in intact cells. Cell Signal 20: 1423–1431. https://doi.org/10.1016/j. cellsig.2008.03.011 37. Schneider CA, Rasband WS, Eliceiri KW (2012) NIH image to ImageJ: 25 years of image analysis. Nat Methods 9:671–675. https://doi.org/10.1038/nmeth.2089 38. Linkert M, Rueden CT, Allan C et al (2010) Metadata matters: access to image data in the real world. J Cell Biol 189:777–782. https:// doi.org/10.1083/jcb.201004104 39. Borner S, Schwede F, Schlipp A et al (2011) FRET measurements of intracellular cAMP concentrations and cAMP analog permeability in intact cells. Nat Protoc 6:427–438. https:// doi.org/10.1038/nprot.2010.198

Chapter 2 Assaying Protein Kinase A Activity Using a FRET-Based Sensor Purified from Mammalian Cells Ashton J. Curtis, Ryan S. Dowsell, and Matthew G. Gold Abstract Protein Kinase A (PKA) is the major intracellular receptor for cAMP. Research into this prototype kinase is supported by kinase assays that are typically performed in vitro using radio-labeled ATP. For in vivo studies, genetically encoded FRET-based sensors have become popular for monitoring PKA activity. Here, we show that it is also possible to apply such reporters in vitro. We describe how to express and purify milligram quantities of a FRET-based PKA activity reporter using cultured human embryonic kidney cells. We demonstrate how to utilize the purified reporter in a plate reader to determine the IC50 for the widely utilized PKA inhibitor H89 in the presence of a physiologically relevant concentration of ATP. The protocol takes advantage of the economical transfection reagent polyethylenimine and can be performed in a standard cell culture facility. Whereas assays based on radiolabelling are more sensitive, the approach presented here has several advantages: It enables continuous measurement of changes in substrate phosphorylation; a single preparation produces enough reporter for thousands of recordings; the reporter has a long shelf life; and it avoids the safety considerations that arise when working with radioactive material. Key words Protein kinase A, Fluorescent reporter, Phosphorylation, cAMP, Kinase assay

1

Introduction cAMP-dependent protein kinase, also known as protein kinase A (PKA), is the major intracellular receptor for cAMP. The enzyme is critical for cellular responses including sympathetic regulation of the heart [1], control of water reuptake in the kidney [2], and longlasting changes in synaptic strength [3], to name only a few. Mutations affecting PKA activity [4] or localization [5] are pathogenic. PKA also serves as a prototype for understanding the broader protein kinase family, and accordingly its catalytic mechanism and the structural basis of its function have been scrutinized in great detail. Methods to assay PKA activity have of course been fundamental to this research effort. For example, PKA activity assays have been utilized in structure-function studies focused on understanding the phospho-transfer mechanism of PKA [6], its mode of

Manuela Zaccolo (ed.), cAMP Signaling: Methods and Protocols, Methods in Molecular Biology, vol. 2483, https://doi.org/10.1007/978-1-0716-2245-2_2, © The Author(s), under exclusive license to Springer Science+Business Media, LLC, part of Springer Nature 2022

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activation by cAMP [7], the molecular determinants of substrate recognition by PKA [8], and regulation of the kinase by different metal ions [9]. Kinase assays are frequently performed with protein immuno-precipitates to identify signaling complexes that contain PKA [10]. Furthermore, PKA assays have supported identification and characterization of small molecule PKA inhibitors including H89 [11], and there is ongoing demand for new assay formats that facilitate high-throughput kinase inhibitor development [12]. The most common method for measuring PKA kinase activity is to monitor transfer of radio-labeled 32P from the γ position of ATP to substrates. PKA assays are typically performed using this approach with basic peptides such as Kemptide [13] that will bind to p81 phosphocellulose paper. This enables the peptide to be recovered and separated from free phosphate and ATP once the reaction has been terminated with, e.g., phosphoric acid [14]. The extent of 32P incorporation is then measured in a scintillation counter. 32P-labeled protein substrates can be recovered after reaction termination using mixed cellulose ester membrane filters [9]. Radioactive kinase assays have also been adapted for 96-well plates [15]. Although 32P-based kinase assays are highly sensitive and accurate, only a single time-point can be collected per reaction. Furthermore, 32P-ATP has a limited shelf life since the half-life or 32 P is only ~14 days, and there are additional safety and logistical considerations associated with working with radioactive material. Non-radioactive methods have become popular for assaying protein phosphatase activity, including assays with the chromogenic substrate p-nitrophenyl phosphate [16] and application of molybdate/malachite-based reagents for colorimetric quantitation of free phosphate [17], but equivalent methods are yet to catch on in the kinase field. Kemptide labeled with either rhodamine or carboxytetramethylrhodamine (TAMRA) enables phospho-peptide formation to be monitored by separating terminated kinase reactions using high performance liquid chromatography [18]. This method avoids radioactivity, but running HPLC separations is time consuming and it is only possible to perform end-point assays using this approach. Genetically encoded reporters are in wide use for monitoring PKA activity in live cells. A popular reporter is A-kinase activity reporter 4 (AKAR4), which contains cpVenus and Cerulean fluorescent proteins on either side of an FHA domain and PKA phosphorylation motif (Fig. 1a). Phosphorylation of the central motif by PKA leads to association of the central elements that increases FRET efficiency between the terminal fluorophores. In theory, sensors of this type could be used for in vitro assays, but it is typically not possible to express these reporters using conventional bacterial and insect cell approaches. In this protocol, we describe how to express and purify a modified version of AKAR4 that contains a C-terminal octa-histidine (8His) tag by transfecting cultured

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Fig. 1 Optimization of PKA activity reporter expression. (a) An octa-histidine (8His, purple) affinity tag was inserted at the C-terminus of the genetically encoded reporter AKAR4 immediately prior to the terminal nuclear export signal (NES, red). Phosphorylation of the reporter at a central PKA consensus site (blue) triggers a conformational change in which the central FHA domain associates with the phosphorylated site and thereby brings cerulean and cpVenus into closer proximity. In this way, increased FRET serves as a readout of PKA phosphorylation. (b) AKAR4-8His-NES expression was monitored in 6-well plates using GFP fluorescence comparing five ratios of PEI:DNA, and three different amounts of DNA per well. GFP fluorescence intensity is shown in wells 3 days after transfection. (c-e) Quantification of AKAR4 expression in transfected HEK293T cells as indicated by fluorescence emission at 530 nm following excitation with blue light. Measurements are shown for different PEI:DNA ratios up to 5 days after transfection using either (c) 0.67 μg, (d) 1.67 μg, or (e) 3.33 μg DNA per well

HEK293T cells. Our protocol takes advantage of the economical transfection reagent polyethyleneimine (PEI) [19]. It enables large-scale expression of AKAR4 in a standard tissue culture facility and yields enough purified reporter for thousands of in vitro PKA assays. Although assaying PKA activity with purified AKAR4 is less sensitive than monitoring 32P incorporation, it has several advantages. It is possible to monitor the full time-course of a single reaction; it is economical; the reporter is stable for >12 months in a
80  C freezer; measurements can be performed in high throughput in 96-well plates; there are no concerns about radiation safety; and assays can be performed quickly with no need for wash

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steps after reaction termination. In this protocol, we first set out a standard procedure for optimizing expression of AKAR4 in HEK293T cells (Subheading 3.1)—this step may also be applied to other available FRET-based sensors of kinase activity [20] and to other genetically encoded reporters including cAMP sensors [21]. We next explain how to purify AKAR4 (Subheading 3.2) and how to calibrate the purified reporter for plate reader assays (Subheading 3.3). Finally, we provide an example application by detailing how to determine the IC50 for H89 inhibition of PKA catalytic (C) subunits in the presence of 1 mM ATP (Subheading 3.4). Common pitfalls, potential modifications, and theoretical insights are highlighted in “Notes” (Subheading 4).

2

Materials Prepare solutions using ultrapure water (e.g., by purifying deionized water with a MilliQ system to a resistivity of >18 MΩ), and analytical grade reagents.

2.1 Mammalian Cell Expression

1. HEK293T cells (ATCC, reference CRL-3216; see Note 1). 2. High-serum culture media: DMEM containing pyruvate and high glucose, mixture of 100 U/mL penicillin and 100 μg/mL streptomycin, 1% (v/v) GlutaMAX, 10% heat-inactivated horse serum. 3. Low-serum culture media: DMEM containing pyruvate and high glucose, 1% (v/v) GlutaMAX, 2% heat-inactivated horse serum (see Note 2). 4. DMEM containing pyruvate and high glucose. 5. Phosphate-buffered saline. 6. Plasticware including 10 cm-diameter tissue culture dishes and 6-well plates. 7. pcDNA3-AKAR4-8His-NES expression vector, ~ 500 μg prepared at >0.5 μg/μL using, e.g., a Plasmid Maxiprep kit (Qiagen) after production in TOP10 cells (Thermo Fisher Scientific). The vector was assembled by introducing an 8His tag prior to the nuclear export signal in AKAR4-NES (Addgene clone 64727, developed by Jin Zhang) using the restriction enzymes EcoRI and XbaI to insert the following annealed primer pair: EcoI_8HisNLS_XbaI (50 AATTCGCCG GCCACCACCACCACCACCACCACCACGGCGCCCTGC CCCCCCTGGAGCGCCTGACCCTGTAAT) and XbaI_8 HisNLS_EcoRI (50 CTAGATTACAGGGTCAGGCGCTCCA GGGGGGGCAGGGCGCCGTGGTGGTGGTGGTGGTGG TGGTGGCCGGCG).

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8. 1 mg/mL PEI (linear, MW25000, transfection grade): Dispense 100 mg powder into a beaker and suspend in 90 mL H2O. Stir while adding HCl dropwise until the pH is just below 2, then cover and stir for approximately 10 min until the powder has completely dissolved. Carefully adjust to pH 7 with NaOH and bring up to 100 mL with H2O. Sterilize with a 0.2 μm filter, and divide into 1 mL aliquots for long-term storage at
20  C (see Note 3). 9. A tissue culture facility equipped with a tissue culture hood, 37  C incubators maintained in a humidified atmosphere with 5% CO2 in air, and a microscope for determining cell confluence. 10. A ChemiDoc gel imaging system (bio-rad) operated using ImageLab software (see Note 4). 11. ImageJ software (NIH) for quantifying AKAR4 fluorescence in images of 6-well plates. 2.2 Reporter Purification

All buffers should be filter sterilized and stored at 4  C for use within 1 week. 1. Lysis buffer: 30 mM Tris–HCl pH 8, 500 mM NaCl, 10 mM imidazole, 1 mM benzamidine, 0.5% Igepal CA-630. Supplement with one cOmplete Protease Inhibitor Cocktail (Roche) per 100 mL immediately prior to cell lysis. 2. Nickel buffer A: 30 mM Tris–HCl pH 8, 500 mM NaCl, 10 mM imidazole, 1 mM benzamidine. 3. Nickel buffer B: 30 mM Tris–HCl pH 7.5, 500 mM NaCl, 300 mM imidazole 1 mM benzamidine. 4. Storage buffer: 25 mM Na Hepes pH 7.5, 100 mM NaCl. 5. Sonicator such as a Q500 sonicator (Fisher Scientific). 6. HisTrap HP, 5 mL column (GE Healthcare). 7. HiPrep 26/10 Desalting column (GE Healthcare). 8. AKTA Start Chromatography System (see Note 5) controlled using Unicorn Start 1.2 software (GE Healthcare). 9. Vivaspin Turbo 15, 10 kDa MWCO PES centrifugal protein concentrators (Sartorius). 10. For running SDS-PAGE gels: NuPAGE 4–12%, Bis-Tris Mini Protein Gels, NuPAGE MES running buffer (20), NuPAGE LDS sample buffer (4), Novex Sharp Pre-stained protein standards (all Thermo Fisher Scientific). 11. Coomassie blue stain (10% ethanol, 30% acetic acid, 0.125% w/v Coomassie R-250) and destain (10% ethanol, 10% acetic acid) solutions. 12. BCA protein assay kit.

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2.3 Ratiometric Plate Reader Assays

1. Reaction buffer: 20 mM Na Hepes pH 7.5, 100 mM NaCl, 10 mM DTT, 20 mM MgCl2, 0.5% Igepal CA-630. Prepare 20 mL and divide into aliquots of 500 μL and store at
80  C for use within 6 months. 2. Injection buffer: 20 mM Na Hepes pH 7.5, 100 mM NaCl, 5 mM ATP. Prepare 50 mL, filter sterilize and divide into aliquots of 1.5 mL. Store aliquots at
80  C for use within 6 months. 3. Dilution buffer: 20 mM Na Hepes pH 7.5, 100 mM NaCl. Store at 4  C for use within 6 months. 4. Purified PKA Cβ subunit (0.3 mg/mL in storage buffer), purified after transgenic expression in bacteria [22] (see Note 6). 5. H89, Dihydrochloride. Prepare a 10 mM solution by dissolving 1 mg in 193 μL DMSO. Store at 4  C for use within 4 months. 6. FLUOstar Omega microplate reader (BMG Labtech) equipped with a 430 nm excitation filter, an emission filter wheel containing both 485 nm and 520 nm emission filters, and an injector (see Note 7). 7. 96-well black-walled microplates for fluorescence-based assays. 8. Data analysis software: MARS (BMG Labtech), Microsoft Excel, and Origin (OriginLab).

3

Methods

3.1 Optimizing Reporter Expression by PEI Transfection

The following procedures should be carried out in a sterile tissue culture hood using aseptic technique unless otherwise stated. This protocol may be applied to optimize expression of other fluorescent reporters besides AKAR4 including the latest PKA activity reporters (see Note 8). 1. Seed 3 6-well plates (labeled low, medium, and high DNA) with HEK293T at a confluence of ~35% in 2 mL high-serum media per well, and incubate the plates at 37  C with 5% CO2. 2. On the following day when the cells have reached a confluence of ~70%, replace the media with 2 mL low-serum media per well an hour before addition of the transfection mixtures and return to the incubator. 3. The aim of this optimization experiment is to compare AKAR4 expression at the following five different PEI:DNA ratios (2:1, 3:1, 4:1, 5:1, 6:1), and at three different concentrations of DNA per well (0.67, 1.67, 3.33 μg per well, see Note 9). Accordingly, add either 0 (control), 20, 30, 40, 50, or 60 μL

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of 1 mg/mL PEI solution to six 1.5 mL Eppendorf tubes, and bring the volume in each tube to 250 μL with DMEM. Vortex each tube for ~3 s. 4. Add 60 μg pcDNA3-AKAR4-8His-NES DNA to a separate 15 mL falcon tube, and bring the volume to 1.5 mL with DMEM. 5. After 20 min, divide the DNA solution between the five Eppendorf tubes containing different PEI concentrations to yield the five desired PEI:DNA ratios and a control solution. Vortex each mixture for 3 s, and leave to stand for 20 min at room temperature. 6. For each PEI-DNA mixture, add either 36 μL/well (low DNA plate), 90 μL/well (medium DNA plate), or 180 μL DNA/well (high DNA plate). Swirl the plate while adding the solutions dropwise, and return the plates to the incubator. 7. On the following morning, replace the media with high-serum culture media (2 mL/well). 8. Twenty four hours after transfection, image AKAR4 fluorescence in the three plates in a ChemiDoc imager using blue epi illumination for excitation and a 530/28 emission filter to detect fluorescence emission. Image using a range of exposure times including very short times (0.05 s) to ensure that day-today comparisons can be made without signal intensity rising out of range on the latter days. 9. On each subsequent day up to 5 days after transfection, carefully substitute in fresh media and re-image the plates (Fig. 1b). 10. Quantify the signal intensity of each well on each day using ImageJ to yield time courses for the five different PEI:DNA ratios with either 0.67 μg DNA/well (Fig. 1c), 1.33 μg DNA/well (Fig. 1d), or 3.33 μg DNA/well (Fig. 1e). For AKAR4-8His-NES, high expression can be achieved three days after transfection with 3.33 μg and 20 μL PEI per well (light blue, Fig. 1e). 3.2 Reporter Purification Following Scaled up Expression in HEK293T Cells

All protein purification steps should be performed on ice or in a refrigerated cabinet. 1. Seed 20  10 cm diameter tissue culture dishes with HEK293T cells at a confluence of ~35% in 10 mL high-serum cell culture media per dish, and return to a 37  C incubator with 5% CO2. 2. On the following morning when the cells have reached a confluence of ~70%, substitute in 10 mL low-serum media 1 h before addition of the transfection mixtures and return to the incubator.

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3. Pipette 400 μg DNA into a 15 mL falcon tube and bring up to 10 mL with DMEM. 4. Mix 2.4 mL PEI (1 mg/mL) with 7.6 mL DMEM in a separate 50 mL falcon tube, vortex for 3 s, and leave to stand at room temperature. 5. After 20 min, add the PEI solution to the DNA solution, vortex for 3 s, and then leave to stand for a further 20 min at room temperature. 6. Pipette 1 mL of the PEI-DNA mixture onto each plate dropwise while swirling the plate gently, and return the plates to the incubator. 7. The following morning, replace the media with 10 mL highserum media per plate. 8. Two days after transfection, substitute in fresh high-serum media again. 9. Three days after transfection, aspirate the media, wash each plate with 10 mL PBS taking care not to detach the cells, aspirate the PBS, and transfer the plates into a
80  C freezer (see Note 10). 10. Thaw plates on ice, then add 1 mL lysis buffer per plate and leave to incubate for 10 min. Resuspend cells using a 1 mL pipette, and collect all of the material (~25 mL) in a 50 mL falcon tube. Sonicate for 3  10 s before clarifying the lysate by centrifugation at 48,000  g for 30 min. 11. Exchange the supernatant into Nickel Buffer A using 2 HiPrep 26/10 desalting columns connected in series (see Note 11) using the AKTA start. 12. Apply the protein solution to a 5 mL HisTrap HP column pre-equilibrated in Nickel Buffer A using the AKTA start, then wash the column for 30 mL after stepping up to 7% Nickel Buffer B (30 mM imidazole final concentration). 13. Elute the protein with a gradient over 30 mL up to 100% Nickel Buffer B (black triangle, Fig. 2a), collecting 2 mL fractions throughout the elution. 14. To detect fractions containing AKAR4-8His-NES, dispense 30 μL from each fraction into a black-walled 96-well plate and measure emission at 520 nm following excitation at 430 nm in the FLUOstar Omega plate reader (see Note 12). The reporter elutes towards the end of the imidazole gradient (green line, Fig. 2a). 15. Take 20 μL from each fraction and mix with 1 μL 1 M DTT, and 7 μL 4 NuPAGE sample buffer. Without heating (see Note 13), separate the samples using a 4–12% NuPAGE gel alongside Novex Sharp Pre-stained protein standards.

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Fig. 2 PKA activity reporter purification. (a) The trace shows UV absorbance (blue) throughout purification of AKAR4-8His-NES following expression in HEK293T cells. Fluorescence emission at 520 nm following excitation at 430 nm was also measured for each fraction and is overlaid on the trace in green. The first datapoint for FRET emission corresponds to the starting lysate. AKAR4-8His-NES was eluted using the indicated imidazole gradient. Fractions including the starting lysate (lys) and flow-through (f/t) were subjected to SDS-PAGE on a 4–12% gel, and the fluorescent reporter was initially detected by in-gel fluorescence emission at 530 nm following excitation with blue light (b). Coomassie staining of the same gel confirmed that AKAR4-8His-NES was present at high purity in fractions 12–14 (c)

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16. After the run has completed, transfer the gel into distilled water and immediately image AKAR4 fluorescence using the ChemiDoc system (Fig. 2b). 17. Transfer the same gel into 50 mL Coomassie stain solution and incubate overnight on a rocker, then destain the following day with 3  1-h incubations in 100 mL destain solution (Fig. 2c). 18. Pool peak fractions containing pure AKAR4 (fractions 12–14 in Fig. 2), exchange into storage buffer using a HiPrep 26/10 desalting column, and concentrate to ~4 mL using a Vivaspin Turbo 15 centrifugal concentrator. 19. Determine the protein concentration by BCA assay. This protocol yields ~2.5 mg purified AKAR4-8His-NES. Dilute the protein to 0.381 mg/mL for a 5 μM working solution, and divide into aliquots of 100 μL for storage at
80  C. 3.3 Calibrating the Reporter for Plate Reader Activity Assays

The aim of this section is to determine a suitable PKA C subunit concentration and time range to use for assaying PKA activity with the purified reporter. It is written in such a way that it could be adapted to a different PKA activity reporter (see Note 14). 1. Prepare serial dilutions of PKA C subunits in dilution buffer at the following nM concentrations: 667, 333, 167, 66.7, 33.3, 1.67, and 0. Addition of 15 μL of each solution into total reaction volumes of 50 μL will achieve the desired final concentrations of 200, 100, 50, 20, 10, and 0 nM C subunits. 2. Prepare 250 μL reaction master mix comprising 180 μL dilution buffer, 50 μL reaction buffer (10x), and 20 μL purified AKAR4 (2 μM stock). 3. Dispense 25 μL master mix into six wells of a black-walled 96-well plate. To each well, add 15 μL of the appropriate C subunit dilution. To a seventh well, add 35 μL dilution buffer and 5 μL reaction buffer (10) to serve as a control well enabling subtraction of background fluorescence. 4. While the plates are equilibrating to room temperature for 15 min, thaw an aliquot of injection solution to 22  C using a heat block, then prime the injector of the plate reader with this solution (see Note 15). 5. Insert the plate into the plate reader and determine a suitable gain for both 520 and 485 nm emission upon excitation at 430 nm using one of the experimental wells. 6. Start the run with the following parameters: Measure 520 and 485 nm emission for each well at 5 s intervals for a total of 31 min (see Note 16). In place of the fourth recording at 15 s, initiate PKA phosphorylation by injecting 10 μL of injection solution into each of the seven wells (see Note 17).

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7. Process the data by first subtracting background fluorescence from each of the six experimental wells according to the value of the control well in which the reporter was omitted. Average the three readings before ATP injection to determine the starting emission ratio. Once background-subtracted values for both channels have been obtained for each datapoint, calculate the 520/485 nm emission ratios. This process can be automated using MARS data analysis software. 8. Plot the data (Fig. 3a), and calculate the rates (expressed as % change in 520 nm/485 nm emission ratio over time) during the initial period of linear change. Slopes can be determined using, for example, the SLOPE function in Excel. 9. Repeat the experiment twice more and plot the initial rates— averaged from three runs—against PKA C subunit concentration. For AKAR-8His-NES, the initial rate follows a linear relationship to PKA concentration up to 200 nM C subunit (Fig. 3b). This data indicates that recording over 20 min with C subunits in the 10–50 nM range would be appropriate for determining the IC50 of H89. 3.4 Determining the IC50 of a PKA Inhibitor

The small molecule H89 inhibits PKA with a Ki ¼ 48 nM by binding competitively to the ATP-binding pocket of the C subunit [11]. Since H89 competes with ATP, its half-maximal inhibitory concentration (IC50) should theoretically be substantially higher at physiological concentrations of ATP. One of the advantages of using AKAR4 to monitor PKA activity is that elevated concentrations of ATP do not dilute out the tracer as would happen in an assay based on 32P. Therefore, in a proof-of-concept experiment, here we describe determination of the IC50 for H89 inhibition of PKA in the presence of 1 mM ATP. This is a much higher concentration of ATP than used in previous studies [11] and is close to physiological levels [23]. 1. Prepare serial dilutions of H89 in dilution buffer at the following μM concentrations: 66.7, 33.3, 16.7, 6.67, 3.33, 1.67, 0.667, 0.333, 0. Addition of 15 μL of each solution into total reaction volumes of 50 μL will achieve the desired final concentrations of 20, 10, 5, 2, 1, 0.5, 0.2, 0.1, and 0 μM H89. 2. Prepare 300 μL of the following master mix: 60 μL reaction buffer (10), 36 μL dilution buffer, 24 μL AKAR4, 180 μL PKA C subunit (from 83.3 nM stock for a final concentration of 25 nM). 3. Dispense 25 μL of the master mix to each of nine wells in a black-walled 96-well plate. Add 15 μL of the appropriate H89 dilution to each well. To a tenth background fluorescence control well add 35 μL dilution buffer and 5 μL reaction buffer (10).

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Fig. 3 Calibration of purified PKA activity reporter measurements in plate reader. (a) Changes in the ratio of emission at 520 (yellow, Y) and 485 (cyan, C) nm emission, upon excitation at 430 nm, were monitored over time in an FLUOstar Omega microplate reader. Time series were collected in triplicate either in the absence of PKA C subunit (green), or with 10 (light blue), 20 (gold), 50 (gray), 100 (orange), or 200 (dark blue) nM C subunit. All reactions were initiated by injection of ATP to a final concentration of 1 mM. (b) Plot of initial rate of change in Y/C emission ratio vs C subunit concentration. Initial rates were calculated between 10 and 50 s for 50, 100, and 200 nM C subunits; and between 10 and 310 s for 0, 10, and 20 nM C subunits. The two variables follow a linear relationship (red line). All data is shown as mean  standard error

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4. While the plates are equilibrating to room temperature for 15 min, thaw an aliquot of injection solution to 22  C using a heat block, then prime the plate reader injector. 5. Insert the plate into the FLUOstar Omega plate reader and determine a suitable gain for both 520 and 485 nm emission channels upon excitation at 430 nm using one of the experimental wells. 6. Start the run with the following parameters: Measure 520 and 485 nm emission for each well at 10 s intervals for a total of 21 min. In place of the fourth recording at 30 s, initiate PKA phosphorylation by injecting 10 μL of injection solution into each of the seven wells. 7. Process the data to obtain background-subtracted 520/485 nm emission ratios. As shown in Fig. 4a, higher H89 concentrations noticeably reduce PKA activity. Calculate the rate of change in 520 nm/485 nm emission ratio between 10 and 310 s using the SLOPE function in Excel. 8. Repeat the experiment twice more before plotting the initial rates against H89 concentration on a logarithmic scale. Determine the IC50 by curve fitting using a four-parameter function such as the Hill1 function in Origin (Fig. 4b). Using this approach, we determined an IC50 ¼ 1.53  0.06 μM for H89 inhibition of PKA, confirming that a physiological ATP concentration elevates the IC50 of H89 into the low micromolar range (see Note 18).

4

Notes 1. It is also possible to perform PEI transfection of suspension mammalian cells such as Freestyle 293F cells (Thermo Fisher Scientific) where suitable facilities are available (an orbital shaker with a humidified atmosphere of 8% CO2 in air). This will reduce plastic waste. 2. Higher transfection efficiency can be achieved by initially omitting antibiotics and reducing the serum concentration in the culture media. 3. Standard transfection reagents such as Lipofectamine-2000 (ThermoFisher) or FuGENE (Promega) may be used in place of PEI if cost is less of a consideration. 4. Alternative imaging systems include the ImageQuant series (Cytiva). A qualitative comparison can be made on a standard long-wavelength UV transilluminator. 5. If an FPLC is not available, the procedure may be performed in batch by diluting the initial clarified lysate into 200 mL Nickel Buffer A, and incubating with 5 mL Ni-NTA agarose (Qiagen)

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Fig. 4 Determination of IC50 for H89 using purified PKA activity reporter. (a) Changes in 520/485 nm emission ratio were measured in a FLUOstar Omega microplate reader. Wells contained 25 nM C subunits mixed with variable concentrations of H89. Reactions were initiated by addition of ATP to a final concentration of 1 mM, and fluorescence readings were collected at 10-s intervals for 20 min as shown in the example recordings. (b) Rates of change in Y/C emission ratio were calculated between 10 and 310 s for each H89 concentration (n ¼ 3), and are shown as mean  standard error plotted against H89 concentration. An IC50 value for H89 was determined by fitting the data to a Hill function

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for 1 h in a roller bottle. After washing with buffer containing 100 mM imidazole, elute the reporter with Nickel Buffer B. The final buffer exchange step into storage buffer may be performed with a PD-10 desalting column packed with Sephadex G-25 resin (Cytiva). 6. Purified PKA C subunit is also commercially available (e.g., Promega, catalog no. V516A). 7. Many alternative microplate readers are available. The key requirements are that the reader can rapidly switch emission filters for ratiometric fluorescence measurements, and that the reader is equipped with at least one injector for initiating reactions with ATP. 8. The most advanced PKA activity reporters include ExRaiAKAR2 [24] and tAKARα [25]. 9. These numbers are equivalent to 4, 10, and 20 μg DNA per 10 cm-diameter cell culture dish. 10. The plates can be left in storage to continue the protocol at a later date, if convenient. 11. Alternatively, divide the supernatant and run two batches of equal volume through a single column. 12. This step is not essential—the SDS-PAGE analysis (Fig. 2b, c) is sufficient to show which fractions contain purified AKAR48His-NES. 13. Heating the samples prior to electrophoresis would reduce subsequent in-gel fluorescence of AKAR4. 14. If adapting the assay for a different reporter, the first step is to determine a suitable concentration of reporter. This can be achieved by reading a serial dilution of the reporter—for AKAR4, the signal is approximately tenfold above background in both channels with the reporter at 0.2 μM. 15. A fresh vial of injection solution should be thawed before each plate reader run otherwise ATP hydrolysis in the solution will lead to reduced apparent PKA activity in later runs. 16. For recordings with the FLUOstar Omega, when reading 7 wells in parallel in “plate” mode, the shortest possible spacing between readings is approximately 5 s. 17. If a second injector is available, this can be exploited to vary the concentration of injected components such as metal ions, nucleotides, or small molecule inhibitors. 18. The elevated IC50 of H89 for PKA at physiological ATP concentrations undermines the applicability of the inhibitor given that it is known to trigger off-target effects in the low micromolar range [26].

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Acknowledgements This work was supported by a Wellcome Trust and Royal Society Sir Henry Dale fellowship to MGG (104194/Z/14/Z), a Wellcome Trust studentship to RSD, and a BBSRC studentship to AJC. References 1. Bers DM, Xiang YK, Zaccolo M (2019) Whole-cell cAMP and PKA activity are epiphenomena, Nanodomain Signaling matters. Physiology (Bethesda) 34(4):240–249. https://doi.org/10.1152/physiol.00002. 2019 2. Nedvetsky PI, Tamma G, Beulshausen S, Valenti G, Rosenthal W, Klussmann E (2009) Regulation of aquaporin-2 trafficking. Handb Exp Pharmacol 190:133–157. https://doi. org/10.1007/978-3-540-79885-9_6 3. Hell JW (2016) How Ca2+
permeable AMPA receptors, the kinase PKA, and the phosphatase PP2B are intertwined in synaptic LTP and LTD. Sci Signal 9(425):e2. https://doi.org/ 10.1126/scisignal.aaf7067 4. Walker C, Wang Y, Olivieri C, Karamafrooz A, Casby J, Bathon K, Calebiro D, Gao J, Bernlohr DA, Taylor SS, Veglia G (2019) Cushing’s syndrome driver mutation disrupts protein kinase a allosteric network, altering both regulation and substrate specificity. Sci Adv 5(8): eaaw9298. https://doi.org/10.1126/sciadv. aaw9298 5. Gold MG, Gonen T, Scott JD (2013) Local cAMP signaling in disease at a glance. J Cell Sci 126(Pt 20):4537–4543. https://doi.org/ 10.1242/jcs.133751 6. Madhusudan TEA, Xuong NH, Adams JA, Ten Eyck LF, Taylor SS, Sowadski JM (1994) cAMP-dependent protein kinase: crystallographic insights into substrate recognition and phosphotransfer. Protein Sci 3(2): 176–187. https://doi.org/10.1002/pro. 5560030203 7. Zhang P, Smith-Nguyen EV, Keshwani MM, Deal MS, Kornev AP, Taylor SS (2012) Structure and allostery of the PKA RIIbeta tetrameric holoenzyme. Science 335(6069): 712–716. https://doi.org/10.1126/science. 1213979 8. Moore MJ, Adams JA, Taylor SS (2003) Structural basis for peptide binding in protein kinase a. role of glutamic acid 203 and tyrosine 204 in the peptide-positioning loop. J Biol Chem 278(12):10613–10618. https://doi.org/10. 1074/jbc.M210807200

9. Knape MJ, Ahuja LG, Bertinetti D, Burghardt NC, Zimmermann B, Taylor SS, Herberg FW (2015) Divalent metal ions mg(2)(+) and ca(2) (+) have distinct effects on protein kinase a activity and regulation. ACS Chem Biol 10(10):2303–2315. https://doi.org/10. 1021/acschembio.5b00271 10. Gold MG, Reichow SL, O’Neill SE, Weisbrod CR, Langeberg LK, Bruce JE, Gonen T, Scott JD (2012) AKAP2 anchors PKA with aquaporin-0 to support ocular lens transparency. EMBO Mol Med 4(1):15–26. https:// doi.org/10.1002/emmm.201100184 11. Chijiwa T, Mishima A, Hagiwara M, Sano M, Hayashi K, Inoue T, Naito K, Toshioka T, Hidaka H (1990) Inhibition of forskolininduced neurite outgrowth and protein phosphorylation by a newly synthesized selective inhibitor of cyclic AMP-dependent protein kinase, N-[2-(p-bromocinnamylamino)ethyl]5-isoquinolinesulfonamide (H-89), of PC12D pheochromocytoma cells. J Biol Chem 265(9): 5267–5272 12. Ma H, Deacon S, Horiuchi K (2008) The challenge of selecting protein kinase assays for lead discovery optimization. Expert Opin Drug Discov 3(6):607–621. https://doi.org/10. 1517/17460441.3.6.607 13. Zhang P, Knape MJ, Ahuja LG, Keshwani MM, King CC, Sastri M, Herberg FW, Taylor SS (2015) Single turnover autophosphorylation cycle of the PKA RIIbeta holoenzyme. PLoS Biol 13(7):e1002192. https://doi.org/10. 1371/journal.pbio.1002192 14. Hastie CJ, McLauchlan HJ, Cohen P (2006) Assay of protein kinases using radiolabeled ATP: a protocol. Nat Protoc 1(2):968–971. https://doi.org/10.1038/ nprot.2006.149 15. Davies SP, Reddy H, Caivano M, Cohen P (2000) Specificity and mechanism of action of some commonly used protein kinase inhibitors. Biochem J 351(Pt 1):95–105. https://doi. org/10.1042/0264-6021:3510095 16. Grigoriu S, Bond R, Cossio P, Chen JA, Ly N, Hummer G, Page R, Cyert MS, Peti W (2013) The molecular mechanism of substrate

Assaying PKA Using Purified AKAR4 engagement and immunosuppressant inhibition of calcineurin. PLoS Biol 11(2): e1001492. https://doi.org/10.1371/journal. pbio.1001492 17. Patel N, Stengel F, Aebersold R, Gold MG (2017) Molecular basis of AKAP79 regulation by calmodulin. Nat Commun 8(1):1681. https://doi.org/10.1038/s41467-01701715-w 18. Luzi NM, Lyons CE, Peterson DL, Ellis KC (2017) Characterization of PKACalpha enzyme kinetics and inhibition in an HPLC assay with a chromophoric substrate. Anal Biochem 532:45–52. https://doi.org/10.1016/j. ab.2017.06.001 19. Aricescu AR, Lu W, Jones EY (2006) A timeand cost-efficient system for high-level protein production in mammalian cells. Acta Crystallogr D Biol Crystallogr 62(Pt 10):1243–1250. h t t p s : // d o i . o r g / 1 0 . 1 1 0 7 / S0907444906029799 20. Greenwald EC, Mehta S, Zhang J (2018) Genetically encoded fluorescent biosensors illuminate the spatiotemporal regulation of Signaling networks. Chem Rev 118(24): 11707–11794. https://doi.org/10.1021/acs. chemrev.8b00333 21. Patel N, Gold MG (2015) The genetically encoded tool set for investigating cAMP: more than the sum of its parts. Front Pharmacol 6:164. https://doi.org/10.3389/fphar. 2015.00164

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22. Walker-Gray R, Stengel F, Gold MG (2017) Mechanisms for restraining cAMP-dependent protein kinase revealed by subunit quantitation and cross-linking approaches. Proc Natl Acad Sci U S A 114(39):10414–10419. https://doi. org/10.1073/pnas.1701782114 23. Gribble FM, Loussouarn G, Tucker SJ, Zhao C, Nichols CG, Ashcroft FM (2000) A novel method for measurement of submembrane ATP concentration. J Biol Chem 275(39):30046–30049. https://doi.org/10. 1074/jbc.M001010200 24. Zhang JF, Liu B, Hong I, Mo A, Roth RH, Tenner B, Lin W, Zhang JZ, Molina RS, Drobizhev M, Hughes TE, Tian L, Huganir RL, Mehta S, Zhang J (2020) An ultrasensitive biosensor for high-resolution kinase activity imaging in awake mice. Nat Chem Biol 17(1): 39–46. https://doi.org/10.1038/s41589020-00660-y 25. Ma L, Jongbloets BC, Xiong WH, Melander JB, Qin M, Lameyer TJ, Harrison MF, Zemelman BV, Mao T, Zhong H (2018) A highly sensitive A-kinase activity reporter for imaging Neuromodulatory events in awake mice. Neuron 99(4):665–679. e665. https://doi.org/ 10.1016/j.neuron.2018.07.020 26. Murray AJ (2008) Pharmacological PKA inhibition: all may not be what it seems. Sci Signal 1(22):re4. https://doi.org/10.1126/ scisignal.122re4

Chapter 3 MultiFRET: A Detailed Protocol for High-Throughput Multiplexed Ratiometric FRET Masoud Ramuz, Ivan Diakonov, Chris Dunsby, and Julia Gorelik Abstract The newly generated software plugin MultiFRET allows for real-time measurements of multiplexed fluorescent biosensors in a near high-throughput fashion. Here we describe a detailed protocol for setup and use of this software for any purpose requiring instant feedback during fluorescence measurement experiments. We further describe its non-primary features including beam splitter misalignment correction, custom calculations through input of simple equations typed in a .txt format, customizable Excel output, and offline bulk analysis of image stacks. Finally, we supply a usage example of a cAMP measurement in cultured rat neonatal cardiomyocytes. Key words FRET, Software, Plugin, Icy, ImageJ, Fluorescence, Real-time, High-throughput, Imageprocessing

1

Introduction Fo¨rster resonance energy transfer (FRET) is a physical phenomenon describing the transfer of energy between a donor molecule that has been excited to a higher electronic energy level to a suitably receptive acceptor molecule within its close (typically on the scale of ~10 nm) vicinity [1]. The efficiency of FRET depends on the distance between the donor and acceptor molecules as well as their relative orientation to one another. Fusion proteins with a pair of fluorophores that change distance and/or angle upon protein–protein interactions may be used to directly assess fluctuations in cellular protein levels [2]. This mechanism has many applications as a research tool in fields including biology and chemistry, allowing for example the relative separation of a population of identical donor-acceptor pairs to be sensed, or the population fraction of two populations of donor-acceptor pairs where each population has a different separation and/or orientation [3]. In the latter case, one of the two populations may represent the binding of the sensor to a

Manuela Zaccolo (ed.), cAMP Signaling: Methods and Protocols, Methods in Molecular Biology, vol. 2483, https://doi.org/10.1007/978-1-0716-2245-2_3, © The Author(s), under exclusive license to Springer Science+Business Media, LLC, part of Springer Nature 2022

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protein of interest. The population fraction can be normalized to its value after a saturating dose of the protein of interest has been applied, yielding a percentage of maximum change. We refer to this methodology as “ratiometric FRET,” as the population fraction is determined by the ratio of emission between the two fluorophores [4]. For most purposes, the FRET measurements are performed post-experiment on images or image stacks captured during the experiment. However, to test the effects of drugs used sequentially on live cells there is a need for hardware and software capable of “real-time” analysis, i.e., analysis of measurements while the experimental conditions of a capture are still relevant, providing a direct feedback on what is occurring rather than separating the capture of images to saved files to be analyzed separately from the experiment [5]. On the hardware side this means a camera with shutter speeds relevant to the experiment, as well as sensitivity to measure the required changes in fluorescence. This camera would be coupled to a microscope along with all the tools required for any ratiometric FRET measurement including: a light source such as an LED or mercury lamp; an appropriate excitation band-pass filter; an appropriate dichroic beam splitter; an appropriate pair of emission bandpass filters with mechanical switching between them or a second dichroic beam splitter arrangement to split the emission light into two separate detection channels that can be acquired simultaneously; and adequate optical magnification. In MultiFRET, we recommend the use of, e.g., an Arduino to synchronize sample illumination and the opening of the camera exposure so that cells are only illuminated when the camera is exposing. The software connected to the camera and light source may vary between laboratories, with the most prominently used being the open-source freeware known as ImageJ with MicroManager. ImageJ version 1.01 was released in March of 1999 and is a Java-based image analysis suite with simple yet intuitive tools for the measurement of pixel intensities in selected regions [6]. This allows ImageJ to be used to measure and compare fluorescent signals. Being open source has allowed for many off-shoots of the software to come into existence, one of which is Micro-Manager. Micro-Manager was published in 2010 and appears as an add-on on top of the original ImageJ functionality, but has quickly become a staple in microscopy due to its primary function to interact with and control microscopy hardware [7]. The most notable functionality of Micro-Manager is the Multi-Dimensional Acquisition (MDA). This allows you to set an interval, as well as select multiple spatial coordinates for Micro-Manager to capture images. MDA paired with a mechanized moving stage and Z-focus drive may be used for temporally multiplexed FRET measurements of multiple spatial locations in the sample. It therefore provides the opportunity to gain more data by recording the fluorescence signal in multiple

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spatial regions during a time-lapse acquisition compared to a timelapse measurement of a single region alone. This approach yields measurements of each region with equal temporal spacing. The number of regions that can be measured in this way depends on the speed of the mechanized stage, Z-focus drive, distance between regions, and the selected time interval for image acquisition. When combined with the proper hardware, MDA allows for temporally multiplexed ratiometric FRET measurements of multiple spatial locations. However, previously described non-proprietary analysis software could only calculate and display FRET ratios from a single region in real time [5]. MultiFRET is a Java-based plugin we developed for the ImageJ off-shoot “Icy” and is designed to optimize accuracy and live analysis of multiplexed regional luminescence data obtained when using a beam splitter to acquire both the donor and acceptor signals simultaneously. MultiFRET uses an Icy plugin for edge detection, named “Active Contours” by Dufour et al. [8], to identify the signal acquired from the donor and acceptor channels and generates an affine transformation to spatially align the two channels. MultiFRET then takes the aligned channels generated from live MDA capture and reports the ratiometric FRET measurement from each spatial location as the measurement progresses. Note that although the main intent of this plugin was to measure FRET, it can be used for any temporally varying visible luminescence. One such example is recently developed single fluorophore sensors based on circularly permuted fluorescent proteins to enable a more straightforward measurement of multiplexed fluorescent biosensors [9]. Furthermore, MultiFRET is compatible with any number of channels, theoretically allowing many FRET biosensors to be monitored at the same time provided that the biosensors are compatible for simultaneous usage. The flexibility of MultiFRET in regard to the type of experiments that can be performed has been taken into account in the form of a freely customizable automated analysis feature. Live analysis can be customized by inputting mathematical equations, using regions of interests as variables, which will be parsed by mxParser by M. Gromada in 2010 and shown live on graphs built through JFreeChart. Post-experiment output comes in the form of an automatically generated Excel sheet, for which a template including formulae can be supplied by the user. This chapter will detail the necessary steps to set up the MultiFRET software, the protocol for running an experiment, variations on the protocol, and lists references to the notes for potential trouble-shooting steps. After describing the software setup, we detail several optional steps that may be taken to customize your experiment and data output. We then detail the sample preparation, using NRVMs transfected with Epac-SH74 [10] as an example for the demonstration of MultiFRET. Epac-SH74 is a FRET sensor for cAMP, consisting of a binding domain for cyclic adenosine

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monophosphate (cAMP) flanked by mTurquoise (ex. 434 nm, em. 474 nm) as donor fluorophore and a Venus (ex. 515 nm, em. 528 nm) dimer as acceptor. Finally, we run through every step of running the MultiFRET plugin. While we demonstrate the protocol using two spectral channels of our four-channel system. MultiFRET is, as previously mentioned, compatible with any number of spectral channels.

2

Materials 1. Eclipse TE2000 Inverted Microscope (Nikon Instruments Europe B.V.) (see Note 1). 2. Tango 3 mini, stepper motor controller (M€arzh€auser Wetzlar, Wetzlar, Germany). 3. SCAN IM 130
85, mechanized stage (M€arzh€auser Wetzlar, Wetzlar, Germany). 4. MFD, focus drive (M€arzh€auser Wetzlar, Wetzlar, Germany). 5. 3-axis digital joystick with Multi-Function wheel (M€arzh€auser Wetzlar, Wetzlar, Germany). 6. Quadview beam splitter (Photometrics, Tucson, AZ, USA). 7. 430 nm OptoLED light source (Cairn Research, UK). 8. Dichroic mirror microscope (Omega, Brattleboro, VT, USA). 9. Dichroic mirrors Quad-view (Semrock, Rochester, New York, USA). 10. Band-pass filters Quad-view (Semrock, Rochester, New York, USA). 11. Longpass filter Quad-view (Omega, Brattleboro, VT, USA). 12. ORCA flash 4.0 camera LT+ (Hamamatsu Photonics, Welwyn Garden City, UK). 13. Arduino Duemilanove (RS Components, UK). 14. Lipofectamine 3000 transfection kit (Thermo Fisher, UK). 15. Neonatal heart dissociation kit (Milteyi Biotech, Germany). 16. Neonatal calf serum (Thermo Fisher, UK). 17. GlutaMAX™ Supplement (Thermo Fisher, UK). 18. Vitamin B12, 98% (Thermo Fisher, UK). 19. Medium 199 with Hanks’ salts and sodium bicarbonate without L-glutamine (Thermo Fisher, UK). 20. Antibiotic Antimycotic Solution (100
), (Thermo Fisher, UK). 21. 35 mm Dish, Uncoated, Coverslip No.1.5, 14 mm Glass Diameter (MatTek, USA).

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22. Sprague Dawley neonates one to two days old (Charles River, UK). 23. Epac-SH74 (kindly donated by Dr Kees Jalink, NJI-AVL, Netherlands) (see Note 2).

3 3.1

Methods Icy Setup

1. Download the Icy Bioimaging suite at http://icy.bio imageanalysis.org/download/. 2. The same page contains a download link for Java, if your device does not have Java 7 or higher installed make sure to download and install the proper version of Java 7 or higher for your OS architecture. This is important as Icy does not come in separate 32/64 bit versions, but rather runs in either mode depending on the Java installation it finds. If you are not sure if Java is installed, confirm installation through the methods listed below. Note that Java version is noted as “1.X.0_###”, with X being the number referred to when Java 7 or 8 is mentioned, and ### being an indicator of the current build of said version. (a) In Windows: WIN+R ! Type “cmd” and hit ENTER ! Type “java -version” and hit ENTER. (b) On a Mac: Command key ! hit the SPACEBAR ! Type “terminal” and hit ENTER ! Type “java -version” and hit ENTER. (c) On Linux: Open a Terminal (method depending on your distribution) ! Type “java -version” and hit ENTER. 3. The MultiFRET plugin comes as a MultiFRET.jar file and should be copied into the Plugins folder under your Icy installation. MultiFRET is available on request from m. [email protected] [email protected]. 4. Install Micro-Manager 1.4, obtained from https://micromanager.org. 5. Start Icy by running Icy.exe or any shortcuts generated by the installer (see Note 3). 6. Using either the top-side search bar or the Online Plugins button under the top-side Plugins tab, confirm that your installation of Icy came with Active Contours and MicroManager For Icy installed (see Note 4). 7. When attempting to start the Micro-Manager plugin for the first time, you are prompted to indicate the location of your Micro-Manager installation. This links the plugin to the actual installation (see Note 5). 8. The Micro-Manager plugin works almost identically to the stand-alone installation, as such it will start with a prompt to

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select a configuration file. This is the file that can be set up through the Micro-Manager configurations wizard and contains all the information needed for Micro-Manager to control your hardware. Within Icy, this wizard can be opened through the menu that follows when clicking on the top-left of the Micro-Manager main window. Setup of the configurations file is performed through the self-explanatory wizard, and a list of compatible devices can be found here: https://micro-manager.org/wiki/Device_Support (see Note 6). 3.2 Initial Setup of MultiFRET

At this point it is assumed that you have a microscope configured with an automated stage, shutter-controlled excitation light, camera, and the appropriate lenses/splitters needed for the experiment. If not, refer to Sprenger et al. [5] for basic instruction and to Subheading 3 of this chapter for our modifications. In this section we show how to either manually select a set of regions of interest (ROI) or use the Active Contours plugin to generate a contour-file, used to correct for beam splitter channel misalignment. We further set up our MDA settings, and optionally prepare an Excel output template and custom calculations file.

3.2.1 Active Contours

1. With no dish on the microscope, turn on the brightfield illumination source, open Icy, then open the Micro-Manager plugin, make sure you have selected the correct binning for your camera (we use 4
4 binning) and activate Live capture. 2. Increase brightfield illumination source until the corners of your channels are clear and sharp in the viewport, but make sure that the light does not “spill over” into the camera chip around the channels, see Fig. 1 (see Note 7). 3. On the Micro-Manager main window, click Snap to capture a still frame. 4. In the Icy top-side pane Region of Interest tab, find the buttons for Rectangle, Circle, Free-hand, etc. ROI drawing tools. Draw an ROI around each of your channels. Rectangle is recommended but any of them will work (see Note 8). 5. Use the search bar to find and run the Active Contours plugin by Dufour et al. [8], this will open a plugin window with many adjustable settings and a Run (►) button at the bottom, see Fig. 2 Input is by default set to the Active Sequence, meaning the last image you have had as the active window, either make sure that the capture from step 3 with ROIs from step 4 is the active window or select it from the Input drop-down selection box. The only settings that need to be changed are (a) Export ROI to ON_NEW_IMAGE or ON_INPUT. (b) Type of ROI to POLYGON. (see Note 9).

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Fig. 1 Uniformly lit channels can be generated by increasing the intensity of bright light or by tweaking the image settings (dragging the blue and red lines in the Histogram pane to encompass only parts of the tonal distribution representing the bright channel). Top left: Before histogram modification. Bottom left: After modification. Right: Histogram pane showing histogram corresponding to the image after modification shown in top/bottom left

6. Hit the ► button to run Active Contours, which will draw out contours starting from each of your drawn ROI and, using an edge-detection algorithm, tightly fit these contours to each channel. The contours are a polygon ROI with many procedurally generated points. These and all other ROI are listed on the right-side pane of the Icy main window under the ROI tab. If the right-side pane is not visible, there will be a very small < arrow on the right border of the Icy main window which expands this pane (see Note 10).

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Fig. 2 An example of Active Contours pane with typical settings to extrapolate freely drawn ROIs (green rectangles) into tight POLYGON contours (colored rectangular shapes)

7. In the right-side pane under the ROI tab, find and rename all the ROI to appropriate channel names. In traditional fashion, the F2 key may be used to start renaming a selected entry. Thus, using the F2, ENTER, and the arrow keys allows for a faster conclusion over mainly using the mouse. Make sure there are no duplicate names, and then select all of the contours and save them to an accessible location using the Save ROI (s) button in the Region of Interest tab of the Icy main window top-side pane. Selection follows typical modern computer selection methods such as SHIFT+leftclick to select all entries between the currently selected and the one clicked on, and CTRL+leftclick to select one additional entry. If you have chosen ON_INPUT in step 5, make sure you are not selecting the initial ROI drawn in step 4. ROI may be deleted by selecting them and hitting the DEL key. Note that it is recommended that contour-files are saved with a date, so that they can be used to re-analyze any saved image stacks from that period.

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Fig. 3 Example of an Excel output workbook with a typical data sheet showing Milestones listed on the left with the C-column containing the average of the last 10 frames before milestone mark. On the ROI name, frame number, recording time (s), mean intensity, and raw data can be found in order. The sheet further contains an automatically generated graph with mean intensity on the Y axis and time on the X axis 3.2.2 Excel Output Template (Optional)

Upon completion of data acquisition, MultiFRET prompts Excel to write to a user-designated workbook. In this workbook every run of MultiFRET will create a header sheet named “Experiment#” where x is an incremental integer counting the number of experiments in the workbook. This header sheet will contain a screen capture of the final state of MultiFRET, including all graphs and sequence viewers. After each header sheet, data sheets will appear named “ ” with the target name being a user-designated label for each position in the sample marked for data acquisition, defaulting to “Pos” with n ranging from 0 to the number of positions acquired minus one. Each of these sheets contains data following the template shown in Fig. 3, with columns for milestones, frame number, frame capture time in seconds, mean intensity, and raw data. Furthermore, a graph based on the mean intensity and time columns is generated. In this template, the column showing the mean intensity of signal (F ) for the target ROI is calculated using Eq. (1) with the number of pixels in the ROI (n) and the intensity (x) of each single pixel (i). If enabled, there will be a further background correction applied to the mean intensity column using Eq. (2). The milestones shown in this template are user-marked events in time that occur during the experiment such as the addition of a drug. For convenience, each milestone comes with a formula next to it which calculates the average over the last 10 frames captured up to and including the milestone. Using the FRET ratio plateau averages provided by milestones, responses can easily be normalized to an experimental condition chosen to saturate the response of the biosensor, allowing a % FRET change to be calculated (Eq. 3). In this section, we detail the steps needed to set up a custom template on top of the one described above. 1. Open either the Excel workbook you intend to use or create and open a new one.

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2. Create a new sheet at the start of the workbook, that is the leftmost sheet, and name it “template.” 3. In this sheet, fill in any formulae and indicators you require while avoiding use of cells that are already in use by the default template. 4. After a completed run of MultiFRET that uses this workbook as its output workbook, any new datasheets will have the contents of your template sheet automatically copied into them before MultiFRET fills these sheets with data and the original template. 5. Depending on your version of Excel, it may be required to force Excel to re-calculate any formulae added to your datasheets in this manner. This may be done using the hotkey CTRL+ALT+F9 to force re-calculation on all open worksheets or with CTRL+ALT+SHIFT+F9 to do so for all sheets. ! n 1 X F ¼ xi ð1Þ n i¼1

FRET ¼

F donor  F donor background F acceptor  F acceptor background

FRET%Change ¼ 100


3.2.3 Custom Calculations (Optional)

F stimulantplateau F saturatorplateau

ð2Þ ð3Þ

MultiFRET is able to parse mathematical formulae through the use of the mXparser by M. Gromada in 2010. MultiFRET implements this parser by scanning a user-designed formula for variables that can be linked to the data obtained from specified ROI. Such a formula can be created in any text editor such as Notepad and stored as a text file (*.txt) with the header for each formula denoted by a “>” at line start, the variable names comma-separated and denoted by a “” at line start, and the formula itself denoted by a “>>>” at line start. For example, the bleed-through correction formula in Eq. (4), with example bleed-through co-efficients 1.468 for the CFP channel to YFP channel and 9.610 for the YFP channel to CFP channel, can be written as in Eq. (5). When saved as a *.txt file, using for example Notepad.exe in Windows, it can be selected in MultiFRET, which gives the option to designate values obtained from a specified ROI as those to be used for, e.g., CellMeasurement or BackgroundMeasurement. The text in square brackets is used to indicate the named spectral channel of this ROI that should be used. Thus, this example lets us measure both the fluorescence signal (e.g., YFP) from a cell (CellMeasurement[YFP]) and the signal from a selected background region in both the YFP channel (BackgroundMeasurement[YFP]) and in the CFP channel

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(BackgroundMeasurement[CFP]), then perform the custom calculation. For simple expressions, it is enough to write them as one would on a graphical calculator, with the exception that any word followed by square brackets will be detected by MultiFRET as a variable, and needs to be designated in the “” denoted line. For more complex functions, comprehensive documentation can be found here: https://mathparser.org/mxparser-tutorial/. 

F Corrected ratio

 ðF CFP F CFP background Þ F YFP  F YFP background  1:468 ¼  ðF YFP F YFP background Þ ð4Þ F CFP  F CFP background  9:610

ð5Þ 3.2.4 Sample Preparation (Example)

Neonatal ventricular mouse cardiomyocytes (NRVMs) were isolated from one- to two-day-old pups according to the protocol provided by the manufacturer of the dissociation kit (Miltenyi Biotech, Germany) (www.miltenyibiotec.com/protocols). All procedures are carried out under the regulations of EU2012. Neonatal rat pups are sacrificed through schedule 1 procedures without the use of anesthesia. Myocytes were plated on glass-bottom Mattek dishes (13 mm culture well diameter, 35 mm dish diameter) in culture medium (90 mL M199 medium, 10 mL neonate serum, 0.5 mL antimycotic/antibiotic solution (100
), 1 mL L-Glutamax (100
), 0.5 mL Vitamin B12 stock (0.4 mg/mL)) and cultured in an incubator with 1% CO2 supply. We detail here a protocol for NVRM isolation. 1. Prepare enzyme mix 1 and enzyme mix 2 according to the Miltenyi Biotec Inc. neonatal heart dissociation kit. (a) Mix 1: 62.5 μL enzyme P and 2.3 mL buffer X preheated for 5 min at 37  C in a 15 mL falcon tube. (b) Mix 2: Combine 25 μL Buffer Y, 12.5 μL enzyme A, and 100 μL enzyme D, then add this to enzyme mix 1. 2. Sacrifice one- to two-day rat pups through cervical dislocation followed by decapitation, following ethical procedures for your laboratory. 3. Pin the bodies to a convenient surface to allow extraction of the heart using sterilized scissors and forceps. 4. Wash the hearts in Hank’s buffer salt solution (HBSS) to remove blood.

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5. Transfer the hearts to a clean petridish containing HBSS and mince them into 1 mm cubes. 6. Aspirate any HBSS and transfer the tissue into a gentleMACS C tube containing 2.5 mL of enzyme mix for enzymatic digestion and tissue dissociation. 7. Place the gentleMACS C tube containing the enzyme mix and tissue with the cap facing down in a 37  C incubator for 15 min. 8. Attach the tube to the gentleMACS dissociator and run the program provided with the machine. 9. Repeat steps 7 and 8 twice. 10. After the third time, add 7.5 mL of culture medium 199 (M199) supplemented with 10% (v/v) FCS, 1% (v/v) VitB12, 1% (v/v) L-glutamine, 200 μg/mL streptomycin, and 200 U/mL penicillin. 11. Pass the sample through a 70 μm gauge mesh filter insert to remove large particles. 12. Centrifuge the filtrate at 179 times g for 5 min and remove the supernatant. 13. Resuspend the pellet in fresh supplemented M199 and transfer to a glass-bottom flask. 14. Incubate for 1 h at 37  C in 1% CO2 to allow for separation between cardiomyocytes and the cardiac fibroblasts which adhere to the flask more rapidly. 15. During incubation, use sterile laminin diluted in PBS to 0.6 mg/mL to coat a required number of glass-bottom Mattek dishes, preferably of the smallest available well-size to reduce usage of cells. 16. Collect the medium containing cardiomyocytes in a tube and centrifuge at 180
g for 5 min. 17. Count the cells using a hemocytometer or other cell counting device. 18. Make sure the Laminin on the Mattek dishes has dried up or aspirate any that is left over. 19. Plate an appropriate number of cardiomyocytes, approximately 20,000 for a 7 mm glass-bottom Mattek dish, by pipetting a droplet into the glass-bottom well. 20. Incubate at 37  C in 5% CO2 for 3 h before topping up with 2 mL of supplemented M199 medium. While MultiFRET works with any luminescent material, we provide here an example procedure for transfection of the cultured NRVMs with a plasmid encoding the Epac-SH74 sensor.

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1. In a sterile hood, prepare two Eppendorf tubes containing 25 μL of Opti-mem medium per dish to be transfected. 2. In the first tube add 1 μg of plasmid DNA per dish to be transfected. 3. To the same tube, add 1 μL of P3000 reagent per dish to be transfected. 4. Incubate this tube at room temperature for 1 min while preparing the second tube. 5. In the second tube, add 1 μL of Lipofectamine 3000 reagent per dish to be transfected. 6. Slowly pipette the contents of the second tube into the first, slowly moving the pipette tip throughout the length of the tube while releasing the mixture. Do not mix. 7. Incubate the combined mixture for 20 min at room temperature (see Note 11). 8. Directly pipette 50 μL of the transfection mixture onto the glass-bottom well of a Mattek dish (see Note 12). 9. Incubate at 37  C in 5% CO2 for 48 h. 3.3 Running MultiFRET

At this point in the protocol, it is assumed you have previously completed the Subheading 3.3 of this chapter and have an up-todate contour-file that represents the current position of your channels on the camera chip. Position the dish onto the microscope stage; immersion oil is required for the 60
lens. 1. Start Icy and open the Micro-Manager plugin, selecting the appropriate configuration file for your system. 2. Set the appropriate binning (4
4 is appropriate in our system)) and exposure time for your camera and experiment. Note that higher levels of binning reduce the impact of readout noise in exchange for a reduction in spatial resolution. The binning you select must be the same as used when setting up the contour-file. 3. Click on the Multi-D Acq. button on the Micro-Manager window to open the MDA window, see Fig. 4. 4. Make sure that the only checkboxes enabled are Time points, Multiple positions (XY), and Save images. Depending on your hardware, Autofocus may also be used. 5. Under Time points, set the Number of time points to an arbitrarily high integer that will not be reached during your experiment, but is low enough not to require more memory than is available. An estimation of the memory cost and experiment duration are given in the Summary box (see Note 13).

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Fig. 4 Example MDA setup

6. Under Time points, set the Interval to a duration appropriate to your experiment, but that will give your stage enough time to loop through all selected cells. The duration selected here is the minimum total time any single cycle of measurements will take. That is, if your cells have all been recorded throughout a current cycle, MDA will wait until the interval duration elapses before starting the next cycle. However, you need to make sure the time it takes for the stage to scan through all the selected cells does not exceed the time interval you have set between

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your consecutive measurements of the same cell. In our hands, with the time-lapse intervals of 6–10 s, the number of cells you can measure is 15–25, depending on how close the cells are located in a dish. 7. Under Save images, set a suitable Directory root for your image stacks to be saved under as well as a suitable Name prefix for the image-stack files. Image stacks will be saved in a sub-folder named _ as __MMStack_.ome.tif. 8. Under Multiple positions (XY), click on Edit position list. . . to bring up the Stage Position List window. Here at the bottom of the window, un-check the use of the Z stage if your machine lacks the capability of digitally tracking vertical movement of the stage. All marked targets after this un-checking will only have their XY coordinates recorded (see Note 14). 9. You are now ready to start selecting cells. Click on Live on the Micro-Manager For Icy main window. A sequence viewer window with a live camera feed will open. You are now ready to locate and select cells using either the camera or the microscope optics. Note that depending on your microscope you may need to switch between camera and optics output using a switch, valve, or lever on the microscope itself. 10. Using only a 3D joystick, move the stage until a target of interest is centered in your channels output. If available, use a mechanized vertical stage that uses either an encoder to inform Micro-Manager of vertical changes and record Z-coordinates in addition to X and Y, or an auto-focus mechanism. You may use the manual focus knob on the microscope but beware that bringing a later target into focus may result in loss of focus for previous targets. With the lack of an encoder or auto-focus, you must find targets that are roughly within the same focal plane and you may use either the 3D Joystick to focus or the manual focus knob on the microscope but beware that bringing a later target into focus may result in loss of focus for previous targets. 11. To select the target of interest, click MarkMark on the Stage Positions List window. Note that the number of targets MultiFRET can support relies solely on your PC’s hardware. Once an adequate number of targets have been selected, you may close the Stage Positions List. (a) To correct a mistakenly selected target, click on the entry within the Stage Positions List and then click the Remove button on the same window. (b) Targets listed in the Stage Positions List may be renamed by double clicking on the entry’s label, or by selecting them and hitting the F2 key. Type in a unique label and hit the ENTER key to confirm.

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(c) To check a previously marked target, select it in the Stage Positions List and then click on the ► Go to button. (d) Note that the positions list may be saved once selections are complete by clicking on the Save As. . . button in the Stage Positions List window. This will prompt you to save your currently marked targets and allows them to be loaded if you wish to restart Icy due to any mistakes. Loading a positions list is done by clicking on the Load button on the same window. 12. Start the MDA by clicking the Acquire! button on the MultiDimensional Acquisition window. This will open a sequence containing an image stack for each position marked in the previous step. Every interval the stage will move to and capture a frame at each position, updating the sequences in the process. Each sequence viewer has a slider at the bottom which allows you to scroll through the image stack. Note that even when the sequence viewers are closed, Micro-Manager is still collecting frames for them in the background as the sequences themselves are still open. 13. Now start MultiFRET, either from the plugins tab on the top pane of the Icy main window or through the search bar. The first run of this plugin will show terms of service window, if you wish to continue you must agree to the terms. Agreement will be saved in C:\Users\\MFIoptions.cfg. Note that this is a local home directory, not a network one. 14. A changelog message will pop-up in the lower right corner of Icy, showing the current version, date of the current version, as well as any fixes and new features that have been added in the current version. This message dismisses itself in 30 s, or when clicked on. 15. A settings window will open containing several rows of options that may be enabled or disabled. (a) Transform: Enables the use of an algorithm to increase accuracy of channel alignment. Rotationally distorted channels in particular will benefit from this option. For this algorithm to function contour edges need to contain minimal deformities, as the algorithm uses the straight lines to detect channel corners. Note that there will be a few seconds of lag upon clicking the Ok button on this settings window, while the plugin runs its calculations. The run itself is further unaffected. It is recommended to keep this option on unless an error pops up after clicking the Ok button. The protocol will run with adequate accuracy regardless of this option if the channels are not rotated excessively.

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(b) Offline: Enable this option if you are loading image stacks into Icy for “offline” analysis. “Offline” analysis implies running through the rest of this protocol on not a live captured image stack, but on a previously saved one. A microscope and Micro-Manager are not required for an offline run, but you must use a contour created for the state of the beam splitter at the time of the capture. Alternatively, you may use the image stack to create a new contour-file for it. The following steps are the same for either an online or an offline run, but the plugin will run much faster in the offline mode, as it will not need to wait for captures. (c) CustomCalcs: Enables the use of a custom calculations file as setup in Subheading 3.2.3 of this chapter. (d) Workspace: Enables a convenient “workspace” tab in the window that opens during the run. This tab will place all sequence viewers and graphs conveniently inside a scrollable section of the window, making them easier to manage. Recommended to be on for most purposes unless it is of utmost importance to be able to view all graphs or sequences, or both, simultaneously. (e) Select Contour-file: Prompts you to select your contourfile. This is mandatory for the run. (f) Select Calculations: Prompts you to select your custom calculations file, as set up in Subheading 3.2.3 of this chapter. The CustomCalcs checkbox on the previous row must be enabled. (g) Save Settings: This will save all options on this window to C:\Users\\MFIoptions.cfg. These will be automatically loaded on the next start-up of MultiFRET. (h) Open a File: This button opens a prompt for you to choose an output Excel workbook or a folder. Alternatively, you may manually enter a file or folder location in the adjacent textbox. If a folder is selected, a Datasheet. xlsx file will automatically be generated. If an existing used Excel workbook is chosen, data will be amended to the end of this workbook in the form of new sheets. It is recommended to frequently change the used workbook, as interruptions of the run may result in corruption of the file. (i) Between the settings and the confirmation buttons you will find a real-time log of the settings loaded and changed in this window. (j) Under the log you will find the Ok button which is used to confirm the settings, and the Cancel button which is used to close MultiFRET.

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16. When the settings are as desired, click on the Ok button. If Transform was enabled, the next window may take a few seconds to load. All MDA acquisition windows are automatically minimized at this point. The MultiFRET window appears and contains a set of tabs on the left, one of which is Workspace (if it is enabled, which is recommended) and contains the sequence viewers. Otherwise, along with the MultiFRET window, a window for each of your marked positions will appear. Whether the sequence viewer is shown in the Workspace or as a separate window, these viewers contain a slider on the left allowing you to switch through channels in alphabetical order (as named during contour generation). Note that while the MultiFRET window is active, any frames captured by the MDA will automatically be stored for analysis by MultiFRET. This means that your data collection includes the time it takes for you to set up the ROIs in the following steps in your experiment. 17. The mean intensity of fluorescence is calculated using Eq. (1), wherein the mean intensity of signal (F Þ in each ROI is calculated from the number of pixels n in the ROI and the intensity x of each single pixel i. Under the Corrections tab, enable any further live corrections required. By default, this tab will contain the option to enable live background correction on a per-channel basis using Eq. (2). 18. If enabled, custom calculation will also appear here. Save the enabled calculations for future runs by clicking the Save corrections and last ROI channel selection button at the bottom of this tab. 19. To proceed, you must draw an ROI for each region of interest in your sequences, and a single background ROI for each position (each sequence). Note that a sequence may have multiple ROI that will all be corrected against the same background ROI. These ROI can be drawn manually using the ROI tools in the Region of interest tab in the Icy main window top pane. It is recommended to use Polygon ROIs for your targets, and to save time use either Circle or Square ROIs for the background ROIs. ROIs can be selected on the image or in the right-pane ROI tab, be moved through dragging or editing the position values in the right-side pane, be deleted using the DEL key, and modified in every way including color and label. As always, if you change the labels make sure they are unique. 20. The tab-list on the left side of the MultiFRET window also contains a tab for each of your marked positions. Every ROI drawn on your images will appear in the appropriate tab as a series of settings. The first drop-down list chooses the numerator for the fluorescence ratio calculation, while the second drop-down list chooses the divisor. Finally, there will be a

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checkbox to designate the ROI as a background ROI. Doing so will ignore and gray out the numerator/divisor settings. If enabled, custom calculations checkboxes will appear after the background checkbox. Each ROI may be designated to be used as one of the variables in your custom calculation equation. Importantly, selection of any of the basic settings here will be committed to memory and automatically applied to any ROI drawn hereafter. This makes it efficient to first draw a single target ROI, adjust the settings, and then draw the rest of them. Similarly, it is most efficient to draw one background ROI, designate it as such, and then draw the rest of them. The memory of the last chosen ROI settings may be saved to C: \Users\\MFIoptions.cfg, using the Save corrections and last ROI channel selection button at the bottom of the corrections tab. 21. As an alternative method and time-saver compared to manually drawing the ROIs for your targets, you may use the Detect button at the bottom of the MultiFRET window to use the automatic detection function to draw a single ROI per sequence automatically. Under the Detection tab, you may choose the method for an automatic ROI drawing functionality built on the Active Contours plugin by Dufour et al. [8]. (a) Max Pixel: This method will find the highest intensity pixel in your image and use it as a starting point to draw your ROI. This method is recommended unless your sample features bright artifacts. (b) Whole Frame: This method will use the borders of your channel as the starting point to draw your ROI. 22. At the bottom of the window, find the Max. Iterations textbox and its Detect button. Max. Iterations controls the amount of time afforded for the automatic ROI drawing function. It is at 10,000 by default, which is normally sufficient. This may be raised if ROIs are drawn unfinished but doing so will increase the duration of the automatic detection procedure. 23. Note that the automatic detection of ROI is not always perfect, so make sure to double-check all ROI. Note that the Detect button will first clear any currently drawn ROIs and will not draw background ROIs (see Note 15). 24. If using multiplexed biosensors with the adequate number of channels to measure each separately, you may duplicate your target ROI as many times as required and assign different numerator and divisor channels to each. 25. Finally, one of the last few buttons at the bottom of the MultiFRET window is the Milestone button. This will create a mnemonic marker allowing you to designate when you have applied experimental events such as the addition of a drug. This

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Fig. 5 An example of MultiFRET workspace showing 2 viewports of different position captures and their corresponding live charts. NRVMs were transfected with Epac-SH74cAMP sensor and the YFP/CFP ratio was displayed upon stimulation with 100 nM isoproterenol, designated as the automatically generated “Milestone#1”. After a plateau was established, cells were saturated for cAMP by use of phosphodiesterase inhibitor IBMX, designated by the user generated “IBMX” marker. Finally, the End of the experiment was also marked by the user. Note that the top chart has been zoomed out for demonstration purposes

button’s effects are only visible once the Start button has been clicked and the graphs are generated but it may be used during the ROI setup phase, allowing experimental events to be registered even while setting up the MultiFRET live calculations. 26. Click on the Start button at the bottom of the MultiFRET window when all sequence viewers contain at least one target ROI designated with the correct numerator/divisor, and exactly one background ROI designated as such, see Fig. 5. All frames captured by the MDA between the appearance of the MultiFRET window and now will automatically be processed, generating a graph with all live corrections for each sequence. These graphs will either show as separate windows or inside the Workspace tab if it is enabled. All further captured frames will

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automatically be processed and shown in the same way after every capture interval. Every Milestone will appear both as a list at the bottom of the MultiFRET window as well as a marker on the graphs. Graphs may be zoomed in and out on, and a rightclick brings up options to set automatic axis constraints. 27. When your experiment has concluded, first, make sure the designated output Excel workbook is not open, then click on the Stop button; this will bring up a prompt window allowing you to select whether the MDA should also be stopped, and whether its sequences should be closed. When stopped, MultiFRET will close and instruct Excel to write the data into a workbook and open it. Excel may take a few seconds to complete this procedure, and it is important to keep Icy open until the Excel workbook appears. Premature closure may corrupt the workbook (see Note 16). 28. To proceed to the next experiment, you have to clear the positions list using the Clear All button in the MDA Stage Position List window; make sure all sequences have been closed and repeat this protocol from step 7.

4

Notes 1. Microscopy System Our microscopy system is based on the same principles as shown by Sprenger et al. [5], but with the addition of a mechanized sample stage and focus drive (M€arzh€auser Wetzlar, Wetzlar, Germany) that allow higher-throughput acquisition of multiple spatial coordinates in one experiment (Fig. 6a), as well as an Arduino controlling an LED instead of the shuttercontrolled mercury light. The Arduino board is set up with Arduino software (https://www.arduino.cc/) and installed according to the guide on the Micro-Manager website (https://micro-manager.org/wiki/Arduino). Individual cell positions (X, Y, and Z) are recorded within Micro-Manager prior to an experiment, allowing each image capture to have a cell centered and in focus. Cells are imaged using an inverted Nikon TE2000 microscope with a Nikon 60
/1.40 Plan Apo objective, a 430 nm LED light source (Cairn Research, UK), and a long-pass 455 nm dichroic mirror (DM455, Omega, Brattleboro, VT, USA). The reflected light is passed through a beam splitter QuadView (Photometrics, Tucson, AZ, USA) to split the light into four spectral channels before it reaches the camera. The beam splitter has three long-pass dichroic mirrors: 514 nm, 552 nm, and 605 nm (Di02 R514, FF552 Di02, FF605 Di02, Semrock, Rochester, New York, USA) and three band-pass filters: 433/24 nm 530/11, 572/15

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Fig. 6 (a) Overview of a typical microscopy system used to run MultiFRET. (b) Internal design of a quadview beam splitter

(center/band-pass) (FF01-433/24, FF01-530/11, FF01572/15 Semrock, Rochester, New York, USA) and a longpass 700 nm filter (700LP,Omega, Brattleboro, VT, USA), for acquisition of cyan, yellow, orange, and red light, respectively (Fig. 6b). Recordings were made using an ORCA-Flash 4.0 camera (Hamamatsu Photonics, Welwyn Garden City, UK). The computer is equipped with an Intel(R) Core (TM) i7-4770 central processing unit (CPU), NVIDIA GeForce GT 630 graphical processing unit (GPU), and 16GB of DDR3 random access memory (RAM). For full specifications, see Supplement A. The software for the recordings was

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Fig. 7 Schematic representation of Epac-SH74 showing two fluorophores flanking an EPAC1 protein with a glutamine to glutamic acid mutation at amino acid position 270

Micro-Manager 1.4 (open source, Vale Lab, University of California, San Francisco, USA), integrated into the Icy imaging suite (open source, http://icy.bioimageanalysis.org). 2. FRET Biosensor The Epac-SH74 (Fig. 7), which was kindly gifted by the Jalink group (Netherlands Cancer Institute), contains a mutated full-length EPAC1 protein as a sensor, flanked by mTurquoise (ex. 434 nm, em. 474 nm) as donor fluorophore and a Venus dimer as acceptor. Upon binding to cAMP, a conformational change occurs which reduces FRET. 3. (a) In Windows: When starting Icy, if an error message is encountered saying that the JAVA_HOME environmental variable is not set, first locate your Java installation directory (example: C:\Program Files\Java\jre1.8.0_251), then hit WIN +R and enter SystemPropertiesAdvanced and click on Environment Variables in the subsequent window, next add a new system variable with the name JAVA_HOME and the path to your java installation directory, and finally adjust the system Path variable by adding %JAVA_HOME%\bin as a new entry. (b) On Mac: If your security settings deny access to Icy, you will need to use control+click and choose Open, to bypass them. (c) On Linux: Make sure to start Icy from its folder to ensure proper loading of VTK libraries. If your distribution shows incompatibilities, you may need to install or compile the dedicated VTK package with java wrapper and move the library (*.so) files to the icy lib/linux64/vtk folder. 4. Icy has a built-in Plugin installer which can be accessed from the plugins tab, or through the search bar. Additionally, any uninstalled plugins searched for through this same bar can be installed by clicking on the search result. Currently, MultiFRET is not included in the automated plugin installer; however, the ActiveContours plugin is needed for this protocol and it can be automatically installed. Icy should come with a MicroManager plugin installed, if not this can also be acquired in the same way. Do note that this plugin is not an actual MicroManager installation, Micro-Manager 1.4 or later should be installed separately from https://micro-manager.org. A link to this download is also provided if the plugin is launched without a Micro-Manager installation.

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5. (a) Note that after this initial setup, the Micro-Manager plugin may not work initially and yield an error when launched. This can be resolved by restarting Icy. If a further error is encountered, this may indicate a conflict between the OS architecture the downloaded micro-manager installation is built for and that of the Java version installed. To clarify, if 64-bit Java 8 is installed Icy will launch in 64-bit and be unable to launch a 32-bit Micro-Manager installation through its plugin, and vice versa. (b) When launching Micro-Manager within Icy you may be prompted to register unless you have already done so or already clicked “Never” within the stand-alone Micro-Manager application. You may notice that the registration window within Icy lacks the “Ok,” “Later,” and “Never” buttons, this is a result of the way Icy internalizes windows. The buttons are still present, but the layout will not show them. However, one may still navigate to and use the hidden “Never” button by activating the lowest text input field and using three presses of the Tab key followed by Enter or Spacebar. 6. (a) If your hardware is not yet supported by Micro-Manager, either search online for a compatible driver built by someone or to build your own driver refer to this guide: https://micro-manager.org/wiki/Building_Micro-Man ager_Device_Adapters 7. (a) If your brightfield illumination source does not have sufficient power to clearly illuminate the channels, you may use the brightness and contrast controls after capturing a frame in step 3 to achieve the same effect. (b) If a channel is not lit up evenly, i.e., there is a gradient, you may have to adjust the positions of your mirrors if you are using a beam splitter. 8. (a) As the next step uses an edge-detection tool designed for cells, you may be quite rough and inaccurate with the ROI drawings. (b) If for any reason you prefer to contour your channels manually and skip the next step, it is recommended to use the polygon ROI tool to do so. Using the mouse-wheel allows you to zoom in and out on the capture, for enhanced accuracy. Note that for comprehensive realignment algorithms to work, you require the plethora of only points provided by the next step. 9. (a) If for any reason Active Contours did not come installed, install in either the same way as launching it through the search bar, through the Online Plugin installer, or from this URL: http://icy.bioimageanalysis.org/plugin/active-contours/

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10. (a) If the contour encompasses dead space around the channel, this may be resolved by increasing contrast between channel space and the unused camera chip space around it in the following ways: l

Increase bright-light output and further close diaphragm.

l

Use the Histogram and colormap settings on the right-side pane under the Sequence tab to enhance contrast by dragging the lines in the histogram to bound only relevant intensities (typically a peak) and drag a point upwards in the colormap to further increase the brightness. (b) If too many contours are shown in the ROI list, this may be due to accidental duplication of initial ROIs, or by running Active Contours again while the contours are already present. Since contours are ROI themselves, they may be used as new starting-off points for subsequent contours.

11. If at any point you see a precipitate forming in the first tube, this could be the result of either too much DNA or adding your reagents before adding the DNA. Make sure to add DNA to the Opti-mem before any lipofectamine reagents. 12. For enhanced transfection efficiency, there are two alternative methods you may use: l

Mix the transfection mixture with the cell suspension just before plating.

l

Aspirate the medium from the Mattek dish, add the transfection mixture onto the glass-bottom well containing the cells and then incubate for 3 h before adding fresh medium on top.

13. Memory here refers to storage memory, you can increase the amount available to your computer by upgrading your HDD, SSD, or connecting an external drive of either type. 14. Note that the Stage Position List window is not an Icy internalized window. This may be resolved in a future update to Icy, but currently brings with it several issues: l

The Stage Position List window may be hidden behind the Icy main window. Hold down the ALT key and hit the TAB key multiple times to cycle through open desktop windows, find it and release ALT bring it back to the front.

l

Experience has prompted the theory that resizing or perhaps even moving the Stage Position List window may result in a crash while running the MDA later. It is best to leave the dimensions and position of this window alone once it is open, and close it when you are finished using it. If a crash occurs, it is important to terminate any lingering Icy process. Do to so, use CTRL+SHIFT+ESC to open the task

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Fig. 8 The Pos1 viewport shows a possible error in cell detection when using the automatic cell contouring function. Note that the viewport has been zoomed out to fully display the nature of this error

manager, click on More Details on the bottom left if needed (depending on your version of Windows and previous task manager usage), then find and end every instance of the Java (TM) Platform SE binary, or Java.exe. On any other operating system, use the appropriate process manager to do the same. 15. (a) Using the automatic detection function will save much time in this process, but it is not a one-button solution. ROIs may be drawn in the wrong position or may incorrectly include background space. Importantly, ROIs automatically drawn may also encompass the area outside of the bounds of your image (Fig. 8). This makes it important to quickly zoom out on each image, using the mouse-wheel, to see if the ROI is put where it should be. Should there be one or more erroneous ROIs, it is important to replace these: select them either through the right-hand pane ROI tab or by simply clicking on the ROI in the viewport, then hit the DELETE key, and finally replace the ROI using the Polygon tool under the top-side Region Of Interest tab. (b) For increased efficiency, copy and paste the same background ROI quickly into each sequence and drag them to a suitable location. This saves the time of having to click on the ROI drawing tool over and over.

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16. (a) If your experiment concludes prematurely for any reason and your Excel workbook output has not been completed, it is still possible to retrieve your data. All data acquired automatically has a back-up amended to a text file in real time. This text file may be found in C:\Users\\MFI, along with a log file containing run-time information useful for debugging. (b) If you end your experiment but MultiFRET cannot find your designated Excel workbook or the workbook is opened or busy for any other reason, you will get a prompt when hitting the Stop button. This prompt contains a Retry and a Choose other .xlsx file. . . button. Retry allows you to close the Excel workbook or place it in the designated location and have MultiFRET attempt to write to it again. Choose other .xlsx file. . . opens a file chooser, allowing you to select a folder where a Datasheet.xlsx file will be generated or an existing .xlsx file. Alternatively, you may type in the name of a non-existing . xlsx file and it will be generated for you.

Acknowledgement This work was funded by the British Heart Foundation grants RM/17/1/33377, RG/17/6/32944, and RG/12/18/30088, and supported by the National Heart and Lung Institute, Imperial College London. References 1. Forster T (1946) Energiewanderung und Fluoreszenz. Naturwissenschaften 33(6): 166–175 2. Heim R, Tsien RY (1996) Engineering green fluorescent protein for improved brightness, longer wavelengths and fluorescence resonance energy transfer. Curr Biol 6(2):178–182 3. Jares-Erijman EA, Jovin TM (2003) FRET imaging. Nat Biotechnol 21(11):1387–1395 4. Spiering D, Bravo-Cordero JJ, Moshfegh Y, Miskolci V, Hodgson L (2013) Quantitative ratiometric imaging of FRET-biosensors in living cells. Methods Cell Biol 114:593–609 5. Sprenger JU, Perera RK, Go¨tz KR, Nikolaev VO (2012) Fret microscopy for real-time monitoring of signaling events in live cells using unimolecular biosensors. J Vis Exp 66:e4081 6. Schneider CA, Rasband WS, Eliceiri KW (2012) NIH image to ImageJ: 25 years of image analysis. Nat Methods 9(7):671–675 7. Edelstein AD, Tsuchida MA, Amodaj N, Pinkard H, Vale RD, Stuurman N (2014)

Advanced methods of microscope control using μManager software. J Biol Methods 1(2):10 8. Dufour A, Thibeaux R, Labruyere T, Guillen N, Olivo-Marin J (2011) 3-D Active Meshes: Fast Discrete Deformable Models for Cell Tracking in 3-D Time-Lapse Microscopy, in IEEE Transactions on Image Processing, 20 (7):1925–1937, https://doi.org/10.1109/ TIP.2010.2099125 9. Mehta S, Zhang Y, Roth RH, Zhang J-F, Mo A, Tenner B et al (2018) Singlefluorophore biosensors for sensitive and multiplexed detection of signalling activities. Nat Cell Biol 20(10):1215–1225 10. Klarenbeek JB, Goedhart J, Hink MA, Gadella TWJ, Jalink K (2011) A mTurquoise-based cAMP sensor for both FLIM and ratiometric read-out has improved dynamic range. PLoS One 26(4):e19170

Chapter 4 Photoactivated Adenylyl Cyclases as Optogenetic Modulators of Neuronal Activity Thilo Henss, Martin Schneider, Dennis Vettko¨tter, Wagner Steuer Costa, Jana F. Liewald, and Alexander Gottschalk Abstract In the past 15 years, optogenetic methods became invaluable tools in neurobiological research but also in general cell biology. Most prominently, optogenetic methods utilize microbial rhodopsins to elicit neuronal de- or hyperpolarization. However, other optogenetic tools have emerged that allow influencing neuronal function by different approaches. In this chapter we describe the use of photoactivated adenylyl cyclases (PACs) as modulators of neuronal activity. Using Caenorhabditis elegans as a model organism, this chapter shows how to measure the effect of PAC photoactivation by behavioral assays in different tissues (neurons and muscles), as well as their significance to neurobiology. Further, this chapter describes in vitro cyclic nucleoside-30 ,50 -monophosphate measurements (cNMP) to characterize new PACs in C. elegans. Key words Photoactivated adenylyl cyclase, PAC, Membrane-bound (mb)PAC, Caenorhabditis elegans, Multi-worm tracker (MWT)

1

Introduction Caenorhabditis elegans is a 1 mm long transparent nematode with known genomic sequence [1]. It is cultivated on E. coli OP50 bacteria and has a generation time of 3–4 days at 20  C [2]. Neurobiology studies are further facilitated by the eutelic nature of the animal, the low cell count of 302 neurons (about 30% of all cells), and the known connectivity pattern [3]. These properties allow adopting optogenetic methods relatively easy, compared to the effort needed in other animal models. A collection of optogenetic methods has been developed for spatiotemporal control of neuronal firing with a minimally invasive approach in C. elegans [4, 5]. The majority of applications use

Supplementary Information The online version of this chapter (https://doi.org/10.1007/978-1-0716-22452_4) contains supplementary material, which is available to authorized users. Manuela Zaccolo (ed.), cAMP Signaling: Methods and Protocols, Methods in Molecular Biology, vol. 2483, https://doi.org/10.1007/978-1-0716-2245-2_4, © The Author(s), under exclusive license to Springer Science+Business Media, LLC, part of Springer Nature 2022

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microbial rhodopsins or their derivates. Widely known examples are Channelrhodopsin-2, a blue light activated cation channel, and Halorhodopsin, a yellow light activated chloride pump [6–9]. However, these proteins do not activate or inhibit neuronal activity in an entirely natural manner, but rather override intrinsic signals that would normally be processed by the neuron or population of neurons of interest, thus perturbing circuit function. Thus, these optogenetic tools do not allow to simply accentuate intrinsic activities of a circuit. Expression of photoactivated adenylyl cyclases (PACs) in the neurons of interest provides another quality of stimulation compared to the canonical rhodopsin optogenetic tools, because they do not evoke neurotransmitter release per se, like depolarization does. Rather, cAMP can affect neuronal firing through at least two different pathways: Via PKA-mediated phosphorylation of downstream targets that affect synaptic vesicle (SV) docking and priming, as well as, at least in cholinergic neurons, filling of SVs with acetylcholine [10]. The second pathway may occur through Epac, an exchange protein activated by cAMP, which directly binds the second messenger generated by PAC activation [11]. Thus, in response to optogenetically evoked cAMP signaling, intrinsic activity will not be overturned, but rather enhanced such that neurons release more transmitter than usual. Consequently, circuit activity becomes accentuated and behaviors are increased, yet in a coordinated fashion. In recent years, several soluble PACs were established in C. elegans, such as the microbial PACs from Euglena (euPAC) and Beggiatoa (bPAC), and the synthetic phytochrome-linked cyclases IlaC22 k27 and PaaC [10, 12–14]. bPAC is composed of a “blue light sensor using FAD” (BLUF) domain which is C-terminally fused to an adenylyl cyclase (AC) domain [15]. Functional bPAC forms dimers, and upon BLUF domain activation by absorption of a photon, it generates cAMP from ATP. BLUF domain activation by light is reversible and does not lead to degradation of the PAC. Generally, bPAC is highly light sensitive, produces a high overall cAMP level and has low dark activity, but turns off slowly following light termination (bPAC: τoff ¼ 12.3 s; KM ¼ 3.7  0.4 μW/mm2 in TRIS-HEPES buffer [15]). Soluble PACs have one drawback, in that they do not mimic physiological cAMP signaling, as induced by membrane-bound (mb) ACs. This occurs within microdomains in close vicinity to the plasma membrane [16, 17], while bPAC generates cAMP at high levels throughout the cytosol. Thus, we generated and established mbPACs in C. elegans. These proteins are based on Cyclase Opsins (CyclOps), which are particular due to their combination of a rhodopsin domain and a C-terminally fused guanylyl cyclase (GC) domain [18, 19]. To obtain mbPACs, we mutated 2–3 key amino acids in the active site of the GC domain of the

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Blastocladiella emersonii and Catenaria anguillulae CyclOps [20]. To further improve the expression and/or membrane targeting of the proteins, we fused them N-terminally with yellow fluorescent protein (YFP) [20]. In this work, we established BeCyclOp (A-2x), YFP-BeCyclOp(A-2x), and YFP-CaCyclOp(A-2x) as new mbPACs for optogenetic cAMP generation in C. elegans, which are marked by high substrate specificity and magnitude of cAMP production. Characterization of the mbPACs and the soluble bPAC in cholinergic neurons (punc-17 promoter) depicted comparable light induced increases of the crawling speed and swimming frequency; however, the mbPACs triggered longer-lasting behavioral effects. Besides their usage as single optogenetic tools, we combined the mbPACs with cyclic nucleotide-gated (CNG) channels to obtain two-component optogenetic systems for activation and silencing of excitable cells. To this end, we combined the mbPACs with the CNG channels TAX-2/-4 (from C. elegans, which is conductive to Na+ and Ca2+), and the cAMP-gated K+-channel from Spirochaeta thermophila (SthK). These two-component optogenetic systems exhibitied variable levels of excitatory or inhibitory activity, kinetics, and long- or short-lasting effects. Working with bPAC and the mbPACs, due to their high light sensitivity, requires that ambient light levels are kept very low, to avoid unwanted (pre-) activation before the actual experiment. This chapter describes how to assess experimental readouts after PAC photo-stimulation in C. elegans neurons and potentially whole neural networks, as exemplified for the cholinergic neurons. These neurons are critically regulating locomotion, by providing excitatory signals to muscles as well as to GABAergic neurons. The latter inhibit muscles on the opposite side of the body, thus their coordinated activity with that of cholinergic neurons evokes a body bend. Cholinergic motor neurons are essential for C. elegans crawling on solid substrate, as well as for swimming in liquid. Here we assess behavioral changes upon PAC activation, for example the swimming behavior, with periodic swimming locomotion (thrashing of the body) at a wavelength about twice the length of the animal. Swimming rate increases when cAMP is increased in these neurons, and also the bending angles are more pronounced. Further, this chapter describes behavioral experiments (locomotion speed and body length measurements) and in vitro cNMP measurements for the characterization of new PACs in C. elegans. Efficiency and applicability of a specific PAC as an optogenetic tool depends on its kinetic properties, compared to the expected outcome of the optogenetic experiment, as well as its dark activity. Furthermore, PACs may be used to investigate signaling cascades activated by cAMP itself, since cAMP has countless modes of action which, by the time of writing of this chapter, are still not fully understood.

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Materials Prepare all solutions using double-distilled water and analytical grade reagents. Prepare and store all reagents at room temperature unless indicated otherwise.

2.1 Common Materials

1. “phosphate buffer”: 1 M KH2PO4, 1 M K2HPO4, pH 6.0. 2. Nematode growth medium (NGM): 0.3% (w/v) NaCl, 1.7% (w/v) Agar, 0.25% (w/v) Tryptone/Peptone, 1 mM CaCl2, 1 mM MgSO4, 25 mM “phosphate buffer,” 0.0005% (w/v) Cholesterol (in ethanol), autoclaved. 3. M9 Buffer: 20 mM KH2PO4, 40 mM Na2HPO4, 85 mM NaCl, 1 mM MgSO4, pH 6.85. 4. 6.5 and 3 cm wide petri dishes. 5. UV Sensor and power meter (i.e., S120UV Sensor with PM 100D power meter; Thorlabs, Germany). 6. Aluminum foil.

2.2 C. elegans Strains

Respective strains are available from the Gottschalk lab, e.g., ZX1569: lite-1(ce314); zxIs53[punc-17::bPAC::YFP; pmyo-2:: mCherry], ZX1940: lite-1(ce314); zxEx960[punc-17::BeCyclOp:: SL2::mCherry,pelt-2::GFP], ZX2316: lite-1(ce314); zxEx1088 [pmyo-3::BeCyclOp[E497K,C566T,H564D]::SL2::mCherry]; zxEx889[pmyo-3::tax-2::GFP; pmyo-3::tax-4::GFP,pmyo-2:: mCherry], ZX2391: lite-1(ce314); zxEx1117[punc-17::BeCyclOp [E497K,C566D]::Sl2::mCherry], ZX2394: lite-1(ce314); zxEx1120[pmyo-3::bPAC::Sl2::mCherry]; zxEX1119 [pmyo-3:: SthK::SL2::GFP; pymo-2::mCh], ZX2396:lite-1(ce314); zxEx1121 [punc-17::SthK::Sl2::GFP; pmyo-3::mCherry]; zxIs53[punc-17:: bPAC::YFP; pmyo-2::mCherry], ZX2403: lite-1(ce314); zxEx1127 [pmyo-3::BeCyclOp[E497K,C566D]::SL2::mCherry]; zxEx889 [pmyo-3::tax-2::GFP, pmyo-3::tax-4::GFP,pmyo-2::mCherry], ZX2405: lite-1(ce314); zxEx1129[pmyo-3::CaCyclOp[E497K, C566D]::SL2::mCherry]; zxEx889[pmyo-3::tax-2::GFP, pmyo-3:: tax-4::GFP,pmyo-2::mCherry], ZX2408: lite-1(ce314); zxEx1132 [pmyo-3::bPAC::SL2::mCherry]; zxEx889[pmyo-3::tax-2::GFP, pmyo-3::tax-4::GFP,pmyo-2::mCherry], ZX2507: lite-1(ce314); zxEx1119[pmyo-3::SthK::SL2::GFP; pmyo-2::mCherry]; zxEx1222 [pmyo-3::CaCyclOp[E497K,C566D]::SL2::mCherry], ZX2606: lite-1(ce314); zxEx1231[punc-17::SthK::SL2::GFP; punc-17::BeCyclOp[E497K,H564D,C566T]::SL2::mCherry; pmyo-2::mCherry], ZX2659: lite-1(ce314); zxEx1255[punc-17::YFP::BeCyclOp [E497K,C566D]::Sl2::mCherry; pmyo-2::mCherry], ZX2660: lite1(ce314); zxEx1256[punc-17::YFP::CaCyclOp[E497K,C566D]:: Sl2::mCherry; pmyo-2::mCherry].

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1. NGM plates: 8.5 ml NGM in 6.5 cm petri dishes. 2. Escherichia coli strain OP50-1. 3. NGM plates containing OP50-1: NGM plate, 300 μl OP50-1 (see Note 1). 4. 100 mM all-trans retinal (ATR) stock solution (in 99% EtOH) (see Note 2). 5. NGM plates containing OP50-1 and ATR: NGM plate, 300 μl OP50-1, 200 μM ATR (see Note 3).

2.4 Analyses of Behavior in Liquid

Tracker platform with equipment necessary for imaging and stimulation of animals, based on the “Multi-Worm Tracker” [21]. 1. Tracker platform equipped with a petri dish holder, a camera (i.e., DALSA, Falcon 4M30, 4 MP (2352  1728), 31 fps, 10 bit) equipped with camera lens (i.e., Rodenstock 60 m, f/4.0 Rodagon Lens) connected via Camera Link cable. 2. As excitation light source, HighPower-LEDs for specific color illumination can be used (e.g., HighPower-LED Green 2.46 W 30 (Signal Construct, Germany); HighPower-LED Red 3 W 30 ; HighPower-LED Blue 3 W 30 ; HighPower-LED Yellow 1 W 60 (Ledxon, Germany)) energized by an LED driver (Mean Well, 40 W Multiple-Stage Output Current LED Power Supply), and dimmed with a 100 kΩ potentiometer (Vishay, 249 ½00 Conductive Plastic and Cermet Potentiometer) controlled by Transistor-Transistor-Logic (TTL) pulses passed on from the computer via a signal connection box (Measurement Computing, 37 conductor shielded signal connection box). Custom made aluminum ring to mount maximum of six LEDs focusing the light onto the center of the petri dish holder (see Fig. 1). 3. Computer equipped with a solid state drive (SSD; SanDisk, Ultra II 960 GB 2.500 , SATA III (SDSSDHII-960G-G25)), a base-configuration Camera Link frame grabber device (National Instruments, Camera Link Image Acquisition Card NI PCIe-1427) and a counter/timer board (Measurement Computing Counter (MCC) card, PCI-CTR05, Low Cost 5 Channel Counter/Timer Board for PCI Bus). 4. As background light source, red to infrared Power LEDs are recommended (e.g., Power LED Red (625 nm) 3 W 120 ; Power LED infrared (850 nm) 3 W 120 (Winger Electronics, Germany)). 5. 6 cm petri dish (empty) and 3 cm petri dish poured with 2.5 ml NGM. 6. M9 buffer.

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Fig. 1 Multi-worm tracker (MWT) with LEDs for illumination of animals, video and analysis. (a) Left Panel: MWT components, camera, stage with tapping device (for mechanosensation experiments), and LEDs mounted on a metal frame. Background lighting from a red filtered screen light source can be seen underneath the stage. More recent adjustments added a dark hood that surrounds the setup, and an LED-based, infrared background lighting system, which alleviates the need for a filter foil (details upon request). Middle and Right Panels: Red and blue LED illumination. (b) Video still from the camera acquisition. (c) Image analysis with individual animals tracked 2.5 Analyses of Behavior on Solid Substrate 2.5.1 Body Length Measurements

1. Epi-fluorescence microscope (i.e., Axio Scope.A1 microscope (Zeiss, Germany)) equipped with a mercury lamp as light source and standard bandpass filters for the desired excitation wavelength, a red bandpass filter (675  50 nm) for red background light and a 10 objective. In general, a light intensity of 0.9 mW/mm2 is used (see Note 4). 2. Video camera for recording of experiment (i.e., Canon G9, or Canon EOS 750, with adapter for stereomicroscope ocular, or C-mount, if the microscope has one). 3. Mechanical shutter (Sutter Instruments, USA) to control the duration of the light pulse. Preferably the shutter is computercontrolled. 4. Analysis programs (i.e., ImageJ, Excel, LabVIEW, KNIME (scripts available from the authors), WormLab (MBF Bioscience), etc.). Body length is normalized to the values of the first few seconds before stimulation light exposure.

2.5.2 Analyses of Crawling Behavior

Tracker platform with equipment necessary for imaging and patterned stimulation of a single animal, based on the “Multimodal illumination and tracking system” [22, 23]. 1. Epi-fluorescence microscope (i.e., Axiovert 35 microscope; Zeiss, Germany), equipped with a three-color LCD projector, a motorized x–y stage, a camera (i.e., AVT Guppy F-033

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(Edmund Optics)) with 320  240 pixel resolution at 25 Hz, a red bandpass filter (675  50 nm) for red background light and a mechanical shutter. 2. Computer with a running installment of “Multimodal illumination and tracking system” software (see Note 5) [22, 23]. Install Multimodal illumination and tracking system from the web site (https://www.ncbi.nlm.nih.gov/pmc/ articles/PMC3189501/#SD1). For proper execution of the software, the computer must have LabView 2009, Vision Acquisition Software, Vision Development Module, and Matlab v2009a installed. 2.6 cNMP Measurements

1. Multi-Plate Reader (i.e., CLARIOstar PLUS (BMG Labtech)).

2.6.1 General Equipment and Materials

3. 1.5 ml Eppendorf tubes.

2. Eppendorf Mixer. 4. Glass beads (0.25–0.5 mm; e.g., from Carl Roth, Karlsruhe, Germany). 5. 3-Isobutyl-1-methylxanthin (IBMX), Dimethyl Sulfoxide (DMSO). 6. M9 buffer. 7. 100 mM IBMX Stock solution (in 100% DMSO). 8. Stimulation buffer: 1 mM IBMX, 1% DMSO (1:100 dilution of IBMX Stock in M9 buffer) (see Note 6).

2.6.2 cAMP Measurements

1. AlphaScreen cAMP Detection Kit (Perkin Elmer). 2. 384-well Optiplate (Perkin Elmer). 3. 10 μM biotin-cAMP Stock solution (10 nM biotin-cAMP in 1 ml 1 PBS). 4. 1 μM biotin-cAMP working solution (1:10 of biotin-cAMP Stock in 1xPBS). 5. 1 Immunoassay buffer (1:10 of 10 Immunoassay buffer in H2O) (see Note 7).

2.6.3 cGMP Measurements

1. cGMP Direct Chemiluminescent ELISA Kit (Arbor Assays). 2. Glass test tubes (see Note 8). 3. Wash buffer (1:10 of 20 Wash buffer in distilled water). 4. Sample Diluent (1:4 of 4 Sample Diluent in distilled water). 5. Cyclic GMP Conjugate (1:20 of Cyclic GMP CLIA Conjugate concentrate with Conjugate Diluent). 6. Chemiluminescent Substrate (1:1 of Substrate solution A and B) (see Note 9).

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Methods

3.1 Optogenetic Tool Expression 3.2 C. elegans Cultivation

PACs and CNG channels are expressed using punc-17 (cholinergic neurons) or pmyo-3 (body wall muscle) promoters. 1. Cultivate C. elegans strains on NGM plates containing OP50-1. 2. One day before the experiment, transfer transgenic animals to NGM plates containing OP50-1 or NGM plates containing OP50-1 and ATR and incubate the plate at RT overnight and in dark (see Note 10).

3.3 Analyses of Behavior in Liquid

1. Prepare assay plates 2 days before experiment. Pour 2.5 ml autoclaved NGM in a 3 cm wide Petri dish (see Note 11). 2. Adjust the light of the LED ring to the desired intensity using UV Sensor and power meter. 3. Transfer 30 animals (under red light >600 nm) to an NGM plate. Animals should be without or with only a minimum of OP50 bacteria. 4. Add 500 μl M9 buffer and wait 10 min to allow animals to adapt to the new environment. 5. Put the plate into the 6 cm petri dish, which is mounted on the petri dish holder (see Note 12). 6. Adjust the LED ring to uniformly illuminate the worms in the center. 7. Start the video acquisition software MS-Acqu. Enter the user settings and press the LabVIEW “Run” button. 8. Switch on the background light source (see Note 13). 9. Focus onto swimming animals using the camera lens. Press the “Start” button to start video acquisition. 10. Use the wrMTrck plugin in ImageJ for automated tracking and quantification of the swimming cycles. Open “wrMTrck ROI Batch” and select the folder containing the videos. After confirming the last region of interest (ROI), automated video analysis starts (see Note 14). 11. To validate tracking, open “compression Validation v3” and choose the mode “Validate Tracking.” Control every video for immobile worms, shadows, or artifacts and delete the wrong tracks in the file “*_tracks.txt” which opens automatically. Validated tracks will be saved into new folder (“edited_tracks”) by pressing “OK.” Proceed until validation of all acquired videos is done. 12. To summarize tracks, copy edited tracks to a new location (e.g., “C:\Users\Swimming\Experiment”), copy the file

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Fig. 2 Example measurements of swimming cycles. Swimming behavior of bPAC expressing animals (pmyo-3 promoter) before and during a 60 s light pulse (470 nm, 0.2 mW/mm2)

“Sw_Sum_weighted_v2.class” into parent directory (“C: \Users\Swimming”). For generation of the summary file, open Windows Command CMD, go to the directory of your saved files (“C:\Users\Swimming”) and enter cd “path” (cd C: \Users\Swimming). Select Sw_Sum_weighted_v2.class by pressing tab. Delete the “.class” and press enter. Summary wA and Summary wB are saved in the main folder, and analysis of the body bending rate could be executed in a program of your choice (e.g., Origin, Prism, Excel); for a possible outcome and representation, see Fig. 2. 3.4 Analyses of Behavior on Solid Substrate 3.4.1 Body Length Measurement

1. Prepare assay plates 2 days before experiment. Pour 8.5 ml autoclaved NGM in a 6.5 cm wide petri dish. 2. Transfer a single animal (under red light >600 nm) on an NGM plate. Wait 2 min to allow the animal to adapt to the new environment (see Note 15). 3. Acquire a video of the animal using the compound scope (Zeiss Axio Scope.A1). In general, the protocol used is: 5 s without light, followed by 2 s light, and 18 s without light (see Note 16). 4. Repeat steps 1 and 2 for each strain and condition to be tested 10–15 times. 5. Calculate the body length over time for each animal using the program of your choice (e.g., ImageJ, LabVIEW). For each strain and condition, normalize the length to the averaged values before illumination using a program of your choice (e.g., Excel, KNIME); for a possible outcome and representation, see Fig. 3.

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Fig. 3 Example measurements of body length. Body length measurement of animals co-expressing (pmyo-3 promoter) bPAC and TAX-2/-4 before, during, and after a 2 s light pulse (470 nm, 0.9 mW/mm2) 3.4.2 Analysis of Crawling Behavior

1. Using the multimodal tracking and illumination device, start the program “Beamer Alignment” to properly align the projector and to create a conversion from camera position to projector position. 2. Start the program “Color Illumination and Tracking.” First, the user has to specify the location and the name of the video that will be saved. In the illumination control panel, click on the “scheduled” button to insert the illumination protocol file. As illumination protocol, 15 s without light, 25 s blue light followed by 15 s without light is recommended (see Note 17). 3. Transfer a single animal (under red light >600 nm) on a NGM plate. Wait 15 min to allow the animal to adapt to the new environment (see Note 18). 4. To start the measurement, click the following buttons in the order “Auto Track,” “Calculate,” “Record,” and “Illuminate.” For light application, the shutter has to be manually controlled. When the green color of the “Illumination” button turns gray, this indicates the end of the illumination protocol. Finally, click the buttons “Record,” “Calculate,” and “Auto Track” to stop tracking and video recording. 5. Repeat steps 2 and 3 for each strain and condition to be tested 45–60 times. 6. Start the program “Head Encode” to encode the location of the head of the animal into the video. Encode the location of the head for all recorded videos.

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Fig. 4 Example measurements of crawling speed and bending angles. Crawling speed (a) and bending angles (b) of bPAC (punc-17 promoter) expressing animals before, during, and after 25 s light pulse (470 nm, 0.9 mW/ mm2)

7. Start the program “Complete Video Analysis” to analyze the head encoded videos for multiple parameters (i.e., Speed, Bending angles, Length). 8. Use your program of choice for analysis of relevant data (e.g., KNIME); for a possible outcome and representation, see Fig. 4. 3.5 cNMP Measurements

1. Transfer 60 animals (under red light >600 nm) into a 1.5 Eppendorf Tube containing 50 μl Stimulation buffer.

3.5.1 C. elegans Extract Preparation

2. Stimulate the animals with blue light (0.4 mW/mm2) for 30 s. 3. Transfer the animals into liquid nitrogen and subject the animals to three freeze–thaw cycles. 4. Add glass beads to the animals, and vortex the mixture for 15 min at RT. The samples may be stored at 20 or can be immediately used for determination of cNMP content. 5. Centrifuge the mixture for 1 min at 2000 rpm (400  g) and use the supernatant for subsequent cNMP measurement.

3.5.2 cAMP Measurements

1. Prepare the cAMP standard serial dilutions in stimulation buffer using the 50 μM cAMP standard, as indicated in Table 1 (see Note 19).

3.5.3 The Subsequent Steps Should Be Carried out in the Dark

1. Prepare the Acceptor bead solution (1:25 of Anti-cAMP Acceptor beads in Stimulation buffer) and the Donor bead solution (1:150 of Streptavidin Donor beads, 1:24 of 1 μM biotin-cAMP in 1x Immunoassay buffer) (see Note 20). 2. Add 5 μl C. elegans extract or cAMP standard serial dilution into a well of the 384-well Optiplate (see Note 21).

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Table 1 Preparation of the cAMP standard serial dilutions in stimulation buffer Dilution 1

[Final] (nM) 1000

(M) 5  10

6 6

2

300

1.5  10

3

100

5  107

4 5

30 10

1.5  10 5  10

7

8 8

6

3

1.5  10

7

1

5  109

8 9

0.3 0.1

1.5  10 5  10

9

10 10

Volume of dilution

Stimulation buffer [μl]

15 μl of 50 μM AMP

135

45 μl of 1

105

45 μl of 2

90

45 μl of 3

105

45 μl of 4

90

45 μl of 5

105

45 μl of 6

90

45 μl of 7

105

45 μl of 8

90

45 μl of 9

105

10

0.03

1.5  10

11

0.01

5  1011

45 μl of 10

90

12 (ctrl; no cAMP)

0

0

–

105

3. Add 10 μl Acceptor bead solution into each well and incubate the plate for 30 min at RT. 4. Add 10 μl Donor bead solution into each well and incubate the plate for 1 h at RT. 5. Measure the cAMP content using a Multi-Plate Reader. 6. Generate the cAMP standard curve using a four-parameter logistic (4PL) regression in a program of your choice (e.g., Origin, Prism, Excel) and calculate the cAMP concentrations. 3.5.4 cGMP Measurements

1. Prepare the cGMP standard serial dilutions in Sample Diluent using the 640 nM cGMP standard (Fig. 5). 2. Prepare the acetylation reagent: Mix one part of acetic anhydride with 2 parts of triethylamine in a glass test tube. Use the acetylation reagent within 60 min of preparation. 3. Sample preparation: Dilute the control samples 1:10 and the samples predicted to have a high cGMP content 1:50–100. 4. Standard and sample acetylation: Pipet 300 μl Sample Diluent into a glass tube to act as the B0 or Zero Standard. Add 15 μl of the acetylation reagent and shake immediately. Pipet 150 μl of each standard dilution into a glass tube, and add 7.5 μl of the acetylation reagent. Pipet 75 μl of each sample into a glass tube and add 3.75 μl acetylation reagent and shake immediately. Proceed to assay within 30 min.

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Fig. 5 Preparation of the cGMP standard serial dilutions in sample diluent

5. Follow the Assay Protocol—“Acetylated format” of the kit to measure the cGMP contents. 6. Use your program of your choice (e.g., Origin, Prism, Excel) for calculation of the cGMP content.

4

Notes 1. Let the OP50 dry for about 2 h before use; this might take longer for freshly poured NGM plates. 2. The ATR stock solution may be stored at 80 for 4–6 months. 3. In general, 2 μl of ATR stock solution are added to 1 ml OP-50, and 300 μl of this solution is spread on NGM plates. 4. The red filter is positioned to allow only red light as background light during the measurement. Cover any possible, unfiltered source of light with aluminum foil. This is crucial, as unfiltered light might photoactivate PAC even before the actual start of your experiment. Work in a dark room protected from sunlight. Our dark room for PAC measurements has a blue light background intensity below 25 nW/mm2. We successfully tried different blue light sources: filtered HBO 50 lamp light (Zeiss, Germany; 450–490 nm); filtered LCD projector light (450–490 nm); DPSS laser illumination (Pusch OptoTech, Baden-Baden, Germany; 473 nm, 25.6 mW/ mm2), or a LED lamp (KSL-70, Rapp OptoElectronic, Hamburg, Germany; 470 nm, 8 mW/mm2). To adjust the light intensity, the diameter of the light spot at the focal plane that

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would be used for recording of the animal is measured, and the intensity could be adjusted using, e.g., UV Sensor and power meter (Thorlabs, Germany). 5. There is a variety of worm tracker software available online. We use different trackers depending on the task. Here we show the usage of Multimodal illumination and tracking system, capable of patterned photo-stimulation of a single animal during video acquisition [22]. For a review on the different trackers, see [24]. A microscope-less alternative is the Multi-Worm Tracker [21]. 6. Aliquots of the IBMX Stock solution may be stored at 20 . The stimulation buffer should be prepared fresh for each assay. 7. The biotin-cAMP solutions may be stored at 4 for 6 months. Further, the used PBS should be free of contaminants. The 1x immunoassay buffer should be prepared fresh for each assay. 8. Caution: The Kit contains triethylamine and acetic anhydride (lachrymators, eye irritants). Work in a hood with proper ventilation and wear appropriate protective safety wear. Also, the included Sample Diluent Concentrate is acidic. 9. The cyclic GMP Conjugate and Chemiluminescent Substrate can be stored for 1 month at 4  C. 10. Since CyclOps require ATR as co-factor, C. elegans strains expressing these proteins had to be cultivated on NGM plates containing OP50-1 and ATR. As a control, animals of the same strain could be transferred onto NGM plates without ATR. 11. Assay plates should be made with care; air bubbles, variation in volume, and surface irregularities on the NGM will decrease the quality of the tracked data. Fresh plates are kept for 1 day at room temperature to allow NGM solidification and surface drying; they can be stored for up to 6 weeks at 4  C. 12. Make sure animals are equally distributed and do not accumulate at the side by smoothly moving the plate in circles. 13. The videos should show a high contrast without being overexposed. The animals should appear as black shapes on a bright background. 14. The wrMTrck plugin must contain the files wrMTrck_.class, wrMTrck_$particle.class, Stack_Deflicker.class, wrMTrck_ROI_ Batch.txt. Analysis of all video files in the folder and subfolders will be initialized. For each video, the corrected video, the maximum and minimum Z-projection, the data for each track, the tracked paths, and a video containing the labels for each animal (display the swimming cycle counter) will be saved in the appropriate subdirectory, whereas the “Results” and “Summary” windows are stored in the main directory.

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15. To obtain convincing behavioral effects, only crawling animals should be measured. In some cases, the adaption time of the animal has to be extended up to 15 min. 16. To enhance the behavioral output, the duration of the light pulse could be extended to 15 s. 17. To facilitate subsequent data analysis, files should be saved as: X wormYYY.avi (X: any number of characters (e.g., strain name); Y: one character). For example, ZX123 ATR worm 01.avi. 18. Worm transfer does change the animal’s behavior for some minutes. C. elegans takes between 5 and 10 min to adapt to the new plate. 19. Prepare the cAMP standard solutions fresh for each assay. 20. The donor bead solution has to be incubated for at least 30 min before adding to the assay plate. 21. Measure the cAMP standard serial dilution samples in duplicates. For each strain and condition, prepare 3 biological samples for cAMP measurement. For PACs, generating a high amount of cAMP, the extract has to be diluted for cAMP measurement (1:10 or higher). References 1. Consortium TCeS (1998) Genome sequence of the nematode C elegans: a platform for investigating biology. Science 282:2012–2018 2. Brenner S (1974) The genetics of Caenorhabditis elegans. Genetics 77:71–94 3. White JG, Southgate E, Thomson JN, Brenner S (1986) The structure of the nervous system of the nematode Caenorhabditis elegans. Philos Trans R Soc Lond B 314:1–340 4. Xu X, Kim SK (2011) The early bird catches the worm: new technologies for the Caenorhabditis elegans toolkit. Nat Rev Genet 12(11):793–801. https://doi.org/10.1038/ nrg3050 5. Husson SJ, Gottschalk A, Leifer AM (2013) Optogenetic manipulation of neural activity in C. elegans: from synapse to circuits and behaviour. Biol Cell 105(6):235–250. https://doi. org/10.1111/boc.201200069 6. Nagel G, Brauner M, Liewald JF, Adeishvili N, Bamberg E, Gottschalk A (2005) Light activation of channelrhodopsin-2 in excitable cells of Caenorhabditis elegans triggers rapid behavioral responses. Curr Biol 15(24):2279–2284. https://doi.org/10.1016/j.cub.2005.11.032 7. Zhang F, Wang L-P, Brauner M, Liewald JF, Kay K, Watzke N, Wood PG, Bamberg E,

Nagel G, Gottschalk A, Deisseroth K (2007) Multimodal fast optical interrogation of neural circuitry. Nature 446(7136):633–639. https://doi.org/10.1038/nature05744 8. Liewald JF, Brauner M, Stephens GJ, Bouhours M, Schultheis C, Zhen M, Gottschalk A (2008) Optogenetic analysis of synaptic function. Nat Methods 5(10): 895–902. https://doi.org/10.1038/nmeth. 1252 9. Liu Q, Hollopeter G, Jorgensen EM (2009) Graded synaptic transmission at the Caenorhabditis elegans neuromuscular junction. Proc Natl Acad Sci U S A 106(26):10823–10828 10. Steuer Costa W, Yu S-C, Liewald JF, Gottschalk A (2017) Fast cAMP modulation of neurotransmission via neuropeptide signals and vesicle loading. Curr Biol 27(4):495–507. https://doi.org/10.1016/j.cub.2016.12.055 11. Gekel I, Neher E (2008) Application of an Epac activator enhances neurotransmitter release at excitatory central synapses. J Neurosci 28(32):7991–8002. https://doi.org/10. 1523/JNEUROSCI.0268-08.2008 12. Etzl S, Lindner R, Nelson MD, Winkler A (2018) Structure-guided design and functional characterization of an artificial red light-

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regulated guanylate/adenylate cyclase for optogenetic applications. J Biol Chem 293(23):9078–9089. https://doi.org/10. 1074/jbc.RA118.003069 13. Ryu M-H, Kang I-H, Nelson MD, Jensen TM, Lyuksyutova AI, Siltberg-Liberles J, Raizen DM, Gomelsky M (2014) Engineering adenylate cyclases regulated by near-infrared window light. Proc Natl Acad Sci U S A 111(28): 10167–10172. https://doi.org/10.1073/ pnas.1324301111 14. Weissenberger S, Schultheis C, Liewald JF, Erbguth K, Nagel G, Gottschalk A (2011) PACα--an optogenetic tool for in vivo manipulation of cellular cAMP levels, neurotransmitter release, and behavior in Caenorhabditis elegans. J Neurochem 116(4):616–625. https:// doi.org/10.1111/j.1471-4159.2010. 07148.x 15. Stierl M, Stumpf P, Udwari D, Gueta R, Hagedorn R, Losi A, Ga¨rtner W, Petereit L, Efetova M, Schwarzel M, Oertner TG, Nagel G, Hegemann P (2011) Light modulation of cellular cAMP by a small bacterial photoactivated adenylyl cyclase, bPAC, of the soil bacterium Beggiatoa. J Biol Chem 286(2): 1181–1188. https://doi.org/10.1074/jbc. M110.185496 16. Bock A, Annibale P, Konrad C, Hannawacker A, Anton SE, Maiellaro I, Zabel U, Sivaramakrishnan S, Falcke M, Lohse MJ (2020) Optical mapping of cAMP signaling at the nanometer scale. Cell 182(6): 1519–1530 e1517. https://doi.org/10. 1016/j.cell.2020.07.035 17. Cooper DM, Tabbasum VG (2014) Adenylate cyclase-centred microdomains. Biochem J 462(2):199–213. https://doi.org/10.1042/ BJ20140560

18. Gao S, Nagpal J, Schneider MW, KozjakPavlovic V, Nagel G, Gottschalk A (2015) Optogenetic manipulation of cGMP in cells and animals by the tightly light-regulated guanylyl-cyclase opsin CyclOp. Nat Commun 6: 8 0 4 6 . h t t p s : // d o i . o r g / 1 0 . 1 0 3 8 / ncomms9046 19. Scheib U, Stehfest K, Gee CE, Ko¨rschen HG, Fudim R, Oertner TG, Hegemann P (2015) The rhodopsin–guanylyl cyclase of the aquatic fungus Blastocladiella emersonii enables fast optical control of cGMP signaling. Sci Signal 8:1–8 20. Scheib U, Broser M, Constantin OM, Yang S, Gao S, Mukherjee S, Stehfest K, Nagel G, Gee CE, Hegemann P (2018) Rhodopsin-cyclases ˚ for photocontrol of cGMP/cAMP and 2.3 A structure of the adenylyl cyclase domain. Nat Commun 9(1):2046. https://doi.org/10. 1038/s41467-018-04428-w 21. Swierczek NA, Giles AC, Rankin CH, Kerr RA (2011) High-throughput behavioral analysis in C. elegans. Nat Methods 8(7):592–598. https://doi.org/10.1038/nmeth.1625 22. Stirman JN, Crane MM, Husson SJ, Wabnig S, Schultheis C, Gottschalk A, Lu H (2011) Realtime multimodal optical control of neurons and muscles in freely behaving Caenorhabditis elegans. Nat Methods 8(2):153–158. https:// doi.org/10.1038/nmeth.1555 23. Stirman JN, Crane MM, Husson SJ, Gottschalk A, Lu H (2012) A multispectral optical illumination system with precise spatiotemporal control for the manipulation of optogenetic reagents. Nat Protoc 7(2):207–220. https://doi.org/10.1038/nprot.2011.433 24. Husson SJ, Costa WS, Schmitt C, Gottschalk A (2013) Keeping track of worm trackers. WormBook:1–17. https://doi.org/10.1895/ wormbook.1.156.1

Chapter 5 Imaging the cAMP Signaling Microdomain of the Primary Cilium Using Targeted FRET-Based Biosensors Danielle T. Arena and Aldebaran M. Hofer Abstract Optical approaches have revolutionized our view of second messenger signaling in organelles, allowing precise time-resolved assessment of soluble signaling molecules in situ. Among the most challenging of subcellular signaling microdomains to assay is the primary cilium. A petite but visually arresting organelle, the primary cilium extends from the cell surface of most non-dividing cells. Recently, the concept of the primary cilium as an independent cAMP signaling organelle has attracted substantial interest. The cilium sequesters a very specific subset of ciliary cAMP-linked GPCRs in its membrane (e.g., 5-HT6, D1R, MCR4, FFAR4, TGR5), as well as other key components of the cAMP signaling machinery that include adenylyl cyclases, GNAS, phosphodiesterases, PKA holoenzyme, and biologically important PKA targets. Here we provide a practical guide to assessing ciliary cAMP signals in live cells using targeted genetically encoded FRET biosensors. Key experimental difficulties include gathering sufficient signal from such a small, photon-limited volume, and the susceptibility of cilia to movement artifacts. Other challenges are associated with the fidelity of sensor targeting and the difficulties in distinguishing between cAMP signals produced exclusively within the cilium vs. those that emanate from the cell body. Here we describe ratio imaging approaches used in our lab for time-resolved visualization of ciliary cAMP in cultured renal cells. These methods can be readily adapted to other cell types and microscopy platforms according to the needs of the user. Key words Primary cilia, cAMP signaling microdomains, PKA, Fluorescent proteins, FRET biosensors, Live-cell ratio imaging

1

Introduction The primary cilium, a highly conserved organelle essential for development, has been increasingly implicated in adult homeostasis and pathology [1, 2]. This elegant non-motile structure is contiguous with the cell plasma membrane and is about the size of a single elongated mitochondrion. The cilium maintains its own unique lipid composition, membrane potential, and permeability barrier to soluble proteins [3–6]. Complex trafficking mechanisms are responsible for directing select proteins to this organelle, including

Manuela Zaccolo (ed.), cAMP Signaling: Methods and Protocols, Methods in Molecular Biology, vol. 2483, https://doi.org/10.1007/978-1-0716-2245-2_5, © The Author(s), under exclusive license to Springer Science+Business Media, LLC, part of Springer Nature 2022

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a very exclusive subset of ciliary GPCRs [7–9]. These GPCRs are invariably connected to the cAMP signaling pathway through Gαs or Gαi [10]. Consequently, the cilium has the unique potential to respond to a distinct panel of GPCR ligands and thereby generate or suppress local intraciliary cAMP signals [11]. The significance of this autonomous signaling circuit is not fully understood, especially since cAMP generated in the cell body travels freely from the cytosol into the cilioplasm [11], but the growing number of identified cilium-specific Epac and PKA substrates in this organelle suggests significant biological potential of the ciliary cAMP microdomain [12, 13]. In this chapter we describe the methods used in our lab to monitor intraciliary cAMP changes using optical reporters. The general approach is similar to that used by our group for many years to image cAMP in other cellular compartments [14– 17]. However, there are special considerations for measuring messengers in the cilium [11, 18] and these will be addressed here. The basic procedures entail transfection with a suitable targeted reporter and plating of cells onto glass coverslips. Cells are subjected to specific culture conditions to induce growth arrest, thereby promoting the formation of cilia. Organelles are then imaged using a simple epifluorescence ratio imaging system equipped with high N.A. 60
or 100
TIRF objectives. A critical parameter is the choice of sensor. Because the cilium is small and is subject to movement, selection of a robust (preferably ratiometric) reporter is paramount. In addition, high-fidelity targeting motifs are necessary to avoid confounding background signals from the cell body. There is a wide selection of known targeting motifs and many types of sensors [18, 19]. Some of these have been combined previously to generate ciliary FRET cAMP sensors such as the pioneering construct from Mark von Zastrow’s lab, SSTR3ICUE3 [20], and a high affinity probe based on a novel bacterial cAMP binding motif from Dagmar Wachten’s group, SSTR3mlCNBD-FRET [21]. Takanari Inoue and colleagues performed a systematic analysis to optimize delivery of genetically encoded calcium indicators to cilia [19]. Following their lead, we generated various combinations of targeting sequences fused to different types of cAMP reporters. We found the best performance using the excellent FRET/FLIM sensors EpacH187 and EpacH188 from the lab of Kees Jalink [22] linked to either Arl13b (a small GTPase) [23] or the ciliary serotonin receptor 5-HT6 [11, 24]. It is possible to obtain near perfect targeting of these FRET probes in ciliated cells. Combined with gentle illumination from epifluorescence systems, we have succeeded to perform time-resolved FRET measurements in live cilia lasting up to 2 h. Simply locating cilia in a field of cells can be a stumbling block for the uninitiated. It’s important to recognize that cells place their

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primary cilium in variable locations (top, bottom, side of the cell), but since the formation of the cilium involves an elaboration of the mother centriole, they usually extend from the plasma membrane overlying the nucleus (as opposed to distal projections of the cell). Cilia in IMCD3 cells typically project up from the top surface of the cell, meaning that they might appear as a bright dot when viewed in epifluorescence. It is easier to image a cilium that is horizontal to the plane of the coverslip or located underneath the cell body next to the coverglass. Examples of these various conformations in live IMCD3 cells expressing different ciliary FRET sensors are shown in the confocal 3D renderings of Fig. 1. To highlight the nucleus and microtubule cytoskeleton, the cells were incubated for 1 h with SPY DNA595 and/or SiR Tubulin [25], respectively. Of note, we found that SiR tubulin (Cytoskeleton, Inc.) can be used to nonspecifically label the microtubule-based axoneme of the cilium in live cells. For additional tips and tricks for viewing live primary cilia, see Ott and Lippincott-Schwartz [26].

2

Materials Prepare all solutions using ultrapure water (purify deionized water to a sensitivity of 18.2 MΩ cm at 25  C).

2.1

Stock Solutions

10
HEPES buffered Ringer’s stock (1.2 M NaCl; 100 mM HEPES; 24 mM K2HPO4; 3.9 mM KH2PO4). 1 M NaOH (for adjusting pH). 1 M CaCl2. 1 M MgSO42.

2.2

Materials

2.3 Working Solutions

Sharp forceps for picking up coverslips (e.g., Dumont #5/45); Dow Corning high vacuum grease; cotton swabs for applying grease to imaging chamber; Whatman #3 filter paper; No. 1 autoclaved rectangular 22
50 mm glass coverslips*; open-top 22
50 mm rectangular flow-through imaging chamber and platform (e.g., Warner Instruments, cat#: JG-23WHP and PM-1)*; stage adapter (e.g., Warner Instruments cat#: SA-20 selected for your microscope); superfusion setup including vacuum line and double flask trap (*see Note 1). 1. Sterile 1
phosphate buffered saline (PBS): 137 mM NaCl, 2.7 mM KCl, 10 mM Na2HPO4, and 1.8 mM KH2PO4. 2. Freshly prepared 1x HEPES Ringer’s solution: Dilute 10
Ringer’s stock, add 5 mM glucose. Magnesium sulfate (1 mM MgSO4) and calcium (1 mM CaCl2) are added after the final dilution of 1
Ringer’s is made to avoid precipitates. Adjust

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Fig. 1 3D live-cell images of mIMCD3 cells expressing FRET sensors showing variable placement of primary cilia. Cilia are typically found extending from plasma membrane in the general vicinity of the nucleus, but can emerge from the top, side, or bottom of the cell facing the coverslip. Placement of cilia can potentially affect access to exogenously added reagents. For example, GPCR agonists may be impeded from reaching receptors on cilia underneath the cell, although these organelles will be easier to image in epifluorescence. In the following images, cells were counterstained with a live-cell nuclear stain SPY DNA595 (magenta) and/or a live-cell cytoskeletal marker SiR tubulin (gray) to provide context. (a) When viewed from the top, a cilium labeled with 5-HT6-H187 (YFP fluorescence depicted in yellow-green color) appears as a small dot (white arrow). (b) 3D side-view of 5-HT6-H187 shows that cilia on mIMCD3 cells typically stand straight up into the bath, complicating ratio imaging in epifluorescence. (c) Top panel: Example of cilium with well-targeted Arl13b-H188 protruding from the side of the cell, horizontal to coverslip; bottom panel: magnified view illustrating labeling of the microtubule core of the organelle with SiR tubulin. (d) Example of mIMCD3 cell in which the cilium is pressed against the coverslip at the bottom of the cell. In this case ciliary targeting of 5-HT6-H187 is imprecise, with much residual sensor in the cell body. Methods: Confocal z-stacks were taken with a Leica TCS SP8 WLL FALCON confocal microscope using a white light laser and resonant scanner and lightning processing. The YFP moiety of the FRET sensor was excited at 505 nm (emission 515–535 nm). SPY DNA595 was excited at 592 nm (emission 607–640 nm) and SiR tubulin was excited at 650 nm (emission 665–700 nm). 3D volume reconstructions were generated with the Leica LAS-X software

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pH to 7.40 to 7.45 using 1 M NaOH. Cell culture media should not be used for imaging as it contains phenol red and other additives that are mildly fluorescent, and in the absence of 5% CO2, the bicarbonate in the medium will cause the pH to rise. 2.4 Cell Culture Media

1. DMEM/F12 supplemented with 10% FBS, 100 U/ml penicillin, 100 μg/ml streptomycin (see Note 2). 2. TrypLE Express/0.05% trypsin-EDTA.

2.5

3

Microscope

A relatively simple inverted epifluorescence microscope capable of performing FRET emission ratio measurements is sufficient for measuring ciliary cAMP. A good high numerical aperture (N.A.) oil immersion objective is critical for ciliary measurements. We use a Nikon Eclipse TE2000U equipped with an undermounted Andor iXON Ultra 888 (1024x1024) EM-CCD camera and Nikon 60X Plan Apo TIRF (N.A. 1.45) or 100X TIRF (N.A. 1.49) objectives. Metafluor software (Molecular Devices) controls Sutter filter wheels and shutters in the excitation and emission path and the camera acquisition (see Note 3).

Methods

3.1 Transfection: “Drop Method”

IMCD3 cells are generally in the optimal condition for cilia imaging experiments two to three days after transfection and serum starving. We use a modification of the manufacturer’s protocol to accelerate the transfection process because of the extra time that is needed for cells to be maintained in serum-free solutions before use. Carry out transfection and serum starving using sterile technique in a tissue culture hood at RT. This procedure is for four coverslips (see Note 4). 1. Aseptically place coverslips in dishes. 2. Add 150 μl sterile OptiMEM to each of two 1.5 ml sterile Eppendorf tubes. In the first tube, add 4.5 μl Lipofectamine LTX. In the second tube, add approximately 1500 ng total DNA of cilium-targeted cAMP biosensor plasmid DNA from maxiprep (if co-transfecting with another construct use 750 ng of each plasmid) and 1.5 μl Lipofectamine Plus reagent. 3. Vortex each of the tubes for 2 s. 4. Mix the contents of the two tubes and incubate at room temperature for 20–30 min (see Note 5). 5. After about 10 min, trypsinize cells in 80% confluent T25 flasks with TrypLE Express and dilute in 8–10 ml complete culture media. Gently pipette up and down to break up clumps, while avoiding bubbles.

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6. Add 1 ml of the cell suspension to the transfection mixture. Mix by pipetting up and down 1–2 times. 7. Plate cells directly on coverslips dropwise, such that surface tension holds the mixture onto the coverslip (see Note 6). 8. Carefully transfer plates to a 5% CO2 incubator at 37  C. 9. Incubate transfection reaction for 6 h or overnight. 3.2

Serum Starving

Serum starving of dividing cells promotes cilia formation, as does contact inhibition in confluent cultures. Note that differentiated non-dividing cells such as cultured primary neurons will have cilia without any adjustment to the culture conditions. Certain cell types tolerate serum removal better than others. IMCD3 cells are particularly hardy and can be kept for up to 4 days in serum-free conditions. They frequently have some ciliated cells even without serum starving. 1. Wash each coverslip with 2 ml sterile DPBS (warmed to 37  C). 2. Add 2 ml serum-free media to each dish (see Note 7). 3. Place in 5% CO2 incubator at 37  C for at least 24 h.

3.3 FRET Measurements

1. Warm up freshly made Ringer’s to room temperature. 2. Cut Whatman #3 filter paper (see Note 8). 3. Prepare the imaging chamber by greasing the backside of the aperture (see Note 9). 4. Remove the coverslip from the dish and place face up on a piece of filter paper. 5. Wick away moisture from the edges of the coverslip, so that the perimeter of the coverslip is completely dry except for the area you will be imaging (see Note 10). 6. Picking up the coverslip close to the edge with forceps, flip it over and place securely in the center of the imaging chamber so that it adheres to the grease. 7. Flip imaging chamber over and further secure the coverslip by pushing straight down on the chamber. Be careful that the coverslip does not slide from position. 8. Add Ringer’s to the cells as gently as possible (avoid adding it directly onto the cells). Steps 4–8 should be done quickly, so that the coverslip does not dry out (see Note 11). 9. Place chamber in the proper platform and stage adapter, according to the microscope being used. Hold down the chamber on both sides as you secure it into the platform to avoid excessive disturbance of the chamber and coverslip.

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10. Use a Kimwipe wetted with distilled water to carefully clean the back side of the coverslip where it will contact the microscope objective. 11. Place the platform/adapter with the secured chamber on the microscope stage. Secure the platform to the stage (we use foam pads underneath the slide clips to achieve a better grip). 12. Insert the vacuum tube to the chamber to test the flow on the vacuum and to rinse the cells before starting the experiment (see Note 12). 13. Turn on the imaging system in the following order: light source, filter wheel, camera, bright field light source, XY controller, computer. 14. Open Metafluor software. 15. Select “New” to start or create a protocol. Fill in the appropriate parameters. 16. Looking through the microscope oculars, find a field of interest by manually switching to a YFP, CFP, or GFP filter cube, selecting “Focus” on the tool bar in Metafluor, then “Open,” then “Start focusing” (see Note 13). 17. Locate a field of cells with well-targeted brightly labeled cilia. Switch the image from the oculars to the monitor and check that the cilia appearing on the monitor are in focus. 18. Once the image is in focus on the monitor, select “Stop focusing,” then “Close” (see Note 14). 19. Switch to the FRET filter on the filter cube to begin imaging. 20. Take an image by selecting “Acq one” on the tool bar. 21. Select “Regions.” 22. Select the wavelength (or ratio) image you would like to use to draw regions. 23. Using the free-form tool, draw a border around each cilium to be measured. Click to start a region and double click to end it. Draw one region per cell/cilium of interest. 24. Once regions are drawn, select “Done” to return to the main screen. This will allow you to view the ratio and intensity vs. time graphs as the experiment proceeds. The regions can be redrawn during later analysis. 25. Select “Save image” on the “Experiment Control Panel” and label the file accordingly. 26. Select “Zero Clock” and then “F4: Acquire.” The interval between acquisition of ratio image pairs (480 nm and 535 nm emissions) should be 5–10 s.

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27. Once the system has started to acquire data, select “Events” from the tool bar, and add basic identifying information about your experiment (i.e., cell type/plasmid type). 28. Double click on the event you create to mark the event (see Note 15). 29. Add Ringer’s to the chamber (either using a perfusion apparatus or by hand using four additions from a 1 ml pipette) and mark it. 30. We recommend collecting a baseline for about 5 min. 31. Add agonists (e.g., 4 additions
1 ml of 25 μM Forskolin). 32. To observe termination of the response, add at least 6 ml Ringer’s to the chamber or (better) rinse using the perfusion apparatus. It is helpful to “calibrate” at the end of your experiment using a supramaximal dose of forskolin plus IBMX. Note that this is not a true calibration; an in situ calibration of ciliary sensors can be performed in digitonin permeabilized cells as described previously by our lab [11] or using the methods of Koschinski and Zaccolo [27] (see Note 16). 33. To stop the experiment, select “F2: Pause.” 34. Select “Close.” 35. Select “Yes.” 36. Double check to make sure the file is properly named. 37. Select “Save.” 38. Select “Replace.” 3.4

Data Analysis

1. Open Metafluor and open the protocol and file to be analyzed. 2. Run through the experiment using “F4: Forward” to pick an area of background in the single wavelength intensity channel (either 480 nm or 535 nm) that remains dark throughout (see Note 17). 3. Select “Regions.” 4. Create a small region around this consistently dark area of background. This will be identified as “Region 1.” 5. Retrace the cilia that you wish to analyze using the free-form tool (see Note 18). 6. Select “Done.” 7. Run through the experiment to make sure your regions are aptly drawn. 8. Under the “Run experiment” tab, select “Reference Images.” 9. Click the check box for “Subtract Backgrounds.” 10. Select “Close.” 11. Select “F4: Forward.”

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12. Reset to the first frame of the experiment. 13. Select “Log Data.” 14. Name the file and select “Save.” 15. Select “F4: Forward” (see Note 19). 16. Take a screenshot to corroborate the regions drawn and the data sets. 17. Select “Close.” 18. Select “Yes.” 19. Select “Save.” 20. Select “OK.” 21. Open .log file in Kaleidagraph or other preferred graphing program. 22. Delimiter should be set to “other.” 23. A window will appear. Scroll through until the second line from the top is the first line of data. The first line should contain the word “Time.” 24. Select “Gallery.” 25. Select “Linear” then “Line.” 26. Check the box labeled “X.” 27. Set the first point, time, as “X” and the ratios of all regions, except R1 R1, as “Y” (i.e., R2 R1 ¼ Y; R3 R1 ¼ Y. . .). 28. Select “Plot.” 29. Format as needed. Two sample traces from FRET (Fo¨rster Resonance Energy Transfer) experiments performed as described above on IMCD3 cells expressing Arl13b-Epac-H188 are shown in Fig. 2.

4

Caveats and Conclusions We have described here a simple epifluorescence microscopy approach for imaging intraciliary cAMP using sensitized emission of targeted FRET reporters. The advantages of this imaging modality are its simplicity, relatively low cost of the instrumentation, and the possibility to run complex, long time courses with minimal photodamage to live cells. The major trade-off compared to measurements on a confocal or spinning disk system is the loss of optical sectioning capability. This means that residual signals from mistargeted fluorophores can potentially contribute to the apparent response in the cilium. Figure 3 provides an analysis of how the contribution of residual signal from the cytosol can be assayed.

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Fig. 2 Typical results from ratiometric FRET measurements performed on mIMCD3 cells transiently expressing Arl13b-Epac-H188 according to the protocols outlined here. Regions of interest incorporating only the cilium were drawn as described in the text (see also Fig. 3 for further discussion). (a) Stimulation using 100 nM PGE1 produced an increase in the 480 nm/535 nm emission ratio (440 nm excitation) in the cilium that was partially reversed by washing with Ringer’s solution. Addition of 1 mM IBMX (3-isobutyl-1methylxanthine) + 50 μM FSK resulted in a large ratio response; typically, this supramaximal dose of agonists elicits the maximal ratio change for mIMCD3 cells. Note that while FRET signals are only being collected from the cilium, the response may reflect a combination of cAMP produced in the cilium and cytosol. (b) As in panel (a), stimulation of cells with 5 μM forskolin caused an elevation in the FRET ratio in the cilium that was partially reversed by washing with Ringer’s solution

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Fig. 3 Analysis of the contribution of non-targeted sensor to the measured response in the primary cilium. (a) IMCD3 cell transfected with Arl13b-H187 was stimulated with 50 μM forskolin and 20 μM rolipram. Top traces show 480/535 emission ratio and bottom traces indicate raw fluorescence intensity collected at each of the two emission wavelengths. This cell had near perfect targeting of the sensor, permitting selective monitoring of FRET changes from the cilium. (b) IMCD3 cell co-transfected with Arl13b-H187 and the arginine vasopressin receptor (V2R) was stimulated with 50 pM arginine vasopressin (AVP). In contrast, this cell had poor targeting of the sensor, permitting simultaneous resolution of cAMP changes in the cytosol after collecting fluorescence over the entire cell body (minus the cilium). For more detailed discussion, see [11]. (This figure is modified after Fig. 1 of Ref. [11])

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Another point to remember, perhaps obvious, is that cAMP produced in the cytosol enters freely into the cilium, so of course detection of signal by a perfectly targeted ciliary reporter doesn’t necessarily mean that the signal was generated locally in the cilium. Less obvious is the situation in which cAMP produced in adjacent cells is communicated through gap junctions into the cytosol, and then into the cilium of that cell where it is detected by the targeted reporter (see Note 20) [11, 28]. Finally, it is apparent that precise targeting of the reporter to the cilium is essential. However, a caveat regarding the best ciliary targeting sequences, e.g., Arl13b and 5HT6, is that they do retain their native biological activity (see Note 21). At the same time ciliary GPCR-sensor fusions in particular afford the exciting opportunity to directly study local receptor-stimulated cAMP production (or suppression) in the environment of the cilium [11]. Given the ever-expanding inventory of ciliary GPCRs, this will provide many new possibilities to examine receptor biology in the relatively unexplored domain of the ciliary membrane.

5

Notes 1. Large format coverslips are easier to handle and the large volume flow-through chamber produces a much quieter superfusion during solution changes. Coverslips of different sizes can be used (e.g., 15 mm round), but this will require a different imaging chamber. Make sure you have the proper thickness glass matched to your microscope objective. Disposable round 35 mm dishes with fixed coverglasses (e.g., MatTek dishes) can also be used, but it is difficult to perform reproducible solution changes. 2. If using other cells line, be sure to use the appropriate medium and additives. 3. In lieu of mechanical filter changers, beam-splitters can also be used to simultaneous collect the two FRET emissions onto a split detector in the camera. 4. The transfection recipe does not need to be altered if using coverslips of different sizes. While we’ve had good results with Lipofectamine LTX, use the transfection method that give the best expression in your cell type. Good expression rates are important because not 100% of the cells on a coverslip will have cilia at a given time, even after serum starving. When multiplied by poor transfection efficiency, it can be challenging to locate cilia suitable for imaging. 5. The length of incubation is flexible (~20–60 min).

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6. Place cell suspension dropwise on center of coverslip, corresponding to where you will be imaging. Plates and coverslips should be dry and coverslips centered, so that surface tension holds the drop in place. 7. Some cell types (e.g., MEFs, NIH-3T3) need a small amount of serum to maintain viability. If your cell line doesn’t survive this treatment, try adding 0.5–1% serum. 8. Cut a larger section of filter paper to fit the coverslip with some overhang, and small strips to dry the outer perimeter on the coverslip surface. 9. Too much or too little grease on the chamber causes issues; excessive amounts will lead to grease on the objective lens, and too little will cause leaking due to an insufficient seal. 10. Insufficient removal of moisture from all areas of the coverslip, except the imaging area, will result in a leaky chamber. The importance of not having leaks on the microscope stage cannot be overemphasized. 11. Steps 2–6 should be done as quickly as possible, so that the coverslip does not dry out. 12. It’s important to thoroughly rinse off any residual tissue culture medium before acquiring data. As noted previously, culture medium contains various fluorescent components. It’s also essential to check the flow of the perfusion system and vacuum before starting the measurement. Otherwise, the sample can dry out mid-experiment, or worse, the chamber can overflow and risk damaging your microscope setup if the vacuum is not correctly positioned and secured. We like to “ground” all tubes that connect to the imaging chamber by securely taping them down to the stage. This reduces vibrations and prevents them from coming loose during the experiment. 13. Pick a good field for FRET measurements. Look for a field that has dark background, minimal cells with non-specific targeting, maximal cells with good targeting, and cilia that are “sitting down,” so that you have a larger surface area for signal quantification. 14. It can sometimes be difficult to locate suitable cilia, but to the extent possible, try to minimize the amount of time spent scanning before imaging; continuous exposure to excitation light promotes photodamage. 15. Be consistent with marking events; mark all events either immediately before or after application. 16. A true in situ calibration that relates the cAMP to a given FRET ratio value can be performed in cells expressing Arl13b- or 5-HT6-linked optical reporters by permeabilizing the cell

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with digitonin [11]. Like other organelles, the cilium has a low cholesterol content relative to the plasma membrane. Judicious use of digitonin can therefore be used to obtain some degree of selective permeabilization of the plasma membrane, leaving the cilium intact [6]. By titrating exogenously supplied cAMP in the bathing solution, it is possible to achieve precise in situ calibration of FRET ratios because reporters are fully retained in the cilium. See also the considerations presented by Koschinski and Zaccolo regarding comparison of FRET ratios under different experimental conditions [27]. 17. It is recommended to use the “pseudocolor” representation of the image rather than “monochrome” for picking the darkest area of background as it is much easier for the human eye to distinguish color differences than subtle shades of gray. 18. Be fastidious about tracing cilia for analysis; validity of signal quantification depends on how accurately the region is drawn. 19. After logging the data by selecting “F4: forward,” do not click on the graph. Clicking on the graph can potentially incorrectly alter the data set that was just logged. 20. Intercellular communication of cAMP can be an issue in certain kinds of experiments involving heterogenous cell populations but can be mitigated by using gap junction inhibitors like 18a-glycyrrhetinic acid (5 μM, preincubated 15 min before experiment, and added to all experimental solutions) [11, 28]. 21. Note that Arl13b and 5-HT6 sensor fusions do retain their respective biological activities. Both cause significant elongation of the cilium when over-expressed, and the Gαs-coupled 5-HT6 receptor has a well-known constitutive activity that is sensitive to inverse agonists.

Acknowledgements We gratefully acknowledge support for our work on ciliary signaling from the Medical Research Service of the Veteran’s Administration (VA-ORD I01 BX005124, VA-ORD IS1 BX004786 and VA-ORD I21 BX004093; to A.M.H.) and from NIH NIDCR (R21 DE025921; to A.M.H.). References 1. Hildebrandt F, Benzing T, Katsanis N (2011) Ciliopathies. N Engl J Med 364(16): 1533–1543. https://doi.org/10.1056/ NEJMra1010172 2. Anvarian Z, Mykytyn K, Mukhopadhyay S, Pedersen LB, Christensen ST (2019) Cellular

signalling by primary cilia in development, organ function and disease. Nat Rev Nephrol 15(4):199–219. https://doi.org/10.1038/ s41581-019-0116-9 3. DeCaen PG, Delling M, Vien TN, Clapham DE (2013) Direct recording and molecular

Imaging cAMP in the Primary Cilium identification of the calcium channel of primary cilia. Nature 504(7479):315–318. https://doi. org/10.1038/nature12832 4. Delling M, DeCaen PG, Doerner JF, Febvay S, Clapham DE (2013) Primary cilia are specialized calcium signalling organelles. Nature 504(7479):311–314. https://doi. org/10.1038/nature12833 5. Lin YC, Niewiadomski P, Lin B, Nakamura H, Phua SC, Jiao J, Levchenko A, Inoue T, Rohatgi R, Inoue T (2013) Chemically inducible diffusion trap at cilia reveals molecular sieve-like barrier. Nat Chem Biol 9(7): 4 3 7 – 4 4 3 . h t t p s : // d o i . o r g / 1 0 . 1 0 3 8 / nchembio.1252 6. Breslow DK, Nachury MV (2015) Analysis of soluble protein entry into primary cilia using semipermeabilized cells. Methods Cell Biol 127:203–221. https://doi.org/10.1016/bs. mcb.2014.12.006 7. Domire JS, Green JA, Lee KG, Johnson AD, Askwith CC, Mykytyn K (2011) Dopamine receptor 1 localizes to neuronal cilia in a dynamic process that requires the BardetBiedl syndrome proteins. Cell Mol Life Sci 68(17):2951–2960. https://doi.org/10. 1007/s00018-010-0603-4 8. Hilgendorf KI, Johnson CT, Jackson PK (2016) The primary cilium as a cellular receiver: organizing ciliary GPCR signaling. Curr Opin Cell Biol 39:84–92. https://doi. org/10.1016/j.ceb.2016.02.008 9. Omori Y, Chaya T, Yoshida S, Irie S, Tsujii T, Furukawa T (2015) Identification of G protein-coupled receptors (GPCRs) in primary cilia and their possible involvement in body weight control. PLoS One 10(6):e0128422. https://doi.org/10.1371/journal.pone. 0128422 10. Mykytyn K, Askwith C (2017) G-protein-coupled receptor signaling in cilia. Cold Spring Harb Perspect Biol 9(9):a028183. https:// doi.org/10.1101/cshperspect.a028183 11. Jiang JY, Falcone JL, Curci S, Hofer AM (2019) Direct visualization of cAMP signaling in primary cilia reveals up-regulation of ciliary GPCR activity following hedgehog activation. Proc Natl Acad Sci U S A 116(24): 12066–12071. https://doi.org/10.1073/ pnas.1819730116 12. Mick DU, Rodrigues RB, Leib RD, Adams CM, Chien AS, Gygi SP, Nachury MV (2015) Proteomics of primary cilia by proximity labeling. Dev Cell 35(4):497–512. https://doi. org/10.1016/j.devcel.2015.10.015 13. Mukhopadhyay S, Wen X, Ratti N, Loktev A, Rangell L, Scales SJ, Jackson PK (2013) The ciliary G-protein-coupled receptor Gpr161 negatively regulates the sonic hedgehog

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pathway via cAMP signaling. Cell 152(1–2): 210–223. https://doi.org/10.1016/j.cell. 2012.12.026 14. Gerbino A, Ruder WC, Curci S, Pozzan T, Zaccolo M, Hofer AM (2005) Termination of cAMP signals by Ca2+ and G(alpha)i via extracellular Ca2+ sensors: a link to intracellular Ca2+ oscillations. J Cell Biol 171(2):303–312. https://doi.org/10.1083/jcb.200507054 15. Nichols JM, Maiellaro I, Abi-Jaoude J, Curci S, Hofer AM (2015) “Store-operated” cAMP signaling contributes to Ca2+activated cl- secretion in T84 colonic cells. Am J Physiol Gastrointest Liver Physiol 309(8):G670–G679 16. Maiellaro I, Lefkimmiatis K, Moyer MP, Curci S, Hofer AM (2012) Termination and activation of store-operated cyclic AMP production. J Cell Mol Med 16(11):2715–2725. https://doi.org/10.1111/j.1582-4934.2012. 01592.x 17. Lefkimmiatis K, Leronni D, Hofer AM (2013) The inner and outer compartments of mitochondria are sites of distinct cAMP/PKA signaling dynamics. J Cell Biol 202(3):453–462. https://doi.org/10.1083/jcb.201303159 18. Jiang JY, Falcone JL, Curci S, Hofer AM (2017) Interrogating cyclic AMP signaling using optical approaches. Cell Calcium 64: 47–56. https://doi.org/10.1016/j.ceca. 2017.02.010 19. Su S, Phua SC, DeRose R, Chiba S, Narita K, Kalugin PN, Katada T, Kontani K, Takeda S, Inoue T (2013) Genetically encoded calcium indicator illuminates calcium dynamics in primary cilia. Nat Methods 10(11):1105–1107. https://doi.org/10.1038/nmeth.2647 20. Marley A, Choy RW, von Zastrow M (2013) GPR88 reveals a discrete function of primary cilia as selective insulators of GPCR cross-talk. PLoS One 8(8):e70857. https://doi.org/10. 1371/journal.pone.0070857 21. Mukherjee S, Jansen V, Jikeli JF, Hamzeh H, Alvarez L, Dombrowski M, Balbach M, Strunker T, Seifert R, Kaupp UB, Wachten D (2016) A novel biosensor to study cAMP dynamics in cilia and flagella. eLife 5:e14052. https://doi.org/10.7554/eLife.14052 22. Klarenbeek J, Goedhart J, van Batenburg A, Groenewald D, Jalink K (2015) Fourthgeneration epac-based FRET sensors for cAMP feature exceptional brightness, photostability and dynamic range: characterization of dedicated sensors for FLIM, for ratiometry and with high affinity. PLoS One 10(4): e0122513. https://doi.org/10.1371/journal. pone.0122513 23. Duldulao NA, Lee S, Sun Z (2009) Cilia localization is essential for in vivo functions of the Joubert syndrome protein Arl13b/scorpion.

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Development 136(23):4033–4042. https:// doi.org/10.1242/dev.036350 24. Brailov I, Bancila M, Brisorgueil MJ, Miquel MC, Hamon M, Verge D (2000) Localization of 5-HT(6) receptors at the plasma membrane of neuronal cilia in the rat brain. Brain Res 872(1–2):271–275. https://doi.org/10. 1016/s0006-8993(00)02519-1 25. Lukinavicius G, Reymond L, D’Este E, Masharina A, Gottfert F, Ta H, Guther A, Fournier M, Rizzo S, Waldmann H, Blaukopf C, Sommer C, Gerlich DW, Arndt HD, Hell SW, Johnsson K (2014) Fluorogenic probes for live-cell imaging of the cytoskeleton. Nat Methods 11(7):731–733. https://doi. org/10.1038/nmeth.2972

26. Ott C, Lippincott-Schwartz J (2012) Visualization of live primary cilia dynamics using fluorescence microscopy. Curr Protoc Cell Biol. Chapter 4:Unit 4 26. https://doi.org/10. 1002/0471143030.cb0426s57 27. Koschinski A, Zaccolo M (2019) Quantification and comparison of signals generated by different FRET-based cAMP reporters. Methods Mol Biol 1947:217–237. https://doi.org/ 10.1007/978-1-4939-9121-1_12 28. Lefkimmiatis K, Moyer MP, Curci S, Hofer AM (2009) "cAMP sponge": a buffer for cyclic adenosine 30 ,50 -monophosphate. PLoS One 4(11):e7649. https://doi.org/10.1371/jour nal.pone.0007649

Chapter 6 Methods to Assess Phosphodiesterase and/or Adenylyl Cyclase Activity Via Heterologous Expression in Fission Yeast Marek Domin and Charles S. Hoffman Abstract Heterologous expression of cyclic nucleotide phosphodiesterases (PDEs) and adenylyl cyclases (ACs) in the fission yeast Schizosaccharomyces pombe can be used in combination with PKA-repressed reporters to either carry out high throughput screens for small molecule inhibitors of these target enzymes or to assess hit compounds and their analogs from such screens. Here, we describe two methods for testing panels of such compounds. The first uses a growth assay for which growth in medium containing the pyrimidine analog 5-fluoro orotic acid (5FOA) occurs in response to inhibiting PDE activity to activate PKA. The second uses mass spectrometry to directly measure the impact of compound treatment to study compounds that modulate either PDE or AC activity. Key words Cyclic nucleotide phosphodiesterase, Adenylyl cyclase, Fission yeast, Schizosaccharomyces pombe, fbp1, Inhibitors

1

Introduction The fission yeast Schizosaccharomyces pombe is an important model organism for the study of molecular mechanisms common to eukaryotes [1, 2]. It can also be used as a host for the expression of target proteins in high throughput small molecule screens, leveraging the ability to identify cell-permeable compounds, while also being able to easily screen out compounds that do not act on the desired target via the use of counter-screening strains that express a distinct, but biochemically similar protein such as the endogenous yeast protein [3]. S. pombe is especially amenable to studies involving cyclic nucleotide signaling. The glucose-cAMP signaling pathway is not essential as cells lacking the cAMP-dependent protein kinase PKA are viable, yet it controls a wide range of biological processes such as mating, stationary phase survival, and transcription of the fbp1 gene [4, 5]. There is a 200-fold increase in fbp1

Manuela Zaccolo (ed.), cAMP Signaling: Methods and Protocols, Methods in Molecular Biology, vol. 2483, https://doi.org/10.1007/978-1-0716-2245-2_6, © The Author(s), under exclusive license to Springer Science+Business Media, LLC, part of Springer Nature 2022

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transcript levels in cells with low PKA activity relative to those with high PKA activity [6, 7]. More importantly, cells expressing the ura4 OMP decarboxylase gene from the fbp1 promoter display reciprocal growth behaviors; growing on medium lacking uracil, but not on medium containing the pyrimidine analog 5-fluoroorotic acid (5FOA) when PKA activity is low. In contrast, these strains fail to grow on medium lacking uracil, while displaying resistance to 5FOA when PKA activity is high [6]. Initially, this was used to identify mutations that caused a defect in glucose sensing and PKA activation, and to clone the genes of the glucose-cAMP pathway [4, 5]; however, it can also be used in high throughput screens to identify PDE inhibitors that elevate cAMP to confer 5-fluoroorotic acid (a pyrimidine analog)-resistant (5FOAR) growth [8]. Alternatively, one can delete the S. pombe AC gene and use exogenous cAMP or cGMP to activate PKA [9]. This platform has led to the identification of PDE4, PDE7, PDE8, and PDE11 inhibitors that show biological activity in cell culture [8, 10–13]. While the fbp1-ura4 reporter is not amenable to screening for compounds that lower cAMP levels due to background growth in medium lacking uracil, an fbp1-GFP reporter has been successfully deployed in a screen for AC inhibitors [14]. Here we provide details for carrying out 5FOA assays as an effective way to characterize PDE inhibitors and their analogs as well as a mass spectrometry method to directly measure the impact of compounds on cyclic nucleotide production or hydrolysis.

2

Materials All media lacking 5FOA or cyclic nucleotides can be stored at room temperature, while media containing 5FOA or cyclic nucleotides should be stored at 4
C. When preparing commercially formulated growth media such as Edinburgh Minimal Medium (EMM), note whether or not autoclaving is an acceptable form of sterilization. Most formulations have heat-labile components and require filter sterilization. Prepare cyclic nucleotide-containing solutions at room temperature and use fresh, although short-term storage at 4
C is acceptable. Filter sterilize solutions using a syringe filterdriven unit for small volumes or a bottle-top filter for large volumes.

2.1

Media

1. 5FOA medium: For liquid medium, combine 950 mL distilled H2O with 80 g glucose, 1.45 g yeast nitrogen base w/o amino acids and w/o (NH4)2SO4, 5 g ammonium sulfate, 0.4 g 5FOA (the amount of 5FOA may vary depending on the strain, Fig. 1), 2 g SC-uracil dropout mix (see Note 1), and 50 mg uracil. Dissolve by stirring with low heat. Filter sterilize. For solid medium, make a 2 concentrated nutrient solution in

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Fig. 1 Optimization of a 5FOA growth assay for PDE11 inhibition. Strain CHP2371 expresses the human soluble AC together with human PDE11A4. (a) Growth response to a PDE11 inhibitor in media containing 0.4 g/L, 0.55 g/L, or 0.65 g/L 5FOA. Note the 0.4 g/L 5FOA is insufficient to inhibit growth, while 0.65 g/L 5FOA reduces the sensitivity to the PDE11 inhibitor. Initial cell density is 105 cells/mL. (b) Growth response to a PDE11 inhibitor in medium containing 0.55 g/L 5FOA with a starting cell density of 0.5  105 cells/mL. Note that the lower starting density reduces the growth response as compared to that seen in panel a

half the volume. After filter sterilization, combine with 490 mL water plus 20 g Bacto agar that has been autoclaved in a 2 L flask (see Note 2). 2. Edinburgh Minimal Medium (EMM) complete medium: For liquid medium, combine 970 mL distilled H2O with 12.33 g EMM w/o dextrose (US Biological) and 30 g glucose. Add 150 mg leucine and 75 mg each of adenine, histidine, lysine, and uracil. Dissolve by mixing under low heat and filter sterilize. For solid medium, make a 2 concentrated nutrient solution in half the volume. After filter sterilization, combine with 490 mL water plus 20 g Bacto agar that has been autoclaved in a 2 L flask. 3. Cyclic nucleotides: Stock concentrations of cyclic nucleotides in growth media are 10 mM for cAMP and 5 mM for cGMP. Mix and make sure cyclic nucleotide is fully dissolved before filter sterilizing (see Note 3). 4. Small molecule compounds: Gently heat (40
C) if the compound stock does not appear to have fully dissolved upon thawing. While some compounds are fully soluble in dimethyl sulfoxide (DMSO) at concentrations of 20–50 mM, it is generally better to work with lower concentration stocks (2–10 mM) (see Note 4).

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2.2 Chemicals for Mass Spectrometry Studies 2.2.1 Chemicals and Standards

1. cAMP standard: High-purity solid or liquid forms of standard. 2. Dissolving buffer: High-purity water. 3. Individual standard stock solution: Make a 10–20 ng/mL solution using the dissolving buffer. 4. Dimethyl sulfoxide. 5. Water + 0.1% formic acid. 6. Acetonitrile + 0.1% formic acid. 7. Methanol.

2.2.2 Chromatography Mobile Phases

1. Mobile phase A: Water + 0.1% formic acid. 2. Mobile phase B: Acetonitrile + 0.1% formic acid. 3. Needle wash solvent: Methanol/Water 50:50 v/v.

2.3

Equipment

2.3.1 Equipment for 5FOA Assays

1. 5FOA-based growth screens for PDE inhibitors use 384-well clear sterile dishes such as the Corning 3680 assay plate. 2. Multichannel pipettes (16 channels) that hold up to 50 μL are useful in delivering medium and cells to wells of a microtiter dish. 3. When large numbers of wells need to be filled with medium, consider using a liquid handler such as the Thermo Multidrop 384 or Wellmate microplate dispenser. 4. A microtiter dish vortexer such as an Eppendorf MixMate is used to resuspend cells prior to reading on a plate reader. 5. A plate reader such as a SpectroMax 5 is used to determine the optical density of the cultures in a 5FOA assay. 6. 13  100 mm and 18  150 mm culture tubes, as well as 125 and 250 mL Erlenmeyer flasks for growing yeast cultures. 7. Hemacytometer for counting cell density.

2.3.2 Equipment for LCMS/MS Measurements

1. Agilent 1200 Series Rapid resolution LC: Binary pump. 2. Chiller. 3. Degasser. 4. Autosampler. 5. Agilent 6460 Triple Quadrupole. 6. 2 mL LC vials. 7. Conical inserts (JG Finneran). 8. MSQ Caps with septa (Supelco). 9. 96-well plate manifold (Phenomenex). 10. 96-well filtration plate multiscreen (Millipore Sigma). 11. 96-well collection plate, 350 μL (Phenomenex). 12. Atlantis T3 column 3 μm 2.1  150 mm.

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Methods Construction of fission yeast strains that express foreign PDE or AC genes has been described elsewhere along with methods for high throughput screening [14, 15]. Below, we describe two methods to evaluate compounds that target PDEs or ACs. PDE inhibitors are best studied using 5FOA growth assays in which growth serves as a simple readout for PDE inhibition while also requiring compounds to be cell permeable and selective for protein binding (compounds that promiscuously bind proteins will likely inhibit S. pombe growth). cAMP assays using mass spectrometry are best used for studying AC inhibitors.

3.1 5FOA Assays to Assess PDE Inhibitors 3.1.1 Strains that Lack AC Activity

We previously described methods for identifying optimal concentrations of cAMP or cGMP to add to the 5FOA growth medium when working with strains that lack the ability to produce cyclic nucleotides ( [15, 16]; see Note 5). Here, we describe the procedure for conducting dose-response assays to further characterize hit compounds and their derivatives. 1. Transfer freshly growing cells from solid medium to 1 mL EMM complete liquid medium containing sufficient cAMP or cGMP to repress expression of the fbp1-ura4 reporter (as previously determined; see Note 6) in a 13  100 mm culture tube and grow overnight at 30
C with shaking. All experiments involving yeast should be carried out using standard microbial sterile techniques. Cells can be taken from plates using sterile loops, toothpicks, or micropipette tips. Work should be done in a sterile hood or on a laboratory bench near a Bunsen burner or alcohol lamp to create an updraft to prevent airborne bacteria, yeasts, or fungi from contaminating plates or cultures. 2. Count cells using a hemacytometer and dilute into fresh medium that will allow for repression of the fbp1-ura4 reporter and will achieve a cell concentration of 1  107 cells/mL the following day (see Note 7). 3. Pellet cells by microcentrifugation for 10 s at top speed and wash twice with 1 mL 5FOA medium containing cAMP or cGMP at the concentration determined for conducting the assay (see Note 8). Adjust cells to the desired cell density (see Note 9). 4. Pipet 40 μL of culture into wells in rows 2–8 of a 384-well microtiter dish (see Note 10). 5. Pipet 79 μL of culture into wells in row 1 of a 384-well microtiter dish.

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6. Add 1 μL DMSO or test compound (from a stock solution at 2–5 mM) to row 1. Using a multichannel pipette set at 40 μL, resuspend compounds in row 1, and then carry out a two-fold dilution of the compounds by transferring 40 μL of culture from row 1 to row 2. Continue carrying out two-fold dilutions from rows 2 to 7 (removing 40 μL of culture from row 7 after mixing to leave 40 μL in the well). Row 8 serves as a no compound control. Pipette 40 μL water to the surrounding wells to reduce evaporation of the cultures. Upon transferring 40 μL from one row to the next, pipette up and down several times to mix the medium, while avoiding the generation of bubbles in the wells. It is not necessary to change tips each time for these dilutions. 7. Incubate microtiter dishes for 48 h at 30
C in a sealed container with wet paper towels to reduce evaporation in the wells. Sandwich the assay dishes between two blank microtiter dishes to reduce evaporation and condensation on the lids of the experimental dishes. 8. After incubation, vortex using a microtiter plate vortex to resuspend cells (alternatively, cells can be resuspended by pipetting). Determine the optical densities in the wells using a plate reader (see Note 11). 3.1.2 Strains that Express a Functional AC Gene

Strains that express the S. pombe git2 AC gene or a foreign cyclase gene but are 5FOAS due to the balance between PDE activity and AC activity do not require the addition of cyclic nucleotides to the 5FOA growth medium. While 0.4 g/L 5FOA is generally used in 5FOA growth medium, higher concentrations may be required to confer growth inhibition by 5FOA (Fig. 1). Cyclic nucleotides or a PDE inhibitor is still required in the EMM medium used to grow the cultures prior to the screen so that the fbp1-ura4 reporter is repressed at the start of the assay. The actual 5FOA assay only differs from the protocol in Subheading 3.1.1 with regard to using 5FOA medium that does not contain cyclic nucleotides when washing and resuspending the cells in step 3.

3.2 Direct Measurement of Cyclic Nucleotide Levels for Assessing AC Inhibitors

Mass spectroscopy of nucleotide extracts allows one to measure levels of both cAMP and cGMP [17]. These assays can be used to assess the impact on cAMP or cGMP production by a strain that expresses an adenylyl or guanylyl cyclase together with a PDE that significantly reduces steady state cyclic nucleotide levels. One can then measure the impact of compound treatment as cyclic nucleotide levels increase due to inhibition of the PDE (see Fig. 2). As such, these assays can also be used to examine PDE inhibition itself as long as the PDE has a significant impact on steady state cyclic nucleotide levels of the assay strain.

3.2.1 Preparation of cAMP Samples

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Fig. 2 Assessment of an AC inhibitor in strains expressing AC4 and AC5 together with GNASR201C and PDE4D2. cAMP extracts were prepared at indicated times relative to the addition of 40 μM Rolipram to inhibit PDE4 activity, which results in a rapid increase in cAMP levels. The cAMP level measured at T ¼ 0, prior to the addition of Rolipram, was subtracted from the values obtained 10, 30, and 60 min after addition. Note that the elevation in cAMP levels in the AC4 strain is significantly reduced by compound treatment in comparison to that of the AC5 strain

1. Grow assay strain to ~107 cells/mL in EMM Complete medium at 30
C with vigorous aeration (either on a rollerdrum or platform shaker; see Note 7). Culture volume depends on the number of conditions to be tested (i.e., the number of test compounds and/or concentrations along with a DMSO control). 2. For each sample (one Time ¼ 0 sample together with one sample for each condition to be tested), prepare two 1.5 mL Eppendorf tubes by placing 0.2 mL acetonitrile in one tube and 0.1 mL high purity water in the second tube. 3. Transfer 1.2 mL culture to a 13  100 mm DMSO control culture tube and 1.0 mL culture to the remaining experimental culture tubes. 4. At Time ¼ 0, remove 0.2 mL culture from the DMSO control tube and transfer this into a 1.5 mL Eppendorf tube containing 0.2 mL acetonitrile. Briefly vortex to mix. 5. Add the PDE inhibitor together with either DMSO or the test compounds to initiate the cyclic nucleotide response (see Note 12).

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6. At the desired timepoint, remove 0.2 mL cultures to the Eppendorf tubes containing acetonitrile (see Note 13; Fig. 2 for an example of the possible timecourse). 7. Allow at least 15 min for the acetonitrile to release the cyclic nucleotides from the cells (see Note 14). 8. Centrifuge samples in a microfuge for 1 min at the top speed. 9. Remove 0.1 mL supernatant to Eppendorf tubes containing 0.1 mL high purity water (see Note 15). 10. Filter the acetonitrile-water mixtures using a 96-well filtration plate in a filtration vacuum manifold to deliver the filtered material to a collection tray. Transfer filtrates to sample vials containing conical inserts to allow for low volume sampling. Close with MSQ caps with septa. 3.2.2 Measure Cyclic Nucleotide Levels Via Mass Spectrometry

Cyclic nucleotide measurements are performed using a targeted approach using reverse phase chromatography on an Agilent 1200 Series Rapid resolution LC (RRLC) system equipped with a binary pump, autosampler with chiller and thermostatted column compartment, and coupled with an Agilent 6460 Triple Quadrupole LC/MS System with Jet Stream technology. Agilent MassHunter Optimizer Software (Version 10.0 SR1, Build 10.0.142) is used with the triple quadrupole LC/MS system for identifying the most abundant multiple MRM transitions along with the associated fragmentor voltages and collisions energies. MassHunter Workstation (version 10.0 SR1 Build 10.0.142) is utilized for data acquisition and MassHunter Qualitative Analysis Software (version 10.0 Build 10.0.10305.0) is used for data processing. Agilent MassHunter Optimizer Software (Version 10.0 SR1, Build 10.0.142) is used with the triple quadrupole LC/MS system for identifying the most abundant multiple MRM transitions along with the associated fragmentor voltages and collisions energies (Table 1). 1. Prime HPLC pumps and autosampler properly, assuring that mass spec temperatures have stabilized as indicated in Table 2. 2. Run one or two water blanks, followed by a standard mixture using the LC-MS/MRM method. 3. Check the water blank run(s) for proper baseline levels and any potential contamination of the system. 4. Check the standard mixture for chromatographic separation and assay sensitivity. 5. The system is ready for biological sample analysis using the method described in Table 3 if all the above tests attain anticipated results.

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Table 1 MRM transitions for cAMP and cGMP, fragmentor voltages, and collision energies Compound name

Precursor ion

Product ion

Fragmentor

Collision energy

cAMP

330.1

312.1

135

17

cAMP

330.1

136.1

135

25

cGMP

346.1

152.1

131

25

cGMP

346.1

135.1

131

25

Table 2 Triple Quadrupole MS Conditions Ion Mode

Positive

Drying gas temperature

320
C

Drying gas flow

10 L/min

Sheath gas temperature

315
C

Sheath gas flow

11 L/min

Nebulizer pressure

50 psi

Capillary voltage

4000 V (positive mode)

Nozzle voltage

500 V

Delta EMV

200 V

Table 3 Liquid Chromatography Run Conditions Column

Waters Atlantis T3 3 μm 2.1  150 mm

Column temperature

30
C

Injection volume

5 μL

Needle wash

3 s in wash port (methanol/water 50:50 v/v)

Mobile phase

Mobile phase A: Water + 0.1% formic acid Mobile phase B: Acetonitrile + 0.1% formic acid

Flow rate

250 μL/min

Linear gradient

2% B for 1 min 2% B to 98% B in 1 min 98% B for 4 min 98% B to 2% B in 0.2 min 10 min END

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Notes 1. Synthetic complete mix lacking uracil (SC-ura) is made by mixing 2 g of each of the following: adenine, alanine, arginine, asparagine, aspartic acid, cysteine, glutamine, glutamic acid, glycine, histidine, isoleucine, lysine, methionine, phenylalanine, proline, serine, threonine, tryptophan, tyrosine, and valine. To this, add 4 g leucine, 0.1 g inositol, and 0.2 g paraaminobenzoic acid. Store at room temperature and keep dry. Make fresh mix every year. 2. Care should be taken when weighing out components for 5FOA medium. Too little 5FOA can lead to leaky growth while too much 5FOA can prevent 5FOAR growth. Too little uracil can prevent 5FOAR growth while too much uracil can allow growth in 5FOA medium in the absence of PKA activation. 3. Cyclic nucleotides may lose activity in liquid medium upon long-term storage. Do not make more than a one-month supply of a given medium. 4. Many compounds found in HTS libraries display poor solubility in yeast growth media. Once added to preheated medium, mix by gentle pipetting rather than vortexing. Prepare sufficient compound for the experiments based on the number of wells and the concentration of compound to be used. 5. PDE inhibitor screens identify compounds that elevate PKA activity by inhibiting the expressed PDE. This is detected by growth in 5FOA medium, thus the starting strain must be 5FOAS due to the expression of the target PDE. 6. To detect PDE inhibition by 5FOAR growth, the fbp1-ura4 reporter must be turned off prior to the start of the screen or else pre-existing Ura4 protein will kill the cells even if the PDE is inhibited. This can be accomplished by adding sufficient cAMP or cGMP to repress the reporter in spite of the PDE activity or by adding both a cyclic nucleotide and a known inhibitor of the PDE. Examination of the cells by microscopy is generally sufficient to determine if PKA is activated as this will lead to cells that are visibly longer than the cells when growing in medium lacking added cAMP or cGMP. 7. The doubling time of a strain will vary depending on its genotype and the growth medium. A typical doubling time is ~3 h. However, S. pombe strains will fail to grow if diluted too much, therefore plan to grow cultures for no more than 6 doublings before starting the assay. The volume of the final culture depends upon the number of cells needed for the experiment. Strains that grow to 2  107 cells/mL generally do not respond well in a 5FOA assay. It is best to start multiple cultures with different cell numbers to assure having a culture at the proper density. 8. The concentration of cAMP or cGMP to be used in the assay is such that it confers 5FOAR growth to a strain lacking the target PDE, but not to the strain expressing the PDE. 9. Optimal starting densities vary from 0.5  105 cells/mL to 2  105 cells/mL and should be determined for each strain individually. 10. A multichannel pipette can significantly reduce the time needed to set up replicate assays or compound dilutions to test more than one strain or treatment. 11. Cells will settle to the bottom of the wells and grow in clumps, leading to uneven OD600 readings unless cells are resuspended before reading. Do not centrifuge plates as this also leads to an uneven distribution of cells in the wells. 12. We generally use strains expressing human PDE4D2, a very potent cAMP-hydrolyzing PDE. For these strains, after removing the T ¼ 0 sample, we add 2 μL of a mixture of 20 mM Rolipram together with DMSO or the experimental compounds to the 1 mL cultures and return the tubes to a shaking 30
C incubator or rollerdrum inside a 30
C incubator. 13. The appropriate time of incubation may vary depending on the strain and the compound. For strains that have a relatively low level of AC activity, one may require 60–120 min incubation to obtain a substantial signal. However, some compounds may be toxic and by reducing cell growth, could appear to lower cAMP production at these longer incubation times. Note that in Fig. 2, an impact on cAMP production by AC4 is detectable within 10 min of PDE inhibition. 14. Acetonitrile permeabilizes the cells and also precipitates material in the growth medium that would otherwise accumulate in the guard column. These assays detect both intracellular cyclic nucleotides and those secreted into the growth medium. Therefore, do not pellet and wash cells in an effort to examine intracellular levels as a substantial portion of the cAMP or cGMP is secreted. 15. This two-fold dilution of the acetonitrile into water is needed to reduce the concentrations of both acetonitrile and DMSO in the samples, for proper passage through the guard column.

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Acknowledgments This work was supported by NIH grants 1R21GM079662 and 1R01AG0678361, the Peter Rieser Lectureship Fund, Orphan Disease Center grant MDBR-19-112-FD/MAS, and a grant from Boston College to C.S.H. References 1. Fantes PA, Hoffman CS (2016) A brief history of Schizosaccharomyces pombe research: a perspective over the past 70 years. Genetics 203: 621–629 2. Hoffman CS, Wood V, Fantes PA (2015) An ancient yeast for young geneticists: a primer on the Schizosaccharomyces pombe model system. Genetics 201:403–423 3. Norcliffe JL, Alvarez-Ruis E, Martin-Plaza JJ et al (2014) The utility of yeast as a tool for cellbased, target-directed high-throughput screening. Parasitology 141:8–16 4. de Medeiros AS, Magee A, Nelson K et al (2013) Use of PKA-mediated phenotypes for genetic and small-molecule screens in Schizosaccharomyces pombe. Biochem Soc Trans 41: 1692–1695 5. Hoffman CS (2005) Glucose sensing via the protein kinase a pathway in Schizosaccharomyces pombe. Biochem Soc Trans 33:257–260 6. Hoffman CS, Winston F (1990) Isolation and characterization of mutants constitutive for expression of the fbp1 gene of Schizosaccharomyces pombe. Genetics 124:807–816 7. Hoffman CS, Winston F (1991) Glucose repression of transcription of the Schizosaccharomyces pombe fbp1 gene occurs by a cAMP signaling pathway. Genes Dev 5: 561–571 8. Ivey FD, Wang L, Demirbas D et al (2008) Development of a fission yeast-based highthroughput screen to identify chemical regulators of cAMP phosphodiesterases. J Biomol Screen 13:62–71 9. Demirbas D, Ceyhan O, Wyman AR et al (2011) Use of a Schizosaccharomyces pombe

PKA-repressible reporter to study cGMP metabolising phosphodiesterases. Cell Signal 23: 594–601 10. Alaamery MA, Wyman AR, Ivey FD et al (2010) New classes of PDE7 inhibitors identified by a fission yeast-based HTS. J Biomol Screen 15:359–367 11. Ceyhan O, Birsoy K, Hoffman CS (2012) Identification of biologically active PDE11selective inhibitors using a yeast-based highthroughput screen. Chem Biol 19:155–163 12. Demirbas D, Wyman AR, Shimizu-Albergine M et al (2013) A yeast-based chemical screen identifies a PDE inhibitor that elevates steroidogenesis in mouse Leydig cells via PDE8 and PDE4 inhibition. PLoS One 8:e71279 13. de Medeiros AS, Wyman AR, Alaamery AA et al (2017) Identification and characterization of a potent and biologically-active PDE4/7 inhibitor via fission yeast-based assays. Cell Signal 40: 73–80 14. Getz RA, Kwak G, Cornell S et al (2019) A fission yeast platform for heterologous expression of mammalian adenylyl cyclases and high throughput screening. Cell Signal 60:114–121 15. de Medeiros AS, Hoffman CS (2015) A yeastbased high-throughput screen for modulators of phosphodiesterase activity. Methods Mol Biol 1294:181–190 16. de Medeiros AS, Kwak G, Vanderhooft J et al (2015) Fission yeast-based high-throughput screens for PKA pathway inhibitors and activators. Methods Mol Biol 1263:77–91 17. Beste KY, Burhenne H, Kaever V et al (2012) Nucleotidyl cyclase activity of soluble guanylyl cyclase alpha1beta1. Biochemistry 51:194–204

Chapter 7 Time-Domain Fluorescence Lifetime Imaging of cAMP Levels with EPAC-Based FRET Sensors Olga Kukk, Jeffrey Klarenbeek, and Kees Jalink Abstract Second messenger molecules in eukaryotic cells relay the signals from activated cell surface receptors to intracellular effector proteins. FRET-based sensors are ideal to visualize and measure the often rapid changes of second messenger concentrations in time and place. Fluorescence Lifetime Imaging (FLIM) is an intrinsically quantitative technique for measuring FRET. Given the recent development of commercially available, sensitive and photon-efficient FLIM instrumentation, it is becoming the method of choice for FRET detection in signaling studies. Here, we describe a detailed protocol for time domain FLIM, using the EPAC-based FRET sensor to measure changes in cellular cAMP levels with high spatiotemporal resolution as an example. Key words FLIM, FRET, cAMP, EPAC, TCSPC

1

Introduction Second messengers such as Ca2+, IP3, cGMP, and cAMP are critical intermediates that relay signals from membrane-bound receptors to intracellular effectors. Cyclic adenosine monophosphate (cAMP) plays a key regulatory role in most types of cells; however, the pathways controlled by cAMP may present important differences between organisms. In rare cases, such as the slime mold Dictyostelium [1], cAMP can also convey extracellular signals. Intracellular concentrations of cAMP change rapidly when it is synthesized from ATP by a family of adenyl cyclases (ACs) or degraded by phosphodiesterases (PDEs). Mammalian ACs are either cytosolic or membrane bound and regulated via (1) phosphorylation by PKA, PKC, and calmodulin-dependent protein kinases (CaMK), (2) second messenger molecules such as Ca2+ and (3) via protein–protein interactions. A prominent mode of activation for mammalian membrane-bound ACs is through interaction with heteromeric G proteins, which in turn are triggered

Manuela Zaccolo (ed.), cAMP Signaling: Methods and Protocols, Methods in Molecular Biology, vol. 2483, https://doi.org/10.1007/978-1-0716-2245-2_7, © The Author(s) 2022

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when extracellular signals, such as hormones and neurotransmitters, bind to G protein-coupled membrane receptors (GPCRs). The alpha subunit of the heteromeric G protein (Gα) exchanges GDP for GTP, and as a consequence, dissociates from the G protein complex into Gα and Gβγ. G protein subunits then bind ACs, and depending on the specific subunits and the type of AC, the cAMP producing enzymes are either activated or inhibited [2, 3]. The cAMP concentration is reduced via hydrolysis by members of the PDEs family. Certain PDEs are selective for cAMP, while others can only degrade a closely related second messenger, cGMP (cyclic guanosine monophosphate); the majority however is capable of degrading both cGMP and cAMP. Just as the ACs, PDEs are regulated by the interplay with a variety of cellular signals such as phosphorylation, small signaling molecules, and protein–protein interactions [4]. PDEs have been associated with several diseases, including heart failure, depression, asthma, and inflammation, and many drugs are developed that selectively target PDEs [5]. It is therefore important to keep developing precise real-time measurement techniques for cyclic nucleotide second messengers, that can help elucidate the regulation of their turnover in healthy and diseased cells. Genetically encoded intramolecular biosensors are powerful tools to study second messenger concentrations with high temporal resolution. Fo¨rster resonance energy transfer (FRET) is the non-radiative transfer of energy from a donor fluorophore to an acceptor fluorophore [6] and can directly report protein conformational changes, thus providing a base for a large variety of fluorescent biosensors. Development of these biosensors started around two decades ago, with pioneers such as a calmodulin-based sensor [7] and troponin C-based sensor [8] for Ca2+, cygnet probes for cGMP [9] (with the regulatory domain of PDEs), sensor based on the cGMP-binding domain B from cGMP-dependent protein kinase (GKI) [10], and cAMP sensors based on protein kinase A (PKA) [11] along with the EPAC-based probes [12–14]. EPAC1 is a guanine nucleotide exchange factor for Rap1 that is activated by direct binding of cAMP [13]. EPAC1 has an N-terminal DEP (Dishevelled, Egl, Pleckstrin) domain that is essential for membrane localization, a cAMP-binding domain, a REM domain (Ras exchanger motif), and a C-terminal GEF catalytic domain (guanine exchange factor) that regulates the GDP/GTP binding affinity of the Ras-like protein Rap. The EPAC2 protein is identical in domain structure except for a second N-terminal cyclic nucleotide monophosphate (cNMP) binding domain [15]. Several groups reported on EPAC-based cAMP FRET sensors [16]. Nikolaev et al. [14] made a compact FRET sensor by fusing the cyclic nucleotide binding domains of EPAC1 and EPAC2 in-between donor and acceptor fluorophores. The full-length

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EPAC1 protein was also used [12]. In our design, we deleted the membrane-binding DEP domain (ΔDEP) of EPAC1, and we also introduced point mutations to render EPAC catalytically inactive (CD, T781A, and F782A) so as to prevent unwanted downstream signaling to Rap1 and Rap2 [13, 17]. This proved a good strategy as this configuration yields very robust FRET changes upon cAMP binding. In our original sensor, EPAC(ΔDEP, CD) was sandwiched between CFP and YFP donor and acceptor fluorophores [13]. Since then, we have reported several rounds of optimization [18–20]. FRET is extremely sensitive to distance: typically, a fluorescent protein needs to be in the range of 1–10 nm for FRET to occur. This characteristic distance range makes FRET ideal to measure protein–protein interactions as well as conformational changes within proteins in living cells. The most common techniques to read out FRET are fluorescence ratiometry and fluorescence lifetime imaging (FLIM). Ratiometry can be carried out with relatively simple and widely available equipment by recording both donor and acceptor emissions. However, ratiometric measurements are not fully quantitative unless endpoint calibrations are performed or quite elaborate corrections are carried out [21, 22]. FLIM recording, on the other hand, is a much more robust and inherently quantitative technique [23]. FLIM reports on FRET because FRET shortens the donor lifetime. FLIM has the important advantages that lifetimes generally are independent of concentration, bleaching, or excitation fluctuations. However, fluorescence lifetimes of excited fluorophores typically are a few nanoseconds, and FLIM therefore requires complex and dedicated machinery. While being the most quantitative method for the detection of FRET, FLIM is also technically demanding. Time-correlated single photon counting (TCSPC) [24, 25] and frequency domain (FD)-FLIM [26, 27] are the two most common techniques to measure fluorescent lifetimes. Traditional TCSPC detectors are photon efficient, but have until recently been extremely slow and hence unable to deliver the speed for biological processes occurring at time scales below tens of seconds. Such limitations in speed and experimental complexity were overcome by efforts of several groups, including our laboratory in tight cooperation with Leica Microsystems, and resulted in the development of FLIM instrumentation like the Leica SP8 FALCON [28]. This system is based on a confocal scan head with field-programmable gate array electronics, pulsed laser excitation and fast, spectral single photon counting detectors. FD-FLIM, which is commonly implemented on widefield microscopes, relies on recording a stack of images at different phases and it is inherently fast. Unfortunately, it is also photon inefficient and somewhat prone to producing artifacts when used with living cells,

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stemming from rapid cellular movements and signal transients. We recently also contributed in methods that overcome these issues by developing, in cooperation with Lambert instruments, a singleimage method called siFLIM [29]. In live-cell time-lapse experiments, this approach provides both photon efficiency and speed as well as excellent signal to noise ratio (SNR) along with immunity to lifetime artifacts. Having designed and perfected fast and photonefficient FLIM techniques for both confocal [28] and wide field microscopy [29] we feel that FLIM should become the method of choice for FRET-based signaling studies as it enables following large numbers of cells in real time, quantitatively, with high data content and minimal photodamage. Here we describe how to use fast TCSPC FLIM recording to measure changes in cellular cAMP levels. We employ our EPACbased sensor (EPAC-SH189), a reporter with a truncated version of the cyan fluorescent protein analog mTurquoise2 [30] as donor and a tandem of non-emitting, circular permutated Venus proteins (a yellow fluorescent protein analog) as acceptor [20]. The donor has an exceptionally high quantum yield and brightness, allowing for dim excitation and thus minimizing bleaching and phototoxicity. Unlike most other fluorescent proteins, the decay of excited mTurquoise2 is well fitted with a single exponent, which makes it especially suited for TCSPC FLIM. The tandem dark Venus moiety minimizes emission of the acceptor, enabling collection of mTurquoise2 emission over a wide range without contamination with acceptor signal. This biosensor has an outstanding signal-to-noise ratio and good fluorophore maturation, and it is biochemically inert. Moreover, the EPAC moiety harbors a single point mutation Q270E, which increases its affinity for cAMP. We describe a stepby-step protocol to perform a cAMP recording using the Leica S TELLARIS 8 FALCON time domain FLIM confocal setup. The protocol described here should be equally applicable to recordings with other intramolecular FRET sensors. Note that whereas this protocol focuses on TD-FLIM application to measure cAMP levels in single cells with exceptional quantitative sensitivity, we have previously provided a detailed protocol [31] on the use of FD-FLIM in combination with EPAC-based biosensors and a detailed protocol [32] for cAMP sensing by ratiometric detection of sensitized emission.

2

Materials All solutions are made with deionized water.

2.1

Stock Solutions

1. 1 M NaCl2. 2. 1 M NaOH.

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3. 2.5 M CaCl2. 4. 1 M CaCl2. 5. 1 M MgCl2. 6. 0.1 M KCl. 7. 1 M Glucose (see Note 1). 8. 1 M HEPES (see Note 1). 2.2

Disposables

1. 0.22 μm filters. 2. Attofluor cell chamber (Invitrogen). 3. 24 mm Ø, 0.17 mm thick glass coverslips (#1.5). 4. non-pyrogenic polystyrene tubes. 5. 6-well cell culture plates.

2.3 Working Solutions

1. 2
HEPES buffered saline (HBS-buffer): 280 mM NaCl, 10 mM KCl, 20 mM HEPES, pH ¼ 7.2 at 37  C. Magnesium (1 mM MgCl2), calcium (1 mM CaCl2), and glucose (10 mM) are added when the dilution to 1
HBS-buffer is made. 2. 2
HBS-buffer (for transfection): 280 mM NaCl, 50 mM HEPES, 1.5 mM Na2HPO4, pH 7.2. The optimal pH depends on the cell line that is used (see Note 2). 3. 1.5:1 W/V polyethylenimine (PEI) (MW ~ 25.000) ethanol solution. Store in glass at 20  C (see Note 3). 4. 1
phosphate buffered saline (PBS): 137 mM NaCl, 2.7 mM KCl, 10 mM Na2HPO4, and 1.8 mM KH2PO4. 5. DMEM supplemented with 10% FCS and penicillin/streptomycin (pen/strep). Use the appropriate medium with additives if other cell lines are used. 6. 0.05% trypsin-EDTA solution. 7. DMEM/F-12 and/or phenol red-free Leibovitz’s L15 is used for imaging (see Note 4).

3 3.1

Methods Microscope

Confocal microscope equipped with fast time correlated single photon counting detector(s). Here we use Leica DMI8 confocal microscope with STELLARIS 8 FALCON system. For FRETdonor excitation we use the 440 nm line of the white light laser. For FRET-donor emission we use the Power HyD detector. The microscope is operated by Leica LasX software. Images were taken with a 63
oil objective.

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3.2

Cell Culture

24 mm Ø, #1.5 glass coverslips are sterilized with 70% alcohol or UV-C and placed in the wells of a 6-well plate. After drying, wells are filled with 2 mL of DMEM with FCS and pen/strep. Cells are trypsinized and resuspended in medium, and approximately 150.000 cells are added to each well (see Note 5). Depending on the cell line, cells are transfected 8–24 h after plating and cultured for another 24–72 h prior to FLIM.

3.3

Transfection

PEI transfection: 2 μL of the PEI solution is mixed with 1 μg of plasmid DNA in 200 μL serum-free medium in a polystyrene tube. Mix gently and incubate for 15–30 min at room temperature. Drop the PEI/medium mixture to the cells, and place the 6-well plate, after gentle swirling, back in the incubator (see Note 6). Calcium phosphate transfection: Put 86 μL of 2
HBS in a polystyrene tube, and add 2–5 μg of plasmid DNA diluted in H2O (final volume is 194.9 μL). Mix gently and add 5.1 μL of 2.5 M CaCl2 and mix again. Incubate for 20 min at room temperature, and add dropwise to the cells (see Note 7). Place the cells back in a CO2 incubator. Optionally replace the medium after 24 h. Any other commercial transfection reagent can also be used according to the manufacturer’s protocol.

3.4

Imaging

The cell chamber and imaging medium are prewarmed to 37  C. A coverslip is taken from the 6-well plate with sharp forceps and carefully mounted in a cell chamber (see Note 8). 1–2 mL HBS+/ + (1
HBS with glucose, CaCl2, and MgCl2) is immediately added to the cell chamber (see Note 9). Place the loaded cell chamber on the microscope. Use the appropriate immersion liquid on the objective, and focus on the cells. After focusing on the cells, wait up to 5 min to equilibrate the temperature.

3.5

TSCPC FLIM

1. Turn on all the hard- and software, wait for the instrument to initialize. Start up the FLIM software extension. 2. Adjust the software and hardware settings for EPAC sensor imaging: Select the 440 nm excitation laser line, Power HyD detector for donor lifetime detection between 450 and 550 nm, pinhole at the desired value. We used 3 Airy Units in this experiment (see Note 10). 3. Mount the cells on the microscope. Focus by looking through the eyepiece onto the cells or by looking at the digital image in “live” mode. 4. Set up the time-lapse experiment. Measuring the dynamics of changes in cellular cAMP concentration requires a temporal resolution in the order of seconds with minimal photodamage to the cells. In the current example we acquire FLIM images with 5 s frame interval.

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Fig. 1 Time-lapse FLIM imaging of HeLa cells expressing the EPAC-SH189 biosensor. (a) Mean FRET donor fluorescence intensity image with a set of ROIs drawn for single-cell analysis; (b, c, d) intensity weighted lifetime images at t ¼ 50 s, t ¼ 120 s, and t ¼ 360 s; (e) lifetime traces corresponding for the selected ROIs in (a): cells stimulated at 55 s with 5 nM isoproterenol (S1), at 170 s with 25 nM isoproterenol (S2), at 280 s with 25 μM forskolin (S3); (f) lifetime decay for the first 50 s (before stimulation, green) and the last 50 s (after 25 μM forskolin, blue); (g) Polar plot: the population of sensor molecules is shifted toward the open confirmation due to rise in cellular cAMP concentration

5. Select a region with one or more healthy cells (see Note 11). Start the actual experiment by recording a baseline for a few frames before stimulating the cells with a concentrated stock of reagent(s) (see Note 12). Follow the changes in lifetime. Optionally, multiple stimuli can be added. When the plateau is reached after addition of the initial stimulus, wait for another few frames. In Fig. 1, the initial baseline is recorded for approximately 1 min. As the first stimulus, beta2-adrenergic receptor agonist isoproterenol is added at 5 nM final concentration resulting in an increase of intracellular cAMP and a concomitant increase of fluorescence lifetime due to the opening of the FRET sensor. The second stimulus is the same agonist at 25 nM resulting in further, yet not saturating increase in

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cellular cAMP levels. The final stimulation with 25 μM forskolin directly activates the ACs and increases the intracellular cAMP concentration to the maximum level. 6. Stop the experiment, and make sure the data is properly saved. 7. Photon arrival time histograms from FRET sensor data typically show a multi-exponential curve, indicating the superposition of different FRET states. These FLIM data are well described by a double-exponential fit: a high FRET state with a lifetime of 0.9 ns, and a low/no FRET state with a lifetime of 3.9 ns (see Note 13). 8. The values of lifetime components of the sensor can be plotted in a polar plot (Fig. 1g). The position within the polar plot gives information about several aspects of the sensor such as the fraction of sensor molecules that are in the open and closed conformation, but also on bleaching, and autofluorescence (see Note 13). 9. Photon arrival times registered by the Power HyD detector are fitted with the manufacturer-supplied software (Leica LasX). For the lifetime analysis image binning (e.g., 2x2 pixels) may be applied to increase pixel SNR. For each pixel the photon arrival time histogram is fitted to a double-exponential function with fixed lifetimes for both the high and the low FRET state (see step 7 and Note 13), yielding two images containing the amplitudes of these two components. These images can then be saved as TIF files, reducing the amount of raw data more than 1000-fold (from orders of GB to MB). Further analysis on the fitted lifetime data can be performed with manufacturersupplied software, or TIF files can be exported for processing with custom scripts in, e.g., Python, Matlab, ImageJ/Fiji (see Note 14). 10. Remove the cell chamber from the microscope and clean the objective with a tissue. Carefully clean the cell chamber, alternatingly with water and 70% alcohol to prevent any residual compound from sticking to the metal and affect future experiments. Prolonged storage of the cell chamber is in 0.5 M NaOH (see Note 15). 11. After all experiments are completed, turn off all equipment.

4

Notes 1. Glucose and HEPES cannot be autoclaved and have to be filtered with a 0.22 μm filter. 2. The optimal pH of the 2
HBS-buffer depends on the cell type used for the experiments. Therefore, a series of different pH buffers is made, ranging from 6.8 to 7.2 in steps of 0.05 and tested on exponentially growing cells (~50% confluent).

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3. After 3 months, transfection efficiency drops dramatically. Storage at 4  C is possible, but 20  C is recommended to prevent evaporation. It is strongly recommended to use glass containers, since ethanol taken from an Eppendorf tube by itself sometimes affects cells. 4. When imaging for extended times, cell culture medium is preferred over 1
HBS-buffer. Keeping the autofluorescence minimal is important during FLIM recordings. DMEM/F-12 and/or phenol red-free Leibovitz’s L15 is a good option. DMEM/F-12 is buffered with CO2. If no CO2 is available at the microscope, Leibovitz’s L15 is a good choice. If other media are used, riboflavins and phenol red may be a major source of autofluorescence. 5. For a homogenous layer of cells, move the plate twice in south/ north direction followed by twice in west/east direction. This will prevent cells from piling up in the middle of the well. 6. After 10 h, the first cells will be fluorescent, and 72 h after transfection, expression is highest. Note that PEI can be toxic to cells, and replacing the medium with fresh medium after 24 h is for some cell types advisable. 7. Do not exceed 20 min of incubation since large precipitates are formed which will decrease transfection efficiency. 8. If the forceps is not sufficiently sharp, a bent needle can be useful for lifting up the coverslip. Prevent leakage by placing the coverslip exactly in the middle of the chamber, and screw the ring tight. However, screwing too tightly can result in breaking the coverslip. 9. Clean the bottom of the coverslip with a paper tissue. Gently press the paper for a second time to the bottom of the coverslip to check if no leakage occurs. If the cell chamber is leaky, either the chamber is not screwed sufficiently tight, the coverslip is not in the middle of the ring or the coverslip is broken. 10. Pinhole (in Airy units, AU) can be opened/closed based on the brightness of the sample and the desired optical sectioning. Increasing the diameter of the pinhole enables collecting more photons at the cost of some image blurring. 11. Make sure the fluorescent cell looks like the non-transfected cells as an indication that they are healthy. Very bright cells may develop a different morphology, e.g., become more rounded, and these are to be avoided. An intermediate bright fluorescent cell is often a good choice for measuring. 12. Usually, a highly concentrated (e.g., 1000
) reagent is used to prevent changes in volume, temperature, and/or osmolarity when adding the stimulus. We mix the reagent by first pipetting a small volume with a yellow tip on a P20. Then we transfer the

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yellow tip carefully to a P200. Now a small drop of reagent is present in the middle of the yellow tip. To stimulate the cells, we carefully put the tip of the pressed pipette in the medium of the cell chamber and release the plunger. Then we gently mix by pipetting up and expelling 200 μL medium for six more times, while avoiding air bubbles. Do not mix directly on top of the cells as you may wash them away. 13. A polar (phasor) plot can show to what extent the FRET sensor is opened, whether fluorophore bleaching occurs, and the possible contribution of autofluorescence [33, 34]. Perfect mono-exponentially decaying fluorophores are located on the semicircle. Lower lifetimes are located at the right part of the semicircle. FRET sensors usually show a mixture of multiple decays and their phasors form a line within the semicircle. The sensor starts in a closed conformation and opens after increase of cellular cAMP (after addition of isoproterenol and further after addition of forskolin). In case opening of the sensor completely prevents energy transfer, the phasor ends on the semicircle for a mono-exponentially decaying donor. With decreasing cAMP concentration, the fraction of sensor molecules with high FRET increases and the phasor travels to the right and downward in this plot. 100% FRET efficiency is typically not reached with these sensors, but if it would, this would render the molecules invisible because all energy is transferred to the acceptors. The intercepts of the extrapolated line with the semicircle provide us with values for the long (3.9 ns) and the short (0.9 ns) lifetime components characteristic for this particular sensor. These values are used for fitting the photon arrival times from the experimental data to obtain quantitative FRET-donor lifetimes. 14. For calculating the weighted lifetimes for each pixel from the 2-component fit intensity images, users may prepare a simple custom Fiji [35] macro, where the exported intensity images for the short and the long lifetime components are multiplied with their respective values. The sum of these resulting images is then divided by the sum of original (non-lifetime weighted) intensities, yielding a final weighted lifetime image with: τ ¼ 0.9
I1 + 3.9
I2/(I1 + I2), in which I1 and I2 denote the intensities of the fast and the slow component, respectively. Transferring the data to ImageJ allows for fast and convenient inspection of the data outside the LAS-X software. 15. Some organic compounds bind to the steel ring and affect the follow-up experiment. Repetitive washing with water and alcohol is sufficient to get rid of most compounds. Other compounds are however not fully removed by washing, and therefore, we place the steel cell chamber in concentrated NaOH for prolonged times when not in use.

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Acknowledgements We acknowledge Dr. Marcel Raspe for his contribution to previously published analogous protocol [31] with the focus on frequency domain FLIM. This research has received funding from the European Union’s Horizon 2020 research and innovation program under the Marie Sklodowska-Curie grant agreement No. 840088. References 1. Wang Y, Chen C-L, Iijima M (2011) Signaling mechanisms for chemotaxis. Develop Growth Differ 53(4):495–502 2. Gancedo JM (2013) Biological roles of cAMP: variations on a theme in the different kingdoms of life. Biol Rev Camb Philos Soc 88(3): 645–668 3. Bassler J, Schultz JE, Lupas AN (2018) Adenylate cyclases: receivers, transducers, and generators of signals. Cell Signal 1(46):135–144 4. Maurice DH, Ke H, Ahmad F, Wang Y, Chung J, Manganiello VC (2014) Advances in targeting cyclic nucleotide phosphodiesterases. Nat Rev Drug Discov 13(4):290–314 5. Baillie GS, Tejeda GS, Kelly MP (2019) Therapeutic targeting of 30 ,50 -cyclic nucleotide phosphodiesterases: inhibition and beyond. Nat Rev Drug Discov 18(10):770–796 6. Fo¨rster T (1948) Zwischenmolekulare Energiewanderung und Fluoreszenz. Ann Phys 437(1–2):55–75 7. Miyawaki A, Llopis J, Heim R, McCaffery JM, Adams JA, Ikura M et al (1997) Fluorescent indicators for Ca2+ based on green fluorescent proteins and calmodulin. Nature 388(6645): 882–887 8. Mank M, Reiff DF, Heim N, Friedrich MW, Borst A, Griesbeck O (2006) A FRET-based calcium biosensor with fast signal kinetics and high fluorescence change. Biophys J 90(5): 1790–1796 9. Honda A, Sawyer CL, Cawley SM, Dostmann WRG (2005) Cygnets. In: Lugnier C (ed) Phosphodiesterase methods and protocols. Humana Press, Totowa, NJ, pp 27–43. Methods In Molecular Biology 10. Nikolaev VO, Gambaryan S, Lohse MJ (2006) Fluorescent sensors for rapid monitoring of intracellular cGMP. Nat Methods 3(1):23–25 11. Adams SR, Harootunian AT, Buechler YJ, Taylor SS, Tsien RY (1991) Fluorescence ratio imaging of cyclic AMP in single cells. Nature 349(6311):694–697

12. DiPilato LM, Cheng X, Zhang J (2004) Fluorescent indicators of cAMP and Epac activation reveal differential dynamics of cAMP signaling within discrete subcellular compartments. Proc Natl Acad Sci U S A 101(47):16513–16518 13. Ponsioen B, Zhao J, Riedl J, Zwartkruis F, van der Krogt G, Zaccolo M et al (2004) Detecting cAMP-induced Epac activation by fluorescence resonance energy transfer: Epac as a novel cAMP indicator. EMBO Rep 5(12): 1176–1180 14. Nikolaev VO, Bu¨nemann M, Hein L, Hannawacker A, Lohse MJ (2004) Novel single chain cAMP sensors for receptor-induced signal propagation. J Biol Chem 279(36): 37215–37218 15. Rehmann H, Prakash B, Wolf E, Rueppel A, de Rooij J, Bos JL et al (2003) Structure and regulation of the cAMP-binding domains of Epac2. Nat Struct Biol 10(1):26–32 16. Kim N, Shin S, Bae SW (2021) cAMP biosensors based on genetically encoded fluorescent/ luminescent proteins. Biosensors 11(2):39 17. de Rooij J, Rehmann H, van Triest M, Cool RH, Wittinghofer A, Bos JL (2000) Mechanism of regulation of the Epac family of cAMP-dependent RapGEFs. J Biol Chem 275(27):20829–20836 18. van der Krogt GNM, Ogink J, Ponsioen B, Jalink K (2008) A comparison of donoracceptor pairs for genetically encoded FRET sensors: application to the Epac cAMP sensor as an example. PLoS One 3(4):e1916 19. Klarenbeek JB, Goedhart J, Hink MA, Gadella TWJ, Jalink K (2011) A mTurquoise-based cAMP sensor for both FLIM and Ratiometric read-out has improved dynamic range. PLoS One 6(4):e19170 20. Klarenbeek J, Goedhart J, van Batenburg A, Groenewald D, Jalink K (2015) Fourthgeneration Epac-based FRET sensors for cAMP feature exceptional brightness, Photostability and dynamic range: characterization

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of dedicated sensors for FLIM, for Ratiometry and with high affinity. PLoS One 10(4): e0122513 21. van Rheenen J, Langeslag M, Jalink K (2004) Correcting confocal acquisition to optimize imaging of fluorescence resonance energy transfer by sensitized emission. Biophys J 86(4):2517–2529 22. Jalink K, van Rheenen J (2009) Chapter 7 FilterFRET: quantitative imaging of sensitized emission. In: Laboratory techniques in biochemistry and molecular biology, Fret and Flim Techniques, vol 33. Elsevier, Amsterdam, pp 289–349 23. Gadella TWJ (2011) FRET and FLIM Techniques. Elsevier, Amsterdam 24. Becker W, Bergmann A, Hink MA, Ko¨nig K, Benndorf K, Biskup C (2004) Fluorescence lifetime imaging by time-correlated singlephoton counting. Microsc Res Tech 63(1): 58–66 25. Liu X, Lin D, Becker W, Niu J, Yu B, Liu L et al (2019) Fast fluorescence lifetime imaging techniques: a review on challenge and development. J Innov Opt Health Sci 12(05):1930003 26. Spencer RD, Weber G (1969) Measurements of subnanosecond fluorescence lifetimes with a cross-correlation phase Fluorometer. Ann N Y Acad Sci 158:361–376 27. Gadella TWJ, Jovin TM, Clegg RM (1993) Fluorescence lifetime imaging microscopy (FLIM): spatial resolution of microstructures on the nanosecond time scale. Biophys Chem 48(2):221–239 28. Alvarez LAJ, Widzgowski B, Ossato G, van den Broek B, Jalink K, Kuschel L, Roberti MJ,

Hecht F (2019) SP8 FALCON: a novel concept in fluorescence lifetime imaging enabling video-rate confocal FLIM. Nat Methods 16(10) 29. Raspe M, Kedziora KM, van den Broek B, Zhao Q, de Jong S, Herz J et al (2016) siFLIM: single-image frequency-domain FLIM provides fast and photon-efficient lifetime data. Nat Methods 6:501–504 30. Goedhart J, von Stetten D, Noirclerc-SavoyeM, Lelimousin M, Joosen L, Hink MA et al (2012) Structure-guided evolution of cyan fluorescent proteins towards a quantum yield of 93%. Nat Commun 3(1):751 31. Raspe M, Klarenbeek J, Jalink K (2015) Recording intracellular cAMP levels with EPAC-based FRET sensors by fluorescence lifetime imaging. Methods Mol Biol 1294: 13–24 32. Klarenbeek J, Jalink K (2014) Detecting cAMP with an EPAC-based FRET sensor in single living cells. Methods Mol Biol 1071:49–58 33. Digman MA, Caiolfa VR, Zamai M, Gratton E (2008) The phasor approach to fluorescence lifetime imaging analysis. Biophys J 94(2): L14–L16 34. Eichorst JP, Wen Teng K, Clegg RM (2014) Polar plot representation of time-resolved fluorescence. Methods Mol Biol 1076:97–112 35. Schindelin J, Arganda-Carreras I, Frise E, Kaynig V, Longair M, Pietzsch T et al (2012) Fiji: an open-source platform for biological-image analysis. Nat Methods 9(7): 676–682

Open Access This chapter is licensed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence and indicate if changes were made. The images or other third party material in this chapter are included in the chapter’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the chapter’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder.

Chapter 8 Disruptors of AKAP-Dependent Protein–Protein Interactions Ryan Walker-Gray, Tamara Pallien, Duncan C. Miller, Andreas Oder, Martin Neuenschwander, Jens Peter von Kries, Sebastian Diecke, and Enno Klussmann Abstract A-kinase anchoring proteins (AKAPs) are a family of multivalent scaffolding proteins. They engage in direct protein–protein interactions with protein kinases, kinase substrates and further signaling molecules. Each AKAP interacts with a specific set of protein interaction partners and such sets can vary between different cellular compartments and cells. Thus, AKAPs can coordinate signal transduction processes spatially and temporally in defined cellular environments. AKAP-dependent protein–protein interactions are involved in a plethora of physiological processes, including processes in the cardiovascular, nervous, and immune system. Dysregulation of AKAPs and their interactions is associated with or causes widespread diseases, for example, cardiac diseases such as heart failure. However, there are profound shortcomings in understanding functions of specific AKAP-dependent protein–protein interactions. In part, this is due to the lack of agents for specifically targeting defined protein–protein interactions. Peptidic and non-peptidic inhibitors are invaluable molecular tools for elucidating the functions of AKAP-dependent protein–protein interactions. In addition, such interaction disruptors may pave the way to new concepts for the treatment of diseases where AKAP-dependent protein–protein interactions constitute potential drug targets. Here we describe screening approaches for the identification of small molecule disruptors of AKAPdependent protein–protein interactions. Examples include interactions of AKAP18 and protein kinase A (PKA) and of AKAP-Lbc and RhoA. We discuss a homogenous time-resolved fluorescence (HTRF) and an AlphaScreen® assay for small molecule library screening and human induced pluripotent stem cell-derived cardiac myocytes (hiPSC-CMs) as a cell system for the characterization of identified hits. Key words Protein kinase A (PKA), A-kinase anchoring protein (AKAP), AKAP18, AKAP-Lbc, Inhibitory peptides, Non-peptidic helix mimetics, Small molecules, Homogenous time-resolved fluorescence (HTRF) assay, AlphaScreen®, Calcium imaging, Line scan imaging, Human induced pluripotent stem cells (hiPSCs)

Ryan Walker-Gray and Tamara Pallien contributed equally to this work. Manuela Zaccolo (ed.), cAMP Signaling: Methods and Protocols, Methods in Molecular Biology, vol. 2483, https://doi.org/10.1007/978-1-0716-2245-2_8, © The Author(s), under exclusive license to Springer Science+Business Media, LLC, part of Springer Nature 2022

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Introduction A-kinase anchoring proteins (AKAPs) comprise a family of scaffolding proteins whose function is the spatial and temporal coordination of cellular signaling processes [1, 2]. The common feature of AKAPs is their ability to bind protein kinase A (PKA) through a structurally conserved 14–24 amino acid long amphipathic helix [3–6]. PKA holoenzyme consists of a homodimer of regulatory (RIα, RIβ, RIIα, or RIIβ) subunits and two catalytic (Cα, Cβ, or Cγ) subunits. The R subunit dimers form N-terminally a dimerization and docking (D/D) domain, which directly interacts with the R-binding domains of AKAPs. Through unique modes each AKAP can directly interact with PKA substrates and additional signaling molecules, including other kinases and phosphodiesterases (PDEs) [7, 8]. A few AKAPs, e.g., AKAP-Lbc, possess enzymatic activity, it acts as a guanine nucleotide exchange factor (GEF) and can specifically activate the small GTPase RhoA [9, 10]. Due to characteristic targeting domains AKAPs are present in every cellular compartment. The lack of a targeting domain renders an AKAP soluble and it is found in the cytosol, such as GSKIP [11]. Each AKAP interacts with a specific set of protein interaction partners in a specific cellular compartment. The composition of an AKAP-based signaling complex, i.e., the set of interaction partners, can vary between different cellular compartments and cells, and between cell states. For example, an elevation of cAMP can cause dissociation of the catalytic subunits of PKA while the regulatory subunits remain AKAPbound. Thus, AKAPs coordinate signal transduction processes spatially and temporally in defined cellular environments. AKAPs are involved in a plethora of cellular processes. For example, AKAP18α, γ, and δ play a role in the control of cardiac myocyte contractility [12, 13], or GSKIP is involved in Wnt signaling [14], a pathway that controls amongst others developmental processes, differentiation, and metabolism. AKAP18δ and AKAP-Lbc control vesicular trafficking and water reabsorption in renal principal cells [15–17]. Functions of AKAPs have been elucidated using knockout and knockin mice, RNA interference studies, or CRISPR/Casmediated knockdown using cell models. Examples are studies of GSKIP or AKAP79 [14, 18–21]. Such studies can define both global functions of an AKAP and also functions of individual protein–protein interactions if a binding domain is modified. However, an alternative approach towards elucidating the functions of individual protein–protein interactions would be disruption with interaction-specific peptidic and non-peptidic pharmacological agents. Pharmacological disruptors are versatile tools that can be used in both animal and cell studies, in any developmental stage or in any cell state, be it resting or stimulated with an external cue. Various peptides for the nonselective disruption of AKAP-

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dependent protein–protein interactions are available [22], mainly peptides for the disruption of AKAP–PKA interactions. They were derived from regulatory R-binding domains of various AKAPs [23– 25] or in silico-designed such as AKAPIS [26]. They all bind the D/D domains of the R subunits of PKA with nanomolar affinity and thereby uncouple PKA from AKAPs [27]. Peptides, although highly selective agents, have limitations with regard to membrane permeation ability, stability, delivery, and bioavailability. Peptidomimetics such as terpyridine-based α-helix mimetics of the RII-binding domain of AKAP18δ [28] or constrained (stapled) peptides [29] overcome some of these drawbacks but are often time- and cost-intensive to generate. In addition, their binding affinity might be lower than those of peptides, e.g., α-helix mimetics bind RII with a KD of 30–150 μM [28]. Small molecules for the inhibition of protein–protein interactions are alternatives to peptides and peptidomimetics. Selective small molecule disruptors of AKAP-dependent protein–protein interactions would be invaluable tools to elucidate physiological functions. They can usually be synthesized in a time- and costeffective manner. The feasibility of undertaking endeavors towards the identification of small molecule inhibitors of AKAP-dependent protein–protein interactions is underlined by the success of targeting other protein–protein interactions. The first small molecule protein–protein disruptors have even reached the market as drugs for the treatment of cancer. Indeed, the first, albeit nonselective, small molecule inhibitor of AKAP–PKA interactions, FMP-API-1, has been identified [30–32]. Other ones, identified by in silico approaches [33] or by screening such as Scaff-10, selectively inhibit the interaction of AKAP-Lbc with RhoA [15]. Since dysregulation of AKAPs and their interactions with PKA is associated with various cardiovascular diseases such as heart failure, inflammatory diseases such as chronic obstructive pulmonary disease (COPD), and neurological disorders such as schizophrenia, disruptors of AKAPdependent interactions may be utilized for the validation of the interactions as drug targets [1, 34–36]. We have previously discussed the use of ELISA-based assays for the search of small molecule inhibitors of AKAP–PKA interactions [37]. In this chapter, we will discuss generalized protocols to identify compounds that interfere with AKAP-dependent protein–protein interactions, and how to investigate the effects of these agents on cells in culture. The method described relies upon initial identification of compounds of interest using homogenous time-resolved fluorescence (HTRF), which are then verified using amplified luminescence proximity homogenous assays (AlphaScreen®). Both assays use proximity-limited interactions between donor and acceptor fluorophores in order to evaluate the binding of the two proteins in solution, with a decrease in acceptor fluorophore emission indicating a reduction in protein–protein interaction. Additionally, each method requires the use of differently tagged

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proteins for donor and acceptor binding. Commercial options are available which allow the same tagged protein pairs to be used in HTRF and AlphaScreen®. HTRF relies upon Fo¨rster Resonance Energy Transfer (FRET) in which excitation of a donor fluorophore that is within range of the acceptor fluorophore can induce non-radiative energy transfer to the acceptor fluorophore, resulting in a shift in the wavelength of emitted radiation (Fig. 1). The probability of energy being transferred in this way is directly dependent on the distance between the

Fig. 1 Homogenous time-resolved fluorescence (HTRF) assay. (a) Relative activity calculations. The ratio of emission at 665 nm to emission at 620 nm is used to produce a relative activity calculation that indicates the change in the proportion of acceptor fluorophores in FRET range of donor fluorophores. For our system this is further interpreted to mean the proportion of bound to unbound AKAP18-D/D complexes. Performing this measurement at a range of concentrations of inhibitor creates a dataset from which an IC50 value can be determined. The graph shown here is a curve showing the inhibition of the interaction of the indicated AKAP18 versions with regulatory RII subunits of PKA created with the peptide AKAP18δ-L314E, which globally inhibits AKAP–PKA interactions. AKAP18α Δ2-10, AKAP18α lacking the N-terminal 10 amino acids with the targeting domain. (b) Schematic representation of the experimental approach. In the absence of an inhibitor, the RII and AKAP proteins bind, bringing their conjugated donor and acceptor within the range of efficient energy transfer, and producing an increase in the amount of 665 nm light emitted by the acceptor molecules. In the presence of an inhibitor, the proteins are unable to bind to one another, meaning the acceptor and donor fluorophores are not brought into range of one another. This is indicated by a higher ratio of 620 nm light to 665 nm light

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fluorophores, and decays logarithmically as the space between the 1 fluorophores increases (E ¼ 1þðr=R 6 ; E ¼ quantum yield, r ¼ dis0Þ tance between fluorophores, R0 ¼ distance at which energy transfer is 50% for this set of fluorophores). HTRF uses fluorophores coupled to antibodies to recognize the protein tags. For our experiments, we used anti-GST-coupled terbium cryptate donor fluorophores in conjunction with anti-polyhistidine conjugated to the fluorescent protein allophycocyanin as the acceptor molecule, though other pairings are commercially available. The ratio of the emission wavelength of the acceptor to the donor can be used to determine relative changes in the proximity of proteins to one another. In the AlphaScreen® assay (Fig. 2), instead of FRET, the activation of the donor releases a reactive oxygen species by a specific wavelength of light which results in the release of singlet oxygen. The singlet oxygen has a half-life of around 4 μs, allowing for a diffusion radius of around 200 nm. If the reactive oxygen species

Fig. 2 Schematic representation of AlphaScreen®. The principle underlying AlphaScreen® is similar to that of FRET. However, instead of a non-radiative energy transfer between the donor and acceptor, a reactive oxygen species (1O2) is emitted from the donor bead when it is activated by 680 nm light. The half-life of the oxygen species and its rate of diffusion dictate the distance over which the donor and acceptor have a high likelihood of interaction. If the acceptor is contacted by single oxygen, it emits light in the 520–650 range. However, if the reactive oxygen does not contact the acceptor within this range, it returns to ground state. In contrast to FRET, there is limited emission fluorescence in the absence of interaction

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encounters the acceptor bead within the time, the activated acceptor releases light. If the singlet oxygen does not reach an acceptor bead, it simply returns to ground state. The most notable difference between the two assay methods is that the AlphaScreen® donor molecule does not emit light upon activation, which can interfere with the measurement of acceptor wavelengths. Each assay may be used as a primary or secondary screening system and allows the determination of IC50 values for identified hits. IC50 values are indicators of the inhibitory potency of a compound, defining the concentration of an inhibitor that decreases the interaction to 50% of the maximum. Hits confirmed in secondary approaches can undergo a hit to lead development towards a drug candidate where selectivity, affinity, and pharmacological properties are optimized. Additional assays that may be adopted for high-throughput screening of small molecule libraries include for example fluorescence polarization-, Biacore- and cell-based automatic microscopy assays. Any screening assay requires adaptation to the particular high-throughput screening platform. As a model system to test the identified hits in a cellular environment, we use human-induced pluripotent stem cell-derived cardiac myocytes (hiPSC-CMs). hiPSCs can be reprogrammed from patient-derived somatic cells such as dermal fibroblasts or peripheral blood mononuclear cells (PBMCs) and readily differentiated to CMs, thus making them an attractive tool to study cardiovascular diseases and evaluate personalized therapeutic options. Several studies have confirmed the similarity of hiPSC-CMs and adult cardiac myocytes with regard to Ca2+ signaling [38, 39]. They are also becoming an integrated part of workflows for drug safety and toxicology screening [40, 41]. However, with regard to certain properties limitations are yet to be overcome. For example, hiPSC-CMs show differences in maturity and ultrastructure compared to adult mammalian cardiac myocytes such as the absence of the t-tubular network [39, 42]. Approaches towards reaching cell maturity include prolongation of cultivation time [38, 43], formation of more complex 3D microtissues including electrical stimulation [43–46] or the use of low glucose, high oxidative substrate maturation medium [47]. However, such systems represent slower, more expensive, or lower throughput validation of compound activity, better suited to later stage translational modeling of candidates. To study the excitation-contraction coupling (ECC) pathway in hiPSC-CMs, Ca2+ fluxes and voltage changes can be visualized using Ca2+- or voltage-sensitive dyes in combination with fluorescence microscopy. These dyes bind to intracellular Ca2+ or detect changes in the membrane potential by changing their fluorescence intensity, respectively [48]. Thus, cells do not need to be genetically manipulated as would be the case when using

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genetically encoded Ca2+ or voltage indicators. Alternative methods to measure cardiac contractility include multielectrode arrays (MEAs) or impedance measurement [48]. However, in order to more directly determine the effect on the cytosolic component of ECC pathways, here we use Ca2+ imaging in monolayer hiPSCCMs to analyze the effect of AKAP-PKA disruptors on Ca2+ cycling.

2 2.1

Material HTRF

1. Assay buffer: 2: 0.1% BSA, 0.1% Tween20 in PBS, pH 7.4. 2. Recombinant GST-tagged AKAP18α-Δ-2-10 [28]. 3. Recombinant His-tagged DD-domain of PKA-RIIα (amino acids 1–44) [28]. 4. Terbium-conjugated anti-GST antibody. 5. XL665-conjugated anti-His antibody. 6. AKAP18δ-L314E peptide 10 μM in DMSO. 7. AKAP18δ-PP peptide 10 μM in DMSO. 8. DMSO. 9. ProxiPlate-384 Plus Shallow Well Microplates. 10. Genios Pro plate reader or another HTRF-certified plate reader.

2.2

AlphaScreen®

1. Recombinant 6xHis-tagged RIIα D/D. 2. Recombinant GST-tagged AKAP18α. 3. Test compounds, 1 mM in DMSO. 4. AlphaScreen® 0.05% BSA).

Assay

Buffer

(PBS,

0.05%

Tween20,

5. ProxiPlate-384 Low volume, white. 6. Biotek Multiflow liquid handling device. 7. Tecan Freedom Evo Workstation liquid handling robot. 8. AlphaScreen® Glutathione Donor Beads. 9. Nickel-Chelate-Acceptor-Beads. 2.3 Human Induced Pluripotent Stem Cells and Differentiation to Cardiac Myocytes 2.3.1 hiPSC Culture

1. 6-well cell culture-treated plates. 2. Essential 8 (E8) medium. 3. Coating solution: Geltrex diluted 1:10 in DMEM/F12. 4. Accutase. 5. Y-27632 ROCK Inhibitor.

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2.3.2 hiPSC-CM Differentiation and Culture

All basal media can be prepared in advance and stored at 4  C for up to 2 weeks. Add supplements fresh on the day of use. 1. Basal differentiation medium: RPMI-1640, B27 Minus Insulin (50; 2% v/v). 2. Cardiac priming medium: basal differentiation medium, 6 μM CHIR-99021. 3. Cardiac induction medium: basal differentiation medium, 5 μM IWR-1-endo. 4. Cardiac maintenance medium: RPMI-1640, B27 Supplement (50; 2% v/v). 5. Lactate selection medium: RPMI-1640 without Glucose, Sodium DL-lactate solution, CDM3 supplement (1; see [49]). 6. 10 TrypLE Select. 7. Freezing medium: Knockout Serum Replacement (KSR), 10% DMSO. 8. Mr. FrostyTM cryogenic storage box.

2.3.3 Quality Control

1. DPBS without Ca2+/Mg2+ (DPBS-). 2. FoxP3 Kit Fix/Perm (contains solutions 1 and 2). 3. FoxP3 Kit Permeabilization buffer. 4. FACS Buffer: DPBS-, 0.5% BSA, 2 mM EDTA. 5. BD FACS tubes with blue cell strainer caps. 6. Flow Cytometry antibodies/dyes: MLC2v-APC, TNNT2FITC, VioBility_405/452 dye, recombinant human IgG-APC/IgG-FITC. 7. MACSQuant VYB.

2.3.4 Thawing of hiPSCCMs

1. DPBS with Ca2+/Mg2+ (PBS++). 2. Resuspension medium: cardiac maintenance medium, KSR (10%, v/v). 3. Plating medium: resuspension medium, RevitaCell Supplement (100; 1% v/v). 4. CellDropTM BF Automated Cell Counter.

2.3.5 Calcium Imaging

1. Glass-bottom dish 35 mm. 2. Human Plasma Fibronectin, diluted to 50 μg/ml in DPBS++. 3. DPBS. 4. Tyrode’s solution: 135 mM NaCl, 4 mM KCl, 1 mM MgCl2, 1.8 mM CaCl2, 5 mM D-Glucose, 10 mM HEPES. Adjust pH to 7.3 and Osmolarity to 300  5 (see Note 15).

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5. Fluo-8®, AM-ester: 1 mM stock, 1 mg Fluo-8 in 955.2 μl DMSO. 6. Pluronic® F-127: 10% stock, 1 g pluronic in 10 ml ddH2O. 7. Peptide L314E: stock ¼ 10 mM in DMSO. Final concentration ¼ 100 μM. 8. Scrambled peptide: stock ¼ 10 mM in DMSO. Final concentration ¼ 100 μM. 2.3.6 Data Analysis

1. Raw images are produced in the Zeiss proprietary LSM file format, which can be read and manipulated using the opensource LSMToolbox plug-in for ImageJ. For further processing the mean pixel value of a given scanned line is produced using the ImageJ plot profile tool, and recorded in a spreadsheet. 2. The isolated mean pixel values are further evaluated based on the line scan time and pacing frequency using the CalTrack algorithm for MATLAB (Christopher Toepfer, Radcliffe Department of Medicine, UK). This analysis produces normalized average transient profiles that provide insight into the biophysics of the pulse through report on a series of statistics, including the duration of the transient (time above baseline) decay time of the signal (t-end), and full width at half magnitude (CD50). 3. Graphs and statistics: GraphPad PrismTM.

3

Methods We established our protocols for screening of various small molecule libraries in the screening unit of the LeibnizForschungsinstitut for Molecular Pharmacology (FMP) Berlin on the interaction of AKAP18 with RIIα. The “FMP20000” library (www.chembionet.info; www.fmp-berlin.de) contains 20,064 diverse, commercially available compounds, the Selleck and LOPAC libraries contain 2862 and 1280 compounds which are approved drugs or drugs that reached clinical trials. First, we illustrate an HTRF-based primary screening, in a second protocol, we describe an AlphaScreen® protocol. Each, HTRF or AlphaScreen® can be used as a primary or secondary screening.

3.1 HTRF for Screening for Small Molecule Inhibitors of AKAP–PKA Interactions

The HTRF interaction assay is suited to high-throughput screening, as it can be carried out in microplates and the method is easily automated. Here we describe a method used to investigate potential inhibitors of PKA-RIIα regulatory subunit docking and dimerization (D/D) domain binding to full length AKAP18α. Recombinant GST-AKAP18δ and full length untagged RIIα were

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encoded in the vector pGEX4T3 and generated in E. coli (strain Rosetta DE3) [28]. The D/D domain of RIIα was recombinantly expressed with 6x histidine (His) tag, while AKAP18α was coupled to GST. Anti-GST antibodies coupled to terbium cryptate acted as the donor molecules, while anti-6xHis antibodies conjugated to allophycocyanin were used as acceptors. 1. Prepare serial dilutions of the compound(s) to be tested; in this case, the AKAP18δ-L314E peptide and the inactive control peptide AKAP18δ-PP (see Note 1). 2. Dilute the AKAP in assay buffer to a final concentration of 50 nM (see Notes 2 and 3). 3. Dilute the D/D domain of RIIα (50 nM) together with the donor and acceptor antibodies (1:100 each) in assay buffer. 4. Dispense 5 μl of the GST-AKAP18α solution (final AKAP concentration 25 nM) to each well of a 384-well plate, except for wells containing the negative control (see Notes 3 and 4). 5. Dispense 0.2 μl of each dilution of the compound into the appropriate wells (see Note 5). 6. Dispense 5 μl of the solution containing the D/D domain (final concentration 25 nM), the XL665-conjugated antibody, and the terbium conjugated antibody (final concentration of antibodies 1:200) into each well (see Note 6). 7. Shake at 2000 rpm for 15 s and briefly centrifuge the plate to make sure all the components are properly mixed and at the bottom of the wells. 8. Incubate for 1–2 h in the dark (see Note 7). 9. Measure fluorescence emission at 620 nm (donor) and 665 nm (acceptor) with a time delay of 60–150 μs after donor excitation at 320 nm in an HTRF-certified plate reader (see Notes 8 and 9). 3.1.1 Data Analysis

1. Calculate the FRET ratio by dividing the fluorescence intensity of the acceptor by the fluorescence intensity of the donor for each well. 2. Subtract the average FRET ratio of the negative control wells. 3. Normalize to the background-subtracted average FRET ratio of the wells containing positive control for a relative measure of the residual interaction. 4. Plot the obtained values in a logarithmic X-axis and calculate an IC50 value by performing a non-linear regression. For this, we use GraphPad Prism. A representative example of results is shown in Fig. 1, which show that the peptide AKAP18δ-L314E effectively inhibits the AKAP18-PKA (see Notes 10–14).

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3.2 AlphaScreen® Assay

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AlphaScreen® assays offer the same advantages as HTRF in terms of scalability and suitability for high-throughput screening. Additionally, the differences in energy transfer between donor and acceptor mean that there is less background fluorescence in the AlphaScreen®, and the combination of the assays helps to identify compounds of interest that may affect the energy transfer, emission, or excitation of the proteins rather than their association, helping to eliminate false positives. 1. 1:1 serial dilutions of the target compound, 11 points; highest concentration of 100 μM, and the lowest 0.0977 μM. 2. Prepare the acceptor mix in assay buffer (5 μl of mix per reaction). 25 nM RIIα D/D. 20 μl/ml Nickel-chelate-acceptor beads. 3. Prepare donor mix in assay buffer (5 μl of mix per reaction) 150 nM AKAP18α. 20 μl/ml Glutathione donor beads. 4. Dispense 5 μl of donor mix (RIIα D/D, acceptor beads) into each experimental well, omitting negative control wells. 5. Dispense 0.2 μl of test compounds into appropriate wells. 6. Incubate for 30 min at room temperature. 7. Dispense 5 μl of acceptor mix (AKAP18α, donor beads) into each experimental well, again omitting negative control wells. 8. As a negative/background control dispense 10 μl of assay buffer containing only acceptor and donor beads, each at a concentration of 10 μg/ml. 9. Mix for 15 s at 2000 rpm. 10. Incubate for 1 h at room temperature. 11. Measure the AlphaScreen® signal with an excitation at 680 nm and an emission at 520–620 nm with an AlphaScreen® compatible reader.

3.2.2 Data Analysis

1. Subtract the average number of counts of the negative control wells from the signal wells. 2. Normalize to the background-subtracted average counts of the wells containing positive control for a relative measure of the residual interaction. 3. Plot the obtained values in a logarithmic X-axis and calculate an IC50 value by performing a non-linear regression. For this purpose, we use the GraphPad Prism.

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Fig. 3 Timescale for the differentiation of hiPSC cells into cardiac myocytes using small molecules 3.3 hiPSC Differentiation to Cardiac Myocytes

This protocol describes the differentiation of hiPSCs into cardiac myocytes (CMs) using an approach optimized for cells cultured in E8 medium. In this protocol, cardiac differentiation is induced using the Wnt pathway activator CHIR-99021 followed by inhibition of this pathway from day 3 with IWR-1 (Fig. 3) [50–52]. An optional metabolic selection step is included in this protocol for further enrichment of hiPSC-CMs.

3.3.1 Geltrex Coating of 6-Well Cell culture Plates

1. Prepare 6-well plates by coating with Geltrex. Thaw a 1 ml aliquot of 1:10 diluted Geltrex on ice and mix with 11 ml cold RPMI-1640. Add 1 ml fully diluted Geltrex solution per well of a 6-well plate and incubate 60 min at 37  C before seeding the cells (see Note 16).

3.3.2 hiPSC Culture

1. hiPSCs are cultured in E8 medium on Geltrex-coated plates with daily medium changes under hypoxic conditions (37  C, 5% O2, 5% CO2). 2. Three to four days before starting CM differentiation, dissociate the cells for 6 min using Accutase and seed onto fresh Geltrex-coated 6-well plates at a density of 2  105 cells/well in E8 medium supplemented with 10 μM of Y-27632 ROCK Inhibitor. 3. The next day, change medium to 2 ml E8 medium without ROCK inhibitor and continue with daily medium change until the cells reach 80–90% confluence (see Note 17).

3.3.3 Cardiac Myocyte Differentiation

1. Day 0 of differentiation, change the medium to 2 ml cardiac priming medium/well (see Note 18). Transfer cells to an incubator with normoxia conditions (37  C, 5% CO2). 2. Day 1, add 4 ml basal differentiation medium/well. Do not remove the old medium. 3. Day 3, replace medium with 4 ml cardiac induction medium/ well. 4. Day 5, add 4 ml basal differentiation medium/well. Do not remove the old medium. 5. Day 7, replace medium with 3 ml cardiac maintenance medium/well.

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6. Day 9, replace medium with 3 ml lactate selection medium/ well (see Note 19). If cell death appears high the next day, replace with fresh selection medium. 7. Day 11, replace medium with 3 ml cardiac maintenance medium/well and keep on changing the medium every 2–3 days (see Note 20). 8. Day 15, dissociate the cells for 12 min using 10 TrypLE and freeze back 1–10  106 cells/cryovial in 1 ml CM freezing medium (see Note 21). Keep 1  106 cells to perform a quality control experiment. 3.3.4 Quality Control

If not otherwise indicated, centrifugation and incubation steps are conducted at RT. 1. Add dissociated hiPSC-CMs in 1 ml resuspension medium to the blue strainer cap of a BD FACS tube. 2. Centrifuge at 300  g for 2 min. 3. Remove supernatant and perform live/dead staining with Viobility dye. Resuspend the cells in 140 μl DPBS- and remove 40 μl cell suspension as unstained control. Add 1 μl dye to the remaining 100 μl DPBS- and incubate for 15 min in the dark. 4. Wash cells by adding 2 ml DPBS- and centrifuge at 300  g for 3 min. 5. Remove supernatant and resuspend in 500 μl Fix/Perm solution (1:4 dilution of solution 1 with solution 2). Incubate in the dark for 20 min (see Note 22). 6. Add 2 ml DPBS- and centrifuge at 300  g for 3 min. Remove supernatant. Resuspend cells in 500 μl permeabilization buffer (1:10 in ddH2O) and incubate in the dark for 30 min. 7. Add 2 ml DPBS- and keep 1 ml of the cell suspension for isotype control staining. Transfer the remaining 1.5 ml to another FACS tube for cardiac TNNT2 and MLC2v staining. Centrifuge at 300  g for 3 min. Remove supernatant and resuspend in 100 μl FACS buffer for staining: for control stain add 2 μl each of recombinant human IgG-APC and FITC isotype control antibodies; add 10 μl anti-MLC2v-APC and 10 μl anti-TNNT2-FITC to the second FACS tube. Incubate for 20 min in the dark. 8. Add 2 ml PBS and spin down at 300  g for 3 min. Resuspend the cells in 400 μl FACS buffer and analyze at MACS Quant VYB. 9. To analyze the FACS results and set up the gates we used FlowJo Version 10.

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Fig. 4 Flow cytometry analysis of hiPSC-CMs at differentiation day 20 after thawing. (a) Representative image of hiPSC before start of differentiation. (b) hiPSC-CMs at day 25 of differentiation seeded on glass-bottom dishes coated with 50 μg/ml fibronectin. (c) Expression of TNNT2 and MLC2v protein at Day 20 of CM differentiation after thawing. Cells were gated for single cells using forward and site scatter channels. The number of viable cells was determined using Vioblue dye staining. Isotype FITC and APC control samples were used to set up the gates for TNNT2 and MLC2v detection

As shown in Fig. 4, the differentiated cardiac myocytes were tested for TNNT2 and MLC2v expression by flow cytometry. The cells show high expression of TNNT2 at Day 20 of differentiation (TNNT2+ ¼ 98.8%) and 46.5% expression of MLC2v indicating a shift into a more ventricular-like phenotype. 3.3.5 Calcium Imaging

The following section provides a protocol for the analysis of Ca2+ signaling in hiPSC-derived cardiac myocytes, using the Ca2+-binding dye, Fluo-8-AM (Fig. 5). Briefly, cells were loaded with 2 μM of Fluo-8-AM in the presence of pluronic acid, which improves water solubility of the dye. The Fluo8-AM dye will enter the cell and will be trapped intracellularly upon de-esterification. To improve Ca2+

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Fig. 5 Representative scheme of line scan acquisition. Cells receive biphasic field stimulation at a rate of 0.5 Hz (Pacing), triggering an action potential which allows fluorescent emission from activated Fluo8 intracellular dye, which is captured as an 8-bit image by the confocal microscope (Line Scan). The underlying 8-bit values (0–255) of these pixels can then be averaged along the width of the line as a measure of near-instantaneous cellular fluorescence at a single axis through the cell (Plotted data). This measurement can be used as an indicator of the time course of release and reuptake of cytoplasmic Ca2+ concentration and further as an indication in the biophysical dynamics of the underlying mechanisms

loading of the sarcoplasmic reticulum (SR), cells were paced at 10 V, 0.5 Hz for 2 ms. For analysis of whole-cell calcium transients the line scan mode has been chosen, measuring 20,000 lines with 1.92 ms per line. Spontaneous subcellular calcium events, called calcium sparks, originate from single or a cluster of ryanodine receptors and can be measured in the line scan mode as well. To specifically analyze the effect of AKAP-PKA disruption on the ECC pathway, we stimulated the cells with AKAP18δ-L314E (L314E) and a scrambled peptide as control (Fig. 6). 3.3.6 Thawing of hiPSCCMs

1. Coat 35 mm glass-bottom dishes with 50 μl fibronectin droplets (1:20 in PBS++, 50 μg/ml) (see Note 23). 2. Collect cryovial(s) from liquid nitrogen and perform a quick thaw in a 37  C water bath. Continue until 90% of the suspension is defrosted. 3. Remove cells from cryovial and gently add to the bottom of a 50 ml Falcon tube. Wash cells with 1 ml resuspension medium and add dropwise to the 50 ml Falcon tube (see Note 24, and [51] for a more detailed protocol). 4. Add another 5 ml resuspension medium to the tube. Centrifuge for 3 min at 300  g.

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Fig. 6 AKAP18δ-L314E (L314E) treatment of hiPSC-CMs affects the duration of Ca2+ transients. (a) Representative averaged transients for all cell measurements taken before treatment (gray) overlaid with those following treatment (colored), taken from a single dish (n ¼ 9 for each condition). (b) The duration of the calcium signal calculated between the point at which the trace initially crosses the baseline threshold in the rising phase of the action potential, and the point at which the signal crosses the threshold in the falling phase. Graph shows the mean of three independent measurements. (c) Timepoint of the intersection between signal (after peak) and baseline. Graph in (b) and (c) show the mean of three replicates, 9 untreated and 9 treated cells measured. All graphs show boxplots with minimum and maximum values for three independent measurements. ****p-value < 0.0001

5. Aspirate the supernatant and resuspend the cell pellet in 1 ml plating medium. Perform a cell count with Trypan blue. 6. Reconstitute the cells 1.5  106 cells/ml.

to

a

final

concentration

of

7. Aspirate the fibronectin droplets and add a 50 μl droplet of cells to the fibronectin-coated area or the dishes (75,000 cells/ droplet). After 2 h incubation, slowly fill up with 1 ml resuspension medium/dish, without dislodging the cells from the droplet areas. 8. The next day, change medium to cardiac maintenance medium. Optional: perform another quality control experiment after 5–7 days of recovery (see Subheading 3.3.4).

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1. Change the medium the day of the imaging experiment to 1 ml cardiac maintenance medium. 2. For calcium imaging, cells are stained with 2 μM Fluo-8 and 0.01% pluronic acid for 10 min at 37  C, 5% CO2. 3. Wash with 1 ml Tyrode’s and leave cells for 20 min at 37  C, 5% CO2 in 1 ml Tyrode’s (see Note 25). 4. For the imaging the following settings are used: pacing at 10 V, 0.5 Hz, 2 ms biphasic pulse, 40 objective, laser ¼ 488 nm, pinhole ¼ open, line scans with 1.92 ms per line with 20,000 lines in total (Fig. 5). 5. For application of compounds, remove 1 ml Tyrode’s used for the baseline measurement and apply the compound in another 1 ml Tyrode’s. Incubate the dish for 10 min at RT and repeat the measurement.

3.3.8 Data Analysis

1. Convert the lsm-files into txt-files using Image J or any other suitable program. 2. Further convert the txt-files into an excel-file format and analyze with the CalTrack software.

4

Notes 1. Ensure that the starting concentration of the proteins is high enough that their storage buffer is significantly diluted. It is essential that a control using covalently linked acceptor and donor tags be carried out in order to ensure that reduced FRET signal is the result of inhibition of the interaction between the target proteins. Without this control it is not possible to differentiate compounds acting upon the target site, and those effecting the interaction between the protein tags and fluorophores. 2. A variety of assay buffers can be used to better suit the requirements of your protein–protein interactions. However, it is important to check the compatibility of the buffer with the acceptor and donor beads using positive and negative controls. 3. For a new HTRF assay, it is important to optimize the concentrations of both recombinant proteins, which can be carried out by cross-titrating them in one plate. This can also be necessary after the assay has been established, e.g., when changing to a new batch of recombinant protein, which can cause a drop in the signal range; too little protein may result in a smaller signal range, with too much protein the assay may not be in a dynamic range, i.e., it is in saturation.

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4. The FRET ratios determined using an HTRF assay are not sufficient by themselves for quantification of the interaction between two proteins. It is essential to include controls for determining the range of the signal. When assessing the inhibitory potency of small molecules towards a PPI, typical controls are: (a) positive control—both proteins and both fluorophoreconjugated antibodies in the absence of compounds (this will generate the maximum FRET ratio; relative activity of 1 or 100%) (b) negative control—no recombinant proteins (or only acceptor) and both fluorophore-conjugated antibodies in the absence of compounds (this will generate the lowest FRET ratio, corresponding to the background of the assay; relative activity of 0 or 0%) Optimization of an HTRF assay is generally aimed at increasing the difference between these two controls; the higher the positive (100%) control is compared to the negative (0%) control, the more sensitive the assay will be. 5. The present protocol was established in a format compatible with high-throughput screening. Where the volumes to be dispensed onto the plate were too small to be accurately measured with an automatic pipette, as is the case with the addition of the peptide AKAP18δ-L314E, a FreedomEvo automated liquid handling platform (Tecan AG, Ma¨nnedorf, Switzerland) was used. 6. The pipetting schemes should avoid carry-over effects; always pipette from the wells containing lower concentration of compounds or proteins to the wells containing higher concentration, and use a new pipette tip when changing compounds or adding protein to wells containing a different compound. 7. When setting up a new HTRF assay, a time course experiment should be performed to determine optimal measurement time. Incubation periods are usually 1–2 h. 8. To improve sensitivity, determining the optimal time delay between excitation and detection may help. The aim is to exclude as much background fluorescence due to contaminants in the mixture (generally short-lived fluorescence) as possible, while allowing for detection of high signals from the HTRF fluorophores, which have a long fluorescence lifetime. 9. Plate readers for HTRF measurements should fulfill two fundamental requirements: they should be able to excite and detect fluorescence emission at the appropriate wavelengths (this depends on the fluorophores used) and they should allow for the introduction of a time delay between the

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excitation and the detection of emitted fluorescence. Several plate readers compatible with HTRF are certified by Perkin Elmer and bear an “HTRF compatibility” sticker. 10. After setting up the conditions for an HTRF assay, it should be validated by using known inhibitors of the interaction. If none is available, recombinant untagged donor or acceptor proteins can be used; these should decrease the FRET ratio by competing with the equivalent tagged protein for binding to the interaction partner without generating a FRET signal. 11. Especially when using HTRF for high-throughput screening of small molecule inhibitors of PPIs, positive hits should be validated in at least one counter-assay, to make sure that the observed effect is due to inhibition of the target interaction and not due to interference with the photophysical properties of the fluorophores. One option is an AlphaScreen®, but other counter-assays have been described [53]. 12. The recombinant proteins should be as free of other contaminating proteins as possible. To avoid proteolytic degradation and oxidation keep proteins at 4  C, include protease inhibitors and reducing agents such as DTT or β-mercaptoethanol. If possible, check beforehand whether the proteins are functional to increase confidence in the method. Aliquoting the proteins helps preventing their inactivation during storage. 13. If the test compounds are dissolved in DMSO, it is advisable to include the same concentration of DMSO in the control wells. 14. White plates generally lead to the best results, although black plates are also suitable. Glass-bottom plates should be avoided. Assay volumes should match well volumes as close as possible to allow for optimal detection. 15. The osmolarity of the measurement solution was monitored every time a new stock is prepared. Adjust the osmolarity with ddH2O or 1 M Glucose. 16. Geltrex has been used in this protocol as an equivalent to the broadly used Matrigel that also works well for coating of glassand plastic-bottom plates/dishes. Fibronectin or Laminin could be used as well, especially when a lower cell number is used [51]. Because Geltrex and Matrigel start to form solid matrices within minutes at RT, all media and materials should be pre-cooled to 4  C. 17. Starting quality, density, and proliferation of hiPSC cells are key factors for a successful differentiation. For the BIHi005-A cell line (see hpscreg.eu/), a confluence of 80–90% at Day 0 has been shown to give the best results. The starting number of cells may need to be adjusted for different cell lines and pluripotency media.

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18. It is recommended to test different CHIR concentrations for each condition, line or new batch of CHIR that is used. To start with, CHIR concentrations in a range of 6–12 μM work for a broad range of cell lines [51]. 19. Selection for cardiac myocytes is recommended to remove potential non-cardiac cells. 20. Around day 7–10, some beating areas should be visible. Cultures that do not show any beating at Day 12 may be discarded. 21. An initial dissociation between Day 12–16 of differentiation is recommended, as cell death following initial dissociation will slightly increase with continued culture [51]. Cells can also be replated directly without cryopreservation. 22. At this point, cells can be resuspended in 1 ml FACS buffer and stored for up to 4 days at 4  C. Tubes should be sealed with parafilm to prevent evaporation of buffer. 23. Different coatings have been tested including Geltrex in DMEM/F12 (1:60), Laminin-221 in PBS++ (5 μg/ml), Fibronectin in PBS++ (50 μg/ml) and Fibronectin/Gelatine (50 μg/ml + 0.1% w/v gelatine). For the first two conditions, areas of the hiPSC-CM monolayers started to either detach or aggregate at around day 5 after thawing, suggesting inadequate adherence. Further, there were slight differences in the calcium transients for the different coatings (data not shown). 24. To avoid osmotic shock and shear stress, the recovery of the cells in resuspension medium should be done carefully, optimally aiming for about 1 drop every 3–5 s with gentle swirling in between [51]. 25. If calcium imaging is done without pacing, the recovery time might be increased to up to 1 h after washing.

Acknowledgements We thank Carolin Genehr from the Stem Cell Facility of the MDC for assisting in the differentiation and freezing of hiPSC-CMs. We are grateful for the help in Ca2+ signal evaluations by Christopher Toepfer, British Heart Foundation Centre of Research Excellence, Oxford, UK, and Francesca Margara, Department of Computer Science, University of Oxford, UK. This work was supported by grants from the Bundesministerium fu¨r Bildung und Forschung (BMBF; 16GW0179K), the Deutsche Forschungsgemeinschaft (German Research Foundation, DFG KL1415/7-1 and the program-project grant 394046635— SFB 1365), DZHK (German Centre for Cardiovascular Research), partner site Berlin, Standortprojekt 81Z0100101, and the German Israeli Foundation (GIF, I-1452-203/13-2018).

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kinase a interaction yields novel protein kinase A-anchoring disruptor peptides. Biochem J 396:297–306 26. Alto NM, Soderling SH, Hoshi N et al (2003) Bioinformatic design of A-kinase anchoring protein-in silico: a potent and selective peptide antagonist of type II protein kinase a anchoring. Proc Natl Acad Sci U S A 100:4445–4450 27. Hundsrucker C, Klussmann E (2008) Direct AKAP-mediated protein-protein interactions as potential drug targets. Handb Exp Pharmacol 186:483–503 28. Scha¨fer G, Milic J, Eldahshan A et al (2013) Highly functionalized terpyridines as competitive inhibitors of AKAP-PKA interactions. Angew Chem Int Ed Engl 52:12187–12891 29. Hanold LE, Fulton MD, Kennedy EJ (2017) Targeting kinase signaling pathways with constrained peptide scaffolds. Pharmacol Ther 173:159–170 30. Christian F, Szaszak M, Friedl S et al (2011) Small molecule AKAP-protein kinase a (PKA) interaction disruptors that activate PKA interfere with compartmentalized cAMP signaling in cardiac myocytes. J Biol Chem 286:9079– 9096 31. Yu X, Li F, Klussmann E et al (2014) G protein-coupled estrogen receptor 1 mediates relaxation of coronary arteries via cAMP/PKAdependent activation of MLCP. Am J Physiol Endocrinol Metab 307:E398–E407 32. Ando F, Mori S, Yui N et al (2018) AKAPsPKA disruptors increase AQP2 activity independently of vasopressin in a model of nephrogenic diabetes insipidus. Nat Commun 9:411 33. Diviani D, Raimondi F, Del Vescovo CD et al (2016) Small-molecule protein-protein interaction inhibitor of oncogenic rho signaling. Cell Chem Biol 23:1135–1146 34. Esseltine JL, Scott JD (2013) AKAP signaling complexes: pointing towards the next generation of therapeutic targets? Trends Pharmacol Sci 34:648–655 35. Troger J, Moutty MC, Skroblin P et al (2012) A-kinase anchoring proteins as potential drug targets. Br J Pharmacol 166:420–433 36. Bucko PJ, Scott JD (2020) Drugs that regulate local cell signaling: AKAP targeting as a therapeutic option. Annu Rev Pharmacol Toxicol 61:361–379 37. Scha¨chterle C, Christian F, Fernandes JM et al (2015) Screening for small molecule disruptors of AKAP-PKA interactions. Methods Mol Biol 1294:151–166 38. Zhang GQ, Wei H, Lu J et al (2013) Identification and characterization of calcium sparks in

cardiomyocytes derived from human induced pluripotent stem cells. PLoS One 8:e55266 39. Zhang XH, Morad M (2020) Ca(2+) signaling of human pluripotent stem cells-derived cardiomyocytes as compared to adult mammalian cardiomyocytes. Cell Calcium 90:102244 40. Blinova K, Dang Q, Millard D et al (2018) International multisite study of humaninduced pluripotent stem cell-derived cardiomyocytes for drug Proarrhythmic potential assessment. Cell Rep 24:3582–3592 41. Gintant G, Burridge P, Gepstein L et al (2019) Use of human induced pluripotent stem cellderived cardiomyocytes in preclinical cancer drug cardiotoxicity testing: a scientific statement from the American Heart Association. Circ Res 125:e75–e92 42. Itzhaki I, Maizels L, Huber I et al (2011) Modelling the long QT syndrome with induced pluripotent stem cells. Nature 471: 225–229 43. Walter A, Saric T, Hescheler J et al (2016) Calcium imaging in pluripotent stem cellderived cardiac myocytes. Methods Mol Biol 1353:131–146 44. Nunes SS, Miklas JW, Liu J et al (2013) Biowire: a platform for maturation of human pluripotent stem cell-derived cardiomyocytes. Nat Methods 10:781–787 45. Ronaldson-Bouchard K, Ma SP, Yeager K et al (2018) Advanced maturation of human cardiac tissue grown from pluripotent stem cells. Nature 556:239–243 46. Mannhardt I, Breckwoldt K, Letuffe-Breniere D et al (2016) Human engineered heart tissue: analysis of contractile force. Stem Cell Rep 7: 29–42 47. Feyen DAM, McKeithan WL, Bruyneel AAN et al (2020) Metabolic maturation media improve physiological function of human iPSC-derived cardiomyocytes. Cell Rep 32: 107925 48. Garg P, Garg V, Shrestha R et al (2018) Human induced pluripotent stem cell-derived cardiomyocytes as models for cardiac Channelopathies: a primer for non-electrophysiologists. Circ Res 123:224– 243 49. Burridge PW, Matsa E, Shukla P et al (2014) Chemically defined generation of human cardiomyocytes. Nat Methods 11:855–860 50. Tohyama S, Hattori F, Sano M et al (2013) Distinct metabolic flow enables large-scale purification of mouse and human pluripotent stem cell-derived cardiomyocytes. Cell Stem Cell 12:127–137

Interference with AKAP Signalling 51. Miller DC, Genehr C, Telugu NS et al (2020) Simple workflow and comparison of media for hPSC-cardiomyocyte cryopreservation and recovery. Curr Protoc Stem Cell Biol 55:e125 52. Zhang JZ, Belbachir N, Zhang T et al (2021) Effects of cryopreservation on human induced pluripotent stem cell-derived cardiomyocytes

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Chapter 9 Micro-2D Cell Culture for cAMP Measurements Using FRET Reporters in Human iPSC-Derived Cardiomyocytes Andreas Koschinski and Manuela Zaccolo Abstract In the last years human induced pluripotent stem cell-derived cardiomyocytes (hIPS-CMs) have emerged as a promising alternative to rodent-derived cardiomyocytes. However, as the differentiation process is lengthy and commercially available cells are expensive, the cell number is limited. Here we provide detailed information on how to scale down 2D cell cultures of hIPS-CMs for the purpose of cAMP FRET measurements, thereby extending the number of possible experiments by more than tenfold. Crucial factors like cell density or cell number to culturing media volume can be maintained exactly as under normal culturing conditions and existing equipment does not need to be modified. The chapter covers the preparation of downscaled cell culture vessels, coating and seeding procedures, transduction or transfection of the cells with a genetically encoded cAMP FRET sensor, performing realtime cAMP FRET measurements with this sensor and the analysis of generated imaging data. Numbers for seeding areas, seeding densities, coating volumes and concentrations, media volumes, and concentrations of reagents are given as guidelines. Key words Micro-2D cell culture, hIPSC-derived cardiomyocytes, Fo¨rster resonance energy transfer, FRET, Biosensor, cAMP, Real-time imaging, Intracellular signaling

1

Introduction Since decades rodent-derived cardiomyocytes have been used as a model system to explore cAMP signaling pathways that are involved in normal and pathogenic processes, with the goal to understand physiology and the mechanisms that lead to cardiac disease. However, it is known that the rodent heart differs significantly from human hearts, including in the expression of components of the cAMP signaling pathway [1, 2]. As in the last decades the body of knowledge on how to re-program nearly any cell type into induced pluripotent stem cells and to differentiate these cells into myocyte-like cells has grown significantly, now human induced pluripotent stem cell

Manuela Zaccolo (ed.), cAMP Signaling: Methods and Protocols, Methods in Molecular Biology, vol. 2483, https://doi.org/10.1007/978-1-0716-2245-2_9, © The Author(s), under exclusive license to Springer Science+Business Media, LLC, part of Springer Nature 2022

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(hIPSC) derived cardiomyocytes have emerged as a promising alternative to the rodent models. Although current protocols do not generate fully mature cardiac myocytes, this approach is already providing important insights into the physiology and pathophysiology of the human heart, with the additional benefit of significantly decreasing the number of animals sacrificed for scientific purposes. A significant drawback is that the use of hIPS-cell cultures and the respective differentiation protocols is an order of magnitude more expensive than preparing and maintaining animal derived cardiomyocytes. In addition, hIPS differentiation is a lengthy process which can last up to several months to achieve a good maturation [3], and once differentiated, the cells don’t divide any more. So the amount of cells and experiments that can be performed with one differentiation is limited. The same applies to commercially available differentiated myocytes. Here we describe a micro-2D culture technique that allows to scale down the necessary amounts of cells for each experiment by more than an order of magnitude. While many existing microscopic setups work with glass coverslips, e.g., with 24 mm diameter coverslips typically kept in 6-well plates or 35 mm Petri dishes, with the protocol described here the number of cells can be reduced to as little as 1% without the need to change anything on an existing setup or the density of the cells. In contrast to existing methods to economize on the cell number where only a little drop of cells is seeded on a coverslip [4] or simply plating cells at very low density on normal coverslips, the 2D-microculture method described here retains a more physiological environment for the cells, as not only the plating area, but also the volume is reduced. This keeps the cell number to plating surface area ratio, as well as the cell to medium volume ratio constant. Therefore gas transfer, pH change and concentrations of secreted metabolites, as well as levels of extracellular messengers and contacts between the cells, are directly comparable to standard cell culture conditions. In addition, the protocol saves significant amounts of coating material, cell culture medium, supplements, transfection reagents, and plasmidic DNA or viral vectors. The method is inexpensive and extremely useful especially for any electrophysiological or imaging approach as these techniques typically use only one cell or only a tiny fraction of the cells seeded. Although shown here for FRET cAMP measurements in hIPSderived cardiomyocytes, the method can be applied to any experimental setup where the number of available cells or the availability/ price of chemicals for incubations is a limiting factor. The possible reduction of cell numbers and volumes is shown in Table 1. In this protocol all necessary steps starting from manufacturing of the adapter rings (see Note 1), passaging and seeding of hIPS-derived cardiomyocytes, transfection or transduction with a

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Table 1 Seeding densities, areas, and medium volume requirements in different cell culture vessel formats (see Note 12). Values are for non-dividing cells and are cell type and experiment-dependent (see Note 13). For imaging purposes lower densities are preferred, to allow for empty “background” areas. The minimal media volumes keep the surface-to-volume ratio approximately constant. However, with these volumes the medium may have to be changed every day, especially with higher cell densities. The recommended volumes are more convenient to work with, as the interval between the next change of medium can be prolonged up to 3 or even 4 days. All area values for conventional formats are calculated by physically measuring the inner diameter of wells of commercially available multiwell plates or 35 mm Petri dishes. Therefore, they might differ slightly from existing values on some websites. “k” stands for “thousand” Surface area

Minimal medium volume

Recommended medium volume

35 mm Petri dish 6-well 180–400k plate

9.08cm2

2000 μl

3000 μl

12-well plate

3.79cm2

850 μl

1500 μl

400 μl

1000 μl

Size

Seeding density

Conventional formats

70–150k

2

24-well plate

40–80k

1.8cm

96-well plate

7–14k

0.36cm2

80 μl

200 μl

12.8 mm ring

30–80k

1.28cm2

280 μl

600 μl

8 mm ring

10–20k

0.5cm2

100 μl

250 μl

70 μl

150 μl

30 μl

70 μl

Self-made rings

6 mm ring 4 mm ring

4–10k 1–3k

0.28cm

2

0.125cm

2

genetically encoded sensor and the general setup of a life-cell FRET imaging station including the appropriate optical filters and dichroics for CFP-YFP FRET imaging are presented. Finally we show an example of the measurement and analysis of intracellular cAMP-changes with the genetically encoded EPAC-SH187 [5] FRET sensor in cardiomyocytes cultured under the optimized, downscaled conditions.

2

Materials

2.1 Cell Culture Equipment

l

l

Biological safety cabinet (laminar flow hood) for sterile working conditions. Cell culture incubator, humidified atmosphere, set to 37
C, 5% CO2.

l

Cell culture microscope, ideally with fluorescence capability.

l

Heated waterbath (37
C).

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2.2 Consumable Supplies

l

Cell counter or counting chamber.

l

Pipette controller.

l

Assortment of variable micropipettes.

l

Stainless steel forceps with fine tips.

l

Vacuum aspiration system (see Note 2).

l

Serological pipettes (2, 5, 10, 25, 50 ml).

l

Disposable plastic aspirating pipettes (2 ml).

l

35 mm cell culture Petri dishes and/or cell culture 6-well plates.

l

145 mm Petri dishes (as tray).

l

Cloning rings or self-made rings (compare Note 1).

l

High viscosity silicone grease (see Note 3).

l

Assortment of sterile micropipette filter tips.

l

0.22 μm sterile syringe filters.

l

50 ml syringes.

l

0.22 μm sterile bottle-top filters and fitting bottles or complete vacuum filter units.

l

Sterile centrifuge tubes (1.5, 2, 15, and 50 ml).

l

High optical quality coverslips.

l

RPMI 1640 cell culture medium with L-Glutamine or alternatively with 25 mM HEPES and Glutamax.

l

B27 cell culture medium supplement.

l

Fetal Bovine Serum (FBS).

l

Phosphate Buffered Saline (PBS or DPBS) without Calcium and Magnesium (DPBS: 137.93 mM NaCl, 8.06 mM Na2HPO4, 2.667 mM KCl, 1.47 mM KH2PO4, pH 7–7.3).

l

TrypLE select 10 cell-dissociation reagent (Gibco) (see Note 4).

l

ROCK-inhibitor (Y-27632).

l

Matrigel extracellular matrix, cat. no. 354234 (Corning) (see Note 5).

l

Lipofectamine Stem transfection reagent (Invitrogen) (see Note 6).

l

cAMP FRET sensor (plasmidic or in a viral vector).

l

hIPS-derived cardiomyocytes.

l

Ethanol.

l

Double distilled or de-ionized water to prepare 70–80% Ethanol for sterilization purposes.

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Fig. 1 Simplified optical path and configuration of a FRET microscope setup for Cyan- and Yellow Fluorescent Protein (CFP-YFP)-based FRET sensors. For these sensors the filter and dichroic setup is: excitation filter 436  25 nm, excitation/emission dichroic 455 nm long-pass, beam splitter dichroic 505 nm long-pass, emission filter CFP 480  15 nm, emission filter YFP 535  20 nm

Specific material for manufacturing self-made rings:

2.3

FRET Equipment

l

Small hacksaw with a blade for metal cutting (small teeth).

l

Good quality sanding paper (wet and dry), grain 150 and 400–1000.

l

Sharp blade (scalpel or small snap-off blade knife).

l

A vice to hold the pipettes while sawing and a piece of wood or a pencil (both not absolutely needed, but very helpful).

As shown in Fig. 1 a typical widefield FRET setup consists of: l

a high-quality inverted microscope with attached beam splitter and PC-coupled camera.

l

high-quality objectives, preferentially water or oil immersion.

l

a PC-controlled excitation light source (see Note 7).

l

a PC with interfaces for controlling the light source and acquisition camera (see Note 8).

l

Software to control the acquisition, storage, and analysis of images (Metafluor) (see Note 9).

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2.4 Consumable Supplies for FRET Measurements

2.5 Analysis Equipment

3

l

Bath chamber/measurement chamber to accommodate the coverslip with cells.

l

pH meter to adjust the pH of extracellular buffers.

l

Extracellular buffer for the FRET experiments, e.g., 140 mM NaCl, 3 mM KCl, 2 mM MgCl2, 2 mM CaCl2, 20 mM Glucose, 10 mM HEPES, adjusted with NaOH to pH 7.2 (see Note 10).

l

Immersion Oil or water.

l

Isobutyl-methyl-xanthin (IBMX).

l

Forskolin (FSK).

l

Dimethylsulfoxide (DMSO) (as solvent for IBMX and FSK).

l

PC with analysis/data extraction software installed (see Note 11).

l

Spreadsheet program (e.g., Excel, Open Office Calc).

l

Graph Pad Prism or comparable scientific analysis and graphing program.

Methods

3.1 Cell Seeding Numbers and Media Requirements for Different Cell Culture Vessel Formats 3.2 Preparation of Rings

Seeding densities and medium volume requirements for a variety of conventionl cell culture vessels and for rings are shown in Table 1 (also see Notes 12 and 13).

1. Cut standard serological plastic pipettes (see Note 14) in about 10 mm high pieces using a small hacksaw with a metal cutting blade (small saw-teeth) (Fig. 2a). 2. Sand the edges of the rings down by circular movements on sandpaper grain size 150 until the edge looks even. Make sure that the rings do not tilt during sanding (see Note 15). 3. If the ends appear to be flat, sand the ring edges a bit more down on sandpaper grain size 400–800. This will give a smooth end-surface. After this step, remove still adherent rubbed-off plastic parts with a sharp blade. The finished ring should stand planar on an even surface without wobbling or any visible gaps between the surface and the ring edge (Fig. 2b). 4. Clean the rings from dust, e.g., with 70% ethanol and let them dry.

Fig. 2 Preparation of the ring-coverslip assemblies. (a) Serological plastic pipettes cut with a metal cutting blade equipped hacksaw. Insets on the top show the hacksaw and the blade. (b) Sanding the rings. The upper inset shows how the rings can be held better and tilting during the sanding process can be avoided (compare Note 15). (c) Four different sizes of ring-coverslip assemblies. From left to right: 12.8 mm, 8 mm, 6 mm, 4 mm. (d) Leak-proofing. Note that rings stored for long time in ethanol become opaque, but still are fully functional, (e) drying the assemblies in a sterile biological safety cabinet. The inset shows how the assemblies should be placed into a Petri dish or well for drying, (f) readily prepared ring-coverslip assemblies in 6-well plates or 35 mm Petri dishes (see Note 19), (g) 145 mm Petri dishes used as trays/storage containers, (h) ringcoverslip assemblies with seeded cells, medium and medium in the space outside the ring

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5. Take high viscosity vacuum grease (compare Note 3) and distribute it on the lid of a 10 cm plastic Petri dish to a thin greasefilm (about 0.5 mm thickness). 6. Dip the ring with one edge into the grease-film, then press the ring with the greased side down onto a high-quality glass coverslip of the desired size. Avoid lateral movement of the ring, as this will smear the growth area with silicone (Fig. 2c). 7. Test the assembly for proper adherence. When lifting the ring with forceps, the coverslip should stay firmly attached and should not fall off. 8. If there is no more previously unused space in the silicone grease to dip more rings, apply a bit more grease and distribute again to a film. Leftover grease can be stored for the next use by simply closing the Petri dish. 3.3 Testing the RingCoverslip Assemblies for Leakage

1. Before sterilizing the ring-coverslip assemblies, test them for potential leaks by filling them to 2/3 with 70–80% ethanol/ dd H2O or pure ethanol (Fig. 2d) (see Note 16). 2. If after a few minutes there is no visible leakage, the assemblies are considered to be tightly sealed and safe to use and can be sterilized.

3.4 Sterilizing the Ring-Coverslip Assemblies

All the following procedures have to be carried out in a sterile environment, e.g., in a laminar flow biosafety cabinet. 1. Sterilize the ring-coverslip assemblies in 70–80% ethanol by submerging them into the ethanol for a few seconds, e.g., in a 100 ml beaker. Use ethanol-sterilized forceps for these manipulations. The following drying process can be accelerated by subsequently submerging the assemblies in pure ethanol and/or by carefully aspirating excess ethanol before drying. For aspiration a sterile 200 μl pipette tip on top of a plastic aspiration pipette can be used. 2. Place the assemblies tilted into the final vessels (Fig. 2e) (see Note 17). 3. After the assemblies are dry and there is also no trace of moisture left at the bottom of the well, move the assemblies fully into the well by either tapping against the wells or using sterilized forceps. Then close the lids. The assemblies are now ready for cell culture (Fig. 2f, g) (see Note 18).

3.5 Coating the Bottom Area Within the Rings

For surface-coating we use Matrigel at a concentration of 8 μg protein per cm2 (see Note 20). The numbers shown here are for eight 8 mm rings (in eight 35 mm Petri dishes in a 145 mm Petri dish as a tray). Amounts for other ring-sizes are shown in Table 2, as well as the typical volumes used to dilute the Matrigel.

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Table 2 Coating volumes, Matrigel protein amount and approximate volumes for different ring-sizes. Shown are the values needed to coat the area defined by one ring. The approximate Matrigel volumes are calculated with an assumed protein amount of 8 mg/ml Matrigel (compare Note 20) Ring-size

Coating volume

Matrigel protein amount

Approximate Matrigel volume

12.8 mm ring

250 μl

10.24 μg

1.28 μl

8 mm ring

100 μl

4 μg

0.5 μl

6 mm ring

50 μl

2.26 μg

0.28 μl

4 mm ring

30 μl

1 μg

0.125 μl

1. Thaw a small aliquot of Matrigel (see Note 21) on ice. 2. Prepare 800 μl of a “dilution medium” without serum or supplement (e.g., RPMI 1640, DMEM/HAM’s F12, or even PBS) in a small sterile tube and keep it on ice. 3. Using cold pipette tips, dilute 32 μg (approximately 4 μl) (see Note 22) of Matrigel into the 800 μl of the dilution medium. 4. Mix well and fill each ring with 100 μl of this mix. Make sure that the whole area at the bottom of the ring is covered. 5. Incubate the rings with the Matrigel solution for at least 1 h at RT or 30 min in a 37
C incubator. 6. Directly before seeding the cells aspirate the Matrigel solution, e.g., with a 200 μl pipette tip on top of a plastic aspirating pipette (see Note 23). 3.6 Seeding hIPS-Derived Cardiomyocytes into Rings

As for differentiation of hIPS-cells into cardiomyocytes very often 6-well plates are used, the volumes in this protocol are meant for dissociating and seeding cells from one well of these plates. However, dividing the volumes roughly by 3 will yield the respective volumes for 12-well plates. 1. Aspirate the medium from the well. 2. Wash the cells 3–4 times with 1–2 ml DPBS. 3. Apply 500 μl TrypLE 10 (compare Note 4) and keep the cells at 37
C (incubator) for some minutes. Monitor the appearance of the cells every 2 or 3 min at the microscope. Wait until the cells have rounded up. Check cell-dissociation/detachment by gently tapping the 6-well plate horizontally against a robust metal part of the microscope. When the cells start to float: 4. Add 3 ml of RPMI 1640 with 10% of serum (e.g., FBS) (see Note 24). 5. Wait 10 s, then resuspend the cells. 6. Spin the cells down for 4 min at 180  g.

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7. Aspirate the supernatant to get rid of excessive serum and resuspend the pellet in maintenance medium, e.g., RPMI 1640 plus B27 medium supplement (see Note 25). 8. Count the cells in a cell counter or a Neubauer counting chamber. A good differentiation should yield about 2–3 million cells/well. Dilute the cells with RPMI 1640 plus B27 to 100,000 cells/ml. For a density of 10,000 cells in an 8 mm ring seed 100 μl of the cell suspension into the coated rings (see Note 26). 9. Apply 2.5 ml of unsupplemented RPMI 1640 into the space of the vessel outside the ring (see Note 27). 10. The day after seeding aspirate the medium from the rings and add 250 μl fresh RPMI plus B27. 3.7 Maintaining hIPS-Derived Cardiomyocytes

3.8 Infection of Human IPS-Derived Cardiomyocytes for cAMP FRET Measurements

Depending on cell density, metabolism, and applied medium volume, change the medium accordingly (see Note 28). For aspiration of the old medium a sterile 200 μl pipette tip on top of a plastic aspiration pipette can be used. The old medium should not be completely removed, but a thin layer of medium should still cover the cells to prevent the cells from drying out before refilling. As the 4 mm rings are too small to be filled using a serological pipette, use sterile 200 μl filter tips for filling instead. Replacing the external medium is only necessary if the volume significantly decreases. After 2–5 days the cells should start to show spontaneous beating again and are ready for measurement (see Note 29). 1. Thaw a virus aliquot on ice. Here we use 1 μl of an adenoviral vector containing the EPAC-SH187 cAMP sensor [5]. 2. Predilute the virus aliquot under sterile conditions 1:100 in cold RPMI 1640 (see Note 30). 3. Mix by gently pipetting 2–3 times up and down, then add the virus at a MOI of about 250–500 directly into the medium in the ring. 4. Place the cells back into the incubator. 5. After 24–48 h control for fluorescence (see Note 31). 6. If fluorescent, the cells are ready for measurement (Fig. 3).

3.9 Transfection of hIPS-Derived Cardiomyocytes for cAMP FRET Measurements

Transfection of non-dividing cells is always a challenge and will not be very efficient. There are detailed protocols available [6, 7], which show that for best transfection efficiency specific reagents and specific time-points of transfection after seeding are required. However, as after seeding the cells need several days up to weeks to restructure and mature again, transfecting them, e.g., 3 days after seeding to yield a maximal transfection efficiency [7] in this respect

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Fig. 3 This figure shows the transduction (left panels) and transfection (right panels) efficiency of hIPS-derived cardiomyocytes with the cytosolic EPAC-SH187 cAMP sensor [5]. The cells were infected with 0.1 μl virus stock (3.4  106 PFU, MOI ¼ 340) or transfected with 0.1 μg plasmidic DNA in 8 mm rings according to the described protocols (see Note 35). They were seeded into 8 mm rings 45 days after differentiation start at a density of 10,000 cells per ring and then cultivated for further 31 days before they were infected/transfected. The images were acquired 24 and 48 h after infection/transfection and show a merge of the differential interference contrast (DIC) and fluorescence images. The scale bar represents 100 μm

is counterproductive. As for imaging experiments in principle only a few transfected cells are sufficient, the following short protocol is adequate and tested up to 31 days after seeding. It shows the procedure for the transfection of the cells in one 8 mm ring with Lipofectamine Stem transfection reagent (see Note 32) and can easily be up-scaled for more rings by multiplying volumes and amounts by the number of rings to be transfected. Media volumes and amounts of DNA for other formats are shown in Table 3. 1. Thaw a plasmid DNA aliquot. Here we used the EPAC-SH187 cAMP sensor [5]. 2. For a transfection of the cells in one 8 mm ring prepare 2 sterile microcentrifuge tubes with 5 μl RPMI 1640 medium without supplement in each (see Note 33). 3. Pipette the volume of the plasmid solution that represents 0.1 μg DNA into the medium in the first tube and mix with the pipette (see Note 34). 4. Pipette 0.3 μl Lipofectamine Stem into the medium in the second tube and mix with the pipette.

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Table 3 Amounts of DNA and reagents for the shown ring-sizes when using Lipofectamine Stem transfection reagent (compare Note 32). Values for 12.8 and 8 mm rings are tested, values for 6 and 4 mm rings are downscaled and given for convenience as a guideline Ring-size

Medium volume

RPMI volume

DNA amount

Lipofectamine Stem reagent

12.8 mm ring

600 μl

2  15 μl

0.3 μg

0.9 μl

8 mm ring

250 μl

2  5 μl

0.1 μg

0.3 μl

6 mm ring

150 μl

2  2.8 μl

0.06 μg

0.18 μl

4 mm ring

80 μl

2  1.3 μl

0.03 μg

0.09 μl

5. Unite both dilutions by pipetting the DNA containing solution into the Lipofectamine Stem containing solution. Mix well by pipetting up and down 2–3 times, then close the tube with the mix. 6. Incubate the mix for 10 min at room temperature. 7. After the incubation add the mix directly into the medium in the ring, mix gently and place the cells back into the incubator. 8. After 24–48 h check for expression (compare Note 31). 9. If fluorescent, the cells are ready for measurement (Fig. 3). 3.10 cAMP FRET Measurements of hIPS-Derived Cardiomyocytes (See Note 36)

On the day of the experiment: 1. Use two forceps to remove the ring from the coverslip. Store the ring in 70–80% ethanol for later cleaning and reuse. 2. Rinse the coverslip with cells 2–3 times with small amounts of extracellular buffer. 3. Mount the coverslip into the measurement chamber and fill the chamber with extracellular buffer. 4. At the microscope, under YFP-illumination (excitation 500  10 nm, emission 535  20 nm) find a representative cell or a representative area of cells. Make sure that there is also some cell-free background. 5. Switch to FRET illumination (excitation 436  25 nm) and acquire one image. On that image check that the signal-tonoise ratio is adequate (see Note 37). 6. Mark the regions of interest (ROIs), check that the images will be saved, then start the acquisition. cAMP-changes typically are slow, therefore acquiring one image every 5–10 s is appropriate. 7. Wait until the cells show a stable FRET-ratio baseline. Monitor this stable baseline for 3–5 min. 8. Apply your stimulus/substance under test. Before adding any other stimulus wait until the signal reaches a stable plateau. At

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Fig. 4 Generation of FRET change curves and calculation of FRET changes on addition of a cAMP raising stimuli. Example curves from an experiment performed in hIPS-derived cardiomyocytes expressing the EPACSH187 cAMP sensor [5]. At the indicated time-points the cell was treated with the unspecific PDE inhibitor IBMX (100 μM) and the adenylyl-cyclase activator Forskolin (FSK, 10 μM). (a) Plot of background-subtracted CFP and YFP emission intensity values over time. (b) Plot of the ratio values (R) calculated from (a) over time. The values used to average the data and to calculate Rt0, Rtx, and Rty are indicated by shaded rectangular areas. (c) Calculation of the ratio, (d) calculation of the ratio change as % increase. R, Em535nm/Em480nm; Rt0, R value before any stimulus; Rtx, R value calculated at the plateau of the response to IBMX; Rty, R value calculated at the plateau of the response to FSK

the end of the experiment the sensor should be driven into saturation by applying, e.g., a combination of Forskolin (10 μM) and IBMX (100 μM) (see Note 38). Figure 4 shows an example of a FRET measurement. 3.11 Analysis of cAMP FRET Measurements of hIPS-Derived Cardiomyocytes

1. Extract the image data with the program of choice. Draw regions of interest (ROIs) around the structures or cells of interest. Make sure to always also have one region in the field of view without any cell for background subtraction. Start the extraction process and export the raw data into a spreadsheet program. 2. Subtract the mean background value of the emission intensities from all other mean ROI-emission values of the respective wavelengths, plot the intensity values against time. Figure 4a shows a representative plot.

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3. Calculate the time course of the FRET change by computing the ratio (R) value for each acquisition time-point using Eq. 1: (see Note 39). R¼

Emission480nm Emission535nm

ð1Þ

4. Plot the R values against time to obtain the ratio change curve (Fig. 4b). 5. Calculate the induced FRET change, ΔR [%] for the timepoints x and y using Eq. 2: ΔRx ½% ¼

ðRtx  Rt0 Þ  100 Rt0

ð2Þ

Here ΔRx [%] is the ratio change expressed as percent change relative to the basal FRET ratio at t0. Rt0 is the average of 5–10 R values before the application of a stimulus, and Rtx is the average of 5–10 R values after the ratio value has stabilized to a new value. Figure 4 shows an example (see Note 40).

4

Notes 1. We here show the preparation of self-made cell culture plastic rings, prepared from serological pipettes. Twenty rings can be generated in about 1 h. This is a very cheap and versatile method, as the raw material (the pipettes) is cheap, and the diameter can be chosen according to needs and available pipettes. However, it should be noted that some ring formats are also commercially available. These so-called “cloning rings” can be purchased as polystyrene or glass rings with various inner diameters, e.g., 4.7 mm (area 0.173 cm2) or 8 mm (area 0.5 cm2). However, at times they have long lead times (e.g., 4 months at the time of preparation of this manuscript) and the sterile glass cylinders are significantly more expensive. If ordering commercially available cloning rings, make sure the height does not exceed the depth of the intended culture vessel (e.g., standard 35 mm Petri dishes can only accommodate rings that are maximally 8 mm high). 2. A vacuum aspiration system is not absolutely necessary for cell culture, but it speeds up medium change significantly. It also is needed for vacuum driven filter systems (compare Note 25). 3. e.g., Dow Corning High Vacuum Grease or Korasilon (formerly Baysilone) Paste M-S 2-200 do have a suitable viscosity. Both compounds also do not show any obvious toxicity to the cells.

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4. We have tried conventional Trypsin/EDTA (0.05%) and Accutase, but found that TrypLE 10 was the most potent, while least damaging dissociation reagent. Trypan-blue exclusion staining showed cell-line dependent only 5.5  1.2 to 11.8  3.6% nonviable cells after up to 10 min incubation (SEM, n ¼ 4, dissociation up to day 45 after differentiation start for each). Trypsin/EDTA was comparably potent, but easily overdigested and damaged the cells. Accutase, although very well working with IPS-cells, had difficulties to dissociate older cardiomyocyte cultures (day 30 or more). TrypLE can be stored in the fridge or as frozen aliquots. We have not seen a significant reduction of activity due to an additional freeze-thaw cycle. TrypLE works best at 37
C, therefore pre-warm an aliquot to 37
C in a waterbath shortly before use. Depending on the thickness of the cell layer and how long the cells had already been cultured in the 35 mm well or dish, even TrypLE 10 might take up to 10 min in the incubator at 37
C to dissociate the cells. 5. There are also other Matrigel formulations and in general other coating agents used in hIPS-derived cardiomyocytes cell culture (e.g., Geltrex, Cultrex, Vitronectin, Laminins, gelatine, and more). However, coating procedures will differ and cell growth on these extracellular matrixes might also be different. 6. Transfection procedures differ for different transfection reagents. In this protocol we show the specific procedure for Lipofectamine Stem. Other transfection reagents will also work (compare Note 32); however then the procedure has to be adapted according to the manufacturer’s instructions. 7. Current LED-light sources are powerful enough for most sensitized FRET applications. They are preferable over the classical high pressure arc lamps because their light output is more stable, they have a much longer lifespan (up to 20,000 h), their intensity can be regulated and their control is much simpler, as they can be just switched on and off as needed, so an additional shutter is obsolete. There is also no risk of explosion with LEDs. 8. Today most simple office PCs fulfill the requirements for standard FRET setups. Most modern cameras and light source controllers are connected via USB. However, some more sophisticated setups, equipped with automated microscopes, stage control, filter wheels, etc. might need an internal expansion card slot. Streaming (high resolution) images with high time resolution will require quick hard disk drives with large storage capacity and sufficient RAM memory. 9. Most microscope companies offer their own, proprietary imaging suites. Metafluor is independent from specific systems and can work on most setups. A common alternative is the freeware

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“MicroManager” (https://micro-manager.org/) which is a plugin for the freely available image analysis program ImageJ (https://imagej.nih.gov/ij/). 10. This buffer is a modified Tyrode-solution and is suitable for a wide variety of cells and experimental conditions. However, as long as a buffer does not contain any colored component that might interfere with the FRET emission wavelength a broad spectrum of physiological extracellular buffers will work. Bicarbonate-based buffers will need additional introduction of CO2, tight pH-control and will require perfusion systems. 11. Typically the imaging suites installed for acquisition can also extract the raw values. For some imaging suites (Leica, Nikon, Zeiss) there are functionally reduced free readers available that can be installed as standalone versions and can extract data or images. These might then be analyzed using, e.g., ImageJ, which also can read many of the original formats. For Metafluor it is possible to either extract the data on the acquisition-PC or with a so-called offline-license version on a different PC. The free readers can be downloaded at the following sites. Be aware that some downloads are free, but require registration. https://www.leica-microsystems.com/products/micro scope-software/p/leica-las-x-ls/ https://www.microscope.healthcare.nikon.com/en_EU/ products/software/nis-elements/viewer https://www.zeiss.com/microscopy/us/products/micro scope-software/zen.html#downloads 12. 12.8 and 8 mm rings are well suited to replace 35 mm wells. They reduce the required cell number by about 83–94%, while still typically providing a sufficiently large number of cells for imaging experiments. HIPS cell-derived cardiomyocytes can stay and mature in these rings for months, and the costs for maintenance compared to 24 mm coverslips in 35 mm wells are reduced by at least 80%. If no high throughput is needed, 6 mm rings can be used to transfer protocols developed for 96-well plates nearly directly. In this case the 6 mm ring-coverslip assemblies are a very cost-efficient alternative to commercial glass-bottom multiwells. 4 mm rings are a bit more difficult to work with, however, with respect to growing small spheroids or other 3-D tissues in gel matrices, they can reduce the costs to one-third compared to a well in a 96-well plate. To achieve an effective filling height of 5 mm, a well in a 96-well plate needs 180 μl, a 4 mm ring with the same filling height requires only 62.5 μl.

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However, with some trade-off regarding the cell number and therefore less choice of cells, also the 6 or 4 mm rings can be used to substitute cultures on coverslips in 35 mm Petri dishes, thereby reducing necessary cell numbers to only 1–2%, with a comparable impact on cell culture costs. 13. The given seeding numbers have been tested for hIPS-derived cardiomyocytes, which are non-dividing cells, but grow in size and ultimately cover large areas (average cell-size of 5435  356 μm2 at day 30 after seeding, SEM, n ¼ 100). For smaller cells (e.g., CHO-cells, average cell-size 1952  113 μm2, SEM, n ¼ 60) the numbers can be increased significantly. For dividing cells the values can be adapted by dividing the preferred final cell number by the fold-increase expected in the time between seeding and imaging. If cells divide once a day, then after 2 days they will have reached about fourfold the original density. Therefore dividing the number by 4 will yield the correct number required for seeding. Note that the cell-to-surface ratio (about 20–40k cells/ cm2) decreases non-linearly at smaller ring-sizes, as the silicone grease increasingly reduces the area cells can attach to. The chosen amount of silicone grease will also have an impact. The numbers provided are therefore intended as a guideline. Although they should guarantee a positive outcome in a trial experiment, they may need to be optimized for the specific application. Consideration may be given to the impact that cell density has on cell maturation. In our experience, even seeding half of the minimal number of hIPS-derived cardiomyocytes still works. However, in this case many cells are not in contact with others, which then typically causes a very large, roundish cell morphology with circular or spiral sarcomeres. Higher seeding densities will lead to better organized sarcomeres and in general to a more mature morphology. This is, however, at the cost of less clearly distinguishable individual cells. 14. Removing the scaling or any printing of the serological pipettes is not absolutely necessary, but it helps controlling the color and filling height of the medium in the ring, as the colored filling indicator stripes of the pipettes may interfere with the medium color. This can be done by rubbing the pipette surface with a paper-tissue soaked with 99% ethanol. This is a somewhat lengthy procedure (works best if the soaked tissue gets warm by friction), but stronger solvents may also corrode the plastic surface. Use disposable plastic aspirating pipettes when preparing 4 mm rings, as these do not have any scaling. 15. Sticking a roughly tailored piece of wood or a wooden pencil into the plastic ring prevents tilting and facilitates sanding (Fig. 2b, upper panel).

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16. Ethanol has less than a third of the surface tension than water. It will therefore more effectively detect small leaks. Leaks can happen either by the ring-end not being planar enough, or the grease not being evenly distributed everywhere at the end. In the first case the ring needs to be re-treated/re-sanded, in the second case take off the ring, clean and dry it, and apply the silicone again. 17. Do not place the coverslips on the bottom of a well unless they are completely dry as otherwise capillary adhesion will make it impossible to get them off the bottom again. In case the coverslip of the assembly accidentally touches the bottom before it is completely dry, it sometimes helps to apply some more ethanol into the vessel so that the assembly can be removed. Setting a scratch with the forceps into the bottom of the well and sliding the coverslip over the scratch also might help to lift off the assembly. Alternatively you could go on with normal culturing, hoping that the later applied medium in the space of the vessel outside the ring (compare Note 27) will dissociate the glass coverslip from the bottom of the well after a while. As this is not always the case, we recommend preparing a new assembly in a new vessel instead. 18. Sterilized ring-coverslip assemblies can be used directly or stored. Up to eight 35 mm Petri dishes can be stored in a 145 mm Petri dish, which also can serve as a tray. To keep the assemblies in the wells sterile, closed “trays” or multiwell plates can simply be sealed with Parafilm. Alternatively they can be wrapped into a plastic bag. In this case the single Petri dishes or plates do not need to be sealed. Although working without antibiotics, we never experienced any contamination due to this sterilization and storage method, even with ring-coverslip assemblies that had been stored for several months. 19. Although the handling of 6-well plates may be easier, single Petri dishes reduce the risk of cross-contamination. If a contamination occurs, the contaminated dish can be discarded, whereas in a 6-well plate the contaminated well has to be decontaminated, with the risk that the contamination might spread to other wells. The second advantage of Petri dishes is that the coverslip can be removed for the measurement under non-sterile conditions, whereas in order not to contaminate the cells on the coverslips in the other wells a 6-well plate must be handled under sterile conditions. 20. Matrigel concentrations for 2D culture vary from 400 μg/cm2 (manufacturers recommendation) over 23 μg/cm2 [8] to 6–9 μg/cm2 [9–12]. We typically use 8 μg/cm2 coated surface. The volume of coating medium should be as small as possible but sufficient to cover the whole surface (refer to Table 2).

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21. Matrigel itself comes sterile, but as the micro cell culture needs only minute quantities (e.g., 4 μl for the coating of eight 8 mm rings) it needs to be aliquoted under sterile conditions into appropriate aliquots (e.g., 25 μl, depending on the preferred ring-format and number) which are kept frozen at 20
C. Even when using sterile microcentrifuge tubes, the handling of these tubes (pouring them on the working surface of the sterile safety cabinet, opening the lids) comes with a small risk of contaminating the Matrigel and therefore later the cells. Matrigel-containing media also cannot be sterile filtered, or only in ice-cold media with an ice-cold filter. Still this might lead to an uncontrollable loss of Matrigel. Therefore it might be worth considering the use of antibiotics, e.g., Penicillin/Streptomycin (typically supplied as 100 solution, final concentration 100 units/ml Penicillin, 100 μg/ml Streptomycin) for the seeding of cells into the rings. Antibiotics are typically banned from hIPS-derived cardiomyocytes culture as they might lead to invisible underlying bacterial contaminations which then will affect the cells. However, if used only for the first day after seeding, they may help to kill an accidental bacterium. If the antibiotics then are removed and had not been able to prevent the contamination, surviving bacteria will grow and the contamination will be visible. The use of Pen/Strep will not affect the cells and some protocols explicitly include it [13]. 22. Matrigel solidifies quickly at room temperature, it should always be thawed and handled on ice/4
C. For aliquoting, diluting, and general handling, tubes, tips, and serological pipettes should be pre-cooled. One method is to keep tips and pipettes in a freezer or fridge until needed. However, in our experience it also works if, e.g., pipette tips are cooled down by pipetting the ice-cold dilution medium 3–4 times up and down before pipetting the Matrigel stock. Serological pipettes also can be cooled down by pipetting ice-cold PBS or medium 2–3 times up and down before use. Using cooled tips and pipettes is mandatory, as they have a high surface to volume ratio, and a significant amount of Matrigel would solidify on their inner surface, thereby reducing the effective concentration in the solution deposited on the coverslip. As long as the stock aliquot is kept on ice all the time, it can be refrozen and thawed several times. For the simple adherence of hIPS-derived cardiomyocytes we haven’t seen any obvious difference, even after 5 freeze-thaw cycles. 23. Matrigel-coated vessels should best be used immediately. However, it is possible to store them at 4
C for 1–2 weeks as long as they don’t dry out. To avoid drying, keep the coating

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medium in the ring and fill the outside of the rings (the unused volume in the 35 mm dish) with 2.5 ml PBS. Then seal the tray or the 6-well plate with parafilm. 24. We maintain hIPS-derived cardiomyocytes in RPMI 1640 plus B27 supplement (maintenance medium) and have cultured the cells for more than 6 months without any sign of cell suffering. The supplementation with Fetal Bovine Serum (FBS) here is to stop the action of TrypLE. If a specific protocol interdicts the use of serum, instead of applying FBS-containing medium resuspend the cells in a larger volume of maintenance medium (a well of a 6-well plate can accommodate up to 5 ml) and further dilute the cell suspension to the maximal volume the vessel used for centrifugation can take (e.g., a 15 ml tube) in order to maximally dilute the dissociation reagent. Then centrifuge (4 min, 180  g) and finally resuspend with fresh medium. This minimizes the activity of residual TrypLE before seeding. 25. RPMI 1640 is stable and has a shelf life of around half a year or more. However, the serum-free supplement B27, once thawed, has a relatively short life. It is recommended not to use media prepared with this supplement for longer than 1–2 weeks. As for the micro cell culture only small amounts of media are necessary, it is therefore better to aliquot the B27, freeze the aliquots, and add them to the medium only before use. However, here the same considerations regarding a possible contamination as for Matrigel apply (compare Note 21). To avoid the use of antibiotics it is therefore advisable to sterile-filter the medium after addition of the B27 aliquot. Depending on the build of the syringe, up to 70 ml can be sterile-filtered using a 50 ml syringe with a 0.22 μm syringe filter, for higher volumes vacuum driven filter systems are more convenient. An alternative would be to prepare the whole amount of B27 in 500 ml Medium and then freeze 40 ml aliquots in sterile 50 ml tubes. They should be frozen upright to prevent uncontrollable loss of medium due to expansion; for the same reason, the tubes should only contain 40 ml. 26. Seeding, or in general passaging differentiated cells will always lead to a certain percentage of cell death. If too many cells die after seeding/passaging, addition of the ROCK-inhibitor Y-27632 to the plating medium might increase survival. Under sterile conditions prepare a stock of 10 mM in DMSO, aliquot into sterile tubes and store frozen at 20
C. Add 1 μl/ml of this stock for a final concentration of 10 μM in the medium. Although the ROCK-inhibitor initially might not be sterile, the prolonged contact with DMSO will kill all usual pathogens. The medium containing this inhibitor should be removed the next day.

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27. Filling the unused space in the well with medium or PBS will prevent evaporation of fluid and the consequent increase in osmolality of the very small volume of medium in the ring. The external medium does not need to contain expensive supplements and can even be substituted by PBS. As this medium will not get in contact with the cells, it may also contain antibiotics to reduce contamination risks. The external medium also prevents the ring-coverslip assembly from irrevocably sticking to the bottom of the well in case a drop of medium (e.g., during medium change) would accidentally drop into a dry well and would be sucked by capillary forces under the coverslip. If necessary, to help the coverslip-ring assembly to submerge, use a sterile pipette tip to press it under the surface of the external medium. 28. If using the recommended volumes (Table 1), and if the cells are not too dense in the rings (e.g., 10k in an 8 mm ring) the medium needs to be changed only every third or fourth day. If protocols do not require absolute exact volumes, e.g., during simple maintenance, filling the rings by drop-counting might be a way to speed up cell culture. One medium drop from a 5 ml serological pipette is about 40 μl. Therefore the 12.8, 8, or 6 mm rings can be filled with 13, 6, or 4 drops (520, 240, or 160 μl), respectively. 29. If kept in culture for longer, the hIPS-derived cardiac myocytes will mature more. Even in the rings the cells can be cultured for several weeks to months. In our experience sarcomeric structures will significantly improve over the first weeks and then stay constant. 30. Our typical virus titers (adenovirus) are in the range of 5  107 PFU/μl. As this would require pipetting very small virus-amounts of 0.1 μl or less, which would not be very precise, we predilute the virus aliquots (1 μl) 1:100 in ice-cold medium (RPMI 1640 plus B27 supplement). Then we add 5–10 μl of this dilution to an 8 mm ring, corresponding to a MOI of 250–500 (2.5–5  106 virus particles to 10,000 cells). Be aware that the necessary MOI might vary depending on maturation state and size of the cells. We also have observed a non-linearity with respect to the size of the vessels. The MOI needs to increase slightly with decreased vessel size. Virus aliquots that were always maintained on ice can be refrozen at least twice, even when already diluted. The infection efficiency might decrease slightly, but for FRET measurements this is not relevant, as one can always find a region with a sufficient number of cells expressing the cAMP sensor. Figure 3a, c shows images of cells infected with 10 μl of the virus-predilution in an 8 mm ring, corresponding to 0.1 μl of the original stock. The used virus aliquot had 2 freeze/thaw cycles after aliquoting.

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31. The EPAC-SH187 cAMP sensor [5] expresses rapidly. Some other constructs may take 48–72 h for sufficient expression. In our experience the cells tolerate a wide MOI-range. Even a tenfold increase will initially not affect cell survival. However, the sensor expression level might then be so high that one can expect cAMP-buffering effects. Therefore it is better to transfect only with a low MOI and wait, e.g., 48 h before measuring. 32. Transfection efficiency will be influenced by age and maturation state of the cells, so DNA and transfection reagent amounts and their ratios may have to be optimized. Transfection was tested only in 12.8 and 8 mm rings, the values for 6 and 4 mm rings are scaled down and given for convenience as a starting point for a trial. In 12.8 and 8 mm rings we had an efficiency of up to 25–30% with hIPS-derived cardiomyocytes from 2 different lines transfected 30–31 days after seeding. Values shown in the table were generated using a DNA to transfection reagent ratio of 1:3. Values for other transfection reagents will differ. We have also tried transfection reagents used for normal cell lines (e.g., PolyPlus JetPrime) according to the manufacturer’s recommendations and at the recommended ratio of 1:2. Even with this reagent we found some transfection. The efficiency was significantly lower, sometimes only a few cells in a ring, but still sufficient for FRET-imaging experiments with single cells. Table 4 shows the tested concentrations. 33. For convenience we did not use the medium recommended by the manufacturer, but we used RPMI 1640 without supplement for premixing the DNA and the transfection reagent instead. In this way it is very simple to perform a quick check regarding the best suited DNA concentration by preparing, e.g., 15 μl of each premix, combine the DNA and reagent premixes, incubate for 10 min and then add 5, 10, and 15 μl of the final mix to the cells (for 0.5, 1, and 1.5 μg DNA) without introducing different amounts of an additional, different medium. Table 4 Volumes and concentrations of DNA for the transfection of hIPSC-CMs with PolyPlus JetPrime transfection reagent. Mind that in the case of this reagent also the medium to buffer ratio should be kept constant by 10:1. Values for 12.8 and 8 mm rings are tested, values for 6 and 4 mm rings are downscaled and given for convenience as a guideline Ring-size

Medium volume

Buffer volume

DNA

Jet-Prime reagent

12.8 mm ring

600 μl

60 μl

0.3 μg

0.6 μl

8 mm ring

250 μl

25 μl

0.1 μg

0.2 μl

6 mm ring

150 μl

15 μl

0.06 μg

0.12 μl

4 mm ring

80 μl

8 μl

0.03 μg

0.06 μl

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34. DNA preparations are not necessarily sterile. Therefore it is advisable to add antibiotics to the medium during the transfection and change back to normal medium after 24 h (compare Note 21). 35. In our experience infection is tolerated better than transfection. The cells tolerate even up to tenfold the amount of virus, whereas the range for plasmidic DNA is significantly narrower and shows an optimum. Therefore an initial verification with a rough concentration-dependency is advisable (compare Note 33). For our hIPS-derived cardiomyocytes transfection with 0.1 μg DNA (8 mm ring) yielded a good expression after 48 h, and the cells survive until day 3 after transfection. Lower concentrations lead to a decrease in the number of expressing cells, whereas higher concentrations (0.15 μg, 0.2 μg) lead to somewhat higher expression already after 24 h, but the cells started to die after 48 h. We have experienced this with cardiomyocytes differentiated from three different hIPS-lines. 36. For a comprehensive video tutorial on FRET imaging including data acquisition and analysis, visit “The secrets of FRETimaging: From theory to practice,” part two: https://www.youtube.com/watch?v¼qWlzR7oD3tY Make sure to select HD-quality. 37. The signal should be significantly higher than the background. In our system we typically use cells showing signals in a range of 50–500% above the background. In order to reduce phototoxicity and bleaching, try to find the lowest possible excitation intensity and exposure time that still yields a good signal to background ratio. Avoid very bright cells, as in such cells the sensor itself may act as a significant buffer for cAMP and might change kinetics or even maximal FRET-change values. 38. Driving the sensor into saturation is important to judge if a stimulus did not work due to its nature or, especially in the case of consecutive applications, because the sensor had been saturated already. In addition the signal at saturation allows normalization to the maximal FRET change, which is important when comparing cAMP responses measured with sensors with different dynamic ranges [14]. In the case of the EPAC-SH187 [5], a combination of IBMX (100 μM) and Forskolin (FSK, 10 μM) is sufficient to saturate the probe. 39. Calculating the FRET ratio by dividing CFP-emission values by YFP emission values for a “loss of FRET” sensor (decreasing FRET with increasing cAMP) like EPAC-SH187 leads to increasing FRET ratios at increasing cAMP concentrations. For a sensor that shows increasing yellow and decreasing cyan emission with increasing cAMP (“gain of FRET”), e.g., the CUTie cAMP-sensor family [15], the ratio can be reversed.

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40. The FRET change can be determined in every spreadsheet program by averaging the values at the respective time-points as mentioned. However, especially with very noisy signals or with drifting baselines it might be more accurate to determine the change graphically as shown in Fig. 4b. Plotting the data into graphs for this purpose, or for high-quality publication can most conveniently be done with specialized scientific analysis/graphing software (e.g., GraphPad Prism or Origin).

Acknowledgments This work was supported by the British Heart Foundation (PG/10/75/28537 and RG/17/6/32944) and the BHF Centre of Research Excellence, Oxford (RE/18/3/34214). References 1. Richter W, Xie M, Scheitrum C et al (2011) Conserved expression and functions of PDE4 in rodent and human heart. Basic Res Cardiol 106:249–262. https://doi.org/10.1007/ s00395-010-0138-8 2. Johnson WB, Katugampola S, Able S et al (2012) Profiling of cAMP and cGMP phosphodiesterases in isolated ventricular cardiomyocytes from human hearts: comparison with rat and guinea pig. Life Sci 90:328–336. https://doi.org/10.1016/j.lfs.2011.11.016 3. Pioner JM, Guan X, Klaiman JM et al (2020) Absence of full-length dystrophin impairs normal maturation and contraction of cardiomyocytes derived from human-induced pluripotent stem cells. Cardiovasc Res 116:368–382. https://doi.org/10.1093/cvr/cvz109 4. Ge´linas R, El Khoury N, Chaix M-A et al (2017) Characterization of a human induced pluripotent stem cell–derived cardiomyocyte model for the study of variant pathogenicity: validation of a KCNJ2 mutation. Circ Cardiovasc Genet 10:e001755. https://doi.org/10. 1161/CIRCGENETICS.117.001755 5. Klarenbeek J, Goedhart J, van Batenburg A et al (2015) Fourth-generation epac-based FRET sensors for cAMP feature exceptional brightness, photostability and dynamic range: characterization of dedicated sensors for FLIM, for ratiometry and with high affinity. PLoS One 10:e0122513. https://doi.org/10. 1371/journal.pone.0122513 6. Tan S, Tao Z, Loo S et al (2019) Non-viral vector based gene transfection with human induced pluripotent stem cells derived

cardiomyocytes. Sci Rep 9:14404. https:// doi.org/10.1038/s41598-019-50980-w 7. Bodbin SE, Denning C, Mosqueira D (2020) Transfection of hPSC-cardiomyocytes using Viafect™ transfection reagent. MPS 3:57. https://doi.org/10.3390/mps3030057 8. Schatten G, Smith J, Navara C et al (2005) Culture of human embryonic stem cells. Nat Methods 2:455–463. https://doi.org/10. 1038/nmeth0605-455 9. Ludwig TE, Bergendahl V, Levenstein ME et al (2006) Feeder-independent culture of human embryonic stem cells. Nat Methods 3: 6 3 7 – 6 4 6 . h t t p s : // d o i . o r g / 1 0 . 1 0 3 8 / nmeth902 10. Lian X, Zhang J, Azarin SM et al (2013) Directed cardiomyocyte differentiation from human pluripotent stem cells by modulating Wnt/β-catenin signaling under fully defined conditions. Nat Protoc 8:162–175. https:// doi.org/10.1038/nprot.2012.150 11. Burridge PW, Matsa E, Shukla P et al (2014) Chemically defined generation of human cardiomyocytes. Nat Methods 11:855–860. https://doi.org/10.1038/nmeth.2999 12. Aisenbrey EA, Torr E, Johnson H et al (2021) A protocol for rapid pericyte differentiation of human induced pluripotent stem cells. STAR Protoc 2:100261. https://doi.org/10.1016/ j.xpro.2020.100261 13. van den Berg CW, Elliott DA, Braam SR et al (2014) Differentiation of human pluripotent stem cells to cardiomyocytes under defined conditions. In: Nagy A, Turksen K (eds)

Micro Cell Culture for cAMP FRET Measurements Patient-specific induced pluripotent stem cell models. Springer, New York, NY, pp 163–180 14. Koschinski A, Zaccolo M (2019) Quantification and comparison of signals generated by different FRET-based cAMP reporters. In: Tiberi M (ed) G protein-coupled receptor signaling: methods and protocols. Springer, New York, NY, pp 217–237

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Chapter 10 Automated Image Analysis of FRET Signals for Subcellular cAMP Quantification Silas J. Leavesley, Naga Annamdevula, Santina Johnson, D. J. Pleshinger, and Thomas C. Rich Abstract A variety of FRET probes have been developed to examine cAMP localization and dynamics in single cells. These probes offer a readily accessible approach to measure localized cAMP signals. However, given the low signal-to-noise ratio of most FRET probes and the dynamic nature of the intracellular environment, there have been marked limitations in the ability to use FRET probes to study localized signaling events within the same cell. Here, we outline a methodology to dissect kinetics of cAMP-mediated FRET signals in single cells using automated image analysis approaches. We additionally extend these approaches to the analysis of subcellular regions. These approaches offer a unique opportunity to assess localized cAMP kinetics in an unbiased, quantitative fashion. Key words Fo¨rster resonance energy transfer, Image cytometry, Microscopy, Cyclic nucleotide

1

Introduction Fo¨rster resonance energy transfer (FRET) is a process in which energy is non-radiatively transferred from one fluorescence molecule (donor fluorophore) to a second fluorescence molecule (acceptor fluorophore) [1, 2]. Because the efficiency at which energy is transferred is highly dependent on the inter-molecular spacing of the donor and acceptor [3, 4], FRET has played a key role in allowing the study of intracellular biological events, such as protein–protein interactions, protein folding, and enzyme–substrate kinetics [5, 6]. By utilizing appropriate fusion protein reporters (such as Epac-based cAMP probes), FRET assays can be employed to measure bulk and intracellular second messenger concentrations—the most wide-spread of which have been the measurement of cAMP [7–12] and cGMP [13, 14]. Coincident with the development of new FRET reporters, there have been a large number of fluorescence microscopy approaches developed to

Manuela Zaccolo (ed.), cAMP Signaling: Methods and Protocols, Methods in Molecular Biology, vol. 2483, https://doi.org/10.1007/978-1-0716-2245-2_10, © The Author(s), under exclusive license to Springer Science+Business Media, LLC, part of Springer Nature 2022

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measure FRET efficiency, including the use of 1, 2, or 3 fluorescence filter cubes [15–17], fluorescence lifetime [18, 19], spectral imaging [20–24], and acceptor photobleaching [25]. Each of these approaches has associated advantages and limitations. However, when performed appropriately, the final outcome of any of these fluorescence microscopy-based FRET assays should be the ability to sample the FRET efficiency for any pixel or region in a fluorescence microscopy image. In theory, FRET efficiency image data should yield a wealth of information describing localized cAMP concentrations. In practice, partly due to signal-to-noise limitations, many FRET-based cAMP assays have simply focused on measuring a bulk cAMP response for the entire population (field of view or large region of interest) or for a single (representative) cell of interest. However, manual selection of regions or cells of interest can introduce two key limitations: use of a small number of cells to represent the population response and introduction of operator bias in the selection of the region. The purpose of this chapter is to present a methodology for automated image analysis to allow selection of many or all cells in a field of view, as well as subcellular regions, to allow more accurate estimations of FRET efficiencies and cAMP concentrations at a cellular and subcellular level, without operator bias. In addition, the automated image analysis approaches, once implemented in a script or pipeline format, significantly reduce analysis time of large fluorescence microscopy image datasets, as operator interaction is largely eliminated. This methodology is presented using the mTurquoise-Venus H188 cAMP FRET reporter [10], although other variations of FRET cAMP FRET sensors are available (see Note 1). Unless otherwise stated, the wording “donor” is used to refer to mTurquoise and “acceptor” is used to refer to Venus.

2

Materials

2.1 FRET-Based cAMP Measurement Components

1. Fluorescence widefield or confocal microscope configured for FRET measurements, such as TE2000, Nikon Instruments (or similar), or inverted spectral confocal microscope (such as A1R, Nikon Instruments, or similar) equipped with spectral imaging capabilities provides the lowest coefficient of variation (CV) when conducting FRET measurements [21] and is preferable to approaches utilizing one or several fluorescence filter cubes (see Note 2). (a) Light path for widefield microscope: A broad-band excitation source (Xe arc lamp), an excitation filter (415 nm with 20 nm bandwidth is appropriate for Turquoise-Venus reporters), dichroic beamsplitter (450 nm long-pass), objective, emission filter (450 nm long-pass), tunable filter for hyperspectral imaging, and CCD camera.

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Light path for confocal microscope: Laser, excitation pinhole aperture, excitation filter, dichroic beamsplitter, objective, emission (barrier) filter, emission pinhole aperture, photomultiplier detector. (b) High numerical-aperture, achromatically corrected, objective, such as a 60
water or oil immersion objective. (c) If using a widefield microscope, a tunable filter for hyperspectral imaging is required, such as HSi-300, Chromodynamics, or VersaChrome filter-based system (VF-5, Sutter Instrument Co.), with appropriate wavelength range for the FRET reporter being used (450–700 nm for CFP-YFP or for Turquoise-Venus reporters). 2. Extracellular buffer solution: 145 nM NaCl, 4 mM KCl, 20 mM HEPES, 10 mM D-Glucose, 1 mM MgCl2, 1 mM CaCl2, pH 7.3. 3. Cells expressing the donor-alone, acceptor-alone, or the donor-acceptor FRET-based cAMP sensor prepared on glass coverslips (see Note 3). Cell culture and infection protocols have been previously described [21, 26]. Alternative procedures such as transfections can also be used to express FRETbased cAMP sensors in the cells as described in [23, 24]. 4. Cellular and membrane labels: A nuclear label, such as DRAQ5 (BioStatus Limited) or Hoechst (Hoechst 33342, Life Technologies), should be used to enable automated image analysis algorithms to automatically locate nuclei. Further labels may be added for identifying other cellular structures (see Note 4). 5. Calibration tools: Although most commercial spectral confocal systems advertise a calibrated spectral detector, a broad-band NIST-traceable light source (LS-1-CAL, Ocean Insight) can be used to check the calibration and if necessary correct spectral image data so that the system provides a flat spectral response. A multi-ion discharge lamp (MIDL) can also be used to verify wavelength precision and spectral resolution (MIDL, Lightform, Inc.). Finally, a fiber-coupled spectrometer (QE65000, Ocean Insight) with integrating sphere (FOIS-1, Ocean Insight) can also be used to verify excitation power for each excitation filter or laser. 2.2 Image Analysis Components

1. Computer workstation, appropriately equipped for image analysis. While computer workstation hardware specifications are continually being improved, the following recommendations represent bare minimum requirements for capabilities for quantitative image processing: dual-core processor, 2 GHz processor speed, 4 GB RAM memory, and 20 GB available hard drive space.

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2. Software for spectral image analysis: Traditionally, analysis of spectral/hyperspectral image data has been performed using a least-squares linear unmixing algorithm with non-negative constraints [21, 27] (see Note 5). While several software programs contain linear unmixing algorithms, such as ENVI (Exelis Visual Information Solutions) and image analysis suites from the major microscope manufacturers (Nikon, Olympus, Zeiss), we typically employ custom linear unmixing scripts using the Matlab (MathWorks) programming environment. 3. Software for automated cell segmentation and quantification: Several software programs are available for automated image analysis and quantification, also called image cytometry (see Note 6). We have used Cell Profiler (The Broad Institute— freely available) and custom scripts written in the MATLAB programming environment.

3

Methods The image analysis process consists of the following general steps: image acquisition, image segmentation, and feature extraction. There are many approaches for performing image segmentation and feature extraction, and many software programs available— from specialized programs such as Cell Profiler (The Broad Institute [28]), to general coding packages such as MATLAB (The MathWorks) (see Note 6). Hence, in addition to the steps provided below, we have provided an example spectral image data set and analysis pipeline (http://www.southalabama.edu/centers/bio imaging/Resources.html) that can be run using Cell Profiler, an open-source software package. The example pipeline performs the basic task of quantifying single-cell FRET for each cell within an image. The pipeline can also be edited to suit a range of further needs (for example, measurement of cellular geometry, intensities of additional labels, etc.). Where appropriate, in the methods section, we have referenced the corresponding steps in the example Cell Profiler pipeline with “*CP*”. A diagram of the image processing steps is shown in Fig. 1.

3.1 Image Acquisition: Spectral Imaging Fluorescence Microscopy of FRETBased cAMP Reporters

Spectral imaging may provide improved ability for quantifying fluorophore signals and separating autofluorescence [23, 29]. Spectral imaging of FRET-based cAMP reporters has been previously described elsewhere [21, 23, 24], and spectral imaging of cGMP reporters has been previously described in Methods in Molecular Biology [26]. A brief summary of the image acquisition methodology is given below. 1. The fluorescence excitation should be configured so as to preferentially excite the donor (~415 nm for CFP or

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Fig. 1 Summary of steps in the methodology for automated single-cell cAMP measurements using a FRET reporter: image acquisition, image segmentation (region selection), and feature extraction, as well as secondary subcellular region selection and feature extraction. Each main set of steps is indicated by blue dashed lines and a label that indicates the corresponding methods subsection

Turquoise) and nuclear label (~415 nm for Hoechst or 561 nm for DRAQ5). 2. The spectral imaging fluorescence detection should be configured to acquire fluorescence over a spectral range that includes both the donor, the acceptor, and the nuclear label emission (e.g., 450–720 nm for Turquoise, Venus, and DRAQ5). 3. Prepare a background/blank sample using a blank coverslip mounted in a coverslip holder and extracellular buffer. 4. Correct the fluorescence emission to a flat spectral response using a NIST-traceable light source [21, 29]. (a) Acquire a spectral image stack of the NIST-traceable lamp, as projected through the coverslip. (b) Acquire a spectral image stack with the NIST-traceable lamp turned off. This will be used as the background spectrum. (c) Calculate the correction coefficient as: CC ¼

I Lamp I Measured  I Background

ð1Þ

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where ILamp is the known (NIST-traceable) lamp spectrum, IMeasured is the measured spectrum, and IBackground is the background spectrum. 5. The spectral (wavelength) resolution can be verified using a multi-ion discharge lamp [29, 30]. 6. Acquire spectral image data for single-labeled samples to use as spectral controls. A separate spectral image scan is needed for each label used. The scan for each label should be performed using identical excitation wavelength as is used for FRET studies (e.g., 405 nm). In some cases, the integration time may need to be adjusted (see Note 7). FRET measurements that are performed using single label controls of donor and acceptor fluorophores may be used to calculate a FRET index. Calculation of FRET efficiency requires further characterization of the assay. One approach to perform FRET efficiency measurements is described thoroughly in [23]. 7. Perform measurements on intact cells with the FRET-based cAMP reporter. (a) Place a coverslip containing adherent cells expressing the cAMP FRET reporter (and additional fluorescent labels) on the microscope. (b) Select a field of view with appropriately expressing cells. We recommend selecting a field of view with moderately expressing cells (see Note 8). (c) Begin the time-lapse study. Acquire images at several baseline time-points before adding reagents. (d) Add reagents to trigger changes in cAMP levels (see Note 9). (e) Acquire further time-lapse images for a period of time longer than the expected cAMP response time (e.g., until the system has reached steady-state; see Note 10). 8. Measure FRET response at minimal and maximal cAMP levels. (a) Minimal cAMP levels can often be approximated by basal conditions. We recommend measuring 30 s to 2 min of basal activity (usually 5–20 time-points) to allow calculation of the standard error of the FRET measurements. Use of an adenylyl cyclase inhibitor, such as 100 μM MDL-12,330A hydrochloride, has also been described for ensuring minimal cAMP levels [31]. (b) Maximal cAMP levels may be approximated by addition of 500 μM IBMX (PDE inhibitor) + 50 μM Forskolin (adenylyl cyclase activator).

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9. Correct images to a flat spectral response. This is achieved by subtracting the background spectrum from the measured image data and then multiplying by the correction coefficient (Eq. 1). This relatively simple correction can be performed using a simple MATLAB script or ImageJ routine. 10. Linearly unmix the spectral image data (see Note 5). Analyzing the spectral images from each time-point will result in an abundance image for each fluorescent label at each timepoint. Abundance refers to the amount of a fluorophore that is detected after spectral unmixing. The abundance images at each time-point are used as the inputs to the automated image analysis procedure (described below) (see Note 6). 3.2 Image Segmentation: Automated Whole-Cell cAMP Measurements

1. Save the signal from each fluorescent label (the linearly unmixed signal, or abundance) as a separate image in tif format (tagged image file format). Examples of linearly unmixed images are given in the example data file (http://www. southalabama.edu/centers/bioimaging/Resources.html). 2. Locate nuclei by thresholding the nuclear image. *CP* In Cell Profiler, use the “IdentifyPrimaryObjects” module to perform nuclear thresholding. (a) Select the thresholding level using a mixture of Gaussians (MoG) algorithm with two intensity populations (foreground and background). (b) Apply the threshold to generate a binary image. This creates a mask which allows geometric analysis of the nuclei and exclusion of not appropriate ones. These binary images are used further for nuclei labeling and matching to the cell boundary. (c) Exclude nuclei that are too large, too small, or touching the border of the image. (d) Assign a label to each nucleus. 3. Locate expressing-cell regions by thresholding the donor and acceptor image. *CP* Use the “IdentifyPrimaryObjects” module. (a) Create a new image that is the sum of the donor and the acceptor images (the linearly unmixed donor and acceptor signals). Save this image in tif format. 4. Filter whole-cell regions so that each cell contains only one nuclei and so that each nuclei has a corresponding cell. *CP* Use the “MaskObjects” module so that only nuclei within expressing cells are considered for analysis. Then use the “IdentifySecondaryObjects” module to identify a single cell corresponding to each masked nucleus.

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(a) Additional filtering can be performed to remove abnormally shaped regions. For example, regions with a diameter of less than a typical cell may be excluded as fragments. In addition, regions with very high intensity of both donor and acceptor correspond to cells with very high FRET reporter expression, which are usually non-viable and may be excluded from analysis. In addition, regions that have a very high circularity are often non-viable or detached cells. These regions can be automatically excluded from analysis by thresholding based on cell radius or circularity. *CP* Use the “MeasureObjectSizeShape” module to extract geometric measurements from each expressing cell. Then use the “FilterObjects” module to identify only cells within the desired cell radius (for example, identify cells with a mean radius between 3 and 15 pixels). 5. Label whole-cell regions with unique identification tags. 3.3 Feature Extraction: Quantitative Measurement of Whole-Cell Data

1. Calculate the FRET efficiency for each region. (a) Measure the pixel-averaged donor intensity (abundance from linear unmixing, ADonor) and acceptor intensity (AAcceptor) for each region. *CP* Use the “MeasureObjectIntensity” module to measure the donor and acceptor intensity within each cell. These measurements can then be exported to Excel (Microsoft Corporation), using the “ExportToSpreadsheet” module. The following equation is then entered into excel. (b) Calculate the FRET efficiency [21] as: FE ¼

A Acceptor  A Donor   Q A Acceptor þ ADonor  QAcceptor  K λ

ð2Þ

Donor

where ADonor and AAcceptor are the unmixed donor and acceptor abundances, respectively; QDonor and QAcceptor are the quantum efficiencies of the donor and acceptor, respectively; and, Kλ is a correction factor (transfer function). These variables and FRET efficiency calculations have been explained in detail [23, 24]. 2. Relate FRET efficiency to cAMP concentrations using information gathered in Subheading 3.1, step 8. Spectrofluorimetry data may also be used to relate FRET efficiency and cAMP concentration (see Note 11). 3. Extract other quantitative whole-cell or cytoplasmic measurements as necessary for the assay (see Note 12).

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1. Perform the steps in Subheadings 3.2 and 3.3 for whole-cell cAMP measurements before quantifying subcellular features. 2. Identify the cytosolic space. *CP* use the “IdentifyTertiaryObjects” module to define the region between the nucleus and cell border as the cytoplasm on a per cell basis. 3. Locate subcellular regions by thresholding each organelle image. (a) Select the thresholding level or threshold calculation algorithm as appropriate for each organelle/region to be identified. (b) Apply the threshold to generate a binary image. This image is used further for extraction of statistical properties of the intensity for the selected subcellular regions. (c) Exclude regions that are too large, too small, or touching the border of the image. This process is done automatically by the mathematical morphology tools of closing and opening applied to binary masks of the subcellular regions. 4. Assign each subcellular region as belonging to only one wholecell region. (a) Assign subcellular regions completely contained within one whole-cell region to the respective cell ID. (b) Subcellular regions overlapping more than one whole-cell region can be either split or removed from consideration. Alternatively, the boundaries of whole-cell regions can be adjusted to better contain appropriate subcellular regions. (c) Discard subcellular regions touching the edge of the image.

3.5 Feature Extraction: Quantitative Subcellular Measurements

1. Calculate the FRET efficiency for each subcellular region. (a) Measure the pixel-averaged donor intensity (abundance from linear unmixing, ADonor) and acceptor intensity (AAcceptor) for each region. (b) Calculate the FRET efficiency for each subcellular region as described in step 1(b) Subheading 3.3. 2. Relate FRET efficiency to cAMP concentrations using information gathered in Subheading 3.1, step 8. Spectrofluorimetry data may also be used to relate FRET efficiency and cAMP concentration (see Note 11). 3. Extract other quantitative measurements of subcellular regions as necessary for the assay (see Note 12).

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Notes 1. The H188 mTurquoise-EPAC-Venus cAMP FRET reporter [10] is described specifically in this methodology, as it represents a fourth-generation cAMP reporter where the mTurquoise and Venus fluorescent proteins may exhibit better performance than previous CFP-YFP FRET reporters. More recently, FRET-based cAMP probes have been developed using alternative constructs that have shown potential for wider changes in FRET efficiency upon cAMP binding [32]. However, there are many alternative cAMP and cyclic nucleotide FRET reporters available, including mTurquoise-Venus reporters with higher or lower cAMP affinity [10] and CFP-YFP reporters [9]. For whichever reporter is selected for cAMP measurement, the fluorescence microscope filters should be selected to optimally detect emission from the donor and acceptor and appropriate calibration and controls should be performed to ensure that the FRET efficiency is calculated accurately. Regardless of the reporter used, once the donor and acceptor signals are identified and the FRET efficiency is calculated, the downstream image analysis steps described in this methodology should be applicable. 2. Several methods have been developed for quantifying FRET in fluorescence microscopy experiments. These include the use of multiple fluorescence filter cubes [15], fluorescence lifetime [33], acceptor photobleaching [25], and spectral/hyperspectral imaging [21, 26]. The effectiveness of the various FRET measurement approaches has been compared in several publications [15, 21, 34]. We have found that spectral imaging allows FRET measurements with reduced coefficients of variation, when compared to multiple fluorescence filter cube approaches [21]. In addition, spectral imaging FRET measurement strategies can facilitate measurement of other fluorescent labels, such as nuclear, organellar, or signaling labels, to give spatial or functional context to FRET-based cAMP measurements. 3. We have previously used a CFP-Epac-YFP FRET reporter that was originally described by Nikolaev and colleagues [9]. We have found this construct to provide a FRET efficiency of approximately 45% at basal conditions and 32% at saturating cAMP concentrations (50 μM), using HEK 293 cells [21]. Alternative FRET-based cAMP probes have also been developed with wider changes in FRET efficiency upon cAMP binding [32]. Because these probes have different fluorescent protein mutants for the donor and acceptor, care should be

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taken to appropriately configure the detector settings of the microscope to ensure adequate measurement of the fluorescence emission. 4. A variety of labels are available for identification of different subcellular structures. Upon identifying these structures using automated image analysis algorithms, it is possible to quantify the changes in FRET within or adjacent to these structures. 5. An example linear unmixing algorithm, implemented in MATLAB, has been previously described for analysis of spectral image data of FRET-based cGMP probes that is also applicable for analysis of FRET-based cAMP probes [26]. Although non-negatively constrained linear unmixing is the standard approach for analysis of spectral microscopy data, alternative approaches are available. Of special interest may be approaches that account for the noise characteristics of image data [35]. 6. Alternative, freely available software programs for automated analysis of cellular microscopy image data include: ImageJ (National Institutes of Health), FIJI [36] (a modified distribution of ImageJ), and Ilastik [37]. MATLAB and Python programming languages can also be used for image processing. In fact, we have developed alternative spectral unmixing and 3D FRET and cAMP visualization algorithms that utilize MATLAB and could also be used [23, 24]. Briefly, the steps involved in calculating 3D FRET efficiency using custom MATLAB scripts include: smoothing (or denoising) of unmixed donor and acceptor images, 3D reconstruction, calculating FRET efficiency, creating a mask using donor + acceptor abundance signals, applying the mask to the FRET efficiency image, reslicing the 3D FRET image, and mapping FRET efficiency to cAMP concentration. These custom MATLAB scripts are available at (http://www.southalabama. edu/centers/bioimaging/Resources.html). 7. In some cases, the integration time may have to be increased to allow sampling of the pure label spectrum with minimal noise artifact (for example, when exciting YFP directly with 405 nm excitation, a longer integration time will be needed than is required when exciting CFP with 405 nm excitation and measuring YFP emission that results from energy transfer). As an example, for a relatively simple preparation utilizing the cAMP FRET reporter and Hoechst 33342, three scans are needed (one of the donor-only, one of the acceptor-only, and one for Hoechst). 8. It is important to select cells with a FRET reporter expression level that is suited for the measurement being made. For example, the FRET reporter level must be sufficient to allow

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accurate detection of changes in FRET efficiency. Likewise, cells expressing excessive levels of the FRET reporter could be effected by buffering of cAMP due to the FRET reporter [38]. 9. Agonist concentration dependence should be examined. Typical agonists concentration ranges include 1–100 μM Forskolin, 10–1000 nM Isoproterenol, and 10–1000 nM Prostaglandin E2. 10. cAMP signaling kinetics may occur over a range of time scales, from tens of seconds to tens of minutes or hours [38]. 11. Spectrofluorimetry measurements of cell lysate may be used to construct a cAMP dose–response curve [21]. In brief, cells expressing the FRET probe are suspended in buffer, lysed with a dounce, and the cell lysate placed in a quartz cuvette. Cell lysate is pre-treated with PDE inhibitors (50 μM Rolipram and 500 μM IBMX). A straightforward calculation suggests that baseline cAMP in the cuvette will be two to three orders of magnitude lower that the cAMP concentration required to elicit half maximal FRET response and ~100-fold lower than threshold cAMP levels detectable with current FRET probes. In brief, 106 cells have a volume of ~3
106 L [39]; this volume is diluted into 1 mL of buffer (~333 dilution). Basal cAMP levels (free and bound) are typically on the order of 1 μM in cells and tissues [40]; thus, baseline cAMP levels in a cuvette are ~3
103 μM, substantially lower than the 0.5–2 μM K1/2 of EPAC-based cAMP sensors. A cuvette containing cell lysates is placed in a spectrofluorometer and cell lysates are treated with PDE inhibitors (50 μM Rolipram and 500 μM IBMX). A baseline scan of the fluorescence emission spectrum is then made while preferentially exciting the donor. Increasing concentrations of cAMP are added to the cuvette, with an emission scan acquired after each sequential addition. Upon completion of the experiment, the emission spectrum is linearly unmixed, and the resultant donor and acceptor abundances are used to calculate the FRET efficiency (Eq. 2) for each cAMP concentration. This data is then fit using the Hill equation, establishing a quantitative relationship between FRET efficiency and cAMP concentration. 12. Other geometric, intensity, or texture features may provide additional information about the cellular physiology [28, 41]. These parameters may be measured for each region (cell, organelle, etc.) using automated image analysis approaches and data extracted for each time-point in the study.

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Acknowledgments This work was supported by NSF grant 1725937 and the Abraham Mitchell Cancer Research Fund. References 1. Fo¨rster T (1948) Zwischenmolekulare energiewanderung und fluoreszenz. Ann Phys 437: 55–75 2. Knox RS (1993) Intermolecular energy migration and fluorescence. In: Mielczarek EV, Greenbaum E, Knox RS (eds) Biological physics: key papers in physics. American Institute of Physics, New York 3. Clegg RM (1995) Fluorescence resonance energy transfer. Curr Opin Biotechnol 6:103– 110 4. Algar WR, Hildebrandt N, Vogel SS et al (2019) FRET as a biomolecular research tool—understanding its potential while avoiding pitfalls. Nat Methods 16:815–829 5. Lee HN, Mehta S, Zhang J (2020) Recent advances in the use of genetically encodable optical tools to elicit and monitor signaling events. Curr Opin Cell Biol 63:114–124 6. Deal J, Pleshinger D, Johnson S et al (2020) Milestones in the development and implementation of FRET-based sensors of intracellular signals: a biological perspective of the history of FRET. Cell Signal 75:109769 7. Mongillo M, McSorley T, Evellin S et al (2004) Fluorescence resonance energy transfer–based analysis of cAMP dynamics in live neonatal rat cardiac myocytes reveals distinct functions of compartmentalized phosphodiesterases. Circ Res 95:67–75 8. Ponsioen B, Zhao J, Riedl J et al (2004) Detecting cAMP-induced Epac activation by fluorescence resonance energy transfer: Epac as a novel cAMP indicator. EMBO Rep 5: 1176–1180 9. Nikolaev VO, Bu¨nemann M, Hein L et al (2004) Novel single chain cAMP sensors for receptor-induced signal propagation. J Biol Chem 279:37215–37218 10. Klarenbeek J, Goedhart J, van Batenburg A et al (2015) Fourth-generation epac-based FRET sensors for cAMP feature exceptional brightness, photostability and dynamic range: characterization of dedicated sensors for FLIM, for ratiometry and with high affinity. PLoS One 10:e0122513 11. Raspe M, Klarenbeek J, Jalink K (2015) Recording intracellular cAMP levels with

epac-based FRET sensors by fluorescence lifetime imaging. In: Zaccolo M (ed) cAMP signaling, Methods in molecular biology, vol 1294. Humana Press, New York, NY 12. Bock A, Annibale P, Konrad C et al (2020) Optical mapping of cAMP signaling at the nanometer scale. Cell 182(6):1519–1530.e17 13. Nikolaev VO, Gambaryan S, Lohse MJ (2006) Fluorescent sensors for rapid monitoring of intracellular cGMP. Nat Methods 3:23–25 14. Honda A, Adams SR, Sawyer CL et al (2001) Spatiotemporal dynamics of guanosine 30 , 50 -cyclic monophosphate revealed by a genetically encoded, fluorescent indicator. Proc Natl Acad Sci 98:2437–2442 15. Gordon GW, Berry G, Liang XH et al (1998) Quantitative fluorescence resonance energy transfer measurements using fluorescence microscopy. Biophys J 74:2702–2713 ´ , Váradi T et al (2014) Maxi16. Nagy P, Szabo´ A mum likelihood estimation of FRET efficiency and its implications for distortions in pixelwise calculation of FRET in microscopy. Cytometry A 85:942–952 ´ , Váradi T et al (2016) rFRET: 17. Nagy P, Szabo´ A a comprehensive, Matlab-based program for analyzing intensity-based ratiometric microscopic FRET experiments. Cytometry A 89: 376–384 18. Wallrabe H, Periasamy A (2005) Imaging protein molecules using FRET and FLIM microscopy. Curr Opin Biotechnol 16:19–27 19. Ishikawa-Ankerhold HC, Ankerhold R, Drummen GPC (2012) Advanced fluorescence microscopy techniques—FRAP, FLIP, FLAP, FRET and FLIM. Molecules 17:4047–4132 20. Levy S, Wilms CD, Brumer E et al (2011) SpRET: highly sensitive and reliable spectral measurement of absolute FRET efficiency. Microsc Microanal 17:176–190 21. Leavesley SJ, Britain A, Cichon LK et al (2013) Assessing FRET using spectral techniques. Cytometry A 83:898–912 22. Zhang J, Lin F, Chai L et al (2016) IIemspFRET: improved Iem-spFRET method for robust FRET measurement. J Biomed Opt 21:105003

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Chapter 11 In Vivo cAMP Dynamics in Drosophila Larval Neurons Isabella Maiellaro Abstract Cyclic adenosine monophosphate (cAMP) is a universal second messenger that mediates a myriad of cell functions across all kingdoms of life. The ability to monitor intracellular changes of cAMP concentration in living cells using FRET-based biosensors is proving to be of paramount importance to unraveling the sophisticated organization of cAMP signaling. Here we describe the deployment of the fruit fly Drosophila melanogaster, specifically the third instar larval stage, as an in vivo model to study the spatio-temporal dynamics of cAMP in neurons. The ubiquity of cAMP signaling and conservation of fundamental mechanisms across species ensures relevance to vertebrate neurons while providing a more structurally and ethically simple model. Key words Animals, Neuromuscular junction (NMJ), FRET, Dissection, Synapses, GPCR, Memory, Plasticity, Model organism, Biosensors

1

Introduction The fruit fly Drosophila melanogaster has been used for more than 100 years as a model organism to answer many fundamental questions, from neuroscience to cancer biology [1–3]. In particular the third instar larval neuromuscular junction (NMJ) of Drosophila has been employed as a model to study synaptic plasticity and memory formation [4, 5], with relevance for human central synapses. This process is highly conserved and mediated in both invertebrate and vertebrate neurons by the second messenger cyclic adenosine monophosphate (cAMP) [6, 7]. Among the first evidence to pinpoint the universal role of cAMP in memory formation was described in Drosophila. The genetic agility of the platform has shown that mutations in components of the cAMP pathway lead to impairment in learning and memory formation [8, 9]. Mutation of the synthetic enzyme (adenylyl cyclase rutabaga (rut) [10–12]), degradative enzymes

Manuela Zaccolo (ed.), cAMP Signaling: Methods and Protocols, Methods in Molecular Biology, vol. 2483, https://doi.org/10.1007/978-1-0716-2245-2_11, © The Author(s), under exclusive license to Springer Science+Business Media, LLC, part of Springer Nature 2022

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(cAMP-specific phosphodiesterase dunce (dnc) [13–15]), or the effector enzyme (cAMP-dependent protein kinase PKA (dco) [8]) all impair memory formation at a structural to behavioral level. These are just some of the evidence that underscore that, despite the anatomical differences between Drosophila and humans, the fundamental molecular mechanisms of signaling are conserved, and Drosophila therefore represents a valuable model organism in which to study neuronal cAMP dynamics in vivo. In this chapter we illustrate how to monitor cAMP dynamics in living third instar Drosophila larval neurons, employing the FRETbased biosensor Epac1-camps [16]. The chapter briefly describes the principle of FRET and of Epac1-based cAMP biosensor [17]. We will describe the GAL4-UAS system used to generate transgenic flies and the basics of the Drosophila larval anatomy required to perform accurate dissections to expose larval neurons for in vivo imaging. 1.1 Epac 1-Camps Based FRET Biosensor

Fluorescence resonance energy transfer (FRET) [18] is a physical phenomenon in which one fluorescent protein (“donor”) can transfer energy non-radiatively to a second fluorophore (“acceptor”). Briefly, this transfer can only occur when and if specific parameters are met: (1) two fluorescent proteins are in physical proximity, typically within a 5%) is not recommended to avoid undesired effects on the development. 2. Prepare at the time of use to avoid auto-decay of CTZ. 3. Some of motorized microscopies are equipped with internal light sources for the self-check of optical path that was not acceptable for bioluminescence imaging. To avoid undesired contamination by these photons, “power-off” in the classical

Kazuki Horikawa and Takeharu Nagai 1 μM cAMP

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Fig. 5 Luminescence signals change of wild type (AX2) or adenylate cyclase A mutant (ACA) cells expressing Nano-lantern (cAMP1.6) was recorded by spectrophotometer. Transient increase of luminescence signal upon cAMP stimulation (1 and 10 μM) was not detected in the ACA mutant

system or activating the bioluminescence mode in the recent system is needed. Using conventional unmotorized microscopies is alternative for a successful bioluminescence imaging. 4. Presence of Ca2+ and Mg2+ is not acceptable since it generates phosphate precipitates in the agar. 5. The thickness of the agar base can be adjusted by the stacking number of the insulation tape. 6. To avoid damages on the high-density cells by oxygen shortage and heating, a cuvette holder equipped with a stirrer and circulating chiller would be suitable. 7. In all cases, bioluminescence signal change is associated with gradual decrease in the baseline. This is due to the auto-decay of CTZ. For a more quantitative analysis, baseline correction considering an exponential decay of CTZ would be acceptable. 8. For a successful detection of bioluminescence signal change, optimal affinity of the indicator for cAMP is essential. Three affinity variants of Nano-lantern (cAMP) with Kds of 0.4, 1.6, and 3.3 μM are available at Addgene [6]. For normal cells at mid-aggregation stage (e.g., 8 h after starvation), Nano-lantern (cAMP1.6) demonstrated the most appropriate cAMP

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dynamics. In other conditions such as the early- or lateaggregation stage when cells are expected to show [cAMP]i changes at low and high concentration range, high and low affinity variants would be more suitable, respectively. 9. To express genetically encoded indicators in D. discoideum cells, cDNAs of humanized codon are not appropriate. Since codon usage in D. discoideum cells is significantly biased to high A-T contents (>70%), humanized cDNAs generally having low A-T contents are not expressed at all. In our study, cDNA of Nano-lantern (cAMP1.6) with a hybrid codon usage was utilized, where Venus moiety was incorporated from Aequorea Victoria’s cDNA having higher A-T contents (61%) than the humanized version (39%), while RLuc moiety was derived from humanized cDNA. Although this allowed acceptably high expression level in the previous study [6], more expression might be realized if the codon of entire cDNA was fully optimized for D. discoideum by synthetic DNA.

Acknowledgments We are grateful to Kenji Osabe for proofreading the manuscript. This work was supported by JST SENTAN KEISOKU program (16812798 to T.N.), Grant-in-Aid for Scientific Research on Innovative Areas “Singularity Biology (No.8007)” (18H05408 to T.N., 18H05415 to K.H.), MEXT, Japan, and the Research Program of “Five-star Alliance” in “NJRC Mater. & Dev.” (T.N., K.H). References 1. Zaccolo M, De Giorgi F, Cho CY, Feng L, Knapp T, Negulescu PA et al (2000) A genetically encoded, fluorescent indicator for cyclic AMP in living cells. Nat Cell Biol 2(1):25–29. https://doi.org/10.1038/71345 2. Kim N, Shin S, Bae SW (2021) cAMP biosensors based on genetically encoded fluorescent/ luminescent proteins. Biosensors 11(2):39. https://doi.org/10.3390/bios11020039 3. Jiang LI, Collins J, Davis R, Lin K-M, DeCamp D, Roach T et al (2007) Use of a cAMP BRET sensor to characterize a novel regulation of cAMP by the sphingosine 1-phosphate/G13 pathway. J Biol Chem 282(14):10576–10584. https://doi.org/10. 1074/jbc.M609695200 4. Fan F, Binkowski BF, Butler BL, Stecha PF, Lewis MK, Wood KV (2008) Novel genetically encoded biosensors using firefly luciferase. ACS Chem Biol 3(6):346–351. https://doi. org/10.1021/cb8000414

5. Binkowski BF, Butler BL, Stecha PF, Eggers CT, Otto P, Zimmerman K et al (2011) A luminescent biosensor with increased dynamic range for intracellular cAMP. ACS Chem Biol 6(11):1193–1197. https://doi.org/10.1021/ cb200248h 6. Saito K, Chang YF, Horikawa K, Hatsugai N, Higuchi Y, Hashida M et al (2012) Luminescent proteins for high-speed single-cell and whole-body imaging. Nat Commun 3(1): 1 2 6 2 . h t t p s : // d o i . o r g / 1 0 . 1 0 3 8 / ncomms2248 7. Loening AM, Fenn TD, Wu AM, Gambhir SS (2006) Consensus guided mutagenesis of Renilla luciferase yields enhanced stability and light output. Protein Eng Des Sel 19(9): 391–400. https://doi.org/10.1093/protein/ gzl023 8. Nagai T, Ibata K, Park ES, Kubota M, Mikoshiba K, Miyawaki A (2002) A variant of yellow fluorescent protein with fast and

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efficient maturation for cell-biological applications. Nat Biotechnol 20(1):87–90. https:// doi.org/10.1038/nbt0102-87 9. Takai A, Nakano M, Saito K, Haruno R, Watanabe TM, Ohyanagi T et al (2015) Expanded palette of nano-lanterns for real-time multicolor luminescence imaging. Proc Natl Acad Sci 112(14):4352. https://doi.org/10.1073/ pnas.1418468112

10. Fey P, Kowal AS, Gaudet P, Pilcher KE, Chisholm RL (2007) Protocols for growth and development of Dictyostelium discoideum. Nat Protoc 2(6):1307–1316. https:// doi.org/10.1038/nprot.2007.178 11. Gaudet P, Pilcher KE, Fey P, Chisholm RL (2007) Transformation of Dictyostelium discoideum with plasmid DNA. Nat Protoc 2(6): 1317–1324. https://doi.org/10.1038/nprot. 2007.179

Chapter 15 Generation of Transgenic Mice Expressing Cytosolic and Targeted FRET Biosensors for cAMP and cGMP Roberta Kurelic´ and Viacheslav O. Nikolaev Abstract Transgenic mice play a significant role in modern biomedical research. In addition to mechanistic studies of a specific gene and protein function, transgenic mice are used as an exciting tool for in vivo or in situ analysis of fluorescent biosensors, which are capable of directly reporting second messenger levels and biochemical processes in real time and living cells. In this chapter, we present a detailed protocol for the generation of plasmid vectors and transgenic mice ubiquitously or constitutively expressing cytosolic and targeted Fo¨rster resonance energy transfer (FRET)-based biosensors for the second messengers 30 ,50 -cyclic adenosine and guanosine monophosphates. These tools and techniques hold great potential for the analysis of second messenger dynamics in physiologically relevant systems. Key words Transgenic mice, FRET, Cardiomyocyte, cAMP, Biosensor

1

Introduction Transgenic mice play an important role in contemporary biomedical research. Many biological and medical questions can be addressed in transgenic animals. For example, they allow detailed analysis of individual gene functions and can be used as genetic animal models of human disease [1–3]. Generally, a specific exogenous DNA fragment can be integrated into a host animal genome to overexpress, knock in, or knock out a specific gene in a constitutive or regulated fashion. There are many approaches to establish transgenic animals, one of which is the microinjection of DNA [4–7]. Murine zygotes before the cell fusion can be harvested and manipulated with specific external DNA by injecting it in the male pronucleus or even both pronuclei. This way, the external DNA can be permanently integrated into the host genome. However, the position where the external DNA is integrated is random, unless special vectors such as containing, for example, Sleeping Beauty transposase are

Manuela Zaccolo (ed.), cAMP Signaling: Methods and Protocols, Methods in Molecular Biology, vol. 2483, https://doi.org/10.1007/978-1-0716-2245-2_15, © The Author(s), under exclusive license to Springer Science+Business Media, LLC, part of Springer Nature 2022

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used [8]. The injected zygotes are transferred into a surrogate mother, and some of the born pups carry the transgene integrated into their genome. Apart from using transgenic mice to study gene function, they can be an exciting tool for the expression of fluorescent biosensors capable of directly reporting second messenger levels and biochemical processes in living cells. For example, they have enabled direct visualization of the second messenger 30 ,50 -cyclic adenosine monophosphate (cAMP) and its real-time monitoring in adult cardiomyocyte [9, 10]. Such biosensors are based on the principle of Fo¨rster resonance energy transfer (FRET) which occurs between the donor and acceptor fluorophores (e.g., cyan (CFP) and yellow (YFP) fluorescent proteins) when they come into close spatial proximity [11]. The simplest cAMP biosensor Epac1-camps is comprised of a cAMP binding domain (derived, e.g., from the Epac1 protein) sandwiched between YFP and CFP [12, 13]. cAMP binding leads to a conformational change in the sensor molecule which can be visualized by a decrease of the FRET signal. Likewise, 30 ,50 -cyclic guanosine monophosphate (cGMP) binding domains from the cGMP-dependent protein kinase flanked by CFP and YFP in the so-called cGi500 biosensor [14] can be used to monitor cGMP levels in real time. In this chapter, we describe a detailed protocol for the generation of plasmid vectors and transgenic mice expressing various cytosolic and targeted cAMP and cGMP biosensors in a tissuespecific and ubiquitous manner.

2 2.1

Materials Equipment

1. Thermocycler. 2. Regular DNA electrophoreses chamber and power supply. 3. Gel documentation system. 4. Laboratory scale. 5. Microcentrifuge. 6. Thermomixer. 7. Nanodrop 2000 device or regular laboratory photometer. 8. FRET imaging system comprised of inverted microscope light source, DualView beam splitter, CCD camera, and imaging software. An example can be found in a previously described comprehensive protocol [15].

2.2

Mice

FVB/NRj mice (Janvier Labs, Saint Berthevin, France).

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Vectors

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1. α-MHC-vector, empty [10]. 2. CAG-vector, empty [16]. 3. α-MHC-Epac-camps plasmid [17]. 4. cGi500 plasmid [14].

2.4

Cells

1. Competent E. coli cells—Top 10. 2. 293A cells.

2.5 Substances and Solutions

1. Agarose peqGOLD Universal. 2. 100 mg/ml Ampicillin (stock solution in aqua bidest). 3. Direct PCR Tail. 4. 100 mM dNTPs (in nuclease-free water). 5. GoTaq DNA Polymerase and Go buffer. 6. 5 KCM: 500 mM potassium chloride, 150 mM calcium chloride, 250 mM magnesium chloride dissolved in nucleasefree water. 7. LB agar: Dissolve powder in deionized water and autoclave. After cooling the solution down to ~50  C, add the desired antibiotic (e.g., 0.1 mg/ml ampicillin) and mix well. Pour this solution into 10 cm Petri dishes, cool down until the agar becomes solidified and store at 4  C. 8. LB medium: Dissolve powder in deionized water, autoclave, and store at 4  C. 9. Loading dye buffer DNA IV. 10. Midori green advance. 11. TAE buffer for DNA electrophoresis. 12. 100 mg/ml Proteinase K (in deionized water), add 10 mM Lascorbic acid as preservative and store the aliquots at 20  C. 13. Pfu polymerase and 10 Pfu buffer. 14. Quick Load DNA Ladder. 15. T4 DNA ligase and buffer. 16. TE buffer: 5 mM Tris–HCl, pH 7.4, 0.1 mM EDTA. 17. Isopropanol. 18. 10 mM Sodium nitroprusside (SNP). Prepare stock solution in water, aliquot and store at 20  C. 19. Nuclease-free water. 20. Transfection reagent Lipofectamine 2000.

2.6

Kits

1. QIAquick Gel Extraction Kit. 2. QIAfilter Plasmid Midi Kit.

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Primers

All primers were ordered from Eurofins Genomics, dissolved in deionized water upon arrival at a final concentration of 100 pmol/μl, and subsequently stored at 20  C. 1. For cloning of the CAG-cGi500 vector to amplify CFP fragment (italic ¼ linker sequence): Forward: 50 AAA GAT ATC TGC AGC GCC ACC ATG GTG AGC AAG GGC G 30 Reverse: 50 AAA GAA TTC CAG CAG GAC CAT GTG ATC G 30 2. For cloning of the α-MHC-E1-CAAX vector (italic ¼ linker sequence): Forward: 50 AAG GAA TTC GAG GAG TTG GCC 30 Reverse: 50 AAA CTC GAG TTA CAT GAT CAC GCA CTT GGT CTT GCT CTT CTT CTT CTT CTT GGA TCC CTT GTA CAG CTC GTC CAT G 30 3. For genotyping: Forward: 50 TGA CAG ACA GAT CCC TCC TAT 30 Reverse: 50 CAT GGC GGA CTT GAA GAA GT 30

2.8 LoxP Stop Sequence

Sequence contains a stop cassette with multiple TAA stop codons flanked by two loxP sites to allow tissue-specific deletion by breeding with Cre expressing mice. Such cassette can be modified in a way to include, for example, a sequence of a red or any other fluorescent protein for the selection of transgenic pups. GCCTCTGCTAACCATGTTCATGCCTTCTTCTTTTTCC TACAGCTCCTGGGCAACGTGCTGGTTATTGTGCTGTCTC ATCATTTTGGCAAAATAACTTCGTATAATGTATGCTATACG AAGTTATTTTGTTAACTTGTTTATTGCAGCTTATAATGGTT ACAAATAAAGCAATAGCATCACAAATTTCACAAATAAAGCA TTTTTTTCACTGCATTCTAGTTGTGGTTTGTCCAAACTCA TCAATGTATCTTATCATGTCTGGATCTTGTTAACTTGTTTA TTGCAGCTTATAATGGTTACAAATAAAGCAATAGCATCAC AAATTTCACAAATAAAGCATTTTTTTCACTGCATTCTAGTT GTGGTTTGTCCAAACTCATCAATGTATCTTATCATGTCTG GATCTTGTTAACTTGTTTATTGCAGCTTATAATGGTTACA AATAAAGCAATAGCATCACAAATTTCACAAATAAAGCATT TTTTTCACTGCATTCTAGTTGTGGTTTGTCCAAACTCAT CAATGTATCTTATCATGTCTGGATCTGCAGATATAACTTCG TATAATGTATGCTATACGAAGTTAT.

2.9 Restriction Enzymes

All restriction enzymes and buffers were from New England BioLabs and used in the following buffers, according to the manufacturer protocol.

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BamHI-HF: buffer CutSmart. EcoRI-HF: buffer CutSmart. EcoRV-HF: buffer CutSmart. KpnI-HF: buffer CutSmart. SpeI-HF: buffer CutSmart. XhoI: buffer CutSmart. 2.10 Other Materials for DNA Purification

3 3.1

1. Slide-A-Lyzer Dialysis Cassette. 2. 0.2 μm Syringe Filter Spartan RC, 13 mm.

Methods Cloning Strategy

To generate the ubiquitous transgenic cGi500 mice we used the empty CAG-vector (Fig. 1) as a backbone for the transgenic construct [16, 18]. CAG acts as a constitutively active promoter (any other well-established constitutively active promoters can be used instead) so that the genes downstream of the promoter are transcribed in virtually every cell type. In this vector, a multiple cloning site is provided with several single-cutter restriction sites. As template DNA we used the previously described cGi500 sensor plasmid, which includes the tandem cGMP binding sites from the cGMP-dependent protein kinase (PKG) flanked by two fluorophores for FRET measurements [13]. Subcloning can be performed in one step but with two separate inserts which need to be ligated into one vector. First, the CFP sequence was amplified by the polymerase chain reaction (PCR). We designed the forward primer which includes the EcoRV restriction site and a linker sequence in front of the Kozak sequence and the start codon (ACC ATG). The reverse primer contained an EcoRI restriction site which can be ligated together with the PKG-YFP fragment. We cut out the second insert DNA fragment (PKG-YFP) with the restriction enzymes EcoRI and XhoI from the cGi500 sensor plasmid. The CAG backbone vector was digested with the restriction enzymes EcoRV and XhoI. The vector backbone was used for a triple ligation with the digested CFP and PKG-YFP fragments (Fig. 1). For transgenic mouse generation, the resultant construct was digested with the restriction enzymes SpeI and BamHI and ran on a 1% agarose gel. The upper band (4661 b.p.) was cut out and further used (see Subheading 3.7), while the lower two bands (2011 and 337 b.p., which contain the antibiotic resistance and other redundant sequences) were discarded. To prove the functionality of the new construct, we transfected it into 293A cells using Lipofectamine 2000, according to the manufacturer protocol. 24 h after transfection, these cells showed

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18 SpeI (1)

CAG promoter

EcoRV + XhoI CAG promoter pCAG empty

pCAG-cGi500

1. CFP PCR (EcoRV + EcoRI)

1719 EcoRV (1)

7009 bp

4813 bp

2. PKG-YFP (EcoRI + XhoI)

5016 BamHI (2)

CFP

4679 BamHI (2)

1719 EcoRV (1) 1746 XhoI (1) 2820 BamHI (2) 2483 BamHI (2)

polyA

2403 EcoRI (1)

polyA 3942 XhoI (1)

PKG YFP

Fig. 1 Cloning strategy for the pCAG-cGi500 construct. The empty pCAG vector serves as a backbone. It is cut with the restriction enzymes EcoRV and XhoI. As an insert, two fragments are ligated into this backbone. They include the CFP sequence amplified by PCR and digested using EcoRV and EcoRI and the PKG-YFP fragment out from the cGi500 plasmid with EcoRI and XhoI

Fig. 2 Proof of functionality of the pCAG-cGi500 construct. (a) shows the fluorescence of transfected 293A cells with the pCAG-cGi500 vector. A FRET measurement is shown in (b). The CFP/YFP ratio is increasing after the addition of sodium nitroprusside (SNP, 50 μM) which is indicative of a cGMP increase in cells

fluorescence and measurable FRET responses to the NO donor sodium nitroprusside (SNP), indicative of an increase in intracellular cGMP (Fig. 2). To achieve tissue-specific expression of the cGi500 construct by using a simple transgenic mouse model, a floxed stop codon sequence (see Subheading 2.7) in front of the start codon of the sensor can be introduced. In this case, transgenic mice will not express the sensor unless bred with a Cre deleter mouse line to drive the expression of the construct in the tissue of interest. To add such a sequence to the original pCAG-cGi500 construct, we cut it with XbaI and EcoRV and inserted the sequence using gene synthesis ordered at the BioCat company (Fig. 3). For generating transgenic animals with cardiac-specific expression of the E1-CAAX biosensor targeted to non-raft membrane microdomains due to the CAAX box targeting sequence, a tissuespecific promoter is required. Alpha myosin heavy chain (α-MHC) is a contractile protein expressed in adult cardiomyocytes, and its

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18 SpeI (1) CAG promoter

pCAG-cGi500

1623 XbaI (1) 1719 EcoRV (1)

7009 bp

CAG promoter

XbaI + EcoRV pCAG-loxP-cGi500

loxP-STOP-loxP

loxP 5816 BamHI (2)

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CFP

STOP cassette loxP 2519 EcoRV (1)

5479 BamHI (2)

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1623 XbaI (1)

7809 bp

polyA

polyA 3942 XhoI (1)

CFP

4742 XhoI (1) YFP

PKG

PKG

YFP

Fig. 3 Cloning strategy for the CAG-loxP-cGi500 construct. The pCAG-cGi500 vector serves as a backbone. It is cut with the restriction enzymes EcoRV and XhoI. As an insert, synthetic sequence encoding for a stop cassette flanked by two loxP site is inserted 1 SpeI (2) 7 BamHI (4)

aMHC-Epac1-camps

Amp

1 SpeI (2) 7 BamHI (5)

aMHC promoter

12174 bp

aMHC-E1-CAAX

aMHC promoter

Amp 12214 bp

3773 EcoRI (3)

7886 SpeI (2) 7880 BamHI (4) polyA 7436 BamHI (4) 7418 EcoRI (3) 7406 XhoI (1) CFP Epac1 6195 EcoRI (3)

KpnI + XhoI

5460 KpnI (1) 5472 BamHI (4) YFP

1. YFP (KpnI + EcoRI) 2. Epac1-CFP-CAAX PCR (EcoRI + XhoI)

3773 EcoRI (3)

7926 SpeI (2) 7920 BamHI (5) polyA 7476 BamHI (5) 7458 EcoRI (3) 7446 XhoI (1) CAAX

5460 KpnI (1) 5472 BamHI (5) YFP

7398 BamHI (5) CFP

Epac1 6195 EcoRI (3)

Fig. 4 Cloning scheme of the α-MHC-E1-CAAX construct. The α-MHC-Epac1-camps vector serves as a backbone. It is cut with the restriction enzymes KpnI and XhoI. As an insert, two fragments are ligated into this backbone. They include the YFP sequence cut out from the α-MHC-Epac1-camps vector with KpnI and EcoRI and the Epac1-CFP-CAAX amplified by PCR and digested using EcoRI and XhoI

promoter is routinely used for tissue-specific expression in adult myocardium. To clone such a construct, we choose the α-MHC-Epac1-camps vector [17] as a backbone (Fig. 4). We digested this vector with KpnI and XhoI. The restriction sites are located behind the α-MHC-promoter and before the poly-A tail. The α-MHC-Epac1-camps vector was also digested with KpnI and EcoRI to excise the YFP fragment. The Epac1-CFP-CAAX sequence was amplified by PCR. In this case, the KpnI restriction site was introduced in front of the start codon and an EcoRI restriction site after the YFP sequence to triple ligate it with EpacCFP-CAAX fragment and the α-MHC-vector backbone. The resulting vector α-MHC-E1-CAAX (Fig. 4) was control-digested with BamHI. This enzyme has five restriction sites in this vector, so five bands are detectable on a gel (5465, 4301, 1926, 444, and 78 b.p.). For microinjections, we digested the α-MHC-E1-CAAX construct with the restriction enzyme SpeI and ran on a 1% agarose gel. The upper band (7925 b.p.) was cut out and further used (see

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339 b.p.

Fig. 5 Example of genotyping PCR analysis. Shown are representative results from genotyping PCR analysis of transgenic founder offspring. Mice with the numbers 3, 4, 6, and 8 contain the E1-CAAX construct in their genome. NC negative control, PC positive control, b.p. base pairs

Subheading 3.7), while the lower band (4289 b.p., which contains the antibiotic resistance and other redundant sequences) was discarded. The linearized and purified DNA sequences were injected into the male pronuclei of the FVB/N mouse zygotes. Microinjections are normally performed by transgenic mouse facilities which we supply with the specially prepared transgenic construct DNA (see Subheading 3.7). Please refer to a previously published comprehensive protocol if you need to establish this procedure in your laboratory [7, 19]. The genotypes of the newborn mice were analyzed by PCR (Fig. 5). The primers for the genotyping were selected to bind both parts of the ligated constructs. We choose a forward primer that binds in the α-MHC promoter sequence and the reverse primer which binds in the middle of the Yfp gene. Mice, where the construct was integrated in the host genome, were classified as founders and used for further breedings. The integration of the external DNA is random. Therefore, the F1 generation has to be analyzed by PCR and FRET measurements to test the functionality of these constructs in the descendants. We usually chose one or two founders within the F1 generation showing robust sensor fluorescence and FRET responses. This founder is then selected to establish a new transgenic mouse line. Below the cloning steps are described in more detail. 3.2 PCR and DNA Extraction

The specific DNA fragment to be integrated into the host DNA must first be amplified by a polymerase chain reaction (PCR). Therefore, the required DNA fragment is amplified using specific primer pairs. 1. For the PCR-mix, pipette 10 μl Pfu buffer, 2 μl dNTPs (10 mM), 2.5 μl each primer forward and reverse (pre-diluted to 10 pmol/μl), 0.5 μl template DNA (0.1–0.3 μg), 83.5 μl nuclease-free water, 1 μl Pfu polymerase in a PCR reaction tube. Prepare this mix on ice. 2. Mix carefully and spin down the solution. 3. Start the following program in a thermocycler:

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94  C: 3 min 94  C: 15 s 55  C: 15 s 72  C: 1.2 min (see Note 1) 72  C: 7 min

30 cycles

4  C: 1

4. Purify the PCR product by running the PCR product on a 1% agarose gel supplemented with Midori green (4 μl per 50 ml of the gel) or ethidium bromide stock solution (3 μl per 50 ml of the gel). 5. Mix 100 μl PCR product with 10 μl loading dye and transfer all on the agarose gel. Start the electrophoresis for 30–60 min at 100 V. 6. Cut out the specific DNA fragment under UV light (see Note 2), determine the weight of the gel piece, and transfer it into a 1.5 ml reaction tube. 7. Extract the DNA out of the gel piece with the QIAquick Gel Extraction Kit. Add a threefold amount of the QG-buffer to the gel piece and melt it in a thermomixer at 50  C. Add a onefold amount of isopropanol to the reaction sample and transfer it to a spin column. Follow the instruction provided by the manufacturer. Elute in 40 μl nuclease-free water. For insertion of a specific DNA fragment in a target vector, both components have to be digested with the same restriction enzymes. Since restriction enzymes generate “sticky ends” (in the majority of cases), the specific DNA fragment (insert) can be easily ligated into the target vector. 3.3 Restriction Digest and Ligation

1. Prepare two reaction samples: (a) 6 μg of the target vector (fill up to 40 μl with nuclease-free water) and (b) 40 μl of the insert (PCR product, see above) in a reaction tube. 2. Add 5 μl of a 10 restriction buffer specific to the selected restriction enzyme to each preparation. Add 0.5 μl 100 BSA, if required by the restriction enzyme. 3. Add 2.5 μl of each of the two reaction enzymes required. 4. Mix the samples and incubate them for at least 2 h at 37  C in a thermomixer. 5. Visualize the DNA digestion on a 1% agarose gel. Cut out the desired fragments under UV light. 6. Purify the DNA with the QIAquick Gel Extraction Kit. Elute the vector in 50 μl and the insert in 25 μl EB buffer.

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7. For ligation, mix 11.5 μl of the digested insert and 1 μl of the linearized vector (corresponds to a ~5:1 insert/vector ratio) in a separate reaction tube. 8. Add 1.5 μl of 10 ligase buffer and 1 μl of the T4 DNA ligase. 9. Mix the sample and incubate for at least 2 h (optimally overnight) at 14  C. For amplification of the ligated reaction sample, transform them into competent E. coli cells. Only bacteria, which absorbed a correctly ligated vector, can grow under antibiotic-specific pressure. 3.4

Transformation

1. To transform the ligation reaction product into competent E. coli cells, add 65 μl nuclease-free water and 20 μl of the 5 KCM buffer to the ligation sample. 2. Store this preparation for 5 min on ice. 3. Then add 100 μl of competent E. coli cells, mix gently and incubate 20 min on ice and afterward 10 min at room temperature. 4. Add 1 ml LB medium (without antibiotic) and incubate for 45 min in a thermomixer at 37  C by 700 rpm. 5. Centrifuge the suspension for 2 min at 2400  g. 6. Collect 100 μl of the supernatant and discard the rest. Resuspend the pellet with 100 μl of LB medium and then transfer and spread the suspension on LB agar plates (with ampicillin). Incubate the plates at 37  C overnight. 7. Pick individual bacteria colonies with a sterile pipette tip and transfer it into a 15 ml reaction tube with 3 ml LB medium. 8. Add the desired antibiotic in a 1:1000 dilution. 9. Incubate this suspension at 37  C in a shaker overnight. To harvest the vector DNA, purify the plasmid DNA from bacteria by using QIAfilter Plasmid Midi Kit according to the manufacturer protocol. Elute the DNA with 100 μl of nucleasefree water. To make sure that you have picked a positive colony with the correctly integrated construct, perform a control digest.

3.5

Control Digest

1. Take 8 μl out of a plasmid DNA sample and add 1 μl of 10 specific restriction enzyme buffer. 2. Add two restriction enzymes (0.5 μl each) to the reaction tube, which leads to a specific banding pattern after digestion. 3. Incubate the tubes for at least 30 min at 37  C. 4. Add 1.5 μl loading dye to each sample and visualize the specific banding pattern on a 1% agarose gel via electrophoresis.

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Positive samples can be amplified to obtain a higher amount of plasmid. 3.6 Amplification of the Constructed Vector

1. Take 100 μl out of the bacterial suspension after transformation (Subheading 3.4, step 4) and inoculate 50 ml LB media containing the specific antibiotic. Incubate these bacteria suspension at 37  C in a shaker overnight (see Note 3). 2. Centrifuge the whole suspension at 11,385  g and 4  C for 5 min. 3. Remove the supernatant and purify the DNA with the QIAfilter plasmid midi kit following manufacturer protocol. This generated purified plasmid DNA could be now used for different applications. For generating transgenic animals via microinjection the DNA has to be linearized and purified in a special way.

3.7 Preparation of the DNA for the Microinjection

1. Linearize 40 μg of the DNA with a specific restriction enzyme (see Note 4). 2. Separate the linearized DNA on a 1% agarose gel, 30–60 min at 80–100 V. 3. Quickly cut out the band of the right size under UV light (see Note 5). 4. Purify the gel pieces with the QIAfilter Plasmid Midi Kit. Use four spin columns and elute each in 100 μl sterile TE buffer. 5. Sterile filter the DNA with a 1 ml syringe and a 0.2 μM filter carefully (see Note 6). 6. For effective removal of salts, dyes, and further contaminations, the DNA has to be dialyzed. Use the Slide-A-Lyzer cassettes according to the manufacturer protocol (see Note 7). 7. Measure the concentration of the dialyzed DNA with a NanoDrop device or a photometer. 8. Adjust the concentration of the sample with sterile TE buffer. For pronucleus injection, a DNA concentration of ~30 ng/μl in TE buffer and a minimum volume of 200 μl have to be submitted to the transgenic mouse unit. This is usually further diluted to 3 ng/μl with the same buffer for injections. After injections and embryo transfer the mouse pups are genotyped at the age of 3–4 weeks to identify the founder animals for further breeding.

3.8

Genotyping

1. Take tail biopsies (by cutting a 3–5 mm piece of tail) from mice aged 3–4 weeks and store them at 20  C. 2. Extract the DNA out of the tissue with 190 μl direct PCR tail lysis buffer and 10 μl Proteinase K in the thermomixer at 55  C overnight under vigorous mixing.

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3. Heat the samples for 45 min to 85  C without shaking. 4. Cool down the samples for 30 min at 4  C. 5. Pipette 0.5 μl of the sample in a PCR reaction tube and add 14.7 μl nuclease-free water, 4 μl 5 GoTaq buffer, 0.5 μl dNTPs, 0.05 μl of the forward primer, 0.05 μl of the reverse primer (both at 100 pmol/μl concentration), and 0.2 μl of the GoTaq polymerase (see Notes 8 and 9). Prepare these steps on the ice. 6. Mix carefully and spin down the reaction mix. 7. Start the PCR program in the thermocycler: 94  C: 4 min 94  C: 30 s 56  C: 30 s

35 cycles



72 C: 1.2 min 72  C: 7 min 4  C: 1

8. Analyze the results on a 1% agarose gel. Usually, about 20–30% of the newly generated mice are PCR positive. Positively identified founders can then be used for further breedings with wild-type mice to establish several heterozygous transgenic mouse lines. The functionality of the construct in the F1 generation can be analyzed by FRET measurements in single isolated cells. Founders which give rise to newborns with a sufficient amount of fluorescence and good FRET responses can be chosen to establish new transgenic mouse lines. There might be a certain variability in the percentage of fluorescent cells and the intensity of fluorescence. We normally prefer working with the lines in which virtually all desired cells are brightly fluorescent to obtain good signal-to-noise ratios in FRET experiments.

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Notes 1. The PCR elongation time is calculated as 2 s per kilobase of DNA. 2. Make sure that you switch on the UV light only for a really short time. Otherwise, strand disruptions may occur. 3. Check the cloudiness of the suspension. For optimal results, the suspension should become cloudy and have an optical density above 1. 4. Due to the high amount of DNA, mix this reaction in the total volume of 200 μl. Add 160 μl of water to the DNA, 20 μl of the

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restriction enzyme buffer, and 20 μl of the restriction enzyme or enzyme mixture. 5. Make sure that you only use the UV light for a very short time. Otherwise strand disruptions could occur. 6. Rinse the reaction tube, the syringe, and the filter with 50 μl TE buffer to make sure that you do not lose DNA. Pipette and filtrate very carefully otherwise the DNA may shear. 7. The membrane of the dialysis cassette has to be hydrated in the TE buffer for 30 s before the sample is loaded into the cassette. Make sure that you use an 18–19 gauge needle for adding the sample; otherwise, the DNA could shear. Dialyze for 2 h at room temperature in TE buffer, change the dialysis buffer and dialyze for another 2 h. Change again the dialysis buffer and dialyze overnight at 4  C. 8. It is advisable to prepare a master mix containing all PCR components except for the tissue DNA. Upscale the quantities of the components according to the number of samples and pipet 19.5 μl of master mix to each DNA sample right before starting the reaction. 9. The primer pair should include parts of the original vector and the insert. Each primer should have a base pair length between 15 and 30, resulting in a melting temperature of around 55  C. Avoid the presence of repeats containing more than four identical nucleotides, especially guanosine phosphates. Use also a positive (plasmid or transgenic mouse DNA) and a negative (water or a wild-type mouse DNA) control for the PCR reaction.

Acknowledgments The work in authors’ laboratories is supported by the Deutsche Forschungsgemeinschaft (grants NI 1301/3-1, FOR 2060, SFB 1328 TP A06), German Center for Cardiovascular Research (DZHK), and the Gertraud und Heinz-Rose Stiftung. References 1. Lampreht Tratar U, Horvat S, Cemazar M (2018) Transgenic mouse models in cancer research. Front Oncol 8:268 2. Masemann D, Ludwig S, Boergeling Y (2020) Advances in transgenic mouse models to study infections by human pathogenic viruses. Int J Mol Sci 21(23):9289 3. Myers A, McGonigle P (2019) Overview of transgenic mouse models for Alzheimer’s disease. Curr Protoc Neurosci 89:e81

4. Gordon JW, Scangos GA, Plotkin DJ et al (1980) Genetic transformation of mouse embryos by microinjection of purified DNA. Proc Natl Acad Sci U S A 77:7380–7384 5. Tian X-L, Wang QK (2006) Generation of transgenic mice for cardiovascular research. Methods Mol Med 129:69–81 6. Liu C, Xie W, Gui C et al (2013) Pronuclear microinjection and oviduct transfer procedures

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for transgenic mouse production. Methods Mol Biol 1027:217–232 7. Pu X-A, Young AP, Kubisch HM (2019) Production of transgenic mice by pronuclear microinjection. Methods Mol Biol 1874:17– 41 8. Ivics Z, Li MA, Máte´s L et al (2009) Transposon-mediated genome manipulation in vertebrates. Nat Methods 6:415–422 9. Lohse MJ, Bu¨nemann M, Hoffmann C et al (2007) Monitoring receptor signaling by intramolecular FRET. Curr Opin Pharmacol 7: 547–553 10. Nikolaev VO, Bu¨nemann M, Schmitteckert E et al (2006) Cyclic AMP imaging in adult cardiac myocytes reveals far-reaching beta1-adrenergic but locally confined beta2-adrenergic receptor-mediated signaling. Circ Res 99: 1084–1091 11. Schleicher K, Zaccolo M (2018) Using cAMP sensors to study cardiac nanodomains. J Cardiovasc Dev Dis 5:17 12. Bo¨rner S, Schwede F, Schlipp A et al (2011) FRET measurements of intracellular cAMP concentrations and cAMP analog permeability in intact cells. Nat Protoc 6:427–438 13. Nikolaev VO, Bu¨nemann M, Hein L et al (2004) Novel single chain cAMP sensors for

receptor-induced signal propagation. J Biol Chem 279:37215–37218 14. Russwurm M, Mullershausen F, Friebe A et al (2007) Design of fluorescence resonance energy transfer (FRET)-based cGMP indicators: a systematic approach. Biochem J 407: 69–77 15. Sprenger JU, Perera RK, Go¨tz KR, Nikolaev VO (2012) FRET microscopy for real-time monitoring of signaling events in live cells using unimolecular biosensors. J Vis Exp (66): e4081. Published 2012 Aug 20. https://doi. org/10.3791/4081 16. Niwa H, Yamamura K, Miyazaki J (1991) Efficient selection for high-expression transfectants with a novel eukaryotic vector. Gene 108:193–199 17. Hu¨bscher D, Nikolaev VO (2015) Generation of transgenic mice expressing FRET biosensors. Methods Mol Biol 1294:117–129 18. Calebiro D, Nikolaev VO, Gagliani MC et al (2009) Persistent cAMP-signals triggered by internalized G-protein-coupled receptors. PLoS Biol 7:e1000172 19. Cho A, Haruyama N, Kulkarni AB (2009) Generation of transgenic mice. Curr Protoc Cell Biol Chapter 19:Unit 19.11

Chapter 16 How to Make the CUTiest Sensor in Three Simple Steps for Computational Pedestrians Florencia Klein, Cecilia Abreu, and Sergio Pantano Abstract Genetically encoded FRET sensors for revealing local concentrations of second messengers in living cells have enormously contributed to our understanding of physiological and pathological processes. However, the development of sensors remains an intricate process. Using simulation techniques, we recently introduced a new architecture to measure intracellular concentrations of cAMP named CUTie, which works as a FRET tag for arbitrary targeting domains. Although our method showed quasi-quantitative predictive power in the design of cAMP and cGMP sensors, it remains intricate and requires specific computational skills. Here, we provide a simplified computer-aided protocol to design tailor-made CUTie sensors based on arbitrary cyclic nucleotide-binding domains. As a proof of concept, we applied this method to construct a new CUTie sensor with a significantly higher cAMP sensitivity (EC50 ¼ 460 nM). This simple protocol, which integrates our previous experience, only requires free web servers and can be straightforwardly used to create cAMP sensors adapted to the physicochemical characteristics of known cyclic nucleotide-binding domains. Key words CUTie, Rational design, FRET, Nanoscale, Location, Sequence alignment

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Introduction The development of genetically encoded sensors based on Fo¨rster resonance energy transfer (FRET) to follow the intracellular dynamics of the second messenger cyclic 3,50 -adenosine monophosphate (cAMP) have revolutionized the study of intracellular events in real time [1–5], achieving nanometric resolution [6]. Monitoring the vast multiplicity of signaling cascades activated by cyclic nucleotides in different subcellular compartments and at different concentrations requires, obviously, sensors with different characteristics [7, 8]. Recently, using a combination of structural bioinformatics and coarse-grained simulation techniques [1, 2], we reported the design of a first-of-its-kind FRET sensor for monitoring intracellular cAMP concentrations named CUTie [9]. The CUTie sensor is made of a single polypeptide chain featuring a

Manuela Zaccolo (ed.), cAMP Signaling: Methods and Protocols, Methods in Molecular Biology, vol. 2483, https://doi.org/10.1007/978-1-0716-2245-2_16, © The Author(s), under exclusive license to Springer Science+Business Media, LLC, part of Springer Nature 2022

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FRET pair of fluorescent proteins and a cyclic nucleotide binding domain (CNBD) that senses the presence of cAMP. A distinctive characteristic of the CUTie sensor is that, unlike other architectures, one of the fluorescent modules in the FRET pair is inserted within a CNBD, while the other fluorescent module is located at the C-terminal of the polypeptide chain. This architecture leaves the CNBD’s N-terminal free to be fused to arbitrary targeting domains determining its subcellular location. The CNBD acts as a hinge, bringing the two fluorescent proteins close to each other upon a rise in cAMP concentration. This conformational transition is mechanically decoupled from the targeting domain, so that the FRET response obtained upon fusion to arbitrary proteins remains constant [9]. Recently, we applied a similar computational protocol to design a cGMP-activated version of CUTie, named CUTie2 [10]. The in vitro characterization of CUTie2 showed a change in FRET efficiency upon cyclic nucleotide binding comparable to that of its cognate CUTie, and an affinity for cGMP and selectivity versus cAMP similar to those reported for the unmodified CNBD [10]. Hence, our computational approach successfully predicted, at quasi-quantitative level, the biophysical characteristics of CUTie and CUTie2 sensors. However, the design protocol required highly specialized knowledge in structural biology, modeling, and coarsegrained molecular dynamics simulations. Therefore, we sought to create a much simpler protocol to design new CUTie sensors based on arbitrary CNBDs using elementary bioinformatics tools available in free-access web servers. As a proof of concept, we applied this simple method to designing a new CUTie version using the CNBD of Mesorhizobium loti, which binds cAMP with an affinity in the 100 nM range. In vitro characterization validated the functioning of the new sensor with an EC50 of 460 nM and the expected FRET variation of nearly 30%. 1.1 Beauty Is in the Eye of the Beholder

Addressing different scientific questions may require the detection of different concentrations of cAMP in subcellular compartments. Hence, how to make the CUTiest sensor, namely, the one that better meets the conditions of a particular scientific question? This may be an extremely challenging question. However, based on our previous experience, it should be possible to craft CUTie sensors with similar characteristics to the desired CNBD. A key feature for the design of the original CUTie sensor consisted in realizing that despite the high structural conservation of the CNBDs [11], the loop connecting β-strands 4 and 5 (named 4–5 loop, see Fig. 1) is poorly conserved, with frequent insertions in different cAMP binding domains. This observation is valid from parasites to mammals [12] and indicates that the 4–5 loop is a structurally permissive region, suggesting it should be possible to

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Fig. 1 Cartoon representation of the CNBD of a cyclic nucleotide-sensitive K+ channel from M. loti (PDB: 2K0G). Structural elements are indicated by colors; gray for helix, pink for extended Beta, white for coils. The cAMP is shown in blue and the loop between beta strands 4 and 5 is evidenced in green. Proline 285, which corresponds to the YFP insertion point, is shown in sticks

insert a fluorescent protein without altering CNBD’s structure or function [9]. We recently proved the validity of this idea by inserting a fluorescent module within the homologous loop of a cGMP binding domain and by demonstrating that the resulting CUTie2 sensor preserves the same affinity and selectivity for cGMP vs. cAMP than the original protein [10]. Based on our previous experience on CNBD modeling [13– 15] in the design of the CUTie and CUTie2 sensors [9, 10], we outline here simple structural bioinformatics rules to design the CUTiest sensor, that is, the best suited for the problem of interest. This procedure only requires simple computational tools freely available at many webservers.

2

Methodology: Designing the CUTiest Sensor in Three Simple Steps Obviously, the selection of the CNBD will depend on the user (see Note 1). In our case, despite its many advantages, the original CUTie sensor, has a relatively high EC50, estimated to be 7.4 μM [9]. Hence, we sought to create a new, complementary, version to detect lower concentrations of cAMP. For this task, we selected the CNBD of a cyclic nucleotide-sensitive K+ channel from M. loti, which has a reported affinity for cAMP of nearly 110 nM [16]. It should be noted that the 3D structure of this CNBD has been determined experimentally (Fig. 1) [17]. We use this structure only

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Fig. 2 A workflow to design new CUTie sensors. YFP (Yellow Fluorescent Protein) and CFP (Cyan Fluorescent Protein)

to illustrate our procedure. However, the availability of a known structure is not necessary for the designing protocol described here (Fig. 2). The first step is to search the CNBD sequence. A recommended database is The Universal Protein Resource (UniProt: https://www.uniprot.org/). In our case, the UniProt code is Q98GN8, which corresponds to the code 2K0G [17] in the Protein Data Bank (PDB: https://www.rcsb.org/). The second step is to align the primary sequence of your protein of interest to other CNBD’s sequences to identify the right insertion points for YFP and CFP. Among many other possible choices we recommend Clustal Ω at MPI Bioinformatics Toolkit [18] (https://toolkit.tuebingen.mpg.de/), which is a web server that provides multiple useful and intuitive tools (see Note 2). Once on the main page, go to Alignment and then Clustal Ω, where you paste all the CNBDs structures you wish to compare. This requires a set of sequences from homologous proteins containing CNBDs (see Note 3). To facilitate this task, we provide here a set of sequences containing CNBDs from a variety of species (namely, mammals, insects, fishes, arachnid, crustaceans, and annelids, see Note 4 and Fig. 3). After aligning the sequences, look for the gap corresponding to the 4–5 loop (see Fig. 3). The middle point in the gap is a good

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Fig. 3 Sequence Alignment of CNBDs. Sequences 1 to 12, and 13 to 24 correspond to cAMP and cGMP binding proteins, respectively. The 25th sequence corresponds to our example case. UNIPROT codes for the cAMP binding domains are: (1 and 2) P00514, (3 and 4) P12369, (5) A0A2J8TG41, (6) E0VRT6, (7 and 8) A0A091CJC2, (9) A0A556UFG6, (10) A9QQ52, (11 and 12) A0A384A0R8. Codes for the cGMP binding modules are: (13) Q13976, (14) Q13237, (15) A0A444U9Q8, (16) A0A443SJC3, (17) B0X970, (18) A0A4Y2N1F1, (19) A0A4Y2BIM3, (20) A0A419PY19, (21) A0A2P8Y1R0, (22) A0A3R7JPE0, (23) A0A444UAH2, (24) A0A0M4ENX8. Our example case corresponds to Q98GN8. Amino acids are colored by physicochemical character according to Clustal. The secondary structure of a typical CNBD and the 4–5 loop are indicated on the top

insertion point for a fluorescent protein (see Notes 5 and 6). The second fluorescent protein can be added to the C-terminal of the CNBD (see Note 7). The procedure outlined above should suffice to generate the sequence of a functional CUTie sensor. However, as an extra precaution aimed to relax potentially problematic structures (see Note 8) we kept the linkers used in the original CUTie sensor between the CNBD and the fluorescent module. In our test case, after performing the alignment, we inserted the linkers and the sequence of the yellow fluorescent protein (YFP)

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Fig. 4 The complete primary sequence of our new CUTie sensor. Each color defines a domain of the sensor: CNBD (violet), linkers (gray), YFP (yellow), and CFP (cyan)

on the position occupied by Proline 285 (Fig. 1 and see Note 5). A cyan fluorescent protein (CFP) was added directly to C-terminal of the CNBD. That is it! The final sequence of the new CUTie is shown in Fig. 4. The next step it to go from in silico to the real world.

3

Experimental Validation To provide a proof of concept of our simple procedure, the recombinant form of the new CUTie biosensor was expressed, and the FRET signal triggered by cAMP and cGMP was measured in vitro using fluorescence spectroscopy. The recombinant form of new CUTie was expressed as an N-terminally six His-tagged protein using the pT7 vector [19] and Escherichia coli BL21 Codon Plus as heterologous expression host. The transformed bacteria were grown in 2 YT medium to an optical density of 0.8 and induced with 1 mM IPTG for 16 h at 20  C. The cell pellet was resuspended in 50 mM Tris-HCl pH 8.0, 300 mM NaCl, containing EDTA-free protease inhibitors cocktail (ROCHE) and lysozyme (end concentration 1 mg/ml). The cell suspension was subjected to sonication and the debris removed by centrifugation at 18,000  g for 1 h at 4  C. The new CUTie was purified with a 1 ml HisTrap column (GE Healthcare) and further polished by a Superdex G-200 (GE Healthcare) size exclusion chromatography run in 50 mM Tris-HCl (pH 8.0), 500 mM NaCl. In a Hellma® fluorescence cuvette, the sensor (100 nM) was treated with different concentrations of cAMP or cGMP (20 nM– 10 mM) in PBS buffer pH 7.4. Upon 1 min of addition of the cyclic nucleotide, the fluorescence emission spectrum (λexc ¼ 435 nm, λem ¼ 460–600 nm) of new CUTie was recorded in a Cary Eclipse fluorimeter using slit widths of 5 nm and a PMT of 800. The fluorescence intensity (FI) values corresponding to CFP (485 nm)

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and YFP (527 nm) in the absence (C0) or presence of different concentrations of the cyclic nucleotides (Cx) were used to calculate theFRET changeas: % FRET ¼ [(FI527nmCx*FI485nmCx)  (FI527nmC0*FI485nmC0)]  100%. The % FRET vs. Log [cNMP] was plotted, fitted to nonlinear regression equations, variable slope and statistically analyzed using the GraphPad Prism six Software (San Diego, CA, USA). The recombinant protein showed a sigmoidal dose response curve (Fig. 5) with a delta FRET of nearly 30% upon saturating concentrations and an EC50 ¼ 460 nM. As indicated in the inset to Fig. 5, the lowest end of the sensitivity range for cAMP is between 100 nM and 200 nM, which nicely coincides with the Kd of 110 nM reported for the CNBD used as cAMP detector [13]. The cGMP curve is right-shifted two log units.

Fig. 5 Dose–response plot for recombinant new CUTie titrated with different concentrations of cyclic nucleotides (cNMP) (20 nM to 10 mM). FRET efficiency was determined by fluorescence spectroscopy and data fitted to nonlinear regression equations, variable slope (cAMP R2  0.91, cGMP R2  0.89). The data represents the mean and SD of two experiments performed in duplicate. The dotted box depicts % FRET vs. linear concentration of cAMP in nM range. Notice the main graph in log scale, while the concentrations of cAMP in inset are presented in linear scale

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Conclusions We presented an effortless procedure to design new versions of the CUTie sensor based on arbitrary CNBDs using elementary bioinformatics tools. The simplification of the process results from our previous experiences in characterizing the structure and dynamics of the CUTie and CUTie2 sensors using molecular simulations. As observed for the CUTie2, the newly created cAMP sensor produced the desired dose-response curve and showed an EC50 compatible with the affinity of the CNBD for cAMP and cAMP/cGMP selectivity [10]. As the new version’s sensitivity ranges from nearly 100 mM, we anticipate it will be highly complementary to the original CUTie, which has an EC50 in the low μM range. Furthermore, CUTie, CUTie2, and the new version introduced here showed a FRET variation of about 30% upon saturating cAMP concentrations (see Note 9). Therefore, we can speculate that new sensors based on arbitrary CNBDs will maintain this dynamic range. Finally, we would like to stress that the CUTie2 and this new sensor were produced in a “single-shot” fashion. Namely, none of them underwent refinement or optimization alternating computational/experimental rounds. This highlights the predictive power of our approach and opens exciting alternatives for future studies of cyclic nucleotide signaling.

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Notes 1. Because of the robustness of the procedure, arbitrary CNBDs can be chosen as cAMP detectors. Ideally, the CNBD of the protein of interest N-terminally fused to the targeting domain would guarantee the measurement of local concentrations of cAMP in the range of interest. 2. Sequence alignments can be performed with any software, algorithm, or web server. The high homology and conservation between CNBDs make the procedure sound and independent of this choice. 3. Use as many CNBD sequences as you want to align. However, the set provided in Fig. 3 is sufficient to pinpoint the insertion point for the fluorescent module within the CNBD. 4. Some proteins contain more than one CNBD. They can be used separately or just one CNBD per protein sequence. For instance, in the alignment shown in Fig. 3, we used the two tandem CNBDs of the PKA-RIa (sequences 1 and 2, Uniprot code P00514), and PKA-RIIb (sequences 3 and 4, Uniprot code P12369).

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5. It is always a good idea (although not strictly necessary) to visualize the loop connecting strands 4 and 5. If the structure of the CNBD has been experimentally determined, the very intuitive visualization tools provided in the Protein Data Bank (www.rcsb.org) will suffice. 6. If the CNBD structure is not known, any predictor will produce acceptable models to confirm the location of the loop. Among many others, the MPI Bioinformatics Toolkit (https:// toolkit.tuebingen.mpg.de/) can build up homology models in a couple of clicks. 7. Notes 5 and 6 also apply to the determination of the C-terminal. Moreover, the CNBD regions are also indicated on the Uniprot pages in the section “Regions.” Thus, you can check for the extension of the CNBD and select the correct amino acid position to fuse the fluorescent module at the C-terminal without the need for a structural model. 8. Inserting a fluorescent module within the CNBD might introduce structural distortions. This problem was evidenced in the course of molecular dynamics simulations performed during the design of the original CUTie sensor. Hence, for a design to be robust to the choice of arbitrary CNBDs, we kept the linkers used in the original CUTie (Fig. 4). This can be considered as an extra precaution aimed to relax potentially problematic structures. 9. The sensors produced using the outlined methodology are expected to produce a delta FRET between 20 and 30%, and EC50 close to the cAMP affinity reported for the CNBD chosen. However, further optimizations could be achieved by applying more intricate computational methodologies. References 1. Nikolaev VO, Lohse MJ (2006) Monitoring of cAMP synthesis and degradation in living cells. Physiology 21:86–92. https://doi.org/10. 1152/physiol.00057.2005 2. Berrera M, Dodoni G, Monterisi S et al (2008) A toolkit for real-time detection of camp: insights into compartmentalized signaling. Springer, Berlin, pp 285–298 3. Meng F, Sachs F (2011) Visualizing dynamic cytoplasmic forces with a compliance-matched FRET sensor. J Cell Sci 124:261–269. https:// doi.org/10.1242/jcs.071928 4. Calamera G, Li D, Ulsund AH et al (2019) FRET-based cyclic GMP biosensors measure low cGMP concentrations in cardiomyocytes and neurons. Commun Biol 2:1–12. https:// doi.org/10.1038/s42003-019-0641-x

5. Heldin CH, Lu B, Evans R et al (2016) Signals and receptors. Cold Spring Harb Perspect Biol 8:a005900. https://doi.org/10.1101/ cshperspect.a005900 6. Chao YC, Surdo NC, Pantano S et al (2019) Imaging cAMP nanodomains in the heart. Biochem Soc Trans 47:1383–1392. https://doi. org/10.1042/BST20190245 7. Sprenger JU, Nikolaev VO (2013) Biophysical techniques for detection of cAMP and cGMP in living cells. Int J Mol Sci 14:8025–8046. https://doi.org/10.3390/ijms14048025 8. Pendin D, Greotti E, Lefkimmiatis K et al (2016) Exploring cells with targeted biosensors. J Gen Physiol 149:1–36. https://doi. org/10.1085/jgp.201611654

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9. Surdo NC, Berrera M, Koschinski A et al (2017) FRET biosensor uncovers cAMP nano-domains at b-adrenergic targets that dictate precise tuning of cardiac contractility. Nat Commun 8:1–14. https://doi.org/10.1038/ ncomms15031 10. Klein F, Sardi F, Machado MR et al (2021) CUTie2: the attack of the cyclic nucleotide sensor clones. Front Mol Biosci 8:48. https:// doi.org/10.3389/fmolb.2021.629773 11. Berman HM, Ten Eyck LF, Goodsell DS et al (2005) The cAMP binding domain: an ancient signaling module. Proc Natl Acad Sci U S A 102:45–50. https://doi.org/10.1073/pnas. 0408579102 12. J€ager AV, De Gaudenzi JG, Mild JG et al (2014) Identification of novel cyclic nucleotide binding proteins in Trypanosoma cruzi. Mol Biochem Parasitol 198:104–112. https://doi. org/10.1016/j.molbiopara.2015.02.002 13. Machado M, Pantano S (2015) Structurebased, in silico approaches for the development of novel cAMP FRET reporters. In: cAMP signaling: methods and protocols. Springer, New York, pp 41–58 14. Pantano S, Zaccolo M, Carloni P (2005) Molecular basis of the allosteric mechanism of cAMP in the regulatory PKA subunit. FEBS

Lett 579:2679–2685. https://doi.org/10. 1016/j.febslet.2005.02.084 15. Pantano S (2008) In silico description of fluorescent probes in vivo. J Mol Graph Model 27: 563–567. https://doi.org/10.1016/j.jmgm. 2008.08.003 16. Cukkemane A, Gru¨ter B, Novak K et al (2007) Subunits act independently in a cyclic nucleotide-activated K + channel. EMBO Rep 8:749–755. https://doi.org/10.1038/sj. embor.7401025 17. Schu¨nke S, Stoldt M, Novak K et al (2009) Solution structure of the Mesorhizobium loti K1 channel cyclic nucleotide-binding domain in complex with cAMP. EMBO Rep 10: 729–735. https://doi.org/10.1038/embor. 2009.68 18. Zimmermann L, Stephens A, Nam SZ et al (2018) A completely reimplemented MPI bioinformatics toolkit with a new HHpred server at its core. J Mol Biol 430:2237–2243. https://doi.org/10.1016/j.jmb.2017.12.007 19. Ortega C, Prieto D, Abreu C et al (2018) Multi-compartment and multi-host vector suite for recombinant protein expression and purification. Front Microbiol 9:1384. https:// doi.org/10.3389/fmicb.2018.01384

Chapter 17 Ion Channel–Based Reporters for cAMP Detection Thomas C. Rich, Wenkuan Xin, Silas J. Leavesley, C. Michael Francis, and Mark Taylor Abstract In the last 20 years tremendous progress has been made in the development of single cell cAMP sensors. Sensors are based upon cAMP binding proteins that have been modified to transduce cAMP concentrations into electrical or fluorescent readouts that can be readily detected using patch clamp amplifiers, photomultiplier tubes, or cameras. Here, we describe two complementary approaches for the detection and measurement of cAMP signals near the plasma membrane of cells using cyclic nucleotide (CNG) channelbased probes. These probes take advantage of the ability of CNG channels to transduce small changes in cAMP concentration into ionic flux through channel pores that can be readily detected by measuring Ca2+ and/or Mn2+ influx or by measuring ionic currents. Key words Cyclic nucleotide-gated channel, cAMP, GPCR, Adenylyl cyclase, Phosphodiesterase

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Introduction The development of real time, single cell cAMP sensors has allowed an understanding of the cellular and molecular mechanisms underlying signaling specificity in the cAMP pathway. Two main classes of cAMP probes have been developed: probes based on cyclic nucleotide-gated (CNG) channels and probes based upon cAMP binding proteins sandwiched between fluorescent proteins that form Fo¨rster resonance energy transfer (FRET) pairs. These probes have complementary strengths and weaknesses that have been described elsewhere [1–3]. Here we outline complementary protocols for the use of CNG channels as cAMP sensors. The first approach takes advantage of divalent cation (Ca2+ or Mn2+) permeability of CNG channels [4, 5]. This approach is typically used to test protocols prior to more technically difficult electrophysiological experiments. This approach is also well suited for high-throughput/high-content screening for agents that trigger increases or decreases in intracellular cAMP levels (see Note 1). This approach can also be

Manuela Zaccolo (ed.), cAMP Signaling: Methods and Protocols, Methods in Molecular Biology, vol. 2483, https://doi.org/10.1007/978-1-0716-2245-2_17, © The Author(s), under exclusive license to Springer Science+Business Media, LLC, part of Springer Nature 2022

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used for single cell detection of cAMP levels using confocal microscopy (see Note 2). The second approach utilizes more direct (and accurate) electrophysiological measurements [6, 7]. Electrophysiological measurements are better suited to take advantage of the strengths of CNG channel-based sensors—their fast activation kinetics and ability to amplify small changes in cyclic nucleotide concentration [6–10]. Importantly, CNG channels have been genetically modified to have high sensitivity and specificity for cAMP [4, 11–13]. It should be noted that electrophysiological measurements of CNG channel activity are technically more difficult than imaging experiments and cannot be used to study Ca2+mediated regulation of cAMP signals due to the Ca2+ permeability of CNG channels [2, 14]. Even with these limitations, electrophysiological measurements of CNG channel activity have been utilized by several groups for the measurement of cAMP signals. They have the highest kinetic resolution for measurement of cAMP signals near the plasma membrane and, as such, offer unique insights into the mechanisms of specificity within the cAMP signaling pathway.

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2.1 Fluorometric cAMP Measurements

1. Spectrofluorometer. A stirred cuvette spectrofluorometer such as the PTI Quanta Master 40. 2. Disposable cuvettes (see Note 3). 3. Extracellular buffer solution 1: 145 mM NaCl, 4 mM KCl, 20 mM HEPES, 10 mM D-Glucose, 1 mM MgCl2, and 1 mM CaCl2, pH 7.4. 4. Cell type of interest (see Note 4). 5. Adenovirus or plasmids encoding CNG channel constructs. The following constructs are based on CNG2 (the wild type olfactory α subunit) and are commonly used for cAMP measurements: WT CNGA2, K1/2 ~ 36 μM cAMP; E583M, K1/2 ~ 10 μM cAMP; C460W/E583M, K1/2 ~ 1 μM cAMP; Δ61-90/ C460W/E583M, K1/2 ~ 12 μM cAMP (see Note 5).

2.2

Reagents

Stock solutions are stored in single use aliquots at
20  C. 1. Vehicle: dimethylsulfoxide (DMSO) or extracellular buffer. 2. 1 mM fura-2/AM in DMSO. 3. 50 mM forskolin in DMSO. 4. 1–10 mM isoproterenol in buffer containing the antioxidants 0.1 mM ascorbic acid and 1 mM thiourea. 5. 1–10 mM prostaglandin E1 in DMSO. 6. 500 mM 3-isobutyl-1-methylxanthine (IBMX) in DMSO.

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7. 10 mM rolipram in DMSO. 8. 10–100 mM cAMP in extracellular buffer or DMSO (see Note 6). 2.3 Electrophysiological cAMP Measurements

1. Electrophysiology setup for whole cell and perforated patch experiments (see Note 7). 2. Rapid perfusion switch system (see Note 8). 3. Pipette puller and polisher (see Note 9). 4. Extracellular buffer solution 2: 145 mM NaCl, 4 mM KCl, 20 mM HEPES, 10 mM D-Glucose, 0.1 mM MgCl2, pH 7.4. 5. Extracellular buffer solution 3: 145 mM NaCl, 4 mM KCl, 20 mM HEPES, 10 mM D-Glucose, 10 mM MgCl2, pH 7.4. 6. Extracellular buffer solution 4: 145 mM KCl, 4 mM NaCl, 20 mM HEPES, 10 mM D-Glucose, 10 mM MgCl2, pH 7.4. 7. Capillary tubing for patch pipettes (see Note 10). 8. Pipette solution 1: 140 mM KCl, 0.5 mM MgCl2, 10 mM HEPES, 5 mM Na2ATP, 0.5 mM Na2GTP, 1 mM cAMP, pH 7.4. 9. Pipette solution 2: 70 mM KCl, 70 mM potassium gluconate, 4 mM NaCl, 0.5 mM MgCl2, 10 mM HEPES, pH 7.4, and 50–200 μg/mL of nystatin. 10. Cell types of interest. 11. Adenovirus or plasmids encoding CNG channel constructs as outlined in Subheading 2.1, item 5.

2.4 Data Analysis Software

1. Software such as Excel (Microsoft), SigmaPlot (Systat Software) or MATLAB (MathWorks) for analysis of spectrofluorometer measurements (see Note 11). 2. PulseFit, Clampfit, or MATLAB software for analysis of electrophysiological experiments (see Note 11).

3

Methods

3.1 cAMP Measurements Using a Spectrofluorometer

1. Adjust spectrofluorometer settings for fura-2 measurements. Several settings in the spectrofluorometer acquisition software must be set prior to data acquisition: (a) The spectrofluorometer should be set for at least two of the following excitation wavelengths: 340, 360, or 380 nm. The combination of 340 and 380 nm is used for Ca2+ measurements, and the combination of 360 and 380 nm is used for Mn2+ quench measurements (see Note 12). We have typically used a dwell time of 0.05–0.10 s at each wavelength.

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(b) The emission wavelength should be set to 510 nm. (c) The excitation and emission slit widths should be adjusted to minimize cross-talk (see Note 13). (d) The speed of the stir bar should be adjusted to maintain cells in suspension without causing cell lysis. A stirred solution is required to maintain the density of cells in the light path and for adequate mixing of reagents. 2. Measure the background/blank signal using a cuvette containing 3 mL extracellular buffer solution 1 and stir bar only. 3. Measure Ca2+ responses. Intact cell measurements are made using cells expressing one or more of the CNG channel constructs. The steps required to make spectrofluorometer-based measurements in cell populations are as follows. (a) Add 3 mL of extracellular buffer solution 1 containing intact cells loaded with fura-2 (1  106 cells/mL) and a stir bar to a cuvette, place the cuvette in the spectrofluorometer, and allow temperature to equilibrate. Note 13 describes conditions for loading fura-2. (b) Start measurement of intracellular Ca2+ (or Mn2+, see Note 12). Record baseline levels for at least 1 min prior to addition of agonists/antagonists. (c) Add agonists/antagonists to trigger changes in intracellular cAMP levels. GPCR agonists include 10–1000 nM isoproterenol or PGE1. Antagonists of PDEs include 100–500 μM IBMX, a broadband PDE inhibitor, and 10 μM rolipram, a PDE4-specific inhibitor. Experiments should be conducted in a stirred cuvette to ensure adequate distribution of agonists/antagonists. (d) Measure the time course of cAMP signals. Measurements are continued until the response has reached steady state. In response to GPCR agonists this typically occurs within 5–10 min [7, 15–21]. 4. Correct for background fluorescence by subtracting the background/blank signal (step 2, Subheading 3.1) from all experimental trials. 5. Calculate fluorescence changes by calculating the ratio of intensities measured at two excitation wavelengths (either F340/F380 or F360/F380, see Note 12). We recommend this approach rather than estimating free Ca2+ because ideally cells will be loaded with high concentrations of fura-2 (see Note 14). Responses are often presented as R/R0 where R0 represents the average baseline fluorescence intensity ratio.

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3.2 cAMP Measurements Using Electrophysiological Approaches

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1. Pull patch pipettes. Place capillary tubing into the pipette puller. Choose settings that yield a pipette resistance of 1–1.5 MΩ (this may take a few iterations to achieve). Polish pipettes to achieve a smooth surface and a final resistance of 1.5–2 MΩ. Make certain that the filament wire has been coated with glass prior to polishing. 2. Place coverslip with cells expressing CNG channel constructs into the perfusion chamber (see Notes 15 and 16). Add buffer to cover cells (typically 0.5–1 mL). Place chamber on microscope stage. Hook up the bulk perfusion system (inflow and outflow tubes). Turn on the system and check for fluid leaks. The flow rate should be sufficient to exchange the bulk bath solution in 30–45 s (this can be readily checked using colored solutions). 3. Fill 0.5–1 mm of the patch pipette tip with the nystatin-containing Pipette Solution 2. Suction may be required. Back fill the remainder of the pipette with Pipette Solution 1 (which does not contain nystatin). Place the patch pipette into the pipette holder. This step is not necessary if alternative electrophysiological approaches are used (see Note 17). 4. Lower the pipette into the bath until the pipette tip is touching a preselected cell. Apply gentle suction until a high resistance seal (>5 GΩ) is formed. This should occur quickly (2–60 s) for most cell types. Subsequently, nystatin will form small holes in the plasma membrane allowing only monovalent ions to cross; this will provide electrical access to the cell. It may take 10–15 min for electrical access to reach a stable level. 5. Capacitive transients are elicited by applying 20 mV steps from the holding potential and recorded at 40 kHz (filtered at 10 kHz) for calculation of series resistance. Capacitance measurements should be made periodically throughout the experiment to track potential changes in series resistance. 6. Wait until a stable electrical access to the cell is achieved. The final series resistance (pipette resistance plus access resistance to the cell) should ideally be between 10 and 20 MΩ. Higher access resistances lead to excessive voltage error. Lower access resistances become unstable leading to a rupture of the small membrane patch beneath the patch pipette, and thus transition to the whole cell configuration. The concentration of nystatin used in Pipette Solution 2 may need to be altered to reach appropriate series resistances. 7. While waiting for the series resistance to stabilize, position the rapid solution switcher near the cell of interest. It is recommended that the actual switch time be estimated by switching from extracellular buffer solution 3 to extracellular buffer solution 4 during a 2 s step from a holding potential of
80 mV to

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0 mV. The voltage protocol will activate endogenous voltagegated K+ channels. Thus, a switch in extracellular K+ concentration will cause a rapid change in the reversal potential; the resultant change in current through K+ channels will accurately track the solution exchange time. We routinely observe switch times 60 ms using the Warner SF-77B fast step system. 8. Electrical recordings should be sampled at ~5 times the fastest signal being measured. We typically sample at 1–5 kHz. 9. Typically the holding potential is maintained at 0 mV. Currents are elicited with 50 ms pulses to +50 and
50 mV and are sampled at 1 kHz. This is done to minimize (inactivate) currents through voltage-gated channels. Residual endogenous currents may need to be inhibited pharmacologically. In cells with higher series resistance, lower magnitude voltage steps may be used to minimize voltage errors due to series resistance. 10. Collect baseline current levels for at least 2 min using the above protocol. At least once per minute briefly (2 s) expose the cell to extracellular solution 3 (using a rapid solution switcher). The high Mg2+ concentration will block currents through CNG channels and allow tracking of baseline currents (through other ion channels) throughout the experiment. Maintain bulk perfusion throughout the experiment to prevent buildup of Mg2+ or experimental reagents in the bath solution. 11. After recording baseline currents, expose cells to experimental reagents (e.g., PDE inhibitors, GPCR agonists or antagonists) for specified amounts of time using the rapid perfusion system. Recordings are typically maintained until responses have reached steady state. In the case of oscillatory responses, several periods should be recorded if possible. 12. After experimental protocols are finished, brief suction is applied to the patch pipette to rupture the patch membrane beneath the pipette (whole cell configuration). This will be readily observed as a broadening of capacitive transients. When the current reaches steady state, block currents with extracellular buffer solution 3 (see Note 18). The Mg2+ blockable current represents maximal current through CNG channels (Imax). 13. Calibrate responses. Responses may be calibrated using the relation: I/Imax ¼ [cAMP]N/([cAMP]N + K1/2N), where I/ Imax is the fraction of maximal current, K1/2 is the cAMP concentration that gives a half-maximal current, and N is the Hill coefficient. Imax is estimated as described in step 12. The K1/2 for different CNG channels has been estimated previously [6, 7]. For example, if I/Imax were found to be 0.7 in an experiment using C460W/E583M channels (K1/2 ~ 1 μM), the estimated cAMP concentration would be ~1.5 μM.

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4

271

Notes 1. High throughput/high content screening. Measurement of Ca2+ influx through CNG channels is readily adaptable to high throughput/high content screening platforms such as the FLIPR2 fluorometric imaging plate reader (Molecular Devices). This system is designed for measurement of intracellular Ca2+ in 96- and 384-well plate formats. Implementation of CNG channel-based assays on high throughput systems allows rapid screening for compounds that regulate the cAMP pathway, including agonists and antagonists of G protein-coupled receptors, adenylyl cyclase, and phosphodiesterase. 2. Detection of localized cAMP signals using single cell Ca2+ influx assays. It is also possible to detect localized changes in cAMP levels using CNG channels as reporters. Cells would be plated on glass coverslips and imaged using confocal microscopy. Localized Ca2+ or Mn2+ influx would then be assessed using automated region of interest software such as LCPro (a freely available ImageJ plugin) [22, 23]. This approach would be particularly useful in detecting cAMP signals initiated at distinct subcellular locations (containing GPCRs, G proteins, adenylyl cyclase, and CNG channels) using low agonist concentrations, for example, kD of the receptor ligand or adenylyl cyclase agonist. 3. In our experience SARSTEDT disposable cuvettes (No. D-51588) minimally attenuate UV illumination, and as such are well suited for the measurement of Ca2+ signals using fura-2 as the Ca2+ indicator. However, standard 10  10  48 mm plastic cuvettes would be appropriate for Ca2+ indicators in the visible range such as fluo-4 or Cal 520. 4. Cells can be either immortalized cell lines such as HEK-293 cells or primary cultures such as mouse embryonic fibroblasts. Cells should be plated on coverslips or 100 mm dishes prior to transfection/infection. (a) Lipid reagent-based transfection: HEK-293 cells are plated at ~60% confluence in either 35 mm dishes with coverslips (electrophysiology) or in 100 mm dishes (spectroscopy). Cells are transfected with constructs encoding either WT, E583M, C460W/E583M, or Δ61-90/C460W/E583M channel constructs using the lipid-based transfection reagent with 1 μg cDNA and typically 3 μL transfection reagent per 35 mm dish or 6 μg cDNA and 18 μL transfection reagent per 100 mm dish. Cells are assayed 48–56 h posttransfection. Typically >90% HEK-293 cells express CNG channel constructs.

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Table 1 Conditions for expression of CNG channels using adenovirus constructs in various cell types. MOI multiplicity of infection, pfu plaque-forming units. *Expression of CNG channels in HEK-293 cells requires addition of 1–2 mM hydroxyurea 2 h postinfection to inhibit viral replication (see Note 2). ** Rich laboratory, unpublished data Cultured cell lines

MOI (pfu/cell)

Incubation time (h)

References

HEK-293

10

24*

[6]

C6-2B

100–200

48–72

[6, 24]

GH4C1

50

24–48

[4]

PC12

100

48

**

A7R5

50–100

24–48

**

Adult ventricular myocytes (rat, rabbit)

500–3000, 100–200

24–48

[15, 25]

Neonatal rat cardiac myocytes (rat)

20–50

24–48

[26]

Vascular smooth muscle (rat)

100–200

48–72

[27]

Airway smooth muscle (human)

20

48

[20]

Pulmonary endothelial cells (rat, mouse)

100–200, 50–100

48

**

Embryonic fibroblasts (mouse)

500

48

[18]

Primary cultures

(b) Adenovirus-mediated gene transduction: HEK-293 cells are plated at ~60% confluence in 35 mm dishes with coverslips or in 100 mm dishes. Cells are infected with adenovirus encoding CNG channel constructs with a MOI of 10 PFU/cell. Two hours postinfection, 1 mM hydroxyurea is added to the media to inhibit viral replication. Cells are assayed 24 h postinfection. Typically, >90% of HEK-293 cells express functional CNG channels. A similar procedure is used for adenovirus-mediated gene transduction in other cell types, with the exceptions that the MOI required for expression is typically higher, 100 PFU/cell, and hydroxyurea is not required (Table 1). 5. Other transfection protocols may be employed. However, transfection efficiency can vary widely depending upon transfection approach and cell type. We recommend the use of cDNA constructs encoding fluorescent protein tagged CNG channel subunits—described in [28]—when using non–viralbased transfection approaches. This will allow simple identification of transfected cells, even when transfection efficiencies are low.

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6. Either extracellular buffer or DMSO may be used as a solvent for cAMP; however, at concentrations of cAMP greater than ~20 mM cAMP tends to precipitate out of salt solutions. At concentrations ~100 mM or higher, cAMP tends to precipitate out of DMSO. 7. We typically use a system based upon the HEKA EPC-10 patch clamp amplifier with Sutter MP-225 micromanipulators. The system should allow perfusion of the bulk bath solution within ~30 s to avoid accumulation of reagents entering the bath solution via the rapid solution switcher. 8. There are a number of fast solution switchers on the market. We use the Warner SF-77B fast step system with the VC6 valve controller. We have also successfully implemented fast solution switch systems using model railroad track switchers obtained from model train/hobby shops. 9. Several commercially available pipette pullers and polishers are available such as the Sutter Instruments P-2000 puller and the Narishige MF-830 polisher. 10. The outer diameter of the tubing must be compatible with the pipette holder. We typically use thin wall borosilicate capillary tubing with filament such as World Precision Instruments TW150-4, 1.5/1.12 mm outer/inner diameter. 11. We typically use custom scripts coded in MATLAB for analysis of emission intensities measured using a spectrofluorometer and electrophysiological experiments. Alternatively, software for analysis of electrophysiological experiments (e.g., PulseFit or Clampfit) is often provided with purchase of the patch clamp amplifier. 12. Measurement of CNG channel activity by monitoring Mn2+ influx. It is possible that in some systems Ca2+ influx through CNG channels will trigger Ca2+-induced Ca2+ release (CICR), which would contribute to the observed Ca2+ responses. A different experimental protocol may be utilized to ensure that altered Ca2+ handling properties of the cells do not contribute to the observed responses: monitoring Mn2+ influx through CNG channels and subsequent quenching of fura-2 [27]. In this protocol, Mn2+ quench of fura-2 is measured at an excitation wavelength of 360 nm (the isosbestic point for fura-2 at different Ca2+ concentrations). Start the protocol as outlined in Subheading 3.1, steps 1–3. Monitor fura-2 fluorescence at excitation wavelengths of 360 and 380 nm. After measuring the baseline fluorescence (F0) for 1 min, add 5 μM MnCl2. A slow decrease in fluorescence due to basal channel activity may be observed. Then add agents that alter cAMP levels. Activation of CNG channels leads to Mn2+ influx that is readily detected as a loss of fura-2 fluorescence monitored at an

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excitation wavelength of 360 nm. Importantly, it is unlikely that changes in free intracellular Mn2+ will significantly alter adenylyl cyclase activity given the low extracellular concentrations of Mn2+ (5 μM) and the high Mn2+ affinity of fura-2, kD ~ 2.8 nM [29]. Normalize data to the prestimulus fluorescence (F0) to correct for variations in dye concentration, and to allow for comparison of results on different batches of cells. To quantify data, perform linear fits to the slope of the agonistand/or antagonist-induced change in fluorescence over time. This protocol describes an approach to measure CNG channel activity (changes in the rate of Mn2+ influx) that is largely independent of changes in intracellular Ca2+ levels. However, in some cell types (e.g., cardiac myocytes) basal Mn2+ influx through endogenous channels is too high to accurately assess agonist- and/or antagonist-mediated changes in cAMP levels. 13. Steps to adjust spectrofluorometer slit width: Adjust excitation and emission slit widths to achieve ~5 nm full width at half maximum height. Load a cuvette containing cells expressing fluorescent donor only. Perform emission scan. Adjust (reduce) excitation and emission slit widths equally and rescan. Continue until the cross talk is 20% of peak donor emission intensity. 14. Fura-2/AM loading. A cell loading solution is prepared by combining 4 μL fura-2/AM stock (1 mM in DMSO) with 2 μL Pluronic F-127 (20% solution in DMSO) in an eppendorf tube and adding 1 mL of extracellular buffer solution 1 (or similar buffer solution) to yield a solution containing 4 μM fura-2/AM, 0.04% Pluronic F-127. Cells are loaded at room temperature for 15–30 min, rinsed with extracellular solution buffer 1, and allowed to sit in the dark at room temperature for an additional 15 min. This provides adequate opportunity for de-esterification of the dye, thereby trapping it in the cells. Loading at temperatures above 35  C should be avoided as it tends to compartmentalize indicator within intracellular organelles of many cell types. In this assay, changes in the rate of Ca2+ influx are indexes of changes in cAMP concentration (cAMP is proportional to the rate of Ca2+ influx through CNG channels). Intracellular fura-2 concentrations should be adequate to displace endogenous Ca2+ buffers and ensure excess of unbound indicator over the experimental time course (i.e., following maximal Ca2+ influx through CNG channels). Thus, higher fura-2/AM loading concentrations (i.e., 16 μM) help preserve fidelity of Ca2+ influx measurements and avoid nonlinear signals at higher Ca2+ concentrations [4]. Lower intracellular fura-2 concentrations are useful for other assays when maintenance and measurement of physiologically meaningful free levels—preserving downstream Ca2+-mediated signaling events—is a priority [30].

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15. Electrophysiological measurements are made in single cells, and, as such, can be made immortalized cell lines such as HEK-293 cells or primary cultures such as mouse embryonic fibroblasts, airway smooth muscle cells, and cardiac myocytes. Cells should be plated on glass cover slips prior to transfection/infection. Note 3 outlines transfection protocols for HEK-293 cells. 16. Cyclic AMP measurements using endogenous cyclic nucleotide regulated channels. Three electrophysiological approaches have been used to detect cyclic nucleotide signals using endogenous ion channels: monitoring protein kinase A (PKA)mediated regulation of L-type Ca2+ channels, monitoring endogenous CNG channel activity, and monitoring endogenous hyperpolarization-activated cyclic nucleotide-gated (HCN) channel activity. (a) Measurement of PKA-mediated regulation of L-type Ca2+ channel activity. The use of PKA-mediated regulation of L-type Ca2+ channel activity to deduce underlying changes in cAMP concentration has been most successfully implemented in cardiac myocytes [15, 31–33]. This approach uses whole cell electrophysiological approaches (see Note 15) to monitor PKA-mediated potentiation of L-type Ca2+ channel activity via well-established voltage protocols to measure L-type Ca2+ channel activity. Two difficulties arise from monitoring PKA-mediated regulation of voltage-gated channels as a surrogate for cAMP measurements. First, most cell types lack sufficient levels of PKA-regulated ion channels for accurate and consistent measurements of channel regulation, and thus accurate inference of underlying cyclic nucleotide levels. Second, more importantly, it is often unclear whether observed agonist/antagonist-induced changes in current occur due to changes in PKA activity, phosphatase activity, or both. Thus, these measurements more accurately reflect agonist/antagonist-induced changes in the balance between PKA and phosphatase activities than underlying cAMP signals. Even with these limitations, this approach was elegantly applied to the study of both cAMP signaling kinetics and cAMP compartmentalization of cAMP signals in cardiac myocyte preparations [31–36]. (b) Measurement of endogenous CNG channel activity. Endogenous CNG channels have been used to monitor cyclic nucleotides in rod and cone outer segments [37–41] and olfactory cilia [42–45]. The concentration of CNG channels in these cellular domains is high, allowing for accurate assessment of currents through CNG channels and the underlying cyclic nucleotide signals. Endogenous CNG

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channels are low abundance proteins in most other cellular systems, making it difficult to accurately assess whether observed agonist-induced changes in current are due to cyclic nucleotide-mediated regulation of the endogenous CNG channels. Thus, endogenous CNG channels are not practical for cAMP measurements in most cellular systems. (c) Measurement of HCN channel activity. Some cell types express high enough levels of HCN channels to monitor cAMP-mediated changes in HCN activity, or HCN channels can be overexpressed for cAMP measurement [46, 47]. In either case, HCN channels are activated by membrane hyperpolarization and regulated by cAMP binding. Thus, accurate measurement of cAMP-mediated changes in voltage-dependent kinetic properties (primarily the voltage dependence of channel activation) of HCN channels is required for quantitative cAMP measurements. In cell types that do not express sufficient endogenous HCN channel levels the CNG channel constructs described in this chapter are in general more desirable because their gating properties are not highly dependent on membrane potential. 17. Alternate electrophysiological approaches for measurement of CNG channel activity. Two alternate electrophysiological approaches for the use of CNG channels as cyclic nucleotide sensors have been utilized: patch cram [48, 49] and whole cell measurements [7, 15–17]. (a) Patch cram measurements: CNG channels are expressed at high levels in Xenopus oocytes. Excised membrane patches expressing high levels of CNG channels are removed from the oocytes and crammed into recipient cells. The advantage of this approach is that the sensitivity of the CNG channels can be measured in the same patches that subsequent measurements of intracellular cyclic nucleotide signaling are made. However, two limitations have precluded this approach from widespread use. First, the patch that is excised from Xenopus oocytes contains a variety of signaling proteins in addition to CNG channels. This makes it difficult to ensure that measured responses are not influenced by signaling proteins within the Xenopus oocyte patch. Second, few cell types are both large enough and robust enough to survive being impaled by a patch pipette. (b) Whole cell measurements. Whole cell measurements of cAMP signals are possible largely because in many cell types near-membrane cAMP levels appear to equilibrate

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slowly with the bulk cytosol, thus dialysis of cAMP from the cytosol into the patch pipette is slow [6, 7, 15, 20]. In whole cell experiments the membrane directly underneath the pipette is ruptured by applying light suction or a brief electrical pulse (the “zap” feature available on many patch clamp amplifiers). This provides electrical access to cells and allows dialysis of solutions from patch pipettes into the cytosol. Thus, the whole cell configuration is particularly useful for allowing known intracellular concentrations of compounds that affect signal transduction (e.g., inhibitors and small proteins) to be introduced into cells [17, 20, 21]. Additional information about electrophysiological approaches can be found in [50]. 18. Calibration of CNG channel measurements. In some cell types—for example cardiac myocytes—the introduction of small concentrations of nystatin triggers a rapid induction of leak currents and loss of plasma membrane integrity (presumably due to interactions with the membranes of intracellular organelles such as the mitochondria). For experiments in these cell types cAMP should be omitted from pipette solution 2 and Imax should be estimated by exchanging the bath solution for extracellular buffer containing high concentrations of membrane permeant CNG channel agonists, for example, 100 μM pCPT-cAMP.

Acknowledgments This work was supported by the Center for Lung Biology and the Colleges of Medicine and Engineering at the University of South Alabama. References 1. van der Krogt GN, Ogink J, Ponsioen B et al (2008) A comparison of donor-acceptor pairs for genetically encoded FRET sensors: application to the Epac cAMP sensor as an example. PLoS One 3:e1916. https://doi.org/10. 1371/journal.pone.0001916 2. Rich TC, Webb KJ, Leavesley SJ (2014) Can we decipher the information content contained within cyclic nucleotide signals? J Gen Physiol 143:17–27 3. Schleicher K, Zaccolo M (2018) Using cAMP sensors to study cardiac nanodomains. J Cardiovasc Dev Dis 5:17. https://doi.org/10. 3390/jcdd5010017 4. Rich TC, Tse TE, Rohan JG et al (2001) In vivo assessment of local phosphodiesterase

activity using tailored cyclic nucleotide-gated channels as cAMP sensors. J Gen Physiol 118: 63–77 5. Frings S, Seifert R, Godde M et al (1995) Profoundly different calcium permeation and blockage determine the specific function of distinct cyclic nucleotide-gated channels. Neuron 15:169–179 6. Rich TC, Fagan KA, Nakata H et al (2000) Cyclic nucleotide-gated channels colocalize with adenylyl cyclase in regions of restricted cAMP diffusion. J Gen Physiol 116:147–161 7. Rich TC, Fagan KA, Tse TE et al (2001) A uniform extracellular stimulus triggers distinct cAMP signals in different compartments of a

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simple cell. Proc Natl Acad Sci U S A 98: 13049–13054 8. Finn JT, Grunwald ME, Yau K-W (1996) Cyclic nucleotide-gated ion channels: an extended family with diverse functions. Annu Rev Physiol 58:395–426 9. Bright SR, Rich ED, Varnum MD (2007) Regulation of human cone cyclic nucleotide-gated channels by endogenous phospholipids and exogenously applied phosphatidylinositol 3,4,5-trisphosphate. Mol Pharmacol 71: 176–183 10. Nache V, Schulz E, Zimmer T et al (2005) Activation of olfactory-type cyclic nucleotidegated channels is highly cooperative. J Physiol 569(Pt 1):91–102 11. Liu M, Chen TY, Ahamed B et al (1994) Calcium-calmodulin modulation of the olfactory cyclic nucleotide-gated cation channel. Science 266:1348–1354 12. Varnum MD, Black KD, Zagotta WN (1995) Molecular mechanism for ligand discrimination of cyclic nucleotide-gated channels. Neuron 15:619–625 13. Fagan KA, Schaack J, Zweifach A et al (2001) Adenovirus encoded cyclic nucleotide-gated channels: a new methodology for monitoring cAMP in living cells. FEBS Lett 500:85–90 14. Rich TC, Karpen JW (2002) Cyclic AMP sensors in living cells: what signals can they actually measure? Ann Biomed Eng 30:1088–1099 15. Rochais F, Vandecasteele G, Lefebvre F et al (2004) Negative feedback exerted by cAMPdependent protein kinase and cAMP phosphodiesterase on subsarcolemmal cAMP signals in intact cardiac myocytes: an in vivo study using adenovirus-mediated expression of CNG channels. J Biol Chem 279:52095–52105 16. Rochais F, Abi-Gerges A, Horner K et al (2006) A specific pattern of phosphodiesterases controls the cAMP signals generated by different Gs-coupled receptors in adult rat ventricular myocytes. Circ Res 98:1081–1088 17. Xin W, Tran TM, Richter W et al (2008) Functional roles of GRK and PDE activities in the regulation of b2 adrenergic signaling. J Gen Physiol 134:349–364. PMCID: PMC2279169 18. Blackman BE, Heimann J, Horner K et al (2011) PDE4D and PDE4B function in distinct subcellular compartments in mouse embryonic fibroblasts. J Biol Chem 286: 12590–12601 19. Willoughby D, Wong W, Schaack J et al (2006) An anchored PKA and PDE4 complex regulates subplasmalemmal cAMP dynamics. EMBO J 25:2051–2061

20. Horvat SJ, Deshpande DA, Yan H et al (2012) A-kinase anchoring proteins regulate compartmentalized cAMP signaling in airway smooth muscle. FASEB J 26:3670–3679 21. Rich TC, Xin W, Mehats C et al (2007) Cellular mechanisms underlying prostaglandin-induced transient cAMP signals near the plasma membrane of HEK-293 cells. Am J Physiol Cell Physiol 292:C319–C331 22. Francis M, Qian X, Charbel C et al (2012) Automated region of interest analysis of dynamic Ca2+ signals in image sequences. Am J Physiol Cell Physiol 303:C236–C243 23. Francis M, Waldrup J, Qian X et al (2014) Automated analysis of dynamic Ca2+ signals in image sequences. J Vis Exp 88. https://doi. org/10.3791/51560 24. Fagan KA, Rich TC, Tolman S et al (1999) Adenovirus-mediated expression of an olfactory cyclic nucleotide-gated channel regulates the endogenous Ca2+-inhibitable adenylyl cyclase in C6-2B glioma cells. J Biol Chem 274:12445–12453 25. Xin W, Yang X, Rich TC et al (2012) All preconditioning-related G protein-coupled receptors can be demonstrated in the rabbit cardiomyocyte. J Cardiovasc Pharmacol Ther 17:190–198 26. Walsh KB, Rich TC, Coffman Z (2009) Development of a high throughput assay for monitoring cAMP levels in cardiac ventricular myocytes. J Cardiovasc Pharmacol 53: 223–230 27. Piggott LA, Hassell KA, Berkova Z et al (2006) Natriuretic peptides and nitric oxide stimulate cGMP synthesis in different cellular compartments. J Gen Physiol 128:3–14 28. Jenkins PM, Hurd TW, Zhang L et al (2006) Ciliary targeting of olfactory CNG channels requires the CNGB1b subunit and the kinesin-2 motor protein, KIF17. Curr Biol 16: 1211–1216 29. Kwan CY, Putney JW (1990) Uptake and intracellular sequestration of divalent cations in resting and methacholine-stimulated mouse lacrimal acinar cells. Dissociation by Sr2+ and Ba2+ of agonist-stimulated divalent cation entry from the refilling of the agonist-sensitive intracellular pool. J Biol Chem 265:678–684 30. Neher E, Augustine GJ (1992) Calcium gradients and buffers in bovine chromaffin cells. J Physiol 450:273–301 31. Jurevicius J, Fischmeister R (1996) cAMP compartmentation is responsible for a local activation of cardiac Ca2+ channels by b-adrenergic agonists. Proc Natl Acad Sci U S A 93: 295–299

CNG Channels as Real Time cAMP Sensors 32. Frace AM, Mery P-F, Fischmeister R et al (1993) Rate-limiting steps in b-adrenergic stimulation of cardiac calcium current. J Gen Physiol 101:337–353 33. Hartzell HC, Mery PF, Fischmeister R et al (1991) Sympathetic regulation of cardiac calcium current is due exclusively to cAMPdependent phosphorylation. Nature 351: 573–576 34. Verde I, Vandecasteele G, Lezoualc’h F et al (1999) Characterization of the cyclic nucleotide phosphodiesterase subtypes involved in the regulation of the L-type Ca2+ current in rat ventricular myocytes. Br J Pharmacol 127: 65–74 35. Goaillard JM, Vincent PV, Fischmeister R (2001) Simultaneous measurements of intracellular cAMP and L-type Ca2+ current in single frog ventricular myocytes. J Physiol 530: 79–91 36. Abi-Gerges N, Szabo G, Otero AS et al (2002) NO donors potentiate the b-adrenergic stimulation of ICa,L and the muscarinic activation of IK,ACh in rat cardiac myocytes. J Physiol 540: 411–424 37. Yau K-W, Lamb TD, Matthews G et al (1979) Current fluctuations across single rod outer segments. Vis Res 19:387–390 38. Baylor DA, Yau K-W, Lamb TD et al (1978) Properties of the membrane current of rod outer segments. Sens Processes 2:300–305 39. Yau K-W, Lamb TD, Baylor DA (1977) Lightinduced fluctuations in membrane current of single toad rod outer segments. Nature 269: 78–80 40. Pugh EN Jr, Lamb TD (1993) Amplification and kinetics of the activation steps in phototransduction. Biochim Biophys Acta 1141: 111–149

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41. Molday RS (1998) Photoreceptor membrane proteins, phototransduction, and retinal degenerative diseases: The Friedenwald Lecture. Invest Ophthalmol Vis Sci 39:2493–2513 42. Gold GH (1999) Controversial issues in vertebrate olfactory transduction. Annu Rev Physiol 61:857–871 43. Lowe G, Gold GH (1993) Contribution of the ciliary cyclic nucleotide-gated conductance to olfactory transduction in the salamander. J Physiol 462:175–196 44. Nakamura T, Gold GH (1987) A cyclic nucleotide-gated conductance in olfactory receptor cilia. Nature 325:442–444 45. Chen CH, Nakamura T, Koutalos Y (1999) Cyclic AMP diffusion coefficient in frog olfactory cilia. Biophys J 76:2861–2867 46. Heine M, Ponimaskin E, Bickmeyer U et al (2002) 5-HT-receptor-induced changes of the intracellular cAMP level monitored by a hyperpolarization-activated cation channel. Pflugers Arch 443:418–426 47. Ponimaskin EG, Heine M, Zeug A et al In: Chattopadhyay A Serotonin receptors in neurobiology. CRC Press, Boca Raton, FL 48. Trivedi B, Kramer RH (1998) Real-time patchcram detection of intracellular cGMP reveals long-term suppression of responses to NO and muscarinic agonists. Neuron 21:895–906 49. Trivedi B, Kramer RH (2002) Patch cramming reveals the mechanism of long-term suppression of cyclic nucleotides in intact neurons. J Neurosci 22:8819–8826 50. Hille B (2001) Ionic channels of excitable membranes, 3rd edn. Sinauer Associates, Sunderland, MA

Chapter 18 Quantitative Phosphoproteomics to Study cAMP Signaling Katharina Schleicher, Svenja Hester, Monika Stegmann, and Manuela Zaccolo Abstract Cyclic adenosine monophosphate (cAMP) signaling activates multiple downstream cellular targets in response to different stimuli. Specific phosphorylation of key target proteins via activation of the cAMP effector protein kinase A (PKA) is achieved via signal compartmentalization. Termination of the cAMP signal is mediated by phosphodiesterases (PDEs), a diverse group of enzymes comprising several families that localize to distinct cellular compartments. By studying the effects of inhibiting individual PDE families on the phosphorylation of specific targets it is possible to gain information on the subcellular spatial organization of this signaling pathway. We describe a phosphoproteomic approach that can detect PDE family-specific phosphorylation changes in cardiac myocytes against a high phosphorylation background. The method combines dimethyl labeling and titanium dioxide–mediated phosphopeptide enrichment, followed by tandem mass spectrometry. Key words cAMP, PKA, Phosphodiesterases, Cell signaling, Phosphorylation, Compartmentalization, Mass spectrometry, Quantitative proteomics, Phosphoproteome

1

Introduction Cyclic nucleotides, such as cyclic adenosine monophosphate (cAMP), are second messengers that translate an external stimulus received via a G protein coupled receptor into an intracellular signal that can propagate in distinct cellular compartments. The paradigm of cyclic nucleotides as second messengers is conserved between a wide range of cell types and can be coupled to different first messengers, ranging from peptides to photons of light. A highly organized subcellular architecture of the pathway ensures that cAMP can convey the information carried by a specific stimulus to the appropriate intracellular machinery, generating the required cellular response. Such architecture relies on spatial confinement of multimeric complexes comprising regulators, effectors and target proteins coupled with uneven subcellular distribution of the second messenger [1]. cAMP is produced by adenylate cyclases

Manuela Zaccolo (ed.), cAMP Signaling: Methods and Protocols, Methods in Molecular Biology, vol. 2483, https://doi.org/10.1007/978-1-0716-2245-2_18, © The Author(s), under exclusive license to Springer Science+Business Media, LLC, part of Springer Nature 2022

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(ACs), and it is hydrolyzed to adenosine monophosphate by phosphodiesterases (PDEs), resulting in termination of the cAMP signal. The spatial distribution of the enzymes that mediate cAMP synthesis and degradation is a key factor in generating cAMP compartmentalization, resulting in different subcellular compartments experiencing different cAMP concentrations. The pattern of cAMP compartmentalization is dependent on the initial stimulus [2, 3] and is responsible for the specificity of the intracellular response. Dissecting the details of the organization and regulation of compartmentalized cAMP signaling is of key importance for our understanding of the physiology and pathophysiology of most organ systems and for the development of improved treatments for a variety of disease conditions [4]. An important effector molecule of cAMP is protein kinase A (PKA) [5, 6]. In the heart, sympathetic activation of betaadrenergic receptors and PKA-mediated phosphorylation leads to increased amplitude of the calcium transient and increased contractility [7, 8], as well as rapid calcium release from the myofilaments and calcium reuptake in the sarcoplasmic reticulum, promoting relaxation [9, 10]. Inhibition of PDEs and concomitant reduction in cAMP degradation potentiates these effects. In parallel, cAMP is essential in maintaining the structure and metabolic homeostasis of cardiac myocytes, as well as in the regulation of gene transcription. Drugs that target the cAMP signaling pathway play a prominent role in the treatment of cardiac disease. However, current therapies are associated with significant side effects and have failed to meaningfully improve the prognosis for many patients with heart failure [11]. Understanding which subcellular compartment and specific pool of cAMP is responsible for different functions is key for the development of therapeutics with improved efficacy and specificity. PDEs are a large group of enzymes, comprising 11 families [12]. Most PDE families include multiple genes and splice variants. These multiple PDE isoforms have specific affinities for cAMP and different velocities of cAMP hydrolysis [13], and they are targeted to distinct cellular compartments [14]. Often multiple PDE isoforms operate in the same compartment. Following activation of a specific G protein coupled receptor, it is the engagement of different PDEs in the local degradation of cAMP at different sites that determines the local level of cAMP and phosphorylation of local targets. Ultimately, local levels of cAMP dictate the specific functional response to that stimulus. As PDEs define the local concentration of cAMP, identification of downstream phosphorylation changes following inhibition of individual PDE families can offer information on the nature and location of the subcellular domains under the control of individual PDEs. This information can be used to map the subcellular localization of cAMP nanodomains [15]. Due to the complex organization of cAMP compartments, functional changes in the phosphoproteome after PDE inhibition

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may be subtle. Depending on their affinity for cAMP, some PDEs may be maximally activated in the context of ongoing cAMP signaling, while others may be only partially engaged, particularly if cAMP concentrations are low [16, 17]. To capture the functional outcomes of PDE inhibition in the context of activated cAMP signaling, the PDE-dependent phosphoproteome must be analysed in a PKA activity background. In such a scenario, the dynamic range of intensity ratios between conditions will be narrow, with many phosphoproteins in a cell showing little or no change if they are not part of the compartment in which the PDE is active. To functionally characterize the local role of distinct PDEs in living cells, a sensitive phosphoproteomics approach is therefore required. Here, we present a phosphoproteomics method that allows for detection of compartmentalized phosphorylation sites in stimulated live cardiomyocytes with a high cAMP background (Fig. 1). This approach combines: (1) dimethyl labeling, which reduces sample processing-related variability by allowing for simultaneous mass spectrometry analysis of up to three conditions in one mass spectrometry run, avoiding reproducibility issues; (2) efficient phosphopeptide enrichment using a matrix of monodisperse microsphere-based immobilized titanium dioxide (TiO2). Although we developed this protocol for analysis of phosphopeptides in cardiac myocytes, the general principles can be applied to any other cellular system.

2

Materials and Reagents

2.1 Materials and Equipment

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Low protein–binding microcentrifuge tubes for sample collection.

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Tube rotator for cell lysis.

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Syringes (1 mL) with 21 G needles for cell lysis.

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Spectrophotometer for measuring protein concentration.

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Benchtop microcentrifuge with fixed-angle rotor (minimum angle 40 ).

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Thermoblock for microcentrifuge tubes.

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Centrifugal concentrators with 10 kDa molecular weight cutoff (MWCO) filters and 500 μL volume for filter-aided sample preparation (FASP) protein digest (e.g., Sartorius Vivacon500 VN01H02 or similar).

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Vacuum manifold for on-column dimethyl labeling.

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C18 3 cm3 vac solid phase extraction cartridges for on-column dimethyl labeling. Spin tips containing monodisperse microsphere-based immobilized TiO2 for phosphopeptide enrichment.

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e isolation Cardiomyocyte

I ntervention condition (e.g., β-adrenergic receptor agonist + PDE inhibitor)

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n isolation Protein ABC-Urea

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P Lys-C/Trypsin n digest

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H2N H2N K K K H2N

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Lightt label (CH3)2N (CH3)2N

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e label Intermediate (CHD2)2N (CHD2)2N

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K(CHD2)2 K(CHD2)2

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( ii)) Combined d phosphoenrichment (CH3)2N

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P TiO2

Combined d LC-MS/MS

Data a analysis Fig. 1 Experimental setup. Schematic representation of the proteomics workflow for one biological replicate combining: (i) dimethyl labeling; and (ii) titanium dioxide (TiO2)–mediated phosphopeptide enrichment. The

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2.2 Working Solutions 2.2.1 For Cell Lysis

2.2.2 For Filter-Aided Sample Preparation (FASP)

2.2.3 For On-Column Stable Isotope Dimethyl Labeling

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Suitable high-resolution mass spectrometer for final sample runs, fitted with an electrospray ionization probe and a 50 cm C18 reverse phase analytical column for a 200 nL/min flow rate (e.g., Orbitrap Elite™ Hybrid Ion Trap-Orbitrap Mass Spectrometer or similar).

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ABC-Urea lysis buffer: 8 M urea, 50 mM ammonium bicarbonate (ABC), PhosSTOP phosphatase inhibitor cocktail, complete protease inhibitor cocktail.

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Bradford solution for measuring protein concentration.

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8 M urea in 100 mM triethylammonium bicarbonate (TEAB).

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10 mM tris(2-carboxyethyl)phosphine (TCEP) in 8 M urea.

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50 mM chloroacetamide in 8 M urea.

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6 M urea in 50 mM TEAB, pH 8.0.

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0.1% trifluoroacetic acid (TFA).

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50% acetonitrile (ACN) in 0.1% TFA.

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Labeling Wash Buffer: 0.1% formic acid.

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Labeling Elution Buffer: 0.1% formic acid + 80% (v/v) ACN.

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Sodium phosphate buffer pH 7.5: Prepare by mixing 3 mL of 50 mM NaH2PO4 with 10.5 mL of 50 mM Na2HPO4. Per sample, 4.5 mL will be needed.

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Formaldehyde labeling solutions (see Note 7): – Light label: 4% (v/v) CH2O. – Intermediate label: 4% (v/v) CD2O.

2.2.4 For Titanium Dioxide (TiO2)-Mediated Phosphopeptide Enrichment

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0.6 M cyanoborohydride (NaBH3CN) (see Note 8).

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Standard protein digest made up of 1:1 (w/w) bovine serum albumin (BSA) and casein. This makes a molar ratio of 1 BSA to 3 casein.

 Fig. 1 (continued) cell type of interest, in this example adult rat cardiomyocytes, are isolated and treated live according to the desired experimental setup. To test effects of PDE inhibition in a cAMP signaling context, two experimental conditions are included: a control condition (i.e., stimulation with a defined amount of a cAMPgenerating agonist), and an intervention condition (i.e., combination of the same amount of cAMP-generating agonist and a PDE inhibitor). Samples are then prepared for phosphoproteomic analysis. The phosphoproteomic approach combines Lys-C/trypsin-mediated protein digest, dimethyl labeling, TiO2-mediated phosphopeptide enrichment of the two conditions combined, and liquid chromatography with tandem mass spectrometry (LC-MS/MS). Final data analysis includes peptide searches and statistical analysis

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2.2.5 For Tandem Mass Spectrometry (LC-MS/MS) Analysis

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2 TiO2 Loading Buffer: 80% Acetonitrile, 10% TFA, 2 M Glycolic acid.

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TiO2 Wash Buffer 1: 80% Acetonitrile, 0.2% TFA.

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TiO2 Wash Buffer 2: 20% Acetonitrile.

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TiO2 Elution Buffer: 5% NH4OH (aq).

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Sample acidification buffer: 20% Formic acid.

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HPLC solvent A: 0.1% formic acid in Milli-Q water.

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HPLC solvent B: 0.1% formic acid in 80% or 100% ACN.

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Isolate live cells according to preferred protocol. At least three biological replicates are recommended for this experiment (see Note 1).

Methods

3.1 Sample Collection

l

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For each condition, treat the live cells in suspension with the stimulant of choice for the desired time period (see Note 3).

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Gently pellet the cells without disrupting any cell membranes. For adult rat ventricular myocytes, preferably pellet by sedimentation, but no more than 50  g centrifugal force.

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Take off the supernatant and snap-freeze the cell pellet in liquid nitrogen.

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For example, for adult rat ventricular myocytes (ARVM) use a Langendorff perfusion apparatus [18]. The general principles of this phosphoproteomics protocol can be applied to any cellular system. For each biological replicate, ensure you have enough cells to provide at least 2 mg of total protein for each condition, in addition to any cells set aside to test the quality of the isolation and/or response to treatment (see Note 2).

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Store cell pellets at 80  C until all biological replicates of the experiment are obtained. Resuspend frozen cell pellets in 500 μL ABC-Urea lysis buffer per 2.5 mg protein pellet and lyse for 30 min rotating at 4  C in the cold room (see Note 4).

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Break residual membranes by passing cell lysate through a 21 G needle (see Note 5).

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Measure the protein concentration of each cell lysate and aliquot 2 mg of protein per condition, into five 400 μg portions for FASP digestion. Each filter for the FASP digestion can take up to 400 μg of protein.

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3.3

FASP Digest

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Wash 10 kDa MWCO centrifugal concentrator columns, one for each sample aliquot, with 200 μL 0.1% TFA in 50% ACN and spin through on a benchtop microcentrifuge at 15,800  g for 10–15 min.

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Load samples into the centrifugal concentrator columns (maximum input per filter is 400 μg protein, total volume per filter: 200 μL).

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Alkylate with 50 mM chloroacetamide (CAA): add 20.4 μL of 0.5 M CAA to the sample for 30 min in the dark. Spin through supernatant at 15,800  g for 20 min. The undigested proteins will remain in the columns, as they are larger than the 10 kDa MWCO. Wash samples 2 with 200 μL 6 M Urea in 50 mM triethylammonium bicarbonate (TEAB) (see Note 6). Change the collection tube. Digest proteins overnight in a thermoblock at 37  C with 1 μg mass spectrometry grade Lys-C protease per 40 μg protein, dissolved in 100 μL 6 M Urea in 50 mM TEAB. Digest for 4 h in a thermoblock at 37  C by adding 1 μg mass spectrometry grade trypsin per 40 μg protein, dissolved in 300 μL 50 mM TEAB. Elute tryptic peptides by spinning the sample through. Keep the flow-through, it contains the tryptic peptides!

3.4 On-column Stable Isotope Dimethyl Labeling

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Wash filter with 150 μL 0.1% TFA.

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Wash filter with 150 μL 50% ACN in 0.1% TFA.

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Check pH of the flow-through (should be pH 3, adjust with 10% formic acid if necessary).

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Pool the five digestions for each condition and use directly for on-column stable isotope dimethyl labeling. For each condition, the total volume of the sample will be 3.5 mL: the elution and wash steps together add up to 700 μL per filter; and the initial 2 mg of protein, aliquoted into five portions for FASP, are now being re-combined.

On-column dimethyl labeling is performed using a vacuum manifold, as adapted from [19]. l

Prepare 5 mL of fresh labeling solution per sample: 4.5 mL sodium phosphate buffer pH 7.5, 250 μL 4% (v/v)

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formaldehyde in water (light or intermediate label), 250 μL of 0.6 M cyanoborohydride in water (see Notes 7 and 8). l

Set up solid phase extraction cartridges on the vacuum manifold and place a 15 mL conical-bottom centrifuge tube under each column to collect the flow-through.

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Wash columns five times with 1 mL acetonitrile.

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Condition the solid phase extraction cartridges ten times with 1 mL Labeling Wash Buffer.

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Adjust the volume of your digested peptide sample to 5 mL with Labeling Wash Buffer.

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Load each sample on a separate solid phase extraction cartridge in 1 mL portions. To allow for optimal adsorption, the flow rate should be 1 drop (approximately 50 μL) per 6 s (i.e., with a flow rate of approximately 500 μL/min).

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Change flow-through collection tube (see Note 9).

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Wash the solid phase extraction cartridges three times with 1 mL Labeling Wash Buffer.

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Soak each of the solid phase extraction cartridges five times with 1 mL of the respective labeling reagent (light or intermediate) by slowly letting the labeling solution flow over the solid phase. The control condition is usually labeled with the light isotope, the intervention condition with the intermediate isotope. For best results, the flow rate should be 1 drop (approximately 50 μL) per 6 s (i.e., with a flow rate of approximately 500 μL/ min).

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Wash the solid phase extraction cartridges three times with 1 mL Labeling Wash Buffer.

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Change the collection tubes to 1.5 mL low protein binding microcentrifuge tubes (see Note 10).

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Elute and collect the labeled samples from the solid phase extraction cartridges using 500 μL of Labeling Elution Buffer.

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Keep an aliquot of 500 ng labeled sample for checking the quality of the labeling, for example on an LTQ XL linear ion trap mass spectrometer, 60-min gradient (see Note 16).

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For each biological replicate, pool the light-labeled control condition with its corresponding intermediate-labeled intervention condition. From now on, these two comparators will be processed together (see Note 11). For three biological replicates you should therefore end up with three combined samples, one condition pair for each biological replicate.

and

change

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3.5 Titanium Dioxide Phosphopeptide Enrichment

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TiO2-mediated phosphopeptide enrichment is performed using spin tips containing monodisperse microsphere-based immobilized TiO2, as adapted from [20] (see Note 12). l

Tip: Use a standard protein digest with phosphorylated and nonphosphorylated peptides alongside your samples as an enrichment control (see Note 13).

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Spin the capped TiO2 columns 30 s in a benchtop microcentrifuge at 220  g to get the loose material to the bottom of the tip.

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Take the lid off each column and discard it.

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Wash the columns with 50 μL TiO2 Elution Buffer and remove the buffer by spinning through at 110  g for approximately 2 min until the bottom of the meniscus touches the TiO2 beads (see Notes 14 and 15). Wash the columns four times with 65 μL TiO2 Loading Buffer and remove the buffer by spinning through at 250  g for approximately 1 min until the bottom of the meniscus touches the TiO2 beads (see Note 14).

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Change tube to a 2 mL low protein binding microcentrifuge tubes.

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Make up each sample 1:1 in 2 TiO2 Loading Buffer (final concentration: 5% TFA, 1 M Glycolic acid).

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l

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l l

l l

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Load the sample, 70–100 μL at a time. For optimal adsorption, slowly pass through the column by centrifuging at 27  g for approximately 5 min per load (see Note 9). Tip: Check the flow rate after 1 min to ensure that the samples are not passing through the column too quickly. Wash twice with 65 μL TiO2 Loading Buffer and remove the buffer by spinning through at 27  g for approximately 5 min until the bottom of the meniscus touches the TiO2 beads. Wash twice with 65 μL TiO2 Wash Buffer 1 and remove the buffer by spinning through at 27  g for approximately 5 min until the bottom of the meniscus touches the TiO2 beads. Wash twice with 65 μL TiO2 Wash Buffer 2 and remove the buffer by spinning through at 27  g for approximately 5 min until the bottom of the meniscus touches the TiO2 beads. Spin at 220  g for 2 min to remove any residual buffer. Change collection tube to a 0.5 mL low protein binding microcentrifuge tube. Add 15 μL 20% formic acid to the tube. Elute phosphopeptides two times with 20 μL TiO2 Elution Buffer each by passing the buffer through over the column at 27  g for 5 min. Spin at 220  g for 2 min to recover residual eluate.

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3.6 Mass Spectrometry Analysis

l

Test the quality of the enrichment by running 20 pmol of the enriched standard protein digest, for example on a 30-min gradient on a LTQ XL linear ion trap mass spectrometer (Fig. 2) (see Note 17).

l

Run the quality control samples for dimethyl labeling and phosphopeptide enrichment as described in the two preceding sections to ensure sample quality is high and to guide any trouble shooting as necessary (see Notes 16 and 17).

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Ensure phosphosensitivity of the liquid chromatography system before the final sample run.

l

3.7 Quantitative Data Analysis

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Wash the column after each biological replicate (see Note 19).

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After the last sample run, wash the column twice using a 15 min BSA gradient, and then run an E. coli or HeLa standard for instrument quality control (see Note 20).

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Search mass spectra against a suitable UniProt reference proteome (for example use the Rattus norvegicus reference proteome for ARVMs) using MaxQuant integrated with the Andromeda search engine [21, 22] (see Note 21).

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For experiments where a light and intermediate label were used, analyze groups with a “Multiplicity” of 2 (see Note 22).

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Specify “Phospho (STY)” as a variable modification along with protein oxidation and N-terminal acetylation.

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Account for carbamidomethyl groups as fixed modifications.

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Select Trypsin/P as specific digestion mode with a maximum of 2 missed cleavages.

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Set the peptide-spectrum match (PSM) false discovery rate (FDR) to 0.01 with a minimum score of 40 and a localization probability of >0.7 for phosphopeptides.

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Enable the “Match between runs” function with a match time window of 0.7 min and an alignment time window of 20 min.

l

Downstream data processing and statistical analysis can be performed using the Perseus software package [23].

3.7.1 Peptide Searches

3.7.2 Downstream Data Processing and Statistical Analysis

Run half (20 μL) of each biological replicate of the labeled and phosphoenriched sample on a 120-min gradient on an Orbitrap Elite™ Hybrid Ion Trap-Orbitrap Mass Spectrometer (Thermo Fisher Scientific) or similar (see Note 18).

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A

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Standard protein digest ( no enrichment) 1.16 x 106 all

7.54 x 105 m/z 722 BSA peptide YICDNQDTISSK 9.04 x 105 m/z 1031 Casein phosphopeptide FQpSEEQQQTEDELQDK 2.81 x 105 m/z 651 Casein phosphopeptide YKVPQLEIVPNpSAEER

B

Standard protein digest after TiO 2 enrichment 9.77 x 106 all

5.84 x 104 m/z 722 BSA peptide YICDNQDTISSK 6.21 x 106 m/z 1031 Casein phosphopeptide FQpSEEQQQTEDELQDK 1.50 x 106 m/z 651 Casein phosphopeptide YKVPQLEIVPNpSAEER

Fig. 2 Phosphopeptide enrichment quality assessment. Mass spectrometry analysis of the standard bovine serum albumin (BSA) and Casein protein digest (a) before and (b) after titanium dioxide (TiO2)-mediated

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Notes

4.1 Sample Collection

1. For quantitative data analysis of the experiment, at least three biological replicates are required for each experiment. That is, control and intervention conditions need to be obtained from three different isolations when using ARVM. Or, if working with cell lines, control and intervention pairs should be prepared in triplicate on three different occasions. 2. To ensure sample quality is high, it is recommended to test the health of the isolated cells with an aliquot of live cells before using the sample as a biological replicate for mass spectrometry. ARVM, for example, can be tested using calcium imaging, fluorescence resonance energy transfer (FRET)-based cAMP sensors and/or contractility analysis of the live cells. Western blot analysis of known phosphorylation sites should also be performed for quality control if possible. 3. Before starting the mass spectrometry experiment, ensure that conditions are optimized for maximum enzyme activation. This includes both the treatment concentrations, and the time needed to achieve stable cAMP levels after treatment. The activity of a PDE at different doses of β-adrenergic receptor agonist can be tested using FRET reporters that are targeted to a compartment in which the PDE is active or using phospho-antibodies for known targets of the PDE [3]. In our experience, it takes about 5–10 min for cAMP concentrations to reach a stable equilibrium after addition of a PDE inhibitor to the cells using FRET-based monitoring of single live ARVM.

4.2

Cell Lysis

4. It is important to let the lysis proceed at 4  C in the cold room to prevent the urea in the lysis buffer from crystallizing. 5. Alternatively, membranes can be disrupted by sonication.

4.3

FASP Digest

6. It is important to use TEAB in this protocol, and not ABC. The digestion buffer needs to be free of primary amines as they would react with the formaldehyde labels in the dimethyl labeling step.

 Fig. 2 (continued) phosphopeptide enrichment shows a depletion of the BSA peak at m/z 722.3, corresponding to the unphosphorylated peptide YICDNQDTISSK, relative to the Casein peaks for m/z 1031.7 and m/z 651.3, corresponding to phosphopeptides FQp SEEQQQTEDELQDK and YKVPQLEIVPN pSAEER, respectively (see Note 17). Red arrowhead points to the depleted BSA peak. Peak intensities to calculate the fold enrichment are noted on the right

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4.4 Dimethyl Labeling

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7. Up to three different labels can be prepared, light, intermediate, or heavy label. For two treatment conditions as described here, we recommend using the light and intermediate labels. There is an option to prepare a heavy label to analyze three treatment conditions in parallel instead of two as described here. To prepare the heavy label, use 4% (v/v) 13CD2O and 0.6 M NaBD3CN to the same specifications as for the preparation of the light and intermediate labels. 8. Cyanoborohydride is very toxic and forms a hygroscopic slush that makes it hard to pipette. Work under the fume hood to make the working solution. To make 2 mL of a 0.6 M solution, pipet 500 μL Milli Q water into a weighing boat and tare the scales. Add 80 mg of NaBH3CN (or NaBD3CN when preparing a heavy label) and then make up the solution to 2 mL with water in a microcentrifuge tube. 9. Troubleshooting: Low phosphopeptide yields may, among other reasons, be the result of suboptimal adsorption of sample peptides to the reverse phase in the dimethyl labeling step or the TiO2 beads in the phosphopeptide enrichment step. To check if adsorption of the peptides is optimal, retain the flowthrough after sample loading and test for residual peptides in the flow-through. Any residual peptides in the flow-through indicate suboptimal adsorption to the column. If this is the case, you may want to adjust the flow rate down to optimize adsorption. 10. Troubleshooting: Similarly, if peptide concentration in the labeled samples is much lower than expected, test the flowthrough of the wash steps for residual peptides to ensure that no peptides are lost during the wash steps. If there are any peptides coming off at the washing stage, prepare fresh buffers and ensure the reverse phase columns are adequately primed in the first steps of the protocol. 11. When using three experimental conditions labeled with light, intermediate, and heavy isotopes, all three would be combined at this stage.

4.5 Phosphopeptide Enrichment

12. TiO2 Loading Buffer, TiO2 Wash Buffer 1, and the TiO2 Elution Buffer should be prepared in the fume hood as their components are volatile and corrosive with a low surface tension. TFA is toxic by inhalation. 13. We use 20 pmol of a standard protein digest consisting of BSA and casein in a 1:3 molar ratio, which is made up of 1:1 (w/w) BSA and casein. 14. TiO2 Loading and Elution Buffer should be prepared fresh for every experiment.

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15. During the experiment, avoid for the columns with the TiO2 beads to completely dry out by ensuring that the liquid phase does not completely spin through, but rather that the bottom of its meniscus is visible after each spin. 4.6 Mass Spectrometry

16. Test dimethyl labeling efficiency by running 500 ng labeled sample before running the final sample preparations on a full gradient, for example on an LTQ XL linear ion trap mass spectrometer, 60 min gradient. We recommend checking the labeled sample before continuing with the phosphopeptide enrichment step to pick up any issues regarding labeling efficiency or sample quality early in the protocol. (a) Labeling efficiency should be >95%. (b) The number of peptides included in the proteomes after dimethyl labeling will depend on sample type, concentration, and efficiency of protein digest. (c) If treatment times in the sample collection step are around 5–10 min, the overlap in the light and intermediate labeled proteomes should be substantial as minor changes in protein expression expected in such a short treatment time. 17. Test phosphopeptide enrichment by running 20 pmol of the enriched standard protein digest control, for example on a 15-min gradient on an LTQ XL linear ion trap mass spectrometer or similar, and compare casein and BSA peaks to 40–80 fmol of the unenriched standard protein digest before running the final sample preparations on a full gradient. When analyzing the standard protein mix of BSA and casein, relative BSA concentration is monitored using a peak at m/z 722.3, corresponding to the BSA peptide YICDNQD TISSK [24]. Relative casein concentration can be monitored using peaks at m/z 1031.7 and 651.3, corresponding to the casein phosphopeptides FQpSEEQQQTEDELQDK and YK VPQLEIVPN pSAEER, respectively [25, 26]. Alternatively, casein peaks at 698 and 733 can be used [20]. Set the search window according to the mass accuracy of the instrument. (a) After phosphopeptide enrichment the casein peptides should have a higher peak intensity than the BSA peptides in the standard protein mix. It is reasonable to expect a 30-fold enrichment or higher. 18. Optimizing the fragmentation method for each peptide ion based on its properties using a data-driven decision tree (DDDT) mass spectrometry protocol can further increase the sensitivity of this phosphoproteomic method. A DDDT protocol will opt for either collision-induced dissociation (CID) or electron transfer dissociation (ETD) depending on

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the charge state and m/z of the MS1 precursor ion [27]. While we recommend DDDT as an optimal approach, conventional data-dependent acquisition mode can also be used. 19. To observe system reproducibility and sensitivity during acquisition, we recommend running 40–80 fmol BSA between runs instead of simple washes. 20. An E. coli or HeLa standard on a 60-min gradient should be run before and after sample acquisition. 4.7 Quantitative Data Analysis

21. UniProt reference proteomes are updated on a regular basis. Make a note of the date the reference proteome was downloaded to facilitate clear data interpretation. 22. For label-free quantification, the “Multiplicity” is 1. Use a “Multiplicity” of 2 when using the light and intermediate label and a “Multiplicity” of 3 when using all three labels, light, intermediate, and heavy.

Acknowledgments This work was supported by the British Heart Foundation (PG/10/75/28537 and RG/17/6/32944) and the BHF Centre of Research Excellence, Oxford (RE/13/1/30181). References 1. Zaccolo M, Zerio A, Lobo MJ (2021) Subcellular organization of the cAMP signaling pathway. Pharmacol Rev 73:278–309. https://doi. org/10.1124/pharmrev.120.000086 2. Zaccolo M, Pozzan T (2002) Discrete microdomains with high concentration of cAMP in stimulated rat neonatal cardiac myocytes. Science 295:1711–1715. https://doi.org/10. 1126/science.1069982 3. Surdo NC, Berrera M, Koschinski A et al (2017) FRET biosensor uncovers cAMP nano-domains at β-adrenergic targets that dictate precise tuning of cardiac contractility. Nat Commun 8:15031. https://doi.org/10. 1038/ncomms15031 4. Zaccolo M (2021) cAMP buffering via liquidliquid phase separation. Function (Oxford) 2: zqaa048. https://doi.org/10.1093/func tion/zqaa048 5. Zaccolo M (2009) cAMP signal transduction in the heart: understanding spatial control for the development of novel therapeutic strategies. Br J Pharmacol 158:50–60. https://doi. org/10.1111/j.1476-5381.2009.00185.x

6. Taylor SS, Kim C, Cheng CY et al (2008) Signaling through cAMP and cAMPdependent protein kinase: diverse strategies for drug design. Biochim Biophys Acta 1784: 16–26. https://doi.org/10.1016/j.bbapap. 2007.10.002 7. Reiken S, Lacampagne A, Zhou H et al (2003) PKA phosphorylation activates the calcium release channel (ryanodine receptor) in skeletal muscle: defective regulation in heart failure. J Cell Biol 160:919–928. https://doi.org/10. 1083/jcb.200211012 8. Gordon AM, Homsher E, Regnier M (2000) Regulation of contraction in striated muscle. Physiol Rev 80:853–924. https://doi.org/10. 1152/physrev.2000.80.2.853 9. Kirchberger MA, Tada M, Repke DI, Katz AM (1972) Cyclic adenosine 3’,5’-monophosphate-dependent protein kinase stimulation of calcium uptake by canine cardiac microsomes. J Mol Cell Cardiol 4:673–680. https://doi.org/10.1016/0022-2828(72) 90120-4

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dimethyl labeling for quantitative proteomics. Nat Protoc 4:484–494. https://doi.org/10. 1038/nprot.2009.21 20. Zhou H, Ye M, Dong J et al (2013) Robust phosphoproteome enrichment using monodisperse microsphere-based immobilized titanium (IV) ion affinity chromatography. Nat Protoc 8:461–480. https://doi.org/10.1038/nprot. 2013.010 21. Cox J, Mann M (2008) MaxQuant enables high peptide identification rates, individualized p.p.b.-range mass accuracies and proteomewide protein quantification. Nat Biotechnol 26:1367–1372. https://doi.org/10.1038/ nbt.1511 22. Cox J, Neuhauser N, Michalski A et al (2011) Andromeda: a peptide search engine integrated into the MaxQuant environment. J Proteome Res 10:1794–1805. https://doi.org/10. 1021/pr101065j 23. Tyanova S, Temu T, Sinitcyn P et al (2016) The Perseus computational platform for comprehensive analysis of (prote)omics data. Nat Methods 13:731–740. https://doi.org/10. 1038/nmeth.3901 24. van de Meent MHM, de Jong GJ (2007) Improvement of the liquid-chromatographic analysis of protein tryptic digests by the use of long-capillary monolithic columns with UV and MS detection. Anal Bioanal Chem 388: 195–200. https://doi.org/10.1007/s00216007-1215-1 25. Unwin RD, Griffiths JR, Leverentz MK et al (2005) Multiple reaction monitoring to identify sites of protein phosphorylation with high sensitivity. Mol Cell Proteomics 4:1134–1144. https://doi.org/10.1074/mcp.M500113MCP200 26. Wu WW, Wang G, Insel PA et al (2011) Identification of proteins and phosphoproteins using pulsed Q collision induced dissociation (PQD). J Am Soc Mass Spectrom 22: 1753–1762. https://doi.org/10.1007/ s13361-011-0197-6 27. Swaney DL, McAlister GC, Coon JJ (2008) Decision tree-driven tandem mass spectrometry for shotgun proteomics. Nat Methods 5: 959–964. https://doi.org/10.1038/nmeth. 1260

Chapter 19 Biochemical Analysis of AKAP-Anchored PKA Signaling Complexes Dominic P. Byrne, Mitchell H. Omar, Eileen J. Kennedy, Patrick A. Eyers, and John D. Scott Abstract Generation of the prototypic second messenger cAMP instigates numerous signaling events. A major intracellular target of cAMP is Protein kinase A (PKA), a Ser/Thr protein kinase. Where and when this enzyme is activated inside the cell has profound implications on the functional impact of PKA. It is now well established that PKA signaling is focused locally into subcellular signaling “islands” or “signalosomes.” The A-Kinase Anchoring Proteins (AKAPs) play a critical role in this process by dictating spatial and temporal aspects of PKA action. Genetically encoded biosensors, small molecule and peptide-based disruptors of PKA signaling are valuable tools for rigorous investigation of local PKA action at the biochemical level. This chapter focuses on approaches to evaluate PKA signaling islands, including a simple assay for monitoring the interaction of an AKAP with a tunable PKA holoenzyme. The latter approach evaluates the composition of PKA holoenzymes, in which regulatory subunits and catalytic subunits can be visualized in the presence of test compounds and small-molecule inhibitors. Key words AKAP, cAMP, DSF, Kinase Inhibitor, Mass Spectrometry, Peptide, Protein kinase A (PKA), SDS-PAGE

1

Introduction The human protein kinome is a remarkable source of signaling enzymes [1]. Investigation of kinase biology requires the exploitation of biochemical and cellular assays that invariably include validated chemical probes [1]. Protein kinase A (PKA) is the textbook example of a second messenger-activated protein kinase, whose regulation by cAMP has been fundamental to the development of the cell signaling field [2]. Recent work raises the prospect of selective intervention in human diseases driven by PKA mutations, such as adrenocortical Cushing’s syndrome [3–5]. The PKA holoenzyme is composed of a tetramer of regulatory (R) and catalytic (C) subunits whose catalytic output is controlled, at least in part, by

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the reversible binding of cAMP to the R subunits. The A Kinase Anchoring Proteins (AKAPs) are signaling scaffolds that play a fundamental role in the spatial and temporal targeting of the PKA holoenzyme [6, 7]. Although AKAPs differ greatly in primary sequence, subcellular localization and their diverse repertoire of binding partners, they share the defining feature of a high-affinity interaction with the regulatory subunits (RI or RII) of the PKA holoenzyme, at a distinct site to those involved in cAMP binding. PKA anchoring proceeds through an amphipathic helix that inserts into a customized groove formed by the docking and dimerization (D/D) of the R-subunit protomers [8–10]. When tethered to AKAPs, the PKA holoenzyme is spatially restricted with access to appropriate cellular substrates that mediate cellular outcomes [11] (Fig. 1). This offers one mechanism to selectively promote cellular events that proceed through the ubiquitous second messenger molecule cAMP [12, 13]. However, the PKA-binding module is only one facet of AKAP action. Other domains of anchoring proteins can interact independently to allow distinct enzymes to integrate other second messenger signals within multivalent assemblies [14–16]. Diversification of these signaling complexes also occurs, because macromolecular assembles contain other kinases, protein phosphatases, adenylyl cyclases, phosphodiesterases, and selected PKA substrates [17–21]. Complexity within PKA signaling is augmented by the utilization of four distinct regulatory subunit isoforms of PKA: RI (RIα and RIβ) and RII (RIIα and RIIβ) which differ in their tissue distribution and concentration, cAMP sensitivity, and affinities for AKAPs. These additional layers finely tune PKA activity [22, 23]. The vast majority of AKAPs selectively bind the RII isoform; however, a limited number of dual-specific AKAPs are thought to interact with RI [9, 24–26]. Moreover, due to the dynamic and compartment-specific nature of interactions with AKAPs and other PKA signaling components, uncovering the intricacies of AKAP-mediated signaling events has proven to be a substantial challenge. To confound matters further, the human genome encodes in excess of 50 AKAP genes and most cell types express at least 10–15 different anchoring proteins [27]. Additionally, the PKA C-subunits are thought to have hundreds of distinct substrates. Likewise, certain AKAPs are expressed as families of alternatively spliced transcripts with distinct biological functions [28, 29]. This level of complexity makes it difficult to definitively elucidate individual roles for each AKAP signaling island. One strategy to study the role of anchoring in signaling events is to selectively displace PKA subtypes from the AKAP platform. Consequently, isoform-selective disruptors have been developed (Fig. 2; Table 1) [30, 31]. Although these reagents are valuable tools to study AKAP–PKA signaling, one major drawback is that these

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Fig. 1 Signaling through AKAP complexes. When intracellular concentrations of cAMP are low, the PKA holoenzyme complex is largely bound to AKAPs. AKAPs are localized to intracellular sites including the plasma membrane and organelles, thereby concentrating PKA to particular locations within the cell. Upon stimulation, intracellular cAMP levels increase. Each R-subunit of PKA binds up to two cAMP molecules and undergoes an allosteric conformational change to loosen contacts with the activated catalytic subunits. Active C subunits are then able to phosphorylate adjacent substrates

Fig. 2 Some published peptide disruptors of AKAP complexes. Isoform-selective disruptors were developed to have specificity of targeting toward either the RI or RII isoform of PKA. Despite considerable sequence divergence between the different disruptor peptides, they all share the common feature of forming an amphipathic helix with a largely hydrophobic binding interface (shown in gray) that complements the binding surface of the D/D domain of the R-subunits. Asterisks represent incorporation of the unnatural amino acid (S)-2-(40 -pentenyl) alanine to form an all-hydrocarbon bridge within the sequence

Table 1 PKA inhibitor compounds for modulation of PKA-mediated signaling PKA inhibitors

Mechanism of action

PKI peptide

Blocks the catalytic site of PKA

H89

ATP-competitive inhibitor of PKA

Rp-cAMPS

Prevents cAMP binding to R-subunits

AT13148

Clinical candidate broad PKA/AGC kinase inhibitor

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inhibitors nonspecifically occlude the AKAP anchoring site on the regulatory subunits. This results in indiscriminate interruption of AKAP interactions with either the RI or RII isoforms. 1.1 RII-Selective Disruptors of AKAP Complexes

The original AKAP disruptor peptide, Ht31, was derived from the PKA-anchoring domain of AKAP-Lbc [32]. The discovery of this peptide set a precedence for investigating docking interactions between AKAPs and the RII subunit. Although Ht31 has limited cell permeability, chemical modification of the peptide increases its overall hydrophobicity [33]. The addition of stearic or myristic acid to the N-terminus of the peptide enhances cellular permeability. However, the conjugation of a lipid moiety contributes to retention of Ht31 in cell membranes. Lipid modified forms of Ht31 and the negative proline analog control (Ht31 and Ht31P) are available as commercial reagents. A bioinformatics approach was subsequently used to identify an RII-specific consensus sequence [34], which was then optimized by peptide array screening to produce a more potent RII inhibitor peptide, AKAP-in silico (AKAP-IS). This peptide was shown to have improved affinity for RII as compared to the Ht31 peptide. The Kd value of AKAP-IS is less than 1 nM for RII, but is in the mid-high nM range for RI. Initially, the AKAP-IS peptide was not cell permeable and had limited solubility in aqueous solution. However, the subsequent addition of a TAT sequence at the N-terminus of AKAP-IS partially improved cell permeability [35]. Despite the enhanced hydrophilicity afforded by the TAT sequence, the conjugated peptide, TAT–AKAP-IS, is still highly hydrophobic and requires solubilization in an aqueous 10% DMSO solution. Using a structure-guided optimization approach (from the structures of the AKAP docking site on RIIα alone and in complex with AKAP-IS) in combination with peptide screening assays, AKAP-IS was further modified to improve the affinity and selectivity, which resulted in SuperAKAP-IS [9]. This peptide disruptor exhibited superior RII selectivity, with fourfold higher affinity for RII and approximately 12-fold less affinity for RI as compared to AKAP-IS. Based on the observation that AKAP18 has a higher affinity for RIIα and that an N-terminally truncated form, AKAP18δ, has an even higher affinity, a new class of disruptor pep tides were generated [36]. This class of peptides possess highaffinity for RIIα with dissociation constants as low as 0.4 nM. Analysis of sequence divergence between these peptides helped to further illuminate important residues for engagement with the RII docking site. Analogous to Ht31, the AKAP18δ peptides were also modified with the addition of a stearate moiety in order to promote cellular uptake. Within the last 10 years, small molecules have also been developed to disrupt AKAP–RII interactions [37, 38]. Very large, and relatively flat surfaced, such as the protein–protein interaction interface between the amphipathic helix of an AKAP and the RII D/D docking site, are notoriously difficult to target using small

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molecule approaches, which could partially explain the relative lack of progress in this field of research. As such, these compounds currently have limited cellular potency (IC50 ¼ 20–40 μM), but serve as a starting point for compound optimization using a small molecule targeting approach. Despite the current limitations, these small-molecule scaffolds remain an exciting research area that merit further investigation. Another promising development in anchoring disruptor peptides is the recent introduction of Stapled AKAP Disruptor (STAD) peptides. Chemically modified RII-specific AKAP disruptors were developed by incorporating nonnatural amino acids into the A-kinase binding (AKB) sequences to bestow small-molecule–like properties onto the peptide sequences [31]. Synthetic libraries were designed based on previously identified AKB or AKB-like sequences, where nonnatural olefinic amino acids were incorporated and cyclized so as to conformationally constrain an alpha-helical fold. This chemical modification was previously shown to promote cellular permeability and proteolytic stability to peptides [39]. The STAD peptides developed in this study are highly cell permeable and effectively block interactions between AKAPs and RII inside cells. The incorporation of the pep tide “staple” introduced significant hydrophobicity to an already hydrophobic sequence, so the addition of a small PEG-3 linker at the N-terminus was required to improved solubility for cell-based experiments. The rapid cellular uptake, resistance to degradation, and relatively long half-lives in cells of the STAD peptides provide a more flexible platform for studying dynamic AKAP signaling events under a variety of conditions. All of the PKA-anchoring disruptor reagents discussed have been patterned after an AKAP motif. Recently, a phage selection procedure was employed that exploited high-resolution structural information to engineer RII D/D domain mutants that are selective for a particular AKAP [40]. Competitive selection screening revealed RII sequences (RSelect) that were preferential for interaction with specific AKAP proteins. Biochemical and cell-based experiments validated the efficacy of RSelect mutants for AKAP2 and AKAP18. This new class of engineered proteins based on the reciprocal surface of the AKAP–PKA interaction has the potential to be used to dissect the contributions of different AKAP-targeted pools of PKA and aid in the design of compounds targeting these subset populations. However, the utility of the RSelect mutants is somewhat limited. Although numerous RII-specific AKAP disruptors have been developed, designing peptides for RI-selective interactions has proven to be more elusive. The first RI-selective peptide inhibitors were identified through peptide array screening nearly a decade after the design of Ht31 and its inactive control Ht31-Pro [41]. The prototype used for the peptide array was derived from

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the A-kinase binding (AKB) domain of AKAP10 [41]. Although the crystal structure of the AKB-binding domain of RI was not solved at the time, the minimal sequence required, and surface residue interactions involved in docking to RI were described through systematic analysis. Based on this study, the AKB binding site on RI was shown to involve multiple interactions with charged residues, while the analogous binding site on RII was shown to largely provide a hydrophobic patch for AKB binding. A major limitation of the peptides identified in this study, as with many unmodified peptides, is that they lack cell permeability and therefore require transfection or genetic encoding in order to characterize their activity in cells. Subsequent studies employed a bioinformatics approach coupled with peptide array screening to yield a RI-selective peptide termed RIAD [42]. The binding sequences from several dualspecificity AKAPs were used as the starting point to develop RI specificity. RIAD was found to have a notably improved binding affinity for RI as well as greater specificity for RI over RII. While the RIAD peptide alone was not cell permeable, the C-terminal addition of 11 arginine residues afforded this property. Although transfection can result in artifacts and compensatory expression changes within the cell, the cell-permeable version of RIAD was utilized to illustrate disruption of RI-specific AKAP interactions in intact, nonmodified cells. RIAD analogs were later developed that incorporated nonnatural and natural amino acids into the sequence to improve proteolytic stability [43]. However, the cell permeability of RIAD analogs remains an issue. Probing the structure of RIα reveals several unique features that differentiate the D/D domain from that of RII, and which are likely key determinants of AKAP specificity [44]. These changes include an altered depth of the binding groove, the presence of an inter-subunit disulfide bridge within the AKAP binding site, and a unique spatial arrangement of restrictive amino acid lining the AKB binding pocket. These structural insights will undoubtedly lead to the development of optimized peptide-based or synthetic scaffolds that can discriminate against RII interactions while maintaining high-affinity binding with RI. Furthermore, additional AKAP selectivity for RI anchoring may involve a separate interface that is upstream of the amphipathic helix, known as the RI-specifier region (RISR), which is capable of augmenting RI binding [45]. Cellular delivery of an RISR peptide was shown to disrupt RI binding and may serve as an additional targeting site for RI-specific disruption. 1.2 cAMPModulatory Effectors

As a means to interrogate AKAP signaling events in cell-based studies, multiple strategies can be applied to stimulate intracellular cAMP production (Table 2). While some reagents stimulate cAMP to physiological levels, many cause inappropriately high concentrations of cAMP. The labdane diterpene, forskolin, is a potent

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Table 2 Classical cAMP-stimulating agents for activation of PKA signaling-complexes cAMP-stimulating agents

Mechanism of action

Forskolin

Activates adenylyl cyclases

IBMX

Inhibits PDEs

Isoproterenol

Indirectly activates adenylyl cyclases

PGE2

Indirectly activates adenylyl cyclases

DB-cAMP

Activates PKA

supraphysiological activator of adenylyl cyclase (AC), and is perhaps the most widely used reagent for modulating cAMP signaling and PKA activity. To date, over 10,000 citations describe use the use of forskolin for this purpose. Forskolin is a natural product isolated from Coleus forskohlii that reversibly increases cAMP concentrations in diverse tissue types [46]. Eight of the nine membranebound isoforms of AC are stimulated to different extents by forskolin [47], with AC9 being the only major outlier [48, 49]. Since the expression profiles of the AC isoforms are tissue-specific, the potency of forskolin in different cellular contexts can vary considerably, and will often result in cAMP concentrations that are not physiologically relevant [47]. An alternative approach for increasing intracellular cAMP is through inhibition of phosphodiesterase (PDE) activity. A nonspecific PDE inhibitor, 3-isobutyl-1-methylxanthine (IBMX), was identified from a panel of xanthine derivatives to have inhibitory effects on PDEs [50]. IBMX is a moderately potent inhibitor of the majority of PDE isoforms but appears to have no effect on PDE8 or PDE9 [51]. Due to its broad inhibitory activity toward PDEs, IBMX is routinely used in conjunction with an AC-stimulating agent (such as forskolin) to further increase overall intracellular cAMP. However, additional caution must be taken when interpreting results from experiments using a forskolin/IBMX cocktail to stimulate PKA as this combination stimulates “unnatural” cAMP production and prolongs the second messenger response well beyond a “typical” time course. A much more physiologically relevant means to stimulate cAMP production is through activation of β1- and β2-adrenergic receptors by isoproterenol (isoprenaline) [52]. Isoproterenol is a synthetic catecholamine that acts as an agonist for this subclass of G protein-coupled receptors (GPCRs). Upon stimulation of β-adrenergic receptors, Gs proteins are activated leading to stimulation of AC activity. After isoproterenol stimulation, cAMP levels rise significantly, but then fall back to near background levels and are resistant to further stimulation even in the presence of persistent isoproterenol treatment [53]. Although β-adrenergic receptors are

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widely expressed in a variety of cells and isoproterenol can elicit a notable effect on cAMP levels, isoproterenol-stimulated cAMP production is useful for short time-course studies but is not effective as a cAMP-stimulating agent for sustained periods. A simple competitive chemical strategy to induce cAMP-sensitive signaling was developed using the cell-permeable cAMP analog, dibutyryl cyclic adenosine monophosphate (DB-cAMP). Although the compound enters the cell in an inactive form, hydrolysis of one of the butyrate groups permits the compound to activate PKA [54]. Although there are clear advantaged of using DB-cAMP analogs, it remains unclear whether they are resistant to all of the cAMP PDE subtypes that exist in a typical cell, in particular PDE8, PDE10, and PDE11 [55]. 1.3 PKA Inhibitors: From Proteins to Small Molecules

The heat-stable protein inhibitor of PKA was first purified from rabbit skeletal muscle over 50 years ago [56]. Tissue protein kinase inhibitor (PKI) is expressed as three isoforms [57] found in a range of cell types. PKI possesses an affinity for PKA in the sub-micromolar range [58], which can be recapitulated biochemically with purified PKA components [59] and also employed in purified recombinant form for the induction of biological responses in vivo [60]. A short, 20-amino-acid sequence was subsequently identified as the inhibitory component of PKI, and a synthetic peptide spanning this sequence was shown to act as a highly selective, potent inhibitor of PKA [61–63]. At the core of the PKI peptide is a pseudosubstrate sequence of RRNAI where the alanine occupies the active site cleft of the C subunit [61–63]. Multiple analogs derived from this 20-mer sequence were synthesized to define the residues that are critical for its inhibitory activity [64, 65]. This sequence was also found to be highly specific for PKA with no inhibitory effect on PKG [64]. PKI not only acts to block the catalytic site on PKA [66], but also serves to mop up any free C subunit inside the cell [67]. A variety of PKI inhibitor peptide analogs and recombinant PKI protein [59] are commercially available that possess a high affinity for PKA and are recognized to have exquisite specificity for PKA at very low concentrations comparable to chemical kinase inhibitors (see below). Recently, very high affinity stapled peptide mimics of the PKI pseudosubstrate with cellular permeability have also been developed [68].

1.4 Chemical Inhibitors of PKA-C

In addition to targeting by a variety of promiscuous kinases inhibitors, such as staurosporine and K252a [59, 69], several early chemical inhibitors of PKA, such as H89, are widely used in the literature. H89 is an isoquinoline-based small molecule that was derived from an earlier inhibitor, H8 [70]. While H8 targeted both PKA and PKG, H89 was found to be a potent inhibitor of PKA but also had inhibitory activity against several other kinases including

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PKG, PKC, casein kinases I and II, and CamKII [69, 71]. Further work identified that H89 acts as a competitive inhibitor of ATP binding to occupy and prevent substrate phosphorylation; this binding event can be probed both directly and indirectly, either enzymatically [72] or by using analytical DSF with purified recombinant C-subunits [59]. While H89 is an effective inhibitor of PKA, numerous off-target effects have been documented, including disruption of intracellular signaling pathways and inhibition of a significant number of related (AGC-kinase) protein kinases, some of which are inhibited even more potently than PKA [69, 73, 74]. Although H89 and its analogs are among the most commonly used of all PKA inhibitors in research, caution should be used when interpreting data, due to its numerous off-target effects. The same is likely to be true for multitargeted AGC kinase inhibitors that inhibit PKA, such as AT13148 [75], which likely exerts anticancer effects through the blockade of multiple signaling pathways. Overall, there remains an urgent need to generate new drug-like molecules that can interfere with PKA signaling, although the ubiquity of PKA in cell biology makes this a challenge currently for kinase inhibitors, in contrast to targeted AKAP blockade [5]. Finally, cyclic nucleotide analogs such as Rp-cAMPS (adenosine-30 ,50 -cyclic monophosphorothioate Rp-isomer) have also been used as indirect inhibitory agents for PKA signaling. Rp-cAMPS is cell permeable and acts as an antagonist of cAMP to prevent activation of PKA by competing with cAMP at nucleotide-binding sites on the regulatory subunits of PKA [76, 77]. This cAMP analog also demonstrates resistance to hydrolysis by phosphodiesterases. Although Rp-cAMPS has limited cell permeability, newer versions such as Rp-8-Br-cAMPS and Rp-8-Cl-cAMPS are recognized to have improved permeability and greater potency [78]. However, since additional signaling elements bind cAMP, it is possible that these analogs may also have other cellular targets aside from PKA-R and can thereby cause unintended secondary effects. The versatility of the PKA holoenzyme system for biochemical analysis comes about in part due to a historical ability to purify stable components from biological tissues and recombinant sources for rapid analysis in different experimental systems [60, 79– 81]. The ready availability of affinity-tagged recombinant proteins for in vitro study is also central for screening procedures to study the mechanism of PKA signaling in simplified systems (Fig. 3). For example, in-frame genetic fusion of the C and RII subunits generates a catalytically active PKA polypeptide that drives substrate phosphorylation in response to agonists in cells depleted of endogenous subunits [11]. This work also suggested that local PKA signaling in cells can occur without physical separation of the C and RII subunits. The anchored chimeric PKA holoenzyme appears to signal within ~200 Å of the AKAP anchor, confirming that

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Fig. 3 Biochemical analysis of the AKAP:RII:C interaction in the presence and absence of exogenous cAMP titrated over a range of concentrations. (a) Cartoon of the assay procedure, which detects free C-subunits released into solution from previously assembled AKAP:RII complexes (b) SDS-PAGE showing the electrophoretic mobility and staining intensity of 3C-generated cleaved AKAP and the relative amounts of associated RII and C subunits. The amount of 3C-cleaved AKAP and RII remain constant in the assay. A high concentration of 50 AMP serves as an internal control, since it does not lead to C-subunit release

spatiotemporal targeting of PKA-based signaling is likely to occur. In addition, we identified that bacterially overexpressed recombinant PKA C-subunit contains at-least 12 autophosphorylation sites not found in a catalytically dead variant [59, 82], and that these proteins can spontaneously assemble into stoichiometric and substoichiometric complexes in solution that can be analyzed by intact MS and DSF [59]. Furthermore, scaffolded cAMP-responsive holoenzyme complexes can be manipulated in vitro for semiquantitative analysis using gel-based approaches [11]. In this chapter, we describe a simple biochemical assay for the evaluation of cAMP effects on an artificially created AKAP-PKA holoenzyme complex. We also describe, in detail, simple protocols for the generation of highly purified recombinant AKAP-79, PKA RII and C-subunits, which can be reassembled into anchored holoenzyme complex in a test-tube. The formation of these high-order complexes can be investigated by a variety of approaches, including Mass Spectrometry [11, 59]. In particular, complex behavior can be assessed after the addition of exogenous reagents to evaluate multivalent protein binding. This is exemplified in the presented example by the physical separation of RII and C-subunits in the presence of different concentrations of cAMP, which can be readily visualized by SDS-PAGE (Fig. 3).

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Materials Prepare all solutions in deionized water at room temperature. Store all reagents at room temperature, unless otherwise indicated. All binding assays should be performed in chilled tubes kept on ice, unless indicated otherwise.

2.1 Expression and Purification of AKAP79, PKA-C, and PKARII Proteins

The following bacterial expression plasmids are validated for the generation of full-length recombinant C and RII PKA subunits and a GST-AKAP truncation mutant in BL21 (DE3) pLysS E. coli cells: pOPINJ-human His-GST-AKAP79297–417 (3C cleavable N-terminal 6His, GST tag, amino acids 297–417, ~20 kDa after cleavage of ~30 kDa GST tag), pET-30 Ek/LIC-mouse PKA-C (N-terminal 6His tag, amino acids 1–351, ~40 kDa), pET-30 Ek/LIC-human PKA-RIIα (N-terminal 6His tag, amino acids 1–381, ~50 kDa). See [11, 59] for further details.

2.1.1 List of Required Chemicals and Reagents

1. Chemically competent E. coli cells: BL21 (DE3) pLysS cells (see Note 1). 2. Luria–Bertani (LB) agar plates: 3.7 g of LB agar in 100 mL water (sufficient to make ~5  20 mL agar plates). Sterilize the agar using an autoclave (121  C under 20 psi for at least 30 min) and allow to cool to 55–60  C. Under sterile conditions, add the appropriate antibiotics and pour the agar in to the required number of petri dishes. Leave the plates to solidify for ~30 min. 3. LB broth: 2.5 g of solid LB is dissolved in 100 mL water. Sterilize using an autoclave. 4. Antibiotics for selection of transformed E. coli: ampicillin— final concentration 50 μg/mL (pOPINJ); kanamycin-final concentration 50 μg/mL (pET-30 Ek/LIC). Both antibiotics are prepared as 1000 stocks in water. BL21 (DE3) pLysS cells also need to be maintained in chloramphenicol—final concentration 35 μg/mL. Chloramphenicol is prepared as a 1000 stock in 100% (v/v) ethanol. All antibiotic stocks should be stored at 22  C. 5. Isopropyl β-D-1-thiogalactopyranoside (IPTG): 100 mM. 1.19 g of IPTG in 50 mL water. Sterilize using a 0.45 μM syringe and store at 20  C. 6. EDTA-free Protease Inhibitor tablets. 7. Ni-NTA agarose resin for purification of His-tagged proteins; reduced glutathione (GSH) Sepharose for purification of GST-tagged proteins. 8. Lysis buffer: 50 mM Tris-HCl, pH 7.4, 300 mM NaCl, 1 mM DTT, 0.1 mM EDTA, 0.1 mM EDTA, 1% (v/v) Triton X-100,

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10% (v/v) glycerol, 10 mM imidazole (see Note 2). Pipette 10 mL of glycerol in to a 100 mL graduated measuring cylinder and add 5 mL of 1 M Tris-HCl, pH 7.4, 10 mL of 3 M NaCl, 100 μL of 1 M DTT, 100 μL of 100 mM EDTA, 100 μL of 100 mM EGTA, 1 mL of 1 M imidazole and 1 mL of Triton X-100 (see Note 3). Adjust volume to 100 mL with water and supplement the buffer with 2 EDTA-free Protease Inhibitor tablets just before use. Lysis buffer can be stored at 4  C. 9. Gel filtration buffer: 50 mM Tris-HCl, pH 7.4, 100 mM NaCl, 1 mM DTT, 10% (v/v) glycerol. Pour 100 mL of glycerol in to a 1 L graduated measuring cylinder and add 50 mL of 1 M TrisHCl, pH 7.4, 33 mL 3 M NaCl, and 1 mL DTT. Adjust the volume to 1 L with water (see Note 4). 10. High salt wash buffer: 50 mM Tris-HCl, pH 7.4, 500 mM NaCl, 20 mM Imidazole. Add 5 mL of 1 M Tris-HCl, pH 7.4, 16.67 mL of 3 M NaCl, 2 mL of 1 M imidazole to a 100 mL graduated measuring cylinder. Add water to a final volume of 100 mL. 11. Ni-NTA elution buffer: 50 mM Tris-HCl, pH 7.4, 100 mM NaCl, 1 mM DTT, 500 mM imidazole. Add 5 mL of 1 M TrisHCl, pH 7.4, 3.33 mL of 3 M NaCl, 500 mL of 1 M imidazole, and 100 μL of 1 M DTT to a 100 mL graduated cylinder and add water to a final volume of 100 mL. 12. GSH elution buffer: Prepare as for Ni-NTA elution buffer, but substitute 500 mM imidazole with 10 mM reduced glutathione (307 mg). 13. Temperature regulated shaking incubator with capacity for holding 2 and 0.1 L volume glass conical flasks. 14. HiLoad 16/600 Superdex 200 size exclusion chromatography (SEC) column (GE Healthcare Life Sciences) or equivalent. 15. Automatic liquid chromatography system (AKTA or similar). 16. Disposable plastic gravity flow columns (e.g., PD-10 columns). 17. Ultrasonic cell disruptor. 2.2 In Vitro AKAPRII-C Binding Assay

1. Purified recombinant PKA-C and RIIα subunits and AKAP79297–417 containing a 3C-protease cleavable GST tag.

2.2.1 List of Reagents

2. Purified Rhinovirus 3C protease (cleaves between a glutamine and glycine of targets containing an LEVLFQ/GP consensus sequence, commercially available). 3. 2 AKAP-PKA binding buffer: 100 mM Tris-HCl, pH 7.4, 200 mM NaCl, 10 mM DTT. Add 10 mL 1 M Tris-HCl, pH 7.4, 6.66 mL 3 M NaCl, and 200 μL 1 M DTT to a 100 mL measuring cylinder, and adjust the volume to 100 mL with water. Use at 1 concentration in the assay. 4. GSH Sepharose.

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5. 1 mM cAMP and 50 -AMP: Dissolve 3.2 mg cAMP in 10 mL water, 3.2 mg 50 -AMP in 10 mL water. Adjust pH of both solutions to 8.0 with NaOH and freeze prior to storage. 6. A heating shaking dry bath.

3

Methods

3.1 Expression and Purification of Recombinant RII, C and AKAP Proteins in BL21 (DE3) pLysS E. coli

1. For each protein to be purified, prepare 4  2 L and 1  200 mL glass conical flasks with 750 mL and 100 mL sterile LB broth respectively. 2. Under sterile conditions, transform 50 μL of competent BL21 (DE3) pLysS cells with 10 ng of plasmid DNA (using standard transformation procedures). Spread the transformed bacteria on to prewarmed agar plates containing the appropriate selection antibiotics and incubate overnight at 37  C. 3. Inoculate 100 mL LB broth (supplemented with the appropriate antibiotics) with a single freshly transformed colony, and incubate overnight at 37  C in an orbital shaking incubator (180–240 rpm). 4. Inoculate each of the 4  750 mL LB broth flasks (supplemented with the appropriate antibiotics) with 5 mL of the overnight culture and incubate at 37  C (240 rpm) for ~2–3 h until the OD600nm reaches 0.6–0.8. Reduce the temperature of the incubator and the bacterial culture to 18  C and induce protein expression with the addition of 0.4 mM (3 mL 100 mM) IPTG. Incubate overnight (~18 h) at 240 rpm. 5. Collect the cells by centrifugation at 5000  g for 10 min, 4  C. 6. Decant the supernatant and collect and pool the cell pellets. Recombinant proteins can either be immediately harvested (Subheading 3.2) or the bacterial pellets can be flash-frozen in liquid nitrogen and stored at 20  C for ~1 week.

3.2 Lysis of the Bacterial Cell Pellet

1. Resuspend the bacterial pellet in ice-cold lysis buffer (~10 mL/ g of bacterial pellet) and transfer to a 100 mL glass beaker. 2. Use an ultrasonic cell disruptor to lyse the bacterial suspension on ice. Sonicate for 30 s and then cool for 1 min to prevent overheating. Repeat for 6–10 cycles or as required (see Note 5). 3. Centrifuge the lysate in a prechilled centrifuge at 43,000  g for 60 min at 4  C. 4. Collect the supernatant and pass through a 0.45 μM syringe filter to remove any remaining cell debris or aggregated material. Keep the clarified supernatant on ice to maintain protein stability throughout the purification procedure. It is recommended that a sample of the total cell lysate is also taken for further analysis.

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3.3 Protein Purification

The following described the procedure for batch affinity purification of PKA and/or AKAP proteins using a “column-free” binding step using a GST-binding resin (such as GSH Sepharose) and a 6His-binding resin (such as Ni-NTA agarose). To minimize proteolysis, perform all of the purification steps at 4  C. 1. Equilibrate 16/600 Superdex 200 pg SEC column with 300 mL gel filtration buffer at a flow rate of 1 mL/min with the pressure limit set to 0.6 MPa. 2. Transfer 2 mL of 50% Ni-NTA or GSH Sepharose slurry (1 mL resting bead volume) in to a disposable plastic gravity flow column. Wash and equilibrate the settled beads by passing through 10 column volumes of water followed by 5 column volumes of cell lysis buffer (see Note 6). Care must be taken to prevent the beads drying. 3. Resuspend the equilibrated affinity resin in the cell lysate and incubate at 4  C with gentle agitation (using a magnetic stirrer) for 1 h to enable binding of the recombinant proteins. Due to the lower binding efficiency of GST to the GSH resin at 4  C, it is recommended that the binding stage for GST-AKAP is extended to ~3 h. 4. Sequentially reapply the lysate containing the suspended affinity resin in to the plastic column until all of the beads have been collected. Collect and sample the “nonbinding” fraction of the lysate for further analysis. 5. Wash the beads in with ~50 column volumes of high salt wash buffer. 6. Elute the bound protein by applying 10 column volumes of the appropriate elution buffer and collect as 500 μL fractions as it comes off the column. 7. Identify protein-containing elution fractions by SDS-PAGE and Coomassie Blue staining (see Note 7). It is recommended that samples of the total lysate and the “nonbinding” Ni-NTA fraction are also analyzed at this point. 8. Pool the protein containing fractions and remove any residual aggregated material by centrifugation at 16,000  g for 20 min (4  C) prior to SEC (see Note 8). 9. If necessary, purify the protein further by SEC, collecting 1.0 mL elution fractions. 10. Analyze eluted fractions after SEC by SDS-PAGE and Coomassie blue staining. 11. Pool the protein-containing fractions. 12. The protein can be flash-frozen in liquid nitrogen and stored at 80  C (see Note 9).

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The following details the specifics of an in vitro GST “pull-down” assay to detect the level of association/dissociation of PKA holoenzyme complexes bound to a purified GST-3C-AKAP79 fragment in the presence of increasing concentrations of cAMP, or the control noncyclic nucleotide 50 -AMP. 1. Prepare 6 microcentrifuge tubes containing serial dilution stocks of cAMP at 10 concentration (1–0.01 mM). Complete dissociation of the PKA holoenzyme complex is achieved at cAMP concentrations 100 μM. 2. Transfer 320 μL of 50% glutathione Sepharose slurry in to a microcentrifuge tube and centrifuge at 5000  g for 1 min. Remove and discard the supernatant, taking care not to disturb the pelleted beads. This will provide a 160 μL packed bead volume which is sufficient to perform eight individual pulldown assays (20 μL per assay). The volumes can be adjusted proportionally depending on the number assays required. 3. Equilibrate the beads by resuspending them in 1 mL 1 AKAP-PKA binding buffer before centrifuging them again at 5000  g (1 min). 4. Carefully remove the supernatant and wash the beads an additional 2 times (see Note 10). 5. Remove the supernatant and add 200 μL of 1 AKAP-PKA binding buffer containing 40 μg of GST-AKAP79297–417 (~5 μg protein per assay). Incubate for ~3 h at 4  C with gentle agitation to allow binding of GST-AKAP79297–417 to the resin. 6. Pellet the beads (5000  g, 1 min) and remove the supernatant. 7. Wash the beads 5 times in 1 mL ice cold AKAP-PKA binding buffer (as for step 3) to remove residual unbound protein. 8. Resuspend the pelleted AKAP-bound GSH beads in 1 mL ice cold AKAP-PKA binding buffer and evenly distribute the suspended bead slurry between eight microcentrifuge tubes (~125 μL per tube). To ensure that all tubes contain an equal volume of AKAP-bound beads, mix the slurry between aliquots by pipetting up and down to prevent bead sedimentation. 9. Centrifuge the microcentrifuge tubes again and carefully remove as much of the supernatant as possible without disturbing the beads. 10. Prepare a 450 μL PKA stock solution containing 16 μM PKA-C, 8 μM PKA-RIIα, and 250 μL of 2 AKAP-PKA binding buffer. Adjust the volume to 450 μL with water. 11. Combine the PKA stock solution with the different concentration of 10 cAMP (45 μL PKA mix + 5 μL 10 cAMP). A control assay containing no cAMP (5 μL water) is also required.

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12. As a negative control, prepare a final assay (as above) containing 100 μM 50 -AMP, which will be incapable of dissociating the PKA holoenzyme. 13. Combine all 8 of the 50 μL PKA solutions with the eight microcentrifuge tubes containing the AKAP-bound GSH beads, and incubate at 30  C for 20 min in a shaking dry bath with enough agitation to maintain the beads in suspension. 14. Pellet the beads by centrifugation as previously describe and wash 3 times in 1 mL ice-cold 1 AKAP-PKA binding buffer to remove nonbound and dissociated protein. Remove the supernatant. 15. Resuspend the beads in 30 μL 1 AKAP-PKA binding buffer supplemented with ~200 ng of 3C. Incubate the beads again in a shaking dry bath at 30  C for 30 min (see Note 11). 16. Pellet the beads by centrifugation and remove 25 μL of the supernatant which will contain the PKA holoenzyme complex and can be analyzed by SDS-PAGE (12% acrylamide gel) and Coomassie blue staining. The order that the proteins will appear on the gel (from top to bottom) will be PKA-RIIα (~50 kDa), PKA-C (~40 kDa), and AKAP79297–417 (~20 kDa). A low-intensity band corresponding to 3C protease can sometimes be detected above AKAP79297–417, although this depends upon the amount of 3C protease used. 17. A concomitant reduction in the intensity of the PKA-C band will be observed as a function of the increasing concentration of cAMP. 50 AMP (which does not bind detectably to R subunit) serves as a convenient negative control (see Fig. 3b). 3.5 SDS-PAGE and Coomassie Blue Staining

1. Resolving gel buffer: 1.5 mM Tris-HCl, pH 8.8: 181.7 g TrisHCL base in 900 mL water. Adjust the pH to 8.8 with HCl and bring the final volume to 1 L with water. 2. Stacking gel buffer: 0.5 mM Tris-HCl, pH 6.8: 60.6 g TrisHCL base in 900 mL water. Adjust the pH to 6.8 with HCl and bring the final volume to 1 L with water. 3. Bis-Acrylamide solution: 29.2:0.8 acrylamide–bis ratio. 4. Ammonium persulfate (APS): 10% (w/v) in water. 0.1 g APS in 1 mL water. 5. N,N,N,N0 -Tetramethyl-ethylenediamine (TEMED). 6. SDS-PAGE running buffer (10): 25 mM Tris-HCL, 192 mM glycine, 3.5 mM SDS. 60.57 g Tris-HCL, 288.3 g glycine and 20 g SDS in 2 L of water and mix. Dilute 100 mL of 10 SDSPAGE running buffer in 900 mL water for to make a 1 solution for use. 7. 5 SDS-PAGE loading buffer.

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8. Prestained molecular mass standards. 9. 1 mM glass gel plates, 1 mM 10 well combs, and electrophoresis system for SDS-PAGE. 10. Coomassie blue: 500 mL methanol, 1 g of Brilliant Blue, 100 mL acetic acid in 1 L (with water). Mix well. 11. Destaining solution: 200 mL methanol, 100 mL acetic acid in 1 L (with water). Mix well. 12. Plastic container for Coomassie blue gel staining. 3.6 Biophysical Analysis of Purified Recombinant PKA Signaling Components

4

Related techniques to evaluate biochemical interactions with purified AKAP, RII, and C subunits include differential scanning fluorimetry (DSF), circular dichroism and native mass spectrometry, and these are described in more detail in [59, 83]. For example, DSF can be employed for analysis of direct binding of cAMP to purified RII subunits discussed in this chapter (and does not require prior unfolding to strip of endogenous cAMP that can copurify from bacteria [84]), or for the titration of small molecules such as kinase inhibitors with C-subunits. In addition, the binding of the heat-stable PKI inhibitor protein and derived peptides to C subunits, and the interaction of a variety of nonspecific kinase inhibitors with the ATP site of C-subunits is described [59]. These studies also include careful evaluation of a “kinase-dead” PKA catalytic domain [60] that does not bind to ATP or PKI in solution, but can still interact with a panel of chemical PKA inhibitors, as well as an R133A RII mutant that does not interact with RII subunits or PKI [59].

Notes 1. Cells can be purchased or made chemically competent in house using standard procedures. 2. Buffer stock solutions: 1 M Tris-HCl, pH 7.4—121.14 g of Tris-HCL base in 800 mL water, adjust to pH 7.4 with HCl and make up the volume to 1 L with water; 3 M NaCl— 175.32 g of NaCl in 1 L water; 1 M imidazole—68.07 g imidazole in 1 L water (adjust pH to 7.4 with HCl); 1 M DTT—15.43 g DTT in 10 mL water (aliquot and store at 20  C); 100 mM EGTA—19 g in 500 mL water; 100 mM EDTA—14.6 g in 500 mL water (adjust the pH of EDTA and EGTA to 8.0 with NaOH). 3. Triton X-100 is extremely viscous. To aid pipetting accuracy, remove the end point of the pipette tip with scissors. 4. Gel filtration buffer must be filtered and degassed for at least 1 h prior to use in SEC.

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5. Sonication conditions will vary depending on the type of system and the size of the probe used. 6. Alternatively, a syringe attached to the end of the column with a small stretch of rubber tubing can be used to manually draw through the liquid phase containing the eluate. 7. We typically use 10% resolving acrylamide gels of 1 mm thickness for SDS-PAGE, loading approximately 5–10 μL of sample per well. 8. It may be necessary to concentrate the pooled eluted protein in order to load it all on to the SEC column. For improved purification resolution, it is recommended that a ~1 mL protein volume is applied to the Superdex 200 16/60 column. 9. It is recommended that the purified protein be aliquoted prior to cryo-storage to avoid damage caused repeated freeze–thaw cycles. 10. GSH beads are provided as a slurry in 20% (v/v) ethanol, which must be removed with successive wash steps to prevent denaturation of the assay proteins. 11. Proteolytic cleavage of the GST tag (which remains bound to the GSH beads) from the AKAP79 protein results in the elution of the intact PKA holoenzyme complex in the mobile phase, which is then collected for analysis by SDS-PAGE.

Acknowledgments This work was supported, in whole or in part, by and BBSRC grants BB/S018514/1, BB/N021703/1, and BB/R000182/1 to D.B. and P.A.E., and National Institutes of Health Grants 1K22CA154600 to EJK, F32DK121415 to MO and NIH DK119186 and DK119192 to J.D.S References 1. Wilson LJ et al (2018) New perspectives, opportunities, and challenges in exploring the human protein kinome. Cancer Res 78(1): 15–29 2. Newton AC, Bootman MD, Scott JD (2016) Second messengers. Cold Spring Harb Perspect Biol 8(8):a005926 3. Beuschlein F et al (2014) Constitutive activation of PKA catalytic subunit in adrenal Cushing’s syndrome. N Engl J Med 370(11): 1019–1028 4. Omar MH, Scott JD (2020) AKAP Signaling islands: venues for precision pharmacology. Trends Pharmacol Sci 41(12):933–946

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Chapter 20 Fluorescent Translocation Reporters for Sub–plasma Membrane cAMP Imaging Oleg Dyachok, Yunjian Xu, Olof Idevall-Hagren, and Anders Tengholm Abstract A wide range of fluorescent sensors with different properties have been developed for imaging of cAMP signals in living cells and tissues. Most cAMP reporters have been designed to undergo changes in fluorescence resonance energy transfer but there are alternative techniques with advantages for certain applications. Here, we describe protocols for cAMP recordings in the sub–plasma membrane space based on detection of translocation of engineered, fluorescent protein–tagged protein kinase A subunits between the plasma membrane and the cytoplasm. Changes in reporter localization can be detected with either confocal or total internal reflection fluorescence microscopy but signal changes are more robust and image analyses less complicated with the latter technique. We show how translocation reporters can be used to study sub–plasma membrane cAMP signals, including oscillations, in insulin-secreting β-cells stimulated with glucose and G-protein-coupled receptor agonists. We also demonstrate how translocation reporters can be combined with other sensors for simultaneous recordings of the cytosolic Ca2+ concentration, protein kinase A activity or plasma-membrane binding of the cAMP effector protein Epac2. Fluorescent translocation reporters thus provide a versatile complement to the growing cAMP imaging toolkit for elucidating sub–plasma membrane cAMP signals in various types of cells. Key words Ca2+, cAMP oscillations, Plasma membrane, Protein kinase A, Translocation, Total internal reflection fluorescence

1

Introduction Second messenger signaling is highly dynamic and often involves distinct temporal patterns and spatial compartmentalization, which help to achieve efficiency and specificity in the control of downstream cellular functions. Such spatiotemporal dynamics have been particularly well recognized for Ca2+, thanks to the early advent of fluorescent indicators to measure the cytoplasmic Ca2+ concentration in individual cells [1]. Measurements of intracellular cAMP dynamics have been more challenging and were for a long time restricted by a lack of suitable tools. There are now a number of available methods for real-time measurements of cAMP (reviewed

Manuela Zaccolo (ed.), cAMP Signaling: Methods and Protocols, Methods in Molecular Biology, vol. 2483, https://doi.org/10.1007/978-1-0716-2245-2_20, © The Author(s), under exclusive license to Springer Science+Business Media, LLC, part of Springer Nature 2022

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in [2, 3] and described elsewhere in this volume). Most cAMP indicators are based on fluorescence resonance energy transfer (FRET). The first version required cumbersome microinjections of fluorescently labeled regulatory and catalytic subunits of protein kinase A (PKA) [4] and suffered from distorted signal as the probe moieties gradually became paired with endogenous PKA subunits. These problems were overcome by genetically encoded versions of the sensor [5, 6]. FRET probes with the additional advantage of being expressed as a single polypeptide chain were soon generated from the cAMP target proteins Epac1 and Epac2 as well as from isolated cAMP-binding domains from PKA, Epac, and the hyperpolarization activated cyclic nucleotide-gated potassium channel 2 [7–10]. Various modifications have resulted in FRET sensors with, for example, increased photostability, dynamic range, and signal-to-noise ratio [11]. However, detection of FRET is not always trivial and the readout is prone to various artifacts [2, 12, 13]. Among cAMP sensors that are not based on FRET, some are comprised of a fusion of the cAMP-sensing component to only one fluorescent protein, which enable single-wavelength detection of intensity changes in response to cAMP [14, 15]. Use of circular permuted fluorescent proteins [16, 17] has provided the advantage of a higher dynamic range, but the circular permuted proteins are typically very sensitive to changes of the intracellular environment, including cytosolic pH [18]. Alternative classes of cAMP sensors include cyclic nucleotide-gated (CNG) channels [19], the activity of which are monitored with patch-clamp current recordings or as changes of the intracellular Ca2+ concentration. Such channels have the advantage of responding quickly and of reporting cAMP in the sub–plasma membrane space where the adenylate cyclases are located and where many important signaling and physiological events take place. Drawbacks with the CNG channel sensors are that their expression may affect the electrophysiological activity of the cell and that the Ca2+ entry may modulate adenylate cyclase and phosphodiesterase activities, thereby affecting the cAMP signal that is being measured. A completely different method, particularly useful for recording sub–plasma membrane cAMP dynamics, is based on protein translocation as a readout [20]. Like the early versions of the FRET biosensors [4, 5] and one of the recently developed singlewavelength sensors [17], it takes advantage of the fact that the regulatory and catalytic subunits of PKA dissociate following binding of cAMP to the regulatory subunit. By targeting the regulatory subunit to the plasma membrane, the holoenzyme becomes located at the membrane under basal conditions [20]. As the sub–plasma membrane cAMP concentration increases the holoenzyme dissociates and the change in localization of the catalytic subunit can be monitored with fluorescence microscopy simply as a change in

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localization. This translocation causes a signal change that is much larger than the simultaneously occurring loss of FRET. We have generated such a translocation sensor with a modified PKA RIIβ regulatory subunit and a Cα catalytic subunit. The regulatory subunits of PKA dimerize and may be localized to specific intracellular sites by binding to A-kinase anchoring proteins (AKAPs) [21]. To avoid dimer formation and unwanted subcellular targeting, the first 90 amino acid residues encompassing the AKAPbinding and dimerization regions were deleted. In the original version of the sensor we added enhanced cyan fluorescent protein (CFP) to the C-terminus and the fluorescent protein was, in turn, extended by the C-terminal polybasic stretch and CAAX-motif from K-Ras4b [20]. The CAAX-motif becomes posttranslationally farnesylated and this lipid modification together with the basic amino acid residues targets the protein to the plasma membrane (Fig. 1a). Other fluorescent proteins can be used, but it is not necessary to have a fluorescent tag on the regulatory subunit (see Note 1). A version of the sensor without fluorescent protein and with the membrane-anchoring sequence added directly to the PKA regulatory subunit is shown in Fig. 1b. The PKA catalytic subunit Cα was used in its full-length wildtype form and tagged with a fluorescent protein at its C-terminus. We have made sensor versions with enhanced yellow fluorescent protein (YFP; Fig. 1a, b) and the red fluorescent protein mCherry (Fig. 1c; see Note 2). Since the regulatory and catalytic subunits must be coexpressed to achieve the intended translocation response, it may be useful to express both proteins from the same vector. For this purpose, we have used a self-cleaving P2A peptide sequence, which induces ribosomal skipping during protein translation, generating two proteins from the same transcript (Fig. 1c; see Note 3). The localization of the reporter can be visualized with confocal microscopy, with which the translocation of the fluorescent Cα subunit can be detected either as changes in membrane or cytoplasmic fluorescence (Fig. 2). However, a better approach is to use total internal reflection fluorescence (TIRF) microscopy, which selectively illuminates a volume within 1.40) objective and beam focusing and positioning optics. Such TIRF illumination devices are available from most of the major microscope manufacturers. An alternative TIRF configuration uses a prism for illumination, which has the advantage of allowing imaging with a low magnification (10–20) objective to obtain information from many cells in parallel (Fig. 4). The authors use a custom-built system, but commercial illuminators are available from, for example, TIRF Labs Inc (Cary, NC).

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6. Commercial confocal microscopes are usually equipped with the necessary peripheral instrumentation but TIRF systems may require custom configuration. 7. A variety of lasers suitable for exciting translocation reporters based on CFP/YFP, mCherry or other fluorescent protein versions are available from several companies. CFP is readily excited with lasers emitting at 442 or 445 nm, YFP with 515 nm light and the red fluorescent protein mCherry with 561 nm light provided by many solid-state lasers. 8. Filters can be obtained from, for example, Chroma Technology (Bellows Falls, VT), Omega Optical (Brattleboro, VT), or Semrock (Rochester, NY). Dual-channel recordings require a device for excitation wavelength switching, such as a filter wheel (e.g., from Sutter Instruments, Novato, CA) or an acousto-optic tunable filter (e.g., from AA Opto-electronic, Orsay, France). Dual wavelength emission recording requires either a fast filter wheel as above or an image splitter, such as Dual-View (Optical Insights, LLC, Santa Fe, NM), OptosplitII (Cairn Research Ltd, Faversham, UK), or W-View Gemini (Hamamatsu Photonics, Hamamatsu, Japan). 9. Data acquisition and image analysis software is usually included with commercial microscope systems but may have to be purchased separately for custom-built setups. The authors use MetaFluor from Molecular Devices (Sunnyvale, CA) and the free ImageJ software [38]. 10. It may be critical to maintain a temperature of 37  C during an experiment as well as to add and remove test substances. We use a peristaltic pump in combination with a custom-built superfusion chamber, chamber heater and microscope objective heater. Similar equipment is available from, for example, Warner Instruments (Hamden, CT). 11. As an alternative to poly-L-lysine, coverslips can be coated with collagen, fibronectin or laminin. 12. This protocol typically yields 30% transfection efficiency 1 day after transfection. The protocol is optimized for insulinsecreting MIN6 β-cells, and may have to be modified for other types of cells. For example, for some primary cells, including pancreatic β-cells, transduction with adenoviral vectors may be required. 13. All cDNA should be transfected at the same time to obtain maximal cotransfection. If more than two plasmids are used it is not recommended to use more than a total of 0.4 μg plasmid DNA per coverslip as higher concentrations may have adverse effects and contribute to reduction of the transfection efficiency and interfere with cell function. Adjust the volume of liposomes when changing the amount of DNA to maintain the ratio of DNA–Lipofectamine 2000 at 1:2.5 (w/v).

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14. Do not allow cells to express the reporter proteins for more than 24 h, since excessive levels may affect cell function. The cAMP-binding part of the reporter may buffer cAMP changes and the catalytic subunit remains catalytically active. We have tried to generate a kinase-dead mutant, but have not succeeded without reducing the ability of the protein to interact with the regulatory subunit, an effect observed also in other studies [39]. 15. Although cells can be stimulated by adding medium into the bath with a pipette, it is preferable to add medium using a superfusion system. Such a system not only permits convenient washout of the stimulus but also eliminates problems with evaporation that otherwise would occur, since the ambient temperature in many cases should be maintained at 37  C to observe normal physiological responses. 16. There is often a large variability in the levels of fluorescent reporter expression between different cells. Since high expression levels potentially may interfere with cellular processes as mentioned in Note 14, it is recommended to choose cells for analysis with relatively low expression levels, approximately in the lower third of the brightness range of all cells. One should also consider that the two reporter components are expressed at roughly equal levels. If there is a deficiency of the membraneanchoring component, the catalytic subunit may bind exclusively to endogenous regulatory subunits at other subcellular locations. When these complexes dissociate following an elevation of the cAMP concentration, the signal at the plasma membrane may increase rather than decrease, giving the impression of an inverted response. To avoid uncertainty about expression of the nonfluorescent membrane-anchoring component in experiments with the single-wavelength version of the reporter, it helps to express the two reporter components from a single vector, as described above (Fig. 1c and see Note 3). 17. Select camera exposure time and gain settings so that no pixels in the image will be saturated. It is important to consider that the fluorescence intensity of the translocating PKA-Cα component might change severalfold during an experiment. It is good to keep the exposure times as short as possible without compromising signal-to-noise ratio, since excessive exposure to excitation light may result in photobleaching and phototoxic effects. If the fluorescence intensity is very low, it is possible with most CCD cameras to combine charges in adjacent pixels to form one pixel in a process named binning. Binning enhances the signal at the expense of optical resolution. On the contrary, if the signal is too bright, it indicates an excessive excitation light intensity. Reduce the laser power or attenuate the light with neutral density filters in the excitation

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beam path. The laser beam should be completely blocked with a shutter between image captures to avoid adverse effects of the light on the specimen. 18. (a) For confocal microscopy images, plasma membrane translocation and dissociation of the biosensor can be quantified by placing a region of interest over the area corresponding to the plasma membrane, either manually or using a segmentation algorithm, such as a threshold or edge detection function. Measurements of membrane fluorescence may be difficult since the cell shape sometime changes over the time-course of an experiment, in particular after cell stimulation. An alternative is to quantify the changes in cytoplasmic fluorescence by defining a region of interest inside the cell that excludes the nucleus, plasma membrane or other conspicuous organelles. This approach only works for analyzing changes in Cα-YFP fluorescence but not for the ΔRIIβ-CFP-CAAX reference channel or for ratio images. (b) In TIRF microscopy images, a region of interest over the cell will always show fluorescence in the plasma membrane and translocation or dissociation is simply recorded as changes of intensity or intensity ratio in this region. (c) An increase of the sub–plasma membrane cAMP concentration results in loss of Cα-YFP intensity but does not affect the ΔRIIβ-CFP-CAAX signal. The CFP/YFP ratio thus increases. If a ratio change is associated with changes in the ΔRIIβ-CFP-CAAX signal it indicates that there are also changes in cell adhesion or other unspecific effects of the stimulus. (d) The cAMP translocation reporter is based on the dissociation of the catalytic and regulatory subunits of PKA, a process with a reported dissociation constant in the nanomolar to low micromolar range. At very high cAMP concentrations, most of the subunits will have dissociated from each other and the sensitivity for detecting changes in cAMP will be reduced. This will not be a problem under most physiological conditions but if saturation is suspected, we recommend complementing the experiments with an alternative biosensor based on the low affinity cAMP-binding Epac protein. (e) To compensate for the differences in expression of the two reporter components, ratio changes can be expressed in relation to the baseline by dividing the ratio value at each time point with the prestimulatory level. In the case only the Cα reporter component is fluorescent, the intensity changes can be expressed as fluorescence intensity changes in relation to baseline (F/F0 or F0/F). 19. Dissolve Cal-590 at 5 mM in DMSO and store at 20  C protected from light. Mix before use and incubate together with cells at 2–5 μM during 45–60 min. The optimal indicator

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concentrations and incubation times vary depending on cell type and cell density. Excessive loading may reduce the Ca2+ response by buffering the concentration changes, whereas too little loading gives poor signal-to-noise ratio. 20. Cal-590 is excited at 561 nm. Simultaneous recording with the cAMP reporter requires three lasers and a triple-band dichroic mirror, such as ZT442/514/561 or the use of a dichroic mirror-free prism-based TIRF system. Cal-590 fluorescence emission is detected, for example, with a 570 nm long-pass filter. Upon Ca2+ binding, the fluorescence intensity increases. Alternation between the Cal-590 and cAMP translocation reporter emission filters using a filter wheel or similar device allows near simultaneous measurements of cytoplasmic Ca2+ concentration and cAMP dynamics in the same cell. If triple excitation and emission is problematic, cAMP can be measured nonratiometrically with an unlabeled regulatory subunit and either a green fluorescent protein-labeled Cα subunit (together with Cal-590) or red fluorescent protein-labeled Cα for combination with a green Ca2+ indicator, such as Cal-520 or Fluo-4. 21. As an alternative to organic Ca2+ indicators, genetically encoded Ca2+ sensors can be used. There are several versions, including red-shifted ones that can be used together with the cAMP translocation biosensor. Two of the redshifted genetically encoded Ca2+ indicators, R-GECO1 [40] and RCaMP [41], are readily excited by 561 nm light while emission is measured >590 nm, providing good separation from both CFP and YFP. Use of genetically encoded Ca2+ indicators require the transfection of an additional plasmid. 22. The PKA activity reporter pm-AKAR3 [37] is based on FRET between CFP and YFP. The easiest way to monitor FRET is by detecting sensitized emission. CFP is excited at 445 nm and emission is detected from YFP at 542/27 nm and from CFP at 483/32. PKA-mediated phosphorylation of pm-AKAR3 results in increased FRET and thereby an increase of the YFP–CFP ratio. cAMP can be recorded at the same time using a red fluorescent, single-color version of the translocation sensor. The mCherry-labeled PKA-Cα shown in Fig. 1 is excited at 561 nm with emission detected >590 nm. 23. GFP is excited at 491 nm and mCherry at 561 nm and emission is collected at 530/50 nm and >590 nm, respectively (filters available from, e.g., Semrock or Chroma Technology). It is also possible to use the CFP/YFP version of the reporter in combination with an mCherry-tagged Epac2 using triple-color imaging as described in Note 20.

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32. Tian G, Sandler S, Gylfe E et al (2011) Glucose- and hormone-induced cAMP oscillations in α- and β-cells within intact pancreatic islets. Diabetes 60:1535–1543 33. Yu Q, Shuai H, Ahooghalandari P et al (2019) Glucose controls glucagon secretion by directly modulating cAMP in alpha cells. Diabetologia 62:1212–1224 34. Li J, Shuai HY, Gylfe E et al (2013) Oscillations of sub-membrane ATP in glucosestimulated beta cells depend on negative feedback from Ca2+. Diabetologia 56:1577–1586 35. Idevall-Hagren O, Barg S, Gylfe E et al (2010) cAMP mediators of pulsatile insulin secretion from glucose-stimulated single β-cells. J Biol Chem 285:23007–23018 36. Miyazaki J, Araki K, Yamato E et al (1990) Establishment of a pancreatic β cell line that retains glucose-inducible insulin secretion: special reference to expression of glucose transporter isoforms. Endocrinology 127:126–132 37. Allen MD, Zhang J (2006) Subcellular dynamics of protein kinase A activity visualized by FRET-based reporters. Biochem Biophys Res Commun 348:716–721 38. Schneider CA, Rasband WS, Eliceiri KW (2012) NIH Image to ImageJ: 25 years of image analysis. Nat Methods 9:671–675 39. Iyer GH, Garrod S, Woods VL Jr et al (2005) Catalytic independent functions of a protein kinase as revealed by a kinase-dead mutant: study of the Lys72His mutant of cAMPdependent kinase. J Mol Biol 351:1110–1122 40. Zhao Y, Araki S, Wu J et al (2011) An expanded palette of genetically encoded Ca2+ indicators. Science 333:1888–1891 41. Akerboom J, Carreras Calderon N, Tian L et al (2013) Genetically encoded calcium indicators for multi-color neural activity imaging and combination with optogenetics. Front Mol Neurosci 6:2. https://doi.org/10.3389/ fnmol.2013.00002

Chapter 21 A Live-Cell Imaging Assay for Nuclear Entry of cAMP-Dependent Protein Kinase Catalytic Subunits Stimulated by Endogenous GPCR Activation Grace E. Peng and Mark von Zastrow Abstract Nuclear entry of cAMP-dependent protein kinase catalytic subunits is typically inferred from changes in net protein amount or kinase activity in the nucleus. Previous methods to directly assess nuclear entry require kinase subunit overexpression and/or supraphysiological cAMP elevation. We describe a method to detect nuclear entry of catalytic subunits expressed at an endogenous level in living cells, stimulated by cAMP in a physiological range, and in real time. Key words Protein kinase A, Confocal fluorescence microscopy, Protein complementation, Nuclear entry

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Introduction The cAMP-dependent protein kinase, or protein kinase A (PKA), is an important mediator of downstream signaling to the nucleus. PKA can reside in the nucleus but is typically present at much higher levels in the cytoplasm, where it is organized and concentrated in localized signaling complexes [1–5]. PKA is inactive in the absence of cAMP because regulatory subunits (PKAreg) suppress activity of catalytic subunits (PKAcat) assembled in holoenzyme complexes. cAMP binding to PKAreg relieves this inhibition and can promote PKAcat dissociation from the complexes. Full dissociation is not essential for activity, but PKAcat is autonomously active as a free monomer [1, 6]. Free PKAcat can diffuse through nuclear pores whereas the holoenzyme complex cannot [7–9]. Together, these properties endow the cell with the ability to mediate longrange signaling to the nucleus, triggered by cAMP-driven dissociation of PKAcat in the cytoplasm followed by diffusion through nuclear pores. PKAcat is rapidly inactivated after entry and then is actively exported, defining a nucleocytoplasmic signaling cycle that

Manuela Zaccolo (ed.), cAMP Signaling: Methods and Protocols, Methods in Molecular Biology, vol. 2483, https://doi.org/10.1007/978-1-0716-2245-2_21, © The Author(s), under exclusive license to Springer Science+Business Media, LLC, part of Springer Nature 2022

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Fig. 1 Schematic depiction of the cell biological basis for the nuclear entry assay. (a) steps in cAMP signaling to the nucleus by free PKAcat entry. cAMP drives PKAcat (C) to dissociate from holoenzyme in the cytoplasm. Free PKAcat enters the nucleus by diffusion through nuclear pores. PKAcat is inactivated by binding its inhibitor (PKI) in the nucleus, PKAcat-PKI complex is actively exported and PKAcat associates with the holoenzyme to complete the cycle. (b) Assay of nuclear PKAcat entry using a split mNG2-based kinetic trap. PKAcat is endogenously labeled with mNG211. The fraction of mNG211-labeled PKAcat that enters the nucleus complements with mNG21–10 localized there by a strong nucleus localization sequence (NLS), creating a kinetic “Trap” specific to this molecular species. mNG21–10 is also expressed in the cytoplasm without an NLS, to effectively “Buffer” uncomplexed PKAcat concentration and reduce assay background

is energetically driven by nuclear export of PKAcat and regulated at the level of entry [8, 10] (Fig. 1a). Two main issues complicate the detection of regulated nuclear entry of PKAcat in intact cells. First, there are other cellular mechanisms for communicating cAMP/PKA signaling to the nucleus. These include cycling of other downstream mediators or PKA targets (e.g., [11]) and local activation of a subpopulation of PKA holoenzyme complexes which are restricted to the nucleus [4, 12]. It is difficult to unambiguously resolve effects of PKAcat nuclear entry, relative to these other mechanisms, using the presently available assays based on nuclear protein phosphorylation or kinase activity. Second, PKAcat is rapidly inactivated after nuclear

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entry by binding to PKI that precedes nuclear export of inactive PKAcat/PKI complexes [10]. This further complicates the experimental detection of PKAcat entry because only a fraction of nuclear PKAcat is active, and downstream transcriptional control is exquisitely sensitive [13]. We became interested in assaying nuclear entry of PKAcat from the cytoplasm in intact cells, motivated by the discovery that GPCRs such as the β2-adrenergic receptor stimulate cAMP signaling from endosomes as well as the plasma membrane [14], and in the course of investigating downstream consequences of endosomal GPCR activation. We initially observed that cAMP production from endosomes efficiently promotes transcription of cAMP target genes [15]. We then learned that nuclear entry of PKAcat is the key step in a downstream transduction pathway dependent on endocytosis [16]. An initial clue to this specific effect emerged from biochemical experiments, using subcellular fractionation, in which endocytic inhibitors were found to strongly inhibit PKAcat accumulation in nuclei despite having only a small effect on global cAMP elevation. Detecting nuclear accumulation in the absence of phosphodiesterase (PDE) inhibitors required very high fraction purity because PKAcat accumulation stimulated by a physiological level of cAMP increase, as defined as that produced by endogenous receptor activation, was only 1–2% of total cellular PKAcat. We found it not feasible to detect such a low level of native PKAcat accumulation using wide field or confocal fluorescence microscopy. Reliable detection of nuclear accumulation by these methods required addition of PDE inhibitors to drive a supra-physiological elevation of cAMP, confirming previous results of others [16, 17]. Nevertheless, fluorescence imaging is a well-established modality for examining nuclear entry of PKAcat [7, 9]. A caveat of these early studies is that the dye-labeled PKAcat species visualized was introduced (by microinjection) in excess to endogenous protein. This caveat has persisted in subsequent imaging studies. For example, nuclear entry of a photoconvertible fluorescent PKAcat fusion protein was detected by confocal imaging in response to the cAMP elevation produced by endogenous adrenergic receptor activation, but the labeled PKAcat visualized was overexpressed [18]. Indeed, we are not aware of any previous method enabling the specific detection of regulated nuclear entry of PKAcat (1) at an endogenous expression level, (2) in response to cAMP produced by the activation of an endogenous receptor and (3) in living cells. The method described below builds on recent advances in labeling endogenous proteins using CRISPR-mediated gene editing and the development of improved split fluorescent proteins [19, 20]. We found that endogenously labeled PKAcat faithfully mimics the native PKAcat protein. In particular, PKAcat labeled with the monomeric green fluorescent protein variant neonGreen2 (mNG2-PKAcat) localized similarly to native PKAcat. In particular,

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Fig. 2 Illustration of the challenge in detecting nuclear accumulation of labeled by live cell imaging in the absence of a kinetic trap. HEK293T cells with endogenous PKAcat labeled with mNG211, and with mNG21–10 expressed only in the cytoplasm, were incubated after adding buffer without a stimulating ligand (“Mock”), the β-adrenergic receptor agonist isoproterenol (100 nM, “Iso”) or forskolin (10 μM, “Fsk”), a direct activator of adenylyl cyclase. Representative confocal images collected at a mid-section focal plane are shown, immediately after the indicated manipulation (“0 min”) and 30 min later after continuous imaging at 37
C (“30 min”). Endogenously labeled PKAcat is detected throughout the cytoplasm, and at highest concentration in punctate structures adjacent to the nucleus, many of which colocalize with Golgi markers (not shown). However, labeled PKAcat is depleted from the nucleus relative to cytoplasm, making nuclei appear dark in confocal optical sections. The fluorescence intensity measured in nuclei is indeed close to assay background, as defined by fluorescence intensity measured outside of cells in the imaging field, under both conditions if a PDE inhibitor is not included. Scale bar, 10 μm

the labeled mNG2-PKAcat, as observed before for native PKAcat, did not visibly accumulate in the nucleus after cAMP production stimulated by endogenous β-adrenergic receptor activation or direct adenylyl cyclase activation if a PDE inhibitor was not also included to further elevate global cAMP (Fig. 2). The method that we describe specifically detects entry and offers sufficiently high sensitivity to detect nuclear entry stimulated by cAMP in the physiological range. It achieves these capabilities by adapting the fluorescent protein technology to generate a distinct molecular species of fluorescent PKAcat that is kinetically trapped after nuclear entry (Fig. 1b). Our method meets all of the assay criteria listed above, and it has sufficient temporal precision to resolve kinetic phases of nuclear entry which differ in dependence on cAMP production from endosomes [16].

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Materials Cell Culture

1. 293T cells with endogenously labeled mNG211-PKAcat and coexpressing cytoplasmic mNG21–10 (see Note 1). 2. Dulbecco’s Modified Eagle Medium (DMEM) with 4500 mg/ L glucose, L-glutamine, sodium pyruvate, and sodium bicarbonate supplemented with 10% fetal bovine serum. 3. Calcium and magnesium free phosphate buffered saline with 0.04% EDTA (PBS-EDTA). 4. TrypLE (Gibco). We use TrypLE to detach cells, but trypsin or PBS-EDTA will also work (see Note 2). 5. 75 cm2 cell culture flasks. 6. Imaging media is composed of DMEM with 4500 mg/L glucose and without glutamine or phenol red, supplemented with 30 mM HEPES. 7. Opti-MEM (Gibco). 8. 35 mm imaging dishes with a #1.5 coverslip bottom. We used 35 mm imaging dishes from MatTek (P35G-1.5-14-C) and CellVis (D35-20-1.5-N). 9. Poly-L-lysine. Stock solution is 0.1% w/v in water kept at room temperature. Dilute stock solution 1 in 100 in pyrogen-free water (final is 0.001% w/v). 10. Lipofectamine2000 (Invitrogen) or equivalent transfection reagent (FuGENE, TransIT, etc.) 11. Ligand.

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Imaging System

A high-quality confocal imaging system capable of maintaining cells at 37
C is required. We specify relevant capabilities of a system that we use in the UCSF Center for Advanced Light Microscopy (calm. ucsf.edu). Other systems that provide similar capabilities should also work. 1. Spinning disk confocal microscope with high magnification oil objectives. We use a Nikon Ti inverted spinning disk microscope fitted with a Yokogawa/Andor Borealis CSU-W1 large field of view confocal scanning unit and quad-band dichroic mirror. We use Nikon Plan Apo VC 60 1.4 NA and Plan Apo VC 100 1.4 NA objectives. 2. Fluorescence excitation source. We use 405 nm and 488 nm solid-state lasers (Coherent) mounted in an Andor laser launch. 3. Camera for fluorescence emission imaging. We use an Andor Zyla 4.2 sCMOS camera fitted to the confocal unit through an electronic filter wheel (Sutter) containing ET450/50 m and ET525/50 m emission filters (Chroma).

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4. Temperature- and humidity-controlled imaging chamber to maintain cells at 37
C. We use an Okolab stage top chamber and objective heater. 5. Image acquisition and analysis software. We use MicroManager 2.1 gamma (micro-manager.org) for microscope control and image acquisition. We use the Fiji implementation of ImageJ (imagej.net) for image analysis.

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3.1 Cell Culture and Transient Transfection

1. Two days before imaging. (a) Coat imaging dishes with poly-L-lysine. • Cover each coverslip in each imaging dish with 0.001% poly-L-lysine. • Incubate at room temperature for 10 min. • Aspirate off the poly-L-lysine solution. Let dry for 10 min. • Wash the coverslip three times with pyrogen free water. • Let dry before seeding adherent cells. (b) Seed adherent cells into imaging dishes at 70% confluency. 2. One day before imaging. (a) Transfect cells with nuclear localized mNG21–10. We transfected cells using 4 μL Lipofectamine2000 (Invitrogen) and 400 ng of nuclear localized mNG21–10 per 35 mm imaging dish (see Note 3). (b) Change media 4–6 h after transfection.

3.2

Imaging

1. Wash the cells with imaging media and replace with 1.5 mL imaging media. 2. Add immersion oil to the objective. 3. Place imaging dish with adherent cells onto the inverted microscope stage for imaging. 4. Focus on the cell monolayer by using transillumination light. 5. Switch to confocal imaging using the blue channel (405 nm laser excitation and ET450/50 m emission filter; henceforth called 405 nm channel). Find a field of view containing cells expressing the nucleus-localized mNG21–10-IRES-TagBFP mNG21–10 indicated by TagBFP fluorescence.

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6. Switch to the green channel (488 nm laser excitation and ET525/50 m emission filter; henceforth called 488 nm channel). Adjust focus to obtain a confocal slice containing a midsection of the cell where the nucleus is clearly defined. 7. Input imaging parameters. For both 405 and 488 nm confocal channels, adjust the exposure time and laser power level to minimize bleaching while obtaining images within the dynamic range of the camera. Select a time interval between individual images (e.g., 30 s to 5 min, see Note 4) and input length of the time lapse (e.g., 30–60 min). Use an autofocus system (we use Nikon Perfect Focus, see Note 5) if available to reduce focus drift during subsequent image acquisition. 8. Acquire a single confocal image of the field using the 405 nm channel. This image will be used as a reference to identify cells expressing nucleus-targeted mNG21–10. 9. Start a time-lapse image acquisition series using the 488 nm laser channel. 10. At 5 min, gently add 4  500 μL agonist (e.g., 500 μL of 400 nM Iso for a final concentration of 100 nM Iso in the dish) in a dropwise manner, evenly distributed throughout the imaging dish. Add additional compounds in a similar fashion as needed (see Note 6). 11. Save the images acquired as full-depth (12 or 16 bit) raw image files. We use TIFF stacks but other formats should work, as long as they do not modify or compress the data. 3.3

Data Analysis

1. In Fiji, open the 405 nm channel image and the 488 nm channel image stack for one experiment. 2. Identify cells that are expressing nuclear localized mNG21–10 for analysis using the 405 nm channel image (Fig. 3a, left). 3. Generate a maximum intensity projection using the 488 nm channel image stack (Fig. 3a, right). Draw an ROI in the nucleus in cells that are expressing nuclear localized mNG21–10 (Fig. 3a, right or Fig. 3b). 4. Select the 488 nm channel image stack. Verify that the ROIs drawn are exclusive to the nucleus for each time point. Adjust if necessary. 5. Measure the intensity of each ROI over time. This can be done by calculating the mean or integrated fluorescence intensity. 6. Determine the ΔF/F0. F0 is defined as the average intensity in the first 5 min (baseline, when no drug has been added; see also Fig. 3c and Note 7).

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Notes 1. We use an 293T (ATCC CRL-3216)-derived cell line generated in collaboration with Bo Huang’s laboratory, in which mNG211 was knocked into the 50 -end of the coding region of the endogenous PRKACA gene and mNG21–10 was stably transfected [16]. We find mNG2 labeling advantageous because it provides a relatively bright fluorescent signal at protein expression levels. Other cell backgrounds and split-FP tagged constructs should also work, such as previously described split GFP-tagged PRKACA lines [19, 21]. 2. We use TrypLE because it detaches the cell monolayer quickly and efficiently into single cells. PBS-EDTA and Trypsin both work for seeding cells prior to transfection. 3. We have focused on nuclear entry stimulated by β-adrenergic receptors that are endogenously expressed in the cell line used. The method can be adapted to assess the effects of other stimuli, including through recombinant receptors expressed heterologously by cotransfection with the nuclear localized mNG21–10 construct. 4. We typically image at 5 s intervals for the first minute after agonist addition, to assure that the cells remain in focus and in the field of view. We then switch to a 30 s interval to minimize photobleaching over the time series. 5. Similar systems are Zeiss’s Definite Focus and Leica’s Adaptive Focus Control. 6. We keep imaging media prewarmed and make the 4 agonist solution right after we start the data acquisition. We add the agonist solution gently in a dropwise manner and complete the addition within 5–10 s. Be careful while adding the solution to avoid bumping the imaging dish, as this may disrupt the focus lock or shift the field of view.

ä Fig. 3 (continued) (“Mock”) or an activator of cAMP production (10 μM forskolin, “Fsk”) is added at 0 min. Scale bars, 10 μm. (a) Cells expressing 2 NLS mNG21–10-IRES-TagBFP are identified by TagBFP fluorescence. A max intensity projection of time lapse images of mNG2-PKAcat cells is used to make ROIs in the nucleus (top). If cells move significantly over time, there will not be an obvious nucleus to draw an ROI in (bottom) and ROIs are drawn per time point as shown in (b). (b) In cells that move over time, equivalent nuclear ROIs must be drawn for each time point. (c) The change in nuclear fluorescence is quantified as a function of time by measuring the mean value in the nuclear ROI and expressing the change relative to the mean fluorescence value measured at the 0 min time point (ΔF/F0). An analysis using Fsk stimulation is shown as an example. Similar results are obtained using isoproterenol to activated endogenous β-adrenergic receptors in the cells (also in the absence of PDE inhibitor)

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7. We typically do not subtract a background or initial fluorescence measurement before ΔF/F0 determination in nuclei because, in our experience, the fluorescence intensity inside the nucleus at baseline is close to extracellular background in healthy cells. We think this is because the NLS-based localization strategy imposes a kinetic but not absolute block on nuclear export due to the strong export activity of PKI [10, 22], suppressing basal accumulation which might otherwise be problematic. If fluorescence measured in nuclei of unstimulated cells is significantly elevated relative to the mean value measured in a region outside of cells, this suggests excessive basal accumulation. In this case, we recommend performing control experiments to verify cell viability, reducing the amount of the nucleus-localized mNG21–10 construct transfected and/or shortening the interval between transfection and the initiation of image acquisition. References 1. Søberg K, Ska˚lhegg BS (2018) The molecular basis for specificity at the level of the protein kinase A catalytic subunit. Front Endocrinol 9: 538 2. Ilouz R, Lev-Ram V, Bushong EA et al (2017) Isoform-specific subcellular localization and function of protein kinase A identified by mosaic imaging of mouse brain. elife 6:e17681 3. Esseltine JL, Scott JD (2013) AKAP signaling complexes: pointing towards the next generation of therapeutic targets? Trends Pharmacol Sci 34:648–655 4. Sample V, DiPilato LM, Yang JH et al (2012) Regulation of nuclear PKA revealed by spatiotemporal manipulation of cyclic AMP. Nat Chem Biol 8:375–382 5. Musheshe N, Schmidt M, Zaccolo M (2018) cAMP: from long-range second messenger to nanodomain signalling. Trends Pharmacol Sci 39:209–222 6. Smith FD, Esseltine JL, Nygren PJ et al (2017) Local protein kinase A action proceeds through intact holoenzymes. Science 356:1288–1293 7. Harootunian AT, Adams SR, Wen W et al (1993) Movement of the free catalytic subunit of cAMP-dependent protein kinase into and out of the nucleus can be explained by diffusion. Mol Biol Cell 4:993–1002 8. Hagiwara M, Brindle P, Harootunian A et al (1993) Coupling of hormonal stimulation and transcription via the cyclic AMP-responsive factor CREB is rate limited by nuclear entry of protein kinase A. Mol Cell Biol 13:4852–4859

9. Adams SR, Harootunian AT, Buechler YJ et al (1991) Fluorescence ratio imaging of cyclic AMP in single cells. Nature 349:694–697 10. Wiley JC, Wailes LA, Idzerda RL et al (1999) Role of regulatory subunits and protein kinase inhibitor (PKI) in determining nuclear localization and activity of the catalytic subunit of protein kinase A. J Biol Chem 274:6381–6387 11. Garmendia-Torres C, Goldbeter A, Jacquet M (2007) Nucleocytoplasmic oscillations of the yeast transcription factor Msn2: evidence for periodic PKA activation. Curr Biol 17:1044– 1049 12. Clister T, Greenwald EC, Baillie GS et al (2019) AKAP95 organizes a nuclear microdomain to control local cAMP for regulating nuclear PKA. Cell Chem Biol 26:885–891.e4 13. Tsvetanova NG, Trester-Zedlitz M, Newton BW et al (2017) G protein-coupled receptor endocytosis confers uniformity in responses to chemically distinct ligands. Mol Pharmacol 91: 145–156 14. Irannejad R, Tomshine JC, Tomshine JR et al (2013) Conformational biosensors reveal GPCR signalling from endosomes. Nature 495:534–538 15. Tsvetanova NG, Von Zastrow M (2014) Spatial encoding of cyclic AMP signaling specificity by GPCR endocytosis. Nat Chem Biol 10:1061– 1065 16. Peng GE, Pessino V, Huang B et al (2021) Spatial decoding of endosomal cAMP signals

Imaging Nuclear Entry of PKA by a metastable cytoplasmic PKA network. Nat Chem Biol 17:558 17. Nigg EA, Hilz H, Eppenberger HM et al (1985) Rapid and reversible translocation of the catalytic subunit of cAMP-dependent protein kinase type II from the Golgi complex to the nucleus. EMBO J 4:2801–2806 18. Mavillard F, Hidalgo J, Megias D et al (2010) PKA-mediated Golgi remodeling during cAMP signal transmission. Traffic 11:90–109 19. Kamiyama D, Sekine S, Barsi-Rhyne B et al (2016) Versatile protein tagging in cells with

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split fluorescent protein. Nat Commun 7: 11046 20. Feng S, Sekine S, Pessino V et al (2017) Improved split fluorescent proteins for endogenous protein labeling. Nat Commun 8:370 21. Leonetti MD, Sekine S, Kamiyama D et al (2016) A scalable strategy for high-throughput GFP tagging of endogenous human proteins. Proc Natl Acad Sci U S A 113:E3501–E3508 22. Fu S-C, Fung HYJ, Cag˘atay T et al (2018) Correlation of CRM1-NES affinity with nuclear export activity. Mol Biol Cell 29: 2037–2044

Chapter 22 Measuring Spatiotemporal cAMP Dynamics Within an Endogenous Signaling Compartment Using FluoSTEP-ICUE Julia C. Hardy, Sohum Mehta, and Jin Zhang Abstract cAMP is a ubiquitous second messenger involved in the regulation of diverse cellular processes. Spatiotemporal regulation of cAMP through compartmentalization within various subcellular microdomains is essential to ensure specific signaling. In the following protocol, we describe a method for directly visualizing signaling dynamics within cAMP microdomains using fluorescent sensors targeted to endogenous proteins (FluoSTEPs). Instead of overexpressing a biosensor-tagged protein of interest to target a microdomain, FluoSTEP Indicator of cAMP using Epac (FluoSTEP-ICUE) utilizes spontaneously complementing split GFP and CRISPR-Cas9 genome editing to localize a FRET-based cAMP biosensor to an endogenously expressed protein of interest. Utilizing this approach, FluoSTEP-ICUE can be used to measure cAMP levels within endogenous signaling compartments, thus providing a powerful tool for studying the spatiotemporal regulation of cAMP signaling. Key words Biosensor, FRET, Live-cell imaging, Compartmentation, Microdomain

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1.1 cAMP/PKA Signaling

30 ,50 -cyclic adenosine monophosphate (cAMP) is a ubiquitous second messenger and a central regulator of cellular functions. cAMP production is typically initiated when an agonist binds to a transmembrane G-protein coupled receptor (GPCR), activating adenylyl cyclases (ACs) to catalyze the production of cAMP from ATP. cAMP signaling proceeds through multiple effector proteins, such as cAMP-dependent protein kinase (PKA), exchange protein activated by cAMP (Epac), and cAMP-gated channel (CNGC), and is terminated when cAMP is digested by cAMP-degrading phosphodiesterases (PDEs). Through these various effectors, cAMP signaling controls numerous biological processes, including cell growth, metabolism, and survival [1–11]. Compartmentalization of cAMP elevations within discrete subcellular microdomains is essential for

Manuela Zaccolo (ed.), cAMP Signaling: Methods and Protocols, Methods in Molecular Biology, vol. 2483, https://doi.org/10.1007/978-1-0716-2245-2_22, © The Author(s), under exclusive license to Springer Science+Business Media, LLC, part of Springer Nature 2022

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cells to achieve spatiotemporal regulation and ensure specificity in cAMP signaling [12, 13]. These cAMP signaling microdomains are frequently implicated in regulating key protein targets of cAMP and its effectors, yet directly investigating the spatiotemporal dynamics of cAMP with respect to specific microdomains can prove challenging. 1.2 FRET-Based cAMP Reporters

The spatiotemporal dynamics of cAMP in living cells were first visualized over 30 years ago using a fluorescence resonance energy transfer (FRET)-based biosensor composed of fluorescent dye-conjugated PKA subunits, which dissociate upon cAMP binding, leading to a FRET decrease [14]. While this originally required labeling purified PKA subunits in vitro and then injecting them into living cells, replacing the fluorescent dyes with FRET-compatible fluorescent proteins (FPs) later enabled the entire sensor to be genetically encoded and produced directly within cells [15]. A wide variety of genetically encoded FRET-based cAMP indicators have since been developed, greatly enhancing our ability to measure cAMP signaling dynamics in living cells [16–18]. These sensors utilize elements from different cAMP-binding proteins, such as PKA [14, 15, 19–21], Epac [19, 22–29], and CNGC [30], as molecular switches that change conformation upon cAMP binding to modulate FRET between a pair of attached FPs. For example, our Indicator of cAMP using Epac (ICUE) probe contains residues 149–881 of Epac1 sandwiched between cyan (CFP) and yellow fluorescent protein (YFP) [23, 24, 31]. Binding of cAMP to ICUE causes the Epac1(149–881) fragment to adopt a more open conformation, leading to a decrease in FRET [23, 24, 31]. FRET involves the nonradiative transfer of excited-state energy from a donor (e.g., an FP) to a compatible acceptor. When FRET occurs, the intensity of donor (e.g., CFP) emission will decrease, and the intensity of acceptor (e.g., YFP) fluorescence emission will increase. In practice, FRET changes are therefore often reported as changes in the acceptor-to-donor (or donor-to-acceptor) emission ratio, although other measurements of FRET efficiency can also be used [32]. The efficiency of energy transfer strongly depends on both physical proximity (e.g.,