Confocal Microscopy: Methods and Protocols (Methods in Molecular Biology, 2304) 1071614010, 9781071614013

Table of contents :
Preface
Contents
Contributors
Chapter 1: Advances in Confocal Microscopy and Selected Applications
1 Introduction
2 Resolution of Intensity
2.1 Sample Preparation
2.1.1 Labeling with Stains
Penetrance
Specificity
Bias
Accuracy
2.1.2 Labeling with Genetically Encodable Tags
Penetrance
Specificity
Bias
Accuracy
2.1.3 Labeling Structures vs Specific Biomolecules
2.2 Detector Efficiency
3 Temporal Resolution
3.1 Resonant Scanners
3.2 Slit Confocal
3.3 Swept-Field Confocal
3.4 Spinning Disk Confocal
4 Spatial Resolution
4.1 Deconvolution
4.1.1 Mechanism of Basic Deconvolution
4.1.2 Weaknesses of Basic Deconvolution
4.1.3 Workflow of Basic Deconvolution
4.1.4 A Note on Conflicting Deconvolution Literature
4.2 Superresolution Imaging
4.2.1 Airyscan Imaging
4.2.2 Depletion Technologies
5 Chromatic Resolution
5.1 Bandpass Filters, Notch Filters, and Dichroic Mirrors
5.2 Adjustable Chromatic Filtering Systems
5.3 Multispectral Imaging
6 Resolution in Additional Orthogonal Dimensions
6.1 Fluorescence Lifetime Imaging
7 Resolution of Analysis Pipelines
8 Resolution of Dynamic Properties of Ensemble Systems
8.1 Photobleaching Recovery Experiments
8.2 Other Intensity Fluctuation Tools
9 Conclusions
References
Chapter 2: Choosing Fluorescent Probes and Labeling Systems
1 Introduction
1.1 Fluorescence Basics
1.2 Labeling Systems
2 Materials
2.1 Labeling Proteins, Peptides, and Thiolated Biomolecules
2.2 Immunofluorescence
2.3 Labeling HaloTag Expressing Mammalian Cells
2.4 Transfecting Mammalian Cells with Fluorescence Protein Expression Vectors
2.5 Extrinsic Labeling of IFP2.0 Expressing Mammalian Cells with Biliverdin
3 Methods
3.1 Labeling Proteins, Peptides, and Thiolated Biomolecules (See Note 2)
3.2 Immunofluorescence
3.3 Labeling HaloTag Expressing Mammalian Cells (See Note 14)
3.4 Transfecting Mammalian Cells with Fluorescence Protein Expression Vectors (See Note 15)
3.5 Extrinsic Labeling of IFP2.0 Expressing Mammalian Cells with Biliverdin
4 Notes
References
Chapter 3: General Considerations for Acquiring a Three-Color Image by Laser Scanning Confocal Microscopy
1 Introduction
2 Materials
3 Methods
3.1 Set Parameters for Three Color Imaging on Three Tracks
3.2 Acquire a Three-Dimensional Image
3.3 Setting the Focus of the Condenser
4 Notes
References
Chapter 4: Microfabricated Devices for Confocal Microscopy on Biological Samples
1 Introduction
1.1 Microfabricated Structures and Biology
1.2 Microfabrication Techniques
1.3 Design Considerations for Confocal Microscopy
1.4 Application Examples
2 Materials
2.1 Template Fabrication
2.2 PDMS Molding
2.3 Reproducing Molds
2.4 In Situ Well Formation
2.5 Device Assembly
3 Methods
3.1 Template Fabrication
3.2 PDMS Molding
3.3 Reproducing Molds
3.4 Stamping Directly onto a Coverglass
3.5 Device Assembly-PDMS Channels on a Glass Coverslip
4 Notes
References
Chapter 5: ZEISS Airyscan: Optimizing Usage for Fast, Gentle, Super-Resolution Imaging
1 Introduction
2 Materials
3 Methods
3.1 Airyscan Imaging
3.1.1 Turn On the System and Launch the Zen (Black) Software
3.1.2 Turn on Lasers
3.1.3 Place the Sample on the Stage and Locate Your Sample
3.1.4 Set the Airyscan Light Path Configuration for Image Acquisition
3.1.5 Aligning the Airyscan Detector
3.1.6 Set Parameters in Channels Window for Acquisition
3.1.7 Saving Images
3.1.8 Tuning off the Microscope
3.1.9 Airyscan Image Processing
3.2 Airyscan FAST Imaging
3.2.1 Set the Airyscan FAST Light Path Configuration
3.2.2 Aligning the Airyscan Detector for the FAST Mode
3.2.3 Acquiring Airyscan FAST Images
4 Notes
References
Chapter 6: High-Resolution Multicolor Imaging of Mitochondria in Lymphocytes
1 Introduction
2 Materials
2.1 Isolation of Primary Cells
2.2 Cell Staining
2.3 Coverslip Preparation and Mounting
2.4 Microscopy, Image Deconvolution and Analysis
3 Methods
3.1 Cell Isolation
3.2 Cell Staining and Adhesion to Coverslip
3.2.1 Cell Staining in Solution: LIVE/DEAD Fixable Green Dead Cell Stain and MitoTracker Red CMXRos
3.2.2 Adhesion of Cells to Coverslip
3.2.3 Cell Staining on Coverslip: TOM20-ATTO-647N and DAPI
3.3 Mounting Coverslip to Slide
3.4 Microscopy, Image Deconvolution, and Analysis
3.4.1 Microscopy Instrumentation and Imaging
3.4.2 Image Deconvolution
3.4.3 Image Analysis
4 Notes
References
Chapter 7: Protein-Retention Expansion Microscopy (ExM): Scalable and Convenient Super-Resolution Microscopy
1 Introduction
2 Materials
2.1 Fixation and Staining
2.2 AcX Anchoring
2.3 Gel Embedding
2.4 Digestion
2.5 Expansion and Imaging
3 Methods
3.1 Fixation and Staining
3.2 AcX Anchoring
3.3 Gel Embedding
3.3.1 Gel Embedding for Cultured Cells
3.3.2 Gel Embedding for Brain Tissue Slices
3.4 Digestion
3.5 Expansion and Imaging
4 Notes
References
Chapter 8: Analysis of B Cell Receptor-Mediated Antigen Extraction by B Lymphocytes from Plasma Membrane Sheets Using Confocal...
1 Introduction
2 Materials
2.1 Preparing Antigen-Containing PMS Using Miniwells
2.2 Internalization of PMS-Bound Antigen by B Cells
2.3 Image Acquisition Using Confocal Microscope
2.4 Image Processing and Analysis
3 Methods
3.1 Preparing Antigen-Containing PMS Using Miniwells
3.2 Internalization of PMS-Bound Antigen by B Cells
3.3 Image Acquisition Using Confocal Microscope
3.4 Image Processing and Analysis
4 Notes
References
Chapter 9: Analysis of Intracellular Vesicles in B Lymphocytes: Antigen Traffic in the Spotlight
1 Introduction
2 Materials
2.1 Cells
2.2 Fixed Samples
2.3 Live Samples
2.4 Transfections
2.5 Microscope
2.6 Software for Image Analysis
3 Methods
3.1 Visualising Antigen Traffic in Fixed Cells
3.1.1 Preparation of Immunofluorescence Samples
3.1.2 Imaging of the Immunofluorescence Samples
3.2 Analyzing Antigen Traffic in Live Cells
3.2.1 Transfections
3.2.2 Preparing the Samples
3.2.3 Imaging of Live Samples
3.3 Image Analysis
3.3.1 Deconvolution by Batch Processing
3.3.2 Analysis of Clustering
3.3.3 Colocalization Analysis in Fiji
3.3.4 Spot Colocalization and Tracking in Imaris
4 Notes
References
Chapter 10: Visualizing Key Signaling Components of Macropinocytosis and Phagocytosis Using Confocal Microscopy in the Model O...
1 Introduction
2 Materials
2.1 Cell Culture
2.2 Cell Preparation for Microscopy
2.3 Confocal Microscopy
2.4 Yeast Preparation
3 Methods
3.1 Cell Culture
3.2 Labeling Yeast with Alexa 633
3.3 Monitoring Temporospatial Distribution of Key Signaling Components During Macropinocytosis and Phagocytosis with Standard ...
3.3.1 Seed Cells into a 4-Well Chamber for Imaging
3.3.2 Monitoring Temporospatial Distribution of Key Signaling Components During Macropinocytosis and Phagocytosis with Standar...
3.3.3 Monitoring Signaling Events of Phagocytosis Using the AiryScan Technique
4 Notes
References
Chapter 11: Imaging GPCR-Mediated Signal Events Leading to Chemotaxis and Phagocytosis
1 Introduction
2 Materials
2.1 Cell Culture and Chemical Stocks
2.2 Time-Lapse Confocal Imaging
2.3 EZ-TAXIScan System
3 Methods
3.1 Time-Lapse Imaging for LPS Chemotactic Signaling
3.2 Micropipette Assay for Folate Chemotaxis
3.3 EZ-TAXIScan Assay for Folate Chemotaxis
3.4 Engulfment Assay of Live Bacteria Particles by Confocal Imaging
4 Notes
References
Chapter 12: High-Throughput Imaging of Arrays of Fluorescently Tagged Yeast Mutant Strains
1 Introduction
2 Materials
2.1 Yeast Mutant Arrays and Query Strain
2.2 General Stock Solutions
2.3 Solid Media for SGA Replica Pinning Protocol
2.4 Liquid Media for HTP Sample Preparation and Imaging
2.5 Accessories and Equipment
2.5.1 SGA
2.5.2 HTP Imaging
3 Methods
3.1 SGA Strategy for Introducing Fluorescent Markers into Yeast Mutant Strain Arrays
3.2 High-Throughput Preparation of Yeast Cells and HTP Imaging
3.2.1 Day 0: Preparing Plasticware and Media (Done Beforehand)
3.2.2 Day 1: Preparing Yeast Overnight Cultures
3.2.3 Day 2: Subculturing
3.2.4 Day 3: HTP Imaging of Plates of the Deletion Collection Array
3.2.5 Day 3: HTP Imaging of Plates of the TS Collection Array
3.3 Image Analysis
4 Notes
References
Chapter 13: Visualizing the Dynamics of T Cell-Dendritic Cell Interactions in Intact Lymph Nodes by Multiphoton Confocal Micro...
1 Introduction
2 Materials
3 Methods
3.1 DC Isolation and Peptide Loading
3.2 Naïve CD4+ T Cell Isolation and Primary Amine Labeling
3.3 Adoptive Transfer
3.4 Minimally Invasive Surgery for the Intravital Microscopy (IVM) of Popliteal Lymph Node
3.5 Acquisition for Intravital Microscopy (IVM) of Popliteal Lymph Node
3.6 Data Analysis for Intravital Microscopy (IVM) of Popliteal Lymph Node
3.6.1 Correct Thermal Drift and Improve Image Quality by Applying Deconvolution
3.6.2 Track Cells and Calculate Colocalization
3.6.3 Calculate the Colocalization and Cell-Cell Distance
4 Notes
References
Chapter 14: Studying Neuronal Biology Using Spinning Disc Confocal Microscopy
1 Introduction
2 Materials
3 Methods
3.1 Coating Plates with Matrigel
3.2 Thawing Human-Induced Pluripotent Stem Cells (i3PSCs)
3.3 Passaging i3PSCs
3.4 Cryopreservation
3.5 Generating Fluorescent i3PSCs
3.6 Differentiation of i3Neurons
3.7 Axon Morphology: Imaging and Measurement
3.7.1 Sample Preparation
Neuronal Branching
Neurite Length
3.7.2 Axon Morphology Imaging
3.7.3 Axon Morphology Analysis
4 Notes
References
Chapter 15: Method for Acute Intravital Imaging of the Large Intestine in Live Mice
1 Introduction
2 Materials
2.1 Animals
2.2 Surgical Materials
2.3 Devices and Reagents for Intravital Microscopy
2.4 Microscopes and Components (See Note 2)
3 Methods
3.1 Surgery to Image Through the Serosa
3.2 Surgery to Image from the Lumen and Intestinal Epithelium (See Note 5)
3.3 Imaging Parameters
3.4 Image Processing
4 Notes
References
Chapter 16: Fluorescence Lifetime Imaging as a Noninvasive Tool to Study Plasmodium Falciparum Metabolism
1 Introduction
2 Materials
3 Methods
3.1 Sample Preparation
3.2 Microscope Setup
3.3 Image Acquisition A-Comparison Between Uninfected and Infected RBCs
3.4 Image Acquisition B-Metabolic Inhibition of Trophozoite-Stage iRBCs
3.5 FLIM Analysis
4 Notes
References
Chapter 17: Developing Analysis Protocols for Monitoring Intracellular Oxygenation Using Fluorescence Lifetime Imaging of Myog...
1 Introduction
2 Materials
2.1 Transfection of Living Cells with Myo-mCherry
2.2 Cell Treatment with Rotenone and Antimycin A
2.3 Imaging Setup
2.4 IRF Measurements (Multiphoton Microscopy)
2.5 Controlled Experimental Environment with a Range of Stable Oxygen Concentrations for Imaging and Calibration
3 Methods
3.1 Transfection of Living Cells with Myo-mCherry
3.2 Fluorescence Two-Photon Image Acquisition
3.3 IRF Measurement (Multiphoton Microscopy)
3.4 Controlled Experimental Environment with a Range of Stable Oxygen Concentrations for Imaging
3.5 Myo-mCherry Calibration and Cell Treatment with Rotenone and Antimycin A
3.6 Measurements of the Oxygen Partial Pressure (pO2) at Each Imposed Oxygen Concentration
3.7 FLIM Analysis
3.8 Obtaining the Intracellular pO2 from Lifetime Data at Each Imposed Oxygen Concentration
3.9 Statistical Analysis
4 Notes
References
Chapter 18: FLIM Imaging for Metabolic Studies in Live Cells
1 Introduction
2 Materials
3 Methods
3.1 Expression of the Sensor Proteins in U2-OS Cells (Day 1)
3.2 Turning on the FLIM System (Day 2)
3.3 Donor-Only Control Using CP-TMR-SMX
3.4 Measure Donor Lifetime with Acceptor
3.5 Control Experiments Using the Inhibitor Sulfapyridine
4 Notes
References
Chapter 19: A Step-by-Step Guide to Instant Structured Illumination Microscopy (iSIM)
1 Introduction
2 Materials
2.1 Hardware and Image Acquisition Software
2.2 Cell Culture
2.3 Immunocytochemistry
3 Methods
3.1 Image Acquisition Protocol (Specific for MetaMorph Acquisition Software)
3.2 Image Post Processing
3.2.1 Image Deconvolution
3.2.2 Image De-striping Using Fourier Space Notch Filers
4 Notes
References
Correction to: Confocal Microscopy
Index

Citation preview

Methods in Molecular Biology 2304

Joseph Brzostowski Haewon Sohn Editors

Confocal Microscopy Methods and Protocols

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-bystep 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.

Confocal Microscopy Methods and Protocols

Edited by

Joseph Brzostowski and Haewon Sohn NIAID, National Institutes of Health, Rockville, MD, USA

Editors Joseph Brzostowski NIAID National Institutes of Health Rockville, MD, USA

Haewon Sohn NIAID National Institutes of Health Rockville, MD, USA

ISSN 1064-3745 ISSN 1940-6029 (electronic) Methods in Molecular Biology ISBN 978-1-0716-1401-3 ISBN 978-1-0716-1402-0 (eBook) https://doi.org/10.1007/978-1-0716-1402-0 © This is a U.S. government work and not under copyright protection in the U.S.; foreign copyright protection may apply and Springer Nature US 2021, Corrected Publication 2021 All rights are reserved 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. Immune cells (green) of the colonic mucosa surrounding the vascular network (magenta) and collagen fibers (cyan). Cover Credit: Marco Erreni and Prof. Roberto Weigert. 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 In the new age of amazing fluorescence imaging technologies, confocal microscopy maintains its prowess as the workhorse imaging modality for cellular biology in laboratories around the world. Beautifully engineered turnkey systems have made confocal microscopy reachable for the everyday biologist and even the theoretical mathematician who might have the need to just sit down and look. Recent advances in scanning speed, electronic noise reduction, detector sensitivity, and super-resolution capability have now made confocal microscopy a preferred platform for live-cell imaging. In addition, creative peripheral equipment such as protein printers and microfluidic devices has widened the highway for investigators to drive their ideas forward into the era of the lab-on-a-chip. The aim of this methods book is to provide step-by-step examples for a wide range of imaging protocols that can be tailored by researchers to fit their favorite organism or cell type. It is assumed that the reader will have had basic training in wet lab techniques and confocal microscopy; however, all protocols will be written in a manner that even a novice to the field should be able to follow successfully. Our authors have provided exciting applications for fixed-cell, live-cell, phenotype screening, super-resolution and intravital imaging techniques. This methods book also offers multiple chapters on fluorescence lifetime imaging microscopy (FLIM) as applied on a laser scanning microscope. We believe that upcoming improvements in scanning speed and detector technology will make FLIM an even more powerful live-cell technique in the near future. We want to thank all the contributors of this work from the bylines to the acknowledgments for their precious time and effort in writing the chapters, improving ideas, making suggestions, and of course, their patient copyedits. We also want to give a special thanks to John Walker, the senior editor, for his support and guidance and the staff at Springer Protocol; without them, this book could not be published. Before we let you go to turn the pages, we would like to leave you with what the great Yogi Berra had purportedly said, “You can learn a lot just by looking.” This T-shirt philosophy, perpetuated over the years by fellow imagers, cannot be understated—for to truly understand the function of a protein within a cell, one must know where it localizes. We are confident that our coauthors share the hope that the protocols and helpful notes within will give you the opportunity to hit home runs throughout your research career. Rockville, MD, USA

Haewon Sohn Joseph Brzostowski

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

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1 Advances in Confocal Microscopy and Selected Applications . . . . . . . . . . . . . . . . . W. Matt Reilly and Christopher J. Obara 2 Choosing Fluorescent Probes and Labeling Systems. . . . . . . . . . . . . . . . . . . . . . . . . Kimberly Jacoby-Morris and George H. Patterson 3 General Considerations for Acquiring a Three-Color Image by Laser Scanning Confocal Microscopy . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Joseph Brzostowski 4 Microfabricated Devices for Confocal Microscopy on Biological Samples . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Nicole Y. Morgan 5 ZEISS Airyscan: Optimizing Usage for Fast, Gentle, Super-Resolution Imaging . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Xufeng Wu and John A. Hammer 6 High-Resolution Multicolor Imaging of Mitochondria in Lymphocytes . . . . . . . Munir Akkaya, Pietro Miozzo, and Margery G. Smelkinson 7 Protein-Retention Expansion Microscopy (ExM): Scalable and Convenient Super-Resolution Microscopy. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Paul Tillberg 8 Analysis of B Cell Receptor-Mediated Antigen Extraction by B Lymphocytes from Plasma Membrane Sheets Using Confocal Microscopy . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Abhijit Ashok Ambegaonkar and Haewon Sohn 9 Analysis of Intracellular Vesicles in B Lymphocytes: Antigen Traffic in the Spotlight. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Sara Herna´ndez-Pe´rez, Marika Runsala, Vid Sˇusˇtar, and Pieta K. Mattila 10 Visualizing Key Signaling Components of Macropinocytosis and Phagocytosis Using Confocal Microscopy in the Model Organism Dictyostelium discoideum . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Xuehua Xu, Joseph Brzostowski, Sharmila Ramachandra, Smit Bhimani, Yan You, and Tian Jin 11 Imaging GPCR-Mediated Signal Events Leading to Chemotaxis and Phagocytosis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Miao Pan and Tian Jin 12 High-Throughput Imaging of Arrays of Fluorescently Tagged Yeast Mutant Strains . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Mojca Mattiazzi Usaj, Dara S. Lo, Ben T. Grys, and Brenda J. Andrews

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Visualizing the Dynamics of T Cell–Dendritic Cell Interactions in Intact Lymph Nodes by Multiphoton Confocal Microscopy . . . . . . . . . . . . . . . . . Billur Akkaya, Olena Kamenyeva, Juraj Kabat, and Ryan Kissinger 14 Studying Neuronal Biology Using Spinning Disc Confocal Microscopy . . . . . . . Javier Manzella-Lapeira, Joseph Brzostowski, and Jenny Serra-Vinardell 15 Method for Acute Intravital Imaging of the Large Intestine in Live Mice. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Marco Erreni, Andrea Doni, and Roberto Weigert 16 Fluorescence Lifetime Imaging as a Noninvasive Tool to Study Plasmodium Falciparum Metabolism . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Javier Manzella-Lapeira and Joseph Brzostowski 17 Developing Analysis Protocols for Monitoring Intracellular Oxygenation Using Fluorescence Lifetime Imaging of Myoglobin-mCherry . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Greg Alspaugh, Branden Roarke, Alexandra Chand, Rozhin Penjweini, Alessio Andreoni, and Jay R. Knutson 18 FLIM Imaging for Metabolic Studies in Live Cells . . . . . . . . . . . . . . . . . . . . . . . . . . Heejun Choi 19 A Step-by-Step Guide to Instant Structured Illumination Microscopy (iSIM) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Alexander Zhovmer and Christian A. Combs Correction to: Confocal Microscopy . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Index . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

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Contributors BILLUR AKKAYA • Laboratory of Immune System Biology, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Bethesda, MD, USA MUNIR AKKAYA • Laboratory of Immunogenetics, National Institute of Allergy and Infectious Diseases, NIH, Rockville, MD, USA GREG ALSPAUGH • Laboratory of Advanced Microscopy and Biophotonics, National Heart, Lung, and Blood Institute (NHLBI), National Institutes of Health (NIH), Bethesda, MD, USA ABHIJIT ASHOK AMBEGAONKAR • Laboratory of Immunogenetics, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Rockville, MD, USA ALESSIO ANDREONI • Laboratory of Advanced Microscopy and Biophotonics, National Heart, Lung, and Blood Institute (NHLBI), National Institutes of Health (NIH), Bethesda, MD, USA BRENDA J. ANDREWS • The Donnelly Centre, University of Toronto, Toronto, ON, Canada; Department of Molecular Genetics, University of Toronto, Toronto, ON, Canada SMIT BHIMANI • Chemotaxis Signaling Section, Laboratory of Immunogenetics, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Rockville, MD, USA JOSEPH BRZOSTOWSKI • NIAID, National Institutes of Health, Rockville, MD, USA ALEXANDRA CHAND • Laboratory of Advanced Microscopy and Biophotonics, National Heart, Lung, and Blood Institute (NHLBI), National Institutes of Health (NIH), Bethesda, MD, USA HEEJUN CHOI • Janelia Research Campus, Howard Hughes Medical Institute (HHMI), Ashburn, VA, USA CHRISTIAN A. COMBS • NHLBI Light Microscopy Facility, National Institutes of Health, Bethesda, MD, USA ANDREA DONI • Unit of Advanced Optical Microscopy, IRCCS Humanitas Research Hospital, Rozzano, Milan, Italy MARCO ERRENI • Unit of Advanced Optical Microscopy, IRCCS Humanitas Research Hospital, Rozzano, Milan, Italy BEN T. GRYS • The Donnelly Centre, University of Toronto, Toronto, ON, Canada; Department of Molecular Genetics, University of Toronto, Toronto, ON, Canada JOHN A. HAMMER • Cell and Developmental Biology Center, National Heart, Lung and Blood Institute, National Institutes of Health, Bethesda, MD, USA SARA HERNA´NDEZ-PE´REZ • Institute of Biomedicine, MediCity Research Laboratories, University of Turku, Turku, Finland; Turku Bioscience, University of Turku and Åbo Akademi University, Turku, Finland KIMBERLY JACOBY-MORRIS • Section on Biophotonics, National Institute of Biomedical Imaging and Bioengineering, National Institutes of Health, Bethesda, MD, USA TIAN JIN • Chemotaxis Signaling Section, Laboratory of Immunogenetics, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Rockville, MD, USA JURAJ KABAT • Research Technologies Branch, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Bethesda, MD, USA

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OLENA KAMENYEVA • Research Technologies Branch, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Bethesda, MD, USA RYAN KISSINGER • Research Technologies Branch, Rocky Mountain Laboratories, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Hamilton, MT, USA JAY R. KNUTSON • Laboratory of Advanced Microscopy and Biophotonics, National Heart, Lung, and Blood Institute (NHLBI), National Institutes of Health (NIH), Bethesda, MD, USA DARA S. LO • The Donnelly Centre, University of Toronto, Toronto, ON, Canada; Department of Molecular Genetics, University of Toronto, Toronto, ON, Canada JAVIER MANZELLA-LAPEIRA • Twinbrook Imaging Facility, Laboratory of Immunogenetics, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Rockville, MD, USA MOJCA MATTIAZZI USAJ • The Donnelly Centre, University of Toronto, Toronto, ON, Canada; Department of Chemistry and Biology, Ryerson University, Toronto, ON, Canada PIETA K. MATTILA • Institute of Biomedicine, MediCity Research Laboratories, University of Turku, Turku, Finland; Turku Bioscience, University of Turku and Åbo Akademi University, Turku, Finland PIETRO MIOZZO • University of Massachusetts Medical School, Worcester, MA, USA NICOLE Y. MORGAN • Microfabrication and Microfluidics Unit, Biomedical Engineering and Physical Science Shared Resource, National Institute of Biomedical Imaging and Bioengineering, National Institutes of Health, Bethesda, MD, USA CHRISTOPHER J. OBARA • Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, VA, USA MIAO PAN • Chemotaxis Signal Section, Laboratory of Immunogenetics, National Institute of Allergy and Infectious Disease, NIH, Rockville, MD, USA GEORGE H. PATTERSON • Section on Biophotonics, National Institute of Biomedical Imaging and Bioengineering, National Institutes of Health, Bethesda, MD, USA ROZHIN PENJWEINI • Laboratory of Advanced Microscopy and Biophotonics, National Heart, Lung, and Blood Institute (NHLBI), National Institutes of Health (NIH), Bethesda, MD, USA SHARMILA RAMACHANDRA • Chemotaxis Signaling Section, Laboratory of Immunogenetics, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Rockville, MD, USA W. MATT REILLY • Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, VA, USA; Department of Molecular & Cellular Biology, Harvard University, Cambridge, MA, USA BRANDEN ROARKE • Laboratory of Advanced Microscopy and Biophotonics, National Heart, Lung, and Blood Institute (NHLBI), National Institutes of Health (NIH), Bethesda, MD, USA MARIKA RUNSALA • Institute of Biomedicine, MediCity Research Laboratories, University of Turku, Turku, Finland; Turku Bioscience, University of Turku and Åbo Akademi University, Turku, Finland JENNY SERRA-VINARDELL • Medical Genetics Branch, National Human Genome Research Institute, National Institutes of Health, Bethesda, MD, USA MARGERY G. SMELKINSON • Research Technologies Branch, National Institute of Allergy and Infectious Diseases, NIH, Bethesda, MD, USA

Contributors

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HAEWON SOHN • Laboratory of Immunogenetics, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Rockville, MD, USA VID SˇUSˇTAR • Institute of Biomedicine, MediCity Research Laboratories, University of Turku, Turku, Finland; Turku Bioscience, University of Turku and Åbo Akademi University, Turku, Finland PAUL TILLBERG • HHMI, Ashburn, VA, USA ROBERTO WEIGERT • Laboratory of Cellular and Molecular Biology, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA XUFENG WU • Light Microscopy Facility, National Heart, Lung and Blood Institute, National Institutes of Health, Bethesda, MD, USA XUEHUA XU • Chemotaxis Signaling Section, Laboratory of Immunogenetics, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Rockville, MD, USA YAN YOU • Chemotaxis Signaling Section, Laboratory of Immunogenetics, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Rockville, MD, USA ALEXANDER ZHOVMER • Laboratory of Molecular Cardiology, National Institutes of Health, Bethesda, MD, USA

Chapter 1 Advances in Confocal Microscopy and Selected Applications W. Matt Reilly and Christopher J. Obara Abstract Over the last 30 years, confocal microscopy has emerged as a primary tool for biological investigation across many disciplines. The simplicity of use and widespread accessibility of confocal microscopy ensure that it will have a prominent place in biological imaging for many years to come, even with the recent advances in light sheet and field synthesis microscopy. Since these more advanced technologies still require significant expertise to effectively implement and carry through to analysis, confocal microscopy-based approaches still remain the easiest way for biologists with minimal imaging experience to address fundamental questions about how their systems are arranged through space and time. In this review, we discuss a number of advanced applications of confocal microscopy for probing the spatiotemporal dynamics of biological systems. Key words Confocal microscopy, Resolution, Linear unmixing, Hyperspectral imaging, Superresolution microscopy, Fluorescence recovery after photobleaching (FRAP), Fluorescence lifetime in photobleaching (FLIP), Fluorescence correlation spectroscopy (FCS), Fluorescence Lifetime Imaging Microscopy (FLIM), Deconvolution, Colocalization, Image analysis, Machine learning, Neural network

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Introduction Over the past several decades, confocal microscopy has emerged as one of the preeminent tools of modern biology. In confocal microscopes, light from planes away from the focal plane of the microscope objective is blocked from reaching the detector through the addition of a pinhole in the conjugate plane of the microscope. The resulting ability to reduce out-of-focus light through optical sectioning has proven invaluable both for its intended purpose of imaging thicker samples and also for improving the signal-tonoise of samples that are quite thin. This technology has expanded greatly in many fields from biology to materials science, and it promises to continue empowering new types of investigation across the scientific community as new implementations and adaptions

Joseph Brzostowski and Haewon Sohn (eds.), Confocal Microscopy: Methods and Protocols, Methods in Molecular Biology, vol. 2304, https://doi.org/10.1007/978-1-0716-1402-0_1, © This is a U.S. government work and not under copyright protection in the U.S.; foreign copyright protection may apply and Springer Nature US 2021

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become available. One of the more remarkable aspects of confocal imaging has been the number of new technologies and approaches that have used a confocal microscope as the base and the broad applications where confocal microscopes have been applied in unusual ways to reveal subtle properties of a system. In this chapter, we provide a brief overview of some of these technologies aimed at the new user of a confocal microscope. Fluorescence microscopy is inherently a multidimensional tool. At the very least, it provides a dataset that is resolved in two spatial dimensions (generally referred to as x and y) and contains a third dimension that roughly corresponds to the amount of fluorescent label present at each location (intensity). In confocal microscopy, even relatively basic applications often can expand this simple threedimensional dataset into multiple additional dimensions. For example, the optical sectioning afforded by confocal microscopy allows users to collect high-quality data at several planes of focus, creating a four-dimensional dataset that contains information about the label intensity in a sample in x, y, and z coordinates. In time lapse imaging, the fluorescence information is collected over time, in addition to space; and multicolor imaging also collects datasets with information that is also resolved in its color. These dimensions (and several additional ones) are in principle distinct from one another, but limitations in labeling and hardware often require sacrifices in some dimensions in order to make gains in other ones. It is often very experiment-dependent which dimensions are of importance, but a lack of resolution in any important dimension can ruin even a carefully planned experiment. Over the years, many clever approaches have been introduced to improve the resolution in each of these dimensions, often with specific penalties in other dimensions that are (hopefully) not of importance for a specific biological question. In our experience, this can become quite overwhelming for new users, as they try to keep track of which components of their experiment are benefiting and which components have been limited. In this chapter, we aim to discuss some of the most common approaches that have been implemented to make specific gains along some of the more frequently limiting dimensions, paying specific attention both to where resolution has been gained and where it has been lost. Whenever possible, we have tried to cite reviews that are accessible to new users.

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Resolution of Intensity A powerful advantage of fluorescence microscopy over electron microscopy is its dependence on fluorescent labels. Fluorescent labels can provide inherently quantitative information about the amount of a specific protein (or other labeled biomolecule) at a defined location. When images are properly acquired from

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appropriately labeled samples, the intensity associated with each pixel of a digital image can serve as a quantitative (or at least semiquantitative) measure of the amount of label that was present at the spatial location (reviewed in [1]). With a proper set of controls, fluorescence intensity can even be converted to an approximate molecular density, assuming some reasonably uniform distribution of labels per molecule [2]. “Resolution” along this axis, then, is defined by difference in the amount of fluorescent label that must be present in two structures in order to confidently identify one as “brighter” than the other. Like all dimensions, the resolving power of intensity is based on the ability of the hardware (and downstream processing) to distinguish signal from noise. Essentially all the components of both the hardware and the software involved in collecting the image are capable of affecting this ability, and it is also heavily dependent upon the preparation and labeling of the sample. A few of the more commonly considered limiting factors for resolution of intensity are discussed briefly below, but keep in mind these are only a few easily selectable factors and many more will be referenced throughout the following sections. 2.1 Sample Preparation

There are far too many sample-specific considerations to list here, but it is worth remembering that even a theoretically perfect microscope could not provide a clear, specific image if the specificity or density of labeling in the sample is poor. Many resources exist from commercial vendors and academic core facilities to help optimize sample preparation for specific confocal microscopy applications. The selection of fluorescent label is of particular importance, and the color, brightness, and photostability all can have a substantial effect on the experiment. For the intensity to be an accurate description of the amount of label, it is imperative that there is an approximately linear relationship between concentration of the label and relative brightness. While this is often approximately true, many factors can confound this relationship. For instance, many dye molecules are subject to self-quenching at high concentrations (e.g., [3]), and most fluorochrome groups show at least some sensitivity to pH and other environmental factors (something to keep in mind if labeling specific subcompartments of cells) (e.g., [4]). In addition to the kind of fluorescent label used, the way in which that label is attached to the structure of interest can also affect the resulting imaging. Labeling is generally performed through staining reagents, genetically encodable tags, or hybrid approaches that combine the two. For each approach, there are specific concerns for the proportion of the target labeled (penetrance), the amount of off-target labeling (specificity), the ability of the label to reach all parts of a population of molecules in an

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unbiased way (bias), and the tendency of the label to correctly report the spatial location of its conjugated biomolecule (accuracy). We briefly discuss some of these concerns in the context of common applications below, but we point readers to much more exhaustive reviews for a more complete discussion (e.g., [5–7]). 2.1.1 Labeling with Stains

There are many diverse tools available to stain cells for fluorescence microscopy, from small molecules like phalloidin (filamentous actin stain) and DAPI (nuclear stain) to antibodies for specific proteins of interest. Besides the obvious considerations of whether they work on fixed cells vs. live cells, etc., there are a few basic qualities of stains that may be beneficial or detrimental, depending on the application.

Penetrance

The penetrance of a stain depends largely on the physical properties of the staining molecule and the conditions under which staining is performed. Larger molecules tend to have a harder time penetrating a dense region of the sample than smaller ones do, and ideally the biophysical characteristics of a dye will match the requirements for penetrating the environment where the target is located. (An obvious example is that a dye for the cytoplasm that cannot pass the plasma membrane cannot be used in live, intact cells.)

Specificity

In general, stains tend to have less specificity than the competing genetically encodable tags, unless the molecule is fluorogenic. No matter how well a user washes a sample, it is unlikely they will ever be able to remove all of the stain molecules that are not bound to the target. Fluorogenic labels surmount this problem by changing their structure such that they only fluoresce when they are bound to the target, but these reagents are not available for most cellular targets of interest.

Bias

It is often difficult to identify whether a particular stain introduces bias without testing it in each specific application directly. Antibodies are particularly prone to this problem, as a result of the highly specific nature of their paratope’s dependence on the epitope on the target. Proteins that are in different conformations or part of large complexes often fail to stain with antibodies under many conditions. Bias can also be derived from spatially inconsistent penetrance of the stain into the sample. This has been demonstrated and quantified for antibodies [8] but is probably true to some extent for most stains. Developments of smaller reagents such as nanobodies and protocols for revealing obscured epitopes can often decrease this source of error (e.g., [9]).

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Accuracy

Problems with staining accuracy primarily arise when the size of the reagent used for staining approaches the resolution of the microscope. In this case, the fluorescence observed from the label can be physically displaced from the actual biomolecule being targeted. Most stains do not have this issue with traditional confocal microscopy, but this becomes an increasing concern when using superresolution techniques as discussed in Subheading 4.

2.1.2 Labeling with Genetically Encodable Tags

Most fluorescent genetically encodable tags are fluorescent proteins [10], though if the biomolecule of interest is RNA additional strategies exist to accomplish the same result [11, 12]. Previously the number of options for fluorescent protein usage was limited to a few basic choices, but the last 20 years have seen the expansion of the fluorescent palette into essentially any visible color or property needed by the experiment. A number of useful resources exist to help users choose their appropriate tool [13–15]. Unlike many protein-specific stains, genetic tags are relatively easy to use for live-cell imaging, but they also require addition of genetic material to the cell either through transient transfection or endogenous genome modification. The tagging is usually accomplished by fusing the gene for the fluorescent tag to the gene of a protein of interest with some flexible peptide linker separating the two, in order to allow each piece to fold correctly. Some considerations for the categories introduced above are listed below.

Penetrance

Fluorescent proteins generally show fantastic penetrance, since they can be in principle translated at a one-to-one ratio with the biomolecule of interest. However, in practice the ratio will not actually be quite as high since not all fluorescent proteins fold correctly, so some proportion of tagged molecules will not be visible [14]. Additionally, the method by which the tagged protein is introduced to the cell will affect the total penetrance of the label into the population. For instance, a transient transfection generates additional tagged versions of the protein on top of the complement of native protein generated by the cell. This effect can be removed by knocking a fluorescent protein into the endogenous gene locus, but this is a relatively time-consuming and expensive process and requires a careful evaluation of the proportion of alleles where it was successfully achieved [16].

Specificity

Direct genetic tagging theoretically ensures very high specificity since the target and tag are translated simultaneously. However, nonspecific signal can confound analysis under a few conditions. First, if the fluorescent tag can be separated from the protein of interest through proteolysis or incomplete translation, it is possible for the protein to become physically displaced from the tag and confuse the analysis. Careful design of linkers and biochemistry controls can alleviate this problem [13]. Second, the tag may fold

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even if the protein itself does not. An example of this that has been well described is that many red fluorescent proteins are resistant to lysosomal degradation and can accumulate in lysosomes even if their tagged molecular partners are misfolded or degraded. This is worth particular care when designing experiments, since the size of a fluorescent protein may not be trivial compared to the object to be tagged, and the placement of the tag may have a substantial effect on localization, folding, or function. Bias

Fluorescent proteins are generally less prone to bias than antibody stains, assuming that the introduction of the tag does not alter the distribution or function of the protein (which generally needs to be checked empirically). Keep in mind that bias can be introduced not only by direct interference of the tag but also by the way the tag is introduced into the sample. Overexpression of a functional tagged protein can still introduce aberrant localization or function as a result of improper protein expression level.

Accuracy

Problems with accuracy are rare for genetically encoded tags, since linker distances keep the fluorescent tag within a few nanometers of the biomolecule of interest. The exception is the labeling of very large biomolecules like RNA or certain extracellular matrix proteins (e.g., collagen), where a genetic tag will be preferentially located to one part of the molecule. In these cases, it is important to remember that there may be regions of the molecule that are distant from the tag. Because of the limitations discussed above for both stains and genetically encoded tags, a number of hybrid approaches have been introduced to attempt to find middle grounds that are advantageous to specific biological situations. Biochemists have used small epitope tags to label proteins of interest for pull downs and other applications for many years, but these tags have also proven useful in microscopy approaches since antibodies to these epitopes tend to be high affinity and relatively insensitive to protein folding state. The small size of these epitopes also permits the tagging of proteins that cannot be tagged with larger constructs like fluorescent tags. There are inherent negatives, however. This approach still requires genetic tagging and management of either laborious genome tagging or the risks of overexpression, and it also requires the application of antibody-based stain, with all the caveats and limitations mentioned above. It will be exciting to see how the use of this application expands in the years to come as tools like nanobodies [17] and new staining procedures like post-expansion staining [18] become more commonplace. Another hybrid application is the development of genetically encodable tags that can be used to directly covalently couple an organic dye molecule to the protein of interest. A number of commercial systems now exist for this, with more under ongoing

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development (e.g., HaloTag, SNAP-tag, etc.). The advantage of these systems is the flexibility in the color of labeling, and often superior photostability and brightness inherent with organic dyes [19]. Historically, there were serious limitations in the ability to stain samples specifically through this mechanism, but recent advances with fluorogenic dyes [20, 21] have substantially improved this. Additionally, these genetic tags permit the coupling of complex chromophores that can report properties of their environment with either fluorescence or spectral shifts (e.g., [22, 23]), or chromophores that resist biochemical environments that would quench traditional fluorescent labels (e.g., [24]). This mechanism of labeling does still retain the inherent negatives of requiring a genetic tag, however. 2.1.3 Labeling Structures vs Specific Biomolecules

As with most scientific experiments, the best approach for sample preparation will depend highly on the experimental question being investigated. As a general rule, labeling a structure made up of many molecular players (e.g., an organelle or a particular cell type) often is better served by prioritizing a highly penetrant approach than by focusing on a high specificity one, since a stain or label that only lights up a few isolated spots on a structure will make the reconstruction of the complete structure very difficult. In contrast, studies examining complexes made up of fewer molecular players (e.g., a particular protein on an organelle) are often confounded by a low specificity stain, since there is smaller amount of true signal present to begin with. It will be more carefully discussed in the following sections, but a number of the techniques designed to improve one dimension of resolution in microscopy will exacerbate or ameliorate each of these sensitivities.

2.2 Detector Efficiency

One component of the microscope hardware that is often a limiting factor for signal-to-noise is the efficiency and sensitivity of the detector used. A more sensitive detector requires a smaller difference in label abundance to distinguish two objects as having different “brightness,” assuming all other optical and sample components are the same. It is important to keep in mind that it is very rare that all other optical components are actually the same, however, so this metric can be a useful place to start but can be misleading if used as the sole criteria for deciding the best way to perform a confocal microscopy experiment. The most common metric reported by manufacturers and microscope companies is the quantum efficiency of the detector. This is theoretically the ratio of detected electrons to the number of incident photons on the detector, but in practice many mechanical and optical factors affect this measurement, so we find it best to think of it as only a relative measure of detector efficiency. Other important considerations include the dynamic range of the

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detector, whether the detector can efficiently detect light at the desired wavelengths, the response time of the detector, and whether the detector can support parallelized detection or not. Collectively, the way these qualities are integrated into the rest of the optical path will define the net sensitivity of the detector to reliably detect differences in intensity. Laser scanning confocal microscopes usually have a one-dimensional detector that sequentially is exposed to light from each position in the resulting image. These detectors are usually based on photomultiplier tubes (PMTs), Avalanche photodiodes (APDs), or hybrid detectors with properties of each (HyDs). These detector types have generally much lower quantum efficiency than the more expensive cameras used in parallelized confocal imaging approaches, but advantages inherent to a one-dimensional optical path, high readout speed, and strong linear amplification steps after initial collection mean that these scopes usually have at least as good of signal-to-noise profiles as their parallelized competitors. Each of these detector subtypes comes at a variety of price points and sensitivities, and to some extent their incorporation into commercial systems is the result of patent rights and company manufacturing pipelines. In general, APDs are more sensitive and have higher quantum efficiency than PMTs, but their highly sensitive nature makes them easy to damage by overexposure to light. In practice, this limits the dynamic range over which they can be used, leading many applications of confocal microscopy to be more easily achieved using PMTs or HyDs. The use of newer materials in the cathode has also increased the sensitivity of modern PMTs, the most commonly used in the current era are gallium arsenide phosphide (GaAsP) and have both sensitivity (and fragility) approaching that of APDs. Confocal microscopes that probe multiple points in a sample simultaneously often use parallelized detectors like cameras to read out information. The standard camera used in fluorescence microscopy is the charge-coupled device (CCD). This is an arrayed detector with many pixels arranged in space that collect light simultaneously and are then read out sequentially one pixel at a time during a time lag when the shutter is closed. Many CCDs implemented in biology use a frame transfer CCD structure, where the charge collected during an exposure is transferred to a dark part of the camera chip during read out so that light can continue to be read out without closing an actual shutter. This increases the speed with which the camera can be operated and minimizes the data lost when a shutter must be closed. These cameras are fairly inexpensive and in traditional front-illuminated form provide quantum efficiencies generally in the range of or slightly higher than modern PMTs and APDs, but in practice they are far less sensitive because of the increased noise inherent in such a large detector area. This can be

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partially overcome by using very slow read out speeds for signal amplification, but with the low signals common in fluorescence microscopy this can often become a major limitation. The sensitivity is greatly increased if the detector is back-thinned, a process that has become more standard for microscopy equipment and can increase the quantum efficiency of the detector up to nearly 95%. Since the emitted light in a confocal experiment can often be quite dim, many high-grade confocal systems will use an electron multiplying-CCD (EM-CCD). EM-CCDs are back-thinned CCD arrays that use an electron amplification step during readout to amplify the signal and achieve single photon sensitivity. This removes much of the readout noise that is inherent in CCD arrays, but the electron amplification step can also introduce noise of a different form, which must be accounted for. Very high sensitivity cameras like EM-CCDs and high-grade CCDs come with a negative: their high sensitivity will also read false positive signals as a result of dark current. This is the detection of electrons that do not arise from photons, and a major source of this noise is thermal energy on the camera chip. For this reason, these cameras normally come equipped with a thermoelectric cooling system for the camera chip, which runs most efficiently at temperatures in the range of
80  C. The resulting cameras are the gold standard for high sensitivity, high signal-to-noise detection, but as you might expect they come at high price point. A more recent parallelized detection strategy to come on the scene is the scientific complementary metal-oxide semiconductor (sCMOS) camera. These cameras use a parallelized array of semiconductor pixels to collect light, but unlike CCD arrays, sCMOS cameras perform electron amplification at the pixel rather than at the readout point. This key difference increases the speed with which the camera can be operated and the size of the field of view but introduces a non-Gaussian source of noise to the image collection. Additionally, many sCMOS cameras read out through a “rolling shutter” mechanism where each row of the image is collected at a slightly different time. This greatly decreases the readout noise, but it can create a time distortion where the top and bottom of the image were collected at slightly different times. It is rare that an object visible in confocal microscopy moves quickly enough for this to be an issue, but it is something for experimenters to keep in mind. Historically, the decreased noise and increased speed of this approach has been revolutionary for parallelized confocal microscopy approaches like spinning disk and swept-field microscopy, but many users working in low light situations still prefer to use EM-CCDs, though that may change as newer, more sensitive sCMOS cameras hit the market.

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Temporal Resolution The early implementations of the confocal microscope mostly used a series of galvanometer mirrors to raster scan a single point of excitation and emission across a sample. This strategy resolves spatially distinct points in a sample by the time when the detector receives the signal from a particular location—essentially trading off time resolution of the detector to gain spatial resolution (Fig. 1). Since the components of this linear system are all quite fast, this has made the resulting scanning point microscopy techniques generally quick enough to manage even many live-cell applications. However, a result of this trade-off is that the speed of scanning point microscopes is inherently coupled to the size of the image taken. Images with more pixels (either because of finer spatial sampling or larger sample size) tend to take longer to image. Over the years, several technological advances have been introduced that attempt to decrease this temporal cost. We discuss briefly a few of the more common technologies for increasing speed below.

3.1 Resonant Scanners

Traditional laser scanning confocal microscopes (LSMs) generally use a series of galvanometer mirrors to raster scan the laser beam back and forth across the sample. Light is collected simultaneously from the same point where it is being excited. In its simplest implementation, the side-to-side motion of one mirror controls the position of the beam in the x dimension, and the rotation of a second mirror controls the position of the beam in the y dimension. Generally, each pixel in the resulting image corresponds to one unique combination of the positions of the two mirrors. Since the range and timing of the mirrors is independently controllable by the signal sent to the galvanometers, this affords traditional LSM confocal the wide range of flexibility in zoom and rotation angle that many users find helpful in capturing the often-chaotic structures present in biological samples. However, in order to ensure correct positioning of the mirrors, there is a mechanical limit to the speed with which the mirror can move from one position to the next. This is the consequence of the time it takes to actually absorb the momentum of the moving mirror to bring it to a stop. The resonant scanner was introduced as a way to speed up the mechanics of the scanning point microscope. Unlike traditional galvanometers, the resonant scanner does not spend a constant amount of time at each pixel. Instead, it oscillates back and forth across its range of motion at defined frequency that is inherent to the material properties of the resonant galvanometer itself. A resonant scanner is paired with a traditional galvanometer that controls one axis of the acquisition and is limited to speeding up a single dimension of acquisition, but more recently a custom system has been introduced that combines a resonant scanner with an acoustic

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Fig. 1 Schematics of data collection by several forms of confocal microscopy. (a) Traditional scanning point confocal microscopes sequentially scan each pixel of an image, spending enough time in each pixel to collect an amount of emitted light that is proportional to the amount of fluorescent label at that location. The theoretical pixels in the first row are labeled according to the time each pixel’s emitted light was collected. (b) The fluorescence intensity of the pixels in the first row of (a). (c) Line scanning and swept-field confocal microscopes parallelize this process, exciting and collecting data from several points in the sample at once. (d) In contrast to (b), parallelization allows the intensity of multiple pixels to be measured simultaneously. (e) Spinning disk confocal microscopes use a rapidly rotating set of disks to sweep the points of interrogation across the sample in an arc, collecting many points simultaneously on an arrayed detector such as a camera

liquid lens for resonant imaging along two dimensions [25]. The increase in speed as a result of implementing resonance comes with a loss of control, however, as the material properties of the resonant mirror are not generally adjustable. It also means that the laser does not spend the same amount of time on each pixel, which can lead to

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slight unevenness in bleaching rates and decreased dynamic range. In practice, the use of a resonant scanner increases the speed of acquisition substantially but decreases the ability to optimize the signal-to-noise of the sample. Slit Confocal

Another approach to speed up acquisition in confocal microscopy is to simply excite and collect light from several points in the sample simultaneously. A variety of approaches for this exist, though each includes some trade-off in order to gain the increased speed. The simplest implementation uses a thin slit instead of a pinhole to block out-of-focus light. This allows the sample to be scanned with a line of focused light and collected on a linear array of detectors, which can greatly increase the speed of acquisition. However, pixels that neighbor one another along the same dimension as the slit are subject to cross-contamination by one another’s out-of-focus light, leading to an uneven resolution along the two dimensions of the image. This cross-contamination can be qualitatively reduced in the final image result using more advanced forms of deconvolution, but this has historically been difficult to use in this context quantitatively. Linear detectors are also generally less efficient than cameras, and consequently this type of technique seems to require more laser power and have more issues with photobleaching. These two traits together have generally made this approach unpopular for most biological questions, but a more advanced implementation of this has been introduced commercially in combination with Airyscan imaging, where the lack of a physical pinhole allows this to be more effectively implemented.

3.3 Swept-Field Confocal

A more common implementation is the simultaneous generation of several true confocal beams using an array of pinholes in a plate. These can then be swept across the sample rapidly through the use of galvanometers. The emission light is then passed back through the pinhole and onto an arrayed detector like a camera. This results in substantially increased speed for large field-of-view applications, but has often led to mild limitations in resolution, since most implementations of this type have a fixed pinhole size that cannot be adjusted appropriately for the wavelength and the large size of the detector surface increases the susceptibility to noise. Some more recent technological implementations have decreased these problems by using slits with adjustable pinholes or increasing the spacing between pinholes to decrease the noise.

3.4 Spinning Disk Confocal

The most popular implementation of parallelized confocal scanning is spinning disk microscopy. In this implementation, the confocal pinholes are arrayed on a rotating Nipkow disk. Excitation light is focused through a set of paired microlenses on another disk rotating in parallel, and multiple points of excitation are swept across the sample in high speed arcs to reconstruct the final image on a more

3.2

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efficient arrayed detector (usually sCMOS or EM-CCD). The functional consequences of this are twofold: Large images can be collected significantly faster than even resonant scanners for the same pixel density, and the fact that each pixel is sampled by many sequential illumination steps means lower laser intensities are often possible. The increased efficiency of these detector types can also increase the signal-to-noise, all other things equal. As always, this increase comes with a trade-off. The closer spacing of the pinholes in the disk means that there is increased interference compared to modern swept-field and scanning point techniques, and most spinning disks have a fixed pinhole width which is not adjustable to optimize as a function of the wavelength, so they pay a small price in spatial resolution to gain these benefits. They also do not generally have the ability to adjust the size or angle of the region being scanned, so long, thin samples can sometimes be difficult to image without tiling approaches, and use of an arrayed detector often limits the ease of many color combinations (see Subheading 5). Despite these limitations, the spinning disk is still the workhorse for many live-cell imaging applications, particularly when large fields of view are necessary.

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Spatial Resolution One common application of light microscopy is to measure distance between or across fluorescently labeled structures. In practice, this is usually achieved using the intensity of specific pixels to decide where an object is located, followed by measuring the distance between the selected locations. While this is probably reasonably accurate in many applications, it is important to remember that the data collected in fluorescence microscopy is not a direct representation of the underlying sample, and some additional factors should be considered. Optical microscopes are limited in their spatial resolution by the diffraction limit of light. This limit represents an inherent negative of the wave nature of light—interference prevents the unequivocal localization of either exciting or emitting light beneath a defined fraction of the respective wavelength [26]. Thus, objects that are closer than this minimum distance cannot be distinguished as separate objects by this technique. However, the effects are even more limiting than a simple barrier. Microscopes are conventionally unable to distinguish objects beneath their resolution limit, but they are also less efficient at transferring information about structures the closer they get to this limit. The efficiency of a microscope as a function of the frequency (1/size) of the information can be measured as an optical transfer function (OTF). The details are beyond the scope of this chapter, but we point interested readers to a number of fantastic references that compare the OTFs of various systems and optical

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configurations [27–29]. The important consequence of this is that the closer an object gets to the resolution limit of the microscope, the more of the photons derived from the object will be erroneously assigned to neighboring pixels. The result is that measuring the spatial size or distance in an optical image is only a reasonably accurate representation of objects with sizes or distances significantly larger than the resolution limit. As you might imagine, this has caused many people to try to find ways to increase the efficiency and accuracy with which these numbers can be resolved, some of the more common of which are discussed below. Deconvolution

The result of the inherent imperfection of microscopes at transferring information of multiple frequencies is that images collected are slightly blurred compared to the actual sample. Smaller structures are more strongly affected by the blurring than larger ones. Technically, the true underlying image is distorted by the OTF. Since the blurring of a signal by a function is mathematically known as a convolution, the attempt to correct for this blurring computationally is a process known as deconvolution (reviewed in [30, 31]). In theory, if you know the true distribution of the label and the OTF of the microscope, you can predict the image the microscope will generate. Conversely, if you know the resulting image and have a reasonable estimate of the OTF with which that image was collected, you can estimate the true distribution of label that was most likely to give rise to the observed image. Like all mathematical processes, the calculation is only as good as the input data, and error can be amplified through the process if care is not taken. Some considerations are listed below.

4.1.1 Mechanism of Basic Deconvolution

Deconvolution has proven to be an essential component of the microscopist’s toolkit, particularly when studying structures or distances close to the resolution limit of the technique, where the OTF is less efficient. This is particularly amplified in threedimensional samples, since the OTF is much less efficient as the sample leaves the center of the focal plane. Thankfully, this blurring is predictable and relates directly to the defined optical properties of the microscope—namely the numerical aperture of the objective lens (NA), the refractive index of the immersion medium (RI), and the wavelength of light being used. The OTF can be measured directly by imaging an object smaller than the resolving power of the microscope, usually a synthetically generated bead of known size. If we image the bead in a perfect focal plane, the result is an image of a diffraction-limited spot. If the focal plane is moved in either direction, however, the image of the point becomes a more diffuse disk of light that increases in size as the focal plane moves farther away. By sweeping through many optical planes, an hourglass-shaped 3D volume representing the signal captured from a single point of light can be constructed. This volume is

4.1

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Fig. 2 Deconvolution with a theoretical PSF. (a) A COS7 cell expressing mApple-Sec61b-TA to label the ER membrane was imaged on a spinning disk confocal microscope. (b) Deconvolution of the raw image data in (a) was carried out using a theoretical PSF. (c) Fluorescence intensity along the lines indicated across a single subdiffraction-limited ER tubule in either (a) or (b), showing increased spatial resolution and signal-to-noise for a small structure (ER tubules in this cell type are ~100–120 nm in diameter)

called the point spread function (PSF), and PSFs for an experiment can be estimated both empirically and theoretically. The true PSF of the microscope is the Fourier transform of the OTF, so once the PSF is known all the information necessary is collected. If the PSFs for a light source are representative, then a mathematical deconvolution can be used to reverse the blurring and provide a more accurate representation of the specimen (Fig. 2). As such, deconvolution provides the microscopist with an opportunity to correct for well-defined, systematic blurring in the image of a specimen. 4.1.2 Weaknesses of Basic Deconvolution

Mathematical processes in science are only as good as the assumptions they make. In practice, there are two approximations implicit in deconvolution that can be problematic if they are not reasonable. Theoretically, the PSF (and OTF) of a microscope are infinite functions that expand (or decay) continuously to infinity with unlimited precision. In practice, we estimate them as finite datasets with voxels estimated at the resolution of the microscope, and as their values approach our limit of detection, noise is introduced. Deconvolution is in many ways a similar process to division—it is normalizing to a known skew in the data. Like division, as the number being divided by approaches zero, the effect on the data becomes larger. Thus, it is important to limit the OTF to frequencies where the efficiency of transfer is significantly nonzero, or the error in the OTF will amplify the error in the data. This generally manifests itself as artifactual, high frequency noise in the image or artificial breaks in continuous structures. Many well-established algorithms exist for calculating where this should be, and most commercial implementations utilize at least one of these approaches, though it is always prudent to check the results of deconvolution by eye, as well.

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The OTF of the microscope for a particular wavelength is the net sum of all the contributing factors in the hardware. In theory, this should be consistent from sample to sample, and if the room and configuration were actually identical, even from day to day. In practice, many small things can aberrate the OTF slightly—from temperature in the room to imperfections on the surface of the coverslip. While these do not have a dramatic effect on the OTF, the exact OTF with which any part of the image was transferred may not match exactly the OTF used for deconvolution. This mismatch can slightly mis-assign fluorescence to improper pixels, introducing a lower confidence in the intensity values that are the result. Some commercial techniques that include deconvolution have found experimental ways to decrease this effect (e.g., Airyscan), and some more advanced deconvolution algorithms use iterated approaches to minimize its contribution. Users should also beware of commercial deconvolution wizards that may normalize or cut data in a way that greatly perturbs the resulting intensity values, if any quantification is to be performed. 4.1.3 Workflow of Basic Deconvolution

To integrate deconvolution into a standard 3D imaging workflow, either a theoretical or empirically determined PSF can be used. A theoretical PSF can be calculated using the known NA, RI, and wavelength of light, and this can be acceptable for many experimental approaches. However, this theoretical PSF does not account for the nontheoretical aberrations that are specific to a given microscope. As a result, deconvolution best practices call for the measurement of PSFs using fluorescent beads that match the wavelength(s) of light being collected in the experiment. These PSFs should be captured using the same parameters that will be used to image the sample (e.g., the same objective, immersion medium, mounting medium, coverslip, etc.). An important consideration for collecting both the PSF and experimental images is the use of ideal Z spacing, which should be based on the optimal Nyquist sampling for a given optical configuration. Most microscope control software can automatically set the optimal Z spacing for a given experiment. It is also important that the entirety of the data is within the dynamic range of the detector, and saturation will cause truncation errors in the deconvolution. The mathematics of deconvolution are outside the scope of this review, but a number of advanced deconvolution algorithms have been integrated into easyto-use commercial software packages. Considering that blurring due to out-of-focus light is well understood and is dependent on known optical parameters, deconvolution can be a crucial step in obtaining the truest image of a biological sample, especially close to the resolution of the technique.

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4.1.4 A Note on Conflicting Deconvolution Literature

There are few topics in optical imaging that are as contentious as deconvolution. Some groups hold that deconvolution is necessary for reasonable imaging, while others avoid it out of fear of introducing artifacts. We will defer to the experts in respective fields for when it should be applied in that context, but we have taken the following approach in our own work. If deconvolution is working, it moves the image slightly closer to the truth. If it is failing, it is introducing artifacts. All calculations based on data propagate some degree of error based on the validity of the assumptions and the confidence in the inputs. Thus, if the user believes the PSF/OTF input to be good, deconvolution can improve the resolution slightly in the spatial dimensions at a price in the confidence of the resolution in the intensity dimension. For measuring distances much larger than the resolution of the microscope, deconvolution is unlikely to have much of an effect, but for distances closer to the diffraction limit, a responsible deconvolution with the associated controls may slightly improve your precision. We also caution against the generalization that deconvolution should never be used before quantifying intensity. While this may be a good general rule due to decreased confidence in the intensity dimension, specific applications may benefit from appropriately normalized deconvolution, especially when the objects to be quantified are close to the resolution limit of the technique.

4.2 Superresolution Imaging

Fluorescence microscopy has allowed the direct study of specific proteins in situ, but it has been of interest for biologists to be able to study the environment where these proteins reside closer to the size scale of a protein. Since these distances remain fully out of reach of conventional confocal microscopy for the reasons described above, there has been considerable effort toward developing techniques that increase the confidence with which small distances can be quantified from microscopy images. Deconvolution provides one route to accomplish this goal, but it comes with a commensurate price in confidence of the intensity values collected. Another route is to directly engineer the microscope and imaging workflow to have a more efficient OTF. The resulting technologies are collectively known as superresolution microscopy, and each superresolution technique uses a unique way of circumventing this barrier (reviewed in [32, 33]). Most superresolution techniques are widefield-based or use post-imaging computation, and as such will not be discussed in this chapter. However, there are a number of reviews and direct comparisons of these technologies that have been published, which we point readers to if they are interested in relative strengths and weaknesses [28, 32–35]. Below, we detail a few of the more common superresolution techniques that use a confocal microscope as their base.

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Fig. 3 Airyscan and confocal imaging of the same cell, showing increased spatial resolution and signal-tonoise afforded by Airyscan reconstruction. The endoplasmic reticulum of a primary mouse fibroblast is visualized with an ER-targeted fluorescent label. The region shown is collected with either laser scanning confocal (a) or Airyscan imaging (b). The indicated regions are enlarged in (c) and (d), respectively. Comparison of the enlarged regions reveals distinguishable substructures in Airyscan imaging that are obscured by the lower spatial resolution of standard confocal microscopy 4.2.1 Airyscan Imaging

Airyscan imaging has become popularized recently with a commercial implementation, but the idea of using a Sheppard’s Sum for reconstructing higher resolution information has been around for some time [36, 37]. In its most common form, Airyscan combines elements of hardware with an obligatory deconvolution step to provide slightly improved spatial resolution and signal-to-noise of the fluorescent label’s distribution in the sample (Fig. 3). The basic principle is to direct the focused, one-dimensional emission light from a scanning point microscope through an open pinhole and onto an array of detectors, rather than cutting the out-of-focus light with the pinhole and directing the readout into a single

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one-dimensional detector. Functionally, this accomplishes two goals: (1) it allows the computational generation of an effectively smaller pinhole while retaining the signal of a larger one (and the commensurate improved spatial resolution), and (2) it provides a direct estimate of the 2D PSF of the microscope at every pixel, which can be used to inform the deconvolution. The former point is accomplished through means of a Sheppard’s Sum, which is described in detail elsewhere [37]. The latter is achieved by moving the detection scheme for a single PSF from a one-dimensional to a two-dimensional detector for each pixel, and the relationship in intensity across the pixel array provides a rough estimate of the two-dimensional PSF of the microscope at each pixel. Recall from earlier that one common source of error in deconvolution is variation in the actual local PSF throughout an image. This effectively allows that source of error to be reduced, providing a “data-driven” deconvolution. Keep in mind that the other major source of deconvolution error is unaffected, and overaggressive Airyscan reconstruction can still introduce high frequency artifacts in the resulting image. Like all image preparation involving deconvolution, Airyscan reconstruction will come with a loss in the accuracy of intensity values on a per pixel basis, but since these are derived from a locally relevant PSF, the error decays within the local neighborhood. Consequently, Airyscan can retain the capacity for quantitative imaging over structures made up of many pixels, but this data should be treated cautiously as those structures become very small or are made up of smaller numbers of pixels. A last benefit of a spatially arrayed detector in a point scanning microscope is that the direct estimation of the PSF can greatly enhance the precision of the deconvolution for PSFs with unusual shapes. This approach has been effectively commercialized in the context of Airyscan to generate a “fast” version. In this mode, Airyscan has essentially become a computationally generated slit confocal, where the x to y distortion (see Subheading 3.2) can be partially rescued by the deconvolution. It will be exciting to see in the coming years if this approach to scanning point microscopy may allow other, more complex PSF structures for a variety of more advanced applications. 4.2.2 Depletion Technologies

As discussed in several sections above, in scanning point microscopes a single point of interrogation is raster scanned through the sample to each pixel sequentially. We generally represent the resulting pixel as a square for ease of visualization, but the volume being interrogated is actually the PSF of the microscope. For shorter wavelengths of light, the PSF is slightly smaller, and the resulting image can be slightly higher resolution. For this reason, it takes more pixels to image the same sample at maximum resolution (i.e., Nyquist sampling) in these conditions. Thus, if the PSF could be artificially reduced in size, smaller pixels could be achieved, and the resulting image would contain higher spatial resolution.

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Depletion technologies take advantage of the fact that the fluorescent labels we use to mark biomolecules of interest in light microscopy actually need to have two things happen to be observed. First, photons from the excitation laser must be absorbed by the chromophore, bringing one or more electrons to an excited state. Second, when these electrons relax to their ground state, they must do so through a process that releases photons of the emission color (other, nonradiative pathways generally exist). The likelihood that an electron changes from one state to another in a particular time window is governed by a statistical operator known as the transition dipole. Thus, the probability at any given time that a particular chromophore releases light is given by the product of two probabilities (Pground ! excited * Pexcited ! ground + hv), each governed by its respective transition dipole. The transition dipoles are complex entities based upon the quantum mechanical properties of the respective states, but they are affected by the likelihood of interacting with specific photons. This can be controlled by the power of the laser applied via the microscope. The first and most widely implemented depletion technology was stimulated emission depletion (STED) microscopy [38]. This approach takes advantage of the fact that the chromophore can additionally interact with photons of other wavelengths, some of which increase the probability of a nonradiative pathway to relaxation. As a result, by exposing a part of the PSF to photons of such a wavelength, the probability that photons are released from that region will decrease in a manner that is roughly proportional to the strength of this laser. STED microscopes use a beam shaping optic to generate a donut shaped “depletion beam,” that is projected colinearly with the excitation beam. This suppresses the probability that dye molecules around the edges of the PSF emit, creating a functionally smaller PSF (reviewed in [35]). Unlike Airyscan and deconvolution, STED and related technologies can provide substantially higher spatial resolution than diffraction-limited confocal microscopy, in extreme cases as much as an order of magnitude higher. The resolution achieved is partially controlled by the power of the depletion beam, so a user can adjust the resolution to some extent, provided the sample can support the increased radiation. This approach can greatly clarify the distribution of small structures that otherwise would be misinterpreted as single large structures, since the accidental excitation of label molecules in neighboring pixels is largely suppressed. However, STED comes with a series of disadvantages that can also confound experiments. First, depletion is a nonlinear process and is not distributed evenly throughout the PSF of the microscope. Effectively quantifying the depleted PSF size can be challenging in practice, and the quantitative analysis of the intensity dimension is a laborious process requiring many label-specific density controls. In its simplest form, the x and y resolution are improved in STED, but the resolution in z is not, leading to an even more highly asymmetric

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PSF than usual. The high laser power needed for conventional STED is often not compatible with photosensitive samples, and phototoxicity or photofixation can create artifactual biological results. As with any technique, the higher the spatial resolution, the more pixels needed to reconstruct the same image. Since most STED microscopes are implemented in a scanning point modality, this decreases the speed with which the same image can be collected, and bleaching can be a problem in practice. A number of improvements have been implemented that help to ameliorate some of these limitations [35, 39]. For instance, systems built with dual objectives or z depletion masks can substantially improve the resolution in the z-dimension. The introduction of time gating with high speed detectors has greatly improved the signal-to-noise of the technique, and the introduction of fluorescent labels with dark states that can be more easily achieved has substantially decreased the amount of radiation required and resulting phototoxicity (e.g., RESOLFT, [40]). The introduction of superresolution imaging has revolutionized many fields that study the distribution of molecules on the nanoscale. An area of emerging interest going forward is the development of approaches for correlating super-resolved imaging data with the context in which the biology occurs. It is also likely that the approaches discussed here will gain in both power and widespread use as commercial systems become more popular that can combine these approaches with complementary advanced imaging techniques based on widefield approaches.

5

Chromatic Resolution Biological samples are highly complex systems made up of many different molecular players interacting in very diverse ways. Understanding the behavior and localization of these components often requires the consideration of more than one type of molecule simultaneously. Multicolor imaging has emerged as a very powerful tool for this application but has historically been limited to a handful of colors due to limitations in the number of distinguishable fluorescent labels. However, a very diverse panel of fluorescent tools has become available over the last 20 years [15], and it has become increasingly a limitation of the microscope hardware whether the spectra of different fluorescent labels can be distinguished. We briefly review common tools in confocal microscopy for separating the emission light from a sample in order to distinguish multiple colors, but keep in mind that most fluorescent tools also have unique excitation spectra. As a result, an optimized multicolor experiment will generally combine the tools discussed below with multiple lasers of different wavelengths in order to maximize chromatic signal-to-noise.

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5.1 Bandpass Filters, Notch Filters, and Dichroic Mirrors

The simplest way to isolate two fluorescent molecules with significantly different emission spectra is by reflecting the photons of undesirable colors before they reach the detector. Optical filters and dichroic mirrors are optical components (usually glass) that are coated with various materials so that they can reflect light of specific wavelengths. They are among the most common tools in all kinds of fluorescence microscopy, since they are fairly inexpensive, generally quite effective, and are not overly fragile. By placing optical filters in the light path, a microscopist can choose the subset of photons that reaches the detector selectively and image multiple colors. For a new user, the terminology used to describe optical filters can be confusing. (1) Bandpass filters are optical filters that only let a defined window of wavelengths pass. They are usually named by wavelength of light at the center of what they allow to pass and the range around the center (in nm, e.g., 525/50). (2) Notch filters are the opposite; they only reflect a defined set of wavelengths (usually a very thin band) and are generally referred to by the band (e.g., 488 notch filter). (3) Dichroic mirrors reflect light that is either of longer wavelength (long-pass) or shorter wavelength (short-pass) than a defined threshold and are referred to by their threshold and the direction (e.g., 647 long-pass). For a new user, two details are the most important to remember. First, no filter is perfect. The thresholds, transmission efficiencies, and reflection efficiencies are rarely 100% and are generally unique to the filter. As you might expect, more expensive filters are usually closer to ideal than less expensive ones, but the filters are usually delivered with reported specifics. Sometimes problems with an imaging experiment can be solved by just verifying that the filter used has the properties desired. Second, filters and dichroic mirrors are usually designed for optimum performance at a specific angle. The right filter at the wrong angle may have very different properties, and in our experience even commercial microscope systems can have filters installed at suboptimal angles, affecting their performance. Optical filters are nearly universally included in fluorescent microscopes. Even scopes with filter-independent emission paths (see below) still often use filters to focus and direct the excitation light into the objective. One strong advantage of using optical filters to separate emission light from a sample is that high-grade filters can be made that have minimal interference to the wavefront. This means that multiple parallel paths can be passed through the same filter, and for this reason nearly all the parallel imaging approaches to confocal microscopy rely almost exclusively on optical filters for emission separation before reaching the camera or arrayed detector. The major weakness of optical filters is they are not adjustable. The reflective nature of the filter is a result of its material properties, so if the color of light that needs to reach the detector changes during the experiment, the filter must be

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physically moved out of place and replaced with another. Many microscopes use motorized filter wheels or turrets to do this in an automated way, but even the fastest of these are orders of magnitude slower than most of the other optical components of the system, and this can slow down a multicolor experiment dramatically. There are also only a defined number of spots for filters in most hardware, so optical filters (in combination with available lasers) often define the colors that can be used on a specific microscope. 5.2 Adjustable Chromatic Filtering Systems

Imaging systems that use cameras or arrayed detectors for detection of spatial or frequency information are limited to using optical filters for splitting colors since the other available tools will distort the wavefront and create aberrations in the image. However, an advantage of scanning point implementations is that the one-dimensional optical path can be redirected in space as a function of its wavelength. This is generally accomplished through the use of a prism, diffraction grating, gradient dichroic, or tunable crystal. While the mechanics of each of these systems is slightly unique, the end result is quite similar. Different colors of light are spatially separated from one another and can be selectively redirected to the detector as a function of their location. An advantage of these approaches is that the range of colors which can be directed to the detector is tunable, and the same microscope can be optimized for essentially any fluorescent tag without physically removing or changing parts. This is of particular importance for use of the rapidly expanding repertoire of ratiometric biosensors, which often have low signal-to-noise and require optimum selection of spectra to effectively record the underlying biology. A last advantage of continuous chromatic splitters is that depending on the way the light is redirected after splitting, it is possible to collect an entire continuous spectrum at each pixel. This is done using a linear arrayed detector downstream of a prism or diffraction grating (i.e., a “spectral detector”). In this arrangement, a scanning point confocal microscope essentially becomes a spatially derived spectrophotometer, and the chromatic properties of different compartments can be examined (see below).

5.3 Multispectral Imaging

All of the tools for chromatic separation listed above are still limited by the requirement that the excitation and emission spectra of the fluorophores be well separated. In cases where fluorophores have overlapping spectra, standard approaches cannot discriminate between the potential sources of emitted light [41]. This has effectively limited most microscopy experiments to a handful of colors. Just as the technologies previously described in this chapter have pushed the resolution in intensity, space, and time, the development of spectral discernment by linear unmixing has substantially improved the resolving power of confocal microscopy in the

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Fig. 4 Multispectral imaging of a COS7 cell with six fluorescently labeled organelles. (a) Raw spectral data was obtained by simultaneously exciting with 3 lasers and passing emitted light through a diffraction grating and across 25 linearly arrayed detectors. Coloring corresponds to the wavelength scale bar. The spectra measured within the highlighted regions are shown in (b). (b, upper panel) Measured spectra of singly labeled cells and (b, lower panel) measured spectra of the three regions depicted in the 6-label cell shown in (a). (c) After linear unmixing of the raw spectral data, a 6-channel image was produced showing minimal bleed-through between the channels. (d) Isolated channels depicted in (c)

chromatic dimension (Fig. 4). A recent implementation of multispectral imaging has shown the successful separation of 120 colors, subject to a priori knowledge about labeling combinations [42], and the use of arrayed spectral detectors has made linear unmixing a tractable experiment in time lapse imaging [43]. In multispectral imaging, the detailed spectral signature of each fluorophore is used to identify and quantify the source(s) of emitted light at each pixel in a recorded image with many competing colors.

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Fluorescent molecules generally emit their photons across a distribution of wavelengths known as an emission spectrum. All of the factors that govern a fluorophore’s emission profile are not known, but at least some of them are the result of bond vibrations in the sample causing slight variations in the energy of excited states. As mentioned in the section on sample preparation, this emission spectrum can be dependent on the local environment of the dye molecule (e.g., pH, crowding, redox state, etc.). In order to account for this, most multispectral imaging experiments require the collection of reference spectra from single-color controls labeled under identical conditions to a multicolor sample. These report the expected spectral distribution for each quantifiable unit of a specific protein. An image of a sample with many labels in it is then collected under the same conditions. For each pixel, the observed spectra can be assumed to be a linear combination of the collected parent spectra, and the relative amount of each of the individual labels can be predicted. Like deconvolution, linear unmixing makes use of a mathematical approach to uncouple several convolved signals. Similarly, the math is only capable of being as accurate as the implied assumptions. If the spectrum of any of the single components in the sample is not effectively represented by the control spectra, linear unmixing will introduce chromatic artifacts. Usually, this manifests itself as bleed-through between the channels. Thankfully, this is relatively easy to spot by eye in the resulting image, though it can require experiments to directly verify that the spectrum has changed rather than just induced colocalization of the two fluorescent labels. A number of commercial instruments now offer a turn-key implementation of both the hardware and software required to perform linear unmixing, but the practice has not yet become widespread in the community. This is probably at least partially because it is laborious to collect and establish proper single-color controls for each experiment. As more work is published using spectral analysis, it will be interesting to see how universal collected spectra are. It may be that this process will be able to be streamlined based on control samples or more advanced computational techniques. An additional area that has not yet been developed but may hold great promise is the use of excitation spectra for unmixing. Some of the work previously mentioned combined excitation and emission unmixing to maximize the number of colors [42], and recent work has implemented an excitation-only unmixing pipeline for light sheet microscopy [44]. Since excitation-based unmixing does not require a spectral detector, this may hold promise for eventually making linear unmixing a viable pipeline for microscopes with parallelized detectors like swept-field and spinning disk confocal microscopes. However, unlike light sheet microscopes that have separate excitation and emission optical paths, these still require the need to split the excitation and emission optical paths from one

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another. With current technology this would still require a moving optical filter, which may slow down the acquisition sufficiently that potential gains would be lost.

6

Resolution in Additional Orthogonal Dimensions Getting information resolved in the four dimensions described thus far in this chapter is the primary goal of most experiments in confocal microscopy. However, there are some more specialized forms of confocal microscopy that collect information about additional independent dimensions. With a few exceptions, implementations of microscopes to measure these additional dimensions are scanning point confocal microscopes. We briefly introduce the basic principles of one of the more common ones here since it is treated in quite a bit more detail in other chapters of this volume.

6.1 Fluorescence Lifetime Imaging

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As discussed earlier, the collection of light from a fluorochrome requires two steps. First, an electron must be stimulated to an excited state by photons from the excitation laser. Then, the electron must return to its lower energy state while releasing photons of the emission wavelength. Each of these transitions is defined by a unique transition dipole, which is partially the result of the quantum mechanical properties of the system. Since every fluorescent label has a unique chemical structure, many labels that radiate at similar wavelengths have transition dipoles of relaxation that vary widely in their probability per unit time. In practice, relaxation times are orders of magnitude faster than the speeds used in confocal microscopy. As a result, if a detector is fast enough and coupled with a pulsed laser excitation source to synchronize excitation, the fluorescence lifetime response curve at each pixel can be collected during the pixel dwell time. If robust calibration is performed, two separate fluorescent species can be distinguished by their lifetime, even if their actual colors are not separable. This is discussed more completely elsewhere [45, 46], but this information can be also very useful in distinguishing fluorescent labels with altered states, such as when performing Fo¨rster resonance energy transfer. In practice, this requires the lifetimes of the excited states be much less than the pixel dwell time, so there is a limitation to speed of imaging or size of field of view.

Resolution of Analysis Pipelines The answers to many questions that can be addressed using confocal microscopy are obvious from representative images, and this remains a primary way confocal microscopy is used in the literature. However, it is difficult for readers of a paper to identify how representative an image is or appreciate the heterogeneity of a

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sample or phenotype from a single (or even a few) images. Many groups have tried to improve upon this by performing qualitative categorizations of cells by eye and reporting the resulting proportion. While this is an improvement over no quantification at all, it is highly prone to implicit bias if the person performing the classification is not blinded to the condition. In a perfect world, the end result of a confocal microscopy experiment is quantified data with little to no human input that can provide bias. In practice, this is next to impossible to achieve for most questions. However, over the years many quantitative and semiquantitative analysis pipelines have been developed for various forms of analysis that can help users answer some questions. Colocalization analysis, for example, has become a common tool for studying spatial relationship between proteins, with several distinct analysis pipelines based on the subtleties of the question (reviewed in [47]). There are also a number of pipelines for detecting and tracking objects that are freely available (reviewed in [48]). It is beyond the scope of this chapter to cover these, but many commercial and academic resources are available to help new users get started. Despite this progress, many apparently simple tasks are very difficult to perform in an automated or blinded way. This remains a major hurdle for the reproducibility of imaging biology, since qualitative classifications are likely to vary substantially from lab to lab or even person to person. One exiting development on this front is the introduction of machine learning techniques (reviewed in [49]). These emerging technologies have revolutionized many fields from economics to speech recognition, but until recently have been technically challenging to access for biologists. The release of ilastik has provided access to a Random Forest classifier [50] that can be easily accessed by biologists with no experience with coding or machine learning protocols (Fig. 5, reviewed in [51]). These technologies and more advanced applications like convolutional neural networks and deep learning approaches have already begun to affect the field of bioimaging [52–54] and promise to revolutionize it in far more dramatic ways in the years to come.

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Resolution of Dynamic Properties of Ensemble Systems Identification and tracking of resolvable structures have been a common application of live-cell confocal microscopy, but the technique is not limited to the ability to study the motion of structures above the spatial limit. Although confocal microscopy and the associated techniques do not generally have the ability to resolve single proteins in the spatial dimensions, the location of proteins is partially encoded in the intensity dimension. Thus, by studying changes in the intensity dimension in defined locations, it is possible to extract significant information about the dynamic properties of things beneath the resolvable limit of the microscope.

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Fig. 5 Machine learning-assisted ER segmentation. (a) The endoplasmic reticulum of a COS7 cell with a fluorescent protein targeted to the luminal compartment visualized by Airyscan imaging. Automated segmentation of the ER network was attempted with (b) a machine learning approach (ilastik pixel classification workflow), (c) low threshold masking, and (d) high threshold masking. Low threshold masking performs well with the sparse ER network at the cell periphery, whereas high threshold masking performs well in the dense nucleus-adjacent ER network. The machine learning approach successfully segments both dense and sparse ER networks 8.1 Photobleaching Recovery Experiments

The most common technique for studying ensemble protein dynamics in living cells is fluorescence recovery after photobleaching (FRAP). To carry out FRAP on a cell expressing fluorescently tagged molecules, all of the fluorophores in a region of interest (ROI) are bleached through application of a high-intensity pulse of laser. Subsequently, increasing intensity is observed within the ROI as a result of movement of unbleached fluorescent molecule into this region. The rate of change of the intensity can be used to infer the mobility and diffusion properties of the tagged molecules. Although this basic approach has been used to study dynamic biological processes for over 40 years, many advancements in recent years have made FRAP a more powerful and widely applicable tool (reviewed in [55]).

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Fig. 6 Differential protein mobility in the ER membrane and lumen, as detected by FRAP. (a) A COS7 cell expressing mApple-Sec61b-TA (ER membrane) and PrSS-mEmerald-KDEL (ER lumen) was imaged before, during, and after a bleaching laser pulse was applied in the region marked by the yellow circle. Fluorescence in the ER lumen recovers rapidly in comparison to the ER membrane, due to rapid diffusion of the soluble KDEL-tagged protein in this compartment. (b, upper panel) Raw fluorescence intensity in the bleached lumenal ROI, as well as in an unbleached control region. Loss of fluorescence in the unbleached region is due to nonspecific imaging-based bleaching throughout the time course (b, lower panel). This nonspecific bleaching can be used to normalize the recovery curve of the bleached ROI. A second bleach step is performed to allow the presence of an immobile fraction to be distinguished from the removal of the bleached molecules from the total fluorescent pool. (c) Normalized recovery curves for the ER membrane and lumenal signals highlight the faster and more complete fluorescence recovery observed in the ER lumen within a defined time window

The FRAP technique can be separated into three distinct stages, which are (1) the pre-bleach, (2) the bleach, and (3) the post-bleach (Fig. 6). In the pre-bleach phase of the experiment, standard confocal images are obtained at low illumination intensity to provide a timepoint-zero reference for the fluorescence intensity

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in the ROI. During the pre-bleach, fluorescence intensity in the ROI is at its maximum (excluding any drastic changes in the state of the biological system being studied, which would render the data unusable). Once the reference image is acquired, the ROI can be photobleached. In this step, a high-intensity laser illuminates the ROI for a very short period of time—usually on the order of tens of milliseconds—to bleach the fluorophores in this region. After the bleach, fluorescence intensity in the ROI should be at its minimum. Best practices call for the high-intensity laser pulse to be as short as possible while fully bleaching the ROI, as localized heating due to longer pulse times may disrupt the biological system [56]. Following these two fast steps, the post-bleach phase involves live-cell imaging of the ROI using the same illumination settings as were used to acquire the pre-bleach reference. During this phase of the experiment, mobile, fluorescently tagged molecules can move into the ROI while bleached molecules are able to diffuse out. In aggregate, then, the fluorescence intensity in the ROI is expected to rise over time from the post-bleach minimum, with the fluorescence eventually stabilizing and reaching its recovery maximum. This post-bleach maximum can be compared to the pre-bleach intensity to calculate a “percent recovery,” which provides information about the fraction of tagged molecules in the ROI that are mobile. Failure to reach full recovery suggests that an immobile fraction of molecules exists, because immobile bleached molecules cannot move out of the ROI and will never regain their lost fluorescence. The interpretation of the plateau in the recovery curve as representing the immobile fraction is based on the assumption that the region being bleached is a negligible volume of the cell, however. For example, if a quarter of the molecules in the cell lie within the ROI and all the molecules are mobile, then one would expect the cell to reach equilibrium at uniformly 75% of its original intensity. A careless microscopist could misinterpret this reduction in the intensity of the plateau as a 25% immobile fraction. To avoid this error, most quantitative FRAP experiments are performed doing at least two subsequent bleach steps. An immobile fraction that was bleached in the first step cannot be “re-bleached” in the second, so the reduction in the plateau after the second recovery would be expected to be less. By comparing these two plateaus, the true resistant fraction can be determined. Whereas percent recovery is a time-insensitive metric, the “recovery rate” measures the speed at which fluorescence in the ROI is regained. Recovery rate is directly related to the ensembleaveraged diffusion coefficient of the tagged molecules, and analysis of recovery can be used to estimate biophysical parameters and binding/dissociation rates for the molecules of interest. As such, FRAP is a powerful approach for quantifying the ensemble dynamics of proteins in living cells, and the development of fast, high-

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quality and reliable turn-key confocal microscopes has made this technique available to a wide array of biologists. However, care must be taken in planning, executing, and analyzing FRAP experiments, as the geometry, volume, and bleach depth of the ROI can substantially change the estimated values of these biophysical parameters. Variations of FRAP include fluorescence loss in photobleaching (FLIP) and inverse FRAP (i-FRAP). In FLIP, an ROI is repeatedly bleached, but the post-bleach imaging is focused on quantifying the loss of fluorescence in other areas of the cell. This loss of fluorescence outside of the ROI is due to the movement of bleached molecules from the ROI into other regions, and as a result, FLIP can be used to determine whether different regions of the cell are interconnected [57]. In contrast to FLIP, i-FRAP measures the gain in fluorescence in regions outside of an ROI due to movement of unbleached molecules. In this approach, the ROI is not bleached, while the entirety of the cell outside the ROI is bleached. Thus, the efflux of fluorescent molecules out of organelles/compartments and into other areas of the cell can be measured. This measurement of a positive signal in i-FRAP is similar to FRAP and contrasts with FLIP, which depends on measurements of the absence of fluorescence. Taken together, FRAP and its many derivative techniques provide biologists with the opportunity to quantify the ensemble dynamics of their biological system of interest, all while using equipment that is readily available in most microscopy facilities. Intensity-based live imaging technologies like FRAP and FLIP have provided much information about the ensemble dynamic properties of proteins in cells, but they suffer from two fundamental limitations. First, they cannot account for the underlying geometries or asymmetric sources of molecular confinement. If proteins do not have uniform access to diffuse into or out of the ROI, the resulting curve will be the average of many variable recovery rates. Second, analysis of the recovery rate is largely dependent on the imaging speed of the microscope. A result of this is that FRAP experiments become increasingly hard to analyze the smaller the ROI, since the recovery curve will occur much faster. This can be modulated slightly by performing FRAP experiments on a parallelized confocal system like a spinning disk or a swept-field confocal, but these require specialized hardware to generate a specific ROI for bleaching, which is not always available. 8.2 Other Intensity Fluctuation Tools

In FRAP experiments, the dynamics of the steady state are analyzed by photobleaching to generate two distinguishable pools of a fluorescently labeled protein. Another way to distinguish pools is using labels that report properties of their environment. Many reagents exist that can report through either intensity or color the concentration of many biophysical properties of their surroundings.

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Perhaps the most famous example is the introduction of GCaMP to offer live imaging-based analysis of neuron firing in tissues or culture [58, 59]. These reagents can be used to study molecular dynamics from fluctuation in the intensity axis. A weakness with studying systems with high speed via intensity analysis using confocal is that the time it takes to raster scan an entire image or read out an entire camera chip can sometimes be an order of magnitude too slow for very rapid biological processes. One family of approaches that can be used to address this accomplish the goal by effectively decreasing the dimensions addressed in the experiment. For example, a scanning point confocal can scan a single line much faster than it can scan an entire image. If the line analyzed can be placed in such a way within the sample that the flow between two compartments can be analyzed, very rapid analysis can be performed on the flux of proteins or ions using kymograph analysis. This approach is called Line Scan microscopy, and it has been used effectively to measure flux between neighboring compartments (e.g., [60]). The dimensions can be decreased even further and used to probe a single point through a process known as fluorescence correlation spectroscopy (FCS). FCS is sufficiently fast that it can often identify biological properties of single molecule motion in and out of the PSF [61]. A more recent implementation performs FCS in a microscope outfitted for STED, which can decrease this volume even further for single molecule resolution [62, 63]. These approaches are based upon the assumption that the biological system where the line or point of interest is placed is static during the time of the experiment, however. While these have provided important biophysical information about many biological systems, their utility is somewhat limited if the system of interest has the ability to move in space while the experiment is being performed.

9

Conclusions The many advances in confocal microscopy-based technology developed over the last 40 years have generated a very versatile and diverse tool for probing the biological properties of living systems. Improvements in culture systems, fluorescent probes, and staging systems like microfluidic devices are likely to ensure the applicability of these tools only increases over time. It will be exciting to see in the coming years how these tools power the gradual transition of biological science from a field heavily dependent on observational imaging and quantitative experimental approaches to a field that can perform quantitative analysis of biological systems in their spatially relevant context.

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Acknowledgments We thank Erin O’Shea and Jennifer Lippincott-Schwartz for support and funding, and members of both labs for comments and suggestions regarding the chapter. We are especially thankful for Andy Moore and Carolyn Ott for critical reading of the manuscript. References 1. Waters JC (2009) Accuracy and precision in quantitative fluorescence microscopy. J Cell Biol 185:1135–1148 2. Jonkman J, Brown CM, Cole RW (2014) Quantitative confocal microscopy: beyond a pretty picture. Methods Cell Biol 123:113–134 3. Penzkofer A, Lu Y (1986) Fluorescence quenching of rhodamine 6G in methanol at high concentration. Chem Phys 103:399–405 4. Kneen M, Farinas J, Li Y et al (1998) Green fluorescent protein as a noninvasive intracellular pH indicator. Biophys J 74:1591–1599 5. Frigault MM, Lacoste J, Swift JL et al (2009) Live-cell microscopy – tips and tools. J Cell Sci 122:753–767 6. Dailey ME, Marrs GS, Kurpius D (2011) Maintaining live cells and tissue slices in the imaging setup. Cold Spring Harb Protoc 2011. pdb.top105 7. Burry R (2010) Immunocytochemistry: a practical guide for biomedical research. Springer, New York 8. Schnell U, Dijk F, Sjollema KA et al (2012) Immunolabeling artifacts and the need for live-cell imaging. Nat Methods 9:152–158 9. Syrbu SI, Cohen MB (2011) Methods in molecular biology. Methods Mol Biol 717:101–110 10. Rodriguez EA, Campbell RE, Lin JY et al (2016) The growing and glowing toolbox of fluorescent and photoactive proteins. Trends Biochem Sci 42:111–129 11. Vera M, Tutucci E, Singer RH (2019) Methods in molecular biology. Methods Mol Biol 2038:3–20 12. Trachman RJ, Truong L, Ferre´-D’Amare´ AR (2017) Structural principles of fluorescent RNA aptamers. Trends Pharmacol Sci 38:928–939 13. Thorn K (2017) Genetically encoded fluorescent tags. Mol Biol Cell 28:848–857 14. Costantini LM, Snapp EL (2013) Fluorescent proteins in cellular organelles: serious pitfalls

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Chapter 2 Choosing Fluorescent Probes and Labeling Systems Kimberly Jacoby-Morris and George H. Patterson Abstract Fluorescence microscopy is advantageous for investigating biological processes and mechanisms in living cells. One of the most important considerations when designing an experiment is the selection of an appropriate fluorescent probe. Equally important is deciding how the probe will be attached to the protein of interest. The advantages and disadvantages of different fluorescent probe types and their respective labeling methods are discussed to provide an overview on selecting appropriate fluorophores and labeling systems for fluorescence-based assays. Protocols are outlined when appropriate. Key words Fluorophores, Fluorescent proteins, Fluorescent probes, Fluorescent tags, Labeling

1

Introduction

1.1 Fluorescence Basics

Fluorescence microscopy provides the ability to directly visualize pathways, localization, and physiological events in biological systems in a minimally invasive manner [1, 2]. Labeling with fluorescent probes allows for measuring ensemble and single-molecule samples in real time. Researchers have some very powerful instruments to image their samples, such as the currently available commercially confocal microscopes [3]. However, even the most sophisticated microscopes are often limited by probes that are used for a given experiment. Thus, effort expended at the onset of imaging experiments to choose the “best-fitting” fluorophore will be rewarded over the course of the project. While it would be advantageous if we could simply recommend a clearly defined “best” fluorescent label, different labels tend to have different advantages which are dependent on the experiment. For example, protein–protein interactions or biosensors will require probe pairs that meet requirements for FRET analysis [1]. Rotational diffusion measurements will require probes that have non-zero anisotropies [4]. The spectral and photophysical properties of the chosen fluorophore(s) will determine the requirements for wavelength and time

Joseph Brzostowski and Haewon Sohn (eds.), Confocal Microscopy: Methods and Protocols, Methods in Molecular Biology, vol. 2304, https://doi.org/10.1007/978-1-0716-1402-0_2, © This is a U.S. government work and not under copyright protection in the U.S.; foreign copyright protection may apply and Springer Nature US 2021

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resolution required for the instrumentation as well [5]. Moreover, the breadth of currently available fluorescent labels makes it prohibitive to cover all in detail. Therefore, we instead provide an overview of basic fluorophore characteristics and some of the common labeling systems to help new researchers in determining the “best-fit” for their experiment. Fluorophores are available in a number of different forms which can be used for both covalent and non-covalent labeling of a target of interest [6, 7]. Broadly defined, fluorophore labeling systems can be placed into intrinsic and extrinsic classes which can be further described by being exclusively intrinsic, exclusively extrinsic, or a mixture. Intrinsic fluorophores are probes in which the molecules are naturally fluorescent, whereas extrinsic fluorophores normally come from exogenous sources and are used to label molecules of interest that are not inherently fluorescent. Intrinsic probe fluorescence derives from cofactors and aromatic amino acid compounds inherent to the structure. Examples of this type of fluorophore are proteins containing cofactors such as flavoproteins, NADHbinding proteins, and pyridoxyl-binding proteins [8]. Investigators often use changes in the fluorescence from these fluorophores as noninvasive monitors of cell physiology rather than probes to label a specific protein of interest [9]. Exceptions are when the cDNA for the proteins binding these intrinsic compounds have been cloned and used in genetic tagging of proteins of interest [10]. This of course requires re-introduction of the chimeric cDNA encoding the protein of interest and cofactor binding protein into the cell or tissue. Once introduced, the protein-probe chimera can be synthesized by the cell from the cDNA, and since the cofactors are normally produced by the cells, it is not necessary to add additional components to the sample in order to provide fluorescence. The genetic introduction of fluorescently labeled molecules into cells, tissues, and organisms has been a subject of intense development over the last 25 years [6, 7]. The cloning and introduction of the green fluorescent protein produced a system in which the probe is synthesized by the cell and also produces a fluorescent structure without exogenously added components [11]. The benefits of not having to add anything other than cDNA encoding chimeras of interest appeals to number of fields, but these proteins often lack the brightness and photostability of synthetic fluorophores. Therefore, in parallel with fluorescent protein development, hybrid solutions have been developed in which genetically encoded proteins were designed to bind covalently to exogenously added synthetic fluorophores [6, 7]. Synthetic fluorophores are advantageous because they are small, bright, and generally photostable compounds [12]. Moreover, the small size of these fluorophores compared to other labels (see Fig. 1) is an important consideration when selecting probes for

Choosing Labels and Fluorophores Fluorescein

SnapTag

HaloTag

IgG

IFP2.0

GFP

39

Quantum dot

5 nm

Fig. 1 Size comparison of common fluorescent labels. Protein data bank files containing the structures of fluorescein (PDB ID 1N0S), SnapTag (PDB ID 3KZY), HaloTag (PDB ID 5UY1), Green Fluorescent Protein (PDB ID 1GFL), and Infrared Fluorescent Protein IFP2.0 (PDB ID 4CQH) and an IgG antibody (PDB ID 1IGT) were used to create a single file in the Swiss-PdbViewer to adequately compare their sizes. The quantum dot size is an estimate based on a similar comparison in Jares-Erijman and Jovin [61]

functionality assessment since structure–function relationships are often dynamic in nature [13]. For instance, a large fluorescent tag could perturb the dynamics of system or cause misfolding/aggregation, thus affecting the functionality of the object of interest. However, attaching a synthetic fluorophore to a protein of interest has typically been a major obstacle for imaging [6, 7]. The aforementioned binding to genetically encoded tagging proteins is a relatively new addition to the labeling toolbox. Prior to such advances, proteins of interest were purified, labeled with a fluorophore, and re-introduced into a cell, or, more commonly, a protein of interest was labeled by immunofluorescence using fluorescently labeled primary antibodies, or fluorescently labeled secondary antibodies which recognize unlabeled primary antibodies against the protein of interest [14]. Both of these methods are still widely used and still have some distinct advantages and disadvantages (see Note 1) compared with more recent developments. Although many types of probes are available, there are some general properties that all probes should possess. The fluorescent probe should be bright, meaning it should absorb excitation light well and emit that absorbed energy as fluorescence with good efficiency. The terms used to describe these photophysical parameters are the extinction coefficient (ε) and quantum yield (QY), respectively. The extinction coefficient can be determined from absorption spectra of a known fluorophore concentration using Beer’s law. Beer’s law describes the absorptivity of a species derived from the relationship between the extinction coefficient (ε), the optical path length (l), and the concentration (c) (see Eq. 1). A ¼ εcl

ð1Þ

The QY is a ratio of the number of photons emitted to the number of photons absorbed [1]. Generally, the higher the value for each of these is indicative of better fluorophores but both are important in determining how bright a fluorophore will be for a

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Fig. 2 Example absorption and emission spectra. Absorption spectra for Cy2 (squares with dashed line) and Alexa488 (circles with dashed line) were converted into extinction coefficient values using 150,000 M1 cm1 at the wavelength of maximum absorption for Cy2 (λex ¼ 492 nm) and 71,000 M1 cm1 at the wavelength of maximum absorption for Alex488 (λex ¼ 499 nm). Emission spectra for Cy2 (squares with solid line) and Alexa488 (circles with solid line) were plotted by assuming the number of photons absorbed was equal to the extinction at 488 nm. These plotted values were scaled by the quantum yield to show the relative levels of fluorescence intensity produced by these two dyes under the same excitation. Note that although Cy2 absorbs >2 times more light at 488 nm than Alexa488, it produces less than half the fluorescence emission of Alexa488. This difference is explained by their respective quantum yield values (ϕCy2 ¼ 0.12 and ϕAlexa488 ¼ 0.92). These spectra were downloaded from https://www.chroma. com/spectra-viewer

given illumination intensity (see Fig. 2). In fact, the two values are often multiplied to provide a single number describing relative brightness which eases comparison across fluorophores. Typically, “good” fluorophores have QY values over 0.1 on a scale from 0 to 1, but researchers can be limited to fluorophores with much lower quantum yields based on other necessary characteristics for their experiments. The extinction coefficient and QY properties can be verified using spectroscopic techniques to characterize each probe [1, 13, 15], but for most fluorophores, tables of photophysical parameters can be found in the literature, online, or from the commercial source. The probe should also have a fluorescence lifetime in the temporal window of the biological process being examined. The lifetime is the average time the molecule exists in the excited state before decaying to the ground state [1]. Since this is generally on the nanosecond time scale for most fluorophores, the majority of confocal microscopy imaging studies are accommodated. The probe should also be as small as possible, photostable, and not perturbing/toxic to the system once added as a label.

Choosing Labels and Fluorophores

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Internal conversion Vibrational relaxation Intersystem crossing

S2 S1 T1 Excitation

No light

Emission

Phosphorescence

S0 Fig. 3 Jablonski diagram showing the electronic states of a molecule and several possible radiative and non-radiative pathways after molecule excitation. Molecules in the ground state (S0) can be excited with wavelengths of light which have energies capable of promoting an electron between the ground state and one of the excited state energy levels (S1 and S2). These result in the typical absorption spectra for fluorophores. Electrons elevated to higher electronic states undergo vibrational relaxation and rapid transition to the lowest energy level of the S1 excited state (internal conversion). From there, the electron can relax back to the ground state via nonradiative processes producing no fluorescence or the transition can result in the production of fluorescence emission (radiative decay). In addition, some fluorophores undergo intersystem crossing into an excited triplet state (T1) which can produce luminescence generally referred to as phosphorescence

It is essential to have some knowledge of the absorption spectrum of the probe of interest. By obtaining the absorption spectrum, one can identify the corresponding wavelength necessary to promote an electron from the singlet ground state to an excited state. See the Jablonski diagram in Fig. 3. Typically, one would select the electronic transition (absorption feature) corresponding to a lower unoccupied excited state and expect the corresponding radiative decay to emit red-shifted from the excitation wavelength. Most fluorophores have absorption in the ultraviolet region of the spectrum, but typically the most red-shifted (lowest energy level) feature in the visible region of the spectrum is selected for excitation. The absorption spectrum is also helpful in determining how well a fluorophore will be excited with a specific laser line (see Fig. 2). Extinction coefficients are reported for a single wavelength, which is commonly the wavelength of maximum absorption, but this wavelength seldom exactly matches the available laser lines on a confocal microscope. Thus, an absorption spectrum provides the researcher with the information needed to estimate the relative excitation at any laser line. The excitation spectrum of a fluorophore of interest should not be overlooked. Rather than determining how much light is absorbed at each wavelength, the readout for this spectrum is

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how much fluorescence is produced when excited at a given wavelength. The excitation spectrum is related to the absorption spectrum, and often, the two closely align. However, fluorophores can absorb light at wavelengths which populate levels in the excited state which preferentially decay to the ground state without producing fluorescence emission. In practice, the excitation spectrum will likely be more helpful when deciding which fluorophore is the “best-fit,” since it can better identify the optimal excitation wavelength for maximum emission from a fluorophore. Similar to the absorption spectrum, it is helpful to plot the excitation spectrum yaxis in extinction coefficient values (see Fig. 2). Such a plot provides a straightforward view for the relative excitation at specific laser lines. The emission spectrum of a fluorophore is a spectral representation of the radiative decay and is often the mirror image of the excitation spectrum (see Fig. 2). The emission is always red-shifted relative to the excitation wavelength because of the vibrational relaxation of an electron to the lowest excited state (the lowest level in S1 in the Jablonski diagram) [1]. This spectrum is crucial for the investigator to determine which filter to select when imaging, and the selection process will differ depending on the specific microscope. For instance, microscopes using predefined and installed emission bandpass filters will likely have only a few choices for the given wavelength range, whereas even the more adaptable instruments using spectral imaging still require the researcher to define the wavelength region of interest. Fortunately, a number of online resources are available which provide excitation/absorption and emission spectra for a number of the most common fluorophores. Examples include the viewer from ThermoFisher (https:// www.thermofisher.com/us/en/home/life-science/cell-analysis/ labeling-chemistry/fluorescence-spectraviewer.html) and from Chroma Technology (https://www.chroma.com/spectra-viewer). As discussed previously, the QY is an important factor in determining the brightness and ultimately the “best-fit” fluorophore. While photophysical characteristics including QY are often available from the commercial vendors offering the fluorophores, it is important to remember that the experimental conditions used to determine these values are unlikely to match the conditions of every imaging experiment. In general, the integrated area of the emission spectrum features can be correlated to the QY of the fluorophore, but numerous competing processes can result in a diminished brightness. For instance, radiative decay from the excited state must compete with electron transfer processes, internal conversion, intersystem crossings, triplet states, excited state proton transfers, and conical intersections [16–18]. The ratio of the radiative decay rate constant (kr) to the summation of all the rate constants (knr) describing competing processes is summarized in Eq. 2 and can also be used to define QY [1].

Choosing Labels and Fluorophores

QY ¼

kr kr þ knr

43

ð2Þ

With extremely complex systems it may also be important to test the pH of the sample buffer/solvent system. Conditions in the immediate environment of the fluorophore can not only affect the QY, they can also lead to solvatochromic shifts in the absorption spectrum as compared to the reported spectrum [19]. However, these phenomena should not necessarily be considered negatively. Such fluorophore behavior can give insight into protonation states, general environment conditions, or solvent accessibility of certain amino acids, all of which may provide new information about a labeled protein of interest [1]. A simple method of measuring absorption, emission, and excitation spectra at varying pH values will allow one to conclude whether chemical shifts can be expected and leveraged appropriately. The fluorescence lifetime (τ) of your probe of interest is also an important selection criterion to consider when designing an experiment. It represents the average time that a fluorophore remains in the S1 state after excitation. Long-lived bright states can be advantageous for single-molecule experiments, FRET, FLIM, τ¼

1 kr þ knr

ð3Þ

anisotropies, and steady-state fluorescence measurements [20]. Based on rearrangement of Eqs. 1 and 2, τ and QY can be related through the radiative emission rate constant (kr). Since kr is usually unknown and likely different for different fluorophores, this relationship would normally have little practical use. However, QY values for fluorophores are often more readily accessible than fluorescence lifetime information. Since plots of QY versus τ for many common fluorophores often show some correlation (see Fig. 4 and [21]), this can be a helpful starting point for new researchers. Here, we discuss three separate phenomena regarding photostability which collectively can produce similar losses of signal during imaging experiments: photobleaching, photoswitching, and photoconversion. Photobleaching or photo-induced fluorophore destruction has long been noted as major obstacle to imaging. And unfortunately for a new researcher, it is most helpful to have some experience with imaging a common fluorophore in order to determine an acceptable level of photobleaching. Values such as photobleaching quantum yield can be found for some fluorophores [22], but in practice, the relative photobleaching half times for fluorophores imaged under similar conditions is more useful [23]. Of course, this is limited by the number of fluorophores that can be reasonably reported for a given study. Therefore, determining the relative photobleaching behavior for fluorophores of interest requires some investigation and comparison across multiple sources. On the other hand, commercial sources often provide a

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Fig. 4 Comparison of fluorescence lifetimes and quantum yields. The fluorescence lifetimes for several common fluorophores were plotted as a function of their quantum yields. A pattern of correlation is observed with higher quantum yield fluorophores having longer fluorescence lifetimes

rough estimate of relative photostability and for other fluorophores, researchers can often find enough overlap between studies to compare probes of interest. More importantly, the prominence of photobleaching has led to the development of numerous “antifade” reagents which greatly reduce photobleaching or photodegradation in many common fluorophores [24]. The basic rationale behind many anti-fade agents is that they rapidly quench fluorophore excited states other than S1, such as the triplet state (see Fig. 3) and return the molecule to the ground state [25]. Photoswitching and fluorescence “on” times are relatively new concerns which commenced with the development and proliferation of single-molecule super-resolution imaging techniques [26]. These are the methods based on the photoactivation/photoswitching behaviors of proteins and dyes designed for these applications. Moreover, with specific buffer treatment, many conventional fluorophores can also be made to photoswitch into dark states from which they can be stochastically switched back “on” [27, 28]. From the point of view of simply imaging a fluorophore, switching to a dark state could simply be considered one of the many nonradiative pathways from the excited to the ground state, albeit much slower than some of the previously mentioned nonradiative pathways. The concern is that if the “on” time of your bright state is too short, it can reduce the observation time and severely diminish signal-to-noise ratio. Since special buffer conditions are required to efficiently induce this behavior in most common fluorophores, this parameter is arguably of less concern.

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On the other hand, for molecules in which the dark state might be long lived [29], it is possible to exploit the blinking phenomena for quantification. In this case, an experiment can be designed specifically to rely on a photoswitchable probe and the rate of switching off to the dark state will dictate the dynamic temporal resolution which one can investigate [30]. If the bright state on time is short, the camera or other detector must also have appropriate specifications and readout rates for quantifying the measurements. 1.2 Labeling Systems

Equally or perhaps even more important than the actual fluorophore and its photophysical characteristics is the approach used to label the molecule of interest. As previously mentioned, synthetic fluorophores are small, bright, and photostable, but specifically labeling a protein of interest in a cell has always been a major obstacle [6, 7]. Genetically encoded fluorescent proteins are convenient but are generally dimmer than synthetic fluorophores and still require introduction of the cDNA into the specimen. Hybrid versions in which a genetically encoded protein binds an exogenously added synthetic fluorophore offer the advantages of both but require the exogenous addition of the fluorophore [6, 7]. For direct labeling of isolated proteins, peptides, or biomolecules, maleimide and iodoacetamide labeling reactions can be coupled with thiol substrates. This is advantageous because it is possible to engineer a protein of interest using site-specific mutagenesis to add or delete cysteines as necessary (it is important to test the activity of the protein when performing mutagenesis to ensure the point mutations do not adversely affect the functionality of the protein). Both coupling reactions are quick and efficient, but one should not expect labeling efficiency to reach 100% or a stoichiometry of 1:1 labeling. Excess uncoupled tags can lead to increased background fluorescence and/or nonspecific labeling. To avoid this contamination in signal, one should consider exploring purification techniques to remove the excess label, e.g., size exclusion chromatography, washing protocols, or affinity chromatography [31, 32]. Direct labeling is often used for single-molecule measurements and FRET/FLIM measurements. The major advantage to labeling a protein of interest directly is that the synthetic fluorophore used is small, bright, and photostable. Its small size makes it less likely to interfere with the protein’s normal trafficking and functions. One major impediment is that the protein must be expressed and purified, probably from bacteria. Unless the purpose is to study the protein’s behavior outside the environment of the cell, the second major impediment will be to get the protein back into the cell. Microinjection on a cellby-cell basis allows a well-defined amount of labeled protein to be added to the cell but can be slow and tedious [33]. Protein

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transduction using peptides as carriers is also an option, but just like microinjection requires some invasive manipulation of the cells [34]. More common for synthetic fluorophores is the labeling of antibodies which are then used to observe the localization of proteins of interest in numerous techniques collectively referred to as immunofluorescence [14]. Protocols for labeling antibodies will have much similarity to the direct labeling protocol above, and often labeling kits containing all reagents with a detailed protocol are available commercially for many synthetic fluorophores. These kits are very helpful and generally produce adequate results even for inexperienced researchers. On the other hand, secondary antibodies (antibodies which recognize a specific antibody isotype from another species) are available from numerous commercial sources with a wide array of synthetic fluorophores. Immunofluorescence techniques and protocols could likely fill volumes of books, but we offer a simple protocol (see Subheading 3.2) to help new researchers get started on one of the most common techniques associated with fluorescence microscopy. Chemically labeled protein tags are highly specific and catalytically inactive and can be genetically engineered into the construct of interest. Some examples of commercially available tags are FlAsH, SNAP-tag, CLIP-tag, and HaloTag [35–37]. The tag for FlAsH is essentially a 10 amino acid peptide genetically fused to the cDNA for the protein of interest. Four cysteines make up the binding sites to which the two arsenics of the fluorescein-based FlAsH reagent bind (see Fig. 5). The other amino acids in the peptide are important for positioning the cysteine side chains for efficient binding. Unlike the small peptide used for FlAsH, other genetically attached protein tags are between 20 and 33 kDa. The SNAP-tag is based on a mutant form of the DNA repair enzyme, O6-alkylguanine-DNA alkyltransferase. This enzyme binds to a benzylguaninederivatized fluorophore and forms a covalent bond with a cysteine amino acid side chain in the binding pocket (see Fig. 6) [38, 39]. The CLIP-tag labeling system was developed from the SNAP-tag by engineering the enzyme to recognize a O2-benzylcytosine-derivatized synthetic fluorophore (see Fig. 7). With relatively good specificity toward their ligands, this allows two-color imaging with the SNAP and CLIP systems. In addition to a fluorescently labeled genetic tag, the reaction products for these reactions include a free guanine and a free cytosine, respectively. The HaloTag is a mutant form of a bacteria enzyme called haloalkane dehalogenase which binds to a ligand consisting of a chloroalkanederivatized synthetic fluorophore (see Fig. 8). The wild-type enzyme normally binds chloroalkanes, removes the halogen, and replaces it with the carboxyl group of a reactive aspartate group [7]. Normally, this intermediate form is short-lived, but the

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Fig. 5 Chemical labeling of a small peptide using FlAsH. The optimal target sequence for the ligand, 40 ,50 -bis(1,3,2-dithioarsolan-2-yl)fluorescein, is a tetracysteine motif containing two pairs of cysteines (C) separated by a proline (P) and glycine (G). The fluorescein fluorescence is quenched until bound to the target peptide

engineered mutant form cannot undergo the hydrolysis reaction to release the alkane. Thus, if the haloalkane has a synthetic fluorophore attached to the other end, the product will be an inactive enzyme labeled with a fluorescent molecule (see Subheading 3.3 for an example labeling protocol). The variety of colors for imaging with these labeling systems is more limited than for immunofluorescence or fluorescent proteinbased imaging. For instance, FlAsH is fluorescein based to produce a green fluorescent signal, and the other choice is resorufin (ReAsH) for red fluorescence [40, 41]. Although less limited, not every synthetic fluorophore is currently commercially available as a

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Fig. 6 Chemical labeling of a SNAP-tag. The target sequence is a mutant form of the DNA repair enzyme, O6-alkylguanine-DNA alkyltransferase. A benzylguanine-derivatized tetramethylrhodamine is depicted here forming a covalent bond with a cysteine amino acid side chain in the binding pocket of the SNAP-tag

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Fig. 7 Chemical labeling of a CLIP-tag. The target sequence is an alternative mutant form of the DNA repair enzyme, O6-alkylguanine-DNA alkyltransferase. The reaction is similar to that of the SNAP-tag system with the exception that a benzylcytosine-derivatized tetramethylrhodamine is depicted

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Fig. 8 Chemical labeling of a HaloTag. The target sequence is mutant form of a haloalkane dehalogenase. The enzyme replaces a halogen attached to an alkane with the carboxyl group of an aspartate within the binding site. This effectively covalently binds the alkane to the enzyme and subsequent hydrolysis removes the alkane to complete the enzymatic reaction. However, the HaloTag is engineered to inhibit the hydrolysis, so the alkane, which is depicted here derivatized with a tetramethylrhodamine fluorophore, remains covalently bound to the tag

functionalized ligand for the other labeling systems either. Nevertheless, a large enough variety of probes including dyes from the Alexa Fluor series, Cy series, diAcFAM, Oregon Green, and Coumarin to allow multi-color imaging. Importantly, the rapid formation of the covalent linkage of the fluorophore to the tag allows for relatively easy removal of excess fluorophore. This labeling method can be used for super-resolution techniques, immunocytochemistry, flow cytometry, live-cell imaging, and fixed-cell imaging. Genetic labeling systems do come with some distinct caveats. The first is that the attachment to the protein of interest can lead to dysfunction or mis-localization, and this is a potential problem for almost every chimera. In some cases, the full functionality or localization of a chimeric protein may not be known simply because the

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full function and location of the protein are not known. If possible, a researcher should use as many tests as reasonably possible to test the viability of the chimera, and these tests will likely differ depending on the protein of interest. A second possible disadvantage is that the labeled protein is artificially produced. Unless the genetic tag affects the normal protein of interest function, it will likely be treated similarly to the endogenous forms of the protein. However, without reduction or negation of the expression of the endogenous protein, the chemically labeled chimera will be expressed at levels abnormal to the cell physiology. Whether this leads to abnormal behavior or results depends on the protein of interest, the level of over expression, and the cell type in which the experiment is being conducted. We do not wish to discourage new researchers, but only warn them concerning potential artifacts. A third caveat is that the genetically encoded component for these types of labels requires introduction of the chimeric cDNA encoding the protein of interest and the genetic tag into the cell. As the system being studied becomes more complex (cell < tissue < organism), incorporating chimeric DNA also generally becomes more complex. Since protocols for expressing a chimera in tissue or whole organisms are beyond the scope of this chapter, we restrict our discussion to work with cultured cells. Therefore, to introduce new researchers with limited experience in transfecting mammalian cells, a simple liposome-based protocol is provided (see Subheading 3.4). Nanoparticle labels such as quantum dots (Qdots) are large semiconductor nanoparticles that are very bright and photostable [42, 43]. Qdots possess narrow emission peaks in spite of their high quantum yield and high extinction coefficient. Because of their composition, size, and functionalized shells (for covalent attachment), Qdots have broad excitation spectra which is advantageous when selecting excitation wavelengths [44]. Their disadvantages include many of the those associated with synthetic fluorophores discussed earlier, such as specifically labeling a protein of interest, incorporating the Qdot into cell or tissue, and the difficult nature of removing excess label. These are exacerbated by the Qdot large size relative to a synthetic fluorophore (see Fig. 1). Nevertheless, if those issues can be overcome, Qdots display very high inherent brightness and photostability. As previously mentioned, the intrinsic fluorophores in combination with a genetic tag have also been a topic of major development. Over the last decade, considerable effort has been placed on pushing the excitation and emission wavelengths for fluorescent markers more toward red wavelengths in efforts to reduce contributions from background autofluorescence and improve excitation light penetration into tissue. Therefore, our discussion for this type of labeling system will focus on the development of bacteria

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phytochromes as infrared fluorescent proteins (IFPs or iRFPs) [10, 45, 46]. These proteins incorporate biliverdin IXα which is an intermediate in heme catabolism and thus will be available in most aerobic cell types. The free form of biliverdin has a red absorption feature centered around 670 nm, but essentially no fluorescence unless bound to one of the phytochrome proteins. Even when bound, the quantum yields are somewhat low with various mutant phytochrome proteins resulting in a range from ~7% to 12% [45]. However, these are commonly the only fluorescence signals observed when imaging cells and tissues at these longer wavelengths, so a less than robust signal is often sufficient. In some cases, treating this system as a chemical labeling system and adding exogenous biliverdin can enhance the signal [10]. The benefit here is that the excess, unbound biliverdin is not fluorescent and does not need to be washed from the system (see Subheading 3.5). Alternatives to bacteria phytochromes are proteins developed from allophycocyanin α-subunit [47–49]. Normally these proteins incorporate phycocyanobilin, but a mutant form named small ultrared fluorescent protein (smURFP) was developed to covalently bind biliverdin. Moreover, this variant is able to bind a biliverdin dimethylester, which should be more membrane permeable and thus more readily facilitate additional exogenous labeling. Perhaps most important for new researchers to remember is that even though these genetic tags do not require exogenous addition of fluorophore as with the previously discussed chemical labeling approaches, they can sometimes benefit from extra exogeneous label. The same techniques required to introduce the genetic tags for the chemical labels into cells will also be necessary for these molecules. Protein of interest overexpression, dysfunction, and mis-localization remain points of concern. The discovery and utilization of green fluorescent protein opened up a world of microscopic exploration [11, 50–53]. Fluorescent proteins can be genetically engineered in constructs of interest for whole cell expression, structure-specific labeling, or signal tracking. Although some of the chemical label genetic tags have a smaller primary sequence, fluorescent proteins form compact beta-barrel structures with a similar three-dimensional size (see Fig. 1). Unique to this class of labels, fluorescent proteins derive their chromophore (the light absorbing and emitting part of the tag) from posttranslational modifications resulting in a cyclized tripeptide located in the center of the beta-barrel. Therefore, other than molecular oxygen, no exogenous components are necessary for the formation of a fluorescent molecule. The location internal to the beta-barrel also allows some control of the immediate environment of the chromophore. Thus, mutagenesis in and around the chromophore has led to the generation of numerous colors with a diverse and wide-ranging spectral palette [54–

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58]. These have also led to a vast array of extinction coefficients, quantum yields, and photostabilities, which give rise to the major disadvantages of using fluorescent proteins compared to their synthetic counterparts: their relative brightness and photostability. On the other hand, a major advantage of fluorescent proteins is that nonlabeling background noise due to excess fluorophore is negated. This allows very straightforward labeling since only the chimeric cDNA must be introduced into the cell. Also unique to this system is that fluorescent proteins must fold into a fully fluorescent structure, which is unlikely to occur with 100% efficiency. However, this is simply analogous to decreased labeling efficiency in chemical labeling systems. Although avoiding issues associated with exogenous labeling, use of fluorescent protein must contend with all the previously discussed points regarding protein of interest dysfunction, mis-localization, and overexpression. Despite their deficient photophysical properties compared to synthetic fluorophores, fluorescent proteins are very popular due to the adaptability and ease of use in a number of systems. New chimeras with proteins of interest often present a challenge in determining the proper functionality. Here, we briefly discuss points for new researchers to consider when designing their experiments. In some experiments, full functionality of the chimera may not be necessary. Often the necessity of the experiment requires the protein of interest to traffic and localize as the endogenous form rather than undertake its full role in the cell. For instance, some chimeras may require only the domain of a protein responsible for its proper localization within the cell. Proper localization can also be problematic to discern. Typically, comparison with immunofluorescence experiments performed on the same protein are helpful in making such a determination. In some systems, it may be reasonable to knockout the endogenous protein and replace it with the chimeric version containing the genetic tag. While this is more complex and time consuming, it will provide the researcher with the opportunity to assess the chimera function and localization with more depth. Even if the chimera functions and localizes properly, one may also have to contend with overexpression artifacts. Again, this will undoubtedly depend on the protein of interest and cell system since different proteins are expressed at different levels in different cells. If the researcher has the cDNA for the endogenous promotor, that is one option to keep the protein levels as close to physiologic as possible. Unfortunately, in-depth knowledge regarding the individual promotors for most proteins may not be available, so most of us try to compensate by imaging at the lowest possible expression levels. Fortunately, this approach has been helped by improvements in the sensitivity of modern confocal microscopes. Nevertheless,

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researchers should also image cells with a large range of chimera expression levels. This may prove useful in determining a maximum expression level simply by qualitatively recognizing obvious artifacts throughout the range.

2

Materials

2.1 Labeling Proteins, Peptides, and Thiolated Biomolecules

1. 50–100 μM proteins, peptides, or thiolated biomolecules (see Note 2). 2. 5–10 nM of Tris-carboxyethylphosphine (TCEP) or dithiothreitol (DTT). 3. Buffer between pH 7.0 and 7.5 such as phosphate-buffered saline (PBS), Tris, HEPES. 4. 1–10 mL of dimethyl sulfoxide (DMSO) or dimethylformamide DMF for solubility of maleimide reagent. 5. Size exclusion column/HPLC/FPLC or electrophoresis for purification (equilibrated using appropriate buffer for sample). Ensure there is excess buffer to use for elution.

2.2 Immunofluorescence

1. COS-7 cells growing on #1.5, 12 mm round coverslips (see Note 3). 2. 10 PBS, pH 7.4. 3. Fetal bovine serum (FBS). 4. Fixative quencher: 10% FBS in 1 PBS pH, 7.4 (see Note 4). 5. Paraformaldehyde: 16% in solution (see Note 5). 6. Saponin: 10% w/v in deionized water. 7. Primary antibody to recognize protein of interest. 8. Fluorophore-labeled secondary antibody to recognize the primary antibody. 9. Mounting medium. 10. Quick dry topcoat nail polish.

2.3 Labeling HaloTag Expressing Mammalian Cells

1. Cells expressing HaloTag fusion protein. 2. 1000 HaloTag Ligand of Interest stock solution (see Note 6). 3. DMEM cell culture medium: prepare the medium containing 10% fetal bovine serum (FBS) and warm to 37  C. 4. FluoroBrite DMEM (ThermoFisher Scientific; #A1896701): prepare the medium containing 10% FBS and warm to 37  C. 5. 1 PBS, pH 7.4. 6. 1 PBS, pH 7.4 containing 10% FBS.

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7. 4% paraformaldehyde: prepare from 16% paraformaldehyde by diluting with threefold 1 PBS, pH 7.4. 8. Cell culture incubator: set the incubator with environmental conditions of 37  C and 5% CO2. 2.4 Transfecting Mammalian Cells with Fluorescence Protein Expression Vectors

1. Plasmid expressing protein of interest: prepare with a working concentration of ~0.5–1 μg/μL. 2. Transfection media: Dulbecco’s modified Eagle media (DMEM) without serum. 3. DNA Transfection Reagent: Xtreme-DNA™ HP (Millipore Sigma Aldrich; #6366244001). 4. COS-7 cells growing in dishes (see Note 7). 5. FluoroBrite DMEM containing 10% FBS.

2.5 Extrinsic Labeling of IFP2.0 Expressing Mammalian Cells with Biliverdin

3

1. 25 μM Biliverdin HCl solution: make in 1 PBS, pH 7.4. 2. COS-7 cells expressing IFP2.0 fusion protein: culture in 35 mm Delta T dishes (see Note 7). 3. FluoroBrite DMEM containing 10% FBS.

Methods Here, we introduce researchers new to the field to some of the common fluorescence labeling systems and fluorophore characteristics. The protocols we present are not meant as end points, but rather starting points for further exploration. The general fluorophore characteristics are worthy of consideration regardless of the experiment. However, as discussed throughout this chapter, every fluorescent label has distinct advantages and disadvantages which are dependent on the questions being asked for a given experiment. Moreover, rather than taut our favorite labels and try to provide reasonable accommodations and adaptations for every type of experiment, our goal here was to provide a sound basis for new researchers to explore and discover which fluorescent labels may provide the most information relevant for their experiment.

3.1 Labeling Proteins, Peptides, and Thiolated Biomolecules (See Note 2)

1. Degas buffers by applying a vacuum for several minutes or bubbling inert gas (nitrogen, argon, or helium) in the buffer (see Note 8). 2. The molecule of interest to be labeled should be diluted or concentrated to the desired concentration (~50 μM) with degassed buffer (see Notes 2 and 8). 3. Add a tenfold molar (~0.5 mM) excess (relative to protein/ peptide/thiolated biomolecule of interest) of reducing agent

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to reduce disulfide bonds, flush with inert gas, and close. Incubate at room temperature for 20 min (see Note 8). 4. Prepare a 20-fold molar (~1 mM) excess (relative to protein/ peptide/thiolated biomolecule) maleimide-conjugated fluorophore in DMSO or fresh DMF. 5. Add dye solution to thiol solution, flush vial with inert gas, and close tightly. 6. Mix the fluorophore solution and the protein of interest thoroughly and incubate at room temperature for 2 h (see Note 2). 7. Remove the unbound dye from the labeled protein by gel filtration, HPLC, FPLC, or electrophoresis. 3.2 Immunofluorescence

1. For each well of a multi-well plate or each 35 mm dish, aspirate the phenol red containing media from COS-7 cells growing on #1.5, 12 mm round coverslip (see Note 3). 2. Wash cells 1 with 1 mL 1 PBS, pH 7.4. 3. Fix cells with 1 mL 4% paraformaldehyde in 1 PBS, pH 7.4 for 10 min at room temperature (see Note 9). 4. Wash cells 3 with 1 mL 1 PBS, pH 7.4 containing 10% FBS. 5. Block nonspecific binding by incubating fixed cells with 1 ml 1 PBS, pH 7.4 containing 10% FBS and 0.1% saponin for 20 min at room temperature (see Note 10). 6. Incubate cells 1 h at room temperature with primary antibody diluted to the appropriate concentration (dilution factor is antibody dependent but is normally 100- to 500-fold dilution) in 10% FBS, 0.1% saponin in 1 PBS, pH 7.4 (see Note 10). If the antibody stock is limited, follow steps 7–12 for a method to minimize the amount used (see Note 11). 7. A humidity chamber for the antibody incubation can be made from a petri or tissue culture dish. The 150 mm size works great, but you can use 100 mm size if you only have six or eight coverslips (12 mm circular). 8. Place a Whatman No. 1 filter paper in the dish and wet it thoroughly with distilled water. 9. On the wet paper, place a clean piece of parafilm (cut to the appropriate size). To distinguish samples, you should label the parafilm rather than the dish cover. 10. In the appropriately labeled area of the parafilm, place 25 μL of the antibody dilution. 11. Using forceps, remove the coverslips from the chamber in which they are washed and place it “cell side” down on the antibody droplet. 12. Cover the chamber for the incubation period.

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13. If steps 7–12 were followed, use forceps to place the coverslip “cell side” up back into the chamber and wash cells 3 with 10% FBS in 1 PBS, pH 7.4. If proceeding from step 6, aspirate primary antibody solution and wash cells 3 with 1 mL 1 PBS pH 7.4 containing 10% FBS. 14. Incubate cells 1 h at room temperature with fluorophorelabeled secondary antibody diluted to the appropriate concentration (dilution factor is again antibody dependent) in 1 PBS pH 7.4 containing 10% FBS and 0.1% saponin (see Note 12). 15. As in step 13, wash cells 3 with 1 mL 1 PBS, pH 7.4 containing 10% FBS. 16. Mount coverslips on glass slides with “cell side” down with an appropriate mounting media (see Note 13). 17. Allow the mounting media to dry for ~2 h. Seal (attach) the coverslips with nail polish at the edge. Be careful not to spread the polish into the middle of the coverslip. Allow the polish to dry 30 min overnight. 18. The coverslip will often have a layer of salt and protein from the dried wash buffer covering the surface. Use a piece of wet lens paper to clean the coverslip surface before imaging. 3.3 Labeling HaloTag Expressing Mammalian Cells (See Note 14)

1. This protocol assumes we are using a 35 mm Delta T dish containing a total volume of 1 mL of medium. The volumes used will need to be scaled accordingly for other chambers. 2. Prepare a 5 HaloTag Ligand of interest solution from the stock solution by making a 1:200 dilution in DMEM cell culture medium warmed to 37  C. Make this dilution immediately prior to adding to cells. In our experiments, we make a total volume of ~200 μL for each Delta T dish of cells to be labeled. 3. For each dish, remove 20% (~200 μL) of the DMEM cell culture medium. 4. Add the 5 HaloTag Ligand solution and mix gently to produce the recommended final labeling concentration (see Note 6). 5. Incubate for 15 min in a cell culture incubator (37  C + 5% CO2). 6. Wash out the excess ligand by aspirating the medium and replacing with 1 mL of 37  C fresh medium. Repeat twice and maintain the cells in DMEM containing 5% FBS. 7. Here, the protocol deviates depending on the HaloTag ligand used (see Note 14). If the ligand is membrane impermeable, you may proceed to step 9 since unbound impermeable ligand should be washed out. Otherwise, incubate cells in DMEM culture medium in a cell culture incubator for 30 min to allow unbound ligand to diffuse out of the cells.

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8. Wash out the excess ligand by aspirating the medium and replacing with 1 mL of 37  C fresh medium. 9. If the cells are to be imaged live, change media to FluoroBrite DMEM containing 10% FBS and image. Otherwise, proceed to fixation starting with step 10. 10. Wash cells 1 with 1 mL 1 PBS (see Note 9). 11. Fix cells with 1 mL 4% paraformaldehyde in 1 PBS for 10 min at room temperature. 12. Wash cells 3 with 1 mL 1 PBS containing 10% FBS. 3.4 Transfecting Mammalian Cells with Fluorescence Protein Expression Vectors (See Note 15)

1. Add 97 μL of the transfection media to 1 μg of plasmid in an Eppendorf tube. 2. Add 3 μL of Xtreme-DNA™ HP DNA Transfection Reagent to tube containing plasmid and DMEM transfection media. Incubate room temperature 15 min. 3. Aspirate media dishes and replace with warmed (37 fresh DMEM.



C)

4. Add the solution containing cDNA, transfection media, and Xtreme-DNA™ Transfection Reagent to cell dishes. 5. Incubate in a cell culture incubator (see Note 16). 6. Change media to FluoroBrite DMEM containing 10% FBS and image. 3.5 Extrinsic Labeling of IFP2.0 Expressing Mammalian Cells with Biliverdin

1. This protocol assumes we are using a 35 mm Delta T dish containing a total volume of 1 mL of medium. The volumes used will need to be scaled accordingly for other chambers. 2. For each dish, remove 20% (~200 μL) of the DMEM cell culture medium. 3. Add 200 μL of 25 μM biliverdin HCl solution into each dish, making the final concentration of 5 μM. 4. Incubate at 37  C for 2 h. 5. Aspirate media and excess biliverdin HCl from dishes (see Note 17). 6. Replace with 1 mL FluoroBrite DMEM containing 10% FBS warmed to 37  C and image.

4

Notes 1. The major advantage of immunofluorescence is that the endogenous protein is detected rather than an overexpressed protein as is often done with a genetically encoded label. One major disadvantage includes the large size of the antibody compared to the synthetic fluorophore (see Fig. 1), which basically negates

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the small size argument. However, the specimen is typically fixed during preparation for immunofluorescence anyway, so the protein will not be undergoing any functions with which the antibody would interfere. 2. Most of our experience has been with labeling antibodies or fluorescent proteins, which are relatively robust molecules. Since different proteins can have vastly different properties, this protocol meant to be considered a basic approach to labeling a protein or peptide of interest. For instance, some proteins are much more heat labile than others, and thus, the protocol would need to be altered to accommodate. The concentration for a purified protein of interest may vary depending on the availability and protein characteristics, such as oligomerization. It is helpful to start with a concentration as high as possible. 3. The culture dish used for immunofluorescence is often a matter of preference and convenience. For a small number of samples, 35 mm dishes work well. For larger numbers, 6- or 12-well plates provide more stability and are easier to keep organized during the numerous incubation and wash steps. 4. A less expensive and sometimes more convenient alternative is 50 mM glycine in 1 PBS pH 7.4. 5. While stock solutions can be made from powder, we find the 10 mL sealed ampules available from numerous commercial sources to work well. 6. Some of the available cell membrane permeant dyes that can be linked to the HaloTag include a red dye, tetramethylrhodamine (TMR; 552ex/578em), the green dyes, diAcFAM (492ex/ 521em), Oregon Green (492ex/520em), and the blue coumarin dye (362ex/450em). The suggested final concentrations for labeling are 5 μM TMR, 1 μM diAcFAM and Oregon Green, and 10 μM Coumarin Ligand. Some of the available cell membrane impermeant dyes that can be linked to the HaloTag include a green dye, Alexa Fluor 488 (499ex/ 518em), and the far-red dye, Alexa Fluor 660 (654ex/ 690em). The suggested final concentrations for labeling are 3.5 μM Alexa Fluor 660 and 1 μM Alexa Fluor 488. 7. The cell culture chambers growing cells for transfection will vary depending on the experimental needs. For instance, if the fluorescent molecule tagged protein of interest will be imaged along with immunofluorescence staining of a separate protein of interest, the cells may be grown on glass coverslips for subsequent immunostaining. If the cells are to be imaged live, we find it helpful to grow the cells in chambers designed for this purpose. While some labs rely on custom imaging chambers, experimenters have several commercial options. Although we do not endorse any products, some with which we have

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worked and had good experiences include MatTek, Lab-Tek, and Bioptechs Delta T dishes. MatTek dishes have diameters of 35, 50, 60, and 100 mm with a coverglass on the bottom to make imaging with an inverted microscope. Lab-Tek chambered coverglasses come in 1-, 2-, 4-, and 8-well versions, which can allow multiple different imaging experiments to be set up with the same sample container. Bioptechs Delta T dishes are 35 mm dishes with a coverslip used in conjunction with other accessories to provide a temperature-controlled environment during imaging experiments. 8. Since cysteine residues can form disulfide bonds which do not react with maleimides, disulfides must be reduced before to the conjugation reaction. Buffers are degassed to remove as much oxygen as possible from the reaction. Since disulfide bridges may be important for overall function, protein activity is generally assessed after labeling to assure the labeling process does not disrupt function. 9. Since chemical fixation with paraformaldehyde is relatively slow, fixation artifacts can be common. It is best to avoid conditions which exacerbate this potential problem. In our experience, the wash step prior to the adding the fixative can be helpful in removing residual medium containing serum which can quench the fixation reaction. The protocol above uses chemical fixation by paraformaldehyde, but you will also find many protocols using an alcohol-based fixation. Although it may not be a problem for synthetic fluorophores, the alcoholbased fixation results in dehydration which can be (although not always) problematic for molecules such as green fluorescent protein which have intricate hydrogen bonding networks around the chromophore and tend to fluoresce better in aqueous solution. 10. The permeabilization step is a necessary evil where artifacts can be generated due to membrane solubilization. Numerous protocols for immunofluorescence can be found throughout the literature which use a variety of amphipathic molecules for permeabilization. The example protocol here uses saponin as opposed to detergents such as Tween20 or Triton X-100. The mechanisms of action differ for these reagents with saponin forming pores in the membrane whereas the detergents solubilize parts of the membrane. With saponin, be sure to maintain it in each of the steps since it can be washed out. To minimize artifacts, use as little as possible of the required permeabilization reagent. 11. Since antibodies are usually precious commodities, using as little as possible for experiments is in your better interest. However, since small volumes are used, care must be taken to

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avoid evaporation during the incubation. Please note that our protocol was developed with 12 mm round coverslips. For other sizes and shapes, volumes will have to be altered accordingly. 12. Often problems associated with immunofluorescence stem from antibody cross-reactivity which results in high background staining. These problems are typically found with the primary antibody which recognizes the protein of interest. This can sometimes be ameliorated by lowering the antibody concentration or reducing the incubation time. Alternatively, trying other available antibodies recognizing the same protein of interest may prove beneficial. Other problems may be that the recognition epitope is masked or lost resulting in no detected signal. Potential troubleshooting steps for this problem would be to change fixative, change permeabilization reagent, and/or alter the concentrations and incubation times. 13. While investigators have the option to make their own, a wide variety of commercial mounting media reagents, such as Fluoromount G, Vectashield, Prolong Gold, etc., are available which we have found to work well. Many also include anti-fade reagents. Since the efficacy of the anti-fade agents may be fluorophore dependent, this may offer some guidance when deciding which to use. Mounting media are also available with different refractive indices which can affect resolution [59]. Finally, mounting media are also sold with added DNA stains, such as DAPI. While this provides a convenience of introducing a nuclear signal with no extra labeling steps, we generally avoid these due to potential DAPI dye photoconversion and signal crosstalk with other imaging channels of interest. 14. HaloTag ligands functionalized with fluorophore come in both cell-permeant and cell-impermeant forms. This can be beneficial or problematic depending on the experiment. For instance, if the protein of interest is located on the exterior of the cell, then the cell-impermeant form could be used. Moreover, if the protein has a subpopulation located on the cell surface and the rest located intracellular, the cell-impermeant form would help assure that only the surface population is labeled. On the other hand, proteins located within the cell would require the cellpermeant form followed by multiple wash steps to remove excess unbound label and avoid higher backgrounds. In our experience, the genetic tags discussed here generally have low backgrounds, and they can even be used to label thick tissues, but new researchers must keep in mind these potential artifacts [60].

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15. Most of our experience has been acquired using COS-7 cells, but the protocol listed above will probably work well on many cultured cell lines. 16. Factors we have found to affect the transfection efficiency include the cell confluency, the expression time, and the liposome to DNA ratio. We try to transfect while the cells are ~50%–70% confluent. Depending on the necessary expression time (how long after transfection the cells are imaged), the cells may need to be on the lower end of the suggested confluency. Typical expression times are 18–24 h for proteins of ~20–30 kDa, but for larger proteins of interest (>40–50 kDa), we typically use 36–48 h. For the liposome– DNA ratio, try to match the ratio suggested by the vendor. Obviously, if the DNA level is too low, the efficiency will not be as high, but we find that higher DNA concentrations can be inhibitory. Approximately 0.1 μg/cm2 (~1 μg per 35 mm dish) is a good starting point. If a transfection is found to be inefficient, we suggest the first troubleshooting step to be confirmation of the plasmid DNA concentration. 17. Although washing out unbound biliverdin HCl can be performed using 1 PBS pH 7.4, we do not find it necessary since the unbound ligand has little to no fluorescence compared to ligand bound to one of the biliverdin-binding proteins. References 1. Lakowicz JR (1999) Principles of fluorescence spectroscopy, 2nd edn. Kluwer Academic/Plenum, New York 2. Lippincott-Schwartz J, Patterson GH (2003) Development and use of fluorescent protein markers in living cells. Science 300:87–91 3. Jonkman J, Brown CM (2015) Any way you slice it-a comparison of confocal microscopy techniques. J Biomol Tech 26(2):54–65 4. Vogel SS, Thaler C, Koushik SV (2006) Fanciful FRET. Sci STKE 2006(331):re2 5. Shaner NC, Patterson GH, Davidson MW (2007) Advances in fluorescent protein technology. J Cell Sci 120(Pt 24):4247–4260 6. Marks KM, Nolan GP (2006) Chemical labeling strategies for cell biology. Nat Methods 3 (8):591–596 7. Schneider AFL, Hackenberger CPR (2017) Fluorescent labelling in living cells. Curr Opin Biotechnol 48:61–68 8. Chaiyen P, Scrutton NS (2015) Special issue: flavins and flavoproteins: introduction. FEBS J 282(16):3001–3002

9. Blacker TS, Duchen MR (2016) Investigating mitochondrial redox state using NADH and NADPH autofluorescence. Free Radic Biol Med 100:53–65 10. Shu X et al (2009) Mammalian expression of infrared fluorescent proteins engineered from a bacterial phytochrome. Science 324 (5928):804–807 11. Chalfie M et al (1994) Green fluorescent protein as a marker for gene expression. Science 263(5148):802–805 12. Zhang J et al (2002) Creating new fluorescent probes for cell biology. Nat Rev Mol Cell Biol 3:906–918 13. Toseland CP (2013) Fluorescent labeling and modification of proteins. J Chem Biol 6 (3):85–95 14. Javois LC (1999) Direct immunofluorescent labeling of cells. Methods Mol Biol 115:107–111 15. Fili N, Toseland CP (2014) Fluorescence and labelling: how to choose and what to do. Exp Suppl 105:1–24

Choosing Labels and Fluorophores 16. Wang Y et al (2015) Excited state structural events of a dual-emission fluorescent protein biosensor for Ca(2)(+) imaging studied by femtosecond stimulated Raman spectroscopy. J Phys Chem B 119(6):2204–2218 17. Tang L et al (2015) Unraveling ultrafast photoinduced proton transfer dynamics in a fluorescent protein biosensor for Ca(2+) imaging. Chemistry 21(17):6481–6490 18. Zhu J et al (2015) Ultrafast excited-state dynamics and fluorescence deactivation of near-infrared fluorescent proteins engineered from bacteriophytochromes. Sci Rep 5:12840 19. Marini A et al (2010) What is solvatochromism? J Phys Chem B 114(51):17128–17135 20. Ha T et al (1999) Single-molecule fluorescence spectroscopy of enzyme conformational dynamics and cleavage mechanism. Proc Natl Acad Sci U S A 96(3):893–898 21. Thorn TLK. Fluorescent protein properties. www.fpvis.org/FP.html. Accessed July 2019 22. Eggeling C et al (1998) Photobleaching of fluorescent dyes under conditions used for single-molecule detection: evidence of two-step photolysis. Anal Chem 70 (13):2651–2659 23. Shaner NC et al (2004) Improved monomeric red, orange and yellow fluorescent proteins derived from Discosoma sp. red fluorescent protein. Nat Biotechnol 22(12):1567–1572 24. Ono M et al (2001) Quantitative comparison of anti-fading mounting media for confocal laser scanning microscopy. J Histochem Cytochem 49(3):305–312 25. Cordes T et al (2011) Mechanisms and advancement of antifading agents for fluorescence microscopy and single-molecule spectroscopy. Phys Chem Chem Phys 13 (14):6699–6709 26. Henriques R et al (2011) PALM and STORM: unlocking live-cell super-resolution. Biopolymers 95(5):322–331 27. Rust MJ, Bates M, Zhuang X (2006) Subdiffraction-limit imaging by stochastic optical reconstruction microscopy (STORM). Nat Methods 3(10):793–795 28. Heilemann M et al (2009) Super-resolution imaging with small organic fluorophores. Angew Chem Int Ed Engl 48(37):6903–6908 29. Betzig E et al (2006) Imaging intracellular fluorescent proteins at nanometer resolution. Science 313(5793):1642–1645 30. Fernandez-Suarez M, Ting AY (2008) Fluorescent probes for super-resolution imaging in living cells. Nat Rev Mol Cell Biol 9 (12):929–943

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31. Kim Y et al (2008) Efficient site-specific labeling of proteins via cysteines. Bioconjug Chem 19(3):786–791 32. Martinez-Jothar L et al (2018) Insights into maleimide-thiol conjugation chemistry: conditions for efficient surface functionalization of nanoparticles for receptor targeting. J Control Release 282:101–109 33. Zhang Y, Yu LC (2008) Single-cell microinjection technology in cell biology. BioEssays 30 (6):606–610 34. Zahid M, Robbins PD (2012) Protein transduction domains: applications for molecular medicine. Curr Gene Ther 12(5):374–380 35. Gautier A et al (2008) An engineered protein tag for multiprotein labeling in living cells. Chem Biol 15(2):128–136 36. Los GV et al (2008) HaloTag: a novel protein labeling technology for cell imaging and protein analysis. ACS Chem Biol 3(6):373–382 37. Sun X et al (2011) Development of SNAP-tag fluorogenic probes for wash-free fluorescence imaging. Chembiochem 12(14):2217–2226 38. Keppler A et al (2004) Labeling of fusion proteins of O6-alkylguanine-DNA alkyltransferase with small molecules in vivo and in vitro. Methods 32(4):437–444 39. Cole NB (2013) Site-specific protein labeling with SNAP-tags. Curr Protoc Protein Sci 73:30.1.1–30.1.16 40. Griffin BA et al (2000) Fluorescent labeling of recombinant proteins in living cells with FlAsH. Methods Enzymol 327:565–578 41. Hoffmann C et al (2005) A FlAsH-based FRET approach to determine G proteincoupled receptor activation in living cells. Nat Methods 2(3):171–176 42. Michalet X et al (2005) Quantum dots for live cells, in vivo imaging, and diagnostics. Science 307(5709):538–544 43. Gu W et al (2007) Measuring cell motility using quantum dot probes. Methods Mol Biol 374:125–131 44. Bruchez M Jr et al (1998) Semiconductor nanocrystals as fluorescent biological labels. Science 281(5385):2013–2016 45. Shcherbakova DM, Verkhusha VV (2013) Near-infrared fluorescent proteins for multicolor in vivo imaging. Nat Methods 10 (8):751–754 46. Stepanenko OV et al (2017) Interaction of biliverdin chromophore with near-infrared fluorescent protein BphP1-FP engineered from bacterial phytochrome. Int J Mol Sci 18(5)

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47. Rodriguez EA et al (2016) A far-red fluorescent protein evolved from a cyanobacterial phycobiliprotein. Nat Methods 13(9):763–769 48. Shemetov AA, Oliinyk OS, Verkhusha VV (2017) How to increase brightness of nearinfrared fluorescent proteins in mammalian cells. Cell Chem Biol 24(6):758–766.e3 49. Ding WL et al (2017) Small monomeric and highly stable near-infrared fluorescent markers derived from the thermophilic phycobiliprotein, ApcF2. Biochim Biophys Acta, Mol Cell Res 1864(10):1877–1886 50. Heim R, Tsien RY (1996) Engineering green fluorescent protein for improved brightness, longer wavelengths and fluorescence resonance energy transfer. Curr Biol 6:178–182 51. Shimomura O (2005) The discovery of aequorin and green fluorescent protein. J Microsc 217(Pt 1):1–15 52. Patterson G et al (2010) Superresolution imaging using single-molecule localization. Annu Rev Phys Chem 61:345–367 53. Shen Y et al (2019) Genetically encoded fluorescent indicators for imaging intracellular potassium ion concentration. Commun Biol 2 (1):18

54. Shaner NC et al (2008) Improving the photostability of bright monomeric orange and red fluorescent proteins. Nat Methods 5 (6):545–551 55. Davidson MW, Campbell RE (2009) Engineered fluorescent proteins: innovations and applications. Nat Methods 6(10):713–717 56. Day RN, Davidson MW (2009) The fluorescent protein palette: tools for cellular imaging. Chem Soc Rev 38(10):2887–2921 57. Hoi H et al (2010) A monomeric photoconvertible fluorescent protein for imaging of dynamic protein localization. J Mol Biol 401 (5):776–791 58. Lam AJ et al (2012) Improving FRET dynamic range with bright green and red fluorescent proteins. Nat Methods 9(10):1005–1012 59. Fouquet C et al (2015) Improving axial resolution in confocal microscopy with new high refractive index mounting media. PLoS One 10(3):e0121096 60. Kohl J et al (2014) Ultrafast tissue staining with chemical tags. Proc Natl Acad Sci U S A 111(36):E3805–E3814 61. Jares-Erijman EA, Jovin TM (2003) FRET imaging. Nat Biotechnol 21(11):1387–1395

Chapter 3 General Considerations for Acquiring a Three-Color Image by Laser Scanning Confocal Microscopy Joseph Brzostowski Abstract Laser scanning confocal microscopy is the workhorse epifluorescence imaging technique used in laboratories worldwide to acquire three-dimensional images of both fixed and live specimens with fine, highcontrast optical sections to discern details that cannot be afforded by standard widefield microscopy. This basic protocol steps the user through a typical three-color imaging experiment using a Zeiss LSM 880 confocal microscope for the example. The extensive Notes section attempts to generalize the method so that concepts and considerations can be applied to other laser scanning confocal systems. Key words Laser scanning confocal microscopy, Pinhole, Epifluorescence, Photomultiplier tube, 3D imaging, Kohler illumination

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Introduction Elegant hardware and software designs from the major manufacturers have made laser scanning confocal microscopes accessible even to the occasional user, allowing these sophisticated instruments to be used for the most routine epifluorescence microscopy experiments when a widefield microscope equipped with a camera would have sufficed. The purpose of this chapter is to walk the new user through a multicolor configuration set up to acquire 2D and 3D images and provide practical commentary behind the settings that are found in common amongst laser scanning confocal microscopes. A section describing how to appropriately focus the transmitted light condenser to obtain a good bright field image is also provided. The basic principles of confocal microscopy and epifluorescence imaging have been thoroughly examined by numerous experts, and recapitulating these concepts here is far beyond the scope of this chapter. We do, however, want to draw the reader’s attention to the excellent chapters in this book by Reilly and Obara,

Joseph Brzostowski and Haewon Sohn (eds.), Confocal Microscopy: Methods and Protocols, Methods in Molecular Biology, vol. 2304, https://doi.org/10.1007/978-1-0716-1402-0_3, © This is a U.S. government work and not under copyright protection in the U.S.; foreign copyright protection may apply and Springer Nature US 2021

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and Jacoby-Morris and Patterson to learn about considerations of and advances in confocal microscopy and fluorescence labeling techniques, respectively. Our facility uses many outstanding sources and refer them to the reader for a deeper appreciation of the core concepts of confocal imaging [1–10]. Of note, there are a number of web-based primers that offer new users very useful interactive tools to learn the fundamentals of microscopy and some of the more exotic techniques (e.g. https://micro.magnet.fsu.edu). Laser scanning confocal microscopy is an epifluorescence imaging technique where excitation light is incident over (epi) the specimen through an objective lens and the resulting fluorescence emission signal is captured back through the same lens (Fig. 1). The excitation source is commonly a continuous wave, monochromatic laser whose beam is focused to the smallest possible point (a diffraction-limited spot) through the objective lens in the specimen plane. To excite fluorophores, the point is scanned laterally in one dimension across the specimen using a set of galvo mirrors. The resulting fluorescence signal that is collected back through the objective lens from each point of excitation in the specimen is de-scanned via the galvo mirrors through a small aperture called a pinhole and incident upon a specialized light detector called a photomultiplier tube (PMT). In epi-illuminated systems, the excitation light travels vertically through the entire specimen, activating fluorophores above and below the intended plane of focus (Fig. 1). The resulting out of focus signal severely degrades image contrast and suppresses the detail resolved by the objective lens. The term confocal is derived from the mechanically adjustable pinhole that is placed in the conjugate focal plane just in front of the PMT. The pinhole lies at the heart of the laser scanning confocal microscope, serving to block the out-of-focus signal to provide thin optical sections with high contrast that can be combined to create a computer-generated 3D rending of the specimen (Fig. 1). The PMT is not a traditional camera sensor with an array of physical pixels, but rather a photon detector that produces and amplifies photoelectrons in response to incident light collected from the excitation spot in the specimen. The amplified signal is converted to digital intensity units to generate the image. The number of “pixels” across each scanned line is assigned in the software by the user to appropriately sample the specimen in lateral (X, Y) space to resolve the finest features allowable by the objective lens and wavelength of light. The resulting digital pixel in the image is a manifestation of the length of time that photoelectrons are registered (digitally converted) from the PMT. If the scan speed of the laser is not slowed, increasing the image pixel resolution results in less photons collected (or averaged) per pixel and more image noise.

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Fig. 1 A very simplified diagram of a laser scanning confocal microscope light path. Excitation light (dashed blue lines) from a laser source through its associated optical elements (not shown) is reflected by the system’s main Beam Splitter (dichroic/polychroic mirror) and scanned laterally by two high-speed vibrating mirrors driven by galvanometer motors (galvo mirrors, not shown). The scanned beam is focused to a point by the objective lens in the specimen focal plane. Excitation light passes through the specimen activating fluorophores above and below the focal plane. In systems using standard continuous wave lasers, the wavelength of emitted fluorescence is longer than the excitation light. Fluorescence signal from the focal plane (solid green lines) and out-of-focus fluorescence from above and below the focal plane (dotted gray lines) pass back through the same optical path, being descanned by the galvo mirrors. The emission signal passes through the beam splitter and is focused by the tube lens at the pinhole aperture, which lies in a plane conjugate to the specimen focal plane. Only the focused rays reach the detector (typically a photomultiplier tube). Out-of-focus rays from above and below the focal plane are blocked by the pinhole, thus providing a thin optical section with high contrast

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Noise is the bane of any imaging technique and is inescapable in laser scanning confocal microscopy; we refer the reader to more in-depth discussions in the references cited above and here [11]. The methods and notes address practical ways to minimize image noise. However, the new user should always keep in mind, especially when working with live specimens, that the settings which provide the “prettiest” image are not necessarily where one ought to collect data over time and/or vertical space because of phototoxic (live cells) and/or photobleaching (live or dead cells) effects that could alter the nature of the biology being imaged. The goal is to collect enough intensity information with minimal invasiveness for quantitative/statistical purposes and to report how the images were obtained. An indelible lesson given to this author was to explore the “ugly” especially when imaging live cells. This advice does not bar acquiring the “money shot” for the representative figure, but, again, it is necessary to report how the representative images were obtained as to not fool the reader.

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Materials 1. Confocal microscope equipped with a 405, 488, and 561 nm laser. In this case, a Zeiss LSM 880 equipped with three PMTs, the aforementioned lasers, and a PlanApo 1.4 NA 40
objective lens. 2. A fixed slide stained with DAPI, Alexa 488, and Alexa 561 or similar.

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Methods Refer to Fig. 2 (Acquisition Tab) and Fig. 3 (Locate Tab) for the software control elements mentioned in the steps below.

3.1 Set Parameters for Three Color Imaging on Three Tracks

1. Turn on system and software. Turn on and warm-up the argon and 561 diode-pumped lasers (see Notes 1–3). 2. Press the Acquisition Tab (Fig. 2) and under the Imaging Setup menu (make sure the “Show all” box on the menu bar is checked), set the optical configuration to image DAPI, Alexa Fluor 488, and Alexa Fluor 568 fluorescence on individual Tracks to avoid crosstalk between detector channels (see Note 4) by adding three Tracks (see Note 5). A transmitted light channel will be added later. 3. Under the Imaging Setup menu, choose “Switch track every: Line” in the dropdown (see Note 6).

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Fig. 2 The Acquisition Tab. Shown is a screenshot of the acquisition control elements in the Zen software as described in Subheading 3

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Fig. 3 The Locate Tab. Shown is a screenshot of the software control elements in Zen software that allows visualization of fluorescence through the eyepiece as described in Subheading 3

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4. Under the Imaging Setup menu, select Track 1 and chose the 488/561 and 405 nm main beam splitters (MBS) for the “Visible Light” and “Invisible Light” excitation optical paths, respectively (see Note 7). 5. With Track 1 still selected, make sure the Channel 1 PMT is checked (see Note 8). In the dropdown menu under “Dye,” choose DAPI from the list to enable the guide histogram. A slider bar below the histogram will be present (or appears when the box is checked). Expand the edges of the slider bar to set the system to collect emission from 415 to 499 nm on Channel 1 (see Note 9). In the dropdown menu under “Color,” choose blue (see Notes 10 and 11). Press the “Invisible Light” laser icon and check the 405 nm laser for this Track (see Note 12). 6. Select Track 2. Uncheck Channel 1 and check Channel 2. In the dropdown menu under “Dye,” choose Alexa Fluor 488 from the list. Using the slider bar below the histogram, expand the edges to set the system to collect emission from 500 to 550 nm (see Note 13). In the dropdown menu under “Color,” choose green (see Notes 14 and 15). Press the “Visible Light” laser icon and check the 488 nm laser for this Track. 7. Select Track 3. Uncheck Channel 1 and check Channel 3. In the dropdown menu under “Dye,” choose Alexa Fluor 568 from the list. Using the slider bar below the histogram, expand the edges to set the system to collect emission from 600 to 650 nm. In the dropdown menu under “Color,” choose red (see Note 16). Press the “Visible Light” laser icon and check the 561 nm laser for this Track. 8. If required with Track 3 still selected, check (activate) the transmitted light PMT (T-PMT, see Note 17) to collect a standard brightfield image or one that is enhanced by differential interference contrast (DIC, see Notes 18 and 19). Correctly aligning and focusing the condenser and resulting brightfield and DIC images are discussed separately in Subheading 3.3. 9. Under the Channels menu (make sure the “Show all” box on the menu bar is checked), select Track 1 (see Note 20). Set the 405 nm laser power to 0.5%. Skip the “Pinhole” setting for the moment. Set the “Gain (Master)” to 600 V and leave the “Digital Offset” at 0 and “Digital Gain” at its default value of 1 (see Note 21). Select Track 2 and set the 488 nm laser power to 1%. This time press the “1 AU” button to collect data on this track with one Airy unit (see Note 22). Set the “Gain (Master)” to 600 V. Leave the “Digital Offset” at 0 and “Digital Gain” at 1. Select Track 3 and set the 561 nm laser power to 1%. Do not adjust the “Pinhole” slider. Set the “Gain (Master)” to 600 V

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for Channel 3. Leave the “Digital Offset” at 0 and “Digital Gain” at 1. Set the “Gain (Master)” to 300 V for the T-PMT for the DIC image. 10. The system is now set for preliminary scanning to find the specimen and set the remaining acquisition parameters. Place a drop of oil on the 40
objective lens (see Note 23), adjust the sliders on the universal sample holder to accommodate the length of the slide, place the fixed-sample slide on the stage holder with the cover glass side down, and raise the objective lens using the coarse focus knob, so that the drop of oil just touches the cover glass. 11. Press the Locate Tab (Fig. 3) to visualize the sample through the eyepiece (ocular). If the sample can be observed in brightfield (i.e., it is not too transparent or thin), then press the “DIC III” button (see Note 24), increase the power of the tungsten light source (or white light LED) if necessary and slowly raise the objective lens to focus to the specimen. 12. If capturing a brightfield image, focus and center the transmitted light source on the specimen by adjusting the condenser as described in Subheading 3.3. 13. Press the Acquisition Tab (Fig. 2) to set the remaining acquisition parameters for each Track individually. 14. Under Acquisition Mode (make sure the “Show all” box on the menu bar is checked), make sure the “Bit Depth” is set to 8 bits for the initial adjustments. Later it will be set to 16 bits (see Note 25). 15. Under the “Channels” menu, uncheck Tracks 2 and 3 (see Note 26). Press “Live” to rapidly scan the sample and adjust focus (see Note 27). Under the image that appears on the right side of the screen, check the “Range Indicator” box to visualize pixels with a value of zero (displayed as blue) and saturated pixels that are out of the range of the detector (displayed as red; see Note 28). 16. Raise (or lower) the intensity in the image such that background (see Note 29) is minimized and that specific fluorescence signal is maximized without reaching saturation (no red pixels). There are three approaches to increase image intensity each having both positive and negative consequences that are detailed in the Notes. (1) Open the pinhole (see Note 30). (2) Raise the laser power (see Note 31). (3) Raise the gain (see Note 32). 17. Raise the “Digital Offset” (usually 1 or 2 U on the slider in 8 bits or several hundred units in 16 bits, see Note 33), so that there are no zero-valued pixels (displayed as blue pixels in the image, see Note 34).

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18. Stop the “Live” acquisition. 19. Under the “Channels” menu, uncheck Track 1. Keep Track 3 unchecked. Check Track 2. Press “Live” and balance the desired fluorescence signal over the background as described above using the “Range Indicator.” Stop the “Live” acquisition. 20. Under the “Channels” menu, uncheck Track 2. Keep Track 1 unchecked. Check Track 3. Press “Live” and balance the desired fluorescence signal over the background as described above for Channel 3 using the “Range Indicator.” Then adjust the “Gain (Master)” for the T-PMT to balance the DIC image, so that there are no saturating pixels. The offset can be adjusted to apply a desired contrast. Stop the “Live” acquisition. 21. If needed, select a subregion (zoom in) of the image to scan (see Note 35). Activate the cropping tool by pressing the “Crop” button on the Dimensions Tab found under the image. A box appears indicating the new region to be scanned, which is a 2
digital magnification (zoom) of the current image. The size of the box can be adjusted at the corners to increase/decrease the zoom. The lines that vertically and horizontally cross the box can be used rotate the image. The single blue line shows the resulting horizontal direction of the scan if the crop box is rotated. The image size can also be manipulated in the “Scan Area” submenu in the “Acquisition Mode” menu (see Note 36). 22. Under the “Acquisition Mode” menu, press the Optimal button to obtain an appropriately sampled image in the X and Y dimensions (see Notes 37–41). 23. Under the “Experimental Manager” header, press “Snap” to obtain a preliminary image with the desired zoom factor and appropriate X/Y sampling (see Note 42). 24. Under the “Averaging” submenu, choose 2 for the “Number” of averages, choose “Line” for the “Mode”, and choose “Mean” for the “Method” to reduce the noise in the image (see Note 43). Press “Snap” to obtain a preliminary image. Press the “New” button under the “Experimental Manager” header (see Note 44). Increase the number of averages to 4, press “Snap” and determine if increased averaging improves image quality (see Notes 45 and 46). 25. Under the “Averaging” submenu, change the “Bit Depth” to 16 bits to capture images with pixels having a gray scale range from 0 (black) to 65,535 (saturated). The “Digital Offset” value set above is automatically multiplied by a factor of 256. The value can be decreased until a few blue pixels appear randomly as the image is scanned in “Live” mode.

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26. Under the “Experimental Manager” header, Press “Live.” Under the “Scan Area” submenu, zoom out to 1
by pressing “1” next to the zoom slider to inspect the image for areas/cells of interest for imaging (see Notes 47 and 48). 27. After choosing the scan area, return to the desired zoom factor. Doublecheck that the scan speed and frame size are at the desired settings. Press “Snap” to capture the final image and save (see Note 49). Figures 4 and 5 show representative images to demonstrate the result of changing scan speed and imaging averaging, and color representation, respectively. 3.2 Acquire a Three-Dimensional Image

The glory of a laser scanning confocal microscope is not fully appreciated until optical “slices” are acquired vertically through the specimen and recombined with the aid of software to create a rendered 3D volume. A “Z-stack” is easily captured on all types of confocal microscopes by simply checking the appropriate box in the software to have the system acquire images sequentially in the vertical dimension. There are several important considerations when performing a Z-stack. (1) Ensure that the specimen is sampled appropriately in the vertical axis to satisfy the Nyquist criterion (see Notes 37 and 49). This “rule,” however, may need to be bent a bit depending on consequences of the next two considerations. (2) Recall that the excitation source transmits through the specimen and activates and photobleaches (depending on laser power) not only at the focal plane, but above and below it as well. The level of photobleaching can be approximated and adjusted for by performing a time series acquisition in 2D with the same number of time points as images in a Z-stack and measuring potential changes in fluorescence intensity at a particular setting. Less laser power, image averaging or under sampling in the vertical dimension are the various compromises available to ameliorate photobleaching. Alternatively, if the rate of photobleaching can be accurately modeled, then adjustments to image intensity can be made post-acquisition through software. (3) If performing live-cell experiments, phototoxic effects on cells scanned in the vertical dimension over time must not be overlooked. 1. Under the Acquisition Tab (Fig. 2) under the Experiment Manager header, check Z-stack to activate the Z-Stack menu; it will appear toward the bottom of the “Multidimensional Acquisition” header. 2. After choosing a cell of interest, and setting the parameters described in Subheading 3.1, temporarily choose one Track (uncheck the others) with a fluorophore that is least vulnerable to photobleaching. Press “Live” and move the focus to the bottom of the specimen nearest the coverslip. In the “Z-Stack”

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Fig. 4 Shown is a comparison of how image averaging and scan speed affect the quality of an image. All images were acquired with a 40
objective lens and a 4
zoom factor. The scale bar on bottom right in both (a) and (b) is 5 μm. (a) Shown is a digitally enlarged region of the full field of view seen in (b) of a nucleus from a mouse kidney section stained with the DNA dye DAPI acquired with either increased averaging or slower scan speed. The top row: From left to right, each image was acquired with the same scan speed (also called pixel dwell time by different confocal manufactures) but with increased image averaging (indicated). Image quality improves at the cost of time, photobleaching, and phototoxicity. Improvement is marginal when averaging is increased to eight times in this example. Bottom row: Each image was acquired with a decreased scan speed and no image averaging. On the Zeiss system, slowing the scan speed (or increasing the pixel dwell time) does not increase the intensity of the image (see Note 38). Image quality is improved with the same aforementioned caveats. The total acquisition time is indicated beneath of each column. The same acquisition time and quality for the images in each column are obtained when an image is acquired either at a scan speed of 7 averaged twice or at a scan speed of 6 with no averaging. (b) The full field of view of a mouse kidney section with DAPI-stained nuclei is shown that differences in image quality are not discernable. From left to right, each image was acquired with the same scan speed but with increased image averaging as indicated

menu, press “Set first.” Move the focus to the top of the specimen and press “Set last.” In the Z-Stack menu, press “Optimal” to appropriately sample the image in Z space (see Note 50).

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Fig. 5 The comparison of a three-channel acquisition presented in the traditional blue, green, red (RGB) color scheme versus a cyan, yellow, magenta (CMY) scheme. For demonstration purposes, the signal from DAPIstained nuclei in a mouse kidney section were acquired on three separate channels with a 40
objective lens and a 4
zoom factor. The scale bar on bottom right in both rows is 5 μm. The laser and gain settings were adjusted such the image intensity for each channel was approximately equal to demonstrate the resulting color in the overlay image (indicated) when all three colors are present or when one channel is turned off

3. Under the Experiment Manager header, press “Start Experiment” to capture a three-dimensional image and save (Fig. 6a). 4. Render the stack of images into a volume with the 3D features found in the software (Fig. 6b). 3.3 Setting the Focus of the Condenser

Critical for both transmitted- and epi-illumination techniques is the ability to generate an even field of illumination on the specimen. Sometimes called Kohlering or setting Kohler illumination after its inventor August Kohler, the technique employs a set of lens elements near the light source (the collector) that lies in the conjugate focal plane, and a second adjustable set of lens elements (the condenser) situated on a moveable rack that lies close to the plane conjugate to the objective’s back aperture. When properly adjusted, Kohlering the microscope’s illumination accomplishes two equally important functions: First, rays that could potentially form an interfering image of the light source in the image plane (e.g. an image of the tungsten filament or LED) are focused by the collector lens to the front focal plane of the condenser lens. In turn, the condenser lens passes these rays parallel through the specimen to avoid forming an image of the light source. Second, rays from the light source emanating at other angles are gathered by the collector

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Fig. 6 Three-channel images and 3D maximum intensity projection. (a) Three-channel acquisition of a mouse kidney section as described in Subheading 3. Two color schemes, CMY (left) and RGB (right), are shown to present the data with a combined overlay of all three channels. DAPI-stained nuclei appear as cyan and dark blue, wheat germ agglutinin-stained structures appear as green and yellow, and actin appears as red and magenta in the left and right panels, respectively. (b) Shown is a 3D maximum intensity projection of DAPIstained nuclei assembled from a Z-series of a mouse kidney section as described in Subheading 3.2. Two rotational views are presented to demonstrate the depth of the acquisition scaled in microns

lens and are focused at the position of the field diaphragm (see below). In turn, the condenser lens focuses these rays at a spot on the specimen to provide an evenly illuminated field. The protocol is described for an inverted microscope platform but also applies to an upright microscope where the moveable condenser is situated below the specimen. 1. Place the specimen on the stage (Fig. 7, I), turn on the transmitted light, and focus the objective lens to form an image as you are looking through the eyepiece.

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Fig. 7 The condenser arm of a typical inverted microscope. Parts are labeled in order as they are described in Subheading 3.3. (I) Specimen stage. (II) Field diaphragm dial. (III) Focus knob. (IV) Centering knobs

2. Situated above the condenser at the top of the swing arm is an adjustable aperture called the field diaphragm (Fig. 7, II). Move the dial completely to the right to close the diaphragm. The diaphragm will always remain slightly open even at this extreme setting. The physical position of the diaphragm is in a space that is conjugate to the specimen and image planes. When the adjustable condenser lens is properly focused, an image of the shadow of the closed diaphragm is visible in the eyepiece. Typically, some adjustment is required, meaning never rely on the previous user. 3. Move the condenser up or down using the focus knob (Fig. 7, III) to focus the image of the diaphragm in the image plane using the knob associated with the condenser housing (see Note 51, Fig. 8). 4. Once focused, the image of the diaphragm may need to be centered. As you look through the eyepiece, turn the two centering knobs (Fig. 7, IV) to move the condenser laterally until it is centered (see Note 52). 5. Once centered, open the diaphragm using the dial (Fig. 7, II), so that its shadow is no longer seen in the field of view through the eyepiece (Fig. 8).

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Fig. 8 Brightfield images of a mouse kidney section after the condenser is correctly aligned and focused. The image of the shadow of the closed field diaphragm is shown in four corners in the first panel. Once centered and focused, the diaphragm is opened just outside of the field of view. Compared are brightfield images acquired with and without DIC

4

Notes 1. This extremely obvious step may cause an eye or two to roll, but it does provide the opportunity to note that warming gas lasers for sensitive measurements, such as FRET experiments in which fluctuations in laser power can confound small percentage changes in image intensity, is quite important. Gas lasers may go the way of the dodo, but many are still around, and likely will survive on systems at least one if not two more postdoctoral lifespans. Advice for the length of warm-up varies, but a good suggestion is to put the laser at running power after the initial startup and wait for 30 min. For routine experiments, where image intensity differences are greater than 10%, following the manufacturer’s for warm-up is fine, which is usually less than 10 min. Laser power can be monitored using a light power meter that can be obtained from many optics companies and is an essential piece of equipment in any core facility. The above advice only applies to gas lasers as modern diode lasers do not require a warm-up period. 2. While this brief protocol only considers a fixed slide at room temperature, it is important to mention that using an environmental chamber (whether a full enclosure around the microscope base or a stage top device) to heat the system to 37  C for live cell work requires at least 30 min for warm-up. What must be appreciated is that while the temperature probes built into the environmental chamber may read the desired set point only after minutes, the surrounding metal in the microscope needs to equilibrate. Temperature deltas cause both stage and focus drift. Many microscopes now have autofocus devices to compensate for focus drift; however, if performing high magnification multi-position imaging, stage drift can occur and would

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require correction post-acquisition. Keep in mind that room HVAC systems are notorious for causing drift. While stage top environmental chambers are extremely convenient, full enclosures do a better job at mitigating temperature deltas in rooms that do not have stable HVAC systems. Temperature can be monitored using a variety of external probes and is an essential piece of equipment in any core facility. Thin wire thermistors can be used at the sample to verify and calibrate the temperature setpoints for environmental chambers. 3. Many confocal systems are equipped with “straight diode” or “diode-pumped” lasers. The former is akin to very nice laser pointers in the sense that a single laser diode provides the desired wavelength and is directly turned on/off by a voltage input controlled via software as opposed to one’s thumb. The power level is also controlled by voltage. The latter is more complex in that a laser diode is used to “pump” a lasing medium to obtain the desired wavelength. The consequence of this design is that these lasers can only be turned on at maximum power and thus need to be attenuated via a secondary device like and acousto-optic tunable filter; such devices are also required for attenuating and selecting specific wavelengths from gas lasers whose power output cannot be rapidly modulated at the laser head. Gas lasers produce multiple wavelengths. For example, a typical argon laser produces four usable wavelengths for confocal systems: 458, 477, 488, and 514 nm. 4. Various confocal manufacturers use different nomenclature to describe what are fundamentally the same principles for directing fluorescence emission of specific wavelengths to a photon detector in the form of a standard photomultiplier tube (PMT) or hybrid detector (see Chapter 1 by Reilly and Obara in this book). For the Zeiss systems, laser light is reflected from a “beam splitter” or polychroic mirror to the specimen. The beam splitter is selected under the Imaging Setup dropdown bar. For example, if only the 488 nm laser is required for singlecolor imaging, then choose the single-reflecting 488 beam splitter. Note that the dual 488/561 beam splitter will work just fine, but the tradeoff would be some light loss. If performing a multi-color imaging experiment, choose the appropriate polychroic beam splitter to reflect the required laser lines that are set for the light path. 5. By default, the “Channel 1” PMT is checked for each added Track. A Track can have one or multiple PMTs associated with it. If multiple PMTs are associated with a single Track, then two fluorescence signals can be acquired simultaneously, which saves time for fixed imaging and is ideal for dynamical live cell imaging; however, crosstalk between signals is a significant consideration. Ideally, two fluorophores whose emission

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spectra do not crosstalk (e.g., Alexa Fluor 488 and 633) are best to image simultaneously. In this protocol, each Track will have just one PMT channel associated with it to prevent crosstalk. 6. Most laser scanning confocal systems provide the user the option to rescan the same line with the excitation wavelength switching for each line scan. In this mode, the image for each Track (with its associated Channel(s)) is alternately generated one line at a time. Laser line switching is handled electronically and does not require moving parts, making the acquisition proceed as quickly as possible. If a filter or mirror change is required due to a more complicated acquisition scheme, the system can be set to fully acquire each Track sequentially by choosing “Frame” in the “Switch track every:” dropdown. Avoid Frame switching at all cost in live cell experiments especially if fluorescent objects move faster than the acquisition time to avoid registration artifacts (e.g., a fast-moving object that is labeled with two fluorophores will have one color phaseshifted in space relative to the other color when the images are overlaid). 7. When “Switch track every: Line” is selected, each Track will be automatically set to have the same components (filters, mirrors, etc.), making set up easier. If “Frame” is selected, then careful attention needs to be paid to the necessary components for each Track. In “Frame” mode, the software will let the user do anything they might like whether it makes sense or not. In our facility, we suggest that users initially set up their new configurations by selecting “Line,” then switch to “Frame” to make specific changes if needed to avoid errors. 8. When a new Track is added, Channel 1 is checked by default and will need to be changed to another Channel. If “Switch track every: Line” is selected, the software does allow the same PMT (Channel) to be used for each added Track. If this scheme is required, then “Switch track every: Frame” needs to be selected, so that the hardware can move between Tracks; this significantly slows the acquisition. 9. The various manufacturers will use different methods (e.g., traditional fixed bandpass/long pass filters or a diffraction grating) to select/separate the desired fluorescence wavelengths emitted by the specimen for detection. The Zeiss 880 employs a holographic grating situated within a spectral recycling loop to efficiently capture and disperse photons emitted by the specimen into the visible light spectrum. Regions of the spectrum are selected and directed toward the desired PMT. It is critical to select a particular bandpass for one’s experiment and stick with it for all acquisitions to allow for intensity comparisons between samples on that day and, potentially, between days.

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10. The PMT reports detected photons (the analog signal) as a digital output in gray scale intensity units—termed analog-todigital units (ADUs). The PMT does not detect color, just photons. The color applied by the user is a “tint” for display purposes only. The resulting “color” image remains as a grayscaled/intensity image for computational purposes unless it is converted and saved in an RGB or other color format. Converted images cannot be used for computational purposes. Always save the original data either in the manufacturer’s data format or as a gray scale TIFF image to preserve the intensity information. 11. It is the unabashed opinion of the author that the traditional blue tint used for DAPI presentation is terrible. Human vision does not have the acuity to adequately sense intensity differences with this hue in print or in presentations at the back of a room. Cyan is a nice alternative, but then requires one to consider an alternative color palette discussed below. However, for the sake of tradition, this protocol will use the red, green, blue palette as well (see Fig. 5). 12. Laser lines on all confocal systems need to be assigned/activated for their specific Track/Channel. The Zeiss system places laser lines on specific beam paths, so that excitation light can be co-focused at the specimen plane. Laser lines from 457 to 641 nm are placed on the “Visible Light” path and laser lines from 440 nm to UV and infrared lasers are placed on the “Invisible Light” path. Having the ability to acquire an image with a laser deactivated (unchecked) can come in handy when trying to determine crosstalk if two channels are acquired simultaneously. 13. When in “Switch track every: Line” mode, the emission wavelength between tracks for each channel cannot overlap (the system will not allow it). If overlap is required, “Frame” mode accommodates this but slows the acquisition time as a tradeoff. 14. Impaired color vision is not that rare in humans—with issues discriminating between red and green being the most common. Unfortunately, the use of red and green to display an overlapping yellow signal to demonstrate colocalization is still quite pervasive. Here is a brief discussion of the topic: https:// www.somersault1824.com/tips-for-designing-scientificfigures-for-color-blind-readers/. Be aware journals are making more accommodations. 15. Yellow is a nontraditional choice for the Alexa Fluor 488 dye. At equal intensity, it blends with cyan to produce a green hue when overlapped (see Fig. 5). Note that human senses 555 nm light (green-yellow) best.

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16. Yellow and magenta produce a pink hue when combined at equal intensity. Cyan and magenta produce a purple hue when combined (see Fig. 5). All three hues combined at equal intensity produce white. 17. The monochromatic light from the laser used to excite a fluorophore transmits through the specimen. The T-PMT (in Zeiss parlance) situated above the condenser captures this transmitted signal. A transmitted light image is obtained “for free” on laser scanning confocal microscopes in the sense that this channel can be combined with (typically) any fluorescence channel and acquired simultaneously on most systems. Since there is no tradeoff in acquisition time (because an additional Track is not required), it is worth checking the box for those occasions when that pesky reviewer wants to see the status of the cell under observation if the fluorescence signal in the image is suspect. 18. A description of the physics underlying the differential interference contrast (DIC) technique is beyond the scope of this chapter and can be found in several excellent web-based primers (e.g., https://micro.magnet.fsu.edu). Simplistically, DIC is one of several optical techniques used to enhance the difference, and hence increase contrast, between the borders of objects in a specimen that otherwise would be transparent in a standard brightfield image. The technique traditionally requires four optical elements: a polarized light source, two birefringent prisms located on opposite sides of the specimen (one of which is adjustable to modify the level of contrast enhancement), and another polarization element (sometimes described as the analyzer), that recombines the transmitted light to create the enhanced image. It also requires a correctly aligned and focused condenser (see Subheading 3.3 and Fig. 8). There are various ways to skin the DIC cat, and depending on the manufacture, the adjustable element is found beneath the objective lens or on the condenser. As emitted photons are a commodity for most specimens imaged by epi-fluorescence, phase contrast (another common brightfield enhancement technique) is not usually used on a confocal system. Phase contrast requires a phase element (a ring) at the back aperture of the objective lens, which incurs a significant light throughput tradeoff. 19. Any laser line on most laser scanning confocal systems can be used to provide the transmitted light channel. Laser light has three major properties: monochromatic (note that gas lasers produce multiple monochromatic peaks that then need to be filtered and diode lasers are notorious for having side bands that also require “cleanup” filters); coherent (the light waves are in sync with one another); and polarized (this final property being essential for producing a DIC image by laser scanning).

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20. Detailed control elements in the Zeiss acquisition software are revealed when the “Show all” boxes are checked. This facility recommends leaving them checked and saving the profile, so that the software opens in the same state. 21. As the voltage (master gain) is raised, electrons on the dynode array (which amplifies the input signal) within the PMT are more apt to be displaced. Most manufacturers will allow the user to raise the voltage on the PMT to its maximum; however, being able to press the pedal-to-the-metal so-to-speak gives the user little benefit. At exceedingly high voltages, electrons will pop off on their own, thus creating randomly generated noise that cannot be compensated for by image averaging. Assuming that raising the laser power or opening the pinhole (which defeats confocality) does not adequately increase in signal, then larger voltages are needed. Zeiss offers a “Digital Gain” to compensate for extremely low signals without introducing more noise into the image. If you find yourself raising the voltage past 900, lower the “Gain (Master)” back to ~800 and add a “Digital Gain” amplification factor to boost the desired signal in the resulting image. Because the signal is digitized directly off the PMT, analog noise from the PMT is not amplified. If a “Digital Gain” factor is applied, then it must be carried out through subsequent experiments if the intent is to compare intensity information. If your signal suffers, however, it is best to improve staining or protein expression whenever possible. 22. The Airy disk and the associated Airy pattern describe the bestfocused spot of light that a lens with a circular aperture can make. The diffraction pattern is formed from a uniformly illuminated, circular aperture; it has a bright central region surrounded respectively by a diminishingly bright series of concentric rings. The diameter of the central Airy disk (the Airy unit) depends on the wavelength of light and the numerical aperture of the objective lens. The central Airy disk contains ~84% of the total light intensity, while the remaining 16% is decreasingly distributed across the concentric rings. Light emanating from a diffraction-limited object in a specimen (an object whose dimension is below the resolving ability of the objective lens—like a 200 nm bead or a small organelle) will, similarly, form an Airy disk pattern in the image. Two diffraction limited objects are said to be resolved when the maximum of the first Airy pattern falls on top of the first minimum of the second airy pattern. Typically, the pinhole is adjusted initially to 1 Airy unit to capture the light from the central Airy disk. This pinhole aperture size blocks the out-offocus light that results from the excitation of fluorophores above and below the focal plane in the specimen to maximize

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contrast in the image. Further reducing the aperture size of the pinhole will indeed result in increased resolution in both the lateral and longitudinal dimensions, but this gain comes at a consequence of either using excessive excitation power (unless the sample is super bright) to collect enough photons to provide adequate signal over noise. 23. A wise investment for a laser scanning confocal microscope system is an excellent 40
Plan Apochromatic objective lens with a high numerical aperture (NA). Plan lenses produce an evenly focused image over the field of view. An uncorrected lens focuses light of different wavelengths to different focal points on the optical axis causing aberrations in the image; an apochromatic (the best correction available) lens converges three colors to the same focal point. The NA describes the light collecting/resolving ability of the objective lens. The higher the NA, the higher the resolving ability of the lens. The resolvable feature size (d; as d decreases, resolution increases) in a fluorescence image can be approximated by the Abbe equation: d ¼ l/2NA, where l is the wavelength of light, NA ¼ n * sinθ, with n being the refractive index of the medium (air, oil, silicone, etc.) between the objective lens and the specimen, and θ being one-half the angular aperture of the objective. Please note that resolution is determined by the wavelength of light and the NA of the lens—not by the magnification. Magnification, however, lets you “see” what the lens resolves. Said another way, an equivalently corrected 40
and 60
lens having the same NA will resolve the distance between two nearby features in a specimen equivalently, but if the features are very small, magnification is required to discriminate the distance in the image. However, there is tradeoff, while image brightness increases as NA increases, brightness decreases as optical magnification increases. Image brightness for a fluorescence image is approximated by the equation: lNA4/M2 with M representing magnification. Interestingly, a useful benefit of a laser scanning confocal system is that the zooming capability afforded by the galvo scanning mirrors provides a digital magnification of the specimen to discriminate resolved image features without a loss in brightness that occurs with optical magnification. 24. When a specimen is too transparent that DIC cannot provide adequate contrast or if there are very few cells on a coverslip, choose an epi-fluorescence channel to aid in finding focus/ specimen. If an option, choose a fluorophore, such as DAPI, where photobleaching will have the least consequence. 25. The dynamic range of intensity values for a pixel can be divided into 256 U (28 or 8 bits), 4096 U (212 or 12 bits), or maximally, 65,536 U (216 or 16 bits) on most confocal systems.

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There is no consensus in the literature for how many shades of gray a human eye perceives (however, it seems safe to say that humans cannot discern 16 bits), regardless, for quantitative purposes, it is best to scan in 16 bits. For new users, we suggest making the change later after adjusting the “Digital offset,” which is discussed below. Getting an appreciation/feel for how the dynamic range scale for any detector (PMT, sCMOS, or CCD) is important and should be paid attention to for any quantitative imaging. 26. Setting up one Track at a time allows the system to scan as fast as possible and does not cause the other fluorophores in the specimen to photobleach such that the same area in the specimen can be used to set up each Track/Channel. 27. At fast scan rates and no imaging averaging, the preview image will be quite noisy. Don’t worry. 28. The “Range Indicator” is a friend who likes to be used and is a tool found on all confocal systems. It rapidly informs if the data falls within the dynamic range of the detector. At this point in the setup, the goal is to ensure the image is not saturating—no red pixels. You can also look at your data graphically by using the “Profile” tool (again common with many confocal systems). Press the “Profile” tab next to the image (actively scanning or captured). A line will appear that can be moved or stretched anywhere in the image. The intensity values under the line will appear in the graph next to the image window. Another piece of advice—in this laboratory, we routinely image dynamic changes in fluorescence intensity in live cells. If a signal will increase during an experiment, remember to leave some overhead to accommodate. A saturated image is generally useless. 29. Noise and background are sometimes inappropriately used interchangeably and confuses the new user. Noise has several sources, and unwanted background signal is one of its contributing factors. Also contributing to noise are photon statistics, electronic noise (such as raising the gain too high causing electrons to spuriously pop off the PMT), and other electronic sources. Noise due to photon statistics can be ameliorated partially by image averaging or raising the laser power. Background signal, however, is part of the sample. Two main sources of background are autofluorescence and the mis-location of the fluorophore of interest. Autofluorescence is inherent to the sample. It can be addressed in a part by using alternative emission band pass ranges in front of the detector or an alternative (less than ideal) excitation laser line. While not discussed here, mathematical spectral unmixing techniques (which require specific hardware for data acquisition) can be

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used to subtract out autofluorescence from the true signal. Background from the fluorophore can be addressed by improving staining methods or how the protein is expressed in a live cell, and, potentially, by decreasing pinhole diameter to increase the contrast between free fluorophore and what is bound to a structure of interest. Raising laser intensity or gain will not improve matters. 30. The Airy unit was discussed above. Opening the pinhole beyond on Airy unit allows out-of-focus light to reach the image plane. Notably for live cell imaging, some out-of-focus light may be acceptable (especially if the structure of interest is larger than the optical section determined by the size of the pinhole aperture) and should be considered for the purposes of minimizing the resulting phototoxic effect on cells if raising laser power is the only alternative to increase the fluorescence signal. How much to break the rule of confocality is to be determined empirically, and then must be applied consistently in experiments for quantitative comparisons. 31. Raising the laser power to increase signal is a great fix if: the background is low; just one image plane is required (for fixed or live specimens); or live cells can take the radiation. This is because, respectively: background fluorescence, if present, will only increase with laser power; the excitation source activates (and will photobleach) the fluorophore above and below the imaging plane; laser light, especially short-wave electromagnetic radiation, is phototoxic to living cells. 32. Raising the gain (voltage) across the PMT is both friend and foe to laser scanning confocal microscopy. Increased gain raises the sensitivity of the PMT to create an image where photons emitted from the specimen are limited; however, this comes at a cost of increased noise from the PMT due to the spurious departure of electrons from the amplifying dynodes. But, because the appearance of noise in the image is random, it can be compensated for by averaging multiple images (or lines) which is itself a tradeoff because photobleaching and phototoxicity need to be considered. There are very few win-wins in imaging—typically you must pay for what you get. 33. For didactic purposes, this protocol suggests doing the initial adjustments in 8 bits then switch to the 16-bit scale. This may become an annoyance once the user is familiar with the system, the dynamic range can be set to 16 bits at the start but just keep in mind the offset values will be in the low hundreds to remove zero-value pixels from the image. 34. Removing zero-valued pixels by raising the offset so that all pixels have a slight gray value in the image during acquisition is as important as making sure there are no saturating pixels in

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areas of the image that require intensity measurements. There is no information in either zero-valued or saturated pixels. On PMT-based systems, there is always some “signal” emanating from the detectors, and it is the best practice to set the dynamic range of the image to show that baseline. This “information” as well as true background can be subtracted from the image post-acquisition, leaving the raw data to always tell the full story, especially, if demanded by a reviewer. The reason is that the image offset can be artificially lowered such that true signal (whether specific or unwanted background) can be eliminated from the captured image. Many new users make the error to lower the offset to produce a “pretty image” with great contrast. But the truth can be hidden in such practices and is easy to spot in review. 35. By selecting a subregion, the laser is scanned across a smaller area of the specimen with the same number of pixels (unless changed, discussed below) and results in a digital magnification of the subregion. The software allows for very large zoom factors, but depending on the NA of the objective and wavelength, excessive zooming leads to big and blurry images (empty magnification). The “useful” magnification is estimated to be between 500 and 1000
the objective’s NA (so, for a lens with a 1.4 NA that would be a 1400
magnification). 36. It is advised to be consistent with one’s zoom factors. It is a nuisance to adjust the size of images obtained at different zoom factors to match scaling when assembling figures for a paper. 37. If the NA of an objective lens provides the resolving ability to discriminate the distance between two objects, then the sample must be scanned (sampled) with enough pixels to discern the objects in the digitally rendered image. To achieve this minimally and meet the so-called Nyquist criterion, the sampling interval must be at least twice the highest spatial frequency (peaks and valleys of intensity across a specimen creates “spatial frequency”) within the specimen to keep the same spatial resolution in the captured digital image. Pressing the “Optimal” button sets the number of pixels required to satisfy the Nyquist criterion in the X and Y dimensions. 38. When more pixels are required to satisfy the Nyquist criterion to adequately sample the image, the software will automatically slow the scan speed thus increasing the pixel dwell time. The increased pixel dwell time decreases the noise in the image but does not increase pixel intensity. Each pixel has an elementary dwell time, and all gray scale values are derived from it such that longer dwell times are the sum divided by the number of underlying elementary pixel dwell times (the average). The

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rationale is that image brightness should not depend on the exposure time, but rather on the pinhole diameter, excitation laser power, and gain. An analogy is that if one “looks” longer at an object, it does not appear brighter unless more light is shone upon it. Be aware that other manufacturers do not employ this method of pixel dwell time averaging, and slower scan speeds result in brighter images as if one were exposing a CCD (film, for those who remember) for longer times. 39. Leave the “Scan Mode” found under the “Acquisition Mode” menu set as “Frame.” The options are Frame, Line, or Spot. The settings define how the mirror is scanned across the specimen. A Frame scan creates an image with X and Y dimensions. A Line scan creates intensity information for a line scanned across the center of the scan area and is useful for measuring rapid intensity changes in the millisecond regime where an image is unnecessary (for e.g., calcium fluxes as measured by reporter fluorophores). Similarly, the Spot scan measures the intensity of a spot in the center of the scan area and is used for techniques such as fluorescence correlation spectroscopy. 40. The user has the option to compromise (decrease) the number of pixels in the Y dimension by increasing the value of the “Line Step” under the “Acquisition Mode” menu. This tradeoff increases the overall acquisition speed. For example, if the “Line Step” value is increased to 2, then the overall acquisition time decreases twofold. This functionality has value in live cell imaging to decrease phototoxicity and/or to increase time resolution to measure fast intracellular events. 41. If a centered specimen has empty space above and below it in a square image, another trick to increase scan speed without compromising pixel resolution is to decrease the number of pixels in the Y dimension next to “Frame Size” under the “Acquisition Mode” menu after pressing the “Optimal” button. This action keeps the pixel “density” the same in both axes as it trims the number of pixels from the top and bottom of the Y axis. For example, to decrease the acquisition time twofold for an optimally sampled image of 512
512 pixels, enter 256 in the Y dimension. The resulting rectangular image will be cropped by 128 pixels from the top and bottom. Note that using “Regions” is another option for increasing scan speed in an “Experiment” acquisition. 42. The software provides an option to continuously scan the specimen using all the parameters set under the “Acquisition Mode” menu by pressing the “Continuous” button under the “Experiment Manager” header. In “Continuous” mode, the specimen is typically scanned at slower speeds relative to a “Live” scan.

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43. Fundamentally, image averaging is the result of summing the intensity detected in the same pixel over multiple scans and dividing by the number of scans. Since noise appears randomly in an image, it is suppressed by imaging averaging. In addition, because computer displays are typically large, the natural tendency is to add imaging averaging to improve what is observed on the screen. Whenever possible, the experimenter should consider the final presentation of the data to minimize photodamage. 44. Be warned, unless the image in the active window is saved or a new empty window is opened by pressing the “New” button, the active image window will be written over if the “Live,” “Continuous,” or “Snap” button is pressed. An “Experiment” acquisition, however, will not be overwritten in an unsaved state. 45. Increasing the number of averages improves the quality of the image but comes at a cost of time and photobleaching. Also, for the sake of data collection, it is usually unnecessary to obtain images that have been excessively averaged with the exception of creating the representative image for the manuscript. The tradeoffs need to be considered empirically. 46. Note that slowing the scan speed such that the acquisition time would equal the acquisition time of an image acquired at a faster scan speed but with multiple averages produces images with no discernable difference in quality. An empirical judgment must be made with one’s specimen to determine if multiple fast passes or one slow pass has a negative effect on a live cell, etc. 47. As noted above, unchecking two Tracks will increase the acquisition speed to aid in searching for areas of interest and to minimize photobleaching of sensitive probes. 48. The software allows a 0.6
zoom factor to increase the field of view. 49. For any confocal system, it is best to save in the native file format to preserve all metadata. The metadata contains all the parameters for how the image was obtained. In the Zeiss software, a convenient “Reuse” button can be found under the open image window and in other redundant locations to set the system with the essential parameters to capture another image with the same settings. 50. As described above, pressing “Optimal” will sample the data appropriately, this time in the Z dimension to satisfy the Nyquist criterion, which is approximately ½ the calculated optical section.

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51. The field diaphragm is at a fixed position and is in a plane along the optical axis that is conjugate to the specimen plane and other image planes. 52. If centration is extremely off, a lower magnification can be used to help find and center the aperture and then centration can be finely adjusted at the higher magnification.

Acknowledgments I would like to warmly thank Drs. Alama Arnold and James McIlvain for their patience and humor while answering all my Zeissrelated questions, my long-time colleague and friend Dr. Haewon Sohn, and my recent (and awesome) trainees: Jack Herrick, Meha Patel, and Jina Yom for critically reading this manuscript. This work is supported by intramural funding from the National Institute of Allergy and Infectious Diseases at the National Institutes of Health. References 1. Conn PM (ed) (1999) Confocal microscopy, Methods in enzymology, vol 307. Academic Press, San Diego 2. Diaspro A (ed) (2002) Confocal and two-photon microscopy: foundations, applications, and advances. Wiley-Liss, New York 3. Gu M (1996) Principles of three-dimensional imaging in confocal microscopes. World Scientific Publishing, Singapore 4. Hibbs AR (2004) Confocal microscopy for biologists. Springer, New York 5. Kino GS, Corle TR (1996) Confocal scanning optical microscopy and related imaging systems. Academic Press, San Diego 6. Matsumoto B (ed) (2002) Cell biological application of confocal microscopy. Academic Press, Cambridge

7. Paddock SW (ed) (2013) Confocal microscopy: methods and protocols, 2nd edn. Humana Press, Totowa 8. Pawley JB (ed) (1995) Handbook of biological confocal microscopy. Springer, New York 9. Sheppard CJR, Shotton DM (1997) Confocal laser scanning microscopy. Springer, New York 10. Stevens JK, Mills LR, Trogadis JE (eds) (1994) Three-dimensional confocal microscopy: volume investigation of biological systems. Academic Press, San Diego 11. Sheppard CJR, Gan X, Gu M, Roy M (1997) Signal-to-Noise ration in confocal microscopes. In: CJR S, Shotton DM (eds) Confocal laser scanning microscopy. Springer, New York

Chapter 4 Microfabricated Devices for Confocal Microscopy on Biological Samples Nicole Y. Morgan Abstract Microfabricated devices have found applications in a range of biomedical research problems in recent years, with thousands of research papers published and multiple commercial devices now available. This chapter is intended to provide an overview of the available options for devices compatible with confocal microscopy, including an overview of fabrication techniques and some examples of device use. Although there are times when off-the-shelf devices are well suited for the problem at hand, in some cases customized devices are necessary or more convenient. Protocols for researchers who wish to make their own devices are outlined below; although fabricating templates for devices requires some specialized equipment, making PDMS or hydrogel devices from templates can be done in a standard laboratory setting. Key words Microfabrication, Microfluidics, Photolithography, PDMS, SU-8

1

Introduction

1.1 Microfabricated Structures and Biology

The use of microfabricated structures in biomedical research has been increasing in the past 15 years, with a variety of commercial products now on the market. Using techniques originally developed for the semiconductor industry, in many cases further refined for biological and microscopy applications, making devices with length scales ranging from a few microns to a few hundred microns is straightforward. These dimensions are well suited for confining and manipulating cells, either for simple positioning or trapping, or for more complicated experiments aimed at understanding the responses of cells to biochemical or mechanical stimuli delivered on length scales relevant to their in vivo environment. Many good reviews of the intersection of microfabrication and microbiology are available [1–3]. An earlier protocol in this series, in a book on microfluidic diagnostics, focused on methods for fabrication of SU-8 templates and PDMS devices [4]. This continues to be the most widely used laboratory-made platform for

Joseph Brzostowski and Haewon Sohn (eds.), Confocal Microscopy: Methods and Protocols, Methods in Molecular Biology, vol. 2304, https://doi.org/10.1007/978-1-0716-1402-0_4, © This is a U.S. government work and not under copyright protection in the U.S.; foreign copyright protection may apply and Springer Nature US 2021

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these systems, and this chapter will focus on those materials in the protocol steps as well. However, I will also outline some other materials options for a researcher new to the field and give an overview of design constraints imposed by typical confocal microscopy instruments. I will also discuss example applications of these devices, including the expanding use of commercial devices in some cases. 1.2 Microfabrication Techniques

The core technologies used for making devices with microscale dimensions are photolithography, laser ablation, and 3D printing; once initial templates or prototypes are made, they can be replicated by molding or hot embossing. Laser ablation can be used to directly pattern channels on a variety of substrates, including thermoplastics [5, 6] and glass [7–9], with the channel width, depth, wall roughness, and surface chemistry somewhat tunable by adjusting laser power, scan speed, and surrounding atmosphere. Typical channel depths and widths are tens to a few hundred micrometers. The resulting surfaces will never be completely smooth, giving rise to optical scatter at the channel walls. 3D printing is a rapidly developing technology, and a recent review article provides a good overview of the different printing technologies and their benefits and drawbacks for microfluidic applications [10]. Commercial vendors are now able to deliver custom-printed plastic pieces made using stereolithography with minimum feature dimensions of 50 μm or less. If desired, pieces like this can be used as molds for optically transparent materials such as PDMS [11] which can then subsequently be attached to coverglass for high resolution microscopy. The surface roughness is still somewhat higher than achievable for lithographic molds, but this can be an accessible and relatively low-cost option for some projects. Photolithography was originally developed for the semiconductor industry and is still the core patterning technology used in that space. It relies on selective exposure of a light-sensitive polymer to create well-defined lateral structures, which can either be used to pattern the underlying metal or semiconductor layers, or as is more typical for microfluidic devices, be used directly as a template. There are positive photoresists, which are broken down when exposed to light and thus more easily removed, and negative photoresists, for which the light initiates a localized crosslinking reaction, making the exposed areas more resistant to removal; a schematic is shown in Fig. 1. The thick photoresists more widely used for microfluidic templates and MEMS are negative resists, in particular SU-8 [12]. These resists can be used with a collimated UV source to create templates with features ranging with lateral dimensions down to a few microns or less, and heights ranging from a micron to hundreds of microns, in which the patterned surfaces are nearly optically flat and for which the features can have aspect ratios (height:width) of ten or more. Because fully crosslinked SU-8 is

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Fig. 1 Photolithography. (a) The wafer is coated with photoresist, and then selectively exposed to light through a patterned mask. (b) The resulting structures after the exposed photoresist is developed, for a positive photoresist (left) and a negative photoresist (right)

mechanically strong and relatively inert, it has been used for freestanding devices including neural probes [13] and for devices that contact cells in culture. For biomedical applications, SU-8 structures are more commonly used as templates for replica molding, which enables rapid and low-cost production of multiple devices. With a template in hand, there are a number of materials which have been used for high-fidelity replication. The most widely used of these is poly (dimethylsiloxane), or PDMS, a silicone elastomer originally developed for potting electronics and first used for microfluidic applications by the Whitesides group [14]. PDMS is convenient to use, optically transparent, and can be irreversibly bonded to clean glass or to other PDMS surfaces after exposure to an oxygen plasma. Because the cured polymer is elastic, it can be used for multilayer structures that provide valving capability, and simple compression fits of tubing for fluid inlets and outlets at moderate pressures. PDMS is oxygen-permeable, which can be an advantage when trying to maintain cultured cells in enclosed chambers. If a more compliant substrate for cells is desired, the most commonly used Sylgard 184 can be mixed with Sylgard 527 to achieve lower-stiffness materials [15]. Other materials that have been used in replica molding from microfabricated templates include hydrogels [16], including PEGDA [17], which can also be crosslinked in situ by selective exposure, or agarose, and novel elastomers with better refractive index matching to water [18]. A number of companies now offer off-the-shelf microfabricated devices compatible with or already mounted on glass or plastic coverslips, including ibidi, Cellix, Micronit, Xona Microfluidics, and microfluidic ChipShop. These can be a good option, especially for common applications such as axonal isolation or for simple microchannel structures. Because these devices are generally

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compatible with existing microscopes, it is also fairly straightforward for a new researcher to use molecular tracers or beads to verify flow rates and device function before incorporating them into a cell biology workflow. 1.3 Design Considerations for Confocal Microscopy

When thinking about how microfabricated structures and devices can be used in conjunction with confocal microscopy, there are a few important design constraints, largely centered on desired working distance and index of refraction mismatches. The most common confocal microscope configuration is an inverted platform with epi-illumination; for high-resolution imaging (40
and above), coverslip-corrected objectives are generally used. In order to be compatible with these imaging systems, microfabricated devices are then typically mounted on coverslips, with the coverslip forming the bottom surface of the channel. Usually the attachment of the device to the coverslip is permanent, as for PDMS plasma bonding to glass, or for commercial thermoplastic devices. However, there are some experiments which require mechanical clamping of a device to a coverslip: for example, when the microchannels are made of a more compliant material such as agarose or other soft hydrogels, or when a device needs to be clamped onto alreadygrowing tissue culture substrates. For these applications, a customized clamp providing physical access for the microscope objective and fluidic inputs/outputs can be readily machined [19]. However, the stage travel will be limited by the clamp, and using the highpower highest-NA objectives may still be difficult because of physical access. The working distance of the objective will determine the depth of the chamber that can be imaged. While cells on or near the coverslip surface can be readily imaged, cells further into the chamber may be less accessible. This can be a particular constraint for organ-on-a-chip style devices in which cells are grown on a membrane between two vertically stacked microchannels, or for devices that aim to image cells in a 3D microenvironment. The image quality near feature edges, such as pillars or channel walls, can be significantly impacted by index of refraction mismatches. In particular, the index of refraction, ni, of PDMS in the visible spectral range (589 nm) is 1.4, and most thermoplastics used in chips are similar to glass, with ni ranging from 1.49 for poly methylmethacrylate to 1.56 for polystyrene. Because these are not well matched to the index of refraction of water (1.33), the feature edges in the microdevice can lead to loss of image quality. Even if the edge is outside the focal volume, if the illuminating light impinges on the edge either above or below the focal plane, there can be some internal reflection in the device features and scattering from random surface roughness, leading to uneven illumination and light collection. For some applications, such as watching cell migration in a 2D chamber, this is not a significant limitation, as the

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imaging can simply be performed well away from the chamber edge; but in cases where the cells are pinned on device features, for example in hydrodynamic traps, the loss of image quality is noticeable at high magnification. For some more advanced microscopy configurations such as light sheet or structured illumination methods, this is even more of a problem. Recently, some researchers have been exploring use of elastomers that have a refractive index of 1.33 [18]; these materials have been successfully used for replica molding and cell confinement, with demonstrated improvements in image quality. However, substantial work in exploring the biocompatibility of these materials and in characterizing their physical properties and stability remains. 1.4 Application Examples

Microfabricated structures have been used in conjunction with confocal microscopy on a range of biological applications, ranging from simple traps to perfused organ-on-a-chip devices. In some cases, this is an enabling technology, permitting measurements that would otherwise be impossible: for example, experiments requiring precise physical shear or chemical stimulation of cells or subcellular structures. In other cases, microfluidic devices provide a substantial improvement in reproducibility and ease-of-use over existing assays, such as the axonal confinement devices. For biology researchers considering whether these structures can be useful for their field, it may be helpful to consider some of the review articles cited below that compare conventional assay platforms and microdevices. A simple example of the utility of microfabricated structures in conjunction with confocal microscopy is the use of microwells to trap cells [20]; several groups have used these structures, generally patterned directly on a coverslip by stamping, in order to image the immune synapse as it forms [21]. If the diameter of the cylindrical wells is tailored to the size of the cells being studied, the immune synapse forms perpendicular to the optical imaging axis, enabling high-resolution imaging [22]. Another widely used device structure uses microgrooves for axonal isolation in neural cell culture, for which two or more large culture areas are connected by microchannels on the order of 4 μm high and wide, thus excluding the soma. Several versions of these devices have been commercialized by Xona microfluidics, but modified geometries can be readily made in the laboratory. In function, these devices are similar to Campenot chambers, first developed in 1977, in which the axonal confinement between the compartments is achieved by scratching a plastic dish with metal pins and sealing reservoirs to the culture surface with vacuum grease. A nice overview of the advantages and disadvantages for different platforms can be found in this review article [23], and discussion of the substantial improvement in capabilities and reproducibility with microfabricated devices can be found here [24]. The

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microfabricated devices are more reproducible, requiring less expertise to assemble and use, and are also well suited for highresolution live-cell imaging. They have been widely used in assessing axonal growth and transport [25, 26]. Microfabricated devices have also been used to study microscale biomechanics with a variety of structures [27, 28]. Some examples are studying confinement of cells to small and shaped adhesive regions, measuring the traction forces exerted by cells by looking at deflections in a micropillared substrate and looking at deformations of cells as they travel through narrow confinements. Microfluidic channels are also convenient for applying shear stress to adherent cells or structures such as microtubules; this can be done with fairly simple device structures. A final example of applications in which microfabricated structures have been used with confocal microscopy is in studies of chemotaxis [29, 30]. Earlier in vitro chemotaxis assays have had very limited gradient control, with the chemokine introduced in a dish via a micropipette, or on one side of a Boyden chamber. In comparison, microfabricated devices can be used to generate much more reproducible gradients on platforms that are fully compatible with confocal imaging; a comparison table between conventional and microfluidic platforms can be found in this review [31]. Generally speaking, there is a trade-off between experimental complexity and fine control over the gradient, with the optimal device design determined by the specific measurement need. In particular, achieving temporal control (e.g., rapidly establishing a gradient that is then stable for an extended period of time) requires active flow in source and sink channels. In contrast, if the gradient stability achievable with diffusion from reservoirs is sufficient, an all-in-one device addressable with pipettes can be used without any additional equipment.

2

Materials

2.1 Template Fabrication

1. Silicon wafers (400 diameter is convenient). 2. SU-8 2000 series photoresist (Microchem). 3. Spin-coater, located in a chemical fume hood. 4. Hotplates. 5. Photomask(s)—pattern can be drawn in CAD or something more specialized like L-edit; a number of companies will generate masks from the files. 6. Collimated i-line UV light source (or a mask aligner, for multilayer templates). 7. SU-8 developer.

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8. Acetone. 9. Isopropanol. 10. DI water. 2.2

PDMS Molding

1. Sylgard 184 (Dow-Corning). 2. 6-in. polystyrene Petri dishes. 3. Tridecafluoro-1,1,2,2-tetrahydrooctyl trichlorosilane (Gelest, Morrisville, PA). 4. Vacuum desiccator.

2.3 Reproducing Molds

1. Sylgard 184 (Dow-Corning). 2. Tridecafluoro-1,1,2,2-tetrahydrooctyl trichlorosilane. 3. Vacuum desiccator.

2.4 In Situ Well Formation

1. Sylgard 184 (Dow-Corning). 2. Coverglass. 3. Silicon wafer (optional). 4. Glass slides. 5. PET film.

2.5

Device Assembly

1. Sylgard 184 (Dow-Corning). 2. Coverglass or glass-bottomed microchamber. 3. Tape (Scotch Magic). 4. Plasma cleaner.

3

Methods There are many possible materials for making microfluidic devices; here I will focus on the device construction most likely to be useful for a laboratory researcher making custom devices for use with confocal imaging, namely a PDMS microdevice mounted on a glass coverslip. Although some researchers will prefer to purchase templates rather than making their own, reviewing the steps in template fabrication may nonetheless help to inform design ideas. The templates can be used many (>50) times to make devices in PDMS or other flexible, moldable materials (e.g., agarose, PEGDA, bio-133). First, some notes on mask design. SU-8 is a negative photoresist, which is to say that exposure to near UV light (350–400 nm) initiates a crosslinking reaction (taken to completion in the postexposure bake step), and that unexposed areas of the resist are removed in the development step (Fig. 1b, right side). The clear areas of the mask correspond to the SU-8 features you are

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Fig. 2 Example alignment marks. Marks used for alignment of two patterned layers. At top left, the feature patterned in the first layer; at top right, the pattern on the mask used in the second (or later) layer for alignment. Simpler structures can be used but may be harder to see in thin SU-8 layers and harder to align as accurately. Typically, at least two alignment marks are used, one on each side of the wafer (bottom). If patterning three or more layers, use additional sets of marks

patterning. Each desired height on the final template requires a separate mask; for example, if you would like reservoirs that are 100 μm tall connected by channels that are 8 μm tall, you will need two masks. Examples of alignment marks used to align the second patterned layer with the first are shown in Fig. 2. Using a mask aligner it is reasonable to expect alignment errors of under 10 μm across the full wafer, and it is possible to do better than this after optimizing protocols. It is helpful to design the masks such that the shorter features protrude into the taller ones; this will have no impact on the final device structure but will prevent small errors in alignment from causing disconnected channels. See Notes 1 and 2 for further design considerations. The mask files can be drawn as DXF files in a CAD program or Solidworks, or in a specialized design format such as Gerber or CIF. Masks can be purchased on photoplotted film or as patterned chrome on glass. The chrome/glass masks are somewhat more expensive, but are more durable, slightly easier to use, and can be used for smaller features. The minimum feature size for film masks is around 5 μm, but chrome-glass features can go down to 3 μm or below, with the cost increasing substantially as the feature size

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decreases further. A good compromise option can be to use film masks for the initial design iterations, and then to switch to glass masks once the design has been optimized for the problem at hand. When patterning SU-8, the datasheets available from Microchem (soon to be Kayaku Advanced Materials) [32] provide an overview of processing conditions and reasonable starting parameters for the different resist formulations. Within a resist series, for example SU-8 2000, the different formulations vary only by the percentage of solids, with the higher concentrations yielding thicker films after spin-coating. 3.1 Template Fabrication

1. Silicon wafers should be cleaned by rinsing sequentially in acetone, isopropanol, and deionized water, then dried with nitrogen gas. 2. CRITICAL STEP: wafers should be subjected to a dehydration bake on a hotplate at 200  C for at least 15 min to remove surface-bound water. 3. OPTIONAL STEP: treat wafer with oxygen plasma for 5 min to enhance adhesion. 4. CRITICAL STEP: before opening or using any photoresist, make sure all lights in the room are filtered to remove wavelengths shorter than 500 nm (e.g., Resistgard plastic shields for standard fluorescent tubes can be purchased from Imtec Acculine, Fremont, CA). Do not expose resist to white light until after development step. 5. Using a spin-coater, coat wafer with the first desired formulation of SU-8, according to manufacturer instructions and desired thickness (see Note 3 for an optional adhesion layer). Typically this involves pouring the resist onto the wafer (~4 mL for a 400 wafer), then using a slow spin speed (~100 rpm) for 10 s to spread the resist, followed by a faster spin speed for 30–40 s. The concentration of solids in the resist and the spin speed in the second step are what determine the final resist thickness, which can range from 2 to 300 μm in a single spin. For thick layers (>50 μm), you must remove the edge bead (a raised ridge of resist that forms on the edge of the wafer) before baking, either in the spin-coating process with EBR-PG (Microchem) or by wiping afterwards (e.g., TX714A swabs from Texwipe, Kernersville, NC). 6. Bake wafer on a leveled hotplate at 65  C/95  C for the times recommended for the resist thickness (see Note 4 for possible adjustments for thick resist layers). Covering the wafers with a glass Petri dish with one end propped up to allow solvent to escape will allow more uniform heating. 7. Bring the resist-coated wafer into contact with the patterned side of the photomask, and expose using a collimated i-line

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(365 nm) UV light source or a mask aligner according to the recommended dose. Your feature resolution will be determined by (1) the proximity of the mask pattern to the resist surface and (2) the collimation of the UV light source. 8. OPTIONAL for resist layers 50 μm or thicker: expose through a filter that cuts out light below 350 nm, for example PL-360-LP (OmegaOptical, Brattleboro, VT), to avoid overexposing the top layer of resist relative to the bottom. Exposure times will need to be slightly higher when using the filter. See Note 5 for additional adjustments needed for thick layers. 9. After exposure, the wafers must again be baked on a hotplate (post-exposure bake) at 65 and 95  C, using the manufacturer recommended times as a reasonable starting point. The pattern should be visible after this baking step. See Note 6 for suggestions on optimizing exposure time. 10. OPTIONAL STEP: if patterning a multilayer device, repeat steps 5–9 with the second mask pattern and desired resist thickness. 11. Working in a chemical fume hood, use two glass crystallization dishes (the 6-in. diameter ones are good for working with 4-in. wafers); fill one with isopropanol and one with SU-8 developer to a depth of approximately 1 in. 12. After post-exposure baking of the last layer, allow the wafer to cool to room temperature. Develop the patterned wafer by placing in SU-8 developer and agitating gently for the recommended development time or until the unexposed resist is clear. 13. OPTIONAL STEP: rinse the wafer in a second dish of SU-8 developer for an additional minute, to remove any residual unexposed resist (see Note 7). 14. Rinse the wafer in isopropanol, by submerging for 30 s with gentle agitation, then rinsing with a spray bottle. 15. Blow dry with nitrogen gas. NOTE: if the template appears cloudy at this stage, development is incomplete, and it should be returned to the SU-8 developer. 16. OPTIONAL STEP: for better durability, hardbake the template by putting it on a 150  C hotplate for 2 min, and then ramping down at 150  C/h until the temperature is below 80  C. 17. OPTIONAL STEP: to lower adhesion of PDMS to the template in subsequent steps, the template can be silanized by placing it in a vacuum desiccator with 30 μL of silane (tridecafluoro-1,1,2,2-tetrahydrooctyl trichlorosilane, Gelest) for at least 1 h.

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PDMS Molding

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1. Measure out ten parts of the Sylgard 184 base and one part of the curing agent by weight. 2. Mix thoroughly, either by stirring for about 5 min until the mixture is uniformly bubbly, or in a planetary mixer. 3. If you have mixed by hand, it may be helpful to degas the PDMS under vacuum before you pour it over the template as well as afterwards. 4. Place the 400 template in a 600 disposable polystyrene dish (see Note 8). 5. Pour the PDMS over the template to the desired thickness. Degas under vacuum for 20 min or until bubbles are gone. 6. PDMS will cure in 90 min at 80  C, 4 h at 65  C, or several days at room temperature (see Notes 9 and 10). 7. The cured devices can be cut out using a razor blade or scalpel, taking care not to apply point stress to the silicon wafer surface when removing the PDMS, and keeping the angle between the PDMS and the wafer relatively low ( Split Channels tool and save individual channels as multistack TIF file with the format: name, file number and the molecule scanned. For example, to save the Ag channel, the filename is Bcell_1_Ag.TIF. 4. Open the AnalyzeInternalizationGUI and enter the path for the folder containing the saved TIF images and the name of the TIF file. Specify the Ag channel and the other channel (B220) (see Note 23 and Fig. 3b). Click on Load to open the image in ImageJ (see Fig. 3b). Specify the order of the planes as bottom first. 5. Calculate the background fluorescence value for the antigen channel by selecting a region of interest on the image opened in ImageJ and clicking Get in the AnalyzeInternalizationGUI. 6. To determine the threshold value for the antigen channel, start with a value about 10
lower than the antigen intensity in the synapse and enter this value in the box for antigen threshold in the AnalyzeInternalizationGUI. Empirically determine the threshold value by evaluating the results. Similarly, determine the threshold value for the surface channel and enter this value in the box for surface threshold. 7. Select the area in ImageJ containing the B cell and click on Run in the AnalyzeInternalizationGUI to analyze internalization in that B cell. The results are displayed in the table of the GUI (see Fig. 3b, lower panel) where the first column (End. #) indicates the number of individual antigen clusters lifted from the substrate and the second column (Int.%) indicates the percent of the antigen found in the endosomes out of the total in the cell. A sideview is displayed in ImageJ if the Display sideview box is checked. In this image, sideways maximum projection of the cell is displayed where the internalized antigen is shown in blue color (see Note 24).

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Notes 1. Use Lab-Tek™ II Chambered Coverglass which is fitted with a no. 1.5 borosilicate glass coverslip to the bottom. This coverslip has ideal optical characteristics for high-resolution confocal microscopy. 2. HEK293A are more useful in preparing PMS due to their larger size and ability to spread over a larger surface area relative to HEK293 or HEK293T cells. 3. Presence of calcium in the buffer is necessary for BCR signaling and BCR mediated antigen uptake. 1
HBSS containing calcium and magnesium from ThermoFisher (Catalog No. 14025076) contains 1.26 mM CaCl2. Hence, to prepare 1 L of wash buffer, add 137.6 mg CaCl2 and 1 g BSA to 1 L of 1
HBSS containing calcium and magnesium. To prepare 1 L of blocking solution, add 137.6 mg CaCl2 and 20 g BSA to 1 L of 1
HBSS containing calcium and magnesium. 4. To prepare the fluorescently conjugated antigens, follow these steps: Mix 100μL of 0.5 mg/mL biotinylated goat anti-human Igλ F(ab0 )2 in PBS and 100μL of 0.5 mg/mL biotinylated goat anti-human Igκ F(ab’)2 in PBS. Add 20μL LL-Rapid Modifier from the Lightning-Link® Rapid Dylight® 650 kit to the antigen mixture. Add the antigen mixture (with added LL-Rapid Modifier) to the lyophilized Lightning-Link® Rapid mix and resuspend gently. Incubate the vial at room temperature in dark for 15 min. Add 20μL of LL-Rapid quencher and incubate the vial at room temperature in dark for 5 min. The antigen mixture is ready to use. The effective concentration of the antibody mixture is 0.4 mg/mL. 5. There are many choices for fluorescent probes to conjugate to antigens. The important considerations are fluorophore photostability, the wavelength of available excitation lasers, detector sensitivity (as some confocal microscope systems may be only equipped with standard PMTs) and the fluorophore conjugated to the antibodies used for labeling cell surface and intracellular proteins (to avoid overlapping emission signals between the PMS and cell). In this protocol, we have used Lightning-Link® Rapid Dylight® 650 kit from Expedeon to conjugate Dylight® 650 to label antigens and used a 633 nm laser to excite fluorescence. Alternatively, a variety of fluorescent probes can be used: DyLight® 405 (405 nm laser excitation); DyLight® 488 and Alexa Fluor® 488 (488 nm laser excitation); Alexa Fluor® 555; DyLight® 550, Alexa Fluor® 594 and DyLight® 594 (561 nm laser excitation); DyLight® 633 and Alexa Fluor® 647 (633 nm laser excitation). Keep in

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mind as long as the wavelength of the excitation source falls near the peak of a fluorophore’s absorption spectrum (see Chapter 2 in this book for more understanding), it can be used. 6. Poly-D-Lysine is resistant to enzymatic degradation and hence would prolong cell adherence. 7. Since the volume of media in the miniwells is only 15μL, make sure the incubator maintains humidity to avoid drying of the media. If necessary, place the chamber in a petri dish containing a wet paper towel to maintain humidity. 8. Seed cells as late as possible in the day so that the incubation period is around 12–14 h. 9. This step is very important in determining the quality and the coverage of the PMS. The HEK293A cells should not be grown for more than 14 h. Overgrown cells tend to peel off during sonication. If the HEK293 cells are less than confluent, the PMS will be sparser and may result in poor PMS quality. 10. For continuous washing and aspiration, attach a 20μL tip to the 2 mL aspirating pipette attached to an aspirator. Attach a 1 mL disposable pipette tip to an appropriate pipette and carefully add buffer to the drop formed in the miniwell while simultaneously aspirating from the other side. Avoid touching the cells with the tips. Also, avoid exposing the cells to air. 11. Use a metal block insert from a 1.5 mL tube heating unit. Typically, these blocks are made from aluminum and will cool quickly. Place the metal block on ice so that the flat surface faces up. This setup keeps cells cold and provides a stable surface to perform the sonication. 12. The time and power setting for the sonicator will need to be adjusted empirically depending on setup. To determine the efficiency of sonication for making PMS, compare the condition of cells before and after sonication by observing under an inverted microscope with 5
zoom. After sonication, the upper part of the cell membrane should peel off and accumulate to one side, while the basal part of the cell membrane should remain attached to the coverslip. 13. Sonication exposes the inner leaflet of the plasma membrane which contains phosphatidylserine that can bind to Annexin-V, which is used during the process of labeling the PMS with fluorescently conjugated antigens. Hence, the quality of the PMS can also be determined by observing under confocal microscope and should look similar to the PMS shown in Fig. 1d. 14. Touch the aspirator tip to the bottom corner of the chamber to avoid drying of the PMS formed in the miniwells. Carefully dry the area around the miniwells by gently touching the sides of the silicon gasket to leave the drop intact on the miniwell.

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15. If the total number of cells is low (5
104–10
104), the cell pellet would be loosely stuck to the bottom of the Eppendorf tube. Hence, it is very important to remove the media gently to avoid aspirating the cells. 16. The mini wells can hold upto 20μL, but it is recommended to use lesser volume. Hence, aspirate buffer from miniwells carefully until it comes to the level of silicone gasket and load around 5μL of cell suspension. Add few μL of incubation buffer if necessary, after adding the cells. 17. Optional: To store the chamber without imaging, exchange wash buffer with 1% PFA in PBS and seal the chamber with parafilm. These samples can be stored at 4  C for 2 weeks. 18. B cells can be stained for any surface molecule of interest (e.g., CD19, HLA-DR, HLA-A, B220, etc.) depending on the availability of antibodies that can used for staining fixed samples. Alternatively, B cells can be stained prior to loading on PMS using fluorescently labeled Fab fragments of the antibodies against the surface molecules. After fixing the samples, intracellular molecules can be labeled with fluorescently labeled antibodies. To do so, permeabilize the cells by exchanging the wash buffer with 0.1% Triton X-100 and incubate for 10 min at room temperature. Wash miniwells with 1 mL wash buffer. Add the antibody mixture to the droplet on miniwells and incubate overnight at 4  C. 19. When detecting multiple fluorophores, it may be necessary to create multiple Tracks to prevent bleed through between fluorophores that have overlapping emission spectra. For example, if two channels are selected for a single Track to detect cells stained with the green fluorescent dye Alexa 488 and the blue nuclear marker 40 ,6-diamidino-2-phenylindole (DAPI), which has a long emission spectrum that expands to green wavelengths, the Channel established for Alexa 488 would detect both signals. 20. Select from standard PMTs, Ch1and Ch2 or the QUASAR detector ChS 1–8. The suggested range for emission filters for some of the common fluorophores is as follows: DyLight® 405 (420–500 mn). DyLight® 488 and Alexa Fluor® 488 (510–575 nm). Alexa Fluor® 555 and DyLight® 550 (565–625 nm). Alexa Fluor® 594 and DyLight® 594 (610–675 nm). DyLight® 633 and Alexa Fluor® 647 (640–750 nm). 21. The optimal resolution is determined based on the Nyquist’s criterion accounting for the numerical aperture of the objective, zoom factor, pixel size, and the excitation wavelength.

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22. Setting Averaging Number of 2 or more is useful in reducing the noise in the image when the signal is weak, or background is high. 23. To confirm that the file details are entered accurately, make sure the filenames appearing above the Load button are exactly as the ones saved from ImageJ. 24. Displaying the result image in the sideview is useful for adjusting the threshold values. If there are no blue pixels at all, try reducing the threshold. If there are many blue pixels throughout the cell, increase the threshold.

Acknowledgment The authors would like to thank Dr. Susan Pierce for her continued support and Dr. Joseph Brzostowski for training in confocal microscopy that has been crucial for the development of the methods described here. This work was supported by the Intramural Research Program of the National Institute of Allergy and Infectious Diseases at the National Institutes of Health. References 1. Hoogeboom R, Tolar P (2016) Molecular mechanisms of B cell antigen gathering and endocytosis. Curr Top Microbiol Immunol 393:45–63 2. Spillane KM, Tolar P (2017) B cell antigen extraction is regulated by physical properties of antigen-presenting cells. J Cell Biol 216:217–230 3. Moore MS, Mahaffey DT, Brodsky FM, Anderson RG (1987) Assembly of clathrin-coated pits

onto purified plasma membranes. Science 236:558–563 4. Nowosad CR, Tolar P (2017) Plasma membrane sheets for studies of B cell antigen internalization from immune synapses. Methods Mol Biol 1584:77–88 5. Hoogeboom R, Natkanski EM, Nowosad CR, Malinova D, Menon RP, Casal A, Tolar P (2018) Myosin IIa promotes antibody responses by regulating B cell activation, acquisition of antigen, and proliferation. Cell Rep 23:2342–2353

Chapter 9 Analysis of Intracellular Vesicles in B Lymphocytes: Antigen Traffic in the Spotlight Sara Herna´ndez-Pe´rez, Marika Runsala, Vid Sˇusˇtar, and Pieta K. Mattila Abstract All eukaryotic cells are delimited by the plasma membrane, separating the cell from its environment. Two critical cellular pathways, the endocytic and the exocytic vesicle networks, shuttle material in and out the cell, respectively. The substantial development of cell biological imaging techniques, along with improved fluorescent probes and image analysis tools, has been instrumental in increasing our understanding of various functions and regulatory mechanisms of various intracellular vesicle subpopulations and their dynamics. Here, using B lymphocytes (B cells) as a model system, we provide a protocol for 3D analysis of the intracellular vesicle traffic in either fixed or living cells using spinning disk confocal microscopy. We also describe the usage of image deconvolution to improve the resolution, particularly important for vesicular networks in lymphocytes due to the small size of these cells. Lastly, we describe two types of quantitative analysis: vesicle distribution/clustering toward the microtubule organizing center (MTOC), and colocalization analysis with endolysosomal markers. Key words B cells, Antigen, Vesicle traffic, Microscopy, Image analysis, Huygens, ImageJ, Imaris, Spinning disk confocal microscopy, Deconvolution, Colocalization, Script

1

Introduction In order to exchange material with the extracellular environment cells critically rely on two interconnected routes: the endocytic and exocytic vesicular pathways. The formation, fusion, fission and trafficking of these vesicles govern numerous cellular functions, including delivery of newly synthesized molecules to their right locations, secretion and uptake of nutrients and metabolites and organelle positioning. Different membrane compartments within the endosomal network serve distinct functions and can be distinguished by features

Supplementary Information The online version of this chapter (https://doi.org/10.1007/978-1-0716-14020_9) contains supplementary material, which is available to authorized users. Joseph Brzostowski and Haewon Sohn (eds.), Confocal Microscopy: Methods and Protocols, Methods in Molecular Biology, vol. 2304, https://doi.org/10.1007/978-1-0716-1402-0_9, © This is a U.S. government work and not under copyright protection in the U.S.; foreign copyright protection may apply and Springer Nature US 2021

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such as morphology, subcellular localization, acidity and protein content. Early endosomes (EEs) are the first compartments to receive the incoming cargo or extracellular fluid from primary endocytic vesicles and they are the main sorting stations of the endocytic pathway, hence also called sorting endosomes (SE). EE/SEs are relatively small and heterogeneous in terms of composition and function. The cargo internalized into EEs reaches a new biochemical environment different from the one in the plasma membrane of the cell. The lumen of the early endosomes is mildly acidic (pH 5.9–6), facilitating conformational changes in proteins that can lead, for instance, to ligand release from internalized receptors [1]. From there, cargo can be directed to late endosomes (LEs) and lysosomes for degradation or recycled back to the plasma membrane either by fast recycling pathways or slow recycling from recycling endosomes (REs) located next to the microtubule organizing center (MTOC) [2, 3]. Meticulous coordination of the endosomal machinery is essential for the viability of all cells, but also critical for many specialized functions of distinct cell types, such as B lymphocytes (B cells). B cells are a crucial part of the adaptive immune system, mounting antibody responses against a vast repertoire of different antigens. Although B cells can also internalize antigen by phagocytosis and fluid-phase endocytosis (pinocytosis), they are the only cells that present antigens of a given specificity [4, 5]. B cells take up and process foreign antigens, recognized by the B cell antigen receptor (BCR), into peptides for loading on MHCII molecules for further exocytosis to the plasma membrane. This process involves intricate specialized endosomal compartments and finally allows presentation of the newly formed peptide antigen-MHCII to T cells, which triggers both B cell differentiation and effector T cell responses. Recent improvements in fluorescence microscopy have helped researchers to unravel the mechanisms of intracellular trafficking, by allowing tracking of different intracellular compartments both in fixed and living cells. The development of new imaging modalities, together with better probes and more sophisticated image analysis tools, has played a critical role in increasing our understanding of different vesicle subpopulations and their dynamic behavior. Endosomal vesicles often fall below or near the resolution of conventional light microscopy, such as confocal microscopy. Thus, possible improvements in the resolution by post-processing techniques should be considered in the analysis pipeline. To fully harness the power of a confocal microscope, image contrast restoration by deconvolution can be used to counteract the blur and noise in the images. Deconvolution is a mathematical operation that uses a Point Spread Function (PSF) to correct the aberrations caused by imperfections of the elements of an optical system, such as distortion caused by a microscope objective lens and other components of the light path, and partially remove the out-of-focus objects from

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Fig. 1 A20 D1.3 B cells activated with Alexa Fluor 647-labeled anti-IgM antibodies as surrogate antigen for 10 min, fixed and imaged with spinning disk confocal microscope. Images were deconvoluted using the Huygens software. Left, raw image. Right, image after deconvolution. A z-stack (summed projection) is shown. Scale bar 3 μm

the image plane. The PSF describes the convolution of the signal originating from a point object, caused by the diffraction of light waves in the imaging system. However, great care has to be taken in using an accurate PSF to avoid the introduction of any new error in the data. Optimally, this is done by measuring the PSF from a test sample prepared and imaged equivalently to the experimental samples, but it can also be estimated based on the imaging settings. Deconvolution helps to increase the signal-to-noise ratio in the images and is especially important when imaging small features close to the resolution limit of light microscopy, such as endosomal vesicles. Deconvolution can also significantly aid quantitative measurements in analysis workflows and in measuring colocalization [6, 7]. Here, using B cells as a model, we provide a protocol for analyzing intracellular vesicle traffic in both fixed samples and living cells using spinning disk confocal microscopy. We describe details for immunofluorescence analysis, or live-cell imaging, of different vesicle markers for quantification of colocalization with specific internalized cargo, in our case, antigen taken up by the BCR. To improve the resolution and separation of the vesicles (50–250 nm in diameter), we apply deconvolution to the three-dimensional image stacks acquired with spinning disk confocal microscopy (Fig. 1). Finally, we present an image analysis workflow to quantify vesicle clustering and translocation toward MTOC, as well as two different methods to measure colocalization using either opensource or commercial software.

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Materials Cells

1. A20 mouse B cell lymphoma line stably expressing the transgenic IgM BCR D1.3 [8] (see Note 1). 2. Growth medium for A20 cells: RPMI 1640 with 2.05 mM Lglutamine supplemented with 10% fetal calf serum (FCS), 50 μM β-mercaptoethanol, 4 mM L-glutamine, 10 mM HEPES and 100 U/mL Penicillin-Streptomycin.

2.2

Fixed Samples

1. 12-Wells PTFE diagnostic slide (Thermo) and 24
60 mm (#1) coverslips. 2. Fibronectin for non-BCR-mediated cell attachment (see Note 2). 3. Imaging buffer: 10% FCS in PBS. 4. Fluorescently labeled anti-BCR antibodies as surrogate antigen (see Note 3). 5. Fixation buffer: 4% PFA in PBS. 6. P&B buffer (Permeabilization and Blocking): 0.3% TritonX100 + 5% serum (see Note 4) in PBS. 7. Staining buffer: 1% BSA, 0.3% Triton X-100 in PBS. 8. Antibodies: see Table 1. 9. Fluoromount-G (with or without DAPI). 10. 1
PBS.

2.3

Live Samples

1. 35 mm coverglass-bottom MatTek dishes. 2. Fluorescently labeled anti-BCR antibodies as surrogate antigen (see Note 3). 3. Fibronectin for non-BCR-mediated cell attachment (see Notes 2 and 5). 4. Live Imaging buffer: 0.5 mM CaCl2, 2 mM MgCl2, 1 g/L Dglucose, 0.5% FCS in PBS (see Note 6).

2.4

Transfections

1. AMAXA electroporation (Biosystems). 2. 2 mm gap width electroporation cuvettes. 3. Recovery medium: growth medium supplemented with 1% DMSO. 4. 2S transfection buffer: 5 mM KCl, 15 mM MgCl2, 15 mM HEPES, 50 mM Sodium Succinate, 180 mM Na2HPO4/ NaH2PO4 pH 7.2 (see Note 7). 5. 6-well plates, cell culture quality. 6. Plasmids encoding the markers of interest. Fluorescent fusion proteins Rab5a-GFP and Rab7a-GFP were used here as an example.

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Table 1 Antibody toolkit for study of intracellular vesicles and organelles Antibody

Company

EEs

Anti-EEA1 Anti-Rab5

Santa Cruz N-19 Cell Signaling C8B1 Technologies (CST)

LEs

Anti-LAMP1 DSHB Anti-Rab7 CST Anti-Rab7 Santa Cruz

REs

Anti-Rab11 Anti-CD71 Anti-CD71 Anti-CD71

Golgi

Anti-Rab6 Anti-GLG1

ER

Anti-KDEL Thermo Fisher Anti-calnexin AbCam

MTOC Anti-PCM1 Anti-PCM1

Clone

Dilution Tested in: 1:75 1:150

Mouse Mouse

1D4B D95F2 B-3

1:50 1:100 1:100

Mouse Mouse Mouse

CST Sigma BD Santa Cruz

D4F5 DF 1513 M-A712 H68.4 and YTA 74.4)

1:200 1:100 1:200 1:50

Mouse Human Human Mouse

CST AbCam

D37C7 ab103439

1:200 1:200

Mice Human

#PA1-013 ab133615

1:150 1:200

Human

G-6 Q15

1:200 1:400

Mouse and human Human

Santa Cruz CST

Antibodies have been tested in mouse (A20 D1.3) or human (Jurkat, HeLa) cells

2.5

Microscope

A spinning disk confocal microscope was used to acquire the images, as it allows fast imaging of z-stacks, large fields of view, and live samples with low bleaching and phototoxicity (see Note 8). Here we used the 3i (Intelligent Imaging Innovations) Marianas spinning disk confocal microscope. The microscope was mounted on a vibration isolation table and was equipped with: 1. Scanner: Yokogawa CSU-W1 (wide imaging area: 16 mm
17 mm) with a maximum imaging speed of 200 fps (full frame 2024
2024), camera port magnification 1
, Pinhole size 50 μm, back-projected pinhole 794 nm and pinhole spacing 7.937 μm. 2. Laser lines and available emission filters: 488 nm (150 mW)—GFP (510–540 nm). 561 nm (100 mW)—Cy3/Alexa 568 (580–654 nm or 580–610 nm). 640 nm (100 mW)—Cy5/Alexa647 (672–712 nm). 730 nm (30 mW)—Alexa 750, IR (768–850 nm). 3. Cameras: For fixed samples: Hamamatsu sCMOS Orca Flash4 v2 C11440-22CU (2048
2048 pixels) for fixed samples. Voxel size 100.594
100.594
270 nm. For live imaging: Photometrics Evolve, 10 MHz Back Illuminated EMCCD (512
512 pixels).

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4. Objective: 63
Zeiss Plan-Apochromat (oil objective, NA 1.4). 5. Incubation system to maintain 37  C for live cells. 6. Image acquisition software: Slidebook 6. 2.6 Software for Image Analysis

3

There are many different tools available for image analysis (see Note 9). Here, we used Fiji (latest version available online; open-source), Huygens Essential 17.10 (SVI, Hilversum, The Netherlands) and Imaris 8.1.2 (Bitplane, Zurich, Switzerland).

Methods

3.1 Visualising Antigen Traffic in Fixed Cells 3.1.1 Preparation of Immunofluorescence Samples

1. Coat clean slides (see Note 10) with 4 μg/mL fibronectin (20 μL/well) for 20–40 min at RT, shield from dust (see Note 11). Remove the fibronectin, rinse once with PBS and leave the wells with PBS. 2. Collect the cells (see Note 1) by gentle centrifugation (50
g, 5 min, RT). Typically, we use approx. 40,000 B cells/well for wells of 5 mm in diameter (see Note 12). 3. Resuspend the cells to a concentration of 2–5
106 cells/mL in cold Imaging Buffer, keep on ice and add fluorescently labeled antigen (10 μg/mL Alexa Fluor anti-IgM). This can be substituted by your ligand of interest. 4. Incubate on ice for 10 min and wash by adding 1 mL of ice-cold PBS and centrifuge (375
g, 2 min, 4  C) (see Note 13). 5. Remove the supernatant and resuspend the cells in 22 μL of Imaging Buffer per well (220 μL for one slide with ten wells). Keep on ice. 6. Add 20 μL of cell suspension per well and transfer to the incubator (5% CO2, 37  C) for desired time points. 7. Remove the imaging buffer and fix the cells for 10 min with 4% PFA, 20 μL per well (see Note 14). 8. Remove the fixative and add P&B buffer, 20 μL/well, for 20 min at RT (see Note 15). 9. Prepare the dilutions of primary antibodies in Staining Buffer. Remove P&B buffer from the wells and add 20 μL of antibody dilution per well. Incubate 1 h at RT or O/N at 4  C. Leave some wells without primary staining to prepare secondary antibody-only controls. 10. Rinse the wells three times with PBS (see Note 16).

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11. Prepare the dilutions of secondary antibodies in PBS. Remove PBS from the wells and add 20 μL of antibody dilution per well. Incubate 30 min at RT. 12. Rinse the wells three times with PBS (see Note 16). 13. Remove the PBS from the wells and add 30 μL of Fluoromount-G (with or without DAPI). Pipette slowly to avoid bubbles and place a coverslip on the slide carefully. 14. Seal the edges of the slide with nail polish to prevent the sample from drying. Let the nail polish dry and image as soon as possible (see Note 17). 3.1.2 Imaging of the Immunofluorescence Samples

1. Turn on your imaging system and optimize the acquisition settings (see Notes 18–20). Image the samples stained with secondary antibodies only to control for unspecific background. When imaging two or more channels, always analyze also single fluorophore samples to control for the potential cross-talk and bleed-through. 2. For the analysis, image representative fields-of-view containing enough cells for analysis (in our system, 15–25 cells per field, 4–5 fields-of-view). 3. Define the top and bottom positions for acquiring a 3D stack. Define the step size by clicking “Optimal” or selecting your preferred value (see Note 21). 4. Double-check the settings for each channel (i.e., exposure time), 3D should be selected under “Capture type” and name the capture. Click “Start” to acquire the stack. 5. Images will be saved as a SlideBook file (.sld) but can also be exported into .tiff using the SlideBook software or opened with Bioformats Importer in Fiji and saved as TIFF.

3.2 Analyzing Antigen Traffic in Live Cells

1. Use 4 mL of recovery medium per well in 6-well plates. Equilibrate the recovery medium in an incubator (37  C, 5% CO2) at least 20 min before transfection (see Note 22).

3.2.1 Transfections

2. Pellet 4
106 cells by centrifugation at 50
g for 5 min (see Note 23). 3. Gently resuspend the cells in 180 μL of 2S transfection buffer containing up to 4 μg of plasmid DNA (see Notes 23 and 24) and transfer the mixture into an ice-cold cuvette. 4. Transfect the cells using Amaxa nucleofector: with A20 D1.3 cells, use program X-001 for higher viability, or program X-005 for higher transfection efficiency. 5. Immediately after nucleofector pulse, fill the cuvette with warm recovery medium and transfer the cells into 6-well plates containing warm recovery media. Rinse the cuvette with recovery medium to collect the remaining cells.

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6. Place the cells in the incubator to recover O/N (see Note 25). Optional: flow cytometry can be used to determine the transfection efficiency and viability of the cells. 3.2.2 Preparing the Samples

1. Functionalize the MatTek dishes with 4 μg/mL of fibronectin in PBS (see Note 5). Incubate 30–40 min at RT and rinse twice with PBS. Keep in PBS until the cells are added and do not let the dishes get dry. 2. Count transfected cells and resuspend them to a concentration of 2–5
106 cells/mL in cold Live Imaging Buffer on ice and add fluorescently-labeled antigen (10 μg/mL anti-IgM). This can be substituted by your ligand of interest. 3. Incubate 5 min on ice and wash by adding 1 mL of ice-cold PBS and centrifuge (375
g, 2 min, 4  C) (see Note 13). 4. Remove the supernatant and resuspend the cells to a concentration of 106/mL in Live Imaging Buffer. Keep the cells on ice. 5. Keep the MatTek dish on ice. Seed the cells on the dish using a minimal volume to ensure covering of the coverslip and incubate for 10 min on ice.

3.2.3 Imaging of Live Samples

1. Turn on the microscope and equilibrate the system by preheating it to 37  C (see Note 26). 2. Wipe dry the bottom of the MatTek dish with the seeded cells and insert it in the sample holder. Find focus, determine the exposure times and start recording as soon as possible (see Note 27). Note the time.

Image Analysis

Here, we provide four examples of image analysis approaches useful for analysis of intracellular vesicles. (1) Using the Huygens software for image deconvolution, improving signal-to-noise ratio. (2) Quantitative analysis of vesicles and their locations in relation to the MTOC using Fiji. (3) Colocalization analysis in Fiji to extract commonly used colocalization parameters: Pearson’s and Manders’ coefficients. (4) Alternative vesicle colocalization analysis using spot detection and spot colocalization in Imaris (see Note 28).

3.3.1 Deconvolution by Batch Processing

Deconvolution can be performed in different software solutions, including Fiji and Slidebook. Here, we describe a deconvolution protocol using Huygens Essential (see Note 29; Movie 1) as, importantly, it enables the use of measured PSF. Huygens can compute a theoretical PSF based on the given imaging parameters, or it can distil an experimental, often more accurate, PSF from images of beads, provided by the user (see Notes 30 and 31).

3.3

1. Open Huygens ESSENTIAL.

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2. Select BATCH from the panel and click “Add task.” 3. Click “Select folder” (or “select files”) and select the right folder containing your .tiff images. Go to the next window with the green arrow. 4. Set Microscopic Parameters: (a) If you do not have a template: fill in the details under “New template” based on your imaging settings. To calculate the Backprojected confocal pinhole needed for the Microscopic Parameters, Huygens Backprojected pinhole calculator can be used (https://svi.nl/ BackprojectedPinholeCalculator). To avoid adjusting microscopic parameters separately each time, save a parameter template for your images using parameter editor. (b) If you already have a saved template, you can load the template for your images captured the same way (see Note 32). To do this, in the bottom part of the window (“Overrule meta data”), select “Overrule all meta data with template.” Click on “New template” and then “Load” the correct template from your files. 5. Scroll the bar down and click “Set all verified.” Go to the next step with the green arrow. 6. Set Deconvolution Parameters: (a) If you do not have a template: Click on “New template.” Go to the “Deconvolution” tab (in the upper part of the window) to change template settings values. Here, you can select the PSF mode: “Measured”/“Theoretical.” If you have selected Measured PSF, go to the “PSF” tab (in the upper part of the window) and select your measured PSF file (see Notes 31 and 33). If you have selected Theoretical PSF it will be calculated on the fly based on the metadata of your images. (b) If you already have a template: Click on “New template” and select “Load” to set the path for the correct template. 7. Press “DONE” when you have the correct settings for your analysis. 8. Select Output format, change it to your preferred file type. Here, we used “16 bit, single file, scale.” 9. Change the output file location clicking on “Select destination.”Press “Run” when everything is set. Meanwhile, you can also add new tasks to the queue. 10. Files will be saved in your output folder and are ready for further analysis (Fig. 1).

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Fig. 2 (a) A20 D1.3 B cells activated with Alexa Fluor 488-labeled anti-IgM antibodies as surrogate antigen for 5 or 45 min, fixed, permeabilized and stained with Alexa Fluor 647-labeled anti-PCM-1 antibodies marking the MTOC. Cells were imaged with spinning disk confocal microscope and deconvolved with Huygens Essential. A z-stack (summed projection) is shown. Scale bar 3 μm. (b) A schematic showing quantification of vesicle clustering. Number of vesicles is counted in each cell and mean distance to the MTOC is calculated as the sum of the distance from each vesicle to the MTOC divided by the number of vesicles in that cell. (c) Vesicles and the MTOC are thresholded on Fiji. Thresholded regions are shown in red in a representative cell (one plane). Scale bar 3 μm. (d) Quantification of the data shown in (a) and (c). Each spot represents one individual cell plotted for the number of vesicles and the mean distance of those vesicles to the MTOC 3.3.2 Analysis of Clustering

Upon B cell activation, antigen is rapidly internalized and trafficked toward a perinuclear compartment close to the MTOC (Fig. 2a). Here, we provide an example of how to use the 3D object counter function in the open-source software Fiji (Fiji Is Just ImageJ) to quantitatively analyze the number of antigen vesicles and their distance from the MTOC, as a proxy of vesicle clustering. MTOC is marked with anti-PCM1 antibodies and identified as the brightest spot in the channel (Fig. 2b).

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We present a series of steps that are adaptable for different image analysis software. The steps can also be combined in a script for semi-automatized batch analysis, such as the script we have made available, together with informative sample images, at the GitHub repository (https://github.com/mattilalab/MiMB). 1. Import separate z-stacks of antigen and MTOC (PCM-1) channels to Fiji (Bioformats plugin). If importing merged images, channels can be split using “Image > Color > Split Channels.” 2. Draw a Region of Interest (ROI) to select an individual cell on the antigen channel and make duplicates of the selections (Right click on the ROI > Duplicate > Check Duplicate stack). 3. Restore the selections on the MTOC channel (Edit > Selection > Restore Selection) and make duplicates of the selections. 4. In order to quantify the intracellular vesicles, set the measurement parameters of choice (Analyze >3D OC Options). A new window will open: click as many parameters as needed in “Parameters to calculate.” Among measured parameters also include the center of mass and mean intensity. 5. Select the antigen channel z-stack image of the desired cell (the duplicate created in step 2) and run “Analyze > 3D Object Counter.” Adjust the threshold manually using the “threshold” slider. The thresholding through all slices can be examined with “slice” slider. If needed, particles below or above certain size can be excluded from the analysis with “Size filter.” To exclude particles that are close to the edges of the image, click “Exclude objects on edges” (Fig. 2c). 6. Click OK and save the output from step 5. 7. Rerun steps 5 and 6 now using the MTOC z-stack and set the threshold high enough to discern MTOC from the possible unspecific dispersed signal and save the output. 8. Repeat steps 2–7 for each of the required number of cells. 9. Open the saved outputs for both channels in a data analysis software (MS Excel or similar). 10. If there are several particles detected in MTOC channel, find the particle with the highest intensity, representing the MTOC of the cell and its center of mass at x, y, z position. 11. Extract and calculate the number of particles in antigen channel. Calculate Euclidean distance of each antigen particle (center of mass x,y,z) from MTOC x,y,z (see Note 34). One can then calculate the average distance of all antigen vesicles from MTOC. Also, other parameters of antigen vesicles, such as mean intensity, integrated density, volume, etc. can be obtained.

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12. Alternatively, automatize steps 2 to 7 in Fiji by recording the steps in the macro recorder (Plugins > Macros > Record). Copy the recording and generalize the used commands in Fiji Scripting console, or adapt our script from GitHub repository for time-saving high-throughput batch analysis. 13. Import the results to a data analysis program, like MS Excel or GraphPad Prism, for graphical presentation and statistical analysis (Fig. 2d). 3.3.3 Colocalization Analysis in Fiji

Colocalization analysis can be performed in different software products, including SlideBook, Fiji, Huygens and Imaris. Here, we describe a typical analysis using the “Colocalization Threshold” function in Fiji. Alternatively, Fiji has also “Colocalization Test” and “Coloc2” plugins for colocalization analysis, and more can be added (e.g., JaCoP). 1. Import raw images or deconvolved images as individual channels to Fiji. When importing merge images, channels can be split using “Image > Color > Split Channels” (Fig. 3a). 2. If there is more than one cell in the field of view, draw a Region of Interest (ROI) to select a cell for the analysis. For consistency, always draw the ROI in the same channel. 3. Click on: “Analyse > Colocalization > Colocalization Threshold.” 4. Select your channels. Always use the same order for the channels when analyzing one set of samples (e.g., Channel 1 your marker of interest, Channel 2 antigen), as it will affect the Manders’ overlap coefficients, M1 and M2. 5. Select the channel containing your ROI in “Use ROI: Channel 1 or Channel 2.” 6. Click the options “Show Colocalized Pixel Map” and “Show Scatter Plot” (see Note 35), and click “OK.” 7. Import the results to a data analysis program, like MS Excel or GraphPad Prism, for graphical presentation and statistical analysis (Fig. 3b).

3.3.4 Spot Colocalization and Tracking in Imaris

Colocalization can also be analyzed in Imaris using the Imaris Coloc module included in the software. However, when studying vesicle colocalization, a complementary approach is also useful. Using Imaris, we use an object-based colocalization analysis using the spot detection algorithms of Imaris to identify vesicles. This more advanced and powerful quantification technique also allows manual filtering of vesicles when the use of thresholding is not optimal. Here, we describe how to segment channels in Imaris using the Spot creation wizard tool (Movie 2). Vesicle colocalisation in segmented channels can then be analyzed using the Spot

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Fig. 3 (a) A20 D1.3 B cells activated with Alexa Fluor 647-labeled anti-IgM antibodies as surrogate antigen (magenta) for 60 min, fixed, permeabilized and stained for an endosomal marker of interest (green). Cells were imaged with spinning disk confocal microscope and deconvolved with Huygens Essential. A z-stack (summed projection) is shown. Examples of colocalization are shown with white arrows. Scale bar 3 μm. (b) Images in (a) were quantified using Fiji to analyze the colocalisation of the antigen with the marker of interest (M2 coefficient) in 10 and 60 min. Data are shown as mean  SD. (c) Images in (a) were segmented in Imaris using the Spot Creation Wizard in 3D (antigen in magenta and marker in green). Examples of colocalization are shown with white arrows. (d) Rendered images (in c) were analyzed using Imaris to quantify the percentage of antigen vesicles colocalizing with the marker in 10 and 60 min. Data are shown as mean  SD

Colocalization tool. Additionally, Imaris can also be used to track spots. Spot tracking can be used to follow and measure the dynamics of the vesicles in live cells. 1. In Imaris, open your image file (merged image) and check image properties (see Note 36). For spot tracking, open your movie. 2. Click on “Add new spots” to open the Spot Creation Wizard. 3. Step 1/3 in the Spot Creation Wizard (see Note 37). When opening a movie, additional options will appear in the Spot Creation Wizard (see Note 38). If you have settings saved from previous analysis, you can load them under “Favourite creation parameters” Click on the blue arrow to move forward.

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4. Step 2/3 in the Spot Creation Wizard. Select here the channel used for segmentation (Source Channel). Enter your Estimated XY Diameter value. Click Model PSF-elongation along Z-axis and enter the estimated Z-value (see Note 39). Background Object Subtraction must be checked. Click on the blue arrow to move forward. 5. Step 3/3 in the Spot Creation Wizard. Adjust the threshold manually to detect all the spots of interest. Detection errors can be corrected later manually adding or deleting spots (see Note 40). Click on the green arrow to finish the wizard tool. Now the spots are shown as a new object in your image tree. 6. Repeat steps 2–5 for channel 2. 7. Once you have the two channels rendered (Fig. 3c), click on “Localize Spots”(see Note 41). A new window will open with the following message: “Get the spots with distance 100 μL volume. (2) Transfer an adequate volume from the dilution microplate A to the beaded deep-well block A containing 600 μL of LF-SDMSG medium. See Table 2 for example volumes for subculturing (see Note 28). 3. Rinse the set of 96 tips in microplate containing 250 μL of distilled water before returning to tip box. Store the tip boxes with rinsed tips for Day 3. 4. Repeat steps 2 and 3 for positions B, C, and D. 5. Grow the yeast cultures in deep-well blocks in an orbital shaking incubator at 30  C (deletion array plates) or 26  C (TS array plates) to OD600 ¼ 0.2–0.4 (see Note 29). See Table 2 for example growth times (see Note 28). 3.2.4 Day 3: HTP Imaging of Plates of the Deletion Collection Array

1. Each set of four deep-well blocks requires: (1) four 96-tip boxes (rinsed, from Day 2); (2) four 96-well optical microplates; (3) one ConA-coated 384-well imaging plate. Perform all pipetting using a 96-tip liquid handler (see Note 16). 2. Transfer 100 μL of yeast cultures from deep-well block A to a 96-well optical microplate. 3. Measure the optical density at 600 nm (OD600) using a plate reader and calculate the average OD600 across the 96 wells (see Note 30). 4. Repeat steps 2 and 3 for deep-well blocks B, C, and D. 5. Transfer the appropriate volume of yeast culture from each deep-well block to the corresponding position of the 384-well imaging plate (see Notes 31 and 32). Ideally, yeast cell density should be kept low on images in order to obtain reliable segmentation/detection of individual cells during image analysis. 6. Centrifuge the 384-well imaging plate at 500 rpm for 30 s using a microplate-compatible centrifuge to collect all cells at the bottom of the well. Alternatively, let the imaging plate

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Table 2 Example dilution volumes for subculturing of haploid strains in LF-SDMSG at 30 ˚C Medium in Volume of overnight culture dilution plate to add to dilution plate (μL)a (μL)a

Growth time Volume of culture from dilution before imaging plate to add to deep-well block (μL) (h)

30

220

30

13

30

220

20

16

20

220

20

19

20

220

10

21

The total volume in the dilution plate after addition of the overnight culture should not exceed 250 μL

a

stand for 10–15 min to allow the cells to settle to the bottom of the wells. 7. Acquire images of yeast cells on an HTP confocal microscope: Several fields of view (four to eight using a 60
objective; see Note 33) should be imaged for each well in order to guarantee high cell numbers for reliable statistical analysis. Ideally, images of several hundred cells should be collected for each strain. Excitation, emission, and exposure settings should be optimized based on the spectral characteristics of the imaged fluorophores, abundance of tagged proteins, and capabilities of the microscope. Z-stack parameters should be determined based on the recommended settings of the microscope setup. As a guideline, five slices with a 1 μm distance between optical sections are in general enough to image yeast cells without under/oversampling using a 60
objective on many HTP microscopes. 8. Start preparing the next 384-well imaging plate so that it is ready for imaging when the previous plate is done. This maximizes throughput and helps maintain comparable growth and physiological conditions between different plates. 3.2.5 Day 3: HTP Imaging of Plates of the TS Collection Array

1. TS array plates are imaged both at the permissive (26  C) and nonpermissive (37  C) temperature. A normal imaging day can therefore usually accommodate two plates from the TS collection array (equal to four plates from the deletion collection array in terms of imaging time). 2. Each set of four deep-well blocks requires: (1) four 96-tip boxes (rinsed, from Day 2); (2) eight 96-well optical microplates; (3) two ConA-coated 384-well imaging plates. Perform all pipetting using a 96-tip liquid handler (see Note 16). 3. For imaging TS array plates at the permissive temperature, follow steps 2–7 of Subheading 3.2.4.

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4. Transfer the four deep-well blocks with the yeast cell cultures grown at the permissive temperature from a 26  C to a 37  C orbital shaker after the 384-well imaging plate is prepared. 3–4 h of growth at the nonpermissive temperature is generally enough to observe morphological changes in the TS mutants. Time the temperature shift so that the nonpermissivetemperature imaging plate is ready only when all permissivetemperature imaging plates are done imaging, and the microscope has been equilibrated to 37  C. Make sure the same amount of incubation time at the nonpermissive temperature is used throughout the screen. 5. Repeat steps 3 and 4 for remaining TS array plate(s) in the batch. 6. After all plates in a batch have been imaged at the permissive temperature, and prior to imaging TS array plates at the nonpermissive temperature, program the microscope platform to image at 37  C. Depending on the microscope setup, this can take up to 1 h. 7. Take the first set of four deep-well blocks out of the 37  C orbital shaker (after 3–4 h of incubation) and follow steps 2–7 of Subheading 3.2.4. Imaging is now done at 37  C. 8. Repeat step 7 for the remaining set(s) of four deep-well blocks incubated at 37  C. 3.3

Image Analysis

Genome-wide screens usually generate several hundred gigabytes of raw images; storage space needs, data transfer protocols, and handling of metadata therefore must be carefully considered ahead of screening. Each image analysis pipeline must be tailored to the acquired images, biological question at hand, number of discernible phenotypes, and any prior knowledge on those phenotypes. Often image analysis represents the most challenging and timeconsuming part of high-content screening. Although there is no single solution for image analysis, most pipelines follow a common workflow. First, single cells are identified in an image, using cell segmentation. Second, phenotypic measurements are taken using feature extraction, typically including measurements of cell or compartment shape and size, pixel intensity, and texture. Depending on the biological question being asked, these quantitative features can then be clustered or classified in a variety of ways to enable an unbiased assessment of phenotypes of interest. For example, a previous study from our lab combined genomewide genetic perturbation of Saccharomyces cerevisiae with highcontent screening and quantitative image analysis to examine DNA damage foci using a Rad52-GFP marker [8]. First, cells were segmented using CellProfiler image analysis software [9]. Each identified cell object was associated with approximately 1000 features measuring different aspects of each image. Second, a

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Fig. 2 Examples of analysis workflows used for yeast cell images. (Panel a) shows workflow described in Styles et al. [8] for automated analysis of DNA damage foci. (Panel b) shows image analysis pipeline used to extract morphological data about endocytic compartments from single cells described in Mattiazzi Usaj et al. [11]. Main steps include object segmentation and feature extraction, construction of classification training sets, and cell classification

classification training set was assembled using CellProfiler Analyst [10] consisting of 1000 cells containing at least one DNA damage focus (positive bin) and 1000 cells that did not contain a DNA damage focus (negative bin). Next, a Wilcoxon rank-sum test was used to select only features that were informative for distinguishing the positive and negative bins (470 features in total). Finally, a support vector machine (SVM) was trained on the compiled training set, and the classifier was used to make label predictions for all identified cells within the screen (focus versus non-focus) (Fig. 2a). A more recent study from our lab combined systematic yeast genetics, high-content screening, and neural network-based image analysis of single cells to screen for genes that influence the morphology of four main endocytic compartments [11]. In this image analysis pipeline, CellProfiler [9] was used to identify individual cells and subcellular compartments and to extract quantitative features describing all segmented objects. Next, principal component analysis (PCA) was applied to the extracted CellProfiler features to reduce the redundancy and correlation of features in the data. We then used an unsupervised outlier detection approach, which finds cells with abnormal compartment morphology, to compile a list of phenotypic classes for each screened marker (21 in total) and

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positive control genes for each class. Next, we labeled a representative set of cells displaying these 21 phenotypes using a custommade, single-cell labeling tool, and used this training set to train a two-hidden layer fully connected neural network for each of the endocytic markers. For each single cell, the marker-associated neural network estimated the probability of each phenotype (from 4 to 7 phenotypes for each marker) and we assigned each cell the phenotype with the highest probability (Fig. 2b). For in depth discussions of image analysis, we refer the reader to review papers that have described the different steps and approaches, and the challenges associated with them [4, 12– 14]. Additionally, versatile software for object segmentation, feature extraction and data analysis, as well as single-cell level image analysis software/code that can be adapted to different biological questions and systems have been developed by us and by other labs [11, 15–18].

4

Notes 1. Irrespective of the strain collection or customized array that is being screened, each plate of the array should have at least a few positions with an isogenic control strain to assure sufficient wild-type cell numbers for downstream statistical analysis. In our standard screening procedure, the arrays have an isogenic control strain at all the positions around the perimeter of the plate (76 positions on a 384-format plate). 2. Other strain collections amenable to analysis by HTP microscopy include, for example: DAmP collection [19], GST overexpression collection [20], and FLEX overexpression collection [21]. 3. This example query strain includes a MATa-specific promoter (STE2pr) driving expression of LEU2, which allows for selection of haploid MATa progeny using medium lacking leucine. A different starting query strain Y7092 contains a MATa-specific promoter (STE2pr) driving expression of Schizosaccharomyces pombe His5, which allows for selection of haploid MATa progeny using medium lacking histidine. Both starting query strains are SGA compatible. For details on SGA markers see Kuzmin et al. [3]. 4. Instead of adding DO powder to the mixture before autoclaving, an appropriate volume of a 10
stock solution can be added after autoclaving. To make a 10
DO stock solution, dissolve 2 g of the powder mixture in 100 mL of distilled water, and filter-sterilize using 0.2 μm filter. Volume of distilled water used to prepare the remaining powder components before autoclaving has to be adjusted accordingly.

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5. Concanavalin A is a lectin that binds carbohydrates on the yeast cell surface and is used for immobilization of live cells for confocal and time-lapse imaging. 6. We recommend using rich medium to prepare lawns of the query strain, since it allows for higher cell densities and therefore better transfer of cells to SGA mating plates. 7. To allow for an effective selection of strains expressing drugresistance cassettes, monosodium glutamate (MSG) is used as a nitrogen source rather than ammonium sulfate both in liquid and solid medium. Ammonium sulfate interferes with the activity of the antibiotics. 8. To prevent contamination by mold, one-fourth of the normally used drug concentration is added to the sporulation plates. 9. The example query strain carries the STE2pr-LEU2 haploid selection cassette (see also Note 3). Haploid strains with the mating type a are selected by excluding leucine from the medium. The deletion of CAN1 and LYP1 genes confers resistance to the amino acid analogs canavanine and thialysine in the absence of arginine and lysine, respectively, and act as haploid selection markers since heterozygous diploid yeast are sensitive to these amino acid analogs. 10. The amino acid(s) that is/are added back to the medium depend(s) on the strain’s genotype. Minimal synthetic medium is used for subculturing and imaging steps, rather than dropout medium, because this results in slower growth, enabling better timing of yeast cell culture growth across the staggered plates that are being imaged in each batch. 11. In addition to lacking ammonium sulfate, low fluorescent yeast nitrogen base (YNB) (e.g., MP biomedicals LLC; #4030-512) does not contain folic acid or riboflavin, which are autofluorescent and reduce the signal-to-noise ratio in acquired images. 12. Low-fluorescent YNB does not dissolve completely during autoclaving. It is therefore recommended to stir the ingredients on high heat for 30 min to 1 h before autoclaving, to dissolve most of the YNB. 13. If both canavanine and thialysine have been present in two or more replica pinning SGA selection steps, they can be omitted from the liquid medium without significant regrowth of diploids. Otherwise, add canavanine and thialysine to the medium at a final concentration of 50 μg/mL each. 14. SGA can also be done using a manual 96-pin or 384-pin replicator. See [3] for a detailed protocol. One-well square plates used in all SGA replica pinning steps must be compatible with the replica pinning robot or manual replicator (e.g., Singer PlusPlates for the Singer RoTor).

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15. Most confocal microscopes with a motorized stage and a microplate holder can be programmed for HTP imaging. Special rings are available which sit around the nose cones of the objectives and create a small pool of immersion liquid, which assures continuous immersion during imaging of a multi-well plate. Some water immersion objectives can be upgraded with a water immersion micro dispenser, that is compatible with longterm live-cell imaging or screening experiments. The time required to image a full 384-well plate is usually longer compared to dedicated automated HTP microscopes. 16. This protocol assumes the use of a (semi)manual 96-tip liquid handler (e.g., Rainin Liquidator, Eppendorf epMotion) that is more accessible to most labs compared to a fully automated liquid handling system. If available, a robotic 96-tip liquid handler is recommended. A liquid handling procedure that accommodates the appropriate volumes and numbers of plates can then be programmed for each of the sample preparation steps. Alternatively, though much more laborious and errorprone, a multi-channel pipette could also be used. This will greatly limit the screening throughput. 17. Alternatively, 96-well plates can also be used. 18. Transfer of colonies from solid to liquid medium can also be done using a 96-pin replica pinning robot with an appropriate solid-to-liquid pinning procedure, or with a manual 96-pin replicator. Use of manual pinning tools is more time consuming, as the metal pins need to be washed between each transfer step (four washes for each 384-format plate). 19. Bagged sporulation plates can be left at room temperature (not recommended above 25  C). 20. Dilution of a saturated overnight culture increases technical reproducibility and helps to minimize the well-to-well variation in cell densities of the subcultures. 21. For example, assuming a simultaneous multi-channel acquisition, collecting z-stacks of five planes for four fields of view takes ~2 h for a full 384-well imaging plate on a PerkinElmer Phenix HTP microscope. A batch of four plates would thus require 8 h of imaging time, with additional time needed to prepare the first plate and for cleanup at the end of the day. If acquiring single-plane images instead of z-stacks, a full 384-well plate is imaged in less than 0.5 h. Depending on the microscope setup, staggering of growth cultures must be adapted to the time required to image one full plate. Depending on the dynamics and sensitivity (e.g., to changes in microenvironment) of the imaged markers, imaging can be done in smaller batches, imaging 192 wells or 96 wells at a time. A pilot screen should be done to determine the variability in the

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morphology of control strain cells on different sized imaging plates. 22. Always bead and autoclave spare plasticware in case any of the plates need to be reimaged. If washing, beading, and autoclaving after every batch of images (or even daily), the total number of plasticware required for a full screen is reduced accordingly. 23. A single glass bead in the well assures more effective mixing of cells and a more uniform growth and minimizes settling of cells to the bottom of the well. If working with fragile strains (cell wall defects, certain drug screens), the glass bead should be avoided in order to prevent cell lysis. 24. Autoclave tape can be used to prevent the lid from falling off the deep-well block and spilling of the glass beads. 25. We recommend doing a growth test in both liquid media before proceeding with aliquoting and sample preparation. The test should include a strain from the final SGA selection plate, and both parental negative control strains (query strain, mutant array strain). 26. If the plates will be used for longer imaging time-courses, a modified ConA-coating protocol described in [22] can be used instead. 27. In this step, colonies from a 384-format agar plate are transferred into four 96-well microplates. In order to prevent strain cross contamination, special attention must be paid to assure proper plate alignment when using pin pads or a manual replicator. When using a robotic liquid handler for the solid-toliquid transfer, cross contamination is not likely. 28. The volumes and growth times listed in Table 2 are meant as a guideline and need to be adapted depending on individual screening conditions and needs. These volumes and incubation times will depend on the growth rate of the yeast cultures, which is affected by the fitness of the yeast strains, medium composition, and incubation temperature. We recommend conducting a small pilot experiment to determine the optimal volumes and growth times for each experimental design. 29. Plate readers report the optical density in arbitrary units. A calibration curve can be used to convert the plate reader’s output to actual cell density. 30. If possible, to normalize cell numbers across all 96 wells, a robotic 96-tip liquid handler that aspirates and dispenses different volumes in each well (variable volume 96-channel head) could be used. 31. The user must determine the appropriate volume of cells to transfer to the imaging plate based on the measured OD600 to obtain the desired cell density in the acquired

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images. To determine this, a calibration curve should be made beforehand. 32. The cell culture volume dispensed to the 384-well imaging plate should be between 20 μL and 100 μL. If the calculated volume is lower than 20 μL, LF-SDMSG media (at least 20 μL) should be added to the imaging plate prior to dispensing the cell cultures. Topping-up the wells with LF-SDMSG medium as well as sealing the imaging plate with aluminum sealing tape are recommended whenever imaging longer time-courses or at elevated temperatures to prevent the samples from drying due to evaporation of the medium. Alternatively, if sealing with aluminum sealing tape is incompatible with the acquisition setup (e.g., when acquiring images with differential phase contrast), wells can be topped-up with LF-SDMSG to a final volume of 90 μL and covered with the lid that comes with the imaging plate. 33. Lower magnifications are not recommended when looking at the subcellular morphology of yeast cells. A higher magnification can be used, but this will usually require imaging more fields of view (FOV) in order to capture enough cells, leading to longer imaging times. The number of required FOVs will depend also on other characteristics of the microscope setup; for example, newer microscopy systems are equipped with cameras with a larger sensor size—the same number of cells can thus be captured with fewer FOVs.

Acknowledgments We thank Helena Friesen and Athanasios Litsios for critical comments. This work was supported by grants from the Canadian Institutes for Health Research (FDN-143264 to BA) and the National Institutes of Health (R01HG00583). References 1. Costanzo M, VanderSluis B, Koch EN, Baryshnikova A, Pons C, Tan G, Wang W, Usaj M, Hanchard J, Lee SD, Pelechano V, Styles EB, Billmann M, van Leeuwen J, van Dyk N, Lin ZY, Kuzmin E, Nelson J, Piotrowski JS, Srikumar T, Bahr S, Chen Y, Deshpande R, Kurat CF, Li SC, Li Z, Usaj MM, Okada H, Pascoe N, San Luis BJ, Sharifpoor S, Shuteriqi E, Simpkins SW, Snider J, Suresh HG, Tan Y, Zhu H, MalodDognin N, Janjic V, Przulj N, Troyanskaya OG, Stagljar I, Xia T, Ohya Y, Gingras AC, Raught B, Boutros M, Steinmetz LM, Moore CL, Rosebrock AP, Caudy AA, Myers CL,

Andrews B, Boone C (2016) A global genetic interaction network maps a wiring diagram of cellular function. Science 353(6306):aaf1420. https://doi.org/10.1126/science.aaf1420 2. Tong AH, Evangelista M, Parsons AB, Xu H, Bader GD, Page N, Robinson M, Raghibizadeh S, Hogue CW, Bussey H, Andrews B, Tyers M, Boone C (2001) Systematic genetic analysis with ordered arrays of yeast deletion mutants. Science 294 (5550):2364–2368. https://doi.org/10. 1126/science.1065810

High-Throughput Imaging 3. Kuzmin E, Sharifpoor S, Baryshnikova A, Costanzo M, Myers CL, Andrews BJ, Boone C (2014) Synthetic genetic array analysis for global mapping of genetic networks in yeast. Methods Mol Biol 1205:143–168. https:// doi.org/10.1007/978-1-4939-1363-3_10 4. Mattiazzi Usaj M, Styles EB, Verster AJ, Friesen H, Boone C, Andrews BJ (2016) High-content screening for quantitative cell biology. Trends Cell Biol 26(8):598–611. https://doi.org/10.1016/j.tcb.2016.03.008 5. Giaever G, Chu AM, Ni L, Connelly C, Riles L, Veronneau S, Dow S, Lucau-Danila A, Anderson K, Andre B, Arkin AP, Astromoff A, El-Bakkoury M, Bangham R, Benito R, Brachat S, Campanaro S, Curtiss M, Davis K, Deutschbauer A, Entian KD, Flaherty P, Foury F, Garfinkel DJ, Gerstein M, Gotte D, Guldener U, Hegemann JH, Hempel S, Herman Z, Jaramillo DF, Kelly DE, Kelly SL, Kotter P, LaBonte D, Lamb DC, Lan N, Liang H, Liao H, Liu L, Luo C, Lussier M, Mao R, Menard P, Ooi SL, Revuelta JL, Roberts CJ, Rose M, Ross-Macdonald P, Scherens B, Schimmack G, Shafer B, Shoemaker DD, Sookhai-Mahadeo S, Storms RK, Strathern JN, Valle G, Voet M, Volckaert G, Wang CY, Ward TR, Wilhelmy J, Winzeler EA, Yang Y, Yen G, Youngman E, Yu K, Bussey H, Boeke JD, Snyder M, Philippsen P, Davis RW, Johnston M (2002) Functional profiling of the Saccharomyces cerevisiae genome. Nature 418 (6896):387–391. https://doi.org/10.1038/ nature00935 6. Li Z, Vizeacoumar FJ, Bahr S, Li J, Warringer J, Vizeacoumar FS, Min R, Vandersluis B, Bellay J, Devit M, Fleming JA, Stephens A, Haase J, Lin ZY, Baryshnikova A, Lu H, Yan Z, Jin K, Barker S, Datti A, Giaever G, Nislow C, Bulawa C, Myers CL, Costanzo M, Gingras AC, Zhang Z, Blomberg A, Bloom K, Andrews B, Boone C (2011) Systematic exploration of essential yeast gene function with temperature-sensitive mutants. Nat Biotechnol 29(4):361–367. https://doi.org/10.1038/nbt.1832 7. Huh WK, Falvo JV, Gerke LC, Carroll AS, Howson RW, Weissman JS, O’Shea EK (2003) Global analysis of protein localization in budding yeast. Nature 425(6959):686–691. https://doi.org/10.1038/nature02026 8. Styles EB, Founk KJ, Zamparo LA, Sing TL, Altintas D, Ribeyre C, Ribaud V, Rougemont J, Mayhew D, Costanzo M, Usaj M, Verster AJ, Koch EN, Novarina D, Graf M, Luke B, Muzi-Falconi M, Myers CL, Mitra RD, Shore D, Brown GW, Zhang Z, Boone C, Andrews BJ (2016) Exploring

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quantitative yeast phenomics with single-cell analysis of DNA damage foci. Cell Systems 3 (3):264–277. https://doi.org/10.1016/j. cels.2016.08.008 9. Carpenter AE, Jones TR, Lamprecht MR, Clarke C, Kang IH, Friman O, Guertin DA, Chang JH, Lindquist RA, Moffat J, Golland P, Sabatini DM (2006) CellProfiler: image analysis software for identifying and quantifying cell phenotypes. Genome Biol 7:R100. https:// doi.org/10.1186/gb-2006-7-10-r100 10. Jones TR, Kang IH, Wheeler DB, Lindquist RA, Papallo A, Sabatini DM, Golland P, Carpenter AE (2008) CellProfiler analyst: data exploration and analysis software for complex image-based screens. BMC Bioinformatics 9 (1):482. https://doi.org/10.1186/14712105-9-482 11. Mattiazzi Usaj M, Sahin N, Friesen H, Pons C, Usaj M, Masinas MP, Shuteriqi E, Shkurin A, Aloy P, Morris Q, Boone C, Andrews BJ (2020) Systematic genetics and single-cell imaging reveal widespread morphological pleiotropy and cell-to-cell variability. Mol Syst Biol 16:e9243. https://doi.org/10.15252/msb. 199243 12. Bray MA, Carpenter AE (2018) Quality control for high-throughput imaging experiments using machine learning in Cellprofiler. Methods Mol Biol 1683:89–112. https://doi.org/ 10.1007/978-1-4939-7357-6_7 13. Caicedo JC, Cooper S, Heigwer F, Warchal S, Qiu P, Molnar C, Vasilevich AS, Barry JD, Bansal HS, Kraus O, Wawer M, Paavolainen L, Herrmann MD, Rohban M, Hung J, Hennig H, Concannon J, Smith I, Clemons PA, Singh S, Rees P, Horvath P, Linington RG, Carpenter AE (2017) Dataanalysis strategies for image-based cell profiling. Nat Methods 14(9):849–863. https://doi.org/10.1038/nmeth.4397 14. Grys BT, Lo DS, Sahin N, Kraus OZ, Morris Q, Boone C, Andrews BJ (2017) Machine learning and computer vision approaches for phenotypic profiling. J Cell Biol 216(1):65–71. https://doi.org/10. 1083/jcb.201610026 15. Eliceiri KW, Berthold MR, Goldberg IG, Ibanez L, Manjunath BS, Martone ME, Murphy RF, Peng H, Plant AL, Roysam B, Stuurman N, Swedlow JR, Tomancak P, Carpenter AE (2012) Biological imaging software tools. Nat Methods 9(7):697–710. https:// doi.org/10.1038/nmeth.2084 16. Kraus OZ, Grys BT, Ba J, Chong Y, Frey BJ, Boone C, Andrews BJ (2017) Automated analysis of high-content microscopy data with deep

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(2006) Mapping pathways and phenotypes by systematic gene overexpression. Mol Cell 21 (3):319–330. https://doi.org/10.1016/j. molcel.2005.12.011 21. Hu Y, Rolfs A, Bhullar B, Murthy TV, Zhu C, Berger MF, Camargo AA, Kelley F, McCarron S, Jepson D, Richardson A, Raphael J, Moreira D, Taycher E, Zuo D, Mohr S, Kane MF, Williamson J, Simpson A, Bulyk ML, Harlow E, Marsischky G, Kolodner RD, LaBaer J (2007) Approaching a complete repository of sequence-verified protein-encoding clones for Saccharomyces cerevisiae. Genome Res 17(4):536–543. https://doi. org/10.1101/gr.6037607 22. Dhar R, Missarova AM, Lehner B, Carey LB (2019) Single cell functional genomics reveals the importance of mitochondria in cell-to-cell phenotypic variation. Elife 8:e38904. https:// doi.org/10.7554/eLife.38904 23. Tong AH, Boone C (2006) Synthetic genetic array analysis in Saccharomyces cerevisiae. Methods Mol Biol 313:171–192. https://doi.org/ 10.1385/1-59259-958-3:171

Chapter 13 Visualizing the Dynamics of T Cell–Dendritic Cell Interactions in Intact Lymph Nodes by Multiphoton Confocal Microscopy Billur Akkaya, Olena Kamenyeva, Juraj Kabat, and Ryan Kissinger Abstract Multiphoton microscopy has provided us the ability to visualize cell behavior and biology in intact organs due to its superiority in reaching deep into tissues. Because skin draining lymph nodes are readily accessible via minimal surgery, it is possible to characterize the intricate interactions taking place in peripheral lymph nodes intravitally. Here we describe our protocol to visualize antigen-specific T cell–dendritic cell interactions in the popliteal lymph node of immunocompetent mice. With this method, behaviors of up to four cell types, such as T cells with different antigen specificities, T cells differentiated into different effector and regulatory lineages and dendritic cells originating from mice that bear mutations in functional genes can be imaged simultaneously. Key words Antigen-specific T cell, Dendritic cell, Primary amine dyes, T cell, Intravital microscopy, Multiphoton

1

Introduction Foreign proteins that enter through skin are cleaved into antigenic peptides and presented by dendritic cells (DCs) [1]. Naı¨ve T cells enter secondary lymphoid organs from blood to assume effector functions based on their ability to recognize these foreign antigenic peptides displayed by DCs [2]. This recognition phase is observed initially as a slowdown, then a complete stop in the T cell mobility preceding the effector functions such as cytokine secretion and killing. It is very important to understand the biology and dynamics of T cell interactions within the intact lymph nodes and because multiphoton microscopy offers great access to deeper structures buried under the capsule such as T cell zone, it has been increasingly used as the gold standard imaging technique to elucidate the contact characteristics of T cells in vivo [3].

Joseph Brzostowski and Haewon Sohn (eds.), Confocal Microscopy: Methods and Protocols, Methods in Molecular Biology, vol. 2304, https://doi.org/10.1007/978-1-0716-1402-0_13, © This is a U.S. government work and not under copyright protection in the U.S.; foreign copyright protection may apply and Springer Nature US 2021

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Here we demonstrate that T cells and DCs isolated from four different sources that are labeled differently can be imaged simultaneously by the intravital multiphoton microscopy of popliteal lymph node. We show that mature DCs can be freshly isolated from spleens of reporter mouse strains, loaded ex vivo with antigenic peptides, and injected subcutaneously via footpad injections for delivery into the popliteal lymph nodes. Likewise, naı¨ve T cells that are isolated from spleen can be labeled with primary amine dyes and adoptively transferred into the blood stream via retroorbital sinus injection immediately after administering DCs into the footpad lymphatic vessels. This results in a T cell response initiated at the popliteal lymph node which is optimally detectable by intravital multiphoton microscopy starting at 18–20 h post adoptive transfer [3, 4]. Here, we also provide the information about the important anatomical landmarks to guide minimally invasive surgery and stabilization of the organ for imaging. Lastly, we show that the biology of cell–cell interactions can be analyzed by Huygens and Imaris software which can be utilized to create surface rendering for the T cells and DCs to determine their contact morphology and to quantify the contact duration, area, and volume.

2

Materials 1. Curved general purpose forceps and scissors. 2. Absorbent bench covers. 3. 70% ethanol in spray bottle. 4. 1.7 mL, 5 mL, 10 mL, and 50 mL sterile conical tubes. 5. 60 mm cell culture dish. 6. 70 μm cell strainer. 7. PBS. 8. R10 media: RPMI 1640 supplemented with 10% heatinactivated fetal bovine serum, 50 U/mL penicillin, 50 μM streptomycin, 1 mM sodium pyruvate, 2 mM L-glutamine, 0.1 mM nonessential amino acids, 50 μM 2-mercaptoethanol, and 10 mM HEPES. 9. MACS buffer: PBS supplemented with 0.5% BSA and 2 mM EDTA. 10. ACK lysing buffer for red blood cell lysis. 11. Collagenase: Liberase TM (Sigma-Aldrich, catalog number: 5401119001). Prepare collagenase by adding 10 mL of R10 to 50 mg Liberase TM for a final concentration of 5 mg/mL. Keep solution on ice, stir gently every 5 min for 30 min until dissolved. Aliquot in 1 mL volume and store at
20  C.

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12. DNAse I: Prepare DNAse I stock solution by adding 10 mL of 0.15 M NaCl to DNAse I for a final concentration of 10 mg/ mL, aliquot into 500 μL, and store at
20  C. Prepare the digestion mixture for DC isolation by mixing 500 μL DNAse stock solution into 1 mL Liberase solution and adding 1 mL of mixed DNAse/Liberase solution into 11 mL complete RPMI medium (R10). 13. Ultrapure mouse CD11c microbeads (Miltenyi Biotec, catalog number: 130-108-338). 14. Naı¨ve mouse CD4+ T cell isolation kit (Miltenyi Biotec, catalog number: 130-104-453). 15. autoMACS Pro Separator (Miltenyi Biotec, catalog number: 130-092-545). 16. Cell Proliferation Dye eFluor™ 450, Cell Proliferation Dye eFluor™ 670 (Thermo Fisher Scientific, catalog numbers are 65-0842-85, 65-0840-85, respectively). 17. 21G, 25G needles, 3–10 mL syringe, 0.3 mL 31G insulin syringe with permanently attached needle. 18. Isoflurane for anesthesia. 19. O2 chamber. 20. Hemocytometer. 21. Trypan blue. 22. Anti-CD31 antibody MEC13.3).

(Alexa

Fluor-647

labeled,

clone

23. Betadine (or other iodine-based surgical scrub) and 70% alcohol prep pads. 24. Ophthalmic ointment & Lomb).

Artelac

Nighttime Gel

(Bausch

25. Surgical scissors and dressing forceps. 26. Small animal hair clipper MiniArco Professional Cordless Trimmer. 27. Stainless-steel tissue holder (NIH Division of Scientific Equipment and Instrumentation Services) (see Note 1). 28. Cyanoacrylate (e.g., superglue). 29. Carbomer-based gel (see Note 2) [5]. 30. Surgical cloth tape. 31. Cotton swab Q-tips. 32. Rodent warmer/heating pad (Braintree Scientific). 33. Isoflurane for anesthesia. 34. Isoflurane anesthesia vaporizer (SurgiVet) with mouse nose cone (Braintree Scientific).

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35. Leica MZ95 Surgical Microscope. 36. Leica SP8 confocal microscope equipped with: two infrared pulsed lasers—a Mai Tai HP laser for 700–950 nm excitation and a Spectra Physics Insight DS laser for 950–1300 nm excitation; and four channel RLD detector (non-descanned detector) containing two HyD detectors and two photomultiplier tube detectors. 37. Huygens software suite. 38. Imaris software.

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Methods

3.1 DC Isolation and Peptide Loading

1. Follow steps 2–8 to remove the spleen. 2. Euthanize the mouse and place it face up on an absorbent bench cover and spray abdomen area liberally with 70% ethanol. 3. Pinch the skin at the midpoint of the abdomen (Fig. 1a) and make a 1 cm incision with curved scissors. 4. Hold both sides of incision and retract the skin in opposite directions (Fig. 1b) (see Note 3). 5. Hold the peritoneum at the left upper quadrant of the abdomen with forceps and make a 5 mm incision and enlarge it to 1–1.5 cm to have a better access to spleen. 6. Gently, lift the spleen with forceps to see its ligaments and surrounding connective tissue. 7. Carefully cut the ligaments, remove connective tissue and fat as much as possible. 8. Put the spleen in 60 mm dish that contains R10 media on ice and move to the next spleen. 9. Insert the needle along the long axis of spleen until one-third of the axis (Fig. 1c) (see Note 4). 10. Flush spleens one by one with ~2.5 mL digestion mixture/ spleen using a 21G needle and forceps to remove the connective tissue (see Note 5). 11. Using forceps and curved scissors, cut the spleens into small pieces (see Note 6). 12. Aspirate the media containing spleen pieces with a 25 mL pipette and put into a sterile 15 mL conical tube (see Note 7). 13. Incubate for 30 min at 37  C. During the incubation, vortex for 10 s at high speed every 5 min.

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Fig. 1 Excision of spleen from euthanized mouse for DC and T cell isolations. Steps for the removal of organ (Franconetti, #1170). Stabilization and flushing of the spleen for DC purification (F)

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14. Place a 70 μm cell strainer into a sterile 60 mm dish and mash spleens through it by the help of a sterile syringe plunger (see Note 8). 15. Pellet the cells by centrifuging at 500  g for 5 min and discarding the supernatant (see Note 9). 16. Add 4 mL ACK lysis buffer, resuspend the pellet up and down a few times, and put on ice for 3 min (see Note 10). 17. Add 10 mL MACS buffer to stop the ACK lysis, centrifuge the tube at 500  g for 5 min, and discard the supernatant. 18. Repeat step 17. 19. Resuspend up to five spleens in 3 mL MACS buffer. 20. Add 90 μL CD11c microbeads per spleen to label the DCs for magnetic selection, vortex, and incubate for 15 min at 4  C. 21. Top off with MACS buffer to fill the 15 mL tube and centrifuge at 500  g for 5 min. 22. While pelleting cell/microbead mixture in the previous step, place a 70 μm cell strainer upside-down on top of a new 15 mL tube. 23. After centrifugation, remove supernatant and add 2 mL MACS buffer onto cell pellet, resuspend by gently pipetting up and down, and pass the cell–bead mixture through the strainer into the new 15 mL tube. Then rinse the initial tube with 2 mL MACS buffer and apply this through the strainer too (see Note 11). 24. Place the tube that contains cells along with two empty tubes labeled as positive and negative into the destined positions on autoMACS rack after removing the caps of all the tubes (see Note 12). 25. Run on autoMACS under the “PosselD2” program. 26. Collect the positive fraction that contains DCs. 27. Pellet DCs by centrifugation at 500  g for 5 min. 28. Resuspend the DCs in 1–2 mL by gently pipetting up and down and add the peptide to reach the desired loading concentration (see Note 13). 29. Vortex and incubate at 37  C for 30 min—3 h (see Note 14). 30. Add 10 mL ice-cold R10 and centrifuge at 500  g for 5 min, discard the supernatant. 31. Add 10 mL ice-cold PBS and centrifuge at 500  g for 5 min, discard the supernatant. 32. Resuspend the pellet with 1 mL ice-cold PBS and count the cells.

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33. Adjust the volume to reach 50 μL (containing 1–2  106 cells) per footpad, gently pipette up and down, transfer into a 1.7 mL tube. 34. Draw the content into 0.3 mL 31G insulin syringe with permanently attached needle (see Note 15). 3.2 Naı¨ve CD4+ T Cell Isolation and Primary Amine Labeling

1. Remove the spleens as in Fig. 1 and place in 10 mL R10 on ice. 2. Set a 70 μm cell strainer into a 60 mm dish and mash spleens through it by the help of a sterile syringe plunger (see Note 16). 3. Centrifuge at 1500 rpm (500  g) for 5 min and discard the supernatant (see Note 17). 4. Add 4 mL ACK lysis buffer, resuspend the pellet up and down a few times and put on ice for 3 min (see Note 18). 5. Add 10 mL MACS buffer and centrifuge the tube at 1500 rpm (500  g) for 5 min. 6. Resuspend the pellet in 10 mL MACS buffer by gently pipetting up and down, take 10–20 μL to count cell number, and centrifuge the rest at 1500 rpm (500  g) for 5 min. 7. Determine cell number with desired method. 8. Resuspend cell pellet in 40 μL of buffer per 107 total cells and add 10 μL of Biotin-Antibody Cocktail per 107 total cells. 9. Vortex and incubate for 5 min in the refrigerator (2–8  C). 10. Add 20 μL of buffer, 20 μL of Anti-Biotin MicroBeads, and 10 μL of CD44 MicroBeads per 107 total cells. 11. Vortex and incubate for 10 min in the refrigerator (2–8  C). 12. Add 10 mL MACS buffer and centrifuge the tube at 1500 rpm (500  g) for 5 min. 13. Aspirate supernatant completely. Resuspend up to 108 cells in 1 mL of buffer, remove the clumps as in “DC isolation step 23” rinsing the empty tube and the strainer with 1 mL of MACS buffer. Cells are in a final volume of 2–4 mL. Proceed to magnetic cell separation. 14. Place tubes in the following autoMACS Chill Rack positions: position A ¼ sample, position B ¼ negative fraction, and position C ¼ positive fraction. 15. Select the program “Depletes” and collect enriched naive CD4 + T cells at position B ¼ negative fraction. 16. Add 10 mL PBS, centrifuge the tube at 1500 rpm (500  g) for 5 min. 17. Aspirate the supernatant, resuspend in 1–2 mL PBS by gently pipetting up and down, and proceed to primary amine labeling (see Note 19).

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18. Reconstitute one vial of Cell Proliferation Dye eFluor™ 670 to a stock concentration of 5 mM with 126 μL of anhydrous DMSO and one vial of Cell Proliferation Dye eFluor™ 450 to a stock concentration of 10 mM with 165 μL of anhydrous DMSO (see Note 20). 19. Prepare a 20 μM (1/500) solution of Cell Proliferation Dye eFluor™ 450 or 10 μM (1/500) solution of Cell Proliferation Dye eFluor™ 670 in PBS pre-warmed to room temperature and cover with foil. 20. Add the 1/500 dye solution at equal volume to the cell suspension to obtain 1/1000 final concentration of the dye. 21. Vortex the cell–dye mixture and incubate for 10 min at 37  C in the dark. 22. Stop labeling by adding 10 mL cold R10 and centrifuge the tube at 1500 rpm (500  g) for 5 min. 23. Wash cells two more times with complete media and for a third time with PBS. 24. Count and adjust the volume accordingly 4–10  106 cells/100 μL in PBS (see Note 21).

for

25. Transfer into a 1.7 mL tube, draw into a 1 mL syringe with 25G needle, and place the syringe on ice (see Note 22). 3.3 Adoptive Transfer

1. Anesthetize animals in the anesthesia induction chamber with 2% isoflurane USP (Burstein, #942) admixed with 3 mmHg oxygen. (Note: Watch as their breathing becomes shallow and rapid and wait for the breathing to become deeper and slower to quickly proceed with the injections.) 2. Insert the 31G needle underneath the digital footpad until the tip of needle reaches metatarsal footpad as described in Fig. 2a (see Note 23). 3. Inject 50 μL and wait for 3 s before removing the needle. (Note: As you remove the needle, it is normal to see a drop of clear fluid coming out of the injection site. It should not contain any blood). 4. Position the animal as in Fig. 2b and quickly proceed to retroorbital injection before anesthesia wears off (see Note 24). 5. Glide the needle through the inner commissure of eye underneath the eyeball until needle opening disappears (see Note 25). 6. Inject 100 μL and wait for 3 s before removing the needle (see Note 26). 7. Return animals to their cage until the microscopy (see Note 27).

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Fig. 2 Footpad and retroorbital injections for DC and T cell adoptive transfers, respectively 3.4 Minimally Invasive Surgery for the Intravital Microscopy (IVM) of Popliteal Lymph Node

Intravital imaging of the mouse popliteal lymph node requires minor surgery; however, this makes it an invasive intravital imaging method and therefore is performed as a non-survival technique. 1. Induce animal anesthesia in induction chamber using 2% isoflurane. Following initial anesthesia, maintain isoflurane influx using a nose cone, at a concentration of ~1.5–1.75% (see Note 28). 2. Inject the mouse with Alexa Fluor-647 labeled anti-CD31 antibody i.v. via retroorbital route to visualize the blood vessels (see Note 29). 3. Place the mouse on a heated pad under the surgical microscope and secure the mouse in ventral recumbency using surgical tape (Fig. 3a, left) (see Note 30). 4. Remove all hair from mouse leg and thigh using hair clipper. 5. Disinfect the surgical site with three alternating scrubs of Betadine (or other iodine-based surgical scrub), and 70% ethanol. 6. Position and focus the binocular microscope on the mouse hind limb. Make 1-cm-long incision on caudomedial side of the knee (Fig. 3a, right), gently pull skin flaps apart exposing semimembranosus muscle and gastrocnemius muscle. Using blunt dissection, separate the muscles to expose the popliteal lymph node. Using micro-dissecting forceps, carefully separate the fat surrounding the lymph node taking care to avoid damage to the arteries, veins, and lymphatic vessels (Fig. 3b). 7. Remove mouse from under the surgical scope, and glue tissue holder to the muscles around exposed lymph node, with the lymph node facing the imaging window (see Note 31).

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Fig. 3 Animal preparation for surgery. (a) The mouse is anesthetized and restrained (left), and 1-cm incision on caudomedial side of the knee is performed (right) to access the popliteal lymph node. (b) A drop of superglue is applied to the tissue holder around its middle window, and the muscles surrounding exposed lymph node are attached to the holder, with the lymph node in the center of the window. (c) Carbomer-based gel is applied to exposed mouse tissues. (d) The mouse is positioned onto the microscope platform, and tissue holder is restrained using surgical tape, with the lymph node facing inverted microscope objective, for time-lapse imaging

8. Immediately after attachment of muscle tissue to the holder, immerse the lymph node in carbomer-based gel. Do not allow lymph node surface to dry (Fig. 3c). 9. For the IVM, flip the holder face down onto the microscope imaging stage, adding more gel as needed, and immobilize the entire mouse leg on the imaging stage using surgical tape (Fig. 3d). Cover the edges of the tissue holder with PBS (37  C) -soaked gauze to prevent any drying (see Note 32). 10. Keep the imaged animal inside the 37  C-heated microscope chamber, placing the temperature probe as close to the animal as possible. That will prevent both hypothermia and overheating of the sedated animal. The duration of imaging should vary from 30 min to 6 h maximum. 11. Once imaging is complete, euthanize the mouse by cervical dislocation while the animal is still anesthetized. 3.5 Acquisition for Intravital Microscopy (IVM) of Popliteal Lymph Node

1. On the Leica SP8 confocal microscope or equivalent, tune the Mai Tai laser to 880 nm and InSight DS laser to 1150 nm wavelengths for simultaneous excitation of collagen fibers as second harmonic generation (SHG) signal, eGFP, dsRed, and e670 dye. (Keep in mind these lasers can be tuned to other

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appropriate wavelengths if using different fluorescent protein/ dye combinations such as replacing SHG with an e450 dye). 2. Allow both the mouse and the motorized stage to reach complete temperature equilibrium before acquiring time-lapse video. 3. Define region of interest (ROI) within T cell zone and set a z-stack over time for imaging a single ROI or set a tiled scan of multiple ROIs if imaging larger area (up to 2 mm2). Number of optical slices within a z-stack and the size of imaged area must be optimized to allow appropriate acquisition speed. 4. While using 25 water-immersion lens, the water drop between objective and imaging stage must be refilled every hour. Sequential 1 h time-lapse videos can be combined later into continuous 6 h time-lapse during post-acquisition data processing using Huygens Professional software. 3.6 Data Analysis for Intravital Microscopy (IVM) of Popliteal Lymph Node

1. Correct for image drift due to thermal changes and other shift or rotation errors using the Huygens Professional software suite by following steps 2–4.

3.6.1 Correct Thermal Drift and Improve Image Quality by Applying Deconvolution

3. Stitch and stabilize image time series using “Object Stabilizer” cross correlation algorithm of the software to minimize shifts and thermal drift deformations between adjacent time frames (Fig. 5). Images are converted to 32-bit float format (see Note 34).

2. Stabilize 3D slices in z direction using “Stabilization Wizard” (Fig. 4) (see Note 33).

4. Deconvolve the image using the “Deconvolution Wizard” (Fig. 6) (see Note 35). 3.6.2 Track Cells and Calculate Colocalization

1. Open the stabilized data in the Imaris software package to proceed with cell tracking, 3D reconstruction, and surface modelling. 2. Select the “Spot” function to create spots of different sizes representing different populations of cells using fluorescent information of each channel (Fig. 7 and 8) (see Notes 36 and 37). 3. In this study, we used Imaris plugins called XTensions written in Matlab. Use XTension of Imaris software called “Colocalize Spots” and set the threshold for cell-to-cell distance as 10μm (Fig. 9) (see Note 38). 4. Calculate the spot-spot colocalization parameters such as number and duration of contacts and export them as excel files (Fig. 9).

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Fig. 4 Huygens Stabilization Wizard showing XY, XZ, and YZ projections of the image volume and correlation selection for rotation detection and iterative filtering during one of the wizard steps 3.6.3 Calculate the Colocalization and Cell–Cell Distance

1. Select “Surface” function to reconstruct the 3D structure of the cells using fluorescent signals (Figs. 10 and 11) (see Note 39). 2. Use XTension “Distance Transformation” to calculate distances of every cell from the surfaces of other cell types represented in different channels at any time point of the time sequence (Fig. 11) (see Note 40). 3. Select “Surface-Surface Colocalization” XTension to calculate 3D surface volume overlap between interacting cells in time and export the data. (Fig. 12) (see Note 41).

4

Notes 1. We use a custom-built 2 cm  5 cm stainless-steel plate with a circular window in the middle. After animal preparation, the window will face water-immersion lens of inverted microscope platform. The tissue holder was designed to stabilize small area within larger animal organ by gluing the organ to the stage and allowing the region of interest to be exposed through the opening.

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Fig. 5 Object Stabilizer of Huygens software using cross correlation to normalize thermal drift of 4D dataset. The cropping frames for all time points based on correlation correction are shown on XY, XZ, and YZ projection as well as total displacement in x direction in voxels. Outliers are adjusted to eliminate one frame with extreme shift

2. We use pre-warmed carbomer-based gel, pH buffered, and matching refractive index of water. This allows exposed animal tissues remain lubricated for hours of imaging and does not interfere optically with using 25 water lens. 3. Slightly turn the mouse on its right side to view the spleen better. 4. Stabilizing the needle between the tongs of forceps helps flushing. 5. After injecting 1.25 mL of the digestion mixture, turn the spleen 180 and inject the next 1.25 mL from the other end of spleen to equally flush all sites. 12 mL digestion mix is enough for up to five spleens, so do not combine more than five in one tube. One spleen yields between 0.5  106 and 1.5  106 purified DCs depending on the strain and age of the wild-type mice. Spleens from 12 weeks old C57BL/6 mice yield approximately 1.0  106 DCs/spleen. 6. Mince into smallest pieces possible.

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Fig. 6 Deconvolution wizard of Huygens software. Channels are split and deconvolved separately using the microscopic template and defining deconvolution template. Window on the left shows channels and their deconvolution progress, yellow window in the middle is deconvolution log showing iteration process and information messages. Top right images show original channel image and changes of the same image during the deconvolution. Deconvolved channels are fused together and saved at the end of the deconvolution wizard

7. Make sure to use 25 mL pipette, as the pieces may clog 10 mL pipette. 8. Apply pressure using rubber end of plunger. Once all the pieces are mashed, aspirate cell suspension from the dish using a sterile pipette and pass through the strainer to get rid of clumps. 9. Aspirate the supernatant by vacuum without disturbing the pellet. 10. Keeping splenocytes in ACK buffer longer than 5 min may destroy the cells other than RBCs. 11. This step would remove all the cell clumps that may otherwise clog autoMACS. 12. Store the reusable autoMACS racks in refrigerator at 4  C until cell isolation. 13. Different peptides can be combined at this stage if the ultimate aim is to load DCs with multiple antigens.

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Fig. 7 (a) Image of the 3D reconstructed volume in Imaris shows all cell populations in different channels/ colors. Green represents YFP+ DCs; blue, red, and magenta represent naı¨ve T cells with different antigen specificities. (b) Image shows spots of different sizes created from previous image using Spot function of Imaris and representing different populations of cells

Fig. 8 Imaris “Spots” function and tracking algorithm for spots are applied sequentially. Image shows tracks of all cell populations in time. Real channel volume signal of the cells and spot objects are overlaid

14. Different peptides have different times for optimal loading. Usually, I-Ab restricted peptides such as OVA(323–339), LCMV GP(61–80), Ea(52–68) would be optimally loaded within 45 min. 15. Insulin syringes with permanently attached needles do not have dead space; therefore, you only need to draw an extra 50 μL.

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Fig. 9 (a) Image shows only colocalized spots representing different spot populations shown in different channel colors and sizes. On the left side, XTension “Colocalize Spots” selection is shown as well as groups of colocalized spots created by this algorithm. Images show tracks of colocalized spots between red and magenta cells (b), tracks of colocalized spots between blue and red cells (c). Real channel volume signal of the cells and spot objects are overlaid

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Fig. 10 (a) Image of the 3D reconstructed volume in Imaris shows all cell populations in different channels/ colors. (b) The same image represented as 3D surface models of the cells in Imaris

Fig. 11 Distance transform channel generated by XTension in Imaris is shown in white color on top of cell volumes in Imaris. Green cell surfaces (on the left image) were used to create distance transform channel outside of the green cells, the minimal distance of the other cells (image on the right) was determined as a minimal intensity of the white distance transform channel between green and other channel surfaces

Collect small air bubbles at the top of the syringe by flicking the syringe with fingers and then push the the plunger to remove the bubble out. 16. Mash up to three spleens in a cell strainer at once. Set another petri dish with cell strainer if you have more than three spleens. Once all the spleens are mashed, aspirate cell suspension from the dish using a sterile pipette and pass through the same strainer to get rid of clumps. 17. Aspirate the supernatant by vacuum without disturbing the pellet.

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Fig. 12 Images showing localization of surface overlap created by “Surface-Surface Colocalization” XTension between red and green cell in Imaris. Image on the top left shows cells in 3D volume, on the top right in reconstructed 3D surface models, on the left bottom with surface overlap shown as a white surface object generated by XTension. Image on the bottom right shows the surface overlap after removing green cell surfaces

18. Wrap the tube with aluminum foil as the next steps will contain light-sensitive material. 19. Once reconstituted, the dye should be protected from light and stored with desiccant at less than or equal to
20  C. Therefore, prepare 5 μL aliquots to avoid freeze–thawing. 20. The cell concentration should be adjusted in accordance with the DC concentration to aim a DC: T cell ratio of one-fourth or one-fifth. 21. Take into account the 100 μL dead space; therefore, you only need to draw 200 μL extra at minimum. Also make sure to remove all the air bubbles by flicking the syringe with fingers and then pushing the top bubble out.

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22. Try not to severe the veins that run parallel to the route of footpad injection. If you accidentally hit the vein, do not proceed with the injection as there will be extravasated blood that fills the subcutaneous space. 23. If you notice that animal starts twitching muscles and/or breathing rapid and shallow, return it into the anesthesia chamber, and wait until muscle movements disappear before proceeding to the injection. 24. Insert the needle with 30 angle to the bench. You should not feel any resistance while inserting the needle, and if so, you might have inserted it too far. 25. As you remove the needle, it is normal to see a drop of blood coming out of the injection site. If any clear fluid pops out as you inject, it means your injection material was not delivered into the vein. Proceed with the other eye. 26. If this preparation starts in the morning on the day before the intravital microscopy, the adoptive transfer can be done by 5.00 pm. This would allow the preparation of animal for microscopy to start at 10.00–11.00 am the next morning. 27. For intravital imaging, anesthesia can be delivered via injection (avertin or ketamine/xylazine) or inhalation (isoflurane). While each anesthetic has advantages and disadvantages for imaging, isoflurane anesthesia typically yields the most consistent results and does not require multiple injections or a catheter application for time-lapse recordings over 30-min. 28. We typically inject 25 μg per mouse in a total volume that does not exceed 100 μL. 29. Either upright or inverted microscope setup can be used for multiphoton intravital imaging [6]. While using upright microscope is a well-established technique [4], we use inverted microscope due to a number of advantages. Traditionally, inverted microscopes are used for life science research, because gravity makes mouse tissues adhere closely to the cover glass and produce even and stable field of imaging, which is especially crucial during in vivo recording of popliteal lymph node. 30. Extra care should be taken to prevent the adhesive from entering the imaging area. 31. PBS and carbomer gel should be warmed to 37  C prior to application to the mouse. The addition of cold buffers alters the movement of cells within the tissue. 32. Use “Rotation Detection” if your 3D tiles are slightly rotated due to the thermal drift and iterative filtering parameter if many slices at the edges are out of focus.

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33. Please adjust the outliers value if your time series does not contain many outliers and if you want to avoid significant data cropping. 34. It is important to acquire images with good sampling rate, as closest to Nyquist rate as possible. Please verify microscopic parameters in deconvolution software before you start deconvolution and make sure everything is correct. This is critical for estimating best PSF for deconvolution. Also make sure you adjust the background subtraction and signal-to-noise ratio to avoid artefacts. 35. Make sure you select the radius and quality threshold parameter for spots to cover most of the cells in 3D. 36. To determine the dynamic behavior of each cell type described by spots, you can use “Track spots” feature and export statistics such as average track speed, length, straightness as excel files. 37. XTension Colocalize Spots measures the distance between the selected groups of spots and based on the threshold value and divides the spots into two groups: “Colocalized” and “Noncolocalized.” Colocalized spots are located in the same area or very near to each other specified by threshold. Please select the threshold value that best suits the size of the cell populations in your dataset. 38. Each cell population requires specific quality threshold and smoothness for surface creation. Make sure you select the threshold that best suits your cells and creates best surface model covering all cell volumes. For splitting the cell surfaces enable “Split touching Objects (Region Growing)” function in connection with specific “Seed Point Diameter.” Eliminate extreme small volumes as debris in the last step of the surface creation wizard. 39. After activating the specific surface “Distance Transformation” XTension, select “Outside of the object.” The XTension automatically converts image into 32-bit float data type (in case it is not 32-bit already) and calculates a mesh of distances from the surface objects starting at the surface and continuing outside of the cell. 40. “Surface-Surface Colocalization” XTension creates masks of two selected surface scenes. It finds the voxels (pixel represented as unit volume in a 3D matrix) inside each surface that overlap with another surface and creates a new channel. Software then uses it to create new surface generated from overlapping regions. Colocalized surface should be generated without smoothing factor.

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Acknowledgments This work was funded by the Intramural Research Program of the National Institute of Allergy and Infectious Diseases, National Institutes of Health. We thank Dr. Randy Clevenger, Animal Surgery Program, National Heart, Lung and Blood Institute for critically reviewing this chapter regarding mouse anatomy. We also thank Dr. Abir Kumar Panda, Laboratory of Immune System Biology, National Institute of Allergy and Infectious Diseases for his help with the illustrations. References 1. Russo E, Nitschke M, Halin C (2013) Dendritic cell interactions with lymphatic endothelium. Lymphat Res Biol 11(3):172–182. https://doi. org/10.1089/lrb.2013.0008 2. Mondino A, Khoruts A, Jenkins MK (1996) The anatomy of T-cell activation and tolerance. Proc Natl Acad Sci U S A 93(6):2245–2252. https:// doi.org/10.1073/pnas.93.6.2245 3. Akkaya B, Oya Y, Akkaya M, Al Souz J, Holstein AH, Kamenyeva O et al (2019) Regulatory T cells mediate specific suppression by depleting peptide-MHC class II from dendritic cells. Nat Immunol 20(2):218–231. https://doi.org/10. 1038/s41590-018-0280-2

4. Liou HL, Myers JT, Barkauskas DS, Huang AY (2012) Intravital imaging of the mouse popliteal lymph node. J Vis Exp 60. https://doi.org/10. 3791/3720 5. Masedunskas A, Sramkova M, Parente L, Weigert R (2013) Intravital microscopy to image membrane trafficking in live rats. Methods Mol Biol 931:153–167. https://doi.org/10.1007/ 978-1-62703-056-4_9 6. Shannon JP, Kamenyeva O, Reynoso GV, Hickman HD (2019) Intravital imaging of vaccinia virus-infected mice. Methods Mol Biol 2023:301–311. https://doi.org/10.1007/ 978-1-4939-9593-6_19

Chapter 14 Studying Neuronal Biology Using Spinning Disc Confocal Microscopy Javier Manzella-Lapeira, Joseph Brzostowski, and Jenny Serra-Vinardell Abstract Cytoskeletal integrity is essential for neuronal complexity and functionality. Certain inherited neurological diseases are associated with mutated genes that directly or indirectly compromise cytoskeletal stability. While the large size and complexity of the neurons grown in culture poses certain challenges for imaging, live-cell imaging is an excellent approach to determine the morphological consequences of such mutants. This protocol details the use of spinning disk confocal microscopy and image analysis tools to evaluate branching and neurite length of healthy iPSC-derived glutamatergic neurons that express specific fluorescent proteins. The protocols can be adapted to neuronal cell lines of choice by the investigator. Key words Spinning disk confocal, Tiling, Neuronal morphology, Cytoskeleton, Human-induced pluripotent stem cells

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Introduction Here we provide a protocol using spinning disk confocal microscopy to capture tiled images to analyze branch and length of neurites in healthy iPSC-derived glutamatergic neurons. Neurons are large polarized cells consisting of a cell body (or soma), multiple dendrites (around 100–300 μm long) that radiate from the cell body and decrease in diameter as they branch (making the dendritic tree), and an extended long axon with a constant diameter that undergoes extensive branching to enable signaling to multiple neurons. The neuronal cytoskeleton is formed by three families of filaments: neurofilaments (important for the transmission of electrical impulses along axons) and two types of polar biopolymers: actin filaments and microtubules. Altogether, these filaments are responsible for the well-compartmentalized neuronal morphology and for its physiological properties.

Joseph Brzostowski and Haewon Sohn (eds.), Confocal Microscopy: Methods and Protocols, Methods in Molecular Biology, vol. 2304, https://doi.org/10.1007/978-1-0716-1402-0_14, © This is a U.S. government work and not under copyright protection in the U.S.; foreign copyright protection may apply and Springer Nature US 2021

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Neurofilaments are made by seven classes of intermediate filaments that constitute the structural core of myelinated axons and modulate the axon diameter [1, 2]. The conduction velocity of electric impulses along axons depends on the myelination and the diameter of the axon [3, 4]. Actin-based microfilaments form a dense network under the plasma membrane that determines the cell shape in response to the physiological needs of neurons. Microfilaments are important for the development of dendritic network, growth cone [5, 6], dendritic spines morphology [7], and synaptic plasticity [8]. The actin cytoskeleton is essential during the development of the nervous system, for post-injury regeneration processes, and allows the actinbased, myosin family motor proteins to transport organelles over short distances [9]. Microtubules are abundant in axons and dendrites, but their organization is different in these both compartments. Microtubules in axons are uniformly oriented with plus-ends pointing outward [10], which in turn determines the neuronal cargo for two families of motor proteins: kinesin-dependent anterograde transport and dynein-dynactin-dependent retrograde transport. In contrast, microtubule orientation is mixed in dendrites and it is not clear how the directional transport is established. Cytoskeletal proteins are essential to develop and maintain the function of the nervous system. There are several inherited neurological diseases that are associated with mutations in cytoskeletal genes or in genes that encode proteins that promote either the destabilization or alter the organization, structure, and/or dynamics of cytoskeletal proteins [11]. Overall, such defects can promote the impairment of neurite outgrowth, dysregulation of the axonal transport, and malformation of dendritic spines and/or synapses. In vitro changes in the cytoskeleton dynamics can be studied by analyzing neuronal branching, neurite length, or organelle trafficking. However, the size and the complexity of the neurons grown in vitro offer special technical challenges for image capture and subsequent analysis. The use of the well-established i3Neurons protocol [12, 13] together with lentiviral particle transduction for the constitutive expression of florescent proteins is very effective for studying the neuronal morphology in real time in cell lines that harbor known mutations that compromise neuronal growth and development. These techniques, combined with spinning disk confocal microscopy for imaging neurons expressing multiple fluorescent genes, have facilitated the speed for capturing real-time dynamics, as well as a decrease in overall laser exposure as compared to conventional widefield fluorescence microscopy [14]. This protocol takes advantage of the fast acquisition speeds associated with spinning disk confocal microscopy and the ability to trigger image capture events, which is especially useful for large tiled acquisitions.

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Materials 1. Human-induced pluripotent stem cells (i3PSCs) (see Notes 1 and 2). 2. Phosphate-buffered saline (PBS) without calcium and magnesium (see Note 3). 3. Dulbecco’s modified Eagle’s medium (DMEM)/F12 medium (GIBCO/Thermo Fisher Scientific; Cat. No. 11320-033). 4. Accutase (StemPro Accutase Cell Dissociation Reagent; GIBCO/Thermo Fisher Scientific; Cat. No. A11105-01). 5. EDTA solution (0.5 mM): Diluted from EDTA (0.5 M) pH 8.0 with PBS. 6. Essential 8 Medium (GIBCO/Thermo Fisher Scientific; Cat. No. A1517001). 7. Induction medium (IM): DMEM/F12 medium (GIBCO/ Thermo Fisher Scientific; Cat. No. 11330-057) containing 1 N2 supplement (GIBCO/Thermo Fisher Scientific; Cat. No. 17502-048), 1 MEM Non-Essential Amino Acids (GIBCO/Thermo Fisher Scientific; Cat. No. 11140-050), 1 GlutaMAX (GIBCO/Thermo Fisher Scientific; Cat. No. 35050-061), and 2 μg/mL doxycycline hydrochloride. 8. Cortical medium (CM): BrainPhys neuronal medium (STEMCELL Technologies, Cat. No. 05790) supplemented with 1 B27 supplement (GIBCO/Thermo Fisher Scientific; Cat. No. 17504-044), 50 ng/mL of brain-derived neurotrophic factor, 50 ng/mL of neurotrophin-3, 1 μg/mL Laminin mouse protein, and 2 μg/mL doxycycline hydrochloride. 9. Cryopreservation medium: 90% EmbryoMax ES Cell Qualified Fetal Bovine Serum (Sigma-Aldrich; Cat. No. ES-009-B) and 10% DMSO. 10. Cryogenic cooler. 11. 10,000 U/mL penicillin–streptomycin (Pen/Strep). 12. Matrigel (Matrigel Matrix hESC-Qualified LDEV-Free; Cat. No. 354277) stock solution: Thaw a bottle of Matrigel stock solution overnight in an ice container placed within a refrigerator and aliquot into prechilled microcentrifuge tubes on ice. The volume of the aliquot is based on the recommended dilution of the manufacturer, that is lot-specific (see Note 4). Freeze the aliquoted Matrigel stock solution at 80  C till the day of use. 13. Borate buffer at pH 8.4: 100 mM boric acid, 25 mM of sodium tetraborate, and 75 mM of sodium chloride. Use the Milli-Q water to make the solutions and the sodium hydroxide (1 M) to adjust pH to 8.4. Filter to sterilize the buffer.

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14. Poly-L-ornithine hydrobromide (PLO; Sigma-Aldrich; Cat. No. P3655) 10 stock solution (w/v): Prepare 10 PLO stock solution by diluting 10 mg of Poly-L-ornithine hydrobromide in 10 mL of borate buffer at pH 8.4 and filtersterilize. 15. ROCK inhibitor (STEMCELL No. Y-27632) (see Note 5).

Technologies;

Cat.

16. 100 RevitaCell supplement (GIBCO/Thermo Fisher Scientific; Cat. No. A2644501) (see Note 6). 17. 6-well tissue culture plates. 18. 15 mL conical tubes. 19. μ-slide 4-well chambers (Ibidi; Cat. No. 80426) (see Note 7). 20. 5 mL round-bottom polystyrene test tubes. 21. 5- and 10-mL serological pipettes. 22. Screw cap microcentrifuge tubes, 1.5 mL. 23. Lentivirus expressing cytosolic mApple. 24. Lentivirus expressing mNeonGreen fused with a nuclear localization sequence (NLS). 25. Fluorescence-activated cell sorter (FACS). 26. Inverted clinical microscope with phase contrast. 27. Hemocytometer. 28. Cell culture incubator set at 37  C and 5% CO2. 29. Nikon Ti2 inverted microscope (or equivalent) equipped with a Plan-Apochromat 20 NA 0.75 objective, motorized stage, incubation system, 488 and 561 nm excitation laser lines, Yokogawa CSU-X1 spinning disk confocal head (or equivalent), and 512  512 EMCCD camera. 30. MATLAB software (MathWorks).

with

Image

Processing

toolbox

31. Imaris 8.4 with Filament Tracer (Bitplane).

3

Methods Carry out procedures in a biological safety cabinet (BSC), preferably a class II BSC/laminar flow hood with a HEPA microfilter. The investigator should use personal protective equipment, including lab coat and gloves. The following steps of the protocol appear in Fig. 1 to have a general overview of the workflow.

3.1 Coating Plates with Matrigel

1. To make the Matrigel coating solution, use one aliquot of the Matrigel stock (see Subheading 2) and dissolve in 25 mL of cold DMEM/F12 media.

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Fig. 1 Overview of the general workflow

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2. Use 1 mL of Matrigel coating solution to coat each well of a 6-well tissue culture plate and incubate at least 1 h at 37  C incubator (overnight coating is better if possible). 3. Aspirate Matrigel solution immediately before use. 3.2 Thawing Human-Induced Pluripotent Stem Cells (i3PSCs)

1. Thaw a cryovial of i3PSCs from liquid nitrogen storage in 37  C water bath or bead bath. 2. Spray the cryovial of i3PSCs with 70% ethanol before transfer into the BSC. 3. Gently add the thawed cell suspension dropwise to 10 mL DMEM/F12 at room temperature (RT) in a sterile 15 mL conical tube. 4. Centrifuge the conical tube containing cells for 5 min at 200  g at RT. 5. Aspirate supernatant and gently resuspend cell pellet with 1 mL of RT Essential 8 Medium supplemented with 10 μM ROCK inhibitor. 6. Count the cells and transfer around 1  106 cells in each well of Matrigel-coated 6-well plate (the number of wells used in this step depends on the number of cells that was in the cryovial initially). 7. Bring the final volume in each well to 2 mL with RT Essential 8 Medium supplemented with 10 μM ROCK inhibitor. 8. Place the plate in the 37  C incubator and gently rock the plate with a side-to-side and front-to-back motion three times to equally distribute the cells. 9. The next day, aspirate the medium and replace with fresh RT Essential 8 Medium without ROCK inhibitor (2 mL/well). 10. Change media daily as in step 9 to keep the culture healthy.

3.3 Passaging i3PSCs

1. When the i3PSCs in the 6-well plates reach the 70–80% confluency, remove the media and rinse the wells with RT PBS. 2. Add 1 mL/well of RT 0.5 mM EDTA solution to the cells and incubate at RT 3–5 min or until cells start to detach from the well. This step should be performed with continuous visualization of the cells using an inverted clinical microscope to be able to detect when the cells start to detach (see Note 8). 3. Aspirate the EDTA solution. 4. With P1000 micropipette, gently dispense 1 mL of RT Essential 8 Medium against the culture surface to dissociate the cells from the well. If necessary, repeat with another mL of Essential 8 Medium to completely detach the cells from the well (see Note 9).

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5. Pipette the aggregate mixture contained in the passaged well up and down once using a 5 mL serological pipette to ensure breakup of any large aggregates that may still be present. 6. Plate the cell clusters directly at the desired density into pre-coated Matrigel wells containing 1.5 mL of RT Essential 8 Medium (see Note 10). 7. Bring the final volume in each well to 2 mL with RT Essential 8 Medium if needed. 8. Place the plate in the 37  C incubator and gently rock the plate with a side-to-side and front-to-back motion three times to equally distribute the cells. 9. The next day, aspirate the medium and replace with fresh Essential 8 Medium (2 mL/well). 10. Change media daily as indicated in step 9 to keep the culture healthy. 11. When the i3PSCs reach the 70–80% confluency, split the culture to transduce with lentivirus vectors for fluorescence labeling (see Subheading 3.5) and cryopreserve the other part (see Subheading 3.4). Very importantly, maintain cultures of nonfluorescent cells for the experimental imaging experiments described in Subheading 3.7. 3.4

Cryopreservation

Cryopreservation is a critical step to preserve i3PSCs for future experiments. We recommend freezing enough cryovials to finish planed experiments. 1. Follow steps 1–5 of the Passaging iPSCs protocol (see Subheading 3.3) to lift the cells from the well. 2. Transfer 2 mL of detached cell aggregates to a conical tube. 3. Centrifuge the conical containing cells for 5 min at 200  g at RT. 4. Remove supernatant and resuspend cells in RT cryopreservation medium. Use 1 mL of RT cryopreservation medium for each well of the 6-well plate. 5. Transfer 0.5 to 1 mL of the cell suspension to each cryovial (see Note 11). 6. Place cryovials in a cryogenic cooler and transfer immediately to a 80  C freezer. 7. Allow the cells to remain at

80  C for 16–36 h.

8. Move cells to liquid nitrogen for long-term storage. 3.5 Generating Fluorescent i3PSCs

The use of homemade or commercial lentiviral vectors that express fluorescent proteins allows the investigator to label specific subcellular structures that can be imaged by fluorescence microscopy in

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living cells (see Note 12). Here we explain how to generate i3PSCs with nuclear green and cytosolic red color. For simplicity, these cells will be referred to as fluorescent i3PSCs. 1. When the i3PSCs (in 6-well plates) reach 70–80% confluency, remove the media and rinse the wells with RT PBS. 2. Incubate the cells with Accutase for 5 min at 37  C for singularizing the cells from the plate. 3. Transfer the cell suspension into a 15 mL conical tube. 4. Centrifuge the cells for 5 min at 200  g at RT. 5. Aspirate supernatant and gently resuspend the cell pellet with RT Essential 8 Medium supplemented with 10 μM ROCK inhibitor. 6. Seed around 0.5  106 cells in each well of Matrigel-coated 6-well plate and place the plate to 37  C incubator. 7. After a minimum wait time for at least 2 h, add the lentivirus vector expressing mNeonGreen fused with a nuclear localization sequence (NLS) (see Note 13) and place the plate back into the 37  C incubator. 8. After 24 h, aspirate the medium and replace with fresh RT Essential 8 Medium (2 mL/well) without ROCK inhibitor. 9. Change media daily until the cells reach 80% confluency (approximately 3–4 days after plating). 10. Incubate the cells with Accutase for 5 min at 37  C for singularizing the cells from the plate. 11. Transfer the cell suspension into a 15 mL conical tube. 12. Centrifuge the 15 mL conical tube containing cells for 5 min at 200  g at RT. 13. Resuspend the cells in 500 μL of Essential 8 Medium supplemented with 1% Pen/Strep (see Note 14). 14. Transfer the cell suspension to a 5 mL round-bottom polystyrene test tube. Keep tube on ice to avoid cell clumping. 15. Using a FACS, collect mNeonGreen positive cells in a 1.5 mL screw-cap microcentrifuge tube containing 500 μL of Essential 8 Medium supplemented with 1% Pen/Strep and 1 of RevitaCell. 16. Transfer the cells to a 15 mL conical tube with DMEM/F12 medium and centrifuge for 10 min at 300  g. 17. Resuspend the pellet by gently pipetting with Essential 8 Medium supplemented with 1% Pen/Strep and 1 RevitaCell supplement. 18. Seed the cells into a well of Matrigel-coated 6-well plate.

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19. Return the plate in the 37  C incubator and gently rock the plate with a side-to-side and front-to-back motion three times to equally distribute the cells. 20. After 24 h, change the media with Essential 8 Medium supplemented with 1% Pen/Strep but without RevitaCell supplement. 21. Change the medial daily with Essential 8 Medium supplemented with 1% Pen/Strep. 22. When the cells reach 70–80% confluency, repeat steps 2–7 but this time add the lentivirus expressing cytosolic mApple (see Note 13). 23. Repeat steps 8–21 but now collect cells double positive for mNeonGreen and mApple. 24. When the cells reach 70–80% confluency, split the culture for further passage (see Subheading 3.3). 25. Remove the Pen/Strep from the media and allow the cells to grow (see Note 15). 26. When the cells reach 70–80% confluency, split the culture for further passage (see Subheading 3.3) and for cryopreservation (see Subheading 3.4). 3.6 Differentiation of i3Neurons

The differentiation protocol has been established by M. Ward and collaborators [13]. The differentiation protocol consists of two parts: a pre-differentiation and a maturation step. During the pre-differentiation step (Days 0–3), the i3PSCs convert to i3Neuronal progenitor cells. After Day 3, i3Neuronal progenitor cells require at least 10 additional days to fully mature to i3Neurons before imaging. It is important to continue culturing and to differentiate nonfluorescent cells as these will be needed for the final culture for imaging. 1. Day 0: When the i3PSC cultures, fluorescent and nonfluorescent, are about 70–80% confluent, remove the medium from the culture and rinse with RT PBS. 2. Incubate the cells with 1 mL/well of Accutase for 5 min at 37  C to release the cells from the plate. 3. Neutralize the Accutase with 2 mL/well of RT DMEM/F12 medium and collect the i3PSCs mixture within a 15 mL conical tube. 4. Add 7 mL of RT PBS in the i3PSCs mixture contained in 15 mL conical tube in order to have the final volume of 10 mL that it is necessary to dilute the Accutase. 5. Centrifuge the 15 mL conical tube for 5 min at 200  g at RT.

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6. Resuspend the pelleted cells, generated from each well, with 1 mL of pre-warmed IM with 10 μM ROCK inhibitor. Several wells can be pooled. Scale volumes appropriately. 7. Count the i3PSCs and seed 1.5  106 cells in each well of a Matrigel-coated 6-well plate in a total volume of 2 mL of RT IM with 10 μM ROCK inhibitor. 8. Day 1 (24 h after plating the i3PSCs): Aspirate the medium and replace with pre-warmed IM. 9. Day 2 (48 h after plating the i3PSCs): Aspirate the medium and replace with pre-warmed IM. 10. Make 1 (v/v) PLO coating solution from fresh 10 PLO stock solution with sterile water. 11. Add 200–300 μL of 1 PLO coating solution to each well of μ-slide 4-well chambers and gently tilt the chamber to ensure full coverage. 12. Incubate chambers in a 37  C incubator overnight. 13. Day 3 (72 h after plating the i3PSCs): Aspirate the 1 PLO coating solution of the wells. 14. Wash the wells with sterile water three times. 15. Aspirate the water from the wells and dry completely in a BSC (drying time takes 30 min). 16. In the meantime, using an inverted clinical microscope, check that all the cells in the culture have some extensions (neurites) to determine if the pre-differentiation step was successful. 17. Incubate the cells with 1 mL/well of Accutase for 5 min at 37  C to release the cells from the plate. 18. Neutralize the Accutase with 2 mL/well of RT DMEM/F12 medium and pipette cells from each well into separate pre-labeled 15 mL conical tubes. 19. Raise the volume of the cell suspensions to 10 mL by adding 7 mL of RT PBS, which is necessary to dilute the Accutase. 20. Centrifuge the 15 mL conical tubes for 5 min at 200  g at RT. 21. Aspirate the supernatant and resuspend the pelleted cells in each tube with 1 mL of pre-warmed CM and determine the number of cells. 22. The i3Neuronal progenitor cells (florescent and nonfluorescent) are ready to be plated in the PLO-coated μ-slide 4-well chambers (see Subheadings 3.7.1). 3.7 Axon Morphology: Imaging and Measurement

We present two main applications for quantitative analysis: branching and neurite length. For each application, we give the needed details for sample preparation and a section detailing the acquisition and analysis of the imaging data. The protocols use a Nikon Ti2 inverted microscope integrated with a Yokogawa spinning disk

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confocal and a Photometrics Evolve Delta EMCCD camera. This protocol can be adapted to any similar microscope, spinning disk model, and camera. 3.7.1 Sample Preparation Neuronal Branching

Neurite Length

1. Seed the fluorescent i3Neuronal progenitor cells at a density of 1:100 with nonfluorescent i3Neuronal precursors into each well of PLO-coated μ-slide 4-well chambers. The total number of cells in each well should equal 100,000. The mixture between fluorescent and nonfluorescent cells will allow visualization of the morphology of fluorescent i3Neurons while maintaining the required cell density to keep the cells alive. Change the media carefully every other day, replacing the half media with fresh pre-warmed CM (see Note 16). 1. Seed 80,000–100,000 cells in a volume of 25 μL in the middle of each well of PLO-coated μ-slide 4-well chambers inside a BSC. 2. Avoid disturbing the cells and allow them to adhere (around 10 min). 3. Check for adherence using an inverted clinical microscope. When cells are adequately adhered, slowly add 800 μL of pre-warmed CM into each well. 4. Change media carefully every other day, replacing the half media with fresh pre-warmed CM (see Note 16). The axons will then grow outwardly from the center of the cell.

3.7.2 Axon Morphology Imaging

1. Assemble stage and incubation apparatuses. Set to 37  C at least 1 h before transferring the Ibidi μ-slide with the i3Neurons. Select a Plan-Apochromat 20 0.75 NA objective lens for these acquisitions. 2. Turn on the CCD camera, spinning disk, laser box, and microscope. The settings of all peripherals are set from the Elements software that runs the microscope. 3. Open the Elements software. If you have multiple cameras, select the Photometrics Evolve camera option when prompted by the software. 4. Select the wide-field optical configuration of either the green or red channel and adjust the laser power while focusing using the eyepiece. Make sure that the PFS autofocus device is turned on (light indicator should be blinking). If you are focused on the cells and the PFS has not engaged, slowly turn the knob counterclockwise. When the PFS engages, it will beep, and you will no longer be able to adjust the focus with the focus knob on the side of the scope. In order to fine-tune the focus, use the PFS focus knob that is sitting on the air table next to the scope.

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5. Configure the parameters for the acquisition of fluorescence from the spinning disk confocal optical configurations (OCs) options. 6. Select CSU 561 and click the “Start” button. Adjust the laser power to obtain the proper SNR in the field of view. In order to test whether the power is photobleaching the signal, move to another position and then return to the initial field of view. If there is a significant photobleaching of the fluorescent proteins, the signal difference between the two fields of view should be apparent (see Note 17). 7. Switch to CSU 488 and locate the neuron nuclei. Repeat the procedure from the previous step using the 488 nm laser. 8. In the Camera Settings tab, select “16-bit No Binning” as the Format option, 20 MHz, EM Gain for Readout, and EM Gain of 50 (slider). Set the exposure time to 50 ms for the CSU 488 configuration and 100 ms for the CSU 561 configuration (see Note 18). 9. Select the optical configuration for triggered acquisition: Activate the 488 and the 561 nm lasers with the laser powers determined above in steps 6 and 7. It is important that the same imaging parameters be used with all samples from the same experiment. 10. On the CSU tab, select the following spinning disk parameters: Set the excitation filter to the quad polychromic mirror (with the following wavelengths: 405, 488, 561, and 640 nm), and the emission filter to the dual band 525/605 pass filter. 11. The acquisition uses the ND Acquisition module. If the ND Acquisition tab is not already displayed, select ND Acquisition from the Devices dropdown menu. 12. Check the box on the Large Image tab. Select the scan area of 30  80 fields, with 15% stitching via blending. Check the “Image Restoration,” “Close active shutter during movement,” and “use PFS” boxes. 13. On the right-hand side of the ND Acquisition tab, select a path and name to have the software save the image (see Note 19). 14. Before the acquisition, move the stage to position the current field of view on the center of the well. 15. Click “Run” to start the acquisition. 16. Once the image acquisition is done, your file is saved as an ND2 image file. If the chamber was seeded for the neurite length analysis, it is possible to crop the image into the upper and lower sections to facilitate the handling of the file for analysis. The same approach can be done for the samples that were seeded for branching analysis.

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Fig. 2 Analysis of branching: (a) Shows the mNeonGreen-labeled nucleus of a neuron in green and the entire mApple-labeled neuron in red before processing. (b) The image shown is produced from combining the binary image of the green channel (in green) and the skeletonized binary trace of the neurons (in purple). (c) Shown is the mean branches per neuron for the wells analyzed

3.7.3 Axon Morphology Analysis

1. Optional: Use a custom-made MATLAB algorithm to remove low-frequency noise and smoothen the images (all MATLAB algorithms available upon request, see Note 20). 2. For the branching analysis, use MATLAB to threshold the red and green channels and skeletonize the red channel. After this process, it is possible to quantify the mean number of branch points per i3Neuron from the skeleton binary of the red channel and the number of connected components from the green channel (number of nuclei). Figure 2 shows a visual representation of the image processing, starting with the elimination of the low-frequency noise shown in Fig. 2a (lookup tables and follow the respective fluorophore channel colors), followed by the final mask of the filaments (purple) superimposed with the mask of the nuclei (green) in Fig. 2b. Figure 2c shows a summary of the branching data for a group of wild-

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Fig. 3 Semiautomatic tracing of individual neurons using Imaris. (a) The active neuron is shown as a yellow filament and the remaining filaments traced appear as white filaments. (b) The zoomed imaged shows how the user can direct the path even when there are overlapping segments

type (Control) and experimental (Exp) samples at week 1 and week 2 post-seeding of the i3Neurons in the μ-slide 4-well chambers. 3. For neurite length, the Imaris software includes analysis tools that are helpful for morphology. Open the ND2 file by dragging the image file to a new container in the Arena. Double click to open the file (see Note 21). 4. Make sure the 3D View option is selected from the top icons. 5. On the Display Adjustment window, shift the dynamic range to visualize the neurons and nuclei. 6. Select the green leaf Filament Tracer icon above the tab on the left (on top of the Scene components). 7. Click “Skip automatic creation, edit manually.” This will take you to the Edit tab (represented by a pencil icon). 8. Select AutoPath. To start a new filament, set the diameter size to match the cell body (14 μm is sufficient), press Shift and right click. 9. To draw the path visible on the screen to the cursor, press Shift and left click. This does not change the start point and does not stop the calculation thread from that start point. 10. To add a weight in cases where there is axon overlap or dim regions, press Shift, Control, and left click. This will continue the same filament path but will start a new calculation from the end of the drawn path (for the automated tracing helper function that highlights the path for the user before it is selected). 11. To add branches, press Shift, Control, and right click. 12. Repeat steps 8–11 to trace more filaments. Refer to Fig. 3 as a visual guide for how to trace the neurons.

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13. To export the data of the filaments, click on the graph icon to the right of the pencil icon that was used to manually create the filaments. Select the parameters to be exported or click the “Export All” button on the lower right-hand corner. One of these parameters included is the neurite length. 14. A second analysis can be done with a custom-made MATLAB program in which the fluorescence intensity can be used as an indicator of neuron/filament density from the center area where most of the nuclei are located. It provides an additional parameter to compare the samples over time (see Fig. 4 and Note 22).

4

Notes 1. i3PSCs are human-induced pluripotent stem cells (iPSCs) with WTC11 background, healthy male subject generated using episomal reprogramming method, [15] that harbors a stably integrated human Neurogenin 2 (NGN2) transgene in the AAVS1 locus under a tetracycline-inducible promotor. These cells were generated by M. Ward and collaborators and are known as i3PSCs [13]. 2. The protocol uses the i3PSC line because it is well established and the inducible integrated NGN2 allows investigators to obtain iPSC-derived neurons (i3Neurons) very quickly and in large numbers compared to conventional protocols. If the interest of the investigator is evaluating the impact of determinate genes on neuronal phenotype, then it will be necessary to use genetic editing tools or mutant iPSCs. 3. Calcium- and magnesium-free PBS is necessary for EDTA treatment. 4. Matrigel is lot-specific. The recommended dilution is stated in the lot certificate and is available online. To avoid Matrigel polymerization that happens at RT, maintain the Matrigel on ice during the aliquot preparation and then keep the Matrigel aliquot stocks at 80  C till the day of use. 5. The ROCK inhibitor is a selective inhibitor of the Rho-associated, coiled-coil containing protein kinase (ROCK); it is used to increase the survival of human embryonic stem cells by preventing apoptosis when they are dissociated to single cells after using Accutase. 6. RevitaCell supplement can also be used for routine single-cell passaging of ES cells instead of the ROCK inhibitor. However, in this protocol, we use RevitaCell supplement (1) to increase the viability of the cells after having been exposed to high pressure during FACS.

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Fig. 4 Analysis of neurite length: (a) Shown is a full-well view of i3Neurons after the first week from when cells are seeded onto coverslips as a drop. Measurements are performed at the periphery. (b) The resulting image after using the Imaris filament tracer for the image is shown in (a). (c) Shown is a cropped region of the mask image produced with MATLAB. Normalized total fluorescence intensity is shown for i3Neurons at week 1 (orange) and week 2 (red) after plating

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7. The μ-slide 4-well chamber (Ibidi; Cat. No. 80426) is our favored choice. The chamber bottoms are composed of a biocompatible plastic material that provides an excellent surface for neurons that avoids cellular aggregation that sometimes appears when cover glass surfaces are used. The material is the thickness of a standard cover glass and is compatible with high NA oil lenses. 8. It is important to monitor the progress of the EDTA solution incubation in the cell culture under a phase-contrast microscope. Aspirate the EDTA solution from the well as cells begin to round and separate uniformly throughout the entire colony but are still attached to the well. Avoid over-incubation with EDTA solution results in the generation of colonies that are too small (2–5 cells), which negatively affects the cell viability. 9. The goal is to disrupt colonies into smaller cell clusters of around 10 cells with the EDTA solution treatment and the force of the media against the well surface. If the timing of EDTA solution treatment has been made accurately, cells will be detached easily from the well and no further pipetting is needed. Too much pipetting could have negative consequences in cell viability. 10. In general, if the i3PSC colonies have an optimal density, they should be passaged every 4–7 days at 1:5 to 1:10 split ratio (i.e., cell aggregates from 1 well can be plated in 5 to 10 wells.) 11. One well of a 6-well plate at ~80% confluency has approximately 1.2  106 cells. 12. It has been described that promotors like CAG, PGK, or EF-1 α should be used in the i3PSCs system in order to avoid being silenced in iPSCs or neuron stage [12]. 13. It is highly recommended that after the production of lentivirus using the standard protocols, the investigator should do a lentivirus titration to ensure that the amount of virus used is optimal to get a good transduction efficiency. 14. The addition of Pen/Strep is recommended during FACS if it is not possible to use 100% aseptic conditions. 15. When the Pen/Strep is removed from the media after the FACS step, it is important to keep monitoring the culture for the possible bacteria appearance. Moreover, performing a mycoplasma test is highly recommended. 16. The half medium change reduces the effect of osmotic shock that occurs with a full medium change due to a sudden change in concentration of media factors. 17. Laser power settings depend on fluorophore expression level. Once these have been set for the first sample, consecutive samples should follow the same laser power settings.

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18. Exposure times may need to be adjusted up or down depending on the experiment. Keep in mind that longer exposure times will increase potential photobleaching. 19. Due to the large file size, it is recommended to automatically save acquisitions. 20. The MATLAB program used to denoise and threshold the fluorescence channels consists of the following workflow: The ND2 image file is imported into its two separate channels. For the red channel: A gaussian filter is applied to the raw image matrix. Then data is converted to the frequency domain using a fast-Fourier transform. The real component of the transform image is then multiplied by a short-pass 2D filter. The inverse Fourier transform is then applied to the matrix and converted to 16-bit unsigned integer. The resulting image is thresholded into a binary, which is then morphologically dilated and then skeletonized. Branch points are calculated from the skeleton. For the channel that labels the nuclei, it is sufficient to apply a Gaussian filter and then threshold the resulting smoothened matrix. Once the image is binarized, connected components are thresholded based on size, and the nuclei are counted. The two parameters obtained yield the mean number of branches per neuron. 21. It is possible to use the MATLAB-filtered image when performing the neurite length analysis with Imaris since that could speedup the process of manually labeling the axons. Alternatively, Imaris also contains a wide range of background subtraction and smoothing procedures that can be performed before the filament tracer analysis. 22. The MATLAB program for neurite length performs thresholding similar to the procedure detailed above. Once the binary mask is obtained, it is multiplied by the filtered grayscale image. The parameters obtained from this image are the total fluorescence and the number of pixels in the mask as a function of distance from the nuclei.

Acknowledgments We would like to thank Eric Balzer from Nikon and Matthew Gastinger from Bitplane for their help with Elements and Imaris and Michael E. Ward (NINDS, NIH) for provide us the i3PSC and the both lentivirus plasmids used in this protocol, the cytosolic mApple and the mNeonGreen-NLS. This work is supported by intramural funding from the National Institute of Allergy and Infectious Diseases at the National Institutes of Health.

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8. Cingolani LA, Goda Y (2008) Actin in action: the interplay between the actin cytoskeleton and synaptic efficacy. Nat Rev Neurosci 9 (5):344–356. https://doi.org/10.1038/ nrn2373 9. Kneussel M, Wagner W (2013) Myosin motors at neuronal synapses: drivers of membrane transport and actin dynamics. Nat Rev Neurosci 14(4):233–247. https://doi.org/10. 1038/nrn3445 10. Kapitein LC, Hoogenraad CC (2011) Which way to go? Cytoskeletal organization and polarized transport in neurons. Mol Cell Neurosci 46(1):9–20. https://doi.org/10.1016/j. mcn.2010.08.015 11. Munoz-Lasso DC, Roma-Mateo C, Pallardo FV, Gonzalez-Cabo P (2020) Much more than a scaffold: cytoskeletal proteins in neurological disorders. Cell 9(2). https://doi.org/ 10.3390/cells9020358 12. Fernandopulle MS, Prestil R, Grunseich C, Wang C, Gan L, Ward ME (2018) Transcription factor-mediated differentiation of human iPSCs into neurons. Curr Protoc Cell Biol 79 (1):e51. https://doi.org/10.1002/cpcb.51 13. Wang C, Ward ME, Chen R, Liu K, Tracy TE, Chen X et al (2017) Scalable production of iPSC-derived human neurons to identify Tau-lowering compounds by high-content screening. Stem Cell Rep 9(4):1221–1233. https://doi.org/10.1016/j.stemcr.2017.08. 019 14. Enoki R, Ono D, Hasan MT, Honma S, Honma K (2012) Single-cell resolution fluorescence imaging of circadian rhythms detected with a Nipkow spinning disk confocal system. J Neurosci Methods 207(1):72–79. https://doi. org/10.1016/j.jneumeth.2012.03.004 15. Miyaoka Y, Chan AH, Judge LM, Yoo J, Huang M, Nguyen TD et al (2014) Isolation of single-base genome-edited human iPS cells without antibiotic selection. Nat Methods 11 (3):291–293. https://doi.org/10.1038/ nmeth.2840

Chapter 15 Method for Acute Intravital Imaging of the Large Intestine in Live Mice Marco Erreni, Andrea Doni, and Roberto Weigert Abstract Intravital microscopy is an imaging technique aimed at the visualization of the dynamics of biological processes in live animals. In the last decade, the development of nonlinear optical microscopy has enormously increased the use of this technique, thus addressing key biological questions in different fields such as immunology, neurobiology and tumor biology. In addition, new upcoming strategies to minimize motion artifacts due to animal respiration and heartbeat have enabled the visualization in real time of biological processes at cellular and subcellular resolution. Recently, intravital microscopy has been applied to analyze different aspect of mucosal immunity in the gut. However, the majority of these studies have been performed on the small intestine. Although crucial aspects of the biology of this organ have been unveiled, the majority of intestinal pathologies in humans occur in the large intestine. Here, we describe a method to surgically expose and stabilize the large intestine in live mice and to perform short-term (up to 2 h) intravital microscopy. Key words 2-photon microscopy, Intravital microscopy, Large intestine, Colon, Cancer

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Introduction Since the discovery of the green fluorescent protein (GFP), several optical microscopy-based techniques, such as time-lapse microscopy, Fo¨ster resonance energy transfer (FRET) or fluorescence recovery after photobleaching (FRAP), have been developed to investigate intracellular events at a molecular level [1]. Recent improvements in temporal and spatial resolution have allowed the development of new and more sophisticated imaging modalities, such as: (1) spinning disk microscopy, which allows high-resolution

The original version of this chapter was revised. The correction to this chapter is available at https://doi.org/ 10.1007/978-1-0716-1402-0_20 Supplementary Information The online version of this chapter (https://doi.org/10.1007/978-1-0716-14020_15) contains supplementary material, which is available to authorized users. Joseph Brzostowski and Haewon Sohn (eds.), Confocal Microscopy: Methods and Protocols, Methods in Molecular Biology, vol. 2304, https://doi.org/10.1007/978-1-0716-1402-0_15, © This is a U.S. government work and not under copyright protection in the U.S.; foreign copyright protection may apply and Springer Nature US 2021, Corrected Publication 2021

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analysis of fast cellular events; (2) total internal reflection microscopy (TIRF), which enables the visualization of events occurring in close proximity to the plasma membrane [2]; and (3) super-resolution microscopy approaches, such as structured illumination microscopy (SIM), photoactivated localization microscopy (PALM), stochastic optical reconstruction microscopy (STORM), and stimulated emission depletion microscopy (STED), which make it possible to resolve structures beyond the diffraction limit of light [3]. Most of these techniques have been primarily applied to reductionist model system (i.e., cell cultures, ex vivo explants, organoids), which although relatively easy to maintain and manipulate, often fail to reconstitute the in vivo complexity of multicellular tissues. The possibility of imaging biology in living multicellular organisms has fascinated scientists for centuries and one of the first attempts to use light microscopy for this purpose was reported 180 years ago, when Rudolf Wagner described the rollover of neutrophils in the blood vessels of live frogs [4]. He can be considered the father of what we now call intravital microscopy (IVM). Since then, IVM has been constantly evolving by taking advantage of the development of different light microscopy-based imaging modalities, such as bright-field microscopy and confocal microscopy [5, 6]. In the last decades, the development of non-linear optical microscopy techniques based on multi-photon excitation (i.e., multiphoton microscopy and harmonic generation) resulted in an enormous increase of IVM applications in different fields such as immunology, neurobiology and tumor biology [7]. One advantage of multiphoton-based approaches is the fact that they rely on nearinfrared and infrared light, which enable imaging areas of tissues located deeper in the imaged organs (up to few hundred microns). Moreover, in non-linear microscopy, the probability of obtaining a fluorescent emission from a fluorophore increases non-linearly with the excitation intensity (e.g., quadratic dependence for two-photon microscopy), thus conferring some unique properties to these approaches. Indeed, different from conventional linear microscopy, the excitation of the fluorophores occurs in a very small volume at the focal point, thus eliminating off-focus excitation and significantly reducing photobleaching and phototoxicity [8, 9]. Another advantage is that the multiphoton absorption spectra of several fluorophores are broader when compared to single-photon excitation, thus permitting the imaging of multiple fluorophores using a single excitation wavelength [10, 11]. In addition to exogenous fluorophores, several cellular structures can be visualized in a labelfree modality. For example, molecules such as NADH and FAD can be imaged by two-photon microscopy, thus providing valuable information on cell metabolism in vivo [12]. Other molecules organized in ordered structures, such as collagen, muscle myosin, myelin or lipids, can be visualized using second (SHG) and third (THG) harmonic generation [7, 13]. When interacting with these

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molecules, incident photons recombine, without any energy loss, into photons with twice/trice the frequency (or half/third the wavelength). These label-free techniques are often combined with multiphoton excitation of exogenous fluorophores, therefore increasing the repertoire of information that can be acquired by IVM. The recent development of strategies to minimize the motion artifacts due to animal respiration and heartbeat has provided the opportunity to visualize subcellular structures with a spatial and temporal resolution comparable to those achieved in imaging model systems (intravital subcellular microscopy, ISMic), thus allowing to investigate for the first-time cell biology in live mammalian tissues [5, 14–16]. The application of IVM techniques for the imaging of the gastrointestinal (GI) tract has been demonstrated to be a useful tool to study mucosal immunology [17]. Various studies focused on the role of adaptive immunity in the small intestine and analyzed the interactions between epithelial and hematopoietic cells. In this context, IVM has been extensively used to investigate the behavior of different immune cell populations in the GI tract: for example, intravital imaging of the intestinal lamina propria showed that CX3CR1+ macrophages send transepithelial extensions to sample Salmonella typhimurium from the gut lumen [18, 19]. In addition, IVM has been used to compare the motility of γδT cells in peripheral lymph nodes with TCR γδ+ intraepithelial lymphocytes within the intestine [20]. More recently, IVM has been used to demonstrate the existence of a structure similar to the blood–brain barrier, called gut–vascular barrier (GVB) in the gut, that controls the translocation of antigens into the bloodstream and prevents entry of the microbiota [21]. Other studies analyzed the impact of sodium dextran sulfate (DSS)-induced mucosal damage and the progression of orthotopic transplanted colorectal cancer organoids in the mouse cecum [22, 23]. Finally, the development of imaging windows, that enables long-term imaging of the small intestine, has permitted to follow tumor progression and visualize cancer stem cell niche [24, 25]. Currently, the application of IVM for the analysis of the intestinal mucosa is mostly limited to the small intestine or the cecum, which is relatively easy to expose and stabilize. However, the majority of intestinal diseases affecting human patients, such as Chron’s disease, ulcerative colitis as well as cancer, prevalently occur in the large intestine [26]. This observation somehow limits the relevance of IVM preclinical studies performed on the small intestine, which only partially reflect what happens in humans. Indeed, some analyses have been performed on the large intestine by multifluorescent endoscopy: light is guided into tens of thousands of optical fibers enclosed in a miniaturized flexible probe to acquire

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confocal microscopy images [27]. This technique has some limitation in terms of tissue penetration (
80 μm), that is restricted to the superficial layer of the large intestine epithelium and mucosa [28]. Here we describe a relatively easy method aimed at the shortterm visualization (1–2 h) of biological processes in the large intestine by using IVM. Briefly, a proximal portion of mouse large intestine is exposed by a longitudinal abdominal excision and carefully detached from the connective tissue using a cauterizer. The organ is subsequently placed on a custom-made imaging support and stabilized using surgical gauze. Tissue hydration is preserved using a previously characterized optical imaging gel, while temperature is constantly monitored and maintained using a water-heated ring. This setup allows to successfully image blood flow and other structural features of the large intestine for a total period of 2–3 h.

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Materials Procedures involving animals and their care conformed to institutional guidelines in compliance with national (D.L. N.116, G.U., suppl. 40, 18-2-1992 and N. 26, G.U. March 4, 2014) and international law and policies (European Economic Community Council Directive 2010/63/EU, OJ L 276/33, 22.09.2010; National Institutes of Health Guide for the Care and Use of Laboratory Animals, U.S. National Research Council, 2011). All the procedures were performed according to the animal protocols approved by the Institutional Animal Care and Use Committee, protocols— LCMB-031 National Cancer Institute. All efforts were made to minimize the number of animals used and their suffering.

2.1

Animals

8- to 12-week-old female and male mice (20–25 g) of the following strains were used: 1. FVB.Cg.Tg (CAG-EGFP) B5 Nagy/J (indicated as B5/EGFP): Ubiquitous expression of cytoplasmic GFP protein [29]. 2. Gt (ROSA) 26 Sor tm4(ACTB-tdTomato,-EGFP)Luo/J (indicated as mT/mG): Ubiquitous expression of the membrane-targeted peptide mTomato, localized on the cell membranes [30]. 3. CX3CR1+/gfp mice on C57/B6 background: The endogenous CX3CR1 locus was disrupted by the insertion of sequence encoding green fluorescent protein (GFP), replacing the first 390 bps of the coding exon (exon 2).

2.2 Surgical Materials

1. Surgical scissors and forceps. 2. Electric trimmer shaver.

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3. Surgical tape. 4. Sterile gauzes. 5. Heated pad. 6. Infrared heat lamp. 7. Sterile syringes. 8. Saline solution. 9. Ketamine (stock solution 100 mg/kg) and xylazine (stock solution 20 mg/kg). 10. Electrosurgical cauterizer. 11. Stereomicroscope. 12. Carbomer-based gel [9]. 2.3 Devices and Reagents for Intravital Microscopy

1. Custom-made intravital stage (Fig. 1). 2. Heated pad. 3. FITC-conjugated dextran (500 kDa), TRITC-conjugated dextran (2000 kDa), and TRITC-conjugated dextran (155 kDa). Injected dose: 1 mg/mouse. 4. Carbomer-based imaging gel, pH 7.2–7.4 (5.47 g 97% D-sorbitol and 0.3 g carbomer (940 or 974P), 15–20 drops of triethanolamine, 100 mL H2O). 5. Mouse temperature control system (see Note 1).

2.4 Microscopes and Components (See Note 2)

1. System 1: Inverted FVMPE-RS Apollo two-photon microscope (Olympus America, Center Valley, PA), equipped with a tunable Excite IR laser (tuning range 690–1300 nm; SpectraPhysics, CA). Emissions were acquired with two cooled multialkali PMT and two GaAsP PMT, using a water immersion 25 objective lens (NA 1.05, XL Plan; Olympus). 2. System 2: Upright TrimScope II two-photon microscope (LaVision Biotech, Bielefeld, Germany), equipped with a tunable Chameleon Ultra II laser (Coherent, CA, tuning range 690–1100 nm). Emission was acquired with two Multi-Alkali PMT and one GaAsP PMT, using a water immersion 20 objective lens (NA 1.00, XLUMPlanFLN; Olympus). 3. Objective heater (H401-T-controller, OkoLab, Italy).

3

Methods The method described below enables performing IVM of the large intestine from either the luminal side, with direct access to the epithelial cells, or the serosa and muscular layer side. Note, that this approach allows to perform imaging for 2–3 h. Proceeding

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Fig. 1 Stage for IVM of the large intestine. (a) Image of the IVM stage for the visualization of the large intestine. The stage was designed and manufactured by the University of Bern, Switzerland. (b) Details of the organ

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Fig. 2 Examples of IVM imaging of the large intestine. (a) Imaging of the large intestine from the mucosal layer: tdTomato mice (red) were injected with FITC- conjugated dextran (green). Scale bar: 50 μm (b) Second harmonic generation signal (gray) was used to visualize collagen distribution in the submucosa. tdTomato expression in mT/mG mice was used to visualize the vasculature of the large intestine (red). Scale bar: 50 μm (c) Schematic representation of the imaging procedure (from the lumen to the serosa) (d, e) NADH endogenous signal (gray) in the mucosal layer of B5/EGFP (green) mice. Scale bar: 80 μm (f) Imaging of the large intestine from the muscular layer: CX3CR1+/gfp mice were injected with TRITC-conjugated dextran (red) to visualize the vasculature. GFP expression (green) identified large intestine-resident CX3CR1-positive macrophages. Scale bar: 50 μm (g) Collagen distribution in the muscular layer (gray) was imaged by second harmonic generation (SHG). TRITC-conjugated dextran (red) and GFP expression (green) were used to visualize the vasculature and the tissue-resident CX3CR1-positive macrophages, respectively. Scale bar: 50 μm (h) Schematic representation of the imaging direction, from the serosa and muscular layer to the lumen ä Fig. 1 (continued) holder, the water-heated ring and the objective heater. Hot water coming from the water bath flows into the “water-in” tube, through the internal cavity of the “water-heated ring” and then returns to the water bath through the “water-out” tube. The objective heater is crucial to maintain the stability of the system and avoid 3D drift effect. (c) Schematic representation of the IVM setup: the large intestine was positioned on the organ holder of the intravital support and covered with a coverslip. The water-heated ring was used to stabilize the coverslip and to maintain the temperature of the exposed organ

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from the lumen, it is possible to visualize the circulation in the blood vessels localized beneath the colonic crypts: Fig. 2a shows the structure of the blood vessels (identified by the mTomato probe on the membrane of endothelial cells) and the FITC-conjugated dextran (green) to visualize the blood flow (Movie 1). Using SHG signal is also possible to identify collagen fibers lining the basement of the blood vessels (Fig. 2b, gray). In addition, acquiring the autofluorescence signal from NADH is possible to monitor the metabolic activity of colonic epithelial cells (Fig. 2d, e, gray). Epithelial cells and colonic crypts can be also visualized by the cytoplasmic expression of the GFP in B5/EGFP mice (Fig. 2d, e, green), even if not all the epithelial cells are GFP positive. Imaging the large intestine from the serosa is possible to visualize the structure of the large intestine, from the muscular layer to the submucosa and, occasionally, to reach the colonic crypts (Movie 2). Injection of TRITC-conjugated dextran allowed to monitor the circulation surrounding the muscular layer and the submucosa (Fig. 2f, g and Movie 3). CX3CR1+/gfp expression (green) is used to detect colonic resident macrophages, while SHG (gray) signal shows the distribution of the collagen fibers within the tissue (Fig. 2g). 3.1 Surgery to Image Through the Serosa

All the surgical procedures were performed using the stereomicroscope. In order to preserve the animal body temperature, keep the mice on the heated pad and under the infrared heat lamp. 1. Anesthetize the mice by an intraperitoneal injection of a mixture of 100 mg/kg ketamine and 10 mg/kg xylazine. 2. Using the electric trimmer, carefully remove the hairs from the abdomen of the mice. 3. Secure the mice to the heated pad in a supine position using the surgical tape. 4. Perform an abdominal incision and, pulling up the cecum, expose the proximal part of the large intestine. 5. Using the cauterizer, carefully separate the large intestine from the connective tissue and the abdominal fat, paying attention to not damage the surrounding blood vessels. 6. Place the exposed portion of the large intestine on the organ holder of the intravital support (see Note 3), previously covered with a hydrated sterile gauze. This approach significantly reduces the large intestine peristaltic movement, without affecting its physiology (see Note 4). 7. Reintroduce the cecum and the proximal region of the large intestine into the abdominal cavity, in order to maintain the temperature of the gastrointestinal tract at physiological levels.

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Table 1 Summary of the imaging parameters as described in Subheading 3.3 Excitation wavelength

Detector

EGFP

870 nm

Cooled multi-alkali PMT

2000 kDa TRITC-dextran

870 nm

Cooled multi-alkali PMT

mT/mG

900 nm

GaAsP PMT

500 kDa FITC-dextran

900 nm

GaAsP PMT

+/gfp

870 nm

Multi-alkali PMT

155 kDa TRITC-dextran

870 nm

GaAsP PMT

SHG

870/900 nm

Cooled multi-alkali PMT or GaAsP PMT

NADH

750 nm

Cooled multi-alkali PMT or GaAsP PMT

CX3CR1

8. Along the entire surgical procedure, it is important to constantly hydrate the organs with saline solution. In case of bleeding, use the cauterizer and the sterile gauzes. 3.2 Surgery to Image from the Lumen and Intestinal Epithelium (See Note 5)

1. Once the large intestine has been positioned on the organ holder of the intravital support, use the cauterizer to perform a longitudinal incision. 2. Carefully, separate the edges of the incision so that the intestinal mucosa will be exposed. 3. Remove the luminal content and wash the intestinal mucosa with the sterile saline solution (see Note 6). 4. To visualize the intestinal vasculature, systemically inject 100 μL of 10 mg/mL fluorescent-conjugated dextran in saline (injected dose: 1 mg/mouse). Specifically, TRITC- or FITCconjugated dextran was used (Table 1) (see Note 7). 5. At the end of the surgical procedure, cover the exposed organ with the carbomer-based gel and gently cover it with a coverslip. 6. Stabilize the coverslip and heat the exposed organ with the water-heated ring (see Note 8). 7. Insert the temperature probe between the coverslip and the organ to monitor the temperature (set to ~36–38  C) (see Note 8). 8. Spread the carbomer-based gel on the coverslip and use it as objective immersion medium.

3.3 Imaging Parameters

Both microscopes described above were used in experiments. Imaging parameters were differently set based on the microscope’s available features (see Note 9).

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System 1: 1. Since system 1 is an inverted microscope, an objective inverter (Model 300, length 235 mm; LSM Technologies) was mounted on the system. 2. Carefully lower the water immersion 25 objective lens (NA 1.05, XL Plan; Olympus) close to the tissue. Use the carbomer-based imaging gel as immersion medium between the lens and the tissue. 3. To detect the expression of the EGFP, set the laser at 870 nm and direct the signal to the cooled multi-alkali PMT. 4. To detect the expression of the tdTomato in mT/mG mice, set the laser at 900 nm and direct the signal to the GaAsP PMT. 5. To detect the signal coming from the TRITC-conjugated dextran, set the laser at 870 and direct the signal to the cooled multi-alkali PMT. 6. To detect the signal coming from the FITC-conjugated dextran, set the laser at 900 nm and direct the signal to the cooled multi-alkali PMT. 7. To collect SHG signal, set the laser at 870 nm or 900 nm and direct the signal to the cooled multi-alkali PMT. 8. To detect NADH autofluorescence, set the laser at 750 nm and direct the signal to the cooled multi-alkali PMT. 9. Begin time lapse using the Olympus FV3000 software, setting laser power and x, y, z, and t dimensions for the acquisition. System 2 1. System 2 is an upright setup allowing the objective lens to dip downwardly into the tissue. 2. Carefully lower the water immersion 20 objection lens (NA 1.00, XLUMPlanFLN; Olympus) close to the tissue. Use the carbomer-based imaging gel as immersion medium between the lens and the tissue. 3. To detect the expression of the CX3CR1+/gfp, set the laser at 870 nm and collect the signal with the multi-alkali PMT. 4. To detect the signal coming from the TRITC-conjugated dextran, set the laser at 870 nm and direct the signal to the GaAsP PMT. 5. To collect SHG signal, set the laser at 870 or 900 nm and direct the signal to the GaAsP PMT. 6. To collect the NADH autofluorescence, set the laser at 750 nm and direct the signal to the GaAsP PMT.

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7. Begin time lapse using ImSpector software (LaVision Biotech), setting laser power and x, y, z, and t dimensions for your acquisition. Imaging parameters are summarized in Table 1. 3.4 Image Processing

Acquired images were processed with Fiji (National Institutes of Health). 3D drift can occur during acquisition and can be corrected using Fiji plugin “Registration-Correct 3D drift”; Briefly: 1. Open image with Fiji. 2. Set the Brightness and Contrast of the image, by going to “Image ! Adjust ! Brightness/Contrast.” 3. To correct 3D drift, go to “Plugins ! Registration ! Correct 3D Drift.” 4. Select channel for registration from the drop-down menu (see Note 10). 5. After registration, crop the corrected image to remove black edges resulting from registration process. Using the “Rectangle” tool from the Fiji toolbar, select the region to crop, then go to “Image ! Crop.” 6. Save the registered file (see Note 11).

4

Notes 1. Temperature is controlled using a single-channel temperature controller. Briefly, mice are placed on a heated pad to maintain the general body temperature. A water-heated metal ring connected to an external water bath is used to control the temperature of the exposed organ (see Fig.1a). The water temperature in the bath should be kept at a value higher than 36–38  C (generally, 45–50  C), to compensate for the heat dispersion along the tubes. To check the temperature of the exposed organ, a probe connected to a single-channel temperature controller is juxtaposed to the organ. Furthermore, the objective heater described in “Note 3” below contributes to maintain the temperature of the system, avoiding 3D drift effect. 2. Both system 1 and system 2, which have been used for the setup of the IVM on large intestine, are equally suitable for IVM acquisition. System 1 has the advantage to be equipped with a tunable IR laser in a 690–1300 nm range of wavelength, allowing the acquisition of THG signals. 3. The intravital support has been provided by the University of Bern, Swiss. It has two metal black supports attached to a base of plexiglass. Support 1 (Fig. 1a) is used to support the water-

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heated ring: a hollow metal ring is connected with two plastic tubes to the water bath, so that the flux of hot water is used to keep the organ temperature at physiologic condition (
36–38  C). Water temperature in the water bath should be set at a value higher than 36–38  C, considering the heat dispersion occurring along the tubes. Support 2 is dedicated to control the positioning of the organ holder (Fig. 1a). A set of different screws allows to finely move the support 2 in the xyz directions and to precisely fix the organ holder in the desired position. Different organ holders can be attached to the support 2 with a dedicated screw. Specifically, the organ holder used in this setup is an L-shape device generated with a 3D printer and reinforced with a metal strip to minimize possible oscillations. 4. During the stabilization of the large intestine on the intravital support, avoid the usage of any surgical glue; this will result in tissue hardening and effects on organ physiology. When exposing the large intestine, pay attention to not stretch or bend the tissue excessively; this could result in the occlusion of some vessels and reduction or even arrest of the blood flow. 5. This procedure enables the visualization of the large intestine from either the serosa or the mucosa layer. The majority of the events in the large intestine occur at the epithelial and submucosal layers. 6. Fecal residues produce a high background signal. It is important to carefully remove them with forceps and then abundantly flush the luminal content with saline solution. 7. For a better visualization of blood vessels, it is advisable to systemically inject the fluorescent probes after the surgical procedure: bleeding can occur during surgery, so the injection at the end of the surgical procedure can avoid dextran leakage and the loss of the relative signal. 8. Maintaining body and organ temperature is crucial to keep the appropriate blood flow and the functionality of the large intestine. Placing mice on the heated pad and the use of the waterheated ring help to keep the temperature around
36–38  C; this temperature should be maintained during both the surgical procedure and the imaging session. 9. The imaging parameters should be optimized based on the characteristics of the microscope. In multichannel imaging, the excitation wavelengths should be adjusted based on the combination of fluorophores; given the wide absorption spectrum of fluorophores in two-photon excitation and the possibility to excite multiple fluorophores with a single wavelength, the excitation wavelength should be appropriately set based on the experimental conditions. Accordingly, the use of cooled

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multi-alkali PMTs, multi-alkali PMTs, or GaAsP PMTs depends on the intensity of the signal collected; generally, GaAsP PMTs have higher sensitivity and their use should be dedicated to the weakest signal. 10. For optimal registration results, it is important to select a channel containing signal collected by a structure that is expected not to move during time lapse. Acquisition of SHG signal can be really helpful for this purpose. 11. Images shown in Fig. 2 have been processed and analyzed using Fiji (National Institutes of Health) and Imaris (Biplane) software. Specifically, 3D drift was corrected using the “Registration-Correct 3D drift” plugin from Fiji, as described above; signal from the SHG was used as reference channel for the registration. Subsequently, image processing and 3D-rendering was performed using Imaris.

Acknowledgments This research was supported by the Intramural Research Program of the National Institutes of Health, National Cancer Institute, Center for Cancer Research (RW). This work has been supported by a UICC International Cancer Research Technology Transfer Fellowship (UICC ICRETT ICR/2015/40447 fellowship to ME). References 1. Weigert R, Porat-Shliom N, Amornphimoltham P (2013) Imaging cell biology in live animals: ready for prime time. J Cell Biol 201 (7):969–979. https://doi.org/10.1083/jcb. 201212130 2. Fish KN (2009) Total internal reflection fluorescence (TIRF) microscopy. Curr Protoc Cytom. Chapter 12:Unit12 18. https://doi. org/10.1002/0471142956.cy1218s50 3. Sahl SJ, Hell SW, Jakobs S (2017) Fluorescence nanoscopy in cell biology. Nat Rev Mol Cell Biol 18(11):685–701. https://doi.org/10. 1038/nrm.2017.71 4. Wagner R (1839) Erlauterungstaflen zur Physiologie und Entwicklungs-geschichte. Leopold Voss, Leipzig, Germany 5. Pittet M, Weissleder R (2011) Intravital imaging. Cell 147(5):983–991. https://doi.org/ 10.1016/j.cell.2011.11.004 6. Secklehner J, Lo Celso C, Carlin LM (2017) Intravital microscopy in historic and contemporary immunology. Immunol Cell Biol 95

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Chapter 16 Fluorescence Lifetime Imaging as a Noninvasive Tool to Study Plasmodium Falciparum Metabolism Javier Manzella-Lapeira and Joseph Brzostowski Abstract Fluorescence lifetime imaging (FLIM) measures the characteristic time that a molecule remains in an excited state prior to emitting a photon and returning to the ground state. It is a state-of-the-art and noninvasive technique that has the potential to obtain signature physiological information during malaria blood-stage infection. The use of autofluorescence signals from intrinsic fluorophores obviates the need to tag the cells with synthetic molecules or to modify their gene expression. Furthermore, it permits time-lapse interrogation of the changes that occur from invasion to the point when the parasite takes over the host for its own survival mechanisms, as well as changes in the health of the parasite due to extrinsically applied metabolic disruptors. In this chapter, we present a protocol to investigate the autofluorescence lifetime signals of both normal red blood cells (RBC) and P. falciparum-infected RBCs. The data shared with this protocol reveals that there is a significant overall increase in autofluorescence lifetime in infected erythrocytes compared to the healthy uninfected ones. We include a metabolic experiment that confirms that the signals obtained from this imaging technique are key metabolites in energetics of the parasites. Furthermore, facilitating these protocols makes it possible to identify infected RBC based on FLIM signals alone, which presents a huge potential for the study of energetic effects of antimalarials and fast, noninvasive diagnosing. Key words Fluorescence lifetime imaging (FLIM), Phasor plots, Label-free fluorescence/autofluorescence, Blood-stage malaria, Plasmodium falciparum

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Introduction Malaria is still one of the deadliest diseases, with over 200 million cases worldwide in 2017 and no signs of incidence reduction between 2015 and 2017 [1]. Recent studies have confirmed the relevance of identifying the changes in metabolism in various stages of red blood cell infection, including the interest in identifying key changes upon the application of metabolic inhibitors [2]. There has been an increasing interest in studying how the parasite utilizes its mitochondria to sustain antimalarial resistance and regulate protein

Joseph Brzostowski and Haewon Sohn (eds.), Confocal Microscopy: Methods and Protocols, Methods in Molecular Biology, vol. 2304, https://doi.org/10.1007/978-1-0716-1402-0_16, © This is a U.S. government work and not under copyright protection in the U.S.; foreign copyright protection may apply and Springer Nature US 2021

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production, underlining the need to study parasite bioenergetics in a noninvasive manner [3, 4]. The autofluorescent properties of several blood cell components, as well as relevant metabolites, have been reported [5, 6]. As laser technologies and photon counting devices improved, fluorescence lifetime imaging provided a tool to discern between various autofluorescent components that could be present within an unlabeled infected RBC (iRBC). Here we introduce a protocol for inspecting P. falciparum iRBCs with FLIM. We have focused on P. falciparum because it is one of the two most prevalent malaria parasites [1]. FLIM is used to assess the abundance of various relevant metabolites, most notably unconjugated NADH, enzyme bound NADH, and FAD. Since it is known that indicators of metabolic activity like NADH have longer autofluorescence lifetimes than that of hemoglobin, we wanted to determine if it were possible to observe a difference in the lifetime signal of an infected erythrocyte and also to assess the lifetime after the application of metabolic disruptors. Fluorescence lifetime data is fitted to the fluorescence decay model in order to obtain mean lifetime parameters. This approach is especially useful for single-exponential and double-exponential decay profiles, or a mixture of two single-exponential fluorophores with overlapping emission wavelength ranges. Often times the fitting of the raw photon count data becomes cumbersome by the contribution of several fluorophores from a region of interest. In this scenario, an additional analysis can be done using the frequency domain of the temporal data. Once the lifetime data is acquired in the time-domain, one can transform the data into its frequency-domain for phasor plot analysis. In the frequency domain, each pixel has two components that correspond to a vector location in 2D. A fluorophore with a lifetime of zero would have vector location of (1,0) and one with an infinite lifetime would have a vector of (0,0). Single-component fluorescence decay is mono-exponential by definition and falls on a semicircle of radius 0.5 and center on (0.5,0). This semicircle, and the area below it, is commonly referred to as the phasor plot [7]. When a pixel contains a mix of two components, the phasor for that decay falls within the semicircle, along a line that connects its individual components. When there are multiple elements contributing to fluorescence decay, their phasors will be influenced by the location and the relative contribution of the single component present. For a particular pixel, or binned pixels, (i,j), the g and s components are given by the following equations: R1 I i,j ðt Þ cos ðωt Þdt g i,j ðωÞ ¼ 0 R 1 0 I i,j ðt Þdt

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R1 s i,j ðωÞ ¼

0

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I i,j ðt Þ sin ðωt Þdt R1 0 I i,j ðt Þdt

Based on the previous explanation, for a fluorophore with single-exponential decay, the equations simplify to cases that yield locations within the half circle of the phasor plot, as follows: g ðωÞ ¼

1 1 þ ðωt Þ2

s ðωÞ ¼

ωt 1 þ ðωt Þ2

The protocols presented in this chapter will describe the procedure of enrichment of trophozoite RBCs and their labeling with mitochondrial fluorophores. It will guide the user on how to set up a FLIM experiment on these samples in order to acquire FLIM signals from the autofluorescent channel, which allows the user to have an additional mitochondrial marker for ROI selection. It will then go over the analysis of the data with both the exponential decay model and the phasor plot.

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Materials 1. Alexa 488 carboxylic acid dye: Prepare 1 μM Alexa 488 carboxylic acid working solution in deionized water. 2. NADH: Prepare 250 μM NADH working solution in 100 μM MOPS buffer, pH 7. 3. Lactose dehydrogenase (LDH): Prepare 1000 units/mL LDS in deionized water. 4. FAD: Prepare 2 g/mL FAD solution in deionized water. 5. RPMI, phenol red-free. Otherwise specified, RPMI is 1 RPMI solution. 6. Phosphate-buffered saline. 7. Bovine serum albumin. 8. Poly-L-lysine. 9. 70% and 40% Percoll-sorbitol solution: Prepare fresh solution in 1 RPMI media from 100% Percoll and 100% sorbitol solutions. Mix 7:0.5:2.5 ratio of Percoll:sorbitol:1 RPMI for 70% solution and 4:0.5:5.5 ratio of Percoll:sorbitol:1 RPMI for 40% solution. 10. 15-mL round-bottom centrifuge tubes. 11. 7.5 μM TMRM stock solution: Prepare from 20 mM tetramethylrhodamine, methyl ester, perchlorate (TMRM) in DMSO. Aliquot and keep in 20  C.

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12. 30 μM MitoTracker Deep Red stock solution: Prepare from 20 mM MitoTracker Deep Red FM in DMSO. Aliquot and keep in 20  C. 13. 40 mM carbonyl cyanide 4-(trifluoromethoxy)phenylhydrazone (FCCP) in DMSO. 14. 8-well LabTek chambers. 15. PDMS stencil, grids of 20  20 mm stencils with 9 square wells of 3  3 mm, Alveole, PDMS stencil. 16. Cinefoil. 17. Zeiss LSM 780 with a 63 objective and an incubation system. 18. Chameleon coherent laser. 19. Becker & Hickl TCSPC hardware (DCC-100 control card) with two HCM-100 detectors. 20. Becker & Hickl Spcm64 acquisition software and SPCImage analysis software. 21. MATLAB software (MathWorks).

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Methods The workflow for these experiments requires the investigator to obtain the pipelines for the red blood cells and the infected cultures before planning the imaging experiments. It is also crucial to familiarize oneself with the handling of blood and parasitized blood samples, including proper protective gear. This section will describe the preparation of the RBC samples and the staining of mitochondria for the parasitized cells. It will then walk the user through the steps necessary for the preparation of the microscope and the setup of the configurations to acquire both the FLIM image and its corresponding fluorescence/DIC image. The last method involves the data analysis, in which the user will process the data to obtain phasor plots and mean lifetime values.

3.1 Sample Preparation

1. Place following control solutions into each well of an 8-well Labtek chamber to ascertain that the FLIM system is working properly and also to guide the user to qualitatively determine the relative contributions of the single components when plotting RBC phasor signals:1 μM Alexa 488 carboxylic acid dye, 250 μM NADH, 1:1 (v/v) of 250 μM NADH:1000 units/mL LDS, and 2 g/mL FAD. 2. Prepare uninfected and infected red blood cell (RBC) samples: Uninfected RBCs were human O+ erythrocytes from Interstate Blood Bank, Inc. These were also used for the infected cultures.

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For the infected RBCs, a GB4 P. falciparum culture was synchronized as is detailed elsewhere [8]. Parasitemia should be 3–5%. Enrich the desired blood stages of the parasite infection [9]. In this protocol, the trophozoite stage will be enriched. 3. Add 4 mL of 70% Percoll-sorbitol in 15-mL round-bottom tubes, then gently add 4 mL of 40% Percoll-sorbitol on top of the first layer. Slowly add 4 mL of the iRBC solution on top of the second layer. 4. Centrifuge at 15,680  g for 30 min and stop without deceleration. 5. Aspirate the cell layer between the bottom Percoll-sorbitol layer and the middle Percoll-sorbitol layer. Add an equal volume of 8:2 of RPMI:10 PBS (this is a hyperosmotic solution) in a 15-mL tube. Incubate at 37  C for 5 min, collect at 3920  g for 5 min. 6. Remove supernatant, add 5 mL of 9:1 RPMI:10 PBS, and incubate at 37  C for 5 min. Centrifuge at 3920  g for 5 min, then aspirate supernatant. 7. Resuspend 5  104 RBCs per sample in RPMI + 1% BSA. Transfer 1 mL to a sterile 1.5 mL Eppendorf tube. 8. Add 1 μL of TMRM and 1 μL of MitoTracker Deep Red stock solutions. Incubate for 30 min in 37  C (see Note 1). 9. Prepare a precut PDMS stencil and place into a well of 8-well chamber for each sample and add 10 μL of 0.01% poly-lysine. 10. Incubate the chamber for 30 min at 37  C. 11. Gently rinse the wells twice with RPMI. Make sure to always leave a small volume of media in the well—the surface should never be dry. 12. Centrifuge the RBC samples at 3920  g for 3 min. Remove media and resuspend in 50 μL of RPMI. 13. Keep samples in the 37  C incubator until you are ready to start imaging. 3.2 Microscope Setup

The protocol below uses a Zeiss LSM 780 confocal microscope integrated with a Coherent Chameleon II Ti:Sapphire femtosecond-pulsed infrared laser. The FLIM acquisition is accomplished using a Becker & Hickl TCSPC DCC-100 control card with two HCM-100 detectors and the Spcm64 acquisition software operating on a second computer. The acquisition is started in the Zen software and photons are counted using the Spcm64 software. This protocol can be adapted to any confocal/FLIM system.

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1. Assemble stage and lens heating apparatuses and set to 37  C at least 1 h before imaging. We used a Plan-Apochromat 63  1.4 NA oil-immersion objective lens for our acquisitions. 2. Turn on the multiphoton laser and the FLIM detectors. Use the Chameleon tuned to 750 nm. 3. On the FLIM computer, open Spcm64 acquisition software. With two detectors, place a dichroic separating the emission at 525 nm, with a BFP filter (480/40) for the reflected shorter wavelength light (see Note 2). 4. Open the System Parameters for each detector. Operation mode should be set to FIFO. Set collection, display, and repeat times to 1 s. Set ADC resolution to 256. The “Image Pixels X” and “Image Pixels Y” should match the pixel resolution that you define with the Zen configuration. Save and close this window (see Note 3). 5. On the main panel, match the “SYNC” bar matches to the laser repetition rate of 80 MHz. It should read 8.00E+7. 6. Open the ZEN software and configure the parameters for the acquisition of a DIC/fluorescence image and for the FLIM scanning (see Note 4). 7. For the confocal configuration, select “LSM” and “Channel” mode, with line switching. 8. Track 1: Activate the 561 nm laser then select the GaAsP detector to collect for the signal between 570 and 630 nm. Select a beam splitter that includes both 561 and 633. Check the PMT box. For each FLIM image, a DIC image was also acquired to distinguish features of iRBCs like the existence of hemozoin. 9. Track 2: Activate the 633 nm laser then select the far-red sensitive PMT to collect signal between 640 and 730 nm. 10. Set the image size to 512  512 pixels, at 2 zoom with a 16-bit depth. Set the scan speed to 9 and average two times. 11. For the FLIM configuration, select “Non-Descanned” and “Channel” mode. Activate the multiphoton laser and tune to 750 nm. Select the 690+ reflector off the invisible light path and select the MP 355/690 beam splitter. Make sure image size in pixels matches the system parameters on your FLIM detectors. 3.3 Image Acquisition A— Comparison Between Uninfected and Infected RBCs

1. Because of the sensitivity of the FLIM detectors, minimize any light source from the microscope room, including box indicator lights and LED screens on apparatuses. The use of cinefoil is recommended to achieve this goal. The screens should be facing away from the microscope and dimmed to at least half their brightest setting.

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2. Prepare an 8-well chamber with two samples: uninfected (uRBCs) and infected RBCs (iRBCs). 104 cells are enough per PDMS well. Use at least 15 μL of RPMI + 2% BSA. Once you place the chamber on the stage, wait 10 min for the temperature to equalize. 3. For the iRBCs sample: In Zen, use the confocal configuration saved in steps 6–10 of Subheading 3.2 to find the right focus and field of view. With “Range indicator” checked, adjust gain and offset settings for the PMTs, as well as laser powers for the mitochondrial markers. 4. Click “Snap” then click “New” and switch to the FLIM configuration saved in step 11 of Subheading 3.2. 5. In Spcm64, enable the FLIM detectors in the DCC-100 control window. There should be some level of background photons detected, but the highest value on the CFD should not be greater than 102. If it is 103, you need to check for background light sources and make sure you cover them. 6. Click “Start” on the menu. The windows should be white, waiting for the trigger from the scanner. On Zen, click “Continuous” acquisition. The FLIM detectors will begin collecting events, and the image will be updated every second with new photons. CFD should be in the 103 to 105 range (see Note 5). 7. Collect 20–30 million events. This could take up to 60 s but will vary depending on the number of cells in the field of view. 8. Click “Stop” on the menu, then stop the continuous acquisition on Zen. Save the FLIM image. 9. In Zen, switch to the Confocal configuration and find a new field of view. Repeat steps 3–8, now with the same laser power settings. Acquire at least 30 cells per experimental condition for statistical analysis. 10. Move the stage to the uRBCs sample. With this sample, the main difference will be that the fluorescent channels will be empty (see Note 6). 11. Replace the chamber with the chamber containing the control solutions. Acquire FLIM images of at least three fields of view from each one of the solutions (see Note 7). 3.4 Image Acquisition B— Metabolic Inhibition of TrophozoiteStage iRBCs

For this section, we use FCCP, a mitochondrial membrane depolarizer. We will compare the FLIM signatures of the iRBCs before and after adding FCCP. 1. Prepare a 40 μM solution of FCCP from a stock dilution. 2. Repeat the “RBC sample preparation” method and use as many wells as you want replicates.

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Fig. 1 FLIM images containing the mean fluorescence lifetime per pixel were thresholded and regions of interest were obtained as single cells for infected trophozoite iRBCs (a) and healthy uRBCs (b). The color bar indicates the mean lifetime levels. Average lifetime values were then calculated by pooling the mean values from each ROI for each group analyzed. The histogram of fluorescence lifetime in the pixels of uninfected versus infected RBCs (c) further reveals the wide range of lifetimes in the pixels from the iRBCs as opposed to uRBCs

3. Once focused on the first well of cells, set up a time lapse with the Confocal configuration: acquire 5 min with 1 s intervals. 4. Start the acquisition, and after the fifth frame, add 1 μL of 40 μM FCCP. When the FCCP reaches the mitochondria, the fluorescence of the TMRM should drop visibly (see Fig. 1). 5. When the time lapse is over, acquire FLIM images and their confocal counterparts, as is detailed in Subheading 3.3. 6. Repeat for each replicate well.

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FLIM Analysis

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SPCImage software (Becker & Hickl) was used to process the raw data acquisitions. Each pixel in the field of view contains timeresolved photon count data. The confocal image corresponding to a FLIM image is then used to produce the mask. This section will go through the steps to be taken to analyze a FLIM data segmented to specific ROIs either with the autofluorescent intensity image or with a confocal image of the mitochondrial label. 1. Load SPCImage and select “Import” under the “File” menu to select a raw SDT data file. Select double-exponential photon decay model: Under “Multiexponential Decay” on the right side of the screen, select two components. Set the bin to 3 and the threshold to 8 (see Note 8). 2. Click the box to open the model options window. Here you need to specify the maximum chi-squared value as 2.0 (see Note 9). 3. To change the image properties, right click on the image and select “Intensity.” This will open a window that allows you to change the intensity and contrast for both the grayscale fluorescence and the color-coded FLIM signal. Right click on the colored histogram to the left of the image. Select “Color,” which will open a window where you can change the min/max values of the color-coded image. 4. Select “Decay Matrix Single Channel” under the “Calculate” menu. Once it finishes processing, click on “Phasor Plot” to create the Phasor components for each pixel. 5. Batch processing is available under the “Calculate” menu. It can be done after you have processed the current file and it will use all the parameters you have selected/modified. 6. Select “Export” under the “File” menu to save the data. Make sure you check the boxes for all parameters necessary. For this protocol, you will need the phasor components, the colorcoded value matrix (this is the mean lifetime data), chi-squared, as well as intensity and color images. 7. Repeat for each sample group. 8. A custom-made MATLAB algorithm can be used to obtain ROIs based on each parasite’s mitochondrial region (available upon request, see Note 10). 9. For the application of FCCP, threshold the mitochondrial regions and then quantify the difference in mean fluorescence intensity before and after the reagent was added (see Fig. 3). 10. A custom MATLAB algorithm can import the ASV data files that are produced by SPCImage and output the lifetime and phasor values for each mitochondrial region or for the control solutions (average of the entire field of view). When comparing

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Fig. 2 FLIM characteristics of control solutions. Solutions of Alexa 488, NADH, NADH conjugated with LDH in a 1:1 ratio, and FAD+ were assayed (920 nm for Alexa 488, 750 nm otherwise). Data in (a) shows localization of the vectors in the phasor plot, with the black shapes representing mean vectors for each group. Mean lifetime is plotted in (b), further confirming lifetime differences but also highlighting the importance of showing the phasor locations to identify fluorophores that would otherwise have similar mean lifetimes

the uninfected with the infected RBCs, use the intensity image TIF file produced by SPCImage (see Note 11 and Figs. 1 and 2).

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Notes 1. TMRM is a cationic dye that serves as an indicator of the polarization of the mitochondrial membrane. When the mitochondrial membrane is depolarized, there will be a loss in fluorescence (see Fig. 3). MitoTracker Deep Red facilitates the selection of regions of interest and can also be fixed. Neither of these dyes affect the autofluorescence signal that we are interested in acquiring. 2. This is the autofluorescence FLIM channel for various components including hemoglobin and conjugated/unconjugated NAD(P)H. If the user has only one FLIM detector, it should be used with a CFP filter for the NAD(P)H channel. 3. If your hardware permits, maximize the ADC resolution, which will allow for higher time sensitivity of the collected photons. For a 60 objective, 512  512 is sufficient. 4. Since there are two fluorophores, set up a configuration that includes both fluorescent markers and vary this configuration as necessary—for instance, since the uninfected RBCs will not have any mitochondrial dye, the specific PMTs could be unchecked and only one laser used for the DIC image.

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Fig. 3 Effects of metabolic inhibitors: iRBCs loaded with TMRM are shown before (a) and (b) after the application of 40 μM FCCP. Mean fluorescence intensity was obtained from each mitochondrion before and after application of FCCP, showing the loss of most of the signal (c). The control group added the same concentration of DMSO that was used to dissolve the FCCP. Statistical significance is at P < 0.0001. Phasor locations of FCCP-treated and control mitochondrial ROIs are plotted in (d), which confirms a metabolic shift towards longer lifetimes and depletion of the shorter lifetime unconjugated NADH. Conversely, inhibition of oxidative phosphorylation with rotenone or antimycin-A or both shift the phasor locations towards a shorter lifetime and different levels of conjugated NADH (e)

5. If the CFD value is above this range, the laser power is too high and might damage the cells. Laser power recommended is 1.5 mW at the objective with a 1% on the laser setting on the software for the particular Chameleon laser suggested. The user might want to test various powers.

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6. When FLIM images combine fluorescence lifetime (color) with fluorescence intensity (brightness of color), the qualitative difference between image groups should be apparent. While uRBCs exhibit a homogenous lifetime, indicative of a cell type in which hemoglobin is diffused evenly throughout the cytoplasm, the uRBCs exhibit a noticeably wider range of lifetimes. There should be a shift expected towards longer lifetimes in infected RBCs. 7. To focus the control solutions, use the photon counts on the ADC display on the Spcm64 software panel for reference. While on continuous scan mode, slowly raise the objective until you begin to see the photon counts increase. You can acquire the FLIM images once the ADC display reaches a maximum photon count. The FLIM image of the control solutions will look relatively even across the entire field of view. 8. Selecting two components refers to the assumption that the decay is biexponential or has two main components (either a mix of two single exponential decays one biexponential decay). Binning the data combines the photons from the neighboring pixels for each particular pixel that is being fitted. The threshold parameter eliminates pixels with a number of photons fewer than the threshold value for a particular time point in the histogram of photon counts for a particular pixel. 9. The chi-squared value measures how accurate the data is fit to the mathematical model. In this case, the user can threshold the data based on the goodness of the fit; how much the chi-squared value differs from a value of 1. 10. The MATLAB program used to threshold the mitochondrial regions consists of the following workflow: The confocal CZI image file is imported and the pixel intensity matrix is extracted for the channel corresponding to the MitoTracker dye emission. A gaussian filter is used with the raw image matrix. Then a k-means algorithm is applied to the frame in order to threshold and binarize (background pixels are False and signal pixels are True). Connected components with True values that have a size under 25 pixels are eliminated. The resulting binary image is then used to create a connected component structure with the separate mitochondria, which is in turn one of the inputs for the “regionprops” function. The second input to the “regionprops” function is either the intensity image or any of the output matrices from the SPCImage software, depending on which mean parameter is desired from the regions. 11. To import ASV files, set the delimiter as double-space (“ “) and use the “importdata” function into a matrix variable. Convert zero values into NaN and use “nanmean” to obtain the mean values for each mitochondrial region.

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Acknowledgment This work is supported by intramural funding from the National Institute of Allergy and Infectious Diseases at the National Institutes of Health. References 1. Organization WH (2018) World Malaria Report 2018. 2019 2. Sakata-Kato T, Wirth DF (2016) A novel methodology for bioenergetic analysis of Plasmodium falciparum reveals a glucose-regulated metabolic shift and enables mode of action analyses of mitochondrial inhibitors. ACS Infect Dis 2 (12):903–916. https://doi.org/10.1021/ acsinfecdis.6b00101 3. MacRae JI, Dixon MW, Dearnley MK, Chua HH, Chambers JM, Kenny S, Bottova I, Tilley L, McConville MJ (2013) Mitochondrial metabolism of sexual and asexual blood stages of the malaria parasite Plasmodium falciparum. BMC Biol 11:67. https://doi.org/10.1186/ 1741-7007-11-67 4. Painter HJ, Morrisey JM, Mather MW, Vaidya AB (2007) Specific role of mitochondrial electron transport in blood-stage Plasmodium falciparum. Nature 446(7131):88–91. https://doi. org/10.1038/nature05572 5. Zheng W, Li D, Zeng Y, Luo Y, Qu JY (2010) Two-photon excited hemoglobin fluorescence.

Biomed Opt Express 2(1):71–79. https://doi. org/10.1364/boe.2.000071 6. Blacker TS, Duchen MR (2016) Investigating mitochondrial redox state using NADH and NADPH autofluorescence. Free Radic Biol Med 100:53–65. https://doi.org/10.1016/j. freeradbiomed.2016.08.010 7. Lakner PH, Monaghan MG, Mo¨ller Y, Olayioye MA, Schenke-Layland K (2017) Applying phasor approach analysis of multiphoton FLIM measurements to probe the metabolic activity of three-dimensional in vitro cell culture models. Sci Rep 7:42730. https://doi.org/10.1038/ srep42730 8. Lambros C, Vanderberg JP (1979) Synchronization of Plasmodium falciparum erythrocytic stages in culture. J Parasitol 65(3):418–420 9. Aley SB, Sherwood JA, Howard RJ (1984) Knob-positive and knob-negative Plasmodium falciparum differ in expression of a strainspecific malarial antigen on the surface of infected erythrocytes. J Exp Med 160 (5):1585–1590. https://doi.org/10.1084/ jem.160.5.1585

Chapter 17 Developing Analysis Protocols for Monitoring Intracellular Oxygenation Using Fluorescence Lifetime Imaging of Myoglobin-mCherry Greg Alspaugh, Branden Roarke, Alexandra Chand, Rozhin Penjweini, Alessio Andreoni, and Jay R. Knutson Abstract Oxygen (O2) is a critical metabolite for cellular function as it fuels aerobic cellular metabolism; further, it is a known regulator of gene expression. Monitoring oxygenation within cells and organelles can provide valuable insights into how O2, or lack thereof, both influences and responds to cell processes. In recent years, fluorescence lifetime imaging microscopy (FLIM) has been used to track several probe concentration independent intracellular phenomena, such as pH, viscosity, and, in conjunction with Fo¨rster resonance energy transfer (FRET), protein–protein interactions. Here, we describe methods for synthesizing and expressing the novel FLIM-FRET intracellular O2 probe Myoglobin-mCherry (Myo-mCherry) in cultured cell lines, as well as acquiring FLIM images using a laser scanning confocal microscope configured for two-photon excitation and a time-correlated single photon counting (TCSPC) module. Finally, we provide step-by-step protocols for FLIM analysis of Myo-mCherry using the commercial software SPCImage and conversion of fluorescence lifetime values in each pixel to apparent intracellular oxygen partial pressures (pO2). Key words Myo-mCherry, Intracellular oxygenation, Hypoxia, Two-photon fluorescence lifetime imaging, Fo¨rster resonance energy transfer

1

Introduction Imaging physically measurable quantities (“quantitative imaging”) within subcellular environments can reveal crucial information about biological processes [1, 2]. Fluorescence lifetime imaging (FLIM) has emerged as a reliable method for measuring physiological processes in cells and subcellular organelles with high signal-tonoise ratios (SNRs) and submicron (
1 μm) planar resolution when combined with confocal or multiphoton microscopy (yielding Z sectioning). Improvements in data acquisition using time-

Joseph Brzostowski and Haewon Sohn (eds.), Confocal Microscopy: Methods and Protocols, Methods in Molecular Biology, vol. 2304, https://doi.org/10.1007/978-1-0716-1402-0_17, © This is a U.S. government work and not under copyright protection in the U.S.; foreign copyright protection may apply and Springer Nature US 2021

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correlated single photon counting (TCSPC) have played a significant role in making FLIM more reliable and of more general use [3–6]. Cell biologists have used FLIM to measure intracellular viscosity, bio-membrane heterogeneity, molecular species concentrations, redox state of cells, pH, and, in conjunction with Fo¨rster Resonance Energy Transfer (FRET), protein–protein interactions, among other parameters [7–9]. We recently published a novel, genetically encoded sensor, myoglobin-mCherry (Myo-mCherry), to monitor intracellular oxygen partial pressure (pO2) that expands the list of measurables accessible through FLIM [10, 11]. In an effort to expand the accessibility of FLIM measurements by other researchers, we herein present a protocol for utilizing FLIM to image Myo-mCherry in transfected cells. The protocol details the use of a laser scanning microscope (LSM) configured for two-photon microscopy, but one-photon excitation is also suitable, and it should yield very similar results in cultured cells, although it is not advisable for tissue slices and thicker samples. We would also like to emphasize that we make the plasmid encoding for the sensor available to the community and requests for material should be addressed to the corresponding author. The fluorescence “lifetime” for a homogenous population of N excited molecules in a singlet state (S1, Fig. 1a) is the time required for this excited state population to be reduced by a factor of e as a result of fluorescence (toward S0, Fig. 1a) [12]. Since the lifetime is a “state function,” its value depends on the type and conformation of the fluorophores, as well as how they interact with their chemical and physical environment [1, 5]. The fluorescence lifetime is not related to a fluorophore’s concentration across a wide range [12]; this makes it an invaluable tool for probing biological environments, which often have unknown concentrations and/or require low fluorophore concentrations (on the order of tens to hundreds of nM) to preserve system integrity. For fluorophore populations with more heterogeneous/complex fluorescence decays, which are often found in biological environments, the molecular mean fluorescence lifetime, τ, corresponds to the weighted arithmetic mean of the individual lifetimes for each component in the decay (Eq. 1) [4]. τ¼

N X

αi τ i =

X

αi

ð1Þ

i¼1

If two different fluorophores are in close proximity to one another (~2–6 nm [13]) and the emission spectrum of one overlaps with the absorption spectrum of the other, energy from the first molecule, the donor, can transfer to the second molecule, the acceptor, through a nonradiative dipole–dipole interaction called FRET [14, 15] (Fig. 1b). This energy transfer results in a reduction

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Fig. 1 (a) Jablonski diagram illustrating the physical mechanism of fluorescence. A photon (or multiple lower energy photons) excites the fluorophore to a higher energy singlet state (S0, 0 ! S1, 3), where it undergoes nonradiative relaxation beyond the scope of this chapter (S1, 3 ! S1, 0). The fluorophore decays to its ground state with emission of a fluorescent photon (S1, 0 ! S0, 2). (b) Jablonski diagram illustrating the physical mechanism of FRET. After excitation and relaxation of the donor, a “virtual photon” would excite the acceptor (S1, 0 (D) ! S0, 0 (A)), leading to acceptor fluorescence. (c) Factors that cause fluorescence decay spectra to deviate from their model. To account for the nonzero width of the excitation pulse (left) and the nonzero temporal response of the detector (middle), the excitation pulse width and the photodetector response function are convolved, generating the IRF for the imaging system (right). ((c) reproduced from Becker 2017 [4] with permission from Becker & Hickl Gmbh)

in both donor intensity and lifetime, as well as an increase in acceptor intensity. This reduction in donor lifetime is one way that enables FLIM to discern where donor–acceptor pairs are in close proximity within a sample based on the lifetimes of the interacting fluorophores [1, 4, 12]. In practice, however, fluorescence decay data do not appear as a sum of exponentials model due to the nonzero width of the excitation beam and the detector’s imperfect temporal response to

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photon detection (Fig. 1c) [4]. To account for these factors, the instrument response function (IRF) for the imaging system is measured, especially in multiphoton microscopy where detected second-harmonic generation (SHG) can confound fluorescence data [4]. After determining the appropriate IRF, a sum of exponentials model would be iteratively reconvolved with the IRF, and the result would be fit to the acquired fluorescence decay data. As discussed at length in prior work [10], Myo-mCherry is a biocompatible FLIM-FRET probe that is capable of tracking pO2 within cells. The probe combines myoglobin, an oxygen (O2) binding protein that experiences spectral shifts between its oxygenated (oxyMyo) and deoxygenated (deoxyMyo) states [16], with the fluorescent protein mCherry. In Fig. 2a, an artistic ribbon depiction of the probe is presented. The absorption spectrum of myoglobin in each biochemical state has different overlap with the emission spectrum of mCherry. The latter acts as a donor for the dark (i.e., virtually nonfluorescent for the excitation wavelength) acceptor myoglobin [16]. The absorption spectrum of deoxyMyo has more overlap with mCherry than when myoglobin is in its oxygenated state. This overlap yields large FRET in deoxyMyomCherry, and as a result, shorter lifetimes are observed in the deoxygenated state of the probe. Upon oxygenation, oxyMyomCherry is formed and the decrease in spectral overlap yields longer lifetimes (see Fig. 2c and full explanation in Ref. 11). Given these two different populations (deoxy and oxyMyo-mCherry), and assuming a single discrete orientation factor in the FRET, a biexponential model can be used to fit this FLIM data: F ðt Þ ¼ a 1 e

t=τ

1

þ a2 e

t=τ

2

ð2Þ

where a1 and a2 are pre-exponential weights used to represent the fractions of fluorophore populations with lifetimes τ1 and τ2, assuming the natural lifetime is constant [17]. Orientation is likely distributed rather than unique, and mCherry itself has been reported to have two lifetimes [18] such that the ideal model must be multiexponential; however, a biexponential model appears to capture the main FRET trends in the Myo-mCherry system and changes in the energy transfer are easily monitored using the average lifetime as an indicator [16]. In prior work [10], cells transfected with Myo-mCherry yielded shorter average lifetimes at lower external pO2 than at higher external pO2 across A549 cells and its mitochondrial DNA-depleted (mtDNA) counterparts, A549 ρ0 cells. Additionally, transfected A549 cells capable of aerobic respiration yielded shorter average lifetimes at all external O2 concentrations (excluding nearanoxia) than A549 ρ0 cells with inhibited aerobic respiration [10]. To image Myo-mCherry using time-domain FLIM, we used a Becker & Hickl (B&H) time-correlated single photon counting

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Fig. 2 (a) The Myo-mCherry construct, where mCherry (PDB: 2H5Q) was coupled to the C-terminus of myoglobin (PDB: 1MBO) by using a two-amino acid linker. (b) Theoretical energy transfer efficiency for the Myo-mCherry system as a function of distance between the two proteins. The dashed line at 4 nm highlights the average distance between the two proteins as inferred from the available structures of the individual proteins. (c) Absorption spectra of oxyMyo and deoxyMyo overlapped with the emission spectrum of mCherry, highlighting the spectral overlap region (shaded area) between the species. R0 is the Fo¨rster radius calculated based on the actual spectral overlap integral (not shown here). (This figure was reproduced from Penjweini 2018 [10] with permission from Journal of Biomedical Optics)

(TCSPC) module. A high-repetition near-infrared (NIR) pulsed laser (80 MHz, 780 nm) excites a sample with a focused beam swept across the sample using a laser scanning microscope configured for two-photon microscopy [1]. The arrival times of the fluorescent photons are then recorded with respect to the laser pulses and the 2D spatial coordinate of the scanner [1]. As a large number of excitation cycles is repeated over time, photons are accumulated and a spatiotemporal histogram of photon counts is built, creating a spatial map of fluorescence decay curves with the highest time resolution and best lifetime accuracy of any FLIM technique [1, 19–21]. To analyze the acquired data, we used the proprietary FLIM analysis software, SPCImage, from B&H configured for batch analysis followed by user corrections and refinement to optimize fitting parameters.

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Here below we outline details on how to setup and configure FLIM measurements, record data, and subsequently analyze them using an appropriate analysis pipeline suitable to extract meaningful information from recordings of the genetically encoded oxygen probe Myo-mCherry expressed in cells.

2

Materials The plasmid containing the Myo-mCherry (or mito-MyomCherry, nucleus-Myo-mCherry) gene is available through our laboratory to anyone interested in using it. The expression of the gene is constitutive and driven by a CMV promoter. The plasmid encodes for antibiotic resistance (kanamycin/neomycin). This feature can be used for amplification and propagation in E. coli, or for transfected cells selection to produce a stable cell line (topics not covered here) (see Notes 1 and 2).

2.1 Transfection of Living Cells with Myo-mCherry

1. Living cells to be transfected. 2. Cell culture media consisting of modified eagle’s medium (DMEM) with 10% non-heat-inactivated fetal bovine serum (FBS, Invitrogen, Grand Island, New York) and 1% penicillin/streptomycin. 3. Phosphate-buffered saline (PBS). 4. Low or serum-free media such as OptiMEM. 5. Lipofectamine® or FuGENE® transfection reagent. 6. Plasmid containing Myo-mCherry DNA. 7. 1.5 mL Eppendorf tubes as needed. 8. Welled microscope slides for cell plating and suitable for confocal microscopy (i.e., Ibidi 4-well μ-Slide, with a single-well volume of 700 μL). Make sure that they are compatible with the stage mounted O2/CO2 incubator.

2.2 Cell Treatment with Rotenone and Antimycin A

1. Transfected cells cultured in cell media.

2.3

1. A laser-scanning confocal microscope configured for two-photon microscopy and equipped with an objective that can transmit IR signal, typically with a numerical aperture (NA) greater than one. For subcellular resolution, an NA above 1.2 (for example, a Leica SP5 or Zeiss LSM 510 with oil objective, NA ¼ 1.4) is best to obtain pixel resolution smaller than subcellular organelles.

Imaging Setup

2. DMSO for preparing stock solutions of drugs. 3. Antimycin A and rotenone, a mitochondrial complex III and complex I inhibitor, respectively [22, 23].

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2. A femtosecond, high-repetition (40–80 MHz) laser tunable to 780 nm (for example, a mode-locked Mai Tai®, Spectra-Physics laser operated at 40 MHz or 80 MHz with a peak wavelength of 780 nm) (see Notes 3 and 4). 3. Dual-band dichroic mirror that transmits the excitation beam into the objective and reflects emitted fluorescent photons consistent with the mCherry emission spectrum to the photodetector (380–740 nm, with a maximum at 610 nm [24]). This dual-band dichroic is an essential component for two-photon fluorescence microscopy (for example, an HFT KP 700/488-nm). 4. Long-pass dichroic mirror that can reflect emitted fluorescent photons downstream into the nondescanned detector (e.g., 735 nm, Thorlabs, DMLP735B). 5. Two short-pass filters that remove residual signals representing scattered light from the laser (e.g., 750 nm, Thorlabs, DMSP750B; 700 nm, Edmund Optics Inc., 12.5 mm diameter, OD 2). 6. Band-pass filter to transmit the mCherry emission to the detector (e.g., 641/75 nm, Semrock, BrightLine® single-band) (see Note 5). 7. A fast (low transit time spread) photomultiplier module (for example, B&H, HPM-100, or Hamamatsu, H7422P-40). 8. Photon counting card equipped with a histogramming TCSPC module that is synchronized with the laser pulses and the pixel and line clocks from the microscope (for example, a B&H SPC-150 or SPC-830). 2.4 IRF Measurements (Multiphoton Microscopy)

1. Same imaging setup described in Subheading 2.3 except: Short-pass filter in   front of the detector that transmits SHG signal λSHG  12 λex: to the photomultiplier (e.g., below 425 nm (for two-photon excitation at 780 nm), Edmund Optics, 25 mm diameter, OD 2). 2. A strong SHG generating source (for example, high purity urea crystals or sucrose [25]) (see Note 6).

2.5 Controlled Experimental Environment with a Range of Stable Oxygen Concentrations for Imaging and Calibration

1. A stage-mounted incubator to provide temperature control and an environment suitable for homeostasis during imaging (i.e., a TC-MI-2046, Bioscience tools, San Diego, CA). 2. An incubator temperature controller, (i.e., a TC-1-100-I, Bioscience Tools). 3. A gas mixing system to deliver mixtures of N2, O2, and 5% CO2 inside the incubator (i.e., CO2-O2-MI, Bioscience Tools) and capable of delivering O2 concentrations from normoxia to hypoxia.

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4. Tanks of high purity CO2 and N2 to supply the gas mixing system. 5. An oxygen monitoring system, (i.e., an OxyLite Pro 2-channel bare-fiber O2 sensor (NX-BF/O/E, Optronix Ltd., Oxford, United Kingdom) for measuring extracellular pO2 and temperature at and above (~100 μm) the cell layer.

3

Methods Methods are partially adapted from Refs. 10, 11. The plasmid provided by our laboratory is generally delivered in a final volume of 50 μL, with a concentration between 0.5 and 1 μg/μL. No further dilution is necessary. For transfection in cell lines (as described below), we generally advise to use plasmid solutions with a concentration between 0.5 and 1.0 μg/μL, to minimize dilution of the transfection reagents used in the following step (see Note 7).

3.1 Transfection of Living Cells with Myo-mCherry

This transfection protocol is suitable for an Ibidi 4-well μ-Slide, with a single-well volume of 700 μL. The protocol can be scaled appropriately for a range of well numbers and volumes so long as the final concentration of DNA per well during the 24–48 h incubation period is 0.57 ng/μL. 1. Seed cells such as at the end of the 24–48 h incubation period confluency will be in the range of 70–80%. High confluency will result in increased O2 consumption, and therefore alter the local O2 environment, and vice versa for low confluency. As such, confluency should be kept consistent across samples. 70–80% provides an adequate pool of cells to choose from and image individually or in groups. 2. Remove old cell culture medium. Add 400 μL of fresh culture media. 3. Add 150 μL of OptiMEM and 400 ng of the Myo-mCherry plasmid to a 1.5 mL tube. 4. Add 150 μL of OptiMEM and 2 μL of Lipofectamine® 2000 to a different 1.5 mL tube. Lipofectamine® 3000 can be used in cell lines, such as suspension and primary cells, that are difficult to transfect [26]. 5. Combine the lipofectamine and plasmid solutions. Mix gently. 6. Incubate the transfection solution for 10–20 min at room temperature. 7. Add dropwise to a single well on the multi-well slide.

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8. Allow the cells to incubate at 37  C and 5% CO2 for 24–48 h. After 24–48 h, wash the cells twice with warm PBS and then cover with 500 μL (2–3 mm) of fresh media. 9. When imaging, use phenol red-free media to reduce background signal. Imaging media can be either DMEM or OptiMEM formulated without phenol red, or Bright-MEM optimized to give low background for imaging. Alternatively, DPBS or HBSS can also be used as low background imaging solutions, care must be taken in this case if longer imaging sessions are planned since these buffer/salt solutions do not contain any nutrients. 3.2 Fluorescence Two-Photon Image Acquisition

1. Turn on and (if necessary) tune the laser apparatus such that the excitation beam provides consistent mode-locked pulses (typically 10,000 peak photons). Then use the sliders in the same curve window to select the start and end of the portion of the TCSPC histogram to be used as the IRF. Store the IRF for use in image analysis by clicking the “curve to IRF” button on the left-hand tool bar. 3. Open a FLIM file containing the desired data to be analyzed. Open the IRF tab from the toolbar and select “Paste from clipboard.” The IRF appears as a green wave form in the curve window, as shown in Fig. 3d. 4. Select a bright pixel in the region of interest using the blue cursor. If there is any unwanted region of the image, select the ROI tool on the left-side menu and click around the perimeter of the region to be included for analysis. Alternatively, use the ROI tool to remove any unwanted areas or use the white cursors on the edge of the image frame to exclude undesired sections. 5. In the presence of the FRET acceptor Myoglobin, the fluorescence decay of mCherry is better described by a multiexponential function with more than two exponential components [29]. However, the four-parameter function in Eq. (1) (two exponentials) adequately represents most data and can be used to glean FRET trends in terms of average lifetime. In the menu on the right side, increase the multiexponential decay components from 1 to 2. 6. For dimmer samples, increase the bin number to values greater than 2 to avoid using decays with a peak count lower than 1000. For our samples, the typical bin value per image is 5 (which corresponds to an area of 11  11 (pixels)2). If such a loss of spatial lifetime resolution is prohibitive, one must collect more frames or increase photon rates (see Note 15). 7. Increase the threshold at least by a factor of N2 + 1, where N is the bin number, to exclude dark pixels. Avoid a high threshold value that results in missing cellular features. 8. The shift of the IRF is determined by fitting the decay of the pixel with the highest intensity in each image, while leaving the shift parameter free. Once the shift is determined from the brightest pixel, it can be fixed for the subsequent pixel-bypixel fitting of the lifetime decay across the whole image. A known standard sample with a single lifetime can be fit with shift allowed to vary. Shift is then fixed for subsequent unknown samples according to the known standard (see Note 16).

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9. Only fix the offset at zero if detectors with low background and negligible afterpulsing are used in conjunction with maximum rejection of ambient and stray light. (for example, an HPM-100 hybrid photodetector from B&H, or Leica HyD detectors) [4]. 10. Unfix scatter correction to account for any photon generation as a result of second harmonic components or other zero-delay scattering [4] (see Note 17). 11. Select Options from the toolbar and open the Color panel. Adjust the width of the lifetime histogram to an interval of 500 to 1500 ps. This covers the range of Myo-mCherry mean lifetimes. If left unspecified, SPCImage will autogenerate the histogram width. 12. Press F2, or select “decay matrix” in the calculate tab to start the pixel-by-pixel fitting procedure. 13. Once the fit is completed, the average lifetime is calculated for each pixel via amplitude weighting, and a lifetime distribution histogram is generated for the image or selected ROI. 14. If you have more than one image, go to the Calculate tab and select Batch Processing to analyze the rest of the images in the set using the same fit parameters. 15. Once the set of images has been batch processed, open another image in the set and observe that the same parameters are used. Readjust fit parameters as needed: to achieve a peak photon count close to 1000 binning might have to be changed; to exclude dark pixels from analysis thresholding might have to be adjusted. Recalculate the fit to improve accuracy of the lifetime values for the current image of the set. Repeat for all images to verify the results. 3.8 Obtaining the Intracellular pO2 from Lifetime Data at Each Imposed Oxygen Concentration

The τ([pO2]) for respiring cells is compared to the data for the same cell type treated with rotenone and antimycin A, which inhibit intracellular O2 consumption [30]. If O2 freely diffuses through cell membranes, and oxidative phosphorylation does not occur in treated cells, the τ([pO2]) cell response in these conditions can determine the relationship between pO2 and lifetime value, thus providing a calibration curve [31]. By using the calibration curve obtained with this method, it is then possible to back calculate the pO2 in untreated cells based on the lifetime of Myo-mCherry measured. Myoglobin has a hyperbolic oxygen dissociation curve [32, 33]. Use MATLAB’s Curve Fitting Toolbox (The MathWorks Inc., Natick, Massachusetts) or any other fitting software to fit a hyperbolic curve to the data:

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Fig. 4 (a) Average Myo-mCherry lifetimes for A549 cells and A549 cells treated with rotenone and antimycin A versus imposed [pO2]. Since rotenone and antimycin A inhibit cellular respiration, there is more oxyMyomCherry in the cell, FRET decreases, and the average lifetime values increase relative to the respiring cell line. (b) Intracellular [pO2] for A549 cells was plotted against imposed pO2. Values were calculated using a, τmax, and τmin values obtained from the calibration curve

τð½pO 2 Þ ¼ ðτmax  τmin Þ

½pO 2  þ τmin a þ ½pO 2 

ð3Þ

where τmax and τmin are the recorded lifetime values at the highest and lowest imposed partial oxygen partial pressure, [pO2] respectively, and a is a fitting parameter related to oxygen affinity [34] (Fig. 4a). 1. Fit τ([pO2]) for the non-respiring cell to the model described in Eq. (3) to obtain a, which serves as a conversion factor between lifetime and external [pO2] (see Note 18). 2. Fit τ([pO2]) for the respiring cell to the same model to confirm the hyperbolic behavior of Myo-mCherry in the cell line of interest. 3. Given a lifetime value for a respiring cell, find the external [pO2] that produces the same lifetime value on the non-respiring cell calibration curve. This [pO2] corresponds to the intracellular [pO2] in the respiring cell (Fig. 4b). 3.9 Statistical Analysis

Using statistics software packages (SPSS, R, etc.), Mann-Whitney U tests can be used to determine differences between groups for sets of cells with at least 30 samples across external [pO2]. MannWhitney U tests are alternatives to t-tests, where the lifetimes for the two sets of cells are not assumed to follow a normal distribution [35].

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Notes 1. The source material and the preparation of the plasmid coding for Myo-mCherry is described in ref. 10. For users with experience in molecular biology, the preparation is straightforward using the plasmid pmCherry N1, obtainable from Clontech (Takara Bio), as a backbone. The gene of myoglobin from Physeter catodon (Sperm Whale) is available on Addgene (plasmid pMB413a, #20058). Although restriction enzyme cloning is a viable option, because of the insertion of the Gly-Ser linker between the two genes, we find it simpler to use a seamless cloning method such as Gibson Assembly (kit available from New England Biolabs), or In-Fusion cloning (kit available from Clontech, Takara Bio). The map of the Myo-mCherry plasmid is available for anyone interested, and it will be sent along with the sample to anyone who will request the plasmid for their experiments. 2. Transfection efficiency depends on factors including the cell line, confluency, antibiotics, and media [36]. For instance, cell lines with inhibited endocytosis will be more difficult to transfect [37]. It is also possible that different reagents display different transfection efficiencies between cell lines, where higher transfection efficiency often occurs in conjunction with higher cytotoxicity [38]. We have observed that some cell lines distribute Myo-mCherry more homogeneously within organelles (Fig. 5). Research on the transfection efficiency of various reagents and their interactions with the Myo-mCherry probe between cell lines is needed. There is also evidence that over time mCherry becomes abnormally localized within lysosomes [39]. This could lead to artifacts from accidental FRET and index of refraction, so careful attention must be devoted to localization. 3. Tuning to 780 nm is convenient because many Ti:Sapphire oscillators (even inexpensive kit lasers lacking tuning) can achieve this wavelength with high power output, and mCherry shows an intense two-photon excitation peak around this wavelength. For in vivo, Myo-mCherry can be excited at 1050 nm using the same setup. 1100 nm excitation would be more appropriate; however, this wavelength is out of range for common Ti:Sapphire lasers, therefore an optical parametric oscillator (OPO) or Nd laser with access to these longer wavelengths would be needed. 4. For single-photon excitation, any laser tunable across the mCherry excitation spectrum (λmax ¼ 587 nm) can be used, but single-photon excitation presents several drawbacks compared to two-photon excitation. First, photobleaching and

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Fig. 5 (a) A C2C12 cell shows homogeneous distribution of the probe. (b) A differentiated C2C12 myo-tube shows heterogeneous distribution within the cytoplasm and organelles

photodamage are no longer constrained to the focal plane because fluorescence excitation occurs throughout the illumination volume rather than only at the laser focal point [40]. Second, descanned detectors behind the confocal pinhole must be used to eliminate out of focus emission, which increases the number of optical elements required in the imaging system [4, 40]. Finally, IRF measurements become more complicated (unless your single-photon microscope has a wideband beam splitter) because dichroic beam splitters do not transmit much scattered light to the detector, meaning that optical components need to be added and/or removed to acquire a strong signal [4]. See [40] for a primer on multiphoton microscopy touching on some of these subjects. 5. For all filters mentioned, check to see if there is a two-photon compatible version of it from your manufacturer of interest. Oftentimes, band-pass filters do not reject IR light efficiently, which can cause issues with acquisition. 6. Anecdotally, reagent urea crystals give a cleaner (tail free) signal than sucrose in our lab. It is not unlikely that sucrose may either contain fluorescent contaminants or act as a substrate for microbial growth. 7. The amount provided is generally sufficient for several transfection reactions. If amplification and propagation of plasmids to replenish the provided stock is not regularly performed in house, we strongly suggest seeking advice from colleagues in the same department/institution that routinely do so. Some companies also offer plasmid amplification services if large-scale production is needed for experiments.

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8. For users using turn-key auto-tuning lasers (e.g., Chameleon, MaiTai, Insight), alignment of the beam is often not required. 9. Performing this syncing procedure is necessary when first setting up a TCSPC system. Typically, the procedure is not required for later measurements on the same system. 10. In addition to turning off room lights, cover the microscope with a thick dark cloth. Furthermore, try to remove any possible entrance point of stray light into the objective from above, below, and the sides of the microscope stage by using nonreflective black masking tape, cardboard, or aluminum foil. 11. In the past, photon distributions were built directly into the hardware of the TCSPC counting card, thereby limiting the number of possible spatial and temporal channels one could record [4]. In contrast, new TCSPC systems running 64-bit acquisition software store spatiotemporal photon distributions directly into the memory and onto the disk of the computer, thus expanding the dimensional capacities of TCSPC systems [4]. The microscope pixel resolution defines the spatial channel constraints. On the temporal side, the FWHM of the IRF acts as the dominant constraining factor on the minimum possible lifetime value one can resolve from a sample, since many TCSPC systems have sub-picosecond channel widths constrained by the full-width half-maximum (FWHM) of the system’s electronic jitter [4, 41]. These electronic limits are an order of magnitude better than current detectors. 12. Filling the field of view with as much of the sample as possible maximizes the spatial resolution of the image, yielding more defined intracellular features. 13. Measuring overly bright cells can result in systemically low lifetime values due to “accidental” intermolecular FRET (see Note 19). 14. For spectral IRF measurements, longer acquisition times with a lower rate of incoming photons provide the best results, since high photon rates can oversaturate the photodetector, resulting in a broadened IRF [42]. Anecdotally, 20,000 counts in the peak time channel provides a reasonable IRF. Ideally, the IRF should be recorded before each experiment to account for potential day-to-day fluctuations in laser output. 15. During data acquisition in laser-scanning microscopy experiments, the point-spread function (PSF) of the microscope lens is oversampled, meaning that the pixel size in an image is smaller than its Airy disk [4] (Fig. 6a). Accordingly, binning of lifetime data from adjacent pixels in the intensity image yields substantially improved lifetime accuracy without loss of spatial resolution; in addition, sampling artifacts are avoided due to overlapping binning [4] (Fig. 6b).

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Fig. 6 (a) Oversampling of the Airy disk in the intensity image (left) and binning of lifetime data (right). Since the pixel size is smaller than the physical image resolution, lifetime data can be binned with no loss in spatial resolution. (b) Overlapping binning of pixels for lifetime calculation. Since adjacent pixels in the intensity image overlap in the binned lifetime data, sampling artifacts are minimized. (This figure was reproduced from Becker 2017 [4] with permission)

16. Effects of IRF, shift, threshold, and bin on FLIM analysis: Obtaining correct scatter values is dependent on the experimental IRF, and as such IRF should be collected before each experiment [5]. As shown in Fig. 7a, four types of IRF data were used in the calculation of mean lifetimes for ten sample Myo-mCherry images. The images were analyzed with IRF data (IRF 1) from an experiment a year prior to imaging and was compared to analysis done with an IRF taken prior to obtaining the sample images (IRF 2). Additionally, shown here are the average lifetimes calculated using the new IRF with a fixed scatter parameter, and those calculated using IRF data auto-generated by SPCImage. The parameters bin, shift, and threshold are used in the fitting procedure to obtain more accurate lifetime measurements by resolving the needs of numerical de-convolution [5]. In order to obtain accurate lifetime measurements using these parameters, it is necessary to know how each one affects fitting, and therefore lifetime. Lifetime binning for a number of pixels (2n + 1)2 results in less sampling artifact, reducing spatial resolution [5]. Increasing pixel size for lifetime calculation through binning will alter lifetimes as dim regions can surpass the set photon count threshold as binning increases. To account for this, threshold must be simultaneously adjusted accordingly to exclude these regions. Thresholding excludes pixels below the set photon count limit from lifetime calculation and limits the influence of dark pixels on lifetime calculation, which otherwise contribute long lived arrival times to the mixture [5]. Shift accounts for differences in detector timing at the IRF collection wavelength and the sample’s emission [5]. Slight changes in shift (less than a single time channel,

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and without significantly changing χ 2) can be used to fine-tune analysis and achieve the best possible lifetime values. The effects of shift, threshold, and binning (all in isolation) on the average lifetime of Myo-mCherry are shown in Fig. 7b–d. Clearly, the collection of a contemporaneous IRF, the calibration of the shift parameter described above, and the exclusion of background with a proper threshold (one that must be changed if binning changes) are all important in recovering stable, accurate mean lifetimes. Doing one without the other may bias results. 17. Obtaining correct scatter values is dependent on the experimental IRF, especially in multiphoton microscopy [4]. If the “automatically generated” IRF (option available in the SPCImage software) is used for data acquired with a multiphoton setup, faster lifetimes will be biased towards longer values and SHG components will be difficult to separate from FLIM data [4]. Likewise, if the scatter parameter is fixed when copying the IRF from the SHG generator data, SHG components are not correctly extracted.

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18. The accuracy of the fitting parameter, a, is dependent on the number of imposed O2 concentrations measured for the cell line of interest, n. In the example provided for the calibration curve where n ¼ 8, a ¼ 7  3 and for τA549 ¼ 1.150 ns, [pO2, int] ¼ 4  2, yielding up to a 50% variation in the calculated [pO2] values depend on the value of a used. As n increases, the variation in a will decrease, yielding more precise calculated [pO2] values. 19. Effect of intermolecular FRET on FLIM analysis: If the distance between chromophores is approximately 3 nm or less, intermolecular FRET can occur [4, 12, 43]. To prevent intermolecular FRET, it would be useful to ensure, when possible, that the concentration of Myo-mCherry does not exceed a few μM. Empirically, while imaging, select cells that do not show oversaturated intensity [44]. Intermolecular FRET can cause self-quenching fluorophore pairs as a result of oversaturation. The unusually high fluorescence results in inaccurate, shortened lifetimes. In Fig. 8, the Myo-mCherry lifetime for a non-respiring SCO2 KO cell at 20% imposed O2 concentration (A) is compared to that measured in the same cell and imposed O2 concentration affected by intermolecular FRET (B). The lifetime histogram distribution is shown for both in Fig. 8c. (B) intentionally has a corrected total cell fluorescence 2.8 times greater than (A) as a result of oversaturation [45].

Fig. 8 Effect of intermolecular FRET on Myo-mCherry lifetime. (a) Lifetime distribution of Myo-mCherry in non-respiring SCO2 KO cell at 20% oxygen concentration. (b) Shorter lifetime of Myo-mCherry due to intermolecular FRET. (c) Lifetime histogram distribution of Myo-mCherry (solid black line with a peak at 1.33 ns) and a shift in the average lifetime (solid white line with a peak at 0.97 ns) due to intermolecular FRET

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Acknowledgments This work was supported by the Intramural Research Program of NHLBI, and in part by funds from the Office of Intramural Training and Education (OITE) of the Office of the Director (OD), National Institutes of Health (NIH). We would like to acknowledge the Light Microscopy Core at the NHLBI (particularly Dr. Christian Combs) for the use of their multiphoton microscopes. References 1. Becker W (2012) Fluorescence lifetime imaging—techniques and applications. J Microsc 247(2):119–136 2. Suhling K, French PM, Phillips D (2005) Time-resolved fluorescence microscopy. Photochem Photobiol Sci 4(1):13–22 3. Becker W (2005) Advanced time-correlated single photon counting techniques. Springer, Berlin 4. Becker W (2017) The bh TCSPC handbook, 7th edn. Becker & Hickl GmbH, Berlin 5. Berezin MY, Achilefu S (2010) Fluorescence lifetime measurements and biological imaging. Chem Rev 110:2641–2684 6. Tinnefeld P et al (2000) Confocal fluorescence lifetime imaging microscopy (FLIM) at the single molecule level. Single Mol 1(2):215–223 7. Singh AK, Das J (1998) Liposome encapsulated vitamin A compounds exhibit greater stability and diminished toxicity. Biophys Chem 73(1–2):155–162 8. Owen DM et al (2006) Fluorescence lifetime imaging provides enhanced contrast when imaging the phase-sensitive dye di-4ANEPPDHQ in model membranes and live cells. Biophys J 90(11):L80–L82 9. Wallrabe H, Periasamy A (2005) Imaging protein molecules using FRET and FLIM microscopy. Curr Opin Biotechnol 16:19–27 10. Penjweini R et al (2018) Intracellular oxygen mapping using a myoglobin-mCherry probe with fluorescence lifetime imaging. J Biomed Optics 23(10):107001. https://doi.org/10. 1117/1.JBO.23.10.107001 11. Andreoni A et al (2019) Genetically encoded FRET probes for direct mapping and quantification of intracellular oxygenation level via fluorescence lifetime imaging (2019). In: Proc. SPIE 10882, Multiphoton Microscopy in the Biomedical Sciences XIX, 108820O. https://doi.org/10.1117/12.2510646

12. Lakowicz JR (1984) Principles of fluorescence spectroscopy. Plenum, New York 13. Selvin PR (2000) The renaissance of fluorescence resonance energy transfer. Nat Struct Biol 7(9):730–734 14. Fo¨rster T (1946) Energiwanderung und Fluoreszenz. Naturwissenschaften 6:166–175 15. Fo¨rster T (1948) Zwischenmolekulare Energiewanderung und Fluoreszenz. Ann Phys Ser 6(2):55–75 16. Sun Q et al (2015) Mechanism of two-photon excited hemoglobin fluorescence emission. J Biomed Opt 20(10):105014 17. Jain A, Blum C, Subramaniam V (2009) Fluorescence lifetime spectroscopy and imaging of visible fluorescent proteins. In: Verdonck P (ed) Advances in biomedical engineering. Elsevier, Amsterdam 18. Wu B et al (2009) Fluorescence fluctuation spectroscopy of mCherry in living cells. Biophys J 96(6):2391–2404 19. Ballew RM, Demas JN (1989) An error analysis of the rapid lifetime determination method for the evaluation of single exponential decays. Anal Chem 61:30–33 20. Ko¨llner M, Wolfrum J (1992) How many photons are necessary for fluorescence lifetime measurements? Phys Chem Lett 200:199–204 21. Phillip JP, Carlsson K (2003) Theoretical investigation of the signal-to-noise ratio in fluorescence lifetime imaging. J Opt Soc Am A Opt Image Sci Vis 20:368–379 22. Li N et al (2003) Mitochondrial complex I inhibitor rotenone induces apoptosis through enhancing mitochondrial reactive oxygen species production. J Biol Chem 278 (10):8516–8525 23. Chen Q et al (2003) Production of reactive oxygen species by mitochondria central role of complex III. J Biol Chem 278 (38):36027–36031

Intracellular Oxygen Monitoring Using Myo-mCherry FLIM 24. Shanser NC et al (2004) Improved monomeric red, orange and yellow fluorescent proteins derived from Discosoma sp. red fluorescent protein. Nat Biotech 22:1567–1572 25. Becker & Hickl GmbH (2008) Recording the instrument response function of a multiphoton FLIM system. Becker & Hickl GmbH. Available via DIALOG. https://www.becker-hickl. com/wp-content/uploads/2018/12/irf-mpv04.pdf. Accessed on October 9, 2019 26. Thermo Fisher Scientific Inc (2017) Invitrogen™ lipofectamine™ 3000 transfection agent. In: Meet our transfection product family. Thermo Fisher Scientific Inc. Available via DIALOG. http://assets.thermofisher.com/ TFS-Assets/BID/Flyers/transfection-prod uct-family-flyer.pdf. Accessed on October 9, 2019 27. Hirsch RP (2016) Introduction to biostatistical applications in health research with Microsoft Office excel. John Wiley & Sons, Inc., Hoboken, New Jersey 28. Wagner BA, Venkataraman S, Buettner GR (2011) The rate of oxygen utilization by cells. Free Radic Biol Med 51(3):700–712 29. Lackowicz JR (2000) On spectral relaxation in proteins. Photochem Photobiol 72 (4):421–437 30. Forkink M et al (2015) Complex I and complex III inhibition specifically increase cytosolic hydrogen peroxide levels without inducing oxidative stress in HEK293 cells. Redox Biol 6:607–616 31. Lodish H, Berk A, Zipursky SL et al (2000) Molecular cell biology, 4th edn. W.H. Freeman, New York 32. Ruiz-Larrera M (2006) A simple question to think about when considering the hemoglobin function. Biochem Mol Biol Educ 30 (4):235–238 33. Schenkman KA et al (1997) Myoglobin oxygen dissociation by multiwavelength spectroscopy. J Appl Physiol 82(1):86–92 34. Kelly JJ et al (1991) Myoglobin oxygen binding curves determined by phosphorescence quenching of palladium porphyrin. Appl Spectrosc 45:1177–1182 35. Schweitzer D et al (2015) Fluorescence lifetime imaging ophthalmoscopy in type

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2 diabetic patients who have no signs of diabetic retinopathy. J Biomed Opt 20(6):061106 36. Thermo Fisher Scientific (2019) Factors influencing transfection agency. Thermo Fisher Scientific. Available via DIALOG. https://www. thermofisher.com/us/en/home/references/ gibco-cell-culture-basics/transfection-basics/ factors-influencing-transfection-efficiency. html. Accessed October 9, 2019 37. Conner SD, Schmid SL (2003) Regulated portals of entry into the cell. Nature 22:37–44 38. Wang T et al (2018) Systematic screening of commonly used commercial transfection reagents towards efficient transfection of single-stranded oligonucleotides. Molecules 23(10):2564 39. Shemmiakina II et al (2012) A monomeric red fluorescent protein with low cytotoxicity. Nat Commun 3(1):1204 40. Piston D et al (2019) Multiphoton microscopy: fundamentals and applications in multiphoton excitation microscopy. In: Microscopy U, The source for microscopy education. Nikon. Available via DIALOG. https:// www.microscopyu.com/techniques/multiphoton/multiphoton-microscopy. Accessed on October 9, 2019 41. Wahl M (2014) Time-correlated single photon counting. PicoQuant Gmbh. Available via DIALOG. https://www.picoquant.com/ images/uploads/page/files/7253/technote_ tcspc.pdf. Accessed on October 9, 2019 42. amamatsu Photonics KK (2019) MCP (microchannel plate) and MCP assembly. Hamamatsu. Available via DIALOG. https://www. hamamatsu.com/resources/pdf/etd/MCP_ TMCP0002E.pdf. Accessed on October 9, 2019 43. Bajar BT et al (2016) A guide to fluorescent protein FRET pairs. Sensors (Basel) 16 (9):1488 44. Wilbur S, Williams M, Williams R et al (2012) Toxicological profile for carbon monoxide. Agency for Toxic Substances and Disease Registry, Atlanta, Georgia. https://www.atsdr.cdc. gov/ToxProfiles/tp.asp?id¼1145&tid¼253. Accessed October 9, 2019 45. Waters JC (2019) Accuracy and precision in quantitative fluorescence microscopy. J Cell Biol 185(7):1135–1148

Chapter 18 FLIM Imaging for Metabolic Studies in Live Cells Heejun Choi Abstract Fluorescent biochemical sensors allow probing metabolic states in a living cell with high spatiotemporal dynamics. This chapter describes a method for the in situ detection of changes in NAD+ level in living cells using fluorescence lifetime imaging (FLIM). Key words Metabolism, Biosensors, FLIM, FRET, NAD+, Mitochondrial NAD+, Nuclear NAD+, Cytosolic NAD+

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Introduction Cells balance their internal biochemical reactions via control of their metabolic flux. These changes allow tight control of key processes that govern growth, maintenance, and survival. With the single-cell techniques to probe metabolic changes inside a cell, we begin to appreciate the cell-to-cell heterogeneity of metabolite synthesis and consumption in basal and perturbed conditions. We start to understand that this regulation also occurs in subcellular compartments, like the nucleus and mitochondria. Confining the measurement in these organelles without perturbing its environment is challenging. This challenge has been addressed by several visualization tools that specifically probe the metabolites within these compartments. By confining the sensors to a spatially restricted compartment through targeting and retention motifs, one can probe a compartment-specific metabolic flux. These methods are incredibly useful as most metabolites are ubiquitous, yet these metabolites in specific compartments are spatially constricted and regulated. Some metabolites like NADH and FAD are intrinsically fluorescent, thus eliminating the necessity of labeling. Therefore, monitoring the level of NADH and FAD can be directly achieved by monitoring their fluorescence. When NADH or FAD are bound to

Joseph Brzostowski and Haewon Sohn (eds.), Confocal Microscopy: Methods and Protocols, Methods in Molecular Biology, vol. 2304, https://doi.org/10.1007/978-1-0716-1402-0_18, © This is a U.S. government work and not under copyright protection in the U.S.; foreign copyright protection may apply and Springer Nature US 2021

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a protein, their fluorescence lifetime changes; this allows label-free monitoring of metabolite states in living cells [1]. However, not all metabolites are fluorescent (such as NAD+, NADP+, glucose), and some intrinsically fluorescent metabolites like NADH and NADPH are not spectrally distinguishable from each other. To overcome this limitation, biosensors capable to monitor these nonfluorescent or fluorescent but spectrally overlapping metabolites have been developed. These biosensors exploit the use of a “sensing” domain that can specifically bind to these “intrinsically nonfluorescent” metabolites. When bound to these metabolites, the resulted conformation change can be read as either fluorescent resonance energy transfer (FRET) between two fluorophores or intensity change when tagged to fluorescent proteins (FPs) or self-labeling tags linked to the “sensing” domain. The changes in FRET or intensity can be monitored as a change in fluorescence lifetime. The list of biosensors used as a lifetime sensor is given in Table 1. There are several advantages in the use of fluorescence lifetime as a parameter to evaluate the concentration of a metabolite. Fluorescence lifetime is nearly independent of the concentration of the fluorophore. In the case of monitoring FRET, only donor fluorescence lifetime needs to be monitored, so that the acceptor does not need to be fluorescent (i.e., dead YFP that is nonfluorescent, but can act as a FRET acceptor [2]). As it is only a function of analyte Table 1 List of metabolite sensors that demonstrated with FLIMa

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For a complete list of biosensors, please visit http://biosensordb.ucsd.edu

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concentration, fluorescence lifetime can also be used to quantitatively report the concentration of the analyte if the proper calibration curve can be achieved. In recent years, there has been considerable effort to make more red-shift fluorophores that can be used as a sensor. In addition to the benefit of combining different color sensors, the red-shifted versions can potentially be imaged deeper with low contribution from autofluorescence (i.e., RCaMP (red Ca2+ indicator) [3]). The recent development of self-labeling tags allowed easy access to changing the spectral properties without compensating the quantum yield through facile exchange of fluorophores. This protocol will describe a method to monitor NAD+ through a novel SNAP-tag-based Indicator with a Fluorescent Intramolecular Tether (SNIFIT) [4]. SNIFIT is composed of three parts: (1) an analyte binding protein (2) a SNAP protein, and (3) a resonance energy transfer acceptor (in this case, HaloTag) [5] (Fig. 1). NAD-SNIFIT contains a mutated version of human sepiapterin reductase (SPR A41D R42W) that specifically recognizes NAD+ (purple) fused with HaloTag and SNAPTag. These tags are linked by 30 proline residues. SMX (a potent SPR inhibitor) interacts with the mutated form of SPR only upon NAD+ binding, causing FRET between CP-TMR-SMX and SiR-Halo. The protocol here describes a method to measure the organelle-specific NAD+ level in U2-OS cells. This tool works in different cell types (HeLa [4], NIH/3T3 [4], HEK293T [4], HT1080 (not published)), but the exact fluorescence lifetime and NAD+ values vary between cell types.

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Materials 1. Complete growth medium (DMEM + 10% FBS + 10 mM Glutamate + Penn/Strep). 2. U2-OS cells (ATCC). 3. Lonza Electroporation kit for U2-OS. 4. Leica FALCON SP8 confocal microscope (White light laser/ HyD SP Gen 2 detector/HC PL APO 86/1.20 W water immersion objective) or comparable system. 5. NAD-SNIFIT plasmid with mammalian expression promoter. 6. CP-TMR-SMX in DMSO (1 mM). 7. SiR-Halo in DMSO (1 mM). 8. Sulfapyridine in medium (2 mM).

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Fig. 1 (a) Structures of the synthetic molecules used for the NAD sensor. CP-TMR-SMX contains SNAP Tag Ligand (O-benzyl-2-chloro-6-aminopyrimidine, CP), tetramethylrhodamine (TMR, green) and sulfamethoxazole (SMX, blue). TMR acts as a FRET donor. SiR-Halo contains silicone rhodamine (SiR) with HaloTag Ligand (Halo). SiR acts as a FRET acceptor. (b) Schematics of NAD-SNIFIT. The protein contains a mutated version of human sepiapterin reductase (SPR A41D R42W) that recognizes NAD (purple) instead of NADP and that is fused with HaloTag and SNAPTag. HaloTag and SNAPTag are linked by 30 proline residues (Pro30). SMX (a potent SPR inhibitor) interacts with the mutated form of SPR upon NAD binding, causing FRET between CPTMR-SMX and SiR-Halo

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1. Culture U2-OS cells in complete medium (DMEM + 10% FBS + 10 mM Glutamate + P/S) or cells under appropriate medium in a T-25 container. 2. Aspirate the medium from cells in the T-25 container.

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3. Add 2 mL of trypsin–EDTA (0.25%) and mix thoroughly in the container. 4. Aspirate the trypsin. 5. Incubate the container for 1–5 min until the cells detach from the container. 6. Resuspend the cells in the medium with FBS to neutralize the trypsin. 7. Count the number of cells using either hemocytometer or automatic cell counter. 8. Centrifuge 106 cells in 14 mL conical tube. 9. Follow the Lonza Electroporation protocol for U2-OS cell. 10. Resuspend the cells in the growth medium and recover cells over 16 h. 3.2 Turning on the FLIM System (Day 2)

1. Start the FLIM system on the microscope and allow the environmental chamber to reach the appropriate temperature and CO2 percentage. 2. Set the emission range from 550 to 610 nm for detection. A 514 nm excitation with 20 MHz pulse frequency is recommended.

3.3 Donor-Only Control Using CP-TMR-SMX

1. Label cells with 250 nM CP-TMR-SMX for 1 h (see Note 1). Representative examples of various subcellular localizations after labeling are shown in Fig. 2. 2. Rinse five times with fresh medium to wash out excess dyes followed by 1 h incubation in dye-free complete medium (see Notes 2–4). 3. Change the medium before taking it to the microscope. 4. Measure the fluorescence lifetime using Leica Falcon SP8 system. 5. Check the photon counts per pixel per cycle. Refer to the detector manual for the upper photon count per pixel per cycle (see Notes 5 and 6). 6. Collect data. 7. Fit the curve with biexponential (see Note 7).

3.4 Measure Donor Lifetime with Acceptor

1. Label cells with 250 nM CP-TMR-SMX and 250 nM SiR-Halo for 1 h (see Note 8). 2. Rinse five times with fresh medium to wash out excess dyes followed by 1 h incubation. 3. Change the medium again before taking it to the microscope. 4. Measure the lifetime of the donor with the acceptor.

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Fig. 2 Representative image of cytosolic (left), nuclear (middle), and mitochondrial (right) NAD-SNIFIT expressed in HT1080. These cells are labeled according to the protocol. The scale bar represents 5 μm

5. Fit the curve using multi-exponential tool in Leica. 6. Compare the lifetime with the donor-only control. 3.5 Control Experiments Using the Inhibitor Sulfapyridine

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Notes 1. The permeability of CP-TMR-SMX is highly dependent on cell type. 10 μM () verapamil hydrochloride may be used with the CP-TMR-SMX to decrease the efflux of intracellular dye concentration. 2. Sometimes CP-TMR-SMX is accumulated in the endolysosomal pathway. To reduce nonspecific binding and accumulation of dyes, we recommend incubating the cells without dye for a few hours post-labeling to reduce the background. 3. Since the NAD-SNIFIT is not a ratiometric sensor, one can use fluorescence lifetime to measure the absolute concentration of NAD+ inside cells. This requires one to permeabilize the cells with a low concentration of digitonin. Then, the medium is exchanged with known concentrations of NAD+ to create a calibration curve by plotting fluorescence lifetime vs. the concentration. Using the calibration, one can determine the concentration of NAD+. However, this may only be done with cytosolic NAD-SNIFIT, as other cellular compartments like mitochondria and nucleus are not permeable in this condition. 4. Although TMR and SiR are stable in many environmental conditions (pH, etc.), the fluorescence lifetime is quite sensitive to the local environment. Thus, the absolute lifetime value may

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change with different biological conditions as it changes the actual dynamic range of the sensor. The control experiments such as sulfapyridine treatment and donor-only control are crucial to show that the change in lifetime is not due to changes in donor or acceptor fluorophore environment, the actual changes in NAD+ concentration. 5. Remember to setup the laser power such that the photon count is sufficiently low to avoid photon pileup effect. This value depends on each system, therefore please check the specification of the system. 6. The region of interest should have sufficient photon counts to provide more reliable fitting of the time-correlated single-photon counting (TCSPC) data. The rule of thumb is to have ~500 counts per pixel in the region of interest. 7. The CP-TMR-SMX shows bi-exponential rather than monoexponential decay, thus the decay curve must be fitted accordingly. The fitting can be done with Leica or PicoQuant software. Follow the step provided by the software. 8. Both CP-TMR-SMX and SiR-Halo need to be at saturation to allow a large dynamic range. The best optimization of labeling is important and is best to build a saturation curve where the maximum signal can be achieved without nonspecific signal. Exceeding 1 μM concentration of dyes causes nonspecific labeling (accumulation of dyes into ER, lysosome, etc.). 9. The exact lifetime of donor fluorescence is (1) cell type dependent and (2) compartment dependent. Therefore, it is important to check the donor-only lifetime to examine the maximum dynamic range and their values. In U2-OS cells, the measured averaged lifetime of the donor-only is 2.89 ns—cytosol, 2.66 ns—nucleus, and 2.63 ns—mitochondria [4]. References 1. Skala MC, Riching KM, Bird DK, GendronFitzpatrick A, Eickhoff J, Eliceiri KW, Keely PJ, Ramanujam N (2007) In vivo multiphoton fluorescence lifetime imaging of protein-bound and free nicotinamide adenine dinucleotide in normal and precancerous epithelia. J Biomed Opt 12(2):024014. https://doi.org/10.1117/1. 2717503 2. Ganesan S, Ameer-beg SM, Ng TTC, Vojnovic B, Wouters FS (2006) A dark yellow fluorescent protein (YFP)-based resonance energy-accepting Chromoprotein (REACh) for Fo¨rster resonance energy transfer with GFP. Proc Natl Acad Sci U S A 103(11):4089–4094. https://doi.org/10.1073/pnas.0509922103

3. Akerboom J, Carreras Calderon N, Tian L, Wabnig S, Prigge M, Tolo J, Gordus A, Orger MB, Severi KE, Macklin JJ, Patel R, Pulver SR, Wardill TJ, Fischer E, Schuler C, Chen TW, Sarkisyan KS, Marvin JS, Bargmann CI, Kim DS, Kugler S, Lagnado L, Hegemann P, Gottschalk A, Schreiter ER, Looger LL (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 4. Sallin O, Reymond L, Gondrand C, Raith F, Koch B, Johnsson K (2018) Semisynthetic biosensors for mapping cellular concentrations of

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nicotinamide adenine dinucleotides. eLife 7: e32638. https://doi.org/10.7554/eLife. 32638 5. Farrants H, Hiblot J, Griss R, Johnsson K (2017) Rational design and applications of semisynthetic modular biosensors: SNIFITs and LUCIDs. In: Stein V (ed) Synthetic protein switches: methods and protocols. Springer New York, New York, NY, pp 101–117 6. Diaz-Garcia CM, Mongeon R, Lahmann C, Koveal D, Zucker H, Yellen G (2017) Neuronal

stimulation triggers neuronal glycolysis and not lactate uptake. Cell Metab 26(2):361–374. e364. https://doi.org/10.1016/j.cmet.2017. 06.021 7. 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. https://doi.org/10.1371/ journal.pone.0019170

Chapter 19 A Step-by-Step Guide to Instant Structured Illumination Microscopy (iSIM) Alexander Zhovmer and Christian A. Combs Abstract Instant structured illumination microscopy (iSIM) allows for rapid multicolor three-dimensional fluorescence imaging at levels of resolution approaching twice the diffraction limit. Here we briefly describe the theory of iSIM and outline a typical hardware setup. We also provide step-by-step guides for generating a cellular-based fluorescent standard, obtaining a multicolor image with iSIM, and the post-processing steps of de-striping and deconvolution using freely distributed software to minimize time and expense. A “Notes” section is also given to inform the reader of the limitations and considerations for the methods shown. Also discussed are alternative methods, quality control checks, and considerations for two-camera alignment. Key words Super-resolution, Fluorescence, Review, Instant structured illumination microscopy, iSIM

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Introduction Instant structured illumination microscopy (iSIM) is a threedimensional (3D) super-resolution (to approximately 145 nm lateral and 350 nm axial) technique that excels at fast and relatively gentle fluorescence imaging [1]. It was first developed by Andrew York and Hari Shroff and built on the idea of spot-scanning or multifocal illumination with photon reassignment for superresolution-structured illumination microscopy (SR-SIM) as described earlier [2–4]. iSIM has since been expanded to both total internal reflection microscopy (TIRF) [5] and two-photon fluorescence microscopy (TPFM) [6]. Traditional SR-SIM relies on spatially varying illumination that excites diffraction limited or smaller regions of the sample [7–10]. To ultimately generate a super-resolution image, the illumination pattern must be shifted and the resulting emission patterns generated from the interactions of the illumination and the sample must be computationally processed to produce a single image. This approach generally takes at

Joseph Brzostowski and Haewon Sohn (eds.), Confocal Microscopy: Methods and Protocols, Methods in Molecular Biology, vol. 2304, https://doi.org/10.1007/978-1-0716-1402-0_19, © This is a U.S. government work and not under copyright protection in the U.S.; foreign copyright protection may apply and Springer Nature US 2021

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least nine images to produce a final 2D image and many more to collect a 3D image [11, 12]. Thorough reviews contrast iSIM imaging strengths and weaknesses compared to other SIM techniques [12] and compare SIM to other super-resolution techniques for biological imaging [13, 14]. The main advantage of iSIM is that it uses fast multifocal excitation and optics on the emission side that allow for obtaining an SR-SIM image instantly, in analog fashion, without the need for the collection of multiple images and subsequent computational reconstruction (although postprocessing deconvolution can enhance resolution and contrast). To understand how this is accomplished, it is necessary to examine the hardware and principles of an iSIM system. Figure 1 shows a schematic of the hardware and concepts associated with an iSIM system. In many respects it is similar to a spinning disk or swept-field confocal microscope in that multifocal excitation beams (from lasers here shown as a light engine) are swept over the sample and the emission light is returned through emission pinholes to sensitive cameras (here shown as sCMOS cameras). The emission spectral bandwidth is determined by fixed bandwidth filters in a filter wheel (in the iSIM scan head) and/or a dichroic mirror (filter cube) in an optical splitter. The main difference is that in iSIM the optics for fluorescence emission collection sample the local region around each multifocal excitation spot. The resulting multiplication of the excitation point spread function (PSF) and each emission PSF offset a distance “X” from the excitation PSF results in a system PSF that is sharper than traditional confocal microscopy, but with lower signal. To restore this signal without sacrificing the improved resolution, the offsets must be removed and the signal from each system PSF summed. In previous methods [2, 4], multiple images are taken as the illumination pattern is swept through the sample and the shifting and summing is achieved computationally (by moving the detected signal X/2 toward the illumination axis). In the iSIM, the same photon reassignment is performed with hardware and only requires one acquired image. This is accomplished through local scaling of the emission light going through a demagnification step (usually 0.5 magnification) to remove the shift optically (Fig. 1, insert above iSIM scan head). This allows for near-resolution doubling at a level of accessibility similar to spinning disk confocal imaging in terms of ease of use, speed, and choice of fluorophores. Here we describe in a step-by-step method how to acquire iSIM images and then process them using the freely distributed software FIJI [15] and associated plugins. Image processing methods will include step-by-step instructions for deconvolution (to increase contrast and resolution) using idealized point spread functions (PSFs) derived from the FIJI plugin “PSF Generator”

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Fig. 1 Illustration of the iSIM setup and concepts. Shown are key elements in a representative iSIM system as described in Subheading 3. The blue excitation light and the green and red emission light are blurred to simulate multifocal beams. The insert above the iSIM scan head is meant to illustrate the key concepts of iSIM including offset excitation and emission PSFs during multifocal scanning and the optical local scaling for pixel reassignment as described in Subheading 1

and deconvolved using the FIJI plugin DeconvolutionLab2 [16]. We also outline a basic way to de-stripe images using FIJIbased Fourier filtering. Striping in iSIM images can occur from diffraction artifacts being added to the various emission PSF’s from the multifocal image collection due to refractive index mismatch or light scattering in thick samples or in misaligned microlenses. These can be removed with specialized optical components or through post-processing algorithms. These processes will be shown using images with a cellular standard that has fluorescently labeled punctate (nuclear pore complexes) and linear (microtubules) sub-resolution structures. A “Notes” section is also provided to detail other methods for deconvolution and de-striping and for other comments on optimizing iSIM imaging experiments.

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Materials

2.1 Hardware and Image Acquisition Software

The iSIM system shown in Fig. 1 consists of an Olympus IX-81 microscope (Olympus Corp., Tokyo, Japan) equipped with an Olympus UPLAPO-HR 100/1.5 NA objective, two Flash-4 scientific CMOS cameras (Hamamatsu Corp. Tokyo, Japan), an iSIM scan head (VisiTech Intl., Sunderland, UK), a multi-laser light engine (BioVision Technologies, Exton, PA), an MS-2000 XY stage, a joystick (ASI, Eugene, OR), and a Nano-Drive piezo Z stage (Mad City Labs, Madison, WI) (see Notes 1 and 2). The iSIM scan head included the VT-Ingwaz optical de-striping unit (VisiTech Intl., Sunderland, UK). The optical splitter (VT-Twincam, VisiTech Intl., Sunderland, UK) and filter cubes with dichroic mirrors and emission filters (Chroma Technology Corp., Bellows, Falls, VT) used to split the emission light between the two cameras were purchased from BioVision Technologies (Exton, PA). The emission filter wheel in the iSIM scan head was populated with DAPI (ET460/50), GFP (ET525/50), Texas Red (ET605/52), and Cy5 (ET655lp) filters (Chroma Technology Corp., Bellows Falls, VT). Image acquisition and system control was through MetaMorph Premier software (Molecular Devices, LLC, San Jose, CA).

2.2

Mouse embryonic fibroblasts were grown in the glass-bottom dishes (P35G-1.5-20-C, MatTek) using DMEM (high glucose, GlutaMAX™ Supplement, HEPES) (10564011, Gibco) with 10% heat-inactivated FBS (110-001-101HI, Quality Biological).

Cell Culture

2.3 Immunocytochemistry

1. CRITICAL: Avoid drying at any step. It can destroy samples. 2. Fix and permeabilize cells with 3 mL of 20  C methanol (322415, Sigma-Aldrich) for 10 min at 20  C. 3. Rinse samples gently three times with 1 mL of 1 Blocker BSA (37525, Thermo Fischer Scientific) in PBS (RGF-3210, KDMedical). 4. Incubate samples with 1 mL 1 Blocker BSA in PBS for 1 h at room temperature to block nonspecific antigen binding. 5. Label microtubules and nuclear pores with 1:500 dilution of primary rabbit anti-alpha tubulin (ab18251, Abcam) and mouse anti-nuclear pore complex proteins (ab24609, Abcam) antibodies in 500 μL of 1 Blocker BSA in PBS overnight at 4  C. 6. Wash samples three times (5 min) with 1 mL of 1 Blocker BSA in PBS.

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7. Label samples with 1:500 secondary Alexa Fluor 488 goat antimouse (A32723, Thermo Fischer Scientific) and Alexa Fluor 568 goat anti-rabbit (A-11011, Thermo Fischer Scientific) antibodies in 500 μL of 1 Blocker BSA in PBS for 2–4 h at room temperature. 8. Wash samples four times (5 min) with 1 mL of 1 Blocker BSA in PBS. 9. Wet mount samples in glass-bottom dishes by adding 1 mL of 90% glycerol (G2025, Sigma-Aldrich) with 1 μg/mL Hoechst 33258 (B2883, Sigma-Aldrich) in PBS. 10. Optional: Add 0.01% sodium azide (71289, Sigma-Aldrich) to the wet mount medium as a preservative to prevent long-term bacterial contamination and degradation of sample.

3

Methods

3.1 Image Acquisition Protocol (Specific for MetaMorph Acquisition Software)

1. Start the iSIM system. 2. Start MetaMorph software (version 7.8.13.0, Molecular Devices, LLC). 3. Choose the 100/1.5NA objective (Devices/Device Control/Control/Magnification). 4. Open multidimensional acquisition panel (Apps/Multidimensional Acquisition). 5. Open laser power panel (Devices/Integrated Confocal System). 6. Put one drop of Olympus Type-F immersion oil (NC0297589, Thorlabs Inc.) on the lens. 7. Place sample in the microscope stage sample holder. 8. Switch to the eyepiece view using View GFP illumination settings (Devices/Device Control/Control/Illumination tab), press Open Shutter button, and find the sample using manual focusing. 9. Switch to camera view using iSIM488 illumination settings and adjust position of the sample using joystick. 10. Go to the multidimensional acquisition panel and select the proper image acquisition parameters for your particular needs. Here we show how to collect Z-stacks for two channels. 11. Choose Z-stack, z-streaming, and multiple wavelengths options (Main/Multiple Wavelengths, Main/Z-series, and Main/Stream). 12. Define a folder for saving the acquired images (Saving). 13. Add the 488 nm and 568 nm channels (Wavelengths) and adjust the exposure time to 250 ms for each channel.

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14. Define the acquisition range and adjust the z-step to 100 nm (Z-series). 15. Check the z-streaming box (Z-Stream). 16. Adjust the laser power to 33% (in laser power panel) for 488 nm and 568 nm channels (see Note 3). 17. Start the image acquisition by pressing the Acquire button in the multidimensional acquisition panel (see Notes 4 and 5). 3.2 Image Post Processing

3.2.1 Image Deconvolution

Typical post processing of acquired iSIM images includes image deconvolution and de-striping if no optical de-striping unit (such as the VT-Ingwaz) is installed. Here, we provide examples of deconvolution and de-striping in FIJI using PSF generator, DeconvolutionLab2, and Fourier transform functions (see Note 6). 1. In FIJI, open the PSF generator plugin (under plugins tab) to begin generating the PSF for subsequent deconvolution for each fluorophore imaged (see Note 7). 2. Generate an excitation PSF by inputting the excitation wavelength used to image a specific fluorophore, numerical aperture of the objective, refractive index of the immersion media, and the “Born & Wolf 3D Optical Model” in the PSF generator plugin. The default XYZ size and the bit depth can be left as default. Press run and save the image stack as the excitation PSF for the fluorophore to be deconvolved. 3. Generate an emission PSF for the same fluorophore by the same method with the only change being entering the wavelength corresponding to the high point of the emission range into the wavelength box of the PSF generator software. After running the plugin, save the resulting image stack as the emission PSF for the fluorophore to be deconvolved. 4. Generate the idealized PSF for deconvolution for the above selected fluorophore by multiplying the excitation PSF by the emission PSF. In FIJI, this is done by opening the excitation and emission PSFs saved in steps 3 and 4 and multiplying these image stacks by using the image calculator (process/image calculator). In the image calculator dialog box, select each opened image into the image 1 and 2 selectors, choose the operator multiply, and select open new window. Save the resulting image stack as the idealized PSF for the selected fluorophore. 5. Repeat steps 2–4 for each fluorophore to be deconvolved. These idealized PSFs can be reused for all image stacks obtained under identical experimental conditions (same fluorophore, numerical aperture of the objective, and refractive index of the immersion media).

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6. Before starting deconvolution, remove the background from the image stack for each fluorophore to be deconvolved. This is accomplished by directly measuring the background in an image containing the specimen, if a pure background region is in evidence, or by imaging a blank slide under the same conditions. In FIJI, draw an ROI in the background area and press Ctrl-M to get the value (to add this measurement, check the box in Analyze/Set Measurements/Mean grey value). The measurement can be repeated in multiple ROIs to get an average value. Once a value representing the background is known, subtract this value from the image stack by going to “Process/Math/Subtract” in FIJI. 7. To begin deconvolution, open the “DeconvolutionLab2” plugin in FIJI (Plugins/DeconvolutionLab2). 8. Input the image to be deconvolved by dragging and dropping the tiff stack corresponding to a selected single fluorophore into the space marked “Image” in the “Deconvolution” tab. 9. Input the idealized PSF generated in step 5 by dragging and dropping the file name corresponding to the PSF file for that fluorophore generated in step 5. 10. Choose the Richardson-Lucy algorithm and 20 iterations (this is what has worked best in our hands) and input a path for the resultant image to be saved in under the “Path” portion of the dialog box. Press “Run” to start the deconvolution. 11. Repeat steps 7–10 for each fluorophore to be deconvolved. 12. Figure 2 shows the result for a two-color image. 3.2.2 Image De-striping Using Fourier Space Notch Filers

1. In FIJI, open the image to be de-striped. This can be a deconvolved image or image stack (see above). If an image stack, scroll to plane where stripes are evident. An example image is shown in Fig. 3a. Note the stripe pattern can change as a function of imaging depth (see Note 8). 2. Use the image function called FFT (Process/FFT/FFT) and Fourier transform the image to show the 2D power spectrum (2D-FFT insert in Fig. 3a) of the image. 3. Use the rectangle or circular ROI selector to outline one of the frequency space spots originating from the stripes in the image and delete it (delete key). This will set the values in those regions to zero. Do this for the other spots in the power spectrum image. 4. Once the spots have been deleted, threshold the image (image /adjust/threshold, or CTRL+Shift+T) by adjusting the bottom of the range to be above zero (a value of 1 will do). Click the dark background button. This will select all the pixels above the notches that were set to zero in step 3.

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Fig. 2 Example of the steps for image deconvolution with freely distributed FIJI plugins. (a) Example of a single iSIM generated raw image from the larger z-stack with nuclear pores labeled with Alexa 488 (green) and microtubules labeled with Alexa 561 (red). (b) Examples of idealized point spread functions (PSFs) for excitation, emission, and their product (system) generated by the FIJI plugin PSF generator. (c) Example

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5. Binarize the 2D power spectrum image from step 4 by clicking the “make binary” under “Process/binarize/make binary” in FIJI. At this point, you should have a 2D spectrum image that looks like the insert in Fig. 3c. Save this image as the FFT filter to be used in step 6. 6. Open the image or image stack to be filtered and the FFT filter created in step 5. Click on the image to be filtered and then select the FFT filter image in the dialog box associated with custom filter from “Process/FFT/Custom Filter.” Image 3C shows a filtered image from this approach compared to image 3A.

Fig. 3 Examples of image de-striping using optical hardware or FFT-based post-processing in FIJI. (a) Example of an iSIM-generated image containing stripe artifacts. The inset shows the 2D Fourier transform (2D-FFT) of the image with the stripe frequencies circled in red. (b) The same image taken with the optical de-striping unit (VT-Ingwaz from Visitech International) operating. Note the lack of stripe frequencies in the inset and lack of stripes in the image. (c) The same image shown after post-processing out the stripes using the 2D-FFT shown in the inset. The details of this process are described in a step-by-step manner in Subheading 3 ä Fig. 2 (continued) image from the larger z-stack shown in (a) deconvolved using the FIJI plugin “DeconvolutionLab2” and the system PSFs generated for each color as shown in (b). The details of this process are described in a step-by-step manner in Subheading 3

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Notes 1. Curd et al. show the steps for constructing a home-built iSIM system [17]. The iSIM system outlined here is from a commercial source (VisiTech Intl., Sunderland, UK). 2. In general, super-resolution imaging requires greater quality control standards to make sure that the microscope system and software are operating correctly in terms of alignment and image processing to get the best resolution. At a minimum, the iSIM system should be tested regularly for XY and Z resolution on known standards. The freely distributed software package PSFj [18] (download at http://www.knoplab. de/psfj/download/) allows for measurement of resolution from sub-resolution beads. Characterization of resolution from sub-resolution-sized biological samples (such as microtubules) can be performed from decorrelation analysis [19] (FIJI plugin can be found on Github at https://github.com/ Ades91/ImDecorr). Demmerle et al. [11] outline the protocols for making imaging standards (like beads) and discuss SIM artifacts (although not all are compatible with iSIM since generation of the raw iSIM image is analog, not digital). 3. Desired speed of acquisition, brightness, and susceptibility to photobleaching of the sample define the actual choice of laser power. 4. If using dual-camera acquisition mode, adjust the camera alignment. 5. The microscope setup with two cameras in Fig. 1 allows for simultaneous two-color imaging. Successful two-color imaging requires precise alignment of the cameras. This can be done by stopping the galvo scanners on the iSIM scan head and using transmitted white light to show the pinhole and microlens pattern from the hardware in the iSIM scan head (Fig. 4). The pattern from the images generated by the two cameras should overlay precisely. In the setup presented in Fig. 1, the alignment screws in the optical splitter can correct for XY shift as seen in Fig. 4b. Different camera rotations with respect to one another are responsible for larger displacements in the periphery compared to the center of the image (Fig. 4a). 6. If necessary, install the image processing software FIJI (https://imagej.net/Fiji/Downloads), PSF generator plugin (http://bigwww.epfl.ch/algorithms/psfgenerator/), and the deconvolutionLab2 plugin (http://bigwww.epfl.ch/deconvolution/deconvolutionlab2/) following instructions on the respective webpages.

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Fig. 4 Illustration of two-camera setup misalignment due to camera rotation or optical displacement. Shown are the overlay of images from two cameras (color coded green or red) for the stationary (non-scanning) pinhole/microlens pattern of illumination. (a) Shows the pattern if camera misalignment includes a rotational and displacement (XY) offset. Note the larger difference in displacement for peripheral spots compared to central spots. (b) Shows just a displacement offset. Note the same distance offset regardless of position. (c) Shows perfect alignment of the two cameras. A live image stream of the two-color-coded camera images allows for camera alignment by rotating the cameras to solve rotational misalignment and to adjust camera XY displacement by adjusting the dichroic position screws in the optical splitter (shown in Fig. 1). This is outlined in Subheading 4

7. Deconvolution of iSIM images can be achieved more rapidly by using freely accessible software [20] developed in the Shroff lab (NIBIB/NIH). The speed increase is due a combination of algorithmic improvement (a Wiener-Butterworth back projector in the Richardson Lucy algorithm) and GPU computing. Also, the software company Microvolution (Cupertino, CA) has a commercially available deconvolution program specific for iSIM that utilizes GPU processing and batch processing that is much faster than what is outlined in this work. They have modules for MetaMorph, FIJI/ImageJ, and other image processing software packages. 8. The simple Fourier based de-striping outlined here and shown in Fig. 3 has several limitations. First, it is usually specific for a specific image. Typically, the 2D-FFT pattern shown in Fig. 3a and the filter shown in Fig. 3c will change as a function of depth in the sample. Secondly, complex patterns that are significantly different from the simple round patterns shown in Fig. 3a are hard to filter out with simple notch patterns. The software company Microvolution (Cupertino, CA) has a commercially available de-striping algorithm available in their deconvolution package which works on each image individually and has more fine control over the level and width of the suppression of the line pattern and works on the entire stack rather than a single image as we have outlined here.

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Acknowledgments The authors thank Erina He of the NIH Medical Arts division for help with the figures in this work and Dr. Hari Shroff and Dr. Min Guo for critical editing and for helpful discussions. This work was supported by the intramural research programs of the National Heart Lung and Blood Institute, NIH. The mention of any company, product, or service in this work is in no way intended as an endorsement by the National Institutes of Health or the authors. References 1. York AG, Chandris P, Nogare DD, Head J, Wawrzusin P, Fischer RS, Chitnis A, Shroff H (2013) Instant super-resolution imaging in live cells and embryos via analog image processing. Nat Methods 10:1122 2. Muller CB, Enderlein J (2010) Image scanning microscopy. Phys Rev Lett 104:198101 3. Sheppard CJR (1988) Super-resolution in confocal imaging. Optik 80:53–54 4. York AG, Parekh SH, Dalle Nogare D, Fischer RS, Temprine K, Mione M, Chitnis AB, Combs CA, Shroff H (2012) Resolution doubling in live, multicellular organisms via multifocal structured illumination microscopy. Nat Methods 9:749–754 5. Guo M, Chandris P, Giannini JP, Trexler AJ, Fischer R, Chen J, Vishwasrao HD, Rey-Suarez I, Wu Y, Wu X, Waterman CM, Patterson GH, Upadhyaya A, Taraska JW, Shroff H (2018) Single-shot super-resolution total internal reflection fluorescence microscopy. Nat Methods 15:425–428 6. Winter PW, York AG, Nogare DD, Ingaramo M, Christensen R, Chitnis A, Patterson GH, Shroff H (2014) Two-photon instant structured illumination microscopy improves the depth penetration of super-resolution imaging in thick scattering samples. Optica 1:181–191 7. Gustafsson MGL (2000) Surpassing the lateral resolution limit by a factor of two using structured illumination microscopy. J Microsc 198:82–87 8. Gustafsson MGL, Agard DA, Sedat JW (2000) Doubling the lateral resolution of wide-field fluorescence microscopy using structured illumination. Proc SPIE 3919:141–150 9. Heintzmann R, Cremer C (1999) Laterally modulated excitation microscopy: improvement of resolution by using a diffraction grating. Proc Soc Photo Opt Instrum 3568:185–196

10. Heintzmann R, Gustafsson MGL (2009) Subdiffraction resolution in continuous samples. Nat Photonics 3:362–364 11. Demmerle J, Innocent C, North AJ, Ball G, Mu¨ller M, Miron E, Matsuda A, Dobbie IM, Markaki Y, Schermelleh L (2017) Strategic and practical guidelines for successful structured illumination microscopy. Nat Protoc 12:988 12. Wu Y, Shroff H (2018) Faster, sharper, and deeper: structured illumination microscopy for biological imaging. Nat Methods 15:1011–1019 13. Godin AG, Lounis B, Cognet L (2014) Superresolution microscopy approaches for live cell imaging. Biophys J 107:1777–1784 14. Turkowyd B, Virant D, Endesfelder U (2016) From single molecules to life: microscopy at the nanoscale. Anal Bioanal Chem 408:6885–6911 15. Schindelin J, Arganda-Carreras I, Frise E, Kaynig V, Longair M, Pietzsch T, Preibisch S, Rueden C, Saalfeld S, Schmid B, Tinevez J-Y, White DJ, Hartenstein V, Eliceiri K, Tomancak P, Cardona A (2012) Fiji: an opensource platform for biological-image analysis. Nat Methods 9:676–682 16. Sage D, Donati L, Soulez F, Fortun D, Schmit G, Seitz A, Guiet R, Vonesch C, Unser M (2017) DeconvolutionLab2: an open-source software for deconvolution microscopy. Methods 115:28–41 17. Curd A, Cleasby A, Makowska K, York A, Shroff H, Peckham M (2015) Construction of an instant structured illumination microscope. Methods 88:37–47 18. Theer P, Mongis C, Knop M (2014) PSFj: know your fluorescence microscope. Nat Methods 11:981 19. Descloux A, Grußmayer KS, Radenovic A (2019) Parameter-free image resolution estimation based on decorrelation analysis. Nat Methods 16:918–924

Guide to Instant Structured Illumination Microscopy 20. Guo, M., Li, Y., Su, Y., Lambert, T., Nogare, D. D., Moyle, M. W., Duncan, L. H., Ikegami, R., Santella, A., Rey-Suarez, I., Green, D., Chen, J., Vishwasrao, H., Ganesan, S., Waters, J. C., Annunziata, C. M., Hafner, M., Mohler, W. A., Chitnis, A. B., Upadhyaya, A., Usdin,

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Correction to: Confocal Microscopy Joseph Brzostowski and Haewon Sohn

Correction to: Joseph Brzostowski and Haewon Sohn (eds.), Confocal Microscopy: Methods and Protocols, Methods in Molecular Biology, vol. 2304, https://doi.org/10.1007/978-1-0716-1402-0

Chapter 15 “Method for Acute Intravital Imaging of the Large Intestine in Live Mice” was previously published with incorrect ESM videos and figure captions. This has now been rectified in the revised version of this book.

The updated online version of this chapter can be found at: https://doi.org/10.1007/978-1-0716-1402-0_15 Joseph Brzostowski and Haewon Sohn (eds.), Confocal Microscopy: Methods and Protocols, Methods in Molecular Biology, vol. 2304, https://doi.org/10.1007/978-1-0716-1402-0_20, © This is a U.S. government work and not under copyright protection in the U.S.; foreign copyright protection may apply and Springer Nature US 2021

C1

INDEX A

F

Active Ras binding domain of Raf1 (RBD)................195, 210, 211 Airyscan ................................................ 11, 16, 18–20, 28, 111–130, 195, 198, 200, 202–205 Antigen extraction................................................ 157–172 Antigens...................................................... 157–160, 162, 163, 165, 167–170, 173–191, 256, 257, 287, 350 Antigen specific T cell ................................................... 257

Fluorescence ................................................. 2, 3, 5, 7, 11, 15, 16, 24, 29–31, 37–44, 46, 47, 50–52, 54, 55, 62, 66–68, 70–73, 80, 81, 83, 85–87, 116, 122, 125–127, 133, 139, 141–143, 152, 168, 169, 198, 202, 211, 213, 216, 218, 271, 276, 279, 280, 282, 302, 304, 306, 308–312, 316–319, 323, 326, 327, 331, 335, 339–341, 343–345, 347, 348 Fluorescence correlation spectroscopy (FCS) .............. 32, 89, 133, 176, 187 Fluorescence lifetime imaging (FLIM) ......................... 24, 301–312, 315–335 Fluorescence lifetime imaging microscopy (FLIM)............... 38, 43, 302–310, 312, 339–345 Fluorescence lifetime in photobleaching (FLIP) ......................................................... 31, 217 Fluorescence microscopy .......................2, 4, 8, 9, 13, 17, 22, 37, 46, 174, 188, 209, 266, 271, 321, 347 Fluorescence recovery after photobleaching (FRAP)................................ 28–31, 113, 119, 285 Fluorescent probes ............................................ 32, 37–62, 147, 163, 169, 208, 211, 222, 296 Fluorescent proteins................................. 5, 6, 28, 38, 39, 52, 53, 59, 60, 147, 149, 154, 197, 211, 213, 217, 222, 253, 271, 276, 288, 340 Fluorescent tags ...............................................5, 6, 23, 39 Fluorophores ..................................23–25, 28, 30, 37–44, 46, 47, 50–53, 55, 56, 58, 60, 61, 66, 67, 71, 80, 81, 83, 84, 86, 87, 89, 125, 143, 147, 161, 171, 179, 188, 202, 204, 233, 276, 281, 286, 287, 296, 302, 303, 310, 316–318, 335, 340, 341, 345, 348, 352, 353 Folate ................................................... 208–211, 213–215 Folic acid receptor 1 (fAR1)............................... 208–211, 213, 215–217 Fo¨rster resonance energy transfer (FRET) ................... 37, 38, 43, 79, 285, 316–318, 327, 329, 330, 332, 335, 340–342

B B cell receptor ............................................................... 158 B cells ..................................................135, 138, 140–142, 157–172, 174–176, 178, 182, 185, 187 Biosensors ...............................23, 37, 195, 197–199, 340 Blood stage malaria ....................................................... 305

C Cancer.......................................................... 287, 288, 297 Chemotaxis............................................ 98, 194, 207–219 Clearing ......................................................................... 155 Colocalization .......................................... 25, 27, 82, 158, 175, 180, 183, 185, 188, 190, 191, 253, 254, 260, 262 Colonic .......................................................................... 292 Confocal microscopy ....................................1–33, 40, 65, 66, 93–108, 112, 133, 138, 139, 157–172, 174, 193–205, 215, 217, 243–263, 265–282, 286, 287, 320, 348 Cytoskeleton...............................207, 208, 210, 265, 266 Cytosolic ............................ 211, 216, 268, 272, 273, 344

D Deconvolution ........................................... 11, 14–20, 25, 112, 113, 122, 130, 133, 134, 138–140, 144, 163, 174, 175, 180, 181, 190, 253, 256, 262, 348, 349, 352–354, 357 Dendritic cells (DCs) .................................................... 243 Dictyostelium ..................... 208, 210, 213–215, 218, 219 Dictyostelium discoideum ....................193–205, 207, 217

E Epifluorescence .........................................................65, 66

G Gel embedding..................................................... 148–151 Genetic screening ................................................. 234, 235 G-protein .............................................194, 208–210, 214

Joseph Brzostowski and Haewon Sohn (eds.), Confocal Microscopy: Methods and Protocols, Methods in Molecular Biology, vol. 2304, https://doi.org/10.1007/978-1-0716-1402-0, © This is a U.S. government work and not under copyright protection in the U.S.; foreign copyright protection may apply 2021

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362 Index

AND

PROTOCOLS

H High-content screening (HCS) .........221, 222, 234, 235 High-resolution.......................................... 94, 95, 97, 98, 133, 143, 158, 169 High-throughput (HTP) microscopy.........................221, 225, 230, 240 Human induced pluripotent stem cells (i3 PSCs) ......................... 267, 270–274, 279, 281 Huygens............................................. 134, 139, 144, 163, 175, 178, 180–183, 185, 187, 189, 244, 246, 253–256 Hypoxia ......................................................................... 321

I Image analysis.................................... 141, 142, 158, 160, 163, 167, 174, 175, 178, 180–186, 189, 191, 221, 222, 232, 234–236, 327 ImageJ......................................................... 160, 165–168, 172, 182, 187, 189, 357 Imaris .........................................134, 139, 141, 165, 178, 180, 183, 185–187, 189, 190, 244, 246, 253, 257, 259, 260, 268, 278, 280, 282, 297 Immune synapses ..................................97, 157, 158, 167 Immuno-staining ................................................. 148, 149 Instant Structured Illumination Microscopy (iSIM) ....................................................... 347–358 Intracellular oxygenation ..................................... 315–335 Intravital microscopy ........................................... 261, 286

K Kohler illumination ......................................................... 75

L Label-free.............................................................. 287, 340 Labeling .....................................................2–7, 24, 37–62, 66, 125, 196, 197, 215, 236, 248, 250, 271, 282, 303, 339, 343, 345 Large intestine...................................................... 285–297 Laser scanning confocal microscopy ................. 66, 68, 87 Linear unmixing........................................................23–25 Lymphocytes ................................................131–144, 174

M Machine learning.......................................................27, 28 Macropinocytosis ................................................. 193–204 Metabolism..........................................131, 286, 301–313 Microfabrication........................................................93–96 Microfluidics................................. 32, 93–95, 97–99, 210 Microscopy ............................................. 2, 6, 7, 9–11, 13, 17, 19, 20, 23, 25, 31, 32, 65, 66, 93, 94, 97, 132–134, 137–139, 147–156, 174, 175, 191, 196, 204, 218, 221, 225, 230, 235, 240, 243,

244, 250, 261, 285, 286, 315, 316, 318–321, 324, 331, 332, 334, 336, 347 Mitochondria......................................131–144, 202, 301, 304, 308, 312, 324, 339, 340, 344, 345 Mitochondrial............................................. 131–144, 303, 307, 309–312, 318, 320, 326, 344 Multiphoton ............................................... 243–262, 286, 287, 306, 315, 321, 324, 331, 334, 336 Myoglobin-mCherry (Myo-mCherry) ............... 315–336

N NAD+ ...................................................340, 341, 344, 345 Neural network ............................................................. 236 Neuronal morphology ......................................... 265, 266 Nuclear .......... 4, 61, 171, 268, 272, 344, 349, 350, 354

P PDMS ......................93–95, 99, 102–107, 304, 305, 307 Phagocytosis ............................... 174, 193–205, 207–219 Phasor plots .........................................302–304, 309, 310 Phosphatidylinositol 3,4,5-trisphosphate (PIP3) .............................195, 199, 201, 210, 211 Phosphatidylinositol 4,5-bisphosphate (PIP2) ......................................194, 195, 197–199 Phosphoinositide 3-kinases (PI3K).............................194, 197, 199, 201, 209, 210 Photolithography ......................................................94, 95 Photomultiplier tube (PMT) ................................. 66, 67, 71, 80–82, 84, 86, 87, 112, 117, 119, 138, 246, 289, 293, 294, 306 Pinholes ........................................... 1, 11, 13, 18, 19, 66, 67, 71, 72, 84, 85, 87, 89, 112, 113, 119, 128, 138, 143, 165, 177, 181, 197, 331, 348, 356, 357 Plasma membrane sheets (PMS) ......................... 157–172 Plasmodium falciparum....................................... 301–312 Pleckstrin homology domain (PH domain) ...............195, 199, 210, 211 Primary amine dyes ....................................................... 244

R Ras...................................... 194, 195, 197–201, 209–211 Resolution ........................................2–32, 37, 45, 61, 66, 85, 88, 89, 102, 111–114, 123–125, 128, 129, 132, 133, 139, 142–144, 147, 148, 165, 167, 171, 174, 175, 198, 201–204, 285, 287, 306, 310, 315, 319, 320, 327, 332, 333, 348, 356 Reviews ..................................................... 2, 4, 16, 17, 20, 88, 93, 94, 97, 98, 112, 236, 348

S Scripts .......................................................... 183, 184, 187 Spectral ............................... 7, 23, 24, 25, 37, 42, 52, 81, 86, 96, 204, 233, 318, 319, 324, 332, 341, 348

CONFOCAL MICROSCOPY: METHODS Spinning disk confocal ......................................11, 13, 15, 25, 175, 177, 182, 185, 268, 274 Spinning disk confocal microscopy ............ 175, 265, 266 Stimulated emission depletion microscopy (STED) ......................................... 20, 21, 32, 133, 134, 137–141, 143, 144, 286 SU-8.......................................... 93–95, 98–102, 105, 106 Super-resolution..................................... 44, 50, 111–113, 119, 122, 125, 129, 198, 347, 348, 356 Super-resolution microscopy ..............133, 147–156, 286

AND

PROTOCOLS Index 363

Tiling ............................................................................... 13 Two-photon fluorescence lifetime imaging................316, 319–321, 323, 330, 331 Two-photon microscopy ....................286, 316, 319, 320

V Vesicle traffic.................................................................. 175

Y Yeast .................. 132, 195–199, 201–204, 209, 221–240

T T cells .......................................................... 141, 142, 174, 243–245, 247, 248, 251, 253, 257, 260, 287 3d imaging........................... 16, 127, 129, 143, 187, 204

Z Zeiss 880 ................................................................ 81, 195