Bioluminescence: Methods and Protocols, Volume 2 (Methods in Molecular Biology, 2525) 1071624725, 9781071624722

Table of contents :
Preface
Contents
Contributors
Part I: Heterogeneous Conjugates
Chapter 1: Quantitative Assessment of the Efficacy of Near-Infrared Photoimmunotherapy with Bioluminescence Imaging
1 Introduction
2 Materials
2.1 Manufacture of IR700 Conjugated Monoclonal Antibody (mAb-IR700 Conjugate)
2.2 In Vitro PIT
2.3 In Vivo PIT
2.4 Bioluminescence Imaging (BLI)
3 Methods
3.1 Conjugation of IR700 with mAb
3.2 In Vitro NIR-PIT
3.3 In Vivo NIR-PIT
3.4 BL Assessment of the Efficacy of In Vivo NIR-PIT
4 Notes
References
Chapter 2: Evaluation of the Efficacy of Saracatinib-Loaded Nanoparticles in Lymphatic Metastases of HNSCC with the Aid of Bio...
1 Introduction
2 Materials
2.1 Reagents
2.2 Instrumentation
2.3 Animals
2.4 Software
3 Methods
3.1 Generation of HN12-Luc2 Cells (See Note 4)
3.2 Establishing the Orthotopic Tongue Tumor Model in NSG Mice (See Note 5)
3.3 Nano-Sar Treatment and Lymph Node Imaging (Fig. 1) (See Note 5)
4 Notes
References
Chapter 3: Reactive Oxygen Species-Responsive and Self-Illuminating Nanoparticles for Inflammation and Tumor Imaging
1 Introduction
2 Materials
2.1 Reagents and labware
2.2 Instrumentation
3 Methods
3.1 Synthesis of a CLP Conjugate
3.2 In Vivo CLI of Peritonitis in Mice
3.3 In Vivo CLI of Acute Liver Injury in Mice
3.4 In Vivo CLI of Ulcerative Colitis
3.5 In Vivo CLI of the Development of Colitis
3.6 In Vivo CLI of Tumors
4 Notes
References
Chapter 4: Antibacterial Activity Evaluation of ZnO, CuO, and TiO2 Nanoparticles in Solution and Thin Films
1 Introduction
2 Materials
2.1 Microbial Strains
2.2 Reagents and Labware
2.3 Instrumentation
3 Methods
3.1 Strain Thawing Method
3.2 Disk Diffusion Method
3.3 ATP Measurement Method
3.4 Germ Carrier Measurement Method
3.5 Validation of the Germ Carrier Measurement Method
3.5.1 Validation of the Slide Washing Protocol According to Standard NF EN 14561
3.5.2 Antibacterial Activity of the Thin Films of NPs
4 Notes
References
Chapter 5: BRET-Based Dual-Color (Visible/Near-Infrared) Molecular Imaging Using a Quantum Dot/EGFP-Luciferase Conjugate
1 Introduction
2 Materials
2.1 Synthesis of NIR-Emitting CdSe/CdS (Core/Shell) QDs
2.2 Surface Modification of QDs with Glutathione (GSH)
2.3 Protein Synthesis (His-EGFP-RLuc-GB1)
2.4 The Other Reagents and Labware
2.5 Instrumentation
3 Methods
3.1 QD Synthesis (CdSeTe/CdS)
3.1.1 Se-Te Stock Solution
3.1.2 CdSeTe Core
3.1.3 Cd-S Stock Solution
3.1.4 CdS Overcoating
3.2 Surface Modification of QDs with Glutathione (GSH)
3.3 Protein Synthesis (His-EGFP-Rluc-GB1)
3.4 BL Spectra of His-EGFP-Rluc-GB1
3.5 Conjugation of His-EGFP-RLuc-GB1 to GSH-QDs
3.6 BRET Spectra for QD-His-EGFP-RLuc-GB1 in the Presence of CTZ
3.7 Flow Cytometric Analysis
3.8 BRET-Coupled Luminescent Molecular Imaging
4 Notes
References
Chapter 6: Polyhistidine-Tag-Enabled Conjugation of Quantum Dots and Enzymes to DNA Nanostructures
1 Introduction
2 Materials
2.1 Reagents and Labware
2.2 Instrumentation
3 Methods
3.1 Preparing Stock Solutions
3.2 Synthesis of DNA Origami Breadboards for Target Immobilization
3.2.1 Preparing Staple Mixtures for DNA Origami Breadboard Synthesis
3.2.2 Synthesis of DNA Origami Breadboard
3.2.3 PEG Precipitation of DNA Origami Breadboard
3.2.4 Addition of AF488-Labeled Strands for Luciferase-Directed BRET
3.3 Immobilization of ZnS-Coated QDs on DNA Origami Breadboard
3.3.1 Histag Conjugation of Peptide-PNA to ZnS-Coated QDs
3.3.2 Immobilizing QDs on the DNA Origami Breadboard
3.3.3 AFM Characterization
3.3.4 Fluorescence (FL) Spectral Characterization Options
3.3.5 Troubleshooting Peptide-PNA Conjugation to QDs with Gel Electrophoresis
3.4 Immobilization of Luciferase on DNA Origami Breadboard
3.4.1 Conjugating NTA to Thiolated ssDNA
Preparation of Reduced Thiol-DNA Oligo
Purification of Thiol-DNA
Addition of NTA
Purification of NTA-DNA and Buffer Exchange from 1x PBS to 100 mM TEAA
3.4.2 Luciferase Expression and Purification
3.4.3 Conjugation of Luciferase to DNA Origami Breadboard
3.4.4 Characterizing Luciferase Immobilization by BRET
3.4.5 BRET Analysis
4 Notes
References
Chapter 7: Real-Time Quantification of Cell Internalization Kinetics by Bioluminescent Probes
1 Introduction
2 Materials
2.1 Reagents and Labware
2.2 Equipment
3 Methods
3.1 Nanocapsule Synthesis
3.1.1 In Situ Polymerization of nFLuc
3.1.2 Varying Surface Charge of nFLuc
3.1.3 Conjugation of Targeting Ligands to nFLuc (See Note 4)
3.1.4 BCA for Protein Concentration Determination (See Note 5)
3.2 Nanocapsule Characterization
3.2.1 Morphology
3.2.2 Size and Surface Charge
3.2.3 KM and Kcat
3.2.4 Cytotoxicity
3.3 Cell Preparation for BL Kinetics Measurement
3.3.1 Addition of D-Luciferin Before Measurement
3.3.2 Cell Count, N
3.4 Real-Time BL Measurement
3.4.1 Addition of nFLuc
3.5 Analysis
3.5.1 Determination of Decay Constant, Kd
3.5.2 Determination of the Intracellular Concentration of nFLuc, [nFLucin]
3.5.3 Determination of the Plateau Concentration, [nFLuc]
3.5.4 Determination of the Initial Rate of Uptake, IRU
3.5.5 Calculating the Partition Coefficient, Keq
4 Notes
References
Part II: Protein Fragment-Complementation Assays
Chapter 8: Quantitative Imaging of Retinoic Acid Activities in Living Mammalian Cells
1 Introduction
2 Materials
2.1 Reagents and Laboratory Equipment
2.2 Instrumentation
2.3 Software Packages
2.4 Animals
3 Methods
3.1 Construction of Mammalian Expression Vectors Encoding RA-Sensitive Single-Chain BL Probes (See Fig. 1a, b)
3.2 Determination of RA-Activated BL Intensities in MDA-MB-231 Cells Expressing pSara #1pSara #13 (See Fig. 1c)
3.3 Establishment of MDA-MB-231 Cells Stably Expressing Sara #2 and Sara #12 with Respective Control Vectors _Sara #2ctrl and ...
3.4 Determination of RA Activities Using MDA-MB-231 Cells Stably Expressing Sara #2 and Sara #12 and Respective Controls (Sara...
3.5 Determination of Ligand-Specific Activation of Sara #12 in MDA-MB-231 Cells Stably Expressing the Sensor (See Fig. 2b)
3.6 Ligand Dose-Response Curves of Sara #12 and Sara #12ctrl (See Fig. 2c)
3.7 Ex Vivo Imaging of Tumor Xenografts of MDA-MB-231 Cells Stably Expressing Sara #12 and Sara #12ctrl Grown in Mouse and Ind...
3.8 Determination of Endogenous at-RA Levels in Mouse Serum and Cerebrospinal Fluid of Mouse Maintained at Normal Housing Cond...
4 Notes
References
Chapter 9: Bioluminescence-Based Complementation Assay to Correlate Conformational Changes in Membrane-Bound Complexes with En...
1 Introduction
2 Materials
2.1 Reagents and LabWare
2.2 Instrumentation
3 Methods
3.1 Transfection of Cells for Measurement of PPI
3.2 Nano-Glo Live Cell Assay to Quantify Heterodimerization of Proteins of Interest
3.3 HVA Assay for Determination of Extracellular H2O2 Production as a Function of NOX4/p22phox Heterodimerization
4 Notes
References
Chapter 10: The NanoBiT-Based Homogenous Ligand-Receptor Binding Assay
1 Introduction
2 Materials
2.1 Peptides
2.2 Reagents and LabWare
2.3 Instrumentation
3 Methods
3.1 Preparation of the SmBiT-Based Binding Tracers
3.1.1 Quantification of Peptides
3.1.2 Activation of SmBiT-Cys (See Note 16)
3.1.3 Conjugation with Ghrelin-Cys or LEAP2-Cys
3.1.4 Quantification of the SmBiT-Based Tracers
3.2 Culture and Transfection of HEK293T Cells
3.2.1 Cell Culture
3.2.2 Transfection
3.2.3 Seeding into 96-Well Microplates (See Note 20)
3.3 NanoBiT-Based Saturation Binding Assays
3.3.1 Tracer Dilution
3.3.2 Binding with sLgBiT-Fused Receptor
3.3.3 BL Measurement
3.3.4 Plotting the Data
3.4 NanoBiT-Based Competition Binding Assays
3.4.1 Dilution of Competitor and Tracer
3.4.2 Binding with sLgBiT-Fused Receptor
3.4.3 BL Measurement
3.4.4 Plotting the Data
4 Notes
References
Chapter 11: Monitoring Hippo Signaling Pathway Activity Using a Luciferase-Based Large Tumor Suppressor (LATS) Biosensor
1 Introduction
2 Materials
2.1 Reagents and LabWare
2.2 Instrumentation
3 Methods
3.1 Construct Design of the LATS-NanoBiT Biosensor
3.2 Molecular Cloning of the LATS-NanoBiT Biosensor
3.3 Measuring LATS-NanoBiT Biosensor Activity by Luciferase Assay
3.4 Approach to Validating the LATS-NanoBiT Biosensor
3.5 Kinome-Wide Kinase Inhibitor Screen Using the LATS-NanoBiT Biosensor
4 Notes
References
Part III: BRET-Based Imaging
Chapter 12: Screening of Protein-Protein Interaction Modulators Using BRET-Based Technology
1 Introduction
2 Materials
2.1 Cell Transfection
2.2 BRET Assay
2.3 Mechanical Lysis
3 Methods
3.1 BRET Saturation Assay in Living Cells
3.2 Mechanical Lysis
3.3 BRET Saturation Assay in Lysates
3.4 Screening for Inhibitors
4 Notes
References
Chapter 13: TRUPATH: An Open-Source Biosensor Platform for Interrogating the GPCR Transducerome
1 Introduction
2 Materials
2.1 Reagents and Labware
2.2 Equipment and Software
3 Methods
3.1 Cell Culture
3.2 Plasmid Transfection
3.3 Microplate Setup
3.4 Running the Assay
3.5 Data Analysis
4 Notes
References
Chapter 14: MERLIN: A BRET-Based Proximity Biosensor for Studying Mitochondria-ER Contact Sites
1 Introduction
2 Materials
2.1 Reagents and Labware
2.2 Instrumentation
3 Methods
3.1 Cell Seeding and MERLIN Expression
3.2 Treatment and RLuc8-Substrate (CTZh) Incubation
3.3 BRET Measurement and Data Analysis
4 Notes
References
Chapter 15: Single-Cell NanoBRET Imaging with Green-Range HaloTag Acceptor
1 Introduction
2 Materials
2.1 Components for Cell Culture
2.2 Plasmids for Expression
2.3 Components for BRET Imaging
2.4 Microscopic Components
2.5 Software
3 Methods
3.1 Cell Culture and Transfection
3.2 Labeling with HaloTag JF525
3.3 Selecting Cells Expressing the Fusion Proteins
3.4 NanoBRET Imaging of PKA Interaction
3.5 Data Processing with Fiji (ImageJ)
4 Notes
References
Chapter 16: Method for Measuring Bioactive Molecules in Blood by a Smartphone Using Bioluminescent Ratiometric Indicators
1 Introduction
2 Materials
2.1 Reagents and Labware
2.2 Reagents and Labware
3 Methods
3.1 Setting of Smartphone
3.2 Preparation for Mouse Blood Collection from Vein
3.3 Setting of Blood Samples with BABI
3.4 Capturing BL Images
3.5 Color Ratio Analysis
4 Notes
References
Chapter 17: BRET Sensors for Imaging Membrane Integrity of Microfluidically Generated Extracellular Vesicles
1 Introduction
2 Materials
2.1 Reagents and Labware
2.2 Instruments
2.3 Softwares
3 Methods
3.1 Construction of MDA-MB-231, 4T1, and NSC Cells Stably Expressing PAL-Nanoluc-RFP BRET Sensor (See Fig. 1)
3.2 Isolation of EVs from MDA-MB-231, 4T1, and NSC Cells Stably Expressing PAL-Nanoluc-RFP BRET Sensor (See Fig. 2)
3.3 Isolation of Cell Membranes from MDA-MB-231, 4T1, and NSC Cells Stably Expressing PAL-Nanoluc-RFP BRET Sensor (See Fig. 3)
3.4 Microfluidic Mediated Loading of microRNAs to EVs and Cell Membranes Isolated from Cells Engineered to Stably Express BRET...
3.5 BRET Imaging of Microfluidic, Processed Cell Membrane Vesicles for Assessing Membrane Integrity and Membrane-Anchored Prot...
4 Notes
References
Chapter 18: In Vivo Assessment of Protein-Protein Interactions Using BRET Assay
1 Introduction
2 Materials
2.1 Cell Lines
2.2 Nucleic Acid Reagents
2.3 Materials and Reagents
2.4 Laboratory Animal
2.5 Instruments
2.6 Software
3 Methods
3.1 Generation of BRET Construct
3.2 Validation of Expression of the Fusion Construct
3.3 In Vitro BRET Assay Development and Validation
3.4 In Vivo BRET Assay
3.5 Data Analysis
4 Notes
References
Chapter 19: Live Cell Imaging of ATP Dynamics in Plant Cells
1 Introduction
2 Materials
2.1 Transformation of Arabidopsis and Isolation of Transgenic Plants
2.2 Microscopic In Vivo Analysis of Chloroplastic ATP Dynamics
2.3 Instrumentation
3 Methods
3.1 Isolation of Transgenic Arabidopsis for In Vivo ATP Imaging
3.2 In Vivo Imaging of ATP Dynamics and Data Analysis
4 Notes
References
Chapter 20: Bioluminescence Resonance Energy Transfer for Global DNA Methylation Quantification
1 Introduction
2 Materials
2.1 Instrumentation
2.2 Reagents and Consumable
3 Methods
3.1 Preparation of MBD-FLuc, CXXC-FLuc, and CXXC-OLuc
3.1.1 Protein Expression
3.1.2 Protein Purification
3.2 Detection of Methylated CpG Content by BRET Assay Using MBD-FLuc
3.3 Detection of Unmethylated CpG Content by BRET Assay Using CXXC-FLuc for Global DNA Methylation Quantification
3.4 Global DNA Methylation Analysis by Multicolor BRET Assay Using MBD-FLuc and CXXC-OLuc
4 Notes
References
Chapter 21: In Vivo Bioluminescent Imaging of Bone Marrow-Derived Mesenchymal Stem Cells in Mice
1 Introduction
2 Materials
2.1 Animals
2.2 Instrumentation
2.3 Reagents and Labware
3 Methods
3.1 In Vivo BLI of Knock-In Mouse Model
3.2 Isolation of Bone Marrow Stem Cells
3.3 In Vivo BLI of BM-MSCs
4 Notes
References
Chapter 22: In Vivo Imaging of Oxidative and Hypoxic Stresses in Mice Model of Amyotrophic Lateral Sclerosis
1 Introduction
2 Materials
2.1 Animal Models
2.2 Solutions
2.3 Instrumentation
2.4 Software
3 Methods
3.1 In Vivo Optical Imaging of Oxidative Stress Using the IVIS Spectrum Imaging System
3.2 In Vivo Optical Imaging of Hypoxic Stress Using the IVIS Spectrum Imaging System
3.3 Analysis of BL or BRET Signals
4 Notes
References
Part IV: Instrumentation
Chapter 23: ATP Sensing Paper with Smartphone Bioluminescence-Based Detection
1 Introduction
2 Materials
2.1 ATP Sensing Paper
2.2 Reagents
2.3 Equipment and Software
3 Methods
3.1 ATP Sensing Paper Preparation (See Fig. 1)
3.2 ATP Sensing Paper Characterization
3.3 BL Assay for ATP Detection (See Fig. 3)
4 Notes
References
Chapter 24: Organ Bioluminescence Imaging Under Machine Perfusion Setting for Assessing Quality of Harvested Organ Preservation
1 Introduction
2 Materials
2.1 Transgenic Rats
2.2 Perfusing Solution
2.3 Machine Perfusion System
2.4 Luminescence Image-Capturing System
3 Methods
3.1 Muscle Pedicle Flap Preparation
3.2 Muscle Pedicle Flap Perfusion
3.3 Measurement
3.4 Analysis
4 Notes
References
Chapter 25: Time-Lapse Bioluminescence Imaging of Hes7 Expression In Vitro and Ex Vivo
1 Introduction
2 Materials
2.1 Set Up the BL Microscopy (See Fig. 1)
2.2 BLI of the PSM Explant Culture
2.3 BLI of PSM-Like Tissues Induced from Mouse ES Cells (iPSM)
3 Methods
3.1 Set Up the BL Microscopy (See Fig. 1)
3.2 BLI of PSM Explant Cultures (See Fig. 3)
3.3 BLI of iPSM (See Fig. 4)
3.3.1 Maintenance of Mouse ES Cells
3.3.2 Generation of iPSM Tissues from Mouse ES Cells
3.3.3 Imaging of iPSM Tissues
3.4 Image Processing and Analysis (See Note 16, Fig. 5)
4 Notes
References
Chapter 26: Bioluminescence-Optogenetics: A Practical Guide
1 Introduction
2 Materials
2.1 Reagents and Labware
2.2 Instruments
3 Methods
3.1 Preparation of HEK293FT Cells
3.2 Preparation of Primary Neurons
3.3 Lipofection in Neurons
3.4 Electroporation of Neurons
3.5 Viral Transduction of Neurons
3.6 Maintenance of Neuronal Culture
3.7 Patch Clamp Recording
3.8 Extracellular Recordings
4 Notes
References
Chapter 27: Applications of Bioluminescence-Optogenetics in Rodent Models
1 Introduction
2 Materials
2.1 Reagents, Labware, Surgical Supplies, and Tools
2.2 Instruments
3 Methods
3.1 Stereotactic Injection of Viral Vectors
3.2 Intracellular Recording Ex Vivo
3.3 Extracellular Recordings Ex Vivo
3.4 CTZ Delivery
3.4.1 Intracranial Delivery
3.4.2 Intravenous Delivery
3.4.3 Intraperitoneal Delivery
3.4.4 Intranasal Delivery
3.5 Whole Animal BLI
3.6 Fiber Photometry
3.7 Awake Intravital Cranial Imaging
3.8 Extracellular Recording In Vivo
4 Notes
References
Chapter 28: One-Channel Microsliding Luminometer for Quantifying Low-Energy Bioluminescent Lights
1 Introduction
2 Materials
2.1 Reagents and Labware
2.2 Instrumental Parts
2.3 Instrumentation
2.4 Software
3 Methods
3.1 Assembly of Mechanical, Optical, and Electric Parts for 1-ch Microsliding Luminometer (See Fig. 1)
3.2 Confirmation of the Fidelity of the 1-ch Microsliding Luminometer Through Determining PLAP Activities (See Fig. 2)
3.3 Determination of Rapamycin-Activated Optical Intensities of FRB-A23-FKBP Using the 1-ch Microsliding Luminometer (See Fig....
3.4 Determination of Estrogen Antagonist-Activated Optical Intensities of a Molecular Strain Probe Using the 1-ch Microsliding...
4 Notes
References
Chapter 29: Compact Eight-Channel Light-Sensing System for Bioassays
1 Introduction
2 Materials
2.1 Chemicals and Labware
2.2 Electronic Parts
2.3 Instrumentation
2.4 Software
3 Methods
3.1 Assembly of Optical and Electric Parts for Constructing Black Box I (BBI)
3.2 Normalization of the Light Sensitivities of the Integrated Eight PMT Channels in BBI
3.3 Determination of SEAP Activities in Mouse Plasma Using BBI
3.4 Diagnosis of Metastases in Mice-Bearing Xenografts of Triple Negative Syngeneic Breast Cancer Using the BBI System
4 Notes
References
Chapter 30: Characterization of Firefly Flashes at Various Temperatures in Different Wavelength Regions
1 Introduction
2 Materials
3 Methods
3.1 Steady-State Recording
3.2 Time-Resolved Recording
4 Notes
References
Chapter 31: Bioluminescent Monitoring of Circadian Rhythms in Isolated Mesophyll Cells of Arabidopsis at Single-Cell Level
1 Introduction
2 Materials
2.1 Plant
2.2 Reagents
2.3 Labware
2.4 Instrumentation for Imaging System (See Fig. 1)
2.5 Software
3 Methods
3.1 Preparation of Arabidopsis Plants
3.2 Preparation of Enzyme Solution
3.3 Protoplast Isolation from Arabidopsis Leaves (See Fig. 2)
3.4 Bioluminescent Imaging (BLI) (See Fig. 1 for the Imaging System)
3.5 BL Quantification
4 Notes
References
Part V: Software
Chapter 32: Exploring Phylogenetic Relationships and Divergence Times of Bioluminescent Species Using Genomic and Transcriptom...
1 Introduction
2 Materials
2.1 Instrumentation
2.2 Software and Dependencies
2.3 Input/Dataset
3 Methods
3.1 Transcript and Gene Prediction Adjustment
3.2 Ortholog Search Using OrthoFinder (See Note 5)
3.3 Species Tree and Concatenated Phylogenomic Reconstruction
3.3.1 Species Tree Using the Software ASTRAL
3.3.2 Concatenated Tree (Supergene and Supertree)
3.4 Divergence Time Estimation Using BEAST2 and TreePL
3.4.1 BEAUti
3.4.2 BEAST
3.4.3 TRACER
3.4.4 TreeAnnotator
3.4.5 Figtree
TreePL
3.5 Computational Resource and Time-Consumption
4 Notes
References
Index

Citation preview

Methods in Molecular Biology 2525

Sung-Bae Kim Editor

Bioluminescence Methods and Protocols Volume 2 Fourth Edition

METHODS

IN

MOLECULAR BIOLOGY

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

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

For over 35 years, biological scientists have come to rely on the research protocols and methodologies in the critically acclaimed Methods in Molecular Biology series. The series was the first to introduce the step-by-step protocols approach that has become the standard in all biomedical protocol publishing. Each protocol is provided in readily-reproducible step-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.

Bioluminescence Methods and Protocols, Volume 2 Fourth Edition

Edited by

Sung-Bae Kim Environmental Management Research Institute, National Institute of Advanced Industrial Science and Technology (AIST), Tsukuba, Ibaraki, Japan

Editor Sung-Bae Kim Environmental Management Research Institute National Institute of Advanced Industrial Science and Technology (AIST) Tsukuba, Ibaraki, Japan

ISSN 1064-3745 ISSN 1940-6029 (electronic) Methods in Molecular Biology ISBN 978-1-0716-2472-2 ISBN 978-1-0716-2473-9 (eBook) https://doi.org/10.1007/978-1-0716-2473-9 © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Science+Business Media, LLC, part of Springer Nature 2022 This work is subject to copyright. All rights are solely and exclusively licensed by the Publisher, whether the whole or part of the material is concerned, specifically the rights of translation, reprinting, reuse of illustrations, recitation, broadcasting, reproduction on microfilms or in any other physical way, and transmission or information storage and retrieval, electronic adaptation, computer software, or by similar or dissimilar methodology now known or hereafter developed. The use of general descriptive names, registered names, trademarks, service marks, etc. in this publication does not imply, even in the absence of a specific statement, that such names are exempt from the relevant protective laws and regulations and therefore free for general use. The publisher, the authors and the editors are safe to assume that the advice and information in this book are believed to be true and accurate at the date of publication. Neither the publisher nor the authors or the editors give a warranty, expressed or implied, with respect to the material contained herein or for any errors or omissions that may have been made. The publisher remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. Cover Illustration Caption: Longitudinal BLI analysis of Australian bat lyssavirus (ABLV)-luc replication in the same mouse on Day 3, Day 5, and Day 10 post-infection. For each image, BLI data are overlaid on a CT image of one B6 Albino mouse infected with ABLV-Luc. Scale bars indicate range of the pseudocolor intensity scale that is used to show mean luminescence intensity (MLI) values. Intensity of viral replication is greatest in the brain. 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 Bioluminescence means a “cold light,” a unique type of chemiluminescence taking place inside a living organism. Historically, bioluminescence has been a source of fascination and curiosity for many people. Bioluminescence from living organisms is so strong as to be visible with naked eyes and intrinsically non-toxic to living organisms. Nowadays, bioluminescence is utilized as a versatile optical readout in multidisciplinary research areas, because it is advantageous in its low background intensity, high signal-tonoise ratios, wide dynamic ranges, versatility, and suitability in the imaging of animal models. Because of these distinctive virtues, bioluminescence has revolutionized molecular imaging and bioanalysis of intracellular molecular events with higher quantitative properties. In these protocol books, we highlight recent advances in molecular imaging techniques, which may be immediately useful in global biolaboratories. The chapters are categorized into nine major parts according to technological properties: (i) Establishment of Luciferins and Luciferases, (ii) Basic In Vitro Applications, (iii) Basic In Vivo Applications, (iv) Multiplex Imaging Platforms, (v) Heterogeneous Conjugates, (vi) Protein FragmentComplementation Assays, (vii) BRET-Based Imaging, (viii) Instrumentation, and (ix) Software. We hope that this book has a role in directing and inspiring researchers to create smarter imaging techniques of the next generation, being truly quantitative, highly sensitive, and readily comprehended. All these efforts on advanced molecular imaging will engender deeper understanding of biological systems and will break new ground in the research fields of life science. I am greatly honored to complete this project with the contributors of this book. They generously accepted contributing one or multiple chapters for these books. The 66 chapters in total, to which major research groups from around the world have contributed, truly reflect the hottest research protocols regarding bioluminescence to date. I am thankful to Professor John Walker and the publishing team for their timely advice and support. Finally, I owe a special thank you to Young-Eun, my wife, and Yun and Hun, my children, for their endless support. I hope that this protocol book is genuinely practical and comprehensive guidance for researchers and technical staff on how to proceed with bioluminescence studies in laboratories. Tsukuba, Ibaraki, Japan

Sung-Bae Kim

v

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

PART I

HETEROGENEOUS CONJUGATES

1 Quantitative Assessment of the Efficacy of Near-Infrared Photoimmunotherapy with Bioluminescence Imaging . . . . . . . . . . . . . . . . . . . . . . . Ryuhei Okada, Aki Furusawa, Peter L. Choyke, and Hisataka Kobayashi 2 Evaluation of the Efficacy of Saracatinib-Loaded Nanoparticles in Lymphatic Metastases of HNSCC with the Aid of Bioluminescence Imaging . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Liwei Lang and Yong Teng 3 Reactive Oxygen Species-Responsive and Self-Illuminating Nanoparticles for Inflammation and Tumor Imaging . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Xiaoqiu Xu, Qi Li, and Jianxiang Zhang 4 Antibacterial Activity Evaluation of ZnO, CuO, and TiO2 Nanoparticles in Solution and Thin Films . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Christine Mielcarek, Rania Dadi, Anne Roynette, Alex Lemarchand, Andrei Kanaev, Karim Senni, Mamadou Traore, and Rabah Azouani 5 BRET-Based Dual-Color (Visible/Near-Infrared) Molecular Imaging Using a Quantum Dot/EGFP-Luciferase Conjugate . . . . . . . . . . . . . . . . . . . . . . . . Setsuko Tsuboi and Takashi Jin 6 Polyhistidine-Tag-Enabled Conjugation of Quantum Dots and Enzymes to DNA Nanostructures . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Christopher M. Green, Divita Mathur, Kimihiro Susumu, Eunkeu Oh, Igor L. Medintz, and Sebastia´n A. Dı´az 7 Real-Time Quantification of Cell Internalization Kinetics by Bioluminescent Probes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Roxanne Castillo, Di Wu, Zheng Cao, Ran Yan, Kalea Fajardo, Jie Ren, Yunfeng Lu, and Jing Wen

PART II

v xi

3

15

21

35

47

61

93

PROTEIN FRAGMENT-COMPLEMENTATION ASSAYS

8 Quantitative Imaging of Retinoic Acid Activities in Living Mammalian Cells . . . 111 Sung-Bae Kim, Rika Fujii, and Ramasamy Paulmurugan 9 Bioluminescence-Based Complementation Assay to Correlate Conformational Changes in Membrane-Bound Complexes with Enzymatic Function . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 123 Sharon O’Neill and Ulla G. Knaus 10 The NanoBiT-Based Homogenous Ligand–Receptor Binding Assay . . . . . . . . . . 139 Ya-Li Liu and Zhan-Yun Guo

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Contents

Monitoring Hippo Signaling Pathway Activity Using a Luciferase-Based Large Tumor Suppressor (LATS) Biosensor . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 155 Alexander Pipchuk and Xiaolong Yang

PART III 12

13

14

15 16

17

18 19 20

21

22

Screening of Protein–Protein Interaction Modulators Using BRET-Based Technology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Lucia R. Ferna´ndez, Jesica Mild, and Martin M. Edreira TRUPATH: An Open-Source Biosensor Platform for Interrogating the GPCR Transducerome . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Jeffrey F. DiBerto, Reid H. J. Olsen, and Bryan L. Roth MERLIN: A BRET-Based Proximity Biosensor for Studying Mitochondria–ER Contact Sites . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Hector Flores-Romero and Ana J. Garcı´a-Sa´ez Single-Cell NanoBRET Imaging with Green-Range HaloTag Acceptor. . . . . . . . Ovia Thirukkumaran and Hideaki Mizuno Method for Measuring Bioactive Molecules in Blood by a Smartphone Using Bioluminescent Ratiometric Indicators . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Mitsuru Hattori, Yukino Itoh, and Takeharu Nagai BRET Sensors for Imaging Membrane Integrity of Microfluidically Generated Extracellular Vesicles . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Ramasamy Paulmurugan, Yi Liu, Uday Kumar Sukumar, Masamitsu Kanada, and Tarik F. Massoud In Vivo Assessment of Protein-Protein Interactions Using BRET Assay. . . . . . . . Aaiyas Mujawar and Abhijit De Live Cell Imaging of ATP Dynamics in Plant Cells . . . . . . . . . . . . . . . . . . . . . . . . . . Ryoichi Sato and Shinji Masuda Bioluminescence Resonance Energy Transfer for Global DNA Methylation Quantification . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Natsumi Taka, Yuji Baba, Yuka Iwasaki, and Wataru Yoshida In Vivo Bioluminescent Imaging of Bone Marrow-Derived Mesenchymal Stem Cells in Mice . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Prakash Gangadaran, Ji Min Oh, Ramya Lakshmi Rajendran, and Byeong-Cheol Ahn In Vivo Imaging of Oxidative and Hypoxic Stresses in Mice Model of Amyotrophic Lateral Sclerosis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Yasuyuki Ohta, Emi Nomura, Shinae Kizaka-Kondoh, and Koji Abe

PART IV 23

BRET-BASED IMAGING 173

185

197 207

219

227

239 259

267

281

289

INSTRUMENTATION

ATP Sensing Paper with Smartphone Bioluminescence-Based Detection . . . . . . 297 Maria Maddalena Calabretta, Ruslan Alvarez-Diduk, Elisa Michelini, and Arben Merkoc¸i

Contents

24

25

26

27

28

29

30

31

Organ Bioluminescence Imaging Under Machine Perfusion Setting for Assessing Quality of Harvested Organ Preservation . . . . . . . . . . . . . . . . . . . . . . Yuhei Higashi, Jun Homma, and Hidekazu Sekine Time-Lapse Bioluminescence Imaging of Hes7 Expression In Vitro and Ex Vivo . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Marina Sanaki-Matsumiya and Ryoichiro Kageyama Bioluminescence-Optogenetics: A Practical Guide . . . . . . . . . . . . . . . . . . . . . . . . . . Matthew A. Stern, Henry Skelton, Alejandra M. Fernandez, Claire-Anne N. Gutekunst, Ken Berglund, and Robert E. Gross Applications of Bioluminescence-Optogenetics in Rodent Models . . . . . . . . . . . . Matthew A. Stern, Henry Skelton, Alejandra M. Fernandez, Claire-Anne N. Gutekunst, Robert E. Gross, and Ken Berglund One-Channel Microsliding Luminometer for Quantifying Low-Energy Bioluminescent Lights. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Sung-Bae Kim and Ramasamy Paulmurugan Compact Eight-Channel Light-Sensing System for Bioassays . . . . . . . . . . . . . . . . . Sung-Bae Kim, Sharon Seiko Hori, Negar Sadeghipour, Uday Kumar Sukumar, and Ramasamy Paulmurugan Characterization of Firefly Flashes at Various Temperatures in Different Wavelength Regions. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Anurup Gohain Barua and Angana Goswami Bioluminescent Monitoring of Circadian Rhythms in Isolated Mesophyll Cells of Arabidopsis at Single-Cell Level . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Shunji Nakamura and Tokitaka Oyama

PART V 32

ix

309

321 333

347

365 377

387

395

SOFTWARE

Exploring Phylogenetic Relationships and Divergence Times of Bioluminescent Species Using Genomic and Transcriptomic Data . . . . . . . . . . 409 Danilo T. Amaral, Monique Romeiro-Brito, and Isabel A. S. Bonatelli

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

425

Contributors KOJI ABE • National Center of Neurology and Psychiatry (NCNP), Tokyo, Japan BYEONG-CHEOL AHN • BK21 FOUR KNU Convergence Educational Program of Biomedical Sciences for Creative Future Talents, Department of Biomedical Science, School of Medicine, Kyungpook National University, Daegu, Republic of Korea; Department of Nuclear Medicine, School of Medicine, Kyungpook National University, Daegu, Republic of Korea; Department of Nuclear Medicine, Kyungpook National University Hospital, Daegu, Republic of Korea RUSLAN ALVAREZ-DIDUK • Nanobioelectronics and Biosensors Group, Catalan Institute of Nanoscience and Nanotechnology (ICN2), CSIC, The Barcelona Institute of Science and Technology, Barcelona, Spain DANILO T. AMARAL • Departamento de Biologia, Centro de Cieˆncias Humanas e Biol
ogicas, Universidade Federal de Sa˜o Carlos (UFSCar), Sorocaba, Brazil; Programa de P
os Graduac¸a˜o em Biologia Comparada, Faculdade de Filosofia, Cieˆncias e Letras de Ribeira˜o Preto, Universidade de Sa˜o Paulo (USP), Ribeira˜o Preto, Brazil RABAH AZOUANI • EBI-Ecole de Biologie Industrielle, Cergy, France YUJI BABA • Graduate School of Bionics, Tokyo University of Technology, Tokyo, Japan ANURUP GOHAIN BARUA • Department of Physics, Gauhati University, Guwahati, India KEN BERGLUND • Department of Neurosurgery, Emory University School of Medicine, Atlanta, GA, USA ISABEL A. S. BONATELLI • Departamento de Ecologia e Biologia Evolutiva, Universidade Federal de Sa˜o Paulo (UNIFESP), Sa˜o Paulo, Brazil MARIA MADDALENA CALABRETTA • Department of Chemistry “Giacomo Ciamician”, University of Bologna, Bologna, Italy; Center for Applied Biomedical Research (CRBA), Azienda Ospedaliero-Universitaria Policlinico S. Orsola-Malpighi, Bologna, Italy ZHENG CAO • Department of Chemical and Biomolecular Engineering, School of Engineering, University of California, Los Angeles, Los Angeles, CA, USA ROXANNE CASTILLO • Department of Chemical and Biomolecular Engineering, School of Engineering, University of California, Los Angeles, Los Angeles, CA, USA PETER L. CHOYKE • Molecular Imaging Branch, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA RANIA DADI • LSPM-CNRS, Laboratoire des Sciences des Proce´de´s et des Mate´riaux, Universite´ Paris 13, Sorbonne Paris Cite´, Villetaneuse, France ABHIJIT DE • Molecular Functional Imaging Lab, ACTREC, Tata Memorial Centre, Navi Mumbai, India SEBASTIA´N A. DI´AZ • Center for Bio/Molecular Science and Engineering, U.S. Naval Research Laboratory Code 6900, Washington, DC, USA JEFFREY F. DIBERTO • Department of Pharmacology, University of North Carolina at Chapel Hill School of Medicine, Chapel Hill, NC, USA MARTIN M. EDREIRA • Universidad de Buenos Aires, Facultad de Ciencias Exactas y Naturales, Departamento de Quı´mica Biol
ogica, Buenos Aires, Argentina; CONICETUniversidad de Buenos Aires, Instituto de Quı´mica Biol
ogica de la Facultad de Ciencias Exactas y Naturales (IQUIBICEN), Buenos Aires, Argentina; Department of

xi

xii

Contributors

Pharmacology and Chemical Biology, School of Medicine, University of Pittsburgh, Pittsburgh, PA, USA KALEA FAJARDO • Department of Chemical and Biomolecular Engineering, School of Engineering, University of California, Los Angeles, Los Angeles, CA, USA ALEJANDRA M. FERNANDEZ • Department of Neurosurgery, Emory University School of Medicine, Atlanta, GA, USA LUCIA R. FERNA´NDEZ • Universidad de Buenos Aires, Facultad de Ciencias Exactas y Naturales, Departamento de Quı´mica Orga´nica, Buenos Aires, Argentina; CONICETUniversidad de Buenos Aires, Unidad de Microana´lisis y Me´todos Fı´sicos Aplicados a la Quı´mica Orga´nica (UMYMFOR), Buenos Aires, Argentina HECTOR FLORES-ROMERO • Institute for Genetics, University of Cologne, Cologne, Germany; Cologne Excellence Cluster on Cellular Stress Responses in Aging-Associated Diseases (CECAD), University of Cologne, Cologne, Germany; Interfaculty Institute of Biochemistry, Eberhard-Karls-Universit€ a t Tu¨bingen, Tu¨bingen, Germany RIKA FUJII • Environmental Management Research Institute, National Institute of Advanced Industrial Science and Technology (AIST), Tsukuba, Japan AKI FURUSAWA • Molecular Imaging Branch, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA PRAKASH GANGADARAN • BK21 FOUR KNU Convergence Educational Program of Biomedical Sciences for Creative Future Talents, Department of Biomedical Science, School of Medicine, Kyungpook National University, Daegu, Republic of Korea; Department of Nuclear Medicine, School of Medicine, Kyungpook National University, Daegu, Republic of Korea ANA J. GARCI´A-SA´EZ • Institute for Genetics, University of Cologne, Cologne, Germany; Cologne Excellence Cluster on Cellular Stress Responses in Aging-Associated Diseases (CECAD), University of Cologne, Cologne, Germany; Interfaculty Institute of Biochemistry, Eberhard-Karls-Universit€ a t Tu¨bingen, Tu¨bingen, Germany ANGANA GOSWAMI • Department of Physics, Pandit Deendayal Upadhyaya Adarsha Mahavidyalaya Dalgaon, India CHRISTOPHER M. GREEN • Center for Bio/Molecular Science and Engineering, U.S. Naval Research Laboratory Code 6900, Washington, DC, USA; National Research Council, Washington, DC, USA ROBERT E. GROSS • Department of Neurosurgery, Emory University School of Medicine, Atlanta, GA, USA ZHAN-YUN GUO • Research Center for Translational Medicine at East Hospital, School of Life Sciences and Technology, Tongji University, Shanghai, China CLAIRE-ANNE N. GUTEKUNST • Department of Neurosurgery, Emory University School of Medicine, Atlanta, GA, USA MITSURU HATTORI • SANKEN (The Institute of Scientific and Industrial Research), Osaka University, IbarakiOsaka, Japan YUHEI HIGASHI • Institute of Advanced Biomedical Engineering and Science, Tokyo Women’s Medical University, Tokyo, Japan; Tokaihit Co., Ltd., Shizuoka, Japan JUN HOMMA • Institute of Advanced Biomedical Engineering and Science, Tokyo Women’s Medical University, Tokyo, Japan SHARON SEIKO HORI • Molecular Imaging Program at Stanford, Stanford University School of Medicine, Palo Alto, CA, USA; Canary Center at Stanford for Cancer Early Detection, Stanford University School of Medicine, Palo Alto, CA, USA YUKINO ITOH • Graduate School of Frontier Biosciences, Osaka University, Osaka, Japan

Contributors

xiii

YUKA IWASAKI • Graduate School of Bionics, Tokyo University of Technology, Tokyo, Japan TAKASHI JIN • RIKEN Center for Biosystems Dynamics Research, Osaka, Japan RYOICHIRO KAGEYAMA • Institute for Frontier Life and Medical Sciences, Kyoto University, Kyoto, Japan; Institute for Integrated Cell-Material Sciences (iCeMS), Kyoto University, Kyoto, Japan; RIKEN Center for Brain Science, Wako, Japan MASAMITSU KANADA • Molecular Imaging Program at Stanford, Stanford University School of Medicine, Palo Alto, CA, USA; Canary Center at Stanford for Cancer Early Detection, Stanford University School of Medicine, Palo Alto, CA, USA ANDREI KANAEV • LSPM-CNRS, Laboratoire des Sciences des Proce´de´s et des Mate´riaux, Universite´ Paris 13, Sorbonne Paris Cite´, Villetaneuse, France SUNG-BAE KIM • Environmental Management Research Institute, National Institute of Advanced Industrial Science and Technology (AIST), Ibaraki, Japan SHINAE KIZAKA-KONDOH • School of Life Science and Technology, Tokyo Institute of Technology, Yokohama, Japan ULLA G. KNAUS • Conway Institute, School of Medicine, University College Dublin, Dublin, Ireland HISATAKA KOBAYASHI • Molecular Imaging Branch, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA LIWEI LANG • Department of Oral Biology and Diagnostic Sciences, Georgia Cancer Center, Augusta University, Augusta, GA, USA ALEX LEMARCHAND • LSPM-CNRS, Laboratoire des Sciences des Proce´de´s et des Mate´riaux, Universite´ Paris 13, Sorbonne Paris Cite´, Villetaneuse, France QI LI • Department of Pharmaceutics, College of Pharmacy, Third Military Medical University (Army Medical University), Chongqing, China; Department of Biomedical Engineering and Medical Imaging, Third Military Medical University (Army Medical University), Chongqing, China YA-LI LIU • Research Center for Translational Medicine at East Hospital, School of Life Sciences and Technology, Tongji University, Shanghai, China YI LIU • Molecular Imaging Program at Stanford, Stanford University School of Medicine, Palo Alto, CA, USA; Canary Center at Stanford for Cancer Early Detection, Stanford University School of Medicine, Palo Alto, CA, USA YUNFENG LU • Department of Chemical and Biomolecular Engineering, School of Engineering, University of California, Los Angeles, Los Angeles, CA, USA TARIK F. MASSOUD • Molecular Imaging Program at Stanford, Stanford University School of Medicine, Palo Alto, CA, USA; Canary Center at Stanford for Cancer Early Detection, Stanford University School of Medicine, Palo Alto, CA, USA SHINJI MASUDA • Department of Life Science and Technology, Tokyo Institute of Technology, Yokohama, Kanagawa, Japan DIVITA MATHUR • Center for Bio/Molecular Science and Engineering, U.S. Naval Research Laboratory Code 6900, Washington, DC, USA; College of Science, George Mason University, Fairfax, VA, USA IGOR L. MEDINTZ • Center for Bio/Molecular Science and Engineering, U.S. Naval Research Laboratory Code 6900, Washington, DC, USA ARBEN MERKOC¸I • Nanobioelectronics and Biosensors Group, Catalan Institute of Nanoscience and Nanotechnology (ICN2), CSIC, The Barcelona Institute of Science and Technology, Barcelona, Spain; Catalan Institution for Research and Advanced Studies (ICREA), Barcelona, Spain

xiv

Contributors

ELISA MICHELINI • Department of Chemistry “Giacomo Ciamician”, University of Bologna, Bologna, Italy; Center for Applied Biomedical Research (CRBA), Azienda OspedalieroUniversitaria Policlinico S. Orsola-Malpighi, Bologna, Italy CHRISTINE MIELCAREK • EBI-Ecole de Biologie Industrielle, Cergy, France JESICA MILD • Universidad de Buenos Aires, Facultad de Ciencias Exactas y Naturales, Departamento de Quı´mica Biol
ogica, Buenos Aires, Argentina; CONICET-Universidad de Buenos Aires, Instituto de Quı´mica Biol
ogica de la Facultad de Ciencias Exactas y Naturales (IQUIBICEN), Buenos Aires, Argentina HIDEAKI MIZUNO • Laboratory of Biomolecular Network Dynamics, Biochemistry, Molecular and Structural Biology Section, Department of Chemistry, KU Leuven, Heverlee, Belgium AAIYAS MUJAWAR • Molecular Functional Imaging Lab, ACTREC, Tata Memorial Centre, Navi Mumbai, India TAKEHARU NAGAI • SANKEN (The Institute of Scientific and Industrial Research), Osaka University, IbarakiOsaka, Japan; Graduate School of Frontier Biosciences, Osaka University, Osaka, Japan SHUNJI NAKAMURA • Department of Botany, Graduate School of Science, Kyoto University, Kyoto, Japan EMI NOMURA • Department of Neurology, Graduate School of Medicine, Dentistry and Pharmaceutical Sciences, Okayama University, Okayama, Japan SHARON O’NEILL • Conway Institute, School of Medicine, University College Dublin, Dublin, Ireland; Legend Biotech, Dublin, Ireland EUNKEU OH • Optical Sciences Division, Code 5600, U.S. Naval Research Laboratory, Washington, DC, USA JI MIN OH • Department of Nuclear Medicine, School of Medicine, Kyungpook National University, Daegu, Republic of Korea YASUYUKI OHTA • Division of Neurology and Clinical Neuroscience, Department of Internal Medicine III, Yamagata University School of Medicine, Yamagata, Japan RYUHEI OKADA • Molecular Imaging Branch, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA REID H. J. OLSEN • Discovery Biology and GPCR Pharmacology, Exscientia PLC., Oxford, UK TOKITAKA OYAMA • Department of Botany, Graduate School of Science, Kyoto University, Kyoto, Japan RAMASAMY PAULMURUGAN • Molecular Imaging Program at Stanford, Stanford University School of Medicine, Palo Alto, CA, USA; Canary Center at Stanford for Cancer Early Detection, Stanford University School of Medicine, Palo Alto, CA, USA ALEXANDER PIPCHUK • Department of Pathology and Molecular Medicine, Queen’s University, Kingston, ON, Canada RAMYA LAKSHMI RAJENDRAN • Department of Nuclear Medicine, School of Medicine, Kyungpook National University, Daegu, Republic of Korea JIE REN • Department of Chemical and Biomolecular Engineering, School of Engineering, University of California, Los Angeles, Los Angeles, CA, USA MONIQUE ROMEIRO-BRITO • Departamento de Biologia, Centro de Cieˆncias Humanas e Biol
ogicas, Universidade Federal de Sa˜o Carlos (UFSCar), Sorocaba, Brazil BRYAN L. ROTH • Department of Pharmacology, University of North Carolina at Chapel Hill School of Medicine, Chapel Hill, NC, USA ANNE ROYNETTE • EBI-Ecole de Biologie Industrielle, Cergy, France NEGAR SADEGHIPOUR • Molecular Imaging Program at Stanford, Stanford University School of Medicine, Palo Alto, CA, USA; Canary Center at Stanford for Cancer Early Detection, Stanford University School of Medicine, Palo Alto, CA, USA

Contributors

xv

MARINA SANAKI-MATSUMIYA • European Molecular Biology Laboratory (EMBL), Barcelona, Spain; Institute for Frontier Life and Medical Sciences, Kyoto University, Kyoto, Japan RYOICHI SATO • RIKEN Center for Sustainable Resource Science, Yokohama, Kanagawa, Japan HIDEKAZU SEKINE • Institute of Advanced Biomedical Engineering and Science, Tokyo Women’s Medical University, Tokyo, Japan KARIM SENNI • EBI-Ecole de Biologie Industrielle, Cergy, France HENRY SKELTON • Department of Neurosurgery, Emory University School of Medicine, Atlanta, GA, USA MATTHEW A. STERN • Department of Neurosurgery, Emory University School of Medicine, Atlanta, GA, USA UDAY KUMAR SUKUMAR • Molecular Imaging Program at Stanford, Stanford University School of Medicine, Palo Alto, CA, USA; Canary Center at Stanford for Cancer Early Detection, Stanford University School of Medicine, Palo Alto, CA, USA KIMIHIRO SUSUMU • Optical Sciences Division, Code 5600, U.S. Naval Research Laboratory, Washington, DC, USA; Jacobs Corporation, Hanover, MD, USA NATSUMI TAKA • Graduate School of Bionics, Tokyo University of Technology, Tokyo, Japan YONG TENG • Department of Oral Biology and Diagnostic Sciences, Georgia Cancer Center, Augusta University, Augusta, GA, USA; Department of Hematology and Medical Oncology, Winship Cancer Institute, Emory University School of Medicine, Atlanta, GA, USA OVIA THIRUKKUMARAN • Laboratory of Biomolecular Network Dynamics, Biochemistry, Molecular and Structural Biology Section, Department of Chemistry, KU Leuven, Heverlee, Belgium MAMADOU TRAORE • LSPM-CNRS, Laboratoire des Sciences des Proce´de´s et des Mate´riaux, Universite´ Paris 13, Sorbonne Paris Cite´, Villetaneuse, France SETSUKO TSUBOI • RIKEN Center for Biosystems Dynamics Research, Osaka, Japan JING WEN • Department of Microbiology, Immunology and Molecular Genetics, David Geffen School of Medicine, UCLA AIDS Institute, University of California, Los Angeles, Los Angeles, CA, USA DI WU • Department of Chemical and Biomolecular Engineering, School of Engineering, University of California, Los Angeles, Los Angeles, CA, USA XIAOQIU XU • Department of Pharmaceutics, College of Pharmacy, Third Military Medical University (Army Medical University), Chongqing, China; Laboratory of Human Disease and Immunotherapies, West China Hospital, Sichuan University, Chengdu, China RAN YAN • College of Biotechnology and Pharmaceutical Engineering, Nanjing Tech University, Nanjing, China XIAOLONG YANG • Department of Pathology and Molecular Medicine, Queen’s University, Kingston, ON, Canada WATARU YOSHIDA • Graduate School of Bionics, Tokyo University of Technology, Tokyo, Japan; School of Bioscience and Biotechnology, Tokyo University of Technology, Tokyo, Japan JIANXIANG ZHANG • Department of Pharmaceutics, College of Pharmacy, Third Military Medical University (Army Medical University), Chongqing, China; State Key Lab of Trauma, Burn and Combined Injury, Third Military Medical University (Army Medical University), Chongqing, China

Part I Heterogeneous Conjugates

Chapter 1 Quantitative Assessment of the Efficacy of Near-Infrared Photoimmunotherapy with Bioluminescence Imaging Ryuhei Okada, Aki Furusawa, Peter L. Choyke, and Hisataka Kobayashi Abstract Near-infrared photoimmunotherapy (NIR-PIT) is a cell-specific cancer therapy in which antibody–photoabsorber conjugates (APCs) are activated by NIR light to induce rapid immunogenic cell death with minimal off-target effects. In preclinical settings, bioluminescence imaging (BLI) is useful to quantitatively assess the efficacy of NIR-PIT for both in vitro and in vivo experiments, especially in the early phase of testing. Here, we describe the detailed methods of the experiments for NIR-PIT and evaluation of its efficacy using BLI. Key words NIR-PIT, Firefly, Luciferase, Luciferin, BLI

1

Introduction Near-infrared photoimmunotherapy (NIR-PIT) is a target cellspecific cancer treatment using an antibody–photoabsorber conjugate (APC) [1–3]. The photoabsorber used in this therapy is IRDye700DX (IR700), which is activated by NIR light at 689 nm. APCs bind the surface of target cells 1–2 days after intravenous (i.v.) injection. Subsequent NIR light exposure induces rapid necrotic/immunogenic cell death (ICD) [4, 5]. The cells treated with NIR-PIT show immediate swelling, bleb formation, and rupture of the cell membranes within a few minutes with minimal off-target effects [1, 6]. When cancer cells are targeted by NIR-PIT, this rapid ICD releases various signaling molecules and tumor-associated antigens from dying cancer cells, resulting in the maturation of immature dendritic cells and following priming, proliferation, and activation of CD8+ T cells [2]. A global phase III human clinical trial of NIR-PIT targeting hEGFR in patients with inoperable head and neck cancer is ongoing from May 2019 (https://clinicaltrials.gov/ct2/show/NCT03769506). In September 2020, the first APC for NIR-PIT, cetuximab-IR700

Sung-Bae Kim (ed.), Bioluminescence: Methods and Protocols, Volume 2, Methods in Molecular Biology, vol. 2525, https://doi.org/10.1007/978-1-0716-2473-9_1, © The Author(s), under exclusive license to Springer Science+Business Media, LLC, part of Springer Nature 2022

3

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Ryuhei Okada et al.

(ASP1929), was conditionally approved and registered for clinical use by the Pharmaceuticals and Medical Devices Agency (PMDA) in Japan. Although this technology originally targeted specific cancer cell antigen, it could equally well be applied to other unfavorable cells, such as immune suppressors, in the tumor microenvironment. In preclinical settings, intratumoral immunosuppressive cells, such as regulatory T cells, were killed with appropriately targeted NIR-PIT [7–9], while cancer-cell-targeted NIR-PIT was also administered. Results showed that this combined approach was superior to monotherapy [10, 11]. The efficacy of cancer-cell-targeted NIR-PIT can also be enhanced by combining it with systemic immune checkpoint inhibitors, such as antiPD-1 or anti-CTLA4, as demonstrated in syngeneic mouse cancer models [12, 13]. Since the target cell killing occurs rapidly after exposure to NIR light, bioluminescence (BL) (e.g., firefly luciferase (FLuc)) and fluorescence (FL) (e.g., GFP) analyses are useful to quantitively evaluate the efficacy of both in vitro and in vivo NIR-PIT preclinical experiments. Luciferase–luciferin BL analysis relies on oxygen and ATP levels in living cells and is superior to GFP FL because rapid ATP loss impedes BL but does not immediately affect GFP FL [14]. In this chapter, we describe step-by-step methods for the in vitro and in vivo experiments to measure the effects of NIR-PIT using BL. Although luciferase- and hEGFR- expressing A431 (A431-luc) cell line and Panitumumab, a monoclonal antibody (mAb) against hEGFR, are exemplified in this chapter, the methods could also be applied to other combinations of cell line and mAb.

2

Materials

2.1 Manufacture of IR700 Conjugated Monoclonal Antibody (mAb-IR700 Conjugate)

1. 10 mM IRDye® 700DX NHS ester (IR700, LI-COR Bioscience) solution: dissolve 5 mg of IR700 NHS ester into 255.9 μL of dimethyl sulfoxide (DMSO). Store at
20  C in the dark. 2. 20 mg/mL of Panitumumab as a monoclonal antibody. Store at 4  C. 3. 0.1 M disodium hydrogen phosphate (Na2HPO4) solution (pH 8–9): dissolve 1.42 g of Na2HPO4 into 100 mL of distilled water. 4. Phosphate-buffered saline (PBS; pH 7.4). 5. 13.5-mL Sephadex G-25 column. 6. Coomassie Brilliant Blue (CBB) protein assay reagent. 7. UV–Vis spectrophotometer (8453 Value System, Agilent).

Bioluminescence Imaging in NIR-PIT Experiments

5

Fig. 1 NIR light sources. Either LED or laser light source of 689 nm is used. Laser diffusers consist of frontal and cylindrical diffusers. The light from a frontal diffuser can be changed to a parallel beam with a collimator. For interstitial light exposure, including endoscopic procedures, cylindrical diffusers are used

2.2

In Vitro PIT

1. A431-luc cell line (hEGFR- and FLuc-expressing cells). 2. Culture medium: add 50 mL of fetal bovine serum (FBS) and 5 mL of 100 antibiotics (penicillin–streptomycin; P/S) to 500 mL of RPMI 1640 medium with or without phenol red. 3. 0.05% (w/v) trypsin/EDTA solution: the diluted solution of 50 mL of 0.5% trypsin/EDTA with 500 mL of PBS. 4. Trypan blue stain solution. 5. NIR light source (e.g., LED or laser) (see Note 1, Fig. 1).

2.3

In Vivo PIT

1. Athymic nude mice, 6–8 weeks old. 2. Isoflurane and vaporizer system. 3. 5 mg/mL of pentobarbital solution. 4. 1-mL syringe and 2527-G needles.

2.4 Bioluminescence Imaging (BLI)

1. D-luciferin solution: dissolve 1.0 g of D-luciferin in 66.6 mL of PBS to make a 15-mg/mL stock solution. Store at
20  C before use. 2. BL imager (Photon Imager, Biospace Lab).

3

Methods Carry out all procedures at room temperature (RT) unless otherwise indicated.

6

Ryuhei Okada et al.

Fig. 2 Purification of APC. (a) PBS is used for the purification of the APC. APC can be confirmed as a blue band (solid arrow), while unconjugated dye remains at a higher level than the APC band (dashed arrow). (b) Just before the APC collection. (c) Collection of the APC 3.1 Conjugation of IR700 with mAb

1. Mix 1 mg of Panitumumab (see Note 2) with 3.3 μL of 10 mM IR700 (IR700/mAb molar ratio ¼ 5) in 0.1 M Na2HPO4 solution (adjust the volume in order to make the final total solution volume to be 300–700 μL). 2. Incubate the above solutions at RT (20–25  C) for 60 min (see Note 3). 3. Wash out the Sephadex G25 column with PBS. 4. Apply the mAB-IR700 mixture on the column. 5. Elute with PBS; collect only the blue-colored band (see Fig. 2). 6. Add 5 mL of the elate to 145 mL of CBB protein assay reagent and measure the absorption at 595 nm as a reference with a UV–Vis spectrophotometer to determine protein concentration. 7. Separately determine the concentration of IR700 by photoabsorption at 689 nm to confirm the mean number of fluorophore molecules conjugated to each mAb molecule (see Note 4). 8. Store at 4  C in the dark before use.

Bioluminescence Imaging in NIR-PIT Experiments

3.2

In Vitro NIR-PIT

7

1. Harvest exponentially growing A431-luc cells with 0.05% (w/v) trypsin/EDTA solution and centrifuge the cells for 3–5 min at 1500 RPM, at 4  C. 2. Count the cell number with trypan blue staining, and re-plate the cells at appropriate density (2–3  105 cells) in each well of the 12-well microplate. 3. Incubate 1 day in a CO2 incubator to allow the cell attachment to the bottom of the well. 4. Aspirate the cell culture medium and add a fresh medium containing mAb–IR700 conjugate (final concentration: 10 μg/mL). The optimal concentration of the mAb–IR700 conjugate will vary for each mAb; however, 10 μg/mL is generally sufficient. 5. Incubate for 1 h at 4 or 37  C in a dark space (see Note 5). 6. Aspirate the cell culture medium, wash the cells with PBS, and add phenol red-free complete medium (see Note 6). 7. Expose the cells to NIR light without the microplate cover. 8. Incubate for 1 h at 37  C. 9. Add 300 μg of D-luciferin (20 μL of D-luciferin solution) per 1 mL medium and incubate for 2 min (see Note 7). 10. Count the photon number for 10–30 s with a BL imager (see Note 8, Fig. 3).

Fig. 3 BL assessment of in vitro NIR-PIT. Luciferase-expressing A431 cell line and Panitumumab–IR700 conjugate are used. The treatment efficacy is assessed in the presence or absence of APC and/or NIR light exposure. The luciferase activity of the cells decreased only when the cells were incubated with APC and exposed to NIR light. Abbreviations: APC+ and APC
, with and without antibody–photoabsorber conjugates; CPM, count per minute

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In Vivo NIR-PIT

1. Harvest exponentially growing A431-luc cells with 0.05% (w/v) trypsin, and collect the cells into a centrifuge tube with the same volume of RPMI 1640 medium as the trypsin/EDTA solution. 2. Centrifuge the cells for 3–5 min at 1500 rpm, at 4  C. 3. Resuspend the cell pellet with cold PBS and quantify the cells with a hemocytometer or a cell counter and dilute cell suspension with PBS so that two million cells are contained in 100 μL (see Note 9). 4. Keep the cell suspension on ice before use. 5. Anesthetize the mouse with 3% (v/v) isoflurane. 6. Mix the cell suspension thoroughly before injection. 7. Aspirate the cell suspension with a 1-mL syringe and 25–27 G needle, and inject 100 μL of the cells subcutaneously into the right dorsum of the mice. Withdraw the needle slowly. 8. Monitor the mouse daily and examine the tumor volume periodically by caliper. Calculate tumor volume as (major axis of tumor)  (minor axis of tumor)2  0.5. 9. Once the tumor volume reaches ~100 mm3, assign the tumorbearing mice randomly to either the treatment group or nontreatment control group (see Note 10). 10. Anesthetize the mice with isoflurane and slowly inject mAb– IR700 conjugate into the tail vein with a 1-mL syringe and 27–30 G needle (see Note 11). 11. On the following day, confirm mAb–IR700 accumulation in the tumor with in vivo FLFL imaging (see Note 12), followed by anesthesia of the mice with isoflurane and intraperitoneal (i.p.) injection of pentobarbital (~750 μg/body). 12. Expose the entire tumor to NIR light under deep anesthesia (see Note 13, Fig. 4). Upon external NIR light exposure, place a piece of aluminum foil with a hole of approximately half-inch diameter (corresponding to the tumor) over the mouse to ensure that the NIR light exposure is limited to the tumor site. When performing interstitial light exposure, cylindrical diffusers are placed through an i.v. catheter or endoscope. 13. Place the mice on heating pads and observe until the recovery from anesthesia. 14. Monitor the mouse daily and examine the tumor volume periodically by caliper. 15. Analyze treatment effects and survival outcomes by comparing them with the control tumors (see Note 14).

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Fig. 4 Light exposure in in vivo NIR-PIT. (a–c) An example of external light exposure. (a) Mouse is located under the laser collimator (*). The black arrow represents the location of the tumor. (b) Upon NIR light exposure, a piece of aluminum foil with a hole is placed over the mouse to shield the rest of the mouse. (c) NIR light is applied onto the tumor site through the hole. (d) An example of interstitial light exposure. A cylindrical diffuser is inserted through the intravenous (i.v.) catheter to allow deeper penetration of interstitial light 3.4 BL Assessment of the Efficacy of In Vivo NIR-PIT

1. Inject D-luciferin solution (15 mg/mL, 200 μL) i.p. before and after NIR-PIT under anesthesia with isoflurane. 2. Obtain cumulative images for 2–3 min with a BL imager (Photon Imager) 10–15 min after the injection of the luciferin solution (see Note 15). Perform this imaging before and after NIR-PIT (see Fig. 5). 3. For quantitative analysis, place regions of interest (ROIs) over the entire tumor using analysis software that comes with the imager or separate software. 4. Calculate the photon counts per minute of relative light units (RLU). 5. Calculate %RLU as (RLU of post-treatment)/(RLU of pre-treatment)  100. 6. Analyze the treatment effects by comparing them with the control tumors.

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Fig. 5 In vivo imaging before and after the NIR-PIT. Luciferase-expressing A431 cell line and Panitumumab– IR700 conjugate are exemplified. Top: The 700-nm FL images. The FL of IR700 is immediately quenched after the NIR light exposure. Bottom: The BL images with luciferase/luciferin reaction. The luciferase activity dramatically decreases 1 day after the light exposure

4

Notes 1. To avoid accidents with laser light, all studies should be carried out in accordance with laser safety regulations. Since the spectrum of laser light is more narrow and closely matched to the absorbance of IR700 than that of LED, laser NIR-PIT has superior efficacy to LED NIR-PIT in both in vitro and in vivo experimental settings [15]. 2. In clinical mAb preparations, vial excipients may inhibit conjugation reactions. By exchanging a solvent with a centrifugal filter or dialysis of the antibody, the potential reaction inhibitors can be eliminated. 3. Higher final concentrations of DMSO may inhibit conjugation. Reaction pH should be adjusted to 8–9 by adding Na2HPO4 solution. 4. It is very important to determine an optimal conjugation number of IR700 molecules per mAb. Conjugating too many IR700 molecules in 1 mAb may result in loss of the binding affinity in vivo even while the same ratio is successful in vitro. Our results indicate that 3–4 IR700 molecules per mAb molecule work well for both in vitro and in vivo experiments; however, the optimal ratio may be different for each mAb. It is also important to confirm whether mAb and IR700 are conjugated or not using HPLC and SDS-PAGE.

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5. Dense accumulation of IR700 dyes to the cells may lead to rapid cell death in response to even weak NIR light contained in the room light so that the cells should be kept in a dark place as much as possible. 6. Phenol red-free medium may be useful to avoid the absorption of NIR excitation light. 7. The cells should be kept warm (e.g., use heating pads). The photon count decreases in a cold environment. 8. The cell viability can also be measured with other alternative assays, such as MTT assay or propidium-iodide staining with flow cytometry. 9. The optimal number of cells for injection depends on cell types and mouse strains. Consistent tumor “take rate” and tumor growth rate are important for the treatment study. 10. When mice with colored hair (e.g., C57BL/6) are used, the hair overlying the tumor site should be shaved before NIR-PIT and imaging studies. Depilation-induced skin pigmentation, which occurs within 1 week after hair removal, precludes accurate NIR light exposure or measurement of BL [16]. When repetitive light exposures or long-term assessment of BL is needed, use white-haired or nude mice. 11. Make sure no air bubbles remain in the syringe. The i.v. administration volume should not exceed 200 μL. 12. It is important to determine the point of the maximal IR700 accumulation within the tumor after mAb-IR700 injection. The amount of IR700 uptake in the tumor is a major determinant of NIR-PIT efficacy. 13. When using an LED light source, measure the power density (mW/cm2) of the light and determine the distance between the LED and target tumor, as power density, measured by an optical power meter, dramatically decreases with distance. When using a laser light source, we usually use the power density of 100–150 mW/cm2. High power density may cause thermal injury on the mouse skin [17]. 14. Measurement of the tumor volume by caliper should be performed by one person to reduce inter-observer errors. 15. The level of BL signal is the maximal between 10–15 min after i.p. D-luciferin injection [18]. For consistency, the same delay time should be applied in all BL experiments.

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Acknowledgments This work was supported by the Intramural Research Program of the National Institutes of Health, National Cancer Institute, Center for Cancer Research (ZIA BC011513). References 1. Mitsunaga M, Ogawa M, Kosaka N, Rosenblum LT, Choyke PL, Kobayashi H (2011) Cancer cell-selective in vivo near infrared photoimmunotherapy targeting specific membrane molecules. Nat Med 17(12): 1685–1691. https://doi.org/10.1038/nm. 2554 2. Kobayashi H, Choyke PL (2019) Near-infrared photoimmunotherapy of cancer. Acc Chem Res 52(8):2332–2339. https://doi.org/10. 1021/acs.accounts.9b00273 3. Kobayashi H, Griffiths GL, Choyke PL (2020) Near-infrared photoimmunotherapy: photoactivatable antibody-drug conjugates (ADCs). Bioconjug Chem 31(1):28–36. https://doi. org/10.1021/acs.bioconjchem.9b00546 4. Ogawa M, Tomita Y, Nakamura Y, Lee MJ, Lee S, Tomita S, Nagaya T, Sato K, Yamauchi T, Iwai H, Kumar A, Haystead T, Shroff H, Choyke PL, Trepel JB, Kobayashi H (2017) Immunogenic cancer cell death selectively induced by near infrared photoimmunotherapy initiates host tumor immunity. Oncotarget 8(6):10425–10436. https://doi. org/10.18632/oncotarget.14425 5. Sato K, Ando K, Okuyama S, Moriguchi S, Ogura T, Totoki S, Hanaoka H, Nagaya T, Kokawa R, Takakura H, Nishimura M, Hasegawa Y, Choyke PL, Ogawa M, Kobayashi H (2018) Photoinduced ligand release from a silicon phthalocyanine dye conjugated with monoclonal antibodies: a mechanism of cancer cell cytotoxicity after near-infrared photoimmunotherapy. ACS Cent Sci 4(11): 1559–1569. https://doi.org/10.1021/ acscentsci.8b00565 6. Ogata F, Nagaya T, Okuyama S, Maruoka Y, Choyke PL, Yamauchi T, Kobayashi H (2017) Dynamic changes in the cell membrane on three dimensional low coherent quantitative phase microscopy (3D LC-QPM) after treatment with the near infrared photoimmunotherapy. Oncotarget 8(61): 104295–104302. https://doi.org/10. 18632/oncotarget.22223 7. Sato K, Sato N, Xu B, Nakamura Y, Nagaya T, Choyke PL, Hasegawa Y, Kobayashi H (2016) Spatially selective depletion of tumor-

associated regulatory T cells with near-infrared photoimmunotherapy. Sci Transl Med 8(352): 352ra110. https://do i.org/10.1126/ scitranslmed.aaf6843 8. Okada R, Maruoka Y, Furusawa A, Inagaki F, Nagaya T, Fujimura D, Choyke PL, Kobayashi H (2019) The effect of antibody fragments on CD25 targeted regulatory T cell near-infrared photoimmunotherapy. Bioconjug Chem 30(10):2624–2633. https://doi.org/10. 1021/acs.bioconjchem.9b00547 9. Okada R, Kato T, Furusawa A, Inagaki F, Wakiyama H, Choyke PL, Kobayashi H (2021) Local depletion of immune checkpoint ligand CTLA4 expressing cells in tumor beds enhances antitumor host immunity. Adv Ther (Weinh) 4(5):2000269. https://doi.org/10. 1002/adtp.202000269 10. Maruoka Y, Furusawa A, Okada R, Inagaki F, Fujimura D, Wakiyama H, Kato T, Nagaya T, Choyke PL, Kobayashi H (2020) Combined CD44- and CD25-targeted near-infrared photoimmunotherapy selectively kills cancer and regulatory T cells in syngeneic mouse cancer models. Cancer Immunol Res 8(3): 3 4 5 – 3 5 5 . h t t p s : // d o i . o r g / 1 0 . 1 1 5 8 / 2326-6066.Cir-19-0517 11. Okada R, Furusawa A, Vermeer DW, Inagaki F, Wakiyama H, Kato T, Nagaya T, Choyke PL, Spanos WC, Allen CT, Kobayashi H (2021) Near-infrared photoimmunotherapy targeting human-EGFR in a mouse tumor model simulating current and future clinical trials. EBioMedicine 67:103345. https://doi.org/10. 1016/j.ebiom.2021.103345 12. Nagaya T, Friedman J, Maruoka Y, Ogata F, Okuyama S, Clavijo PE, Choyke PL, Allen C, Kobayashi H (2019) Host immunity following near-infrared photoimmunotherapy is enhanced with PD-1 checkpoint blockade to eradicate established antigenic tumors. Cancer Immunol Res 7(3):401–413. https://doi.org/ 10.1158/2326-6066.Cir-18-0546 13. Maruoka Y, Furusawa A, Okada R, Inagaki F, Fujimura D, Wakiyama H, Kato T, Nagaya T, Choyke PL, Kobayashi H (2020) Near-infrared photoimmunotherapy combined with CTLA4 checkpoint blockade in syngeneic mouse

Bioluminescence Imaging in NIR-PIT Experiments cancer models. Vaccines (Basel) 8(3):528. https://doi.org/10.3390/vaccines8030528 14. Maruoka Y, Nagaya T, Nakamura Y, Sato K, Ogata F, Okuyama S, Choyke PL, Kobayashi H (2017) Evaluation of early therapeutic effects after near-infrared photoimmunotherapy (NIR-PIT) using luciferase-luciferin photoncounting and fluorescence imaging. Mol Pharm 14(12):4628–4635. https://doi.org/ 10.1021/acs.molpharmaceut.7b00731 15. Sato K, Watanabe R, Hanaoka H, Nakajima T, Choyke PL, Kobayashi H (2016) Comparative effectiveness of light emitting diodes (LEDs) and lasers in near infrared photoimmunotherapy. Oncotarget 7(12):14324–14335. https:// doi.org/10.18632/oncotarget.7365 16. Mu¨ller-Ro¨ver S, Handjiski B, van der Veen C, Eichmu¨ller S, Foitzik K, McKay IA, Stenn KS,

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Paus R (2001) A comprehensive guide for the accurate classification of murine hair follicles in distinct hair cycle stages. J Invest Dermatol 117(1):3–15. https://doi.org/10.1046/j. 0022-202x.2001.01377.x 17. Okuyama S, Nagaya T, Ogata F, Maruoka Y, Sato K, Nakamura Y, Choyke PL, Kobayashi H (2017) Avoiding thermal injury during nearinfrared photoimmunotherapy (NIR-PIT): the importance of NIR light power density. Oncotarget 8(68):113194–113201. https://doi. org/10.18632/oncotarget.20179 18. O’Neill K, Lyons SK, Gallagher WM, Curran KM, Byrne AT (2010) Bioluminescent imaging: a critical tool in pre-clinical oncology research. J Pathol 220(3):317–327. https:// doi.org/10.1002/path.2656

Chapter 2 Evaluation of the Efficacy of Saracatinib-Loaded Nanoparticles in Lymphatic Metastases of HNSCC with the Aid of Bioluminescence Imaging Liwei Lang and Yong Teng Abstract Head and neck squamous cell carcinoma (HNSCC) remains a deadly disease despite concerted efforts to improve its diagnosis and treatment in recent decades. Metastasis of advanced HNSCC nearly always occurs first in neck lymph nodes before the development of distant metastasis. However, the development of preclinical animal models and therapeutic treatments for metastatic HNSCC is lagged from bench to clinic. In this protocol, we exemplify an orthotopic tongue tumor model that can recapitulate the cervical lymphatic metastases of HNSCC and the application to study the effect of novel saracatinib-loaded nanoparticles (Nano-Sar). By taking advantage of bioluminescence imaging (BLI), the present protocol reveals the strong anti-metastatic efficacy of Nano-Sar in the experimental setup. Collectively, the protocol with a novel metastatic mouse model shows great potential to evaluate treatments on metastatic diseases with the aid of bioluminescent technology. Key words HNSCC, The orthotopic model, Lymphatic metastases, Saracatinib, Nanoparticles, Bioluminescence (BL)

1

Introduction Head and neck squamous cell carcinoma (HNSCC) accounts for over 90% of head and neck cancer, ranking seventh of the most common cancers globally [1, 2]. HNSCC arises from mucosal surface of oral cavity, sinonasal cavity, pharynx, larynx, and nasopharyngeal, and more than 60% of patients with advanced HNSCC (at tumor late-stage III and IV) develop marked local invasion and regional lymphatic metastases [3]. Even with improved standard therapies and currently approved immunotherapy, the 5-year overall survival rate of advanced HNSCC is less than 50% [2, 4]. Lymphatic metastasis of HNSCC is driven by molecular dysregulations. Src is one vital member of the Src kinase family of nonreceptor tyrosine kinases (SFKs), conducting signaling

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transductions to regulate multiple cell biological processes, including epithelial-to-mesenchymal transition (EMT) for cancer metastasis [5, 6]. HNSCC has been reported as one of several cancer types with aberrant Src expression and elevated Src activation [5, 7, 8]. Saracatinib is a novel anilinoquinazoline inhibitor of Src activation, showing limited antitumor activity on advanced HNSCC in Phase II clinical trials [9, 10]. Recently, we and others uncovered that saracatinib is a potent anti-metastatic reagent through EMT inhibition in HNSCC [10, 11]. To deliver saracatinib to the population of HNSCC cells efficiently and specifically, we developed a novel tumor-seeking saracatinib-loaded nanoparticles (Nano-Sar) [10]. In the previous study, Nano-Sar has shown a superior inhibitory effect on HNSCC metastasis in the subcutaneous xenograft model compared with the free drug [10]. However, the subcutaneous xenograft model cannot recapitulate specific tumor microenvironment for local invasion and cervical lymphatic metastases [12], which hinders the translation of Nano-Sar from bench to clinical trial. Therefore, there is an urgent need to generate an orthotopic tumor model for HNSCC to monitor lymph node metastasis using in vivo imaging system and evaluate anti-metastatic effects of novel drugs like Nano-Sar. Condon-optimized firefly luciferase (FLuc, Luc2 for gene), which generates strong bioluminescence (BL) signal in mammalian cells, is an ideal reporter gene for tracking cancer cells in vivo. HN12 cells were derived from lymph node metastatic lesions belonging to one HNSCC patient, showing strong invasion potential in vitro and metastatic capacity to lymph nodes [13, 14]. In this protocol, we introduce an orthotopic tongue tumor model with lymphatic metastases by direct injection of HN12 cells that are stably expressing Luc2 (i.e., HN12-Luc2) into the tongue of NOD.Cg-Prkdcscid Il2rgtm1Wjl/SzJ (NSG) mice. Moreover, we apply this tongue tumor model to determine the effect of NanoSar on cervical lymph node metastasis. Our approach has methodological advantages in evaluating cancer metastasis in preclinical animal models using BL technology, which would advance the development of novel treatments for metastatic HNSCC.

2 2.1

Materials Reagents

1. Mammalian expression vector: pGL4.50 [luc2/CMV/Hygro] (Promega; Madison, WI, USA) (see Note 1). 2. Hygromycin B (InvivoGen, San Diego, CA, USA). 3. Saracatinib-loaded nanoparticle (Nono-Sar) [10] (see Note 2). 4. Steady-Glo® Luciferase Assay System (Promega; Madison, WI, USA).

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5. Lipofectamine™ 3000 transfection reagent (Invitrogen, Carlsbad, CA, USA). 6. HN12 cells derived from primary tongue lesions and lymph node metastases (see Note 3). 7. Dulbecco’s modified Eagle medium (DMEM) containing 10% (v/v) fetal bovine serum (FBS), 100 IU/mL penicillin, and 100 μg/mL streptomycin. 8. D-luciferin potassium salt (GoldBio, St Louis, MO, USA). 9. 96-well black-frame microplate (Nunc). 10. 35-mm culture dish. 2.2

Instrumentation

1. GloMax® 20/20 luminometer (Promega). 2. IVIS-200 in vivo imaging system (Xenogen Corporation, Waltham, MA, USA) (see Note 6). 3. EZ-AF9000 autoflow anesthesia system (Palma, PA, USA).

2.3

Animals

Six-week-old NSG mice (Jackson Laboratory, Bar Harbor, ME, USA).

2.4

Software

1. Living Image Ver. 4.7.

3

Methods

3.1 Generation of HN12-Luc2 Cells (See Note 4)

1. Seed HN12 cells in a 35-mm culture dish with 90% confluency, and incubate cells at 37  C under 5% (v/v) CO2 overnight. 2. Transfect 1 μg of pGL4.50 into HN12 cells using Lipofectamine 3000. 3. After 24 h following transfection, seed the cells in five 96-well black-frame microplates using limiting dilution to obtain monoclones. 4. Incubate the cells with 600 μg/mL of hygromycin B and select hygromycin-resistant cells over 3 weeks. 5. Subculture single hygromycin-resistant resistant clones for expansion. 6. Determine the FLuc expression in each clone using SteadyGlo® Luciferase Reporter Assay system. Get the reading of relative light unit with a GloMax® 20/20 luminometer.

3.2 Establishing the Orthotopic Tongue Tumor Model in NSG Mice (See Note 5)

1. Harvest HN12-Luc2 cells and prepare cell suspension with DMEM medium/Matrigel (v/v, 3:1) mixture in a concentration of 4  105 cells. 2. Anaesthetize NSG mice with isoflurane using an EZ-AF9000 auto flow anesthesia system, and inject 50 μL of HN12-Luc2

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Fig. 1 Nano-Sar inhibits lymph node metastasis of orthotopic tongue tumor in NSG mice. (a) The expression of FLuc in HN12-Luc2 cells. The reading of relative light unit (RLU) is measured in 10,000 cells. (b) Lymph node metastasis is monitored on Day 20 after indicated drug treatment by examining BLI using an IVIS-200 in vivo imaging system. Representative BL images and quantitative data are respectively shown in the left and right panels, **p < 0.01

cell suspension (2  104 cells) into the anterior part of the mouse tongue. 3.3 Nano-Sar Treatment and Lymph Node Imaging (Fig. 1) (See Note 5)

1. Intravenously (i.v.) inject 100 μL of Nano-Sar (5 mg/kg) or its control nanoparticles (without saracatinib encapsulation) into the HN12-Luc2-bearing mice (see Note 6), respectively. The nanoparticles are given once every 4 days for a total of 20 days. 2. Intraperitoneally (i.p.) inject the BL substrate D-luciferin (150 mg/kg) into mice at the end of the animal experiment (see Note 7). 3. Ten minutes after D-luciferin injection, image the bioluminescence intensity (BLI) of the lymph nodes using an IVIS-200 in vivo imaging system and quantify BLI using Living image software (Fig. 1).

4

Notes 1. pGL4.50[luc2/CMV/Hygro] encodes a codon-optimized FLuc reporter gene luc2 (Photinus pyralis) for mammalian expression. It contains a synthetic hygromycin B-resistance gene for HN12 cell selection. 2. Preparation and characterization of Nano-Sar as previously described [10]. 3. HN12 cells are a strain of the OPC-22 oral and pharyngeal cancer cell line panel. 4. Ensure that all procedures for the use of human cell lines have to be approved beforehand by an affiliated institutional biosafety committee.

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5. Ensure all animal handling should be performed in accordance with your affiliated Institutional Animal Care and Use Committee guidelines. You may also refer to the NIH Guide for the Care and Use of Laboratory Animals. 6. The first dose of Nano-Sar and its control nanoparticles should be given to mice at least 7 days after HN12-Luc cell implantation until the tongue tumor cells are stabilized and form a noticeable volume. 7. If the BLI is too weak to be detected in living animals, euthanize mice after 10 min of D-luciferin injection and remove the tongue quickly, followed by making a wide cervical incision to expose lymph nodes of the neck.

Acknowledgments This research was supported by NIH grants R03DE028387 and R01DE028351 (to Y.T.). References 1. Bray F, Ferlay J, Soerjomataram I, Siegel RL, Torre LA, Jemal A (2018) Global cancer statistics 2018: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J Clin 68:394–424 2. Chow LQ (2020) Head and neck cancer. N Engl J Med 382:60–72 3. Braakhuis B, Brakenhoff R, Rene´ Leemans C (2012) Treatment choice for locally advanced head and neck cancers on the basis of risk factors: biological risk factors. Ann Oncol 23: x173–x177 4. Canning M, Guo G, Yu M, Myint C, Groves MW, Byrd JK, Cui Y (2019) Heterogeneity of the head and neck squamous cell carcinoma immune landscape and its impact on immunotherapy. Front Cell Dev Biol 7:52 5. Irby RB, Yeatman TJ (2000) Role of Src expression and activation in human cancer. Oncogene 19:5636–5642 6. Summy JM, Gallick GE (2003) Src family kinases in tumor progression and metastasis. Cancer Metastasis Rev 22:337–358 7. Teng Y, Cai Y, Pi W, Gao L, Shay C (2017) Augmentation of the anticancer activity of CYT997 in human prostate cancer by inhibiting Src activity. J Hematol Oncol 10:1–10 8. Mayer EL, Krop IE (2010) Advances in targeting SRC in the treatment of breast cancer and other solid malignancies. Clin Cancer Res 16: 3526–3532

9. Kopetz S, Shah AN, Gallick GE (2007) Src continues aging: current and future clinical directions. Clin Cancer Res 13:7232–7236 10. Lang L, Shay C, Xiong Y, Thakkar P, Chemmalakuzhy R, Wang X, Teng Y (2018) Combating head and neck cancer metastases by targeting Src using multifunctional nanoparticle-based saracatinib. J Hematol Oncol 11:1–13 11. Lang L, Shay C, Zhao X, Xiong Y, Wang X, Teng Y (2019) Simultaneously inactivating Src and AKT by saracatinib/capivasertib co-delivery nanoparticles to improve the efficacy of anti-Src therapy in head and neck squamous cell carcinoma. J Hematol Oncol 12:132 12. Sano D, Myers JN (2009) Xenograft models of head and neck cancers. Head Neck Oncol 1:32 13. Gao L, Zhao X, Lang L, Shay C, Andrew Yeudall W, Teng Y (2018) Autophagy blockade sensitizes human head and neck squamous cell carcinoma towards CYT997 through enhancing excessively high reactive oxygen speciesinduced apoptosis. J Mol Med (Berl) 96: 929–938 14. He L, Gao L, Shay C, Lang L, Lv F, Teng Y (2019) Histone deacetylase inhibitors suppress aggressiveness of head and neck squamous cell carcinoma via histone acetylation-independent blockade of the EGFR-Arf1 axis. J Exp Clin Cancer Res 38:84

Chapter 3 Reactive Oxygen Species-Responsive and Self-Illuminating Nanoparticles for Inflammation and Tumor Imaging Xiaoqiu Xu, Qi Li, and Jianxiang Zhang Abstract Reactive oxygen species (ROS) play a key role in various physiological and pathological processes. Abnormally elevated ROS levels are generally related to the pathogenesis of inflammatory diseases and tumors. Real-time imaging and quantification of ROS can not only provide new insight into mechanistic understanding of diseases associated with ROS but also facilitate high-throughput and high-content drug screening for these diseases. Here, the present protocol introduces ROS-responsive and self-illuminating nanoparticles with chemiluminescence (CL) and fluorescence (FL) properties that can serve as an effective nanoprobe for imaging of pathophysiology, including inflammation and tumor. Key words Reactive oxygen species, Self-illuminating, Nanoparticles (NPs), Chemiluminescence imaging (CLI), Inflammation, Tumor

1

Introduction Reactive oxygen species (ROS) are essential chemicals for human metabolism, which have both beneficial and detrimental effects under pathophysiological conditions [1, 2]. The physiological levels of ROS are maintained by the intracellular redox homeostasis [3]. Once the redox balance is out of control, high levels of ROS can be generated endogenously or exogenously in response to environmental and pathological stresses e.g., exposure to ultraviolet (UV) light, ionizing radiation (X- or γ-rays), and environmental toxins [1, 4]. Sustained high levels of ROS can cause damage to cellular and extracellular constituents such as DNA, proteins, and lipids [5], and these oxidative stress-related disorders are manifested in various diseases, such as the initiation and progression of inflammation and cancer [2, 6]. Monitoring of the ROS levels is a promising strategy for early diagnosis and therapeutic assessment in ROS-related diseases. Accumulation of high levels of ROS at specific tissues may cause

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oxidative microenvironments that are notably different from those of normal tissues, which can be utilized to evaluate pathological degrees [7–10]. Therefore, various methods have been used for the quantification of ROS levels under both normal and diseased conditions. For example, traditional techniques, such as highperformance liquid chromatography (HPLC), mass spectrometry (MS), and electron spin resonance spectroscopy (ESR), have been exploited for in vitro and in vivo measurements of ROS levels in biological or tissue samples [11, 12]. Also, ROS-responsive fluorescent and/or self-luminescent probes were developed for realtime imaging of ROS levels in vitro and in vivo [13]. Recently, there is increasing interest in the design and development of ROS-responsive self-luminescent nanoprobes for in vivo chemiluminescence imaging (CLI) of diseases related to inflammation and oxidative stress [14]. As for generally used small-molecule probes and nanoprobes, their limitations such as poor biocompatibility (especially for inorganic nanoparticles (NP)) [15], rapid excretion (for small molecules like indocyanine green, methylene blue, and luminol) [16], poor tissue penetration (mainly due to light scattering and attenuation), and in vivo tissue autofluorescence [17, 18] hamper direct and accurate monitoring of ROS levels in deep tissues. To tackle these issues, the present protocol introduces ROS-responsive and self-illuminating NPs, which consist of a ROS-responsive and self-luminescent donor (luminol) and a fluorescent acceptor [chlorin e6 (Ce6)], for in vivo CLI of inflammation and tumor in the absence of external excitation [19]. In this modality, the luminol activity can be translated to chemical energy through ROS-initiated redox reactions that turn the probe from the “off” to the “on” state. The subsequent internal energy transfer to Ce6 leads to near-infrared (NIR) light emission for CLI. We exemplify this bioresponsive nanoprobe to show a high signal-tonoise ratio, thereby permitting sensitive and real-time detection of ROS in diverse animal models of inflammation and tumors [19, 20].

2

Materials Prepare all solutions using deionized water and analytical grade reagents. Prepare and store all reagents at 4
C unless stated otherwise.

2.1 Reagents and labware

1. Chlorin e6 (Ce6). 2. 5-Amino-2,3-dihydrophthalazine-1,4-dione (luminol). 3. Monomethyl terminated polyethylene glycol monoamine (mPEG-NH2) (molecular weight: 2,000 Da).

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4. 1-Ethyl-3-(3-dimethylaminopropyl) carbodiimide hydrochloride (EDCHCl). 5. N-Hydroxysuccinimide (NHS). 6. Dimethyl sulfoxide (DMSO), anhydrous grade. 7. Chemiluminescence probe (CLP) NP solution: dissolve the CLP conjugate in saline to produce two different concentrations: i.e., 50 mg/mL for inflammation imaging and 25 mg/ mL for tumor imaging (see Note 1). 8. Luminol solution: dissolve luminol sodium in saline to obtain a solution at the same dose of luminol unit in the CLP NP solution (see Note 2). 9. N-Acetyl-L-cysteine (NAC) solution: dissolve NAC in saline to obtain a 6.49-mg/mL stock solution. 10. Sterile, pyrogen-free saline (0.9% (w/v) NaCl). 11. Zymosan (Saccharomyces cerevisiae) solution: dissolve zymosan in saline to obtain a 2-mg/mL stock solution. 12. Acetaminophen (N-acetyl-p-aminophenol, APAP) solution: dissolve APAP in saline to make a 15-mg/mL stock solution. 13. 3% (w/v) dextran sulfate sodium salt (DSS, molecular weight: 36,000–50,000 Da) solution triggers colitis in mice when it is administered via drinking water. 14. Fetal bovine serum (FBS). 15. Culture medium: Dulbecco’s modified Eagle medium (DMEM) containing 10% (v/v) FBS and 1% (v/v) penicillin– streptomycin solution. 16. Sterile phosphate-buffered serine (PBS). 17. Trypsin/EDTA solution: 0.025% (w/v) trypsin and 0.01% (w/v) EDTA in PBS. 18. Depilatory creams. 19. Dialysis tubing (MWCO 3500 Da). 20. 0.22-μm syringe filter. 21. Cell culture flasks. 22. 1-mL syringes and straight feeding needles (18-G, 80-mm) for enema administration in colitis imaging. 23. 1-mL syringes and 21-G needles for intraperitoneal and intravenous administration in imaging of peritonitis, acute liver injury, and tumor. 24. Isoflurane. 25. Forceps. 26. Scissors. 27. Disposable pipet tips.

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28. Caliper. 29. BALB/c mice for the use in imaging of peritonitis and tumor (see Note 3). 30. C57BL/6 mice for the use in imaging of acute liver injury and colitis. 2.2

Instrumentation

1. IVIS Spectrum imaging system (IVIS® Spectrum, Perkin Elmer). 2. Inverted microscope (Olympus).

3

Methods In all cases, analyze the raw data using the Image Analysis Software provided by the manufacturer and subtract the corresponding background values for all unless indicated otherwise.

3.1 Synthesis of a CLP Conjugate

1. Dissolve 50 mg (0.08 mmol) Ce6, 236 mg (1.23 mmol) EDCHCl, and 144 mg (1.25 mmol) NHS in 10 mL of anhydrous DMSO. 2. Stir the mixture at 50
C in a dark room for 17 h. 3. Add 28 mg (0.16 mmol) of luminol to the mixture and stir for 3 days under the same reaction conditions. 4. Add 320 mg (0.16 mmol) of mPEG-NH2 to the above-stirred solution and further stir constantly for 4 more days. 5. Dialyze the stirred solution against deionized water using dialysis tubing to remove unreacted reagents and by-products. 6. Filtrate the stirred solution through a 0.22-μm syringe filter. 7. Lyophilize the solution and harvest a dark green powder of the desired conjugate.

3.2 In Vivo CLI of Peritonitis in Mice

1. Remove the hair on the region of interest (ROI) of mice using depilatory creams (see Note 4). 2. Intraperitoneally (i.p.) inject the mice with 0.5 mL of a sterile zymosan solution (1 mg of zymosan per mouse) to induce acute peritonitis in mice. 3. Randomly divide the mice into the CLP, luminol, and saline groups (four mice per group). 4. Inject the mice i.p. with 0.1 mL of CLP NP solution in the case of the CLP group at 6-h post-zymosan challenge. 5. In the same manner, inject the mice in the luminol and saline groups with 0.1 mL of luminol solution and saline, respectively. 6. Anesthetize the mice with isoflurane in a chamber.

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7. During anesthesia, place the mice in the supine position to expose the abdomen. 8. Measure CL signals by an IVIS Spectrum imaging system (setting signal level: exposure time ¼ 5 min, f/stop ¼ 1, binning ¼ 8, FOV D ¼ 21.8 cm) (see Note 5). 9. As a separate experiment, randomly divide mice into three groups (four mice per group): negative control group (PeritonitisCLP+), luminol-treated peritonitis group (Peritonitiand CLP-treated peritonitis group s+Luminol+), + + (Peritonitis CLP ). 10. Intravenously (i.v.) inject the peritonitis mice with 0.1 mL of an aqueous solution containing CLP NPs for the Peritonitis+CLP+ group at 5.5-h post-zymosan challenge. Inject the peritonitis mice with 0.1 mL of an aqueous solution of luminol for the Peritonitis+Luminol+ group in the same manner. At the same time, i.v. inject healthy mice (the negative control group) with 0.1 mL of an aqueous solution containing CLP NPs for the PeritonitisCLP+ group. 11. At 30 min after i.v. injection, anesthetize the mice with isoflurane in a chamber and place the mice in the supine position to expose the abdomen. 12. Measure CL using an IVIS Spectrum imaging system following the signal level setting as mentioned in step 8 in Subheading 3.2 and quantify the intensities of ROI (see Note 6), as shown in Fig. 1. 3.3 In Vivo CLI of Acute Liver Injury in Mice

1. Inject healthy mice i.p. with an APAP solution at a dose of 300 mg/kg body weight to induce acute liver injury (see Note 7). After administration, return the mice to the cage so as to free access to food and water. 2. Remove the hair on the ROI site of the mice with depilatory creams. 3. Randomly divide diseased mice into three groups (four mice per group), which are separately treated with (i) saline, (ii) luminol, or (iii) CLP NPs at 24 h after APAP stimulation. 4. Administer the mice i.v. with 0.1 mL of saline for the saline group, 0.1 mL of luminol solution for the luminol group, and 0.1 mL of the CLP NP solution for the CLP group, respectively. 5. Anesthetize the mice with isoflurane in a chamber, and maintain the anesthesia condition while the mice are in the imaging chamber of the IVIS Spectrum imaging system. 6. Place the mice in the supine position to expose the abdomen.

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Fig. 1 In vivo imaging of peritonitis sites in mice. (a) Chemiluminescence imaging (CLI) of mice with peritonitis sites immediately after i.p. administration of saline, luminol, and CLP nanoparticle (NP) solution, respectively. (b) CLI of peritonitis sites in mice at 0.5 h after i.v. injection of different probes. In the PeritonitisCLP+ group, healthy mice were treated with CLP NPs. Peritonitis mice in the Peritonitis+Luminol+ and Peritonitis+CLP+ groups were administered with free luminol and CLP NPs, respectively. In all cases, the left panels show representative CL images, while the right panels illustrate quantified intensities. Data are means  SEM (n ¼ 4); ***P < 0.001. (Reproduced from ref. [19] with permission from American Association for the Advancement of Science (AAAS))

7. Acquire in vivo CL images immediately with the acquisition parameters as mentioned in step 8 in Subheading 3.2. 8. Euthanize the mice, clean the skin over the abdomen by spraying with 70% (v/v) ethanol, and excise the livers from the mice, followed by rinsing with PBS (see Note 8). 9. Detect ex vivo CL (setting signal level: exposure time ¼ 5 min, f/stop ¼ 1, binning ¼ 8, FOV B ¼ 6.5 cm) and FL intensities (using the auto-exposure option to automatically regulate acquisition parameters, excitation filter is set at 430 nm, emission filter is at 680 nm) for isolated liver tissues (see Fig. 2).

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Fig. 2 In vivo and ex vivo imaging of acute liver injury in mice. (a) Imaging of acute liver injury in mice at 24 h after challenging of acetaminophen (APAP) at 300 mg/kg. (b) Ex vivo CLI of isolated hepatic tissues from mice with APAP-induced acute liver injury. (c) Ex vivo FL images indicating the accumulation of CLP NPs in the liver. In all cases, the left panels show representative images, while the right panels illustrate the quantified intensities. Data are means  SEM (n ¼ 4); ***P < 0.001. (Reproduced from ref. [19] with permission from AAAS) 3.4 In Vivo CLI of Ulcerative Colitis

1. Administer mice with 3% (w/v) DSS solution via drinking water for a total of 7 days to induce acute colitis. Periodically inspect the health condition of the mice throughout the course of the experiment (see Note 9).

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2. Remove the hair on the ROI site of the mice with depilatory creams. 3. Randomly divide the mice with colitis into three groups, five mice per each, i.e., (i) saline-treated colitis group (model group), (ii) luminol-treated colitis group (luminol group), and (iii) CLP-treated colitis group (CLP group). Use healthy mice in the normal control group (control group), which is the fourth group. 4. Anesthetize the mice in an anesthesia chamber with 1.5–2.0% (v/v) isoflurane-mixed gas after induction of colitis. 5. During anesthesia, administer the colitis mice with 0.1 mL of an aqueous solution containing CLP NPs or luminol via enema in the CLP or luminol group, respectively. Administer healthy mice with 0.1 mL of an aqueous solution containing CLP NPs via enema in the control group. Treat the colitis mice with 0.1 mL of saline via enema in the model group. 6. Hold mice in the vertical position for 1 min. Clear rectal discharge with PBS and medical cotton balls to avoid interference (see Note 10). 7. Place the mice in the supine position into the anesthesia manifolds in the imaging chamber of the IVIS Spectrum imaging system. 8. Acquire in vivo CLI at 15 min post-administration under the same measurement parameters as mentioned in step 8 in Subheading 3.2. 9. Quantify in vivo FL intensities by auto-exposure and excitation at 430 nm with emission at 680 nm. 10. Euthanize the mice by isoflurane anesthesia plus cervical dislocation. 11. Place the mice in the supine position on a surgical pad and perform a ventral midline incision. Remove the entire colon, followed by rinsing with PBS. 12. Place the colon tidily on a black background. 13. Acquire ex vivo CL and FL images of the colon tissues immediately following the signal level setting as mentioned in step 9 in Subheading 3.3. 14. Separately, acquire time-lapse in vivo CL images of the mice with DSS-induced colitis after enema administration of the CLP NP solution or luminol solution in each animal following the signal level setting as mentioned in step 8 in Subheading 3.2 (see Note 11). 15. Quantify CL signals at standardized ROIs defined on the abdomen or colonic tissues in the images, as shown in Fig. 3.

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Fig. 3 CLI and FLI of mice with ulcerative colitis. (a) In vivo CLI of mice with colitis induced by 3% (w/v) dextran sulfate sodium (DSS) for 7 days. (b) In vivo FLI of ulcerative colitis sites. (c) Ex vivo CL images of colonic tissues isolated immediately after in vivo imaging. (d) Ex vivo FLI of colonic tissues. (e) Time-lapse in vivo CLI of mice with DSS-induced colitis after local administration of CLP NPs or luminol in each animal. In all cases, the left panels show the representative images, while the right panels illustrate the quantified intensities. Data are means  SEM (a–d, n ¼ 5; e, n ¼ 4); *P < 0.05, **P < 0.01, ***P < 0.001. (Reproduced from ref. [19] with permission from AAAS) 3.5 In Vivo CLI of the Development of Colitis

1. Randomly divide mice into 0-, 1-, 3-, 5-, and 7-day experimental groups (four mice per group), and treat the mice in each experimental group with DSS solution via drinking water for 1, 3, 5, and 7 days, respectively. Untreated-mice are set as day 0 group (see Note 12). 2. Depilate the ROI of 20 mice using depilatory creams. 3. At the time points examined, administer mice with the CLP NP solution via enema during anesthesia. 4. Repeat steps 6–8 in Subheading 3.4 as shown in Fig. 4, for example.

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Fig. 4 In vivo CLI of the development of colitis. The CL images (a) and quantitative analysis (b) of mice with colitis induced by 3% (w/v) DSS for different periods of time. For each time point, images were acquired at 15 min after enema administration of 5 mg of CLP NPs in each mouse. (c) Changes in disease activity index during colitis development. Data are means  SEM (n ¼ 4); *P < 0.05, **P < 0.01, ***P < 0.001. (Reproduced from ref. [19] with permission from AAAS)

3.6 In Vivo CLI of Tumors

1. Culture 4T1 cells in cell culture flasks in an incubator at 37
C with 5% (v/v) CO2. 2. When the cells reach approximately 70–90% confluency, carefully remove the culture medium from the culture flasks. 3. Wash out the cells with sterile PBS to remove remained FBS in the residual culture medium. Tip the flask slightly and then completely remove it off. 4. Add 1 mL of trypsin/EDTA solution to the flask and incubate 1–2 min at 37
C, and keep incubation until the cell layer looks detached and dispersed upon observation under an inverted microscope (see Note 13). 5. Add 3–5 mL of the culture medium and harvest the 4T1 cells (see Note 14). 6. Centrifuge the cells at 400  g and 4
C for 5 min. Remove the supernatant and resuspend the cells in 1 mL of PBS and keep on ice. 7. Quantitate the number of cells using a hemocytometer and adjust the cell concentration to be 1  107 cells/mL through further dilution with ice-cold PBS. 8. Implant 1  106 4T1 cells dissolved in 100 μL of sterile PBS subcutaneously (s.c.) into the right hind back of each mouse. 9. Randomly divide mice into two groups (four mice per each group) to study CLI ability of CLP NPs in the 4T1 tumor model, i.e., (i) the 4T1CLP+ group, which denotes healthy mice treated with the CLP NP solution; (ii) the 4T1+CLP+ group, which means 4T1 tumor-bearing mice treated with the CLP NP solution.

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10. Periodically measure the tumor sizes using a caliper, typically every 3 days, once they are noticeable. 11. Start the CLI experiments if the tumor volume reaches approximately 100 mm3 on average (see Note 15). 12. Administer the tumor-bearing mice with 0.1 mL of an aqueous solution containing 2.5 mg CLP NPs by intratumoral (i.t.) injection in the 4T1+CLP+ group. Inject healthy mice in the 4T1CLP+ group with the same dose of CLP NPs by s.c. injection at the site that is the same as that of tumorbearing mice. 13. Acquire time-lapse variation in in vivo CL images (setting signal levels: exposure time ¼ 5 min, f/stop ¼ 1, binning ¼ 8, FOV D ¼ 21.8 cm). Immediately acquire FL images after each CL imaging by auto-exposure and excitation at 430 nm with emission at 680–780 nm, every 20-nm a step (see Note 16). 14. As a separate study, randomly divide the 4T1 tumor-bearing mice into the CLP and CLP + NAC groups (five mice per group). 15. First, intratumorally inject the mice only in the CLP + NAC group with 4 μmol NAC. Two hours later, intratumorally administer all the mice in both groups with 0.1 mL of an aqueous solution containing 2.5 mg CLP NPs. 16. Acquire in vivo CL and FL images before and at 1.5 h postadministration of the CLP NP solution, respectively. Set signal level parameters following step 13 in Subheading 3.6. 17. Euthanize mice. 18. Dissect the organs and rinse with PBS. 19. Quantify ex vivo CL and FL images with the same signal parameters as described in step 13 in Subheading 3.6 (exemplified in Fig. 5).

4

Notes 1. The solution is prepared just before the experiment. 2. Calculate the concentration of luminol by UV-visible spectrophotometry. 3. Mice are acquired from the Animal Center at the Third Military Medical University (Army Medical University). Animal housing, care, and management need to be conducted in accordance with the Guide for the Care and Use of Laboratory Animals proposed by each affiliated institution of researchers. In the case of the authors, all procedures and protocols were approved and supervised by the Animal Ethics Committee at

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Fig. 5 In vivo and ex vivo imaging of solid tumors in mice using CLP nanoparticles. (a) Time-lapse in vivo CLI after local administration of CLP NPs. (b) In vivo FL images at different time points after treatment with CLP NPs. (c) In vivo and (d) ex vivo imaging shows the attenuation of the CL of CLP NPs in 4T1 tumors by pretreatment with NAC for 2 h. (e) In vivo and (f) ex vivo FLI indicate the localization of CLP NPs in 4T1 tumors. For all images, the left panels are the representative images, while the right panels show the quantitative data. Data are means  SEM (a, b, n ¼ 4; c–f, n ¼ 5); *P < 0.05, ***P < 0.001. (Reproduced from ref. [20] with permission from American Chemical Society (ACS))

Third Military Medical University. Keep the mice on a strict 24-h reverse light-dark cycle in a ventilated room at 23  2
C, 5010% humidity with a condition freely accessible to food and water. 4. Depilatory creams need special attention in the deployment time so as not to irritate mouse skin. The deployment time longer than 2 min may irritate mouse skin. 5. The measurement parameters of the CCD camera of the IVIS system need to be set equal during the image acquisition. The parameters include the exposure time, binning (CCD resolution), and f/stop (aperture). 6. ROI tools only measure surface intensities. 7. APAP has poor solubility. Improve the solubility by intensive ultrasound sonication together with heating. 8. Wash isolated livers carefully to suppress autofluorescence caused by residual red blood cells. Red blood cells emit endogenous FL, the wavelengths of which are superimposed to many commonly used fluorescent probes. This makes it difficult to distinguish assay FL from endogenous FL. 9. Mice should be blocked from accession to any other water source. Weight loss and bloody diarrhea may be observed as early as Day 3 in this DSS administration schedule. 10. An unexpected noisy signal induced by the discharge of CLP NPs can interfere with the results.

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11. The first CL images before administration of the CLP NP solution are set as the time point of 0 min. The time point to acquire CL images immediately after enema treatment of the CLP NP solution is considered as 5 min. For each image, acquire the images 5 min before the indicated time point. The periodic determination of the CL images with the same exposure time is required in order to estimate the long-lasting effects of CLP NPs nanoparticles for CLI. 12. Meaningless is the difference in colitis disease activity index (DAI) caused by DSS consumption. As shown in Fig. 4c, the relationship between CL intensity and disease activity index (DAI) is clear. DAI is an important criterion to evaluate the health condition of colitis mice. However, in this protocol, different administration periods of DSS are the only influencing factor. 13. Ensure that the trypsin/EDTA solution immerses the cell layer. Avoid over-trypsinization that can severely damage the cells. 14. The culture medium contains 10% (w/v) FBS that inactivates trypsin. 15. The tumor volumes are difficult to be measured immediately after tumor implantation. It takes at least 2 weeks until the cells are stabilized and form xenografts. 16. Determine the contribution of autofluorescence in the in vivo FL images by a spectral unmixing option.

Acknowledgments This study was supported by the National Natural Science Foundation of China (No. 81971727) and the Program for Distinguished Young Scholars of TMMU. References 1. Winterbourn CC (2008) Reconciling the chemistry and biology of reactive oxygen species. Nat Chem Biol 4(5):278–286. https:// doi.org/10.1038/nchembio.85 2. Dro¨ge W (2002) Free radicals in the physiological control of cell function. Physiol Rev 82(1): 47–95 3. D’Autre´aux B, Toledano MB (2007) ROS as signalling molecules: mechanisms that generate specificity in ROS homeostasis. Nat Rev Mol Cell Biol 8(10):813–824. https://doi.org/10. 1038/nrm2256

4. Leach JK, Van Tuyle G, Lin P-S, SchmidtUllrich R, Mikkelsen RB (2001) Ionizing radiation-induced, mitochondria-dependent generation of reactive oxygen/nitrogen. Cancer Res 61(10):3894–3901 5. Saravanakumar G, Kim J, Kim WJ (2017) Reactive-oxygen-species-responsive drug delivery systems: promises and challenges. Adv Sci 4(1):1600124. https://doi.org/10. 1002/advs.201600124 6. Suomalainen A, Battersby BJ (2017) Mitochondrial diseases: the contribution of organelle stress responses to pathology. Nat Rev Mol

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Cell Biol 19(2):77–92. https://doi.org/10. 1038/nrm.2017.66 7. Yang B, Chen Y, Shi J (2019) Reactive oxygen species (ROS)-based nanomedicine. Chem Rev 119(8):4881–4985. https://doi.org/10. 1021/acs.chemrev.8b00626 8. Tapeinos C, Pandit A (2016) Physical, chemical, and biological structures based on ROS-sensitive moieties that are able to respond to oxidative microenvironments. Adv Mater 28(27):5553–5585. https://doi.org/10. 1002/adma.201505376 9. Tao H, Guo J, Ma Y, Zhao Y, Jin T, Gu L, Dou Y, Liu J, Hu H, Xiong X, Zhang J (2020) Luminescence imaging of acute liver injury by biodegradable and biocompatible nanoprobes. ACS Nano 14(9):11083–11099. https://doi. org/10.1021/acsnano.0c00539 10. Guo J, Tao H, Dou Y, Li L, Xu X, Zhang Q, Cheng J, Han S, Huang J, Li X, Li X, Zhang J (2017) A myeloperoxidase-responsive and biodegradable luminescent material for real-time imaging of inflammatory diseases. Mater Today 20(9):493–500. https://doi.org/10.1016/j. mattod.2017.09.003 11. Krumova K, Cosa G (2016) Overview of reactive oxygen species. In: Singlet oxygen: applications in biosciences and nanosciences, vol 1. Comprehensive series in photochemical & photobiological sciences. Royal Society of Chemistry, Cambridge, pp 1–21 12. Chiesa M, Giamello E, Che M (2010) EPR characterization and reactivity of surfacelocalized inorganic radicals and radical ions. Chem Rev 110(3):1320–1347 13. Antaris AL, Chen H, Cheng K, Sun Y, Hong G, Qu C, Diao S, Deng Z, Hu X, Zhang B, Zhang X, Yaghi OK, Alamparambil ZR, Hong X, Cheng Z, Dai H (2015) A smallmolecule dye for NIR-II imaging. Nat Mater

15(2):235–242. https://doi.org/10.1038/ nmat4476 14. Chen X, Wang F, Hyun JY, Wei T, Qiang J, Ren X, Shin I, Yoon J (2016) Recent progress in the development of fluorescent, luminescent and colorimetric probes for detection of reactive oxygen and nitrogen species. Chem Soc Rev 45(10):2976–3016 15. Fadeel B, Garcia-Bennett AE (2010) Better safe than sorry: understanding the toxicological properties of inorganic nanoparticles manufactured for biomedical applications. Adv Drug Deliv Rev 62(3):362–374. https://doi. org/10.1016/j.addr.2009.11.008 16. Vahrmeijer AL, Hutteman M, van der Vorst JR, van de Velde CJH, Frangioni JV (2013) Imageguided cancer surgery using near-infrared fluorescence. Nat Rev Clin Oncol 10(9):507–518. h t t p s : // d o i . o r g / 1 0 . 1 0 3 8 / n r c l i n o n c . 2013.123 17. Choy G, Choyke P, Libutti SK (2003) Current advances in molecular imaging: noninvasive in vivo bioluminescent and fluorescent optical imaging in cancer research. Mol Imaging 2(4): 15353500200303142 18. Ntziachristos V (2010) Going deeper than microscopy: the optical imaging frontier in biology. Nat Methods 7(8):603–614. https:// doi.org/10.1038/nmeth.1483 19. Xu X, An H, Zhang D, Tao H, Dou Y, Li X, Huang J, Zhang J (2019) A self-illuminating nanoparticle for inflammation imaging and cancer therapy. Sci Adv 5(1):eaat2953 20. An H, Guo C, Li D, Liu R, Xu X, Guo J, Ding J, Li J, Chen W, Zhang J (2020) Hydrogen peroxide-activatable nanoparticles for luminescence imaging and in situ triggerable photodynamic therapy of cancer. ACS Appl Mater Interfaces 12(15):17230–17243. https://doi.org/10.1021/acsami.0c01413

Chapter 4 Antibacterial Activity Evaluation of ZnO, CuO, and TiO2 Nanoparticles in Solution and Thin Films Christine Mielcarek, Rania Dadi, Anne Roynette, Alex Lemarchand, Andrei Kanaev, Karim Senni, Mamadou Traore, and Rabah Azouani Abstract This chapter introduces unique methodology of antibacterial activity evaluation of nanoparticles in both solution and thin films. Nanoparticles of ZnO, TiO2, and CuO are synthesized via the sol-gel method. Antibacterial tests are carried out against Gram-positive Staphylococcus aureus and Gram-negative Escherichia coli bacteria using disk diffusion and bioluminescence. To perform antibacterial tests on thin films and to overcome bacterial strains recuperation on the supports, a new method of bacterial detaching from the slides is developed based on French standard NF EN 14561. Key words CuO, ZnO, TiO2 nanoparticles, Antibacterial, Disk diffusion method, ATP bioluminescence

1

Introduction Antiseptics like disinfectants are formulations containing chemical antimicrobial agents used to fight against microorganisms. They are indispensable in medical and veterinary sectors as well as in various industrial fields and in the environment. Their main objective is to reduce or even destroy harmful or potentially pathogenic microbial flora. The expected effect is direct when the application is made on the contaminated site or indirect when looking for an interruption of the routes of transmission and dissemination of microorganisms [1]. Recently, nanotechnology advances in metal oxide nanoparticle synthesis and functionalization are likely to lead to the development of new antibacterial agents to control the transmission and dissemination of pathogenic bacteria. They could be used to limit nosocomial infections caused by antibiotic-resistant bacteria at the hospital level by acting on the surface in direct contact with humans, the

Sung-Bae Kim (ed.), Bioluminescence: Methods and Protocols, Volume 2, Methods in Molecular Biology, vol. 2525, https://doi.org/10.1007/978-1-0716-2473-9_4, © The Author(s), under exclusive license to Springer Science+Business Media, LLC, part of Springer Nature 2022

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most efficient way to fight bacterial infection by preventing the spread of pathogenic bacteria [2]. Metal oxide nanomaterials as antibacterial are very promising and have growing interest [3–5]. Several types of nanoparticles (NPs) have received much attention for their antimicrobial effects, such as silver oxide (Ag2O), titanium dioxide (TiO2), calcium oxide (CaO), zinc oxide (ZnO), copper oxide (CuO), and magnesium oxide (MgO), and they have been identified as having antibacterial activity. The mechanism of interaction between metal NPs and bacteria is still under discussion, but three main mechanisms are assumed: the formation of reactive oxygen species (ROS), ion release, and interaction of NPs with the cell membrane [6, 7]. The size of the NPs mainly influences the antibacterial mechanism [8, 9]. In this chapter [10, 11], we introduce a sol-gel method to elaborate ZnO and CuO NPs. We demonstrate significant toxicity to Gram-positive (Staphylococcus aureus; S. aureus) and Gramnegative (Escherichia coli; E. coli) bacterial strains. In this chapter, we exemplify disk diffusion and bioluminescence (BL) methods to confirm the antibacterial activity of NPs. It is in this research context that we adapt antibacterial test methods on our NPs in solution and thin films. The methods to analyze the antibacterial activity of NPs in solution are the following: • Agar disk diffusion method (see Note 1) [12]. • Adenosine triphosphate (ATP) BL test (see Note 2). To evaluate the antibacterial activity of NPs in thin films, we also exemplify a method with a protocol adapted from European Standard BS EN 14561:2006 [13], which specifies a test method and the minimum requirements for bactericidal activity of chemical disinfectant products in the decontamination and disinfection of clinical instruments and environments.

2 2.1

Materials Microbial Strains

These bacterial strains have different phenotypic characters and habitats. They are used as germ tests in many international standards (see Table 1). Strains should be maintained at 80  C on cryobeads (AES Laboratoire, France). 1. S. aureus (ATCC 6538), Gram-positive bacteria. 2. E. coli (ATCC 8739), Gram-negative bacteria.

2.2 Reagents and Labware

1. 200-μL sterile tips with micropipettes. 2. Sterile Petri dishes, 90-mm.

Metal Oxides Antibacterial Activity Evaluation

37

Table 1 Characteristics of the different test germs Escherichia coli ATCC 8739

Staphylococcus aureus ATCC 6538

Gram stain

BG

CG+

Habitat

Intestinal flora

Commensal flora of the skin and mucosa of warm-blooded animals

Virulence factors

Presence of different pathovars due to specific properties

Surface proteins, factors inhibiting phagocytosis, toxins and enzymes

Pathogenicity Intestinal and extra-intestinal infections Germs tests

Suppurative infections and toxic infections

Used as a preparatory test control, testing Used in testing media, sterility, sanitizers, disinfectants, antimicrobial preservatives, of antimicrobial handwashing bacterial resistance in carpets and textiles, formulations, assay of antimicrobial and as a control strain for biosynthesis preservatives, and media testing products

3. Cryobeads (GROSSERON, 04091136). 4. Plate Count Agar (PCA). 5. Trypcase soy broth (TSB). 6. Physiological water. 7. Sterile filter disk, 3-mm in diameter. 8. Sterile forceps 5. 9. Sterile swab. 10. Mueller Hinton culture media. 11. 96-well microplates, white. 12. ATP-free pipette tips. 13. ATP reagent kit (Promicol®) comprising the following reagents: (a) Standard ATP solution at 10 μmol/L (160-6106, Promicol). (b) Promex reagent (834-003, Promicol). (c) Proclean (100-0155, Promicol). (d) Proflush (100-0255, Promicol). (e) Prolux-CTM (130-3974, Promicol). (f) Prolux diluent (834-002, Promicol). 14. Germ carrier in glass (L  W: 70  26 mm, thickness: 1.1mm). 15. Sterile forceps. 16. 3-mm diameter glass beads (see Note 12). 17. Peptone water broth. 18. Ethylenediamine tetraacetic acid (EDTA) solution at 0.1% (w/v) (see Note 3).

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Instrumentation

1. Magnetic stirrer. 2. Biological safety cabinet. 3. Incubator. 4. Vortex. 5. Autoclave. 6. BL microplate reader.

3

Methods All antibacterial activity tests are carried out under sterile conditions.

3.1 Strain Thawing Method

1. Preserve strains by cryopreservation at taining 30 cryobeads.

80  C in tubes con-

2. Thaw the strains. 3. Inoculate a bead into a 9-mL tube of TSB. 4. Incubate overnight at 35  C. 5. Verify the purity of the strains with the streak plate method (see Note 4). 6. Incubate the pure strain overnight at 35  C on PCA Petri dishes. 7. Use the purified colonies to make the bacterial suspensions. 3.2 Disk Diffusion Method

The method is based on the French reference method NF U47-107 [14] to test bacterial susceptibility to antibiotics. 1. Prepare Petri dishes containing 20 mL of Mueller Hinton culture media. 2. Inoculate a few colonies from a fresh culture isolated from PCA media into a tube of 9 mL of TSB. 3. After overnight incubation at 35  C, harvest approximately 109 bacterial/mL. 4. Then make two successive dilutions to the tenths in tubes of 9 mL of physiological water to obtain a concentration of 107 bacterial/mL. 5. Inoculate the surface of the media with a bacterial suspension of 107 UFC/mL using a sterile swab (see Note 5). 6. Place in an empty Petri dish a filter disk of 6 mm in diameter with a sterile forceps. 7. Drop 20 μL of the test solution with an automatic pipette and let the solvent evaporate under the biological safety cabinet (BSC).

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Fig. 1 Illustration of the disk diffusion method

Fig. 2 Disk diffusion method measuring the effects of ZnO/S. aureus (a) and CuO/S. aureus (b). The numbers indicate the concentration of the colloids in mol/L. The diameter of Petri dishes is 9 cm

8. Place the different impregnated disks on the surface of the Mueller Hinton culture media with a sterile forceps (see Note 6). 9. Incubate the Petri dishes for 24 h at 35  C. 10. Measure the diameter of the inhibition zone as shown in Fig. 1. 11. Note that NPs inhibit bacteria growth at different NP concentrations, considering the results of the disk diffusion method with CuO and ZnO NPs against S. aureus (see Fig. 2). 3.3 ATP Measurement Method

The ATP levels are measured in relative light units (RLUs) in 96-well microplates white for BL study, and ATP-free laboratory equipment like pipette tips are applied in the experiment. Samples

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are analyzed using the Promicol ATP reagent kit (see Note 7) by a BL microplate reader. The steps are as follows: 1. Incubate a few colonies from a fresh culture isolated from PCA media into a tube of 9 mL of TSB. 2. After overnight incubation at 35  C, we obtain approximately a concentration of 109 bacterial/mL. 3. Fill the wells with reagents as follows: • Two wells with a positive control containing 100 μL of standard ATP solution at 10 μmol/L. • Two wells with a negative control containing 50 μL of NP solution at 1.5 mol/L. • The other wells with 50 μL of NPs at 1.5 mol/L and 50 μL of bacterial suspension at 109 UFC/mL. 4. Add 50 μL of the Promex reagent, where its role is to release ATP from the bacterial cells in the sample’s wells. 5. Prepare the BL microplate reader. • Clean the pump with Proclean and Proflush functions (see Note 8). • Place the Prolux reagent in the injection compartment and connect the automatic injection pump (see Note 9). • Set up the BL microplate reader according to Table 2. 6. Place the microplate in the BL microplate reader and start the analysis. 7. Read the RLUs in the well. Table 2 Setup of OMEGA software parameters for luminescence analysis Basic settings

Measurement type: Microplate name:

Luminescence GREINER 96 F-BOTTOM

Endpoint (equidistant) settings

Measurement interval time (s):

1

Optic settings

Emission: Gain:

Lens 3,600

Injection settings

Volume of pump (μL): Used pump: Pump speed (μL/s): Injection start time (s):

20 1 300 0

General settings

Measurement head: Setting time (s): Reading direction:

7 (fluorescence head) 0.2 Unidirectional, vertical right to left, top to bottom Set off

Target temperature ( C):

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Fig. 3 ATP measurement of ZnO and CuO colloids against S. aureus

8. Procedure for shutting down the unit: cleaning the pumps, emptying the trash can, and cleaning the receptacle. 9. Note that the antibacterial effect of CuO NPs as a function of concentration is more efficient than ZnO NPs (see Fig. 3), considering the result of ATP measurement of ZnO and CuO NPs against S. aureus. 3.4 Germ Carrier Measurement Method

In order to perform antibacterial tests on NP thin films, we exemplify a method to validate and determine the number of viable bacteria after deposition on the support over time, according to an adapted protocol from standard BS EN 14561:2006 [13]. A detailed block diagram is shown in Fig. 4. 1. Inoculate a few colonies from a fresh culture isolated from PCA media into a tube of 9 mL of TSB. 2. After overnight incubation at 35  C, harvest approximately 109 bacteria/mL. 3. Then make four successive dilutions to the tenths in tubes of 9 mL of peptone broth to obtain a concentration of 105 bacteria/mL. 4. Inoculate 50 μL of the bacterial suspension on the first third of the germ carrier so that the deposit is immersed in the stalling solution (see Note 10). 5. Place the Petri dish containing the germ carrier in an incubator at 37  C for up to 1 h (see Note 11).

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Fig. 4 Different steps of germ carrier measurement method

6. Place the germ carrier in a sterile container with 20 mL of 0.1% (w/v) EDTA solution and 5 g of 3-mm diameter glass beads [11]. 7. Vortex 3 times for 1 min. 8. Make two successive dilutions to the tenth by transferring 1–9 mL of physiological water solution and enumerate the three dilutions by the surface plating technique from NF EN ISO 4833-2 in PCA [15]. 9. Place the germ carrier in a Petri dish and cover with 10 mL of supercooled PCA at 50  C. 10. Count viable and cultivable microorganisms 24 h after incubation at 35  C. 3.5 Validation of the Germ Carrier Measurement Method

The following three stages are important according to the European standard NF EN 14561(2007-03) [14] (see Note 13): (i) mechanism of adhesion of bacteria to the supports, (ii) mechanism of detachment of these bacteria [16, 17], and (iii) protocol of counting the viable bacterial population.

3.5.1 Validation of the Slide Washing Protocol According to Standard NF EN 14561 [13]

Not only to determine the appropriate physical treatments as well as their duration but also to choose the concentrations of the chemicals to be combined to carry out the detachment of bacteria, we have implemented several protocols based on the NF EN 14561 standard (see Note 14). 1. Cultivate the cryobeads of each strain studied in a TryptoCasein-Soybean (TCS) nutrient broth. 2. Incubate for 18 h.

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Fig. 5 Validation of slide washing protocol

3. Reseed from 1 mL of the culture in 9 mL of TCS. 4. Incubate for 18 h. 5. Suspend the bacterial suspensions of E. coli and S. aureus in peptone water broth. 6. Deposit volume of 50 μL at a concentration of 105 colonyforming unit (CFU)/mL on glass slides containing the thin layers of NPs. 7. Dry the sample in an oven at 35  C for 1 h. 8. Wash the glass slides in contact with 20 mL of EDTA solution at 0.1% (w/v). 9. Add or not glass beads with vortex stirring or ultrasonication. 10. Note that the 0.1% (w/v) EDTA disodium salt coupled with the physical vortex + glass beads treatment is the best-suited protocol. All the germs deposited on the slide are recovered with this method, as shown in Fig. 5. 3.5.2 Antibacterial Activity of the Thin Films of NPs

1. Prepare the colloidal solution of CuO, ZnO, and TiO2 NPs [10]. 2. Clean and dry the support at room temperature (RT). 3. Immerse the clean support (slide) in the NP colloidal solution.

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Fig. 6 Antibacterial effect of thin layers of CuO, ZnO, and TiO2 in contact with E. coli and S. aureus

4. Withdraw slowly at a constant speed to form a uniform thin layer by sol-gel dip-coating performed on dip-coater KSV Mina KN 4001. 5. Treat thermally the layers at 500  C for 1 h. 6. Note that the thin films (CuO, ZnO, and TiO2) exhibit strong antibacterial activity, considering that a 5 log CFU/mL suppression of the deposited bacterial population is observed for the S. aureus strain (see Fig. 6).

4

Notes 1. It is the most used method for antimicrobial susceptibility testing due to its many advantages; simplicity, low cost, and applied on various numbers of microorganisms and antimicrobial agents. According to these advantages, this method is used in several fields. 2. A sensitive, rapid, and selective method. Indeed, ATP is a molecule found only in living cells. It is quantified by measuring the light produced through its reaction with the enzyme luciferase using a BL microplate reader. The amount of light

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produced is directly proportional to the amount of live bacteria present in the sample. 3. Add 0.1 g (quantum satis) to 100 mL of demineralized water. Then, the solution is autoclaved and used at RT. 4. Streak plate method is one of the important methods in microbiology to isolate a single type of bacteria from a source that contains many, which dilutes the individual cells by spreading them over the surface of an agar plate. Single cells reproduce and create millions of clones, which all pile up on top of the original cell. The piles of bacterial cells observed after an incubation period are called colonies. Each colony represents the descendants of a single bacterial cell, and therefore, all of the cells in the colonies are clones. Therefore, when you transfer a single colony from the streak plate to new media, you have achieved a pure culture with only one type of bacteria [18]. 5. To ensure homogeneous bacteria growth on all the surface, make with the swab three rotations with an angle of 60 . 6. For a better diffusion, place the disk on the side where the solution was deposited. 7. Before use, ensure that all reagents are at RT (18–23  C) because the use of cold reagents will give low results. 8. These reagents are used to clean and neutralize the device, including the injection needles and attached tubing. 9. This reagent is prepared by adding Prolux-CTM (130-3974, Promicol) and Prolux diluent (834-002, Promicol). After reconstitution of the enzyme, the vial can be stored at 4  C for 7 days. 10. For better stability and sterility, place with a forceps the slide in a Petri dish. 11. For better drying, the lid of the Petri dish should be half-open. 12. The beads are pre-weighed into glass tubes, autoclaved, and dried for 1 day at 37  C. 13. There are currently few methods/standards for assessing antibacterial activity in the form of thin films. In this work, we were inspired by a European standard NF EN 14561 (2007-03) [14], which consists of a qualitative test of germ carriers to assess the antimicrobial efficacy for instruments used in human medicine. The “detachment” by cleavage of the bacterial adhesion system is done by physical (vortex, ultrasound) or chemical (detergents, chelators) techniques [16, 17]. 14. The different methods are based on the use of a detergent solution combined with physical techniques (ultrasonication or vortex): Eugon LT100 solution + 3-mm glass beads + 3 min vortex.

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• Eugon LT100 solution + ultrasonication + 30 s vortex  3 • 0.001% (v/v) sodium lauryl sulfate (LSS) solution + 3-mm glass beads + 1 min  3 vortex • 0.1% (w/v) EDTA solution + 3-mm glass beads + 1 min  3 vortex References 1. Bosgiraud C (2003) Microbiologie ge´ne´rale et sante´. ESKA Edition 2. Davies J, Davies D (2010) Origins and evolution of antibiotic resistance. Microbiol Mol Biol Rev 74:417–433. https://doi.org/10. 1128/MMBR.00016-10 3. Huh AJ, Kwon YJ (2011) Nanoantibiotics: a new paradigm for treating infectious diseases using nanomaterials in the antibiotic’s resistant era. J Control Release 156(2):128–145. https://doi.org/10.1016/j.jconrel.2011. 07.002 4. Beyth N, Houri-Haddad Y, Domb A, Khan W, Hazan R (2015) Alternative antimicrobial approach: nano-antimicrobial materials. Evid Based Complement Alternat Med 2015: 246012. https://doi.org/10.1155/2015/ 246012 5. Huang W, Tao F, Li F, Mortimer M, Guo L-H (2020) Antibacterial nanomaterials for environmental and consumer product applications. NanoImpact 20:100268., ISSN:2452-0748. https://doi.org/10.1016/j.impact.2020. 100268 6. Zhao L, Ashraf MA (2016) Influence of silverhydroxyapatite nanocomposite coating on biofilm formation of joint prosthesis and its mechanisms. West Indian Med J 64(5): 506–513. https://doi.org/10.7727/wimj. 2016.179 7. Leung YH, Ng AM, Xu X et al (2014) Mechanisms of antibacterial activity of MgO: non-ROS mediated toxicity of MgO nanoparticles towards Escherichia coli. Small 10(6): 1171–1183. https://doi.org/10.1002/smll. 201302434 8. Azam A, Ahmed AS, Oves M, Khan MS, Memic A (2012) Size-dependent antimicrobial properties of CuO nanoparticles against Grampositive and -negative bacterial strains. Int J Nanomedecine 7:3527–3535. https://doi. org/10.2147/IJN.S29020 9. Zakharova OV, Godymchuk AY, Gusev AA, Gulchenko SI, Vasyukova IA, Kuznetsov DV

(2015) Considerable variation of antibacterial activity of Cu nanoparticles suspensions depending on the storage time, dispersive medium, and particle sizes. Biomed Res Int 2015:412530. https://doi.org/10.1155/ 2015/412530 10. Dadi R, Azouani R, Traore M, Mielcarek C, Kanaev A (2019) Antibacterial activity of ZnO and CuO nanoparticles against gram positive and gram negative strains. Mater Sci Eng C 104:109968 11. Dadi R, Kerignard E, Traore M, Mielcarek C, Kanaev A, Azouani R (2021) Evaluation of antibacterial efficiency of zinc oxide thin films nanoparticles against nosocomial bacterial strains. Chem Eng Trans 84:13–18. https:// doi.org/10.1016/j.msec.2019.109968 12. Mounyr B, Moulay S, Saad KI (2016) Methods for in vitro evaluating antimicrobial activity: a review. J Pharm Anal 6:71–79. https://doi. org/10.1016/j.jpha.2015.11.005 13. BS EN 14561:2006 Chemical disinfectants and antiseptics. Quantitative carrier test for the evaluation of bactericidal activity for instruments used in the medical area. Test method and requirements (phase 2, step 2) 14. Norme NF (2007) Agence Franc¸aise de normalisation 15. NF EN ISO 4833-2 Microbiology of the food chain – Horizontal method for the enumeration of microorganisms – Part 2: colony count at 30 C by the surface plating technique 16. Corpe WA (1973) Detachment of marine periphytic bacteria from surfaces of glass slides. Dev Ind Microbiol 15:251–287 17. Bryant RD, Costerton JW, Aishley EJ (1984) The role of Thiobacillus albertis glycocalyx in the adhesion of cells to elemental sulfura. Can J Microbiol 30:81–90. https://doi.org/10. 1139/m84-015 18. Ahern H (2018) Microbiology: a laboratory experience. Open SUNY Textbooks, Milne Library. State University of New York, Geneseo

Chapter 5 BRET-Based Dual-Color (Visible/Near-Infrared) Molecular Imaging Using a Quantum Dot/EGFP-Luciferase Conjugate Setsuko Tsuboi and Takashi Jin Abstract By virtue of its high sensitivity, bioluminescence imaging (BLI) is an important tool for biosensing and bioimaging in life sciences. Compared to fluorescence imaging (FLI), BLI has a superior advantage that the background signals resulting from autofluorescence are almost zero due to the unnecessity of external excitation. In addition, BLI can permit a long-term observation of living cells because BL results in very low photocytotoxicity toward the host cells. Although BLI has such superior properties over FLI, the available wavelengths in BLI are mostly limited to the visible region. Here we present bioluminescence resonance energy transfer (BRET)-based visible and near-infrared dual-color molecular imaging using a quantum dot (QD) and luciferase–protein conjugate. Key words Bioluminescence resonance energy transfer (BRET), Bioluminescence imaging (BLI), Near-infrared imaging, Molecular imaging, Quantum dot (QD), Enhanced green fluorescent protein (EGFP)

1

Introduction Despite the high sensitivity of bioluminescence imaging (BLI), its potential use in bioimaging has been confined in the visible region [1]. Bioluminescence (BL) emitted at the visible region is strongly attenuated in biological samples [2]. To overcome this drawback, the chemical structure of the common BL substrates (luciferin) has been modified to emit BL at longer wavelengths [3]. Luciferin analogs with extended π-conjugation have been used for red-shifted BLI [3]. Although commonly used BL substrates such as D-luciferin and coelenterazine (CTZ) emit BL at around 560 nm and 480 nm, respectively, a recently developed D-luciferin analog, AkaLumine, can emit BL in the near-infrared (NIR) region exceeding 700 nm [4]. This D-luciferin analog has shown the capability of NIR BL for highly sensitive deep-tissue imaging in living mice.

Sung-Bae Kim (ed.), Bioluminescence: Methods and Protocols, Volume 2, Methods in Molecular Biology, vol. 2525, https://doi.org/10.1007/978-1-0716-2473-9_5, © The Author(s), under exclusive license to Springer Science+Business Media, LLC, part of Springer Nature 2022

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The use of bioluminescence resonance energy transfer (BRET) is an alternative method to achieve red-shifted BLI [5]. BRET consists of (i) a luciferase–luciferin system as the resonance energy donor and (ii) a fluorescent protein or dye as the energy acceptor. A NIR fluorescent dye combined with a luciferase (RLuc 8.6) has been used for BRET-based NIR imaging in living mice [6– 8]. Recently, an Alexa Fluor 680-conjugated BRET probe has been used for the NIR detection of ubiquitin-protease system-regulated hypoxia-inducible factor [9]. A BRET-based NIR imaging system has also been reported using a fluorescent phthalocyanine dye, NIR775, as the resonance energy acceptor [10]. Quantum dots (QDs) are widely used as the energy acceptors for BRET-based NIR imaging [11–19]. The BRET in QD–luciferase conjugates has been applied to NIR tumor imaging in living mice [10]. In the BRET-based tumor imaging, a cyclic arginine– glycine–aspartic acid (cRGD) peptide is mostly used as a targeting ligand to integrin αvβ3, which is expressed in many tumor cells [10]. In this chapter, we introduce BRET-based dual-color (visible and NIR) molecular-imaging, which can be used for the highly sensitive detection of the membrane receptors in living cells. We exemplify a BRET-based dual-color probe, where BRET occurs from luciferase/luciferin to QDs or to EGFP. This BRET probe emits visible and NIR BL from EGFP or QDs. Since the present BRET-based dual-color probe contains an immunoglobulin binding domain (GB1) [20] of protein G, this probe is easily functionalized with a variety of antibodies (IgG). We present BRET-based dual-color molecular imaging of human epidermal growth factor receptor 2 (HER2) in breast cancer cells [21].

2

Materials

2.1 Synthesis of NIREmitting CdSe/CdS (Core/Shell) QDs

1. Selenium (Se) (powder, 99.999% (w/w)). 2. Tellurium (Te) (shot, 1–2 mm, 99.99% (w/w)). 3. Cadmium 2,4-pentanedionate. 4. n-Octadecylphosphonic acid (ODPA). 5. Trioctylphosphine oxide (TOPO). 6. Trioctylphosphine (TOP). 7. Tributylphosphine (TBP). 8. Hexadecylamine (HDA, 90% (w/w)). 9. Sulfur (S, crystalline, 99.9999% (w/w)). 10. Chloroform. 11. Methanol.

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12. Tetrahydrofuran (THF). 13. Inert gas (nitrogen). 14. Three-necked flask. 15. Syringe. 2.2 Surface Modification of QDs with Glutathione (GSH)

1. Glutathione (GSH, reduced form). 2. Tetrahydrofuran (THF). 3. Potassium t-butoxide. 4. 0.45-μm membrane filter. 5. Aqueous solution of Na2CO3 (10 mM). 6. 50,000 MW membrane.

2.3 Protein Synthesis (His-EGFP-RLuc-GB1)

1. Plasmids used as a polymerase chain reaction (PCR) template encoding the cDNA sequences of EGFP, RLuc and GB1. 2. Bacterial expression vector for high-level expression of proteins with 6
His tag, such as pRSET plasmid (ThermoFisher). 3. PCR mixture: PCR primers, high-fidelity DNA polymerase, PCR buffer, dNTPs, and nuclease-free water. 4. Tris–acetate EDTA (TAE) buffer: 40 mM Tris, 20 mM acetic acid, 1 mM EDTA. 5. 1% agarose gel: 1% (w/v) agarose dissolved in TAE buffer. 6. Ethidium bromide solution (0.5 μg/mL). 7. Gel extraction kit. 8. Cloning enzyme set: DNA ligase, restriction enzymes. 9. Single Step (KRX) Competent Cells (Promega) for cloning and protein expression. 10. Super Optimal broth with Catabolite repression (SOC) medium. 11. Luria broth (LB) agar plates with antibiotic appropriate for the plasmid. 12. Plasmid extraction kit. 13. LB medium dissolving antibiotics, L-rhamnose, isopropyl β-D1-thiogalactopyranoside (IPTG). 14. Binding buffer: 50 mM Tris, 500 mM NaCl, 20 mM imidazole. 15. Elution buffer: 50 mM Tris, 500 mM NaCl, 500 mM imidazole. 16. Protease inhibitor cocktail. 17. Ni Sepharose 6 Fast Flow kit (GE Healthcare). 18. Gel filtration column such as PD-10 column (GE Healthcare).

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19. Phosphate-buffered saline (PBS). 20. SDS-PAGE set: 10% (w/v) polyacrylamide gel, Tris–glycine– SDS buffer, Coomassie Brilliant Blue (CBB). 2.4 The Other Reagents and Labware

1. Phosphate-buffered saline (PBS) solution of His-EGFP-RLucGB1 (1 mg/mL). 2. Ethanol solution (1 mg/mL) dissolving native coelenterazine (CTZ). 3. Phosphate-buffered saline (PBS). 4. PBS solution of GSH-QDs (1 μM in 10 mM Na2CO3, optical density (OD) ¼ ca. 3 at 488 nm). 5. PBS solution dissolving QD-His-EGFP-RLuc-GB1 (1 μM). 6. KPL-4 cell line [22]. 7. Cell culture medium: Dulbecco’s modified Eagle’s medium (DMEM), 10% (v/v) fetal bovine serum (FBS), streptomycin (0.1 mg/mL), and penicillin (100 U/mL). 8. 35-mm cell culture dish. 9. Trypsin (2.5 g/L)/EDTA (1 mmol/L) solution. 10. Herceptin (anti-HER2 monoclonal antibody). 11. Erbitux (anti-EGFR monoclonal antibody). 12. Normal human IgG. 13. 40-μm cell strainer mesh.

2.5

Instrumentation

1. Luminescence spectrometer. 2. Humidified CO2 incubator. 3. Inversed microscope. 4. Flow cytometer. 5. BL microscope: LV200 (Olympus, Japan). 6. Fluorescence (FL) spectrometer. 7. Centrifuge. 8. Sonicator. 9. Thermal cycler. 10. DNA Sequencing system. 11. Ultrasonic homogenizer. 12. Ultracentrifuge. 13. Rotator. 14. Spectrophotometer. 15. SDS polyacrylamide apparatus.

gel

electrophoresis

(SDS-PAGE)

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3

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Methods NIR-emitting CdSeTe/CdS QDs are prepared by a hightemperature decomposition method [23, 24]. The surface of QDs is overcoated with glutathione (GSH) to solubilize the QDs to the water phase [24]. A recombinant protein, His-EGFP-RLucGB1, is prepared by the transformation of pRSET-EGFP-RLucGB1 plasmid into Escherichia coli (E. coli). His-EGFP-RLuc-GB1 protein conjugated CdSeTe/CdS QDs are prepared by the direct binding of six histidine tags of His-EGFP-RLuc-GB1 protein to the surface of QDs (see Figs. 1 and 2) [25].

Fig. 1 Schematic diagram for the preparation of a recombinant protein, His-EGFP-RLuc-GB1-functionalized CdSeTe/CdS QDs (QD-His-EGFP-RLuc-GB1), where His-EGFP-RLuc-GB1 protein directly binds to the surface of GSH-coated CdSeTe/CdS QDs (GSH-QDs). BRET occurs from CTZ to EGFP and QD inside QD-His-EGFPRLuc-GB1

Fig. 2 Optical spectra of CTZ, EGFP, and GSH-QDs. Blue-dotted line shows a BL spectrum of CTZ. Green solid and dotted lines show the FL and absorption spectrum of EGFP, respectively. Red solid and dotted lines show the FL and absorption spectrum of GSH-QDs, respectively. (Reproduced from ref. [21] with permission from the Royal Society of Chemistry (RSC))

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3.1 QD Synthesis (CdSeTe/CdS) 3.1.1 Se-Te Stock Solution

Carry out all procedures for QD synthesis in a fume food unless otherwise specified. 1. Dissolve 24 mg of Se and 13 mg of Te in 1 mL of TBP at room temperature (RT) (see Note 1). 2. Preserve the Se-Te stock solution under a nitrogen atmosphere.

3.1.2 CdSeTe Core

1. Load a mixture of 150 mg of cadmium 2,4-pentanedionate, 300 mg of ODPA, 1 g of TOPO, 3 g of HDA, and 0.5 mL of TOP into a 25-mL three-necked flask and heat to 330  C under a nitrogen atmosphere. 2. At this temperature, quickly inject 1 mL of a Se-Te stock solution into the mixture by using a syringe, which causes an immediate change in the solution color from colorless to brown. 3. By monitoring the FL spectra, check the formation of CdSeTe QDs (ca. 790 nm emission) (see Note 2). After the CdSeTe QDs are formed, cool down the solution to 60  C, and add 10 mL chloroform to the solution. 4. Precipitate CdSeTe QDs by the addition of methanol. 5. Harvest the QDs by centrifugation (see Note 3). 6. Resuspend the QDs in 2 mL of TBP.

3.1.3 Cd-S Stock Solution

1. Dissolve 40 mg of sulfur in 10 mL of TBP at 100  C. After sulfur is completely dissolved, cool down the sulfur solution to RT. 2. Add 388 mg of cadmium 2,4-pentanedionate to the sulfur solution, and warm the solution at 100  C to dissolve cadmium 2,4-pentanedionate.

3.1.4 CdS Overcoating

1. Load a TBP solution of CdSeTe QDs and 3 g of HDA into a 25-mL three-necked flask and heat up to 250  C. 2. Add 0.25 mL of Cd-S stock solution to the solution of CdSeTe/CdS QDs. 3. Check the formation of QDs (ca. 790 nm emission) by monitoring the FL spectra. After confirmation of the QDs, cool down the solution to 50  C. 4. Add 10 mL of chloroform to the solution. 5. Precipitate CdSeTe/CdS QDs by addition of 10 mL of methanol. 6. Harvest CdSeTe/CdS QDs by centrifugation (see Note 3). 7. Resuspend the CdSeTe/CdS QDs into 20 mL of THF.

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3.2 Surface Modification of QDs with Glutathione (GSH)

53

1. Add 1 mL of an aqueous solution (50 mg/mL of GSH) to 2 mL of THF solution dissolving CdSeTe/CdS QDs (1 μM, OD ¼ ca. 3 at 488 nm) at RT under sonication. 2. Separate and harvest QD precipitates by centrifugation. 3. Add 2 mL of an aqueous solution of potassium t-butoxide (20 mg/mL) to the QD precipitates. 4. Sonicate the solution for 5 min. 5. Filter the solution through a 0.45-μm membrane filter to remove excess GSH and potassium t-butoxide. 6. Dialyze the solution with an aqueous solution of Na2CO3 (10 mM) using a 50,000-MW membrane (see Note 4).

3.3 Protein Synthesis (His-EGFP-Rluc-GB1)

1. Subclone the DNA fragments of EGFP, Rluc, and GB1 amplified by PCR into a bacterial expression vector. 2. Transform the constructed His-EGFP-RLuc-GB1 plasmid into competent cells. 3. Grow the precultures overnight in the LB medium containing antibiotic appropriate for the expression plasmid at 37  C. 4. Dilute overnight precultures 1:100 into LB containing antibiotic appropriate for the expression plasmid and grow the cultures at 37  C until their density reaches 0.5–0.6 in OD 600. 5. When the cultures reach 0.5–0.6 in OD 600, cool down the temperature to 15–25  C and induce the protein expression by adding IPTG (final concentration: 0.2 mM) and L-rhamnose (0.1% (w/v)). 6. Grow the cultures overnight at 15–25  C with shaking at 190 rpm. 7. Harvest the cells by centrifugation at 5,000
g for 10 min. 8. Resuspend the cell pellet in the binding buffer resolving a trace amount of a protease inhibitor cocktail. 9. Sonicate the cell suspension 7–10 times with 10 s burst at middle-intensity with 10 s of a cooling interval between each sonication. All sonication is done on ice. 10. Centrifuge the lysate at 20,000
g for 20 min to eliminate the cell debris. 11. Purify the protein according to the manufacturer’s instruction using Ni Sepharose 6 Fast Flow kit. 12. Exchange the buffer of eluted fractions with PBS using a gel filtration column. 13. Confirm the size of the purified His-EGFP-RLuc-GB1 protein by SDS-PAGE (see Fig. 3). Use a size marker to compare the molecular weight of His-EGFP-RLuc-GB1. The expected molecular size for His-EGFP-RLuc-GB1 protein is 73.6 kDa.

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Fig. 3 SDS-PAGE image of the fusion protein, His-EGFP-RLuc-GB1

Fig. 4 BL spectrum (black line) of His-EGFP-RLuc-GB1 in the presence of CTZ. Blue and green dotted lines show the spectral contribution from CTZ and EGFP emission. The emission spectrum of EGFP confirms BRET from CTZ to EGFP. (Reproduced from ref. [21] with permission from RSC)

3.4 BL Spectra of His-EGFP-Rluc-GB1

1. Add 20 μL of an aqueous solution of His-EGFP-Rluc-GB1 (1 mg/mL) to 2 mL of PBS. 2. Add 10 μL of an ethanol solution dissolving CTZ (1 mg/mL). 3. Measure the BL spectrum using a luminescence spectrometer with 1-min integration time (see Fig. 4, Note 5).

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Fig. 5 Agarose gel electrophoresis of (1) GSH-QDs and (2) QD-His-EGFP-RLucGB1

Fig. 6 BRET spectrum of QD-His-EGFP-RLuc-GB1 in the presence of CTZ. (Reproduced from ref. [21] with permission from RSC)

3.5 Conjugation of His-EGFP-RLuc-GB1 to GSH-QDs

1. Add 0.2 mL of His-EGFP-RLuc-GB1 (1 mg/mL in PBS) to 0.4 mL of GSH-QDs (1 μM in 10 mM Na2CO3 solution). 2. Purify the His-EGFP-RLuc-GB1-conjugated QDs using a gel filtration column with PBS to remove unconjugated His-EGFP-RLuc-GB1. 3. Confirm the formation of QD-His-EGFP-RLuc-GB1 using agarose gel electrophoresis (see Fig. 5).

3.6 BRET Spectra for QD-His-EGFP-RLucGB1 in the Presence of CTZ

1. Add 20 μL of an aqueous solution of QD-His-EGFP-RLucGB1 (1 μM) to 2 mL of PBS. 2. Add 10 μL of an ethanol solution dissolving CTZ (1 mg/mL). 3. Measure the BRET spectra using a luminescence spectrometer (see Fig. 6, Note 6).

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3.7 Flow Cytometric Analysis

1. Trypsinize the KPL-4 cells (see Subheading 2.4). 2. Harvest the cells by centrifugation (300
g, 3 min), discard the supernatant, and resuspend the cell pellet in PBS to be ~106 cells/mL. 3. Transfer 1 mL of the cell suspension to a 1.5-mL sample tube. 4. Incubate the cell suspension with 1 μM of antibody (Herceptin, Erbitux, normal human IgG) or vehicle for 10 min at 37  C. 5. (Washing step) Harvest the cells by centrifugation (300
g, 3 min), discard the supernatant, and resuspend the cell pellet in 1 mL of PBS. Harvest the cells by centrifugation again and discard the supernatant. 6. Incubate the cell suspension with 50 nM of probe (QDs-HisEGFP-RLuc-GB1) for 10 min at 37  C. 7. Repeat the washing step described in step 5 five times. 8. Resuspend the cell pellet in 0.5 mL of PBS and pass through the cell suspension to a 40-μm cell strainer. 9. Analyze the cell population by flow cytometry immediately (see Fig. 7, Note 7).

3.8 BRET-Coupled Luminescent Molecular Imaging

1. Seed the KPL-4 cells in 35-mm cell culture dishes (3
105 cells per dish) and incubate in DMEM containing 10% FBS overnight at 37  C.

Fig. 7 Flow cytometric analysis of KPL-4 cells treated with QD-His-EGFP-RLuc-GB1 (None), normal human IgG and QD-His-EGFP-RLuc-GB1 (Normal IgG), Herceptin and QD-His-EGFP-RLuc-GB1 (Herceptin), and Erbitux and QD-His-EGFP-RLuc-GB1 (Erbitux). FL signals were detected at >750 nm for QD emission (a) and at 525 nm for EGFP emission (b). (Reproduced from ref. [21] with permission from RSC)

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Fig. 8 (a) Bright field (BF) and BL images of KPL-4 cells treated with Herceptin and QD-His-EGFP-RLuc-GB1. (b) Magnification images for the square regions in the above images. BL images are taken at the wavelength of >715 nm for QD emission and 495–540 nm for EGFP emission. Exposure time for the BLI was set to 3 min. (Reproduced from ref. [21] with permission from RSC)

2. Then, add Herceptin (final concentration of 1 μM) to the cells and incubate for 10 min at 37  C. 3. Repeat the washing step (discard the liquid content of the dish, add 2 mL of PBS and then discard PBS) twice. Then, incubate with 50 nM of probe (QDs-His-EGFP-RLuc-GB1) for 10 min at 37  C. 4. Repeat the washing step described in step 3 four times and fill the cell culture dish with 2 mL of PBS. 5. Add CTZ (final concentration of 50 μM) to the cells just before the NIR images are taken in the next step. 6. Take BRET-coupled NIR images (at >715 nm for QD emission and 495–540 nm for EGFP emission) by using a BL microscope, LV200 with 3-min exposure time (see Fig. 8).

4

Notes 1. Use a freshly prepared TBP solution. When the Se and Te are not completely dissolved in TBP, sonicate the TBP solution. 2. For this purpose, we use an FL spectrometer nearby a fume food and monitor the FL spectra of QDs.

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3. For the separation of QDs, we employ glass test tubes for centrifugation. Control the speed of centrifugation so as not to give the glass test tubes the damage. 4. For the exchange of PBS in the aqueous solution of QDs with Na2CO3 (10 mM), a gel filtration column (PD-10 column, GE Healthcare) can be used. 5. Upon determination of BL spectra of His-EGFP-RLuc-GB1, the BL spectra should be measured immediately after addition of CTZ to the aqueous solution of His-EGFP-RLuc-GB1 so as not to get seriously influenced by the BL decay. 6. To get excellent BL spectra of QD-His-EGFP-RLuc-GB1, one should measure the spectra immediately after the addition of CTZ to the aqueous solution of QD-His-EGFP-RLuc-GB1. 7. Flow cytometric analysis maybe performed using the MACSQuant Analyzer (Miltenyi Biotec Inc.). QD emission is collected through an FL7 filter (ex: 635 nm/em: 750 nm longpass), and EGFP emission is collected through an FL2 filter (ex: 488 nm, em: 525  50 nm).

Acknowledgments This work is partly supported by the Ministry of Education, Science, Sport, and Culture of Japan (Grant-in-Aid for Scientific Research (B) 19H04459 to TJ). References 1. Badr CE, Tannous BA (2011) Bioluminescence imaging: progress and applications. Trends Biotechnol 29:624–633 2. Weissleder R (2001) A clearer vision for in vivo imaging. Nat Biotechnol 19:316–317 3. Anderson JC, Grounds H, Jathoul AP, Murray JAH, Pacman SJ, Tisi L (2017) Convergent synthesis and optical properties of near-infrared emitting bioluminescent infra-luciferins. RSC Adv 7:3975–3982 4. Iwano S, Sugiyama M, Hama H, Watakabe A, Hasegawa N, Kuchimaru T, Tanaka KZ, Takahashi M, Ishida Y, Hata J, Shimozono S, Namiki K, Fukano T, Kiyama M, Okano H, Kizaka-Kondoh S, McHugh TJ, Yamamori T, Hioki H, Maki S, Miyawaki A (2018) Singlecell bioluminescence imaging of deep tissue in freely moving animals. Science 359:935–939 5. Wu C, Mino K, Akimoto H, Kawabata M, Nakamura K, Ozaki M, Ohmiya Y (2009) In vivo far-red luminescence imaging of a biomarker based on BRET from Cypridina

bioluminescence to an organic dye. Proc Natl Acad Sci U S A 106:15599–15603 6. Takai A, Nakano M, Saito K, Haruno R, Watanabe TM, Ohyanagi T, Jin T, Okada Y, Nagai T (2015) Expanded palette of nano-lanterns for real-time multicolor luminescence imaging. Proc Natl Acad Sci U S A 112:4352–4356 7. Branchini BR, Ablamsky DM, Rosenberg JC (2010) Chemically modified firefly luciferase is an efficient source of near-infrared light. Bioconjug Chem 21:2023–2030 8. Rumyantsev KA, Turoverov KK, Verhusha VV (2016) Near-infrared bioluminescent proteins for two-color multimodal imaging. Sci Rep 6: 36588 9. Kuchimaru T, Suka T, Hirota K, Kadonosono T, Kizaka-Kondoh S (2016) A novel injectable BRET-based in vivo imaging probe for detecting the activity of hypoxiainducible factor regulated by the ubiquitinproteasome system. Sci Rep 6:34311

BRET-Based Dual-Color Molecular Imaging 10. Xiong L, Shuhendler AJ, Rao J (2012) Selfluminescing BRET-FRET near-infrared dots for in vivo lymph-node mapping and tumour imaging. Nat Commun 3:1193 11. So MK, Xu C, Loening AM, Gambhir SS, Rao J (2006) Self-illuminating quantum dot conjugates for in vivo imaging. Nat Biotechnol 24: 339–343 12. So MK, Loening AM, Gambhir SS, Rao J (2006) Creating self-illuminating quantum dot conjugates. Nat Protoc 1:1160–1164 13. Hasegawa M, Tsukasaki Y, Ohyanagi T, Jin T (2013) Bioluminescence resonance energy transfer coupled near-infrared quantum dots using GST-tagged luciferase for in vivo imaging. Chem Commun 49:228–230 14. Alam R, Zylstra J, Fontaine DM, Branchini BR, Maye MM (2013) Novel multistep BRETFRET energy transfer using nanoconjugates of firefly proteins, quantum dots, and red fluorescent proteins. Nanoscale 5:5303–5306 15. Samanta A, Walper SA, Susumu K, Dwyer CL, Medintz IL (2015) An enzymaticallysensitized sequential and concentric energy transfer relay self-assembled around semiconductor quantum dots. Nanoscale 7:7603–7614 16. Kamkaew A, Sun H, England CG, Cheng L, Liu Z, Cai W (2016) Quantum dot–nanoLuc bioluminescence resonance energy transfer enables tumor imaging and lymph node mapping in vivo. Chem Commun 52:6997– 7000 17. Alam R, Karam LM, Doane TL, Coopersmith K, Fontaine DM, Branchini BR, Maye MM (2016) Probing bioluminescence resonance energy transfer in quantum rod– luciferase nanoconjugates. ACS Nano 10: 1969–1977 18. Tsuboi S, Jin T (2017) Bioluminescence resonance energy transfer (BRET)-coupled annexin V-functionalized quantum dots for near-

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infrared optical detection of apoptotic cells. ChemBioChem 18:2231–2235 19. Tsuboi S, Jin T (2018) Recombinant protein (luciferase-IgG binding domain) conjugated quantum dots for BRET-coupled near-infrared imaging of epidermal growth factor receptors. Bioconjug Chem 29:1466–1474 20. Gronenborn AM, Filpula DR, Essig NZ, Achari A, Whitlow M, Wingfield PT, Clore GM (1991) A novel, highly stable fold of the immunoglobulin binding domain of streptococcal protein G. Science 253:657–661 21. Tsuboi S, Jin T (2019) BRET based dualcolour (visible/near-infrared) molecular imaging using a quantum dot/EGFP-luciferase conjugate. RSC Adv 9:34964–34971 22. Kurebayashi J, Otsuki T, Tang CK, Kurosumi M, Yamamoto S, Tanaka K, Mochizuki M, Nakamura H, Sonoo H (1999) Isolation and characterization of a new human breast cancer cell line, KPL-4, expressing the Erb B family receptors and interleukin-6. Br J Cancer 79:707–717 23. Jin T, Yoshioka Y, Fujii F, Komai Y, Seki J, Seiyama A (2008) Gd3+-functionalized nearinfrared quantum dots for in vivo dual modal (fluorescence/magnetic resonance) imaging. Chem Commun 44:5764–5766 24. Jin T, Fujii F, Komai Y, Seki J, Seiyama A, Yoshioka Y (2008) Preparation and characterization of highly fluorescent, glutathionecoated near infrared quantum dots for in vivo fluorescence imaging. Int J Mol Sci 9:2044– 2061 25. Alam R, Karam LM, Doane TL, Zylstra J, Fontaine DM, Branchini BR, Maye MM (2014) Near infrared bioluminescence resonance energy transfer from firefly luciferase—quantum dot bionanoconjugates. Nanotechnology 25:495606. (7pp)

Chapter 6 Polyhistidine-Tag-Enabled Conjugation of Quantum Dots and Enzymes to DNA Nanostructures Christopher M. Green, Divita Mathur, Kimihiro Susumu, Eunkeu Oh, Igor L. Medintz, and Sebastián A. Dı´az Abstract DNA nanostructures self-assemble into almost any arbitrary architecture, and when combined with their capability to precisely position and orient dyes, nanoparticles, and biological moieties, the technology reaches its potential. We present a simple yet multifaceted conjugation strategy based on metal coordination by a multi-histidine peptide tag (Histag). The versatility of the Histag as a means to conjugate to DNA nanostructures is shown by using Histags to capture semiconductor quantum dots (QDs) with numerical and positional precision onto a DNA origami breadboard. Additionally, Histag-expressing enzymes, such as the bioluminescent luciferase, can also be captured to the DNA origami breadboard with similar precision. DNA nanostructure conjugation of the QDs or luciferase is confirmed through imaging and/or energy transfer to organic dyes integrated into the DNA nanostructure. Key words DNA nanostructures, Histidine peptide tag (Histag), Quantum dots, Luciferase, peptide nucleic acids, Bioluminescence resonance energy transfer (BRET)

1

Introduction The control of DNA as a genetic code has us on the precipice of a new technological era, yet DNA can offer one more boon as a programmable biopolymer optimal for nanotechnology. DNA, through the development of DNA origami and later the DNA brick methodology, has been shown to self-assemble into almost any arbitrary 2-, and 3-dimensional architecture [1]. Additionally, the benefits of DNA nanotechnology include its “green” nature; economic DNA synthesis makes experiments high-throughput, and more importantly, modular; open-source computational modeling tools make the design learning curve very accessible; and commercial vendors are available that can provide many of the chemical modifications required. These properties are all in support of the critical trait of interest, its bottom-up approach to templating or

Sung-Bae Kim (ed.), Bioluminescence: Methods and Protocols, Volume 2, Methods in Molecular Biology, vol. 2525, https://doi.org/10.1007/978-1-0716-2473-9_6, © The Author(s), under exclusive license to Springer Science+Business Media, LLC, part of Springer Nature 2022

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scaffolding other functional materials. While DNA nanostructures in themselves have some limited applications, when combined with their capability to precisely position and orient dyes, drugs, nanoparticles (NPs), peptides, and proteins, among others, the technology truly shines. The application space for these functionalized DNA nanostructures is enormous, from quantum computing and nanoelectronics to biosensors, vaccines, and many things in-between [2–7]. The full breadth of conjugation strategies to DNA nanostructures is well beyond the scope of this chapter; we direct the interested reader to the following reviews [8–11]. Our focus is on an incredibly versatile and simple methodology based on metal coordination by a multi-histidine peptide tag, often simply referred to as a Histag. As we show, Histags can be readily adapted for conjugating things as diverse as colloidal semiconductor quantum dots (QDs) or enzymes on a DNA nanostructure. Though the Histag conjugation is not covalent, the strength of the conjugation is such (dissociation constant, Kd ~ nM) that the structures can be worked with at the picomolar level [12]. The need for simple purification strategies for enzymes and the observation that certain peptide sequences would flocculate in the presence of metal ions led to the development of tagging enzymes with short peptide tails that would allow for selective extraction on immobilized metal affinity chromatography (IMAC) [13]. A particularly successful sequence is the above-mentioned polyhistidine tag; the Histag term is used to denote more than five continuous histidines with six being the most common length, but longer peptides are not uncommon [14]. High specificity can be obtained as only histidines and cysteines have high affinity to transition metal ions, and both are relatively scarce in most enzyme sequences [15]. Furthermore, the tag can be placed on either the C- and N-terminus and is small and neutrally charged, making it minimally invasive to the enzyme’s functionality. The Histag is most often IMAC purified using a Ni2+-nitrilotriacetic acid (NTA, see Fig. 1, [16]). Subsequently, it was shown that this same metal coordination capture strategy could be transferred to functional substrates [17]. This includes capturing enzymes on inorganic nanoparticles, where the NTA-Ni-Histag conjugation has been shown to optimize functionality in comparison to nonspecific adsorption [18, 19]. Similarly, the NTA-Ni functionalization has been shown to be conjugatable to DNA [20] and thereby amenable to integration into DNA nanostructures for enzyme capture. The first example was shown by the Norton lab, where they captured Histag-labeled green fluorescent protein (Histag-GFP) on a DNA origami breadboard [21]. We provide a method for using the NTA-Ni to capture Histag-luciferase enzymes onto DNA nanostructures. This allows users to integrate the BL properties of

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Fig. 1 Expression and purification of Histag-conjugated luciferase enzyme. (a) Histag-conjugated luciferase enzyme (Histag-Luc, Histag-Luciferase or Luc-Histag) can be expressed in bacteria by preparing a Histagcontaining plasmid that would express and result in a mixture of wild-type and Histag-modified enzyme. (b) Histag-Luc can be purified from wild type using NTA-Ni2+ affinity column; the matrix is modified with NTA groups. After equilibrating with buffer and loading the sample onto the column, Histag-Luc is trapped within the column by Ni2+ coordination with NTA while wild-type enzyme passes in the flowthrough. On addition of the imidazole solution, Histag-Luc is released from the matrix and collected into a vial

luciferase into the nanoscaffold for applications such as sensing, energy harvesting, and cryptography [22–24]. Another very successful application of the Histag has been its use to conjugate enzymes to semiconductor QDs (see Fig. 2). These NPs are often exploited for their useful fluorescent properties in imaging as sensors and are even exploited commercially in screens in the QLED (quantum dot light-emitting diode) technology [27– 29]. A common preparation of QDs is chalcogenides based on CdSe and ZnS, and it is the transition metal ions (most often Zn2+) on the surface of CdSe/ZnS core/shell QDs that efficiently coordinate with the Histags [30]. This strategy allows Histagpeptide hybrids to be synthesized with any desired chemical moiety

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Fig. 2 Semiconductor quantum dots. (a) TEM images of CdSe/CdS/ZnS, emission max 525 nm, Diam. 4.1
0.6 nm. (b) CdSe/CdS/CdZnS/ZnS, emission max 585 nm, Diam. 8.5
0.8 nm. (c) CdSe/CdS/ZnS, emission max 640 nm, Diam. 15
1 nm. (d) Absorbance spectra of QDs in (a–c) normalized to the first absorption band. (e) Normalized FL spectra of QDs in (a–c). (f) Schematic of maltose-binding protein (brown) conjugated to the QD through a Histag (purple). QD surface sulfur atoms are in teal and zinc atoms in pink. (Reproduced with permission from [25]. Copyright (2004) National Academy of Sciences USA.) (g) QD conjugated with Histag DNA hybrid. The Histag of the peptide is shown as a yellow ribbon attached to the DNA. Individual DNA strands within the dsDNA structure are shown in orange and yellow. The rotational extension of the dye molecules is shown by the magenta spheres. Two possible orientations of the DNA relative to the QDs are shown. (i) DNA extending linearly outward from the QD surface and (ii) representation of average distance of DNA from QD surface. (Reprinted with permission from [26]. Copyright 2010 American Chemical Society (ACS))

that can then be efficiently conjugated to the QD in a self-assembly manner [31, 32]. While most DNA–QD conjugation has been realized through a biotinylated DNA strand in combination with streptavidin-QDs, this strategy is inherently bulkier and limits the achievable precision placement of the QD on a DNA nanostructure [26, 33]. DNA–Histag hybrids have been used to capture QDs within DNA nanostructures, but the chemistry of synthesizing DNA–peptide hybrids is generally inefficient, making the capture strand economically limiting [34]. The use of peptide nucleic acids (PNA), nucleotides where the sugar–phosphate backbones are replaced by N-(2-aminoethyl) glycine-based polyamides, is an alternative [35, 36]. PNA can be easily conjugated with peptides [37] while demonstrating higher affinity to complementary nucleic acids than DNA (greater melting temperatures) and still maintaining base-pairing rules [38]. PNA–Histag hybrids can therefore be used to efficiently and precisely position QDs onto DNA nanostructures (see Fig. 3) [36].

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Fig. 3 QD and luciferase immobilization on the DNA origami breadboard. (a) The process of immobilization entails (i) conjugation of peptide-PNA to QDs by combined incubation and (ii) addition of QD-peptide-PNA to DNA origami breadboard displaying the requisite capture strands. (iii) Shown here are four QD immobilization sites on the DNA breadboard with three capture strands per site. (b) For the immobilization of luciferase enzyme, (i) NTA-DNA is first immobilized on the DNA breadboard, (ii) followed by co-incubation of Histagmodified luciferase with the DNA breadboard in the presence of excess Ni2+. (iii) Enzyme immobilization on the DNA breadboard is driven by Ni2+ coordination between NTA and Histag

In this chapter, we aim to show the versatility of the Histag as a means to conjugate moieties to DNA nanostructures. We show that the Histag can be used to capture QDs with numerical and positional precision onto a DNA origami breadboard. We also show that Histag-expressing enzymes capable of producing bioluminescence (BL), such as luciferase, can also be captured to the DNA origami breadboard with similar precision, allowing for energy transfer to other components integrated into the DNA nanostructure.

2

Materials

2.1 Reagents and Labware

1. Ultrapure water with 18 MΩcm resistivity such as water from a Milli-Q water purification (Millipore, Billerica, MA). 2. Sodium chloride (NaCl). 3. Magnesium chloride hexahydrate (MgCl26H2O). 4. 1 M Tris–HCl, pH 8.0. 5. 10 Tris–borate EDTA (TBE buffer, 890 mM Tris, 890 mM boric acid, 20 mM EDTA).

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6. 1 M 4-(2-hydroxyethyl)-1-piperazineethanesulfonic (HEPES buffer).

acid

7. Phosphate-buffered saline (PBS), pH 7.5, 20 stock solution: 1 PBS contains 137 mM NaCl, 2.7 mM KCl, 10 mM phosphate buffer. 8. Triethylammonium acetate buffer (TEAA, pH 7, 2 M stock solution). 9. Tris(2-carboxyethyl)phosphine hydrochloride (TCEP, 40 mM solution). 10. Polyethylene glycol (PEG)-8000 powder. 11. 15-mL centrifuge tubes. 12. 0.2-mL PCR tubes. 13. 0.5- and 1.5-mL DNA LoBind microcentrifuge tubes from Eppendorf. 14. 0.2–2.5, 2–20, 10–200, and 200–1,000 μL micropipettes and tips. 15. 1-mL sterile syringe with Luer-lock tip. 16. 0.2-μm syringe filter. 17. PD-10 columns (3 per DNA strand) (GE Healthcare). 18. V1 grade mica. 19. M13mp18 circular ssDNA of 7249 nt length from Bayou Biolabs (P-107). 20. 408 synthetic ssDNA oligos in two 384-well plates from Integrated DNA Technologies (IDTDNA, for strand list, see Note 1). 21. Cy3-labeled ssDNA from TAGCCGTTACTCTTGCTC).

IDTDNA

(/5Cy3/

22. Alexa Fluor™ 488 (AF488)-labeled ssDNA from IDTDNA (/ 5Alex488N/TAGCCGTTACTCTTGC TCAATCTACTATCTCATCTTTC). 23. Thiolated-ssDNA from IDTDNA TTGAGCAAGAGTAACGGCTA).

(/5ThioMC6-D/

24. Water-soluble QDs (CdSe/ZnS core/shell) coated with CL4, a compact ligand derived from dihydrolipoic acid (DHLA) [30, 39, 40] (see Note 2). 25. His6-peptide-PNA (N0 , HHHHHHAGSGGC:TCTACTATCTCATC, C0 , where the italicized portion is peptide and the nonitalicized portion is PNA) purchased commercially (e.g., PNA-Bio Inc., Thousand Oaks, CA). 26. Maleimido-C3-NTA (NTA 10 mg stock vial) (e.g., Cat. #M035-10 Dojindo Molecular Technologies).

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27. Histag-luciferase N-terminus (Creative Biomart; Cat. #Luciferase-787F) or C-terminus (BPS Bioscience Cat. #100576-1). In-house synthesis of Histag-luciferase (e.g., Renilla reniformis luciferase (RLuc) (EC # 1.13.12.5)) can also be performed with the help of published procedures [22, 23]. 28. Coelenterazine h (CTZh) substrate for luciferase resuspended to 5 mM in 100% (v/v) ethanol. 29. (Optional) Glycerol. 30. (Optional) Agarose powder. 2.2

Instrumentation

1. Personal safety equipment: safety glasses, lab coat, and latex or nitrile gloves. 2. Waste collection beaker. 3. Dry bath/block heater. 4. Thermal cycler such as a ProFlex PCR system (Thermo Fisher Scientific). 5. 12-channel pipette with 5–50 μL transfer such as Finnpipette™ Novus Electronic Multichannel Pipette. 6. Ultrasonic bath for sonicating. 7. Benchtop centrifuge (such as an Eppendorf 5418 centrifuge). 8. Speed vac centrifuge. 9. Quartz cuvette. 10. UV–Vis absorbance spectrophotometer (such as a Nanodrop 2000 Spectrophotometer (Thermo Fisher Scientific) and/or Agilent Cary 4000). 11. Fluorescent spectrofluorometer (such as Horiba FluoroMax-3 spectrofluorometer). 12. Spectrophotometric multiwell plate reader (Tecan Spark® Multimode Microplate Reader). 13. Atomic force microscope. 14. (Optional) Electrophoresis unit and gel mold (such as Cytiva HE 33 Mini Electrophoresis Unit). 15. (Optional) Electrophoresis power supply (such as Fisherbrand FB300). 16. (Optional) An automatic liquid handling system such as an Eppendorf epMotion 5070 or Labcyte Echo 525 acoustic liquid handler.

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Methods

3.1 Preparing Stock Solutions

1. Prepare 10 mL of each of the following stock solutions in 15-mL centrifuge tubes. (a) 5 M NaCl: combine the following. (i) 7 mL of deionized water. (ii) 2.922 g of NaCl. (b) 1 M MgCl2: combine the following. (i) 7 mL of deionized water. (ii) 2.033 g of MgCl26H2O. 2. Agitate the solutions until the components have been almost fully dissolved, then bring the total volumes of each solution to 10 mL, and agitate until the components are fully dissolved. 3. Transfer 10 mL of 10 TBE into a 15-mL centrifuge tube. 4. Transfer 10 mL of 1 M Tris–HCl into a 15-mL centrifuge tube. 5. Using the prepared stock solutions, prepare 10 mL of 0.5 TBE and 12.5 mM MgCl2 by combining the following in a 15-mL centrifuge tube and agitating lightly. (a) 9.375 mL of deionized and purified water. (b) 500 μL of 10 TBE. (c) 125 μL of 1 M MgCl2. 6. Using the prepared stock solutions, prepare a PEG solution (15% (w/v) of PEG-8000 in 5 mM Tris–HCl, 505 mM NaCl, and 12.5 mM MgCl2) by combining the following in a 15-mL centrifuge tube. (a) 6 mL of deionized and purified water. (b) 1.5 g of PEG-8000. (c) 50.0 μL of 1 M Tris–HCl. (d) 1.01 mL of 5 M NaCl. (e) 125.0 μL of 1 M MgCl2. 7. Agitate the PEG solution until all components have been fully dissolved, then bring the total volume to 10 mL with deionized water, and agitate to mix fully. 8. The stock solutions listed below can be stored at room temperature (RT) for the duration of the experiment. (a) 5 M NaCl. (b) 1 M MgCl2. (c) 10 TBE (pH 8.3).

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(d) 1 M Tris–HCl (pH 8.0). (e) 0.5 TBE and 12.5 mM MgCl2. (f) 15% (w/v) PEG-8000 in 5 mM Tris–HCl, 505 mM NaCl, and 12.5 mM MgCl2. 3.2 Synthesis of DNA Origami Breadboards for Target Immobilization

The Histag-based approaches described below are viable for almost all DNA nanostructures, but we will describe the use of a DNA origami breadboard as it is a well-known and characterized system used in many labs [7, 41–43] (see Note 3). The scale of the DNA origami breadboard (~100  60  3 nm) provides a large 2-D structure that can be targeted with multiple moieties, including photonically active moieties such as organic dyes, QD, and BL enzymes, with 1012 viral genomes (vg)/mL for adeno-associated virus) should be resuspended in sterile Dulbecco’s phosphate-buffered saline (DPBS) supplemented with 0.001% (v/v) Pluronic F-68. 3. Coelenterazine (CTZ): Native or analogs (see Note 2). 4. CTZ stock solution in acidified alcohol (0.06 N HCl in either 100% (v/v) methanol or ethanol). For example, 0.2 mL 3 N q.s. to 10 mL of alcohol. 5. CTZ stock solution in β-cyclodextrin [12, 13]: Dissolve 250 μg of CTZ in 10 μL of 100% (v/v) ethanol and then add 500 μL of sterile-filtered 20 mM β-cyclodextrin in PBS to make a final stock solution of 600 μM CTZ. Pipette up and down and vortex to insure complete dissolution. Aliquot and store at 80  C. 6. CTZ stock solution in NanoFuel (NanoLight Technology): A proprietary solvent for CTZ use in vitro. Add 23.6 μL of NanoFuel to a vial containing 500 μg of CTZ to obtain a 50 mM solution (see Note 3). Pipette and vortex to insure complete dissolution. Store the stock solution in the same vial with a tightly closed lid at 80  C for continued use up to several months. The day of experiments, aliquot small samples of CTZ as needed (typically 1 μL) and keep on ice. Just before application, dilute them with a buffer of choice (HEPESbuffered saline or PBS) to a specified final concentration (typically 1:500 for a 100 μM solution) (see Note 4). 7. HEK293FT cells (or any animal cell line of your choice) in a cryovial or in ongoing culture. 8. Brain tissue: Either the neocortex or the hippocampus harvested from embryonic rat pups (embryonic day 18), postnatal mouse pups (postnatal day 0 or 1), or commercially available tissue (BrainBits Inc.).

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9. Culture medium for the cell line: Dulbecco’s Modified Eagle Medium (DMEM) supplemented with 10% (v/v) fetal bovine serum (FBS), 25 mM HEPES, 2 mM L-glutamine, and an appropriate antibiotic (500 μg/mL G-418 for HEK293FT cells). 10. Culture medium for primary neurons: Neurobasal (Plus) Medium supplemented with 5% (v/v) FBS, 0.5 mM GlutaMAX I, and 1 μg/mL gentamicin. 11. Serum-free medium for neurons: Neurobasal (Plus) Medium supplemented with 2% (v/v) B-27 (Plus) and 0.5 mM GlutaMAX I. 12. All-trans retinal: The culture medium should be supplemented with 1–10 μM all-trans retinal (see Note 5). 13. Coverslips: 12 or 18 mm; uncoated or coated with poly-Dlysine. 14. Multi-well microplate: 24-well microplate for 12-mm coverslips, 12-well microplate for 18-mm coverslips, or 96-well microplate for microplate assays. 15. Multielectrode array (MEA): Planner 16 electrodes in a 1-well (Alpha Med Scientific) or 9 electrodes in a 6-well format (Multi Channel Systems). 16. Papain dissociation kit (Worthington). 17. Lipofectamine 3000 (Invitrogen). 18. Microcentrifuge tubes (e.g., Eppendorf 1.7-mL). 19. DPBS: Supplemented with calcium, magnesium, glucose, and pyruvate. 20. Recovery medium (e.g., low-calcium RPMI). 21. Recording buffer: HEPES-buffered saline. 2.2

Instruments

1. Humidified 5% (v/v) CO2 incubator maintained at 37  C. 2. Electroporation apparatus such as Lonza 4D-Nucleofector in the cuvette format. 3. Inverted fluorescence (FL) microscope: To achieve adequate magnification for single-cell recordings, a 40  0.8 NA water immersion objective or 60  1.35 NA oil immersion objective can be used. Appropriate epifluorescence light sources (e.g., arc lamp) and filter cubes (e.g., GFP for ChR2-based LMOs) should be used. 4. MEA recording system, such as Multi Channel Systems MCS GmbH and Alpha MED Scientific Inc.

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Methods

3.1 Preparation of HEK293FT Cells

1. Place uncoated coverslips into a multi-well microplate using sterile forceps. 2. Resuspend HEK293FT cells in a cryovial or in ongoing culture in culture medium for the cell line. 3. Seed cells with 0.5–1 mL of cell suspension per well. For a 96-well microplate, seed with 100 μL. 4. Grow the cells overnight. 5. Transfect with an LMO plasmid using Lipofectamine 3000 following the manufacturer’s instructions when the cell confluency is still low (~30%).

3.2 Preparation of Primary Neurons

1. Dissociate primary neurons from the brain tissue using the papain dissociation kit following the manufacturers’ instructions. Adjust the cell density to one million cells/mL. 2. Seed the primary neurons onto poly-D-lysine-coated coverslips in the volume of 0.5 mL or 1 mL in a 24-well or 12-well microplate, respectively. 3. Maintain them in a humidified 5% (v/v) CO2 incubator at 37  C overnight. 4. Replace the media with the serum-free media for neurons the following day (days in vitro [DIV]1; see Note 6). 5. Maintain the neuronal culture for a week until lipofection.

3.3 Lipofection in Neurons

1. Collect half of the culture media and store in the CO2 incubator. 2. Transfect the neurons in the wells with an LMO plasmid using Lipofectamine 3000 following the manufacturer’s instructions with a modification that one-tenth of the recommended amount for Lipofectamine and DNA in the recommended volume of Opti-MEM is used. 3. Change the media 6–8 h after the transfection. Use the saved media in step 1 supplemented with fresh serum-free media for neurons.

3.4 Electroporation of Neurons

1. Immediately after dissociation (step 1, Subheading 3.2), spin down 2–6 million cells per reaction at 200  g for 10 min to isolate the cells from the media, decant the supernatant, and then resuspend the cells in 100 μL of the proprietary resuspension solution or in DPBS. 2. Add 3 μg of an LMO plasmid and transfer all of the resulting cell suspension mixture to a cuvette.

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electroporation

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4. Add 500 μL of pre-equilibrated recovery media in 5% (v/v) CO2 at 37  C to the mixture. 5. Incubate for 5 min in the CO2 incubator, and then seed onto poly-D-lysine-coated coverslips in wells containing pre-equilibrated culture media for primary neurons in 24- or 12-well microplate (see Note 7). 6. Perform a full medium change with pre-equilibrated serumfree media for neurons the following day. 3.5 Viral Transduction of Neurons

1. Dilute the viral vector in pre-equilibrated serum-free media for neurons (see Note 8).

3.6 Maintenance of Neuronal Culture

1. Perform a one-half media change with pre-equilibrated serumfree media for neurons every week. The neurons should be ready for experiments after DIV10 (see Note 9).

3.7 Patch Clamp Recording

1. Transfer coverslips with cells transfected with LMOs (Subheadings 3.1, 3.3, 3.4, or 3.5) to a recording chamber positioned with an inverted FL microscope.

2. Perform full media change with the serum-free media containing the viral vector on DIV1.

2. Perfuse the recording chamber with the recording buffer at ~500 μL/min at room temperature (RT). 3. Identify a cell for recording using epifluorescence to excite the fluorescent protein tag domain included in all LMO variants (e.g., YFP variants in LMO and iLMO2). 4. Obtain a whole cell patch recording in either voltage or current clamp mode. BL-induced photocurrents can be measured under voltage clamp. 5. Before activating LMOs with CTZ, take a photocurrent measurement in response to wide-field photostimulation at saturating intensity using the appropriate aforementioned light source and filter cubes (see Note 10). 6. Dilute CTZ stock solution in acidified alcohol, β-cyclodextrin, or NanoFuel in the recording buffer (see Note 11). 7. To obtain BL images at the same time, ensure that the filter turret is in an open position (see Note 12). 8. Start recording and apply the CTZ solution up to half of the volume of the chamber (e.g., 500 μL) at the final concentration of 50 μM in the bath in complete darkness. 9. Record the photocurrent in response to the wide-field photostimulation by epifluorescence to verify that the recording

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conditions do not vary during the application of the CTZ solution. 3.8 Extracellular Recordings

1. Prepare the MEA according to the manufacturer’s recommendations (for cleaning, sterilization, and coating; see Note 13). 2. Seed the MEAs with dissociated cortical neurons similar to Subheading 3.2 (see Note 14). 3. At DIV1, perform the transduction as in Subheading 3.5 using a viral vector. 4. Perform a half volume media change every 7 days. 5. Around DIV14, place the MEA in the MEA recording system and confirm if the spontaneous synchronous bursting activity is observable (see Fig. 3c, the beginning of the bottom trace). 6. Use an inverted FL microscope to enable simultaneous BL recording through the optically transparent MEAs (see Fig. 3a, Note 15). Alternatively, recording can be done in a humidified 5% (v/v) CO2 incubator, which is already shielded from ambient light and electrical noise. Delivery of the CTZ solution and detection of BL in steps 9 and 10 can be

Fig. 3 Simultaneous BL detection and electrophysiology in vitro. (a) Primary neurons were dissociated from the embryonic mouse cortex, seeded onto a multielectrode array, and transduced with iLMO2. An FL image shows 16 electrode contacts in black and transduced neurons in white. Yellow scale bar: 100 μm. (b) Diagram of recording setups for fiber photometry inside of an incubator (left) or optical imaging in a warmed MEA recording chamber sealed around an immersion lens objective (right). Yellow tubing: CTZ delivery; white tubing: optical fiber; light blue tubing: humidified CO2 delivery. (c) Representative traces showing BL detected by an amplified photodiode through fiber optics (top) and population firing rate recorded by the MEA (bottom)

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performed remotely through a tubing connected with a syringe and fiber optics, respectively (see Fig. 3b). 7. (Optional) Perform photostimulation either using an external LED light source or the epifluorescence lamp for the microscope. 8. Record a baseline of electrical activity of neurons on the MEA. 9. Add the CTZ stock solution in acidified alcohol, β-cyclodextrin, or NanoFuel to the MEA at a volume of 5 μL per 200 μL of culture volume to reach a final concentration of 10–50 μM (see Note 16). 10. Record BL images using the microscope or BL intensities using the fiber optics in the CO2 incubator (see Fig. 3c; see Notes 12 and 17).

4

Notes 1. In order to ensure the best possible transfection, we recommend using high-quality DNA prepared from an endotoxinfree midi prep kit (e.g., NucleoBond Xtra Midi EF, TaKaRa Bio, Inc.). 2. All LMOs use the marine luciferin, CTZ, as their substrate. These include its native form for LMOs with GLuc, as well as several analogs (i.e., eCTZ, CTZh) for RLuc-based iLMO2. These are commercially available through third parties although it is important to note that the purity standards for different companies vary considerably. CTZ is light-sensitive and thus should be protected from light before and after reconstitution. 3. 50 mM is a concentration higher than the company’s recommendation. This is to minimize the amount of solvent used and avoid possible unwanted side effects [14]. 4. Unlike the stock solution, CTZ diluted in a buffer can only last several hours at RT. Darkening or loss of color of the sample indicates autoxidation and loss of activity. 5. The opsin moiety of LMO molecules requires its chromophore, all-trans retinal, which is typically absent from culture media. 6. We make 50 mL of culture media at a time and store it in a tissue culture flask with a filtered cap. Exchange the media one well at a time so that the well does not dry up. 7. Typically, a higher density than those of cells being seeded in preparation for lipofection is desired.

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8. To ensure 100% transduction efficacy, a final virus concentration should be chosen to achieve a multiplicity of infection of greater than 10. For example, if a well contains one million cells and the lentivirus titer is 109 tu/mL, dilute 10 μL (107 tu) of the viral vector suspension in the culture media. 9. If Neurobasal media has not been supplemented with B-27 with vitamin A, the neurons will not be able to produce retinal, and therefore, all-trans retinal must be added to the media to a final concentration of 1 μM the day before experiments. All-trans retinal can also be added to the extracellular solution. 10. This will correspond to the total expression levels. This should serve as a benchmark to which you can compare the photocurrents generated by BL. 11. We have found that the best way to apply CTZ is through a small-diameter tubing line (e.g., Tygon or silicone) loaded with a CTZ solution, connected to a syringe (can be operated manually or automatically), and delivered to the recording chamber though a micro-manifold (e.g., AutoMate Scientific Inc.). A filing solution should be used to separate the syringe from the CTZ solution. This approach minimizes disruption to the cells and, in turn, the recording. Manually pipetting CTZ directly into the recording chamber is too disruptive. While precise delivery can be achieved using a picospritzer, the CTZ quickly oxidizes in the glass pipette rendering it unusable. 12. Electrophysiological recordings can be synchronized with recording BL through the microscope by either using the exposure signal or the shutter signal of the microscope to initialize the electrophysiology recording. The microscope should be covered in a light impenetrable material so that no instrument or monitor light contaminates the recording. The room for recording should be dark to maximize BL collection. For recording the BL, CCD cameras can be used with a 4  4 binning, and CMOS cameras can be used without binning. Exposure times of 1–5 s should be used for GLuc-based LMOs and 10–20 s for RLuc-based LMOs. 13. There are often several options for coatings (e.g., laminin, poly-L-lysine). We have found that coating conditions have a critical impact on cell viability, and we recommend optimizing your coating conditions prior to beginning your experiments. 14. We recommended using a density of 50,000–100,000 cells for a 1-well array (1 mL) and 12,500–25,000 cells per well for a 6-well MEA (500 μL; sizing based upon Multi Channel Systems). 15. For spontaneous bursting to occur, it is critical that the neurons are maintained in appropriate conditions throughout the recording. Some recording stages are equipped with

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temperature regulation (37  C) and a chamber with a line for humidified 5% (v/v) CO2, such as those from Multi Channel Systems. However, for our recordings using less specialized equipment, we found it necessary to build in those components. Classically, an inverted microscope is used for these experiments, but we have had reasonable success building a humidified chamber sealed around our objective and MEA when needing to record using an upright microscope (see Fig. 3b). 16. A CTZ stock solution can be added directly as MEA extracellular recording is less delicate than the intracellular recording. However, in the case of the sealed recording chamber with an upright microscope, we recommend the syringe-driven smalldiameter tubing approach (as discussed in Note 11) to maintain the proper atmospheric recording conditions for the MEA at this crucial point in the experiment. 17. When performing simultaneous BLI, we recommend using single-well plates to minimize signal contamination from the other wells.

Acknowledgments Support for this project was provided by NSF CBET-1512826 (KB/REG), NIH F31NS115479 (MAS), R21NS112948 (REG), S10OD021773 (KB), DOD W81XWH1910776 (REG), and the Mirowski Family Foundation (REG). References 1. Yizhar O, Fenno LE, Davidson TJ, Mogri M, Deisseroth K (2011) Optogenetics in neural systems. Neuron 71(1):9–34. https://doi. org/10.1016/j.neuron.2011.06.004 2. Aravanis AM, Wang LP, Zhang F, Meltzer LA, Mogri MZ, Schneider MB, Deisseroth K (2007) An optical neural interface: in vivo control of rodent motor cortex with integrated fiberoptic and optogenetic technology. J Neural Eng 4(3):S143–S156. https://doi.org/10. 1088/1741-2560/4/3/s02 3. Sparta DR, Stamatakis AM, Phillips JL, Hovelso N, van Zessen R, Stuber GD (2012) Construction of implantable optical fibers for long-term optogenetic manipulation of neural circuits. Nat Protoc 7(1):12–23. https://doi. org/10.1038/nprot.2011.413 4. Berglund K, Stern MA, Gross RE (2021) Bioluminescence-Optogenetics. In: Yawo H, Kandori H, Koizumi A, Kageyama R (eds)

Optogenetics: light-sensing proteins and their applications in neuroscience and beyond. Advances in experimental medicine and biology, vol 1293, 2nd edn. Springer, Singapore, pp 281–293. https://doi.org/10.1007/978981-15-8763-4_17 5. Sternson SM, Roth BL (2014) Chemogenetic tools to interrogate brain functions. Annu Rev Neurosci 37(1):387–407. https://doi.org/10. 1146/annurev-neuro-071013-014048 6. Berglund K, Birkner E, Augustine GJ, Hochgeschwender U (2013) Light-emitting channelrhodopsins for combined optogenetic and chemical-genetic control of neurons. PLoS One 8(3):e59759. https://doi.org/10.1371/ journal.pone.0059759 7. Berglund K, Clissold K, Li HE, Wen L, Park SY, Gleixner J, Klein ME, Lu D, Barter JW, Rossi MA, Augustine GJ, Yin HH, Hochgeschwender U (2016) Luminopsins integrate opto-

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and chemogenetics by using physical and biological light sources for opsin activation. Proc Natl Acad Sci U S A 113:E358. https:// doi.org/10.1073/pnas.1510899113 8. Park SY, Song SH, Palmateer B, Pal A, Petersen ED, Shall GP, Welchko RM, Ibata K, Miyawaki A, Augustine GJ, Hochgeschwender U (2020) Novel luciferase-opsin combinations for improved luminopsins. J Neurosci Res 98(3):410–421. https://doi.org/10.1002/ jnr.24152 9. Berglund K, Fernandez AM, Gutekunst CAN, Hochgeschwender U, Gross RE (2020) Stepfunction luminopsins for bimodal prolonged neuromodulation. J Neurosci Res 98(3): 422–436. https://doi.org/10.1002/jnr. 24424 10. Tung JK, Gutekunst CA, Gross RE (2015) Inhibitory luminopsins: genetically-encoded bioluminescent opsins for versatile, scalable, and hardware-independent optogenetic inhibition. Scientif Rep 5:14366. https://doi.org/ 10.1038/srep14366 11. Berndt A, Yizhar O, Gunaydin LA, Hegemann P, Deisseroth K (2009) Bi-stable

neural state switches. Nat Neurosci 12(2): 229–234. https://doi.org/10.1038/nn.2247 12. Teranishi K, Shimomura O (1997) Solubilizing coelenterazine in water with hydroxypropyl-β-cyclodextrin. Biosci Biotechnol Biochem 61(7):1219–1220. https://doi.org/10.1271/ bbb.61.1219 13. Naumann EA, Kampff AR, Prober DA, Schier AF, Engert F (2010) Monitoring neural activity with bioluminescence during natural behavior. Nat Neurosci 13(4):513–520. https://doi. org/10.1038/nn.2518 14. Prakash M, Medendorp WE, Hochgeschwender U (2020) Defining parameters of specificity for bioluminescent optogenetic activation of neurons using in vitro multi electrode arrays (MEA). J Neurosci Res 98(3):437–447. https://doi.org/10.1002/jnr.24313 15. Berglund K, Gross RE (2020) Optochemogenetics with luminopsins: a novel avenue for targeted control of neuronal activity. J Neurosci Res 98(3):407–409. https://doi. org/10.1002/jnr.24473

Chapter 27 Applications of Bioluminescence-Optogenetics in Rodent Models Matthew A. Stern, Henry Skelton, Alejandra M. Fernandez, Claire-Anne N. Gutekunst, Robert E. Gross, and Ken Berglund Abstract In the preceding chapter, we introduced bioluminescence-optogenetics (BL-OG) and luminopsin fusion proteins (LMOs), an emerging method of molecular neuromodulation. In addition to reviewing the fundamental principles of BL-OG, we provided a discussion of its application in vitro, including with cell lines and primary cells in culture in vitro. BL-OG is mediated by an easily diffusible molecule, luciferin, and when applied systemically in rodents, the substrate can spread throughout the body, including the brain, achieving powerful molecular neuromodulation with convenience even in awake and behaving animals. In this chapter, we provide a practical guide for BL-OG and LMO applications in rodent models of the nervous system, both ex vivo and in vivo. Key words Bioluminescence (BL), Coelenterazine (CTZ), Luciferase, Luciferin, Luminopsins (LMOs), Mice, Rats, BLI, Electrophysiology, Awake, Anesthetized

1

Introduction Bioluminescence-optogenetics (BL-OG) and Luminopsin fusion proteins (LMOs) have been used as a neuroscience tool in in vivo applications to modulate neuronal activity and downstream behaviors. They have also been used as neuromodulatory agents in a variety of disease models. For instance, LMOs have been used to establish a mechanism for the therapeutic effects of physical exercise on peripheral nerve injury [1] and this understanding was then leveraged to promote axon regrowth following such an injury [2]. Additionally, LMOs have been applied as a potential therapeutic for epilepsy (using a multi-nodal approach) [3], and as agents to increase functional integration of transplanted induced pluripotent

Matthew A. Stern and Henry Skelton contributed equally. Sung-Bae Kim (ed.), Bioluminescence: Methods and Protocols, Volume 2, Methods in Molecular Biology, vol. 2525, https://doi.org/10.1007/978-1-0716-2473-9_27, © The Author(s), under exclusive license to Springer Science+Business Media, LLC, part of Springer Nature 2022

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stem cells in models of both Parkinson’s disease [4] and ischemic stroke [5]. In this chapter, we describe the essential elements of BL-OG usage in rodent models. First, we describe the preparation and handling of the animals and then follow this by detailing the electrophysiology, optical imaging and behavioral procedures in ex vivo brain slice and in vivo anesthetized and awake rodents. Whereas in vitro cultures of dissociated cortical neurons can provide a field of neurons that emulates a network, there can be utility in preserving naturally occurring synaptic connectivity to some degree while still retaining the tight regulation afforded by reduced models for intracellular and extracellular recordings. Ex vivo murine slice recordings serve this purpose. These preparations can be used for both BL-OG and traditional optogenetic photostimulation through the opsin moiety of LMOs. To prepare the tissue for slice recording, the brain must first express an LMO. This is achieved by first intracranially injecting a mouse or rat with an adeno-associated viral (AAV) vector (number of viral particles equivalent to >109 viral genomes or vg). We perform our microinjections stereotactically at our desired targets through pulled glass capillary tubes. Multielectrode array (MEA) recording chambers also exist for slice recordings from the same manufacturers as the cell culture MEAs (see Subheading 2, Chap. 26). Therefore, ex vivo extracellular recordings can also be performed using LMO-mediated neuromodulation. We perform the surgical procedure using traditional approaches to stereotactic rodent surgery as described in Subheadings 3.1, 3.2, and 3.3. There are inherent limitations to in vitro and ex vivo experiments, and while they provide valuable platforms for highly controlled experiments, it is most desired to perform neuromodulatory experiments in models that are more ecologically valid. LMOs have been used in vivo in several experiments to modulate both normal physiology and pathophysiology in intact, minimally perturbed neural networks. LMOs can be introduced into rodent models through injection of viral vectors for transduction (see methods in Subheading 3.1). High-titer AAVs (>109 vg) packaging an LMO gene are traditionally used. These can be injected into a single site as well as multiple targets to facilitate multifocal neuromodulation. In addition to virally mediated transduction, a transgenic mouse line for Cre-dependent expression of excitatory LMO3 has been developed [6] and is now available through a commercial source (JAX stock number: 034853). The transgenic mice provide genetically targeted expression of LMOs in the central and peripheral nervous systems with ease and convenience [2]. Coelenterazine (CTZ) can be delivered by several routes of administration enabling experimental design flexibility. It is important to note that in vivo preparations must be used (i.e., sterile Inject-A-Lume or water-soluble CTZ). In rodents, intravenous

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administration of CTZ results in fast onset of effects (rapid rise in spike rate within seconds of CTZ entering the bloodstream), a peak 20–30 s after injection, and a slower decay (several minutes). Delivery of CTZ to the brain via intraperitoneal (i.p.) injection is slower compared to other routes of administration, but it is the route of choice for chronic and repeated delivery. Intranasal delivery of CTZ to the brain occurs within minutes of administration. In cortical studies, as well as some peripheral nerve studies, the BL emitted from LMO-expressing cells can be detected using a noninvasive whole animal imaging method. Fiber photometry allows for the in vivo measurement of population-level BL deep in the brain. This entails expressing LMOs in the cell population of interest and implanting an optical fiber to collect light from it. For capturing spatiotemporal BL (and from a larger population than that afforded by fiber photometry), awake intravital imaging through a cranial window can be performed in mice head fixed underneath an objective, supported on a specialized platform (e.g., air-cushioned ball, treadmill, running disk, or an air-suspended stage). Here, the animals will undergo surgery for intracranial AAV injection except, instead of a small burr hole, a larger craniotomy is performed through which an injection will be performed, and a cranial window will be placed. This is implemented using standard protocols [7] with an injection similar to that performed in Subheading 3.1. Extracellular electrophysiology allows for the in vivo assessment of BL-OG manipulation of neuronal activity. This includes single-cell and population-level measurements. This requires the stereotactic implantation of a probe within the LMO-expressing cell population of interest. There are many available probes, all of which are generally suitable for this purpose, and which should be chosen based on the anatomy of the target and the required channel count. Chronically implanted multielectrode arrays (e.g., Innovative Neurophysiology) are generally a reasonable approach and will allow for the collection of multiple single units from awake or anesthetized animals over multiple recording sessions. In addition to only collecting electrophysiologic data, electrodes can be combined with optical fibers to create bimodal optrodes for combined optogenetics and electrophysiology. This makes it possible to conduct electrophysiological recordings in concert with BL recordings or traditional, extrinsic light-based activation of the opsins. The behavioral effects of BL-OG neuromodulation depend on the specific cell population targeted and the opsin used. Lateralizing effects on movement via unilateral transduction of motor circuits are among the simplest to assess. As an example, modulation of the basal ganglia (e.g., substantia nigra pars reticulata) can be measured according to contraversive rotational behavior [8– 10]. Although we do not provide step-by-step protocols in this chapter, in general, any behavioral assay should include a long

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(~30 min) baseline assessment followed by substrate administration and an experimental assessment that captures a previously established time course of BL. As some of the solvents used with CTZ have sedative properties, it is best to use water-soluble CTZ for these experiments (Subheading 2.1).

2

Materials Below, we list materials, reagents, and instruments needed for in vivo application of BL-OG in rodent models. Some components have overlaps with the materials listed in Chap. 26 for in vitro applications.

2.1 Reagents, Labware, Surgical Supplies, and Tools

1. AAV packaging an LMO construct payload: A high titer (>1012 vg/mL) suspended in sterile Dulbecco’s PBS supplemented with 0.001% (v/v) Pluronic F-68. 2. Coelenterazine (CTZ): Native or analogs (see Note 1) in a presterilized powder form in individual vials (e.g., Inject-ALume, NanoLight Technology, Prolume Ltd). 3. CTZ solution in sterile NanoFuel Solvent (NanoLight Technology) for in vivo: CTZ comes in sterile injection vials with a low retention volume and with sterile NanoFuel diluent. Thaw NanoFuel at room temperature (RT) and then add 100–200 μL into a vial containing 500 μg CTZ (see Note 2). 4. CTZ solution with water-soluble native CTZ or CTZh: commercially available through NanoLight Technology, both of which can be dissolved in sterile water (up to 500 μg/100 μL depending on experimental needs) without additional diluents. 5. Isoflurane: Vaporized in oxygen for general anesthesia. 6. Sterile saline: 0.9% (w/v) sodium chloride in water. 7. Surgical tools including a scalpel, microtip forceps, a needle, or dissection pin. 8. Pulled glass needle for injection: Attached to NanoJect. 9. Artificial cerebrospinal fluid (aCSF): Bubbled and equilibrated with 5% (v/v) CO2 in O2 to obtain physiological pH of 7.4. 10. Planner multielectrode array (MEA) for brain slice recording. 11. MEA with wire electrodes for in vivo recording: For example, Innovative Neurophysiology. 12. Cannulas for microinjection: A set of guide, dummy, and injection cannulas. 13. Insulin syringe with a 30-G needle for intravenous injections. 14. Tuberculin syringe with a 25-G needle for i.p. injections. 15. Micropipette for intranasal injections.

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16. An optical fiber ferrule: With high numerical aperture, such as a 1.25-mm diameter ceramic ferrule with a 200 μm, 0.39 NA, and 10-mm fiber. It can be either assembled in the lab or commercially available. 17. Dental acrylic: Ortho-Jet (Lang Dental) or Metabond (Parkell Inc.). 18. Cyanoacrylate glue: For example, 3M VetbondTM. 19. Fiber optics including optical patch cables. 20. Dexamethasone (4 mg/mL). 21. Cotton swabs. 22. 3% (w/v) hydrogen peroxide. 23. Gel foam. 24. Coverslip: Round; thickness #1. Diameter selected based upon experimental need. 25. Headplate. 2.2

Instruments

1. Stereotactic apparatus. 2. Stereoscope for surgery. 3. Dental handpiece or stereotactic drill. 4. NanoJect microinjector: Company.

From

Drummond

Scientific

5. Vibratome for brain slicing. 6. Upright fluorescence microscope: A wide-field microscope equipped with a CCD or sCMOS camera, micromanipulators, and patch-clamp amplifiers. Alternatively, a laser-scanning microscope, such as confocal or two photon, equipped with photomultiplier tube (PMT) detectors. 7. Syringe pump. 8. Heat lamp: Can be substituted by a beaker with warm water. 9. Rodent restrainer for tail vein injection. 10. Whole animal BLI system: A general BLI system including a chemiluminescence imaging systems equipped with CCD or CMOS detectors such as those used for Western blot and gel imaging (e.g., Fuji Film LAS-3000). Set up a nose cone to deliver vaporized isoflurane balanced in oxygen with an active activated charcoal vacuum scavenger to enable long imaging sessions (up to 4 h) as well as a heating pad to maintain the mouse’s body temperature throughout the session. 11. High sensitivity photodiode, sensitive down to fW. 12. Analog-to-digital converter and the acquisition software. 13. Recording platform: Neurotar Mobile HomeCage.

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14. Headstage and recording system for in vivo electrophysiology. 15. Analysis software for electrophysiology, such as MATLAB and KiloSort.

3

Methods

3.1 Stereotactic Injection of Viral Vectors

The first step for both ex vivo and in vivo experiments is to prepare animals so that LMOs can express in cell types of interest in the nervous system. 1. Induce and maintain the animal under general anesthesia, such as isoflurane balanced in oxygen. 2. Prepare the surgical area, incise the scalp, and stereotactically align the animal. 3. Perform burr hole craniotomies (diameter: 0.5–1 mm) using a dental handpiece or stereotactic drill at the sites corresponding to the planned injection trajectories being careful to not damage the cortical surface or meningeal layers. 4. Maintain the exposed skull as moist, at a cool temperature throughout by periodically applying chilled saline to the skull surface. 5. At each injection site, using microtip forceps, a needle, or a dissection pin, slightly nick the dura and stereotactically lower your injection needle loaded with AAV. 6. Allow the needle to equilibrate for 5 min at its target. 7. Perform the injection using a NanoJect platform (volume per injection site 200–1,000 nL, at a rate of 1–2 nL/s for mice and 2–4 nL/s for rats). 8. Keep the needle at the target for five more minutes before retracting to minimize efflux. 9. Close the incised skin after all desired injections have been performed. 10. Wait at least 10 days for LMO expression to accumulate before beginning the following experiments (see Fig. 1).

3.2 Intracellular Recording Ex Vivo

1. Sacrifice the animal by overdosing isoflurane. 2. Immediately extract the brain and put it in chilled aCSF. 3. Acquire the slices for recording using a vibratome at a thickness of 300 μm. 4. Incubate them in aCSF bubbled with 5% (v/v) CO2 in O2 for 1 h at RT. 5. Transfer the slice to a recording chamber for electrophysiology perfused with aCSF bubbled with 5% (v/v) CO2 in O2. The

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Fig. 1 Viral vector-mediated transduction of the hippocampus with LMOs. AAV carrying the LMO gene was injected into the mouse hippocampus. Expression was confirmed in the dentate gyrus granule cells through the fluorescent tag in a histological section

recording chamber should be set up for simultaneous BL recording and patch-clamp recording. 6. Perform ex vivo recordings using a similar protocol and recording parameters as the in vitro recordings described in Subheading 3.7, Chap. 26 (see Note 3). 7. Perform photostimulation of LMOs using wide-field excitation light for epifluorescence. Alternatively, photostimulation can be done using a laser light source and a scanning microscope. 8. Obtain a baseline for comparison. 9. Reconstitute CTZ (10–50 μM) in the aCSF, and apply it through the perfusion line (see Note 4). 10. Perform a photostimulation recording after the recordings with CTZ have finished and the CTZ has been washed out to ensure the recording conditions have not changed throughout the experiment. 3.3 Extracellular Recordings Ex Vivo

1. Prepare the animals and subsequent slices the same as they are in Subheading 3.2 except the recording chambers used are the slice MEA. 2. Take extracellular recordings using the same approach as in Subheading 3.8, Chap. 26 (see Note 5).

3.4

CTZ Delivery

3.4.1 Intracranial Delivery

Below, we summarize delivery routes of CTZ for in vivo applications. 1. Surgically implant a guide cannula 1.0 mm above the region expressing LMOs. A dummy cannula should be kept inserted when an injection cannula is not used.

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2. Insert an injection cannula protruding 0.5 mm from the tip of the guide cannula filled with CTZ solution. 3. Slowly inject CTZ solution using a syringe pump up to 1 μL. In rats, inject a 600 μM or 1 mM CTZ solution to activate LMOs [9, 10] (see Note 6). 4. Replace the injection cannula with the dummy cannula. 3.4.2 Intravenous Delivery

1. Prepare CTZ solution in water for mice or in NanoFuel solvent for rats. 2. Warm up the animal using a heat lamp prior to being placed in a tail vein restrainer to improve circulation. Alternatively, once in the restrainer, the tail can be immersed in warm water for 90 s for dilation of the lateral veins (see Note 7). 3. Once the veins are visualized, inject the CTZ solution into one of the veins using a 30-G insulin syringe. To achieve a dosage of 4 mg/kg, inject 50–75 μL per mouse or 200–250 μL per rat of CTZ solution (5 mg/mL). There are also single-use catheters available for tail vein injections.

3.4.3 Intraperitoneal Delivery

1. Prepare CTZ solution in water or NanoFuel solvent for mice. 2. Hold a mouse in one hand by the scruff of its neck and by the tail in other fingers. 3. Using the other hand, inject with the CTZ solution at a dosage of 5–20 mg/kg into the peritoneal cavity using a 25G tuberculin needle [2] (see Note 8).

3.4.4 Intranasal Delivery

1. Prepare CTZ solution in water. 2. Using a micropipettor with a 100-μL pipette tip, draw up 10 μL of the CTZ solution at a time. 3. Scruff a mouse securely and pipette out the CTZ solution slowly drop by drop directly in front of the mouse’s nostril until it breathes it in. Right and left nostrils should be alternated between each drop. 4. Repeat the procedure until the desired amount of CTZ is delivered (2–5 mg/kg) [5].

3.5

Whole Animal BLI

1. After waiting for LMO expression, induce and maintain the mouse under general anesthesia, and place the mouse in the whole animal BLI system. 2. Administer CTZ solution by the chosen route (Subheadings 3.4.2, 3.4.3, or 3.4.4). 3. Take an image of the mouse before the imaging session for surface anatomical reference.

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Fig. 2 Whole animal BLI in vivo. Transgenic mice expressing LMO3 in cholinergic motor neurons received i.p. injections of CTZ. (a) A representative image of BL (in pseudo color) superimposed onto a bright-field image (gray). A background-subtracted BL image was thresholded to show only significant signal. (b) Representative time courses of BL following injections of different dosage of CTZ. (c) Quantification of peak BL. An asterisk indicates significant difference from the rest. N ¼ 3 males (in blue) and 3 females (in red). (Reproduced with permission from MDPI, Ref. [2])

4. Perform the collection of BL images. BL signals from LMOs expressed in the cortex and some peripheral nerves are strong enough to be detected through the intact skin and skull (see Fig. 2), but up to 1 min exposure time is required for transdermal imaging with a CCD camera (see Note 9). 5. Periodically check if the mouse is properly anesthetized throughout the imaging session. 6. Take a reference image of the mouse after the session to verify that it has not moved.

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Fiber Photometry

1. Implant an optical fiber ferrule using standard stereotactic techniques, either in combination with viral transduction (Subheading 3.1) or during the subsequent steps (see Note 10). 2. Secure the mouse’s head in a stereotactic frame, with the scalp opened sufficiently to visualize skull surface landmarks and the surgical entry point under a surgical microscope. 3. Clean the skull surface and remove the periosteum to promote adherence of the acrylic headcap (see Note 11). 4. Attach the fiber ferrule to an arm of the stereotactic apparatus, and determine its position in stereotaxic space by placing it on the relevant anatomical landmarks. 5. Make a craniotomy large enough to fit the fiber at the entry point with a handheld drill, followed by a careful durotomy with a fine needle (see Note 12). 6. Move the fiber to the stereotactic coordinates, lower to the brain surface, and then slowly lower to the determined depth below the brain surface. 7. Build an acrylic headcap around the fiber to secure it to the skull, with skin secured to the edges using cyanoacrylate glue (e.g., VetbondTM, 3M). Wait for the acrylic to solidify in place such that the fiber can be unclamped from the stereotactic apparatus. 8. Remove the mouse and wait until the mouse recovers from anesthesia. 9. Give the mouse 2 days for recovery or 10 days for expression if the viral vector was injected in the same surgery. 10. Using an appropriate optical patch cable, attach a cranial fiber ferrule to a high-sensitivity photodiode in an enclosure (see Fig. 3, Note 13). The mouse can be under anesthesia or awake. 11. Connect the photodiode to an analog-to-digital converter with appropriate software for recording. 12. Record for a long (~30 min) baseline period, followed by an i.p. injection of CTZ solution (Subheading 3.4.3), and then a postinjection recording long enough to capture the time course of BL.

3.7 Awake Intravital Cranial Imaging

1. Inject the mouse with dexamethasone (4.0 mg/mL; 4.8 mg/ kg) intramuscularly into the hind limb 4–8 h before surgery to minimize brain swelling and inflammation. 2. Anesthetize the mouse, prepare the surgical site, and open, retracting a few square millimeters of scalp along the midline. 3. Strip the periosteum using a sterile cotton swab and scalpel, lightly crosshatch the skull surface and etch the surface using

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Fig. 3 BL fiber photometry in vivo. The setup includes a dark box, a stereotactic device for gas anesthesia, and fiber optics connected to an amplified photodiode. The analog-to-digital converted signal (ADC) is recorded in a PC. The display shows a representative trace with an abrupt increase of the signal at the time of CTZ injection due to the opening of the dark box

3% (w/v) hydrogen peroxide applied for 1 min, and then remove using sterile saline (see Note 14). 4. Throughout the process moving forward, ensure that gel foam saturated in chilled saline is kept on the surface of the skull outside of the site being directly operated on. 5. Stereotactically align the animal and demarcate the boundaries of the window. 6. Drill the craniotomy using a dental handpiece being careful to prevent damage to the dura and ensuring a clean edge (see Note 15). 7. Perform the stereotactic injection at the targeted site, maintaining the area with chilled saline-saturated gel foam. 8. Place the thin glass coverslip cranial window, and adhere it to the skull using dental acrylic making sure that no air gets trapped underneath the window. 9. Place a metal headframe around the window for awake head fixation, adhere it to the skull using dental acrylic, and close the skin to the headplate and skull using either dental acrylic or cyanoacrylate tissue adhesive. 10. Wait for at least 2 weeks for an expression level appropriate for imaging. During this time, habituate the mouse to the recording platform, slowly building up to the duration of a full recording session.

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Fig. 4 BL detection through a cranial window in vivo. (a) A head-fixed awake mouse with a chronically implanted cranial window under an upright fluorescence microscope. (b) Example of two-photon fluorescence imaging of iLMO2-expressing cortical neurons in layer II/III. The image is a maximum intensity Z projection obtained around 135 μm deep from the dura with 960 nm excitation light. Scale bar: 50 μm. (c) Representative trace of BL through the cranial window recorded with a PMT

11. Place the mouse under an upright fluorescence microscope (see Fig. 4, Note 16). 12. Acquire a baseline recording and then administer CTZ to the mouse using the desired route and dose. 13. Perform BLI through the window, with recording parameters similar to those used in previous sections for the various detectors (see Note 17). 3.8 Extracellular Recording In Vivo

1. Implant an MEA stereotactically, using a procedure similar to Subheading 3.6 (see Note 18). 2. Wait several days after surgery for recovery, prior to conducting experiments, awake or under anesthesia. 3. Plug a headstage into the implanted probe, connected by a cord to the recording system (see Note 19). 4. Record for a long (~30 min) baseline period followed by CTZ injection (Subheadings 3.4.1, 3.4.2, 3.4.3, or 3.4.4) and a recording long enough to capture for the extent of the previously established time course of BL.

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5. To measure single-unit spike times, high-pass filter, and common-average-reference the signal across channels and then identify and cluster spikes. 6. Analyze local field potentials using time-frequency decomposition (see Note 20).

4

Notes 1. All LMOs use the marine luciferin, CTZ, as their substrate. These include its native form for LMOs with GLuc, as well as several analogs (i.e., e-CTZ, CTZh) for RLuc-based iLMO2. These are commercially available through reagent suppliers although it is important to note that the purity standards for different companies vary considerably. CTZ is light-sensitive and thus should be protected from light before and after reconstitution. 2. NanoFuel allows one to dissolve CTZ at high concentrations without precipitating it. Inject-A-Lume is available for native CTZ, CTZh, and eCTZ. Inject-A-Lume is typically stored in the freezer. The volume of NanoFuel added to the CTZ vial depends on the experimental needs (e.g., route of administration, species, etc.). 3. Importantly, this ex vivo approach enables the recording not only of neurons expressing LMOs but also neurons synaptically downstream. The microscopes used here can be either widefield fluorescent scopes equipped with an epifluorescence unit and CCD or CMOS detectors or laser scanning microscopes, such as confocal or two-photon, equipped with PMT detectors. Especially with the PMTs, it is essential that these experiments are performed in absolute darkness for adequate noise reduction to detect the BL signal. 4. We recommend diluting the stock solution immediately before application to avoid autoxidation of CTZ by the oxygensaturated buffer. 5. The electrode contacts are on the bottom of the dish. If recordings from the neurons in plane with the imaging is important, an inverted scope must be used for wide-field epifluorescence recordings. For upright scopes, two-photon imaging is ideal given its ability to penetrate deeper into the tissue for optical sectioning. 6. Although this causes very rapid action only near the injection site, it may cause irreversible damage to the injection site if the injection rate is too fast or a large volume is injected. 7. Acclimation to the restrainer should be done for 3–5 days prior to the day of the experiment to reduce stress on the mouse.

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8. Any unused CTZ solution should immediately be stored at 80  C and can be thawed immediately before injection. This route results in first-pass metabolism of CTZ, and therefore, higher concentrations may need to be used. 9. We recommend using albino mice or shaving the hair of black or brown mice to facilitate transdermal imaging. 10. The stereotactic coordinates should target the end of the optical fiber just outside of the cell population of interest, with consideration of its anatomy, the light transmission characteristics of the BL signal in tissue, and the light collection properties of the optical fiber. As BL tends to be dim compared to the fluorescent signals more commonly recorded with fiber photometry, it is necessary to use an efficient configuration with high numerical aperture optics and an accurately placed fiber. 11. Skull screws may be placed for additional security and should be arranged close to the entry point. 12. In a combined viral injection/fiber implant surgery, this entry point will often be the same as the one used for injection, and a single craniotomy sized for the fiber can be used for both. 13. As the signal is typically quite small, it is critical that the entire light path is in a light-controlled environment, which can be achieved by enclosing the entire experiment in an opaque enclosure (see Fig. 3). As this prevents visual monitoring of the mouse, it requires a reliable, safe in vivo apparatus. In particular, awake tethered mice should be attached to rotary joints and anesthetized mice to vital sign monitors. Periods of time that require the introduction of ambient light, primarily CTZ injection, will appear as large increases in signal due to contamination along the light path. These times should be noted during the experiment and excluded from analysis. 14. This is important to ensure proper adherence of the headplate and window to the skull. 15. If experimentally needed, drill any burr holes using a dental handpiece for electrodes for simultaneous electrophysiology recording. You can place and adhere electrodes also using dental acrylic either before opening the craniotomy for the window or after the window placement. 16. Note that with using a PMT, you will lose the spatial information from your BL as the light detected will be that of the whole population unlike laser scanning fluorescence microscopy, where the fluorescence detected can be tied to the scan path coordinates. We use a two-photon microscope that is also equipped for targeted photostimulation using a dedicated laser path, but photostimulation can also be performed using an LED, an arc lamp, or implanted fiber optics. As with the

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simultaneous electrophysiology and BLI in Subheading 3.2, the recordings can be synchronized using the shutter signals from the microscopes. The microscope can also be used to visualize cells expressing LMOs in vivo by selecting an excitation wavelength suitable for the fluorescent protein tags on LMOs. 17. The mice can be imaged multiple times using this chronic window placement approach. However, it is important to monitor window clarity as it can vary overtime due to inflammation, dural thickening, and skull regrowth. 18. Most MEAs will require a larger craniotomy and skull screws for headcap strength and to secure ground/reference wires. 19. Analog-to-digital conversion may take place in the headstage or in a separate component. Attention should be paid to mitigating electromagnetic noise. For awake experiments, the headstage and tether should be as small and unobstructive as possible, and the tether cable should be connected to the rest of the apparatus via a commutator. 20. The time course of BL via systemic administration of the chemical substrate precludes establishing that single cells are being directly modulated, and all effects must be interpreted on a network level. Combining electrophysiology with BL recordings requires attaching both electrical and light recording apparatuses to a synchronized recording system. For this purpose, optrodes are available commercially or can be fabricated using a guide cannula that will allow accurate freehand implantation of a fiber above the site of interest (see Fig. 5). This can provide a ground truth that the BL component of BL-OG is working, in combination with a measure of the physiologic output. This allows for a temporally specific assessment of BL-OG constructs. Using an opsin with appropriate kinetics makes it possible to perform temporally precise optotagging of LMO-expressing cells, on which the effects of BL-OG approaches can then be studied. In addition, using SFLMOs (Subheading 1, Chap. 26), BL excitation can be temporarily quenched using extrinsic light of longer wavelength.

Acknowledgments Support for this project was provided by NSF CBET-1512826 (KB/REG), NIH F31NS115479 (MAS), R21NS112948 (REG), S10OD021773 (KB), DOD W81XWH1910776 (REG), and the Mirowski Family Foundation (REG).

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Fig. 5 Custom-made cannula electrode. (Left) A 16-channel electrode array was superglued to a guide cannula. An optical fiber was inserted through the cannula to deliver blue excitation light for the channelrhodopsin moiety of LMO3. (Right) Implantation surgery of the cannula electrode in the mouse brain. The optical fiber illuminated neurons while recording from them. Subsequent CTZ injections were given through the same guide cannula References 1. Jaiswal PB, Tung JK, Gross RE, English AW (2020) Motoneuron activity is required for enhancements in functional recovery after peripheral nerve injury in exercised female mice. J Neurosci Res 98(3):448–457. https:// doi.org/10.1002/jnr.24109 2. English AW, Berglund K, Carrasco D, Goebel K, Gross RE, Isaacson R, Mistretta OC, Wynans C (2021) Bioluminescent Optogenetics: a novel experimental therapy to promote axon regeneration after peripheral nerve injury. Int J Mol Sci 22(13):17. https://doi. org/10.3390/ijms22137217 3. Tung JK, Shiu FH, Ding K, Gross RE (2018) Chemically activated luminopsins allow optogenetic inhibition of distributed nodes in an epileptic network for non-invasive and multisite suppression of seizure activity. Neurobiol Dis 109:1–10. https://doi.org/10.1016/j. nbd.2017.09.007

4. Zenchak JR, Palmateer B, Dorka N, Brown TM, Wagner LM, Medendorp WE, Petersen ED, Prakash M, Hochgeschwender U (2020) Bioluminescence-driven optogenetic activation of transplanted neural precursor cells improves motor deficits in a Parkinson’s disease mouse model. J Neurosci Res 98(3):458–468. https://doi.org/10.1002/jnr.24237 5. Yu SP, Tung JK, Wei ZZ, Chen DD, Berglund K, Mong WW, Zhang JY, Gu XH, Song MK, Gross RE, Lin SZ, Wei L (2019) Optochemogenetic stimulation of transplanted iPS-NPCs enhances neuronal repair and functional recovery after ischemic stroke. J Neurosci 39(33):6571–6594. https://doi.org/10. 1523/jneurosci.2010-18.2019 6. Medendorp WE, Bjorefeldt A, Crespo EL, Prakash M, Pal A, Waddell ML, Moore CI, Hochgeschwender U (2021) Selective postnatal excitation of neocortical pyramidal neurons results in distinctive behavioral and circuit

BL-OG in Rodents deficits in adulthood. iScience 24(3):102157. https://doi.org/10.1016/j.isci.2021.102157 7. Goldey GJ, Roumis DK, Glickfeld LL, Kerlin AM, Reid RC, Bonin V, Schafer DP, Andermann ML (2014) Removable cranial windows for long-term imaging in awake mice. Nat Protoc 9(11):2515–2538. https://doi.org/10. 1038/nprot.2014.165 8. Berglund K, Clissold K, Li HE, Wen L, Park SY, Gleixner J, Klein ME, Lu D, Barter JW, Rossi MA, Augustine GJ, Yin HH, Hochgeschwender U (2016) Luminopsins integrate optoand chemogenetics by using physical and biological light sources for opsin activation.

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Proc Natl Acad Sci U S A 113(3):E358. https://doi.org/10.1073/pnas.1510899113 9. Berglund K, Fernandez AM, Gutekunst CAN, Hochgeschwender U, Gross RE (2020) Stepfunction luminopsins for bimodal prolonged neuromodulation. J Neurosci Res 98(3): 422–436. https://doi.org/10.1002/jnr. 24424 10. Tung JK, Gutekunst CA, Gross RE (2015) Inhibitory luminopsins: genetically-encoded bioluminescent opsins for versatile, scalable, and hardware-independent optogenetic inhibition. Scientif Rep 5:14366. https://doi.org/ 10.1038/srep14366

Chapter 28 One-Channel Microsliding Luminometer for Quantifying Low-Energy Bioluminescent Lights Sung-Bae Kim and Ramasamy Paulmurugan Abstract A high-throughput quantitative determination of multiple light-emitting samples is a virtue of many light determination systems. In this chapter, we introduce a compact and efficient light determination system with a microsliding platform, a single-channel photomultiplier tube (PMT), and the controlling software for quantitative imaging of low-energy lights. The microsliding platform is uniquely designed to hold a multichannel microslide or an 8-lane PCR tube strip. The platform supports consecutive measurement of the multiple light samples through sliding the microslide or the PCR tube strip like a conveyor belt. We exemplify determination of multiple alkaline phosphatase samples and single-chain bioluminescent probes using this system. We also outline the mechanical specification of the system. This unique luminometer is an important addition to compact on-site quantitative light determination systems that are useful in various research fields including analytical chemistry, biology, and basic science in medicine. Key words Microsliding, Alkaline phosphatase, Luminometer, Photomultiplier tube, High throughput, FRB-A23-FKBP

1

Introduction Photomultiplier tube (PMT) was invented in the mid-1930s, and since then, it has been popularly utilized to measure weak light signals at very fast reading rate [1]. PMT is advantageous in optical instrumentation over other modalities with respect to its low cost, high sensitivity to detect weak signal, compactness, and fast response time. Because of such unique instrumental advantages, PMT has been utilized in a wide range of light-sensing instruments such as luminometer, positron emission tomography (PET), gamma camera, and many others [2–4]. On the other hand, the abovementioned instruments have limitations with respect to the sample throughput, where each optical sample should be determined independently one by one, and the amount of times needed for imaging. Some light-imaging

Sung-Bae Kim (ed.), Bioluminescence: Methods and Protocols, Volume 2, Methods in Molecular Biology, vol. 2525, https://doi.org/10.1007/978-1-0716-2473-9_28, © The Author(s), under exclusive license to Springer Science+Business Media, LLC, part of Springer Nature 2022

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Fig. 1 Construction of a 1-ch microsliding luminometer for determining samples emitting low-energy lights. (a) A schematic diagram of the electric circuit of the luminometer. The light from a sample is focused on the PMT through the fiber optical taper and the mirror cap. The electric signal from the PMT is amplified, discriminated, and finally counted. Inset a shows the pictures of components of the luminometer. (b) The highlighted sample stage, which consists of a mirror cap, three slide decks, a slide holder, a stander, and a fiber optic taper. The light samples are embedded in the mirror cap and deployed on the top slide deck, which is designed to be conveyed on the middle slide deck with a 9 mm interval like a conveyer belt

systems take optical images with CCD camera in a dark chamber. However, they are expensive and neither compact nor portable. In this chapter, we introduce a unique one-channel (1-ch) microsliding luminometer for a high-throughput determination of nonradioactive light samples. This luminometer consists of (i) a 1-ch microsliding platform (sample stage) with a mirror cap, (ii) a light control unit, (iii) a photon counter assembly, (iv) a power supply, and (v) a controlling software installed in a personal computer (PC) interface with the luminometer. The microsliding platform is designed with three decks. The top deck is for carrying a mirror cap holding light samples, the middle deck is for conveying the top slide with a same interval, and the bottom deck is for carrying optical filters (see Fig. 1). The mirror cap has slantingly bored holes at the side, where the pitches

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between the channels are designed to exactly fit to the light detection window on the sample stage. The top and bottom slides are mechanically movable with adjusting bars from the outside. The optical intensities from the light samples are consecutively determined by the photon counter assembly consisting of PMT, discriminator, photon counter, and the controller. The assembly is finally connected to a personal computer (PC) via a USB interface cable and controlled with the custom-designed software. We first exemplify the utility of the microsliding luminometer using placental alkaline phosphatase (PLAP) as a model chemiluminescent light standard. The luminometer is placed inside the chamber of an IVIS Lumina II system (simplified to IVIS system hereafter), and the optical intensities are simultaneously determined using both the microsliding luminometer and the IVIS system. The results show a very strong correlation between the relative luminescence unit (RLU) values of the microsliding luminometer and the average radiance (p/s/cm2/sr) values of the IVIS system. We further exemplify the biomedical utility of the microsliding luminometer to bioassays through rapid determination of (i) the rapamycin-activated bioluminescence (BL) intensities of a single-chain bioluminescent probe named FRB-A23-FKBP and (ii) the estrogen antagonist-driven BL intensities of a single-chain bioluminescent probe entitled ERLBD-RLuc8-SH2. This 1-ch microsliding luminometer is an important addition to biomedical instrumentation for a preclinical diagnosis of light samples with high sensitivity, improved sample throughput, portability, and rapid on-site quantitative analyses of low-energy lights.

2

Materials

2.1 Reagents and Labware

1. Phosphate-buffered saline (PBS). 2. Great EscAPe SEAP Chemiluminescence Kit 2.0 (Takara Bio, Mountain View, CA): This kit includes placental alkaline phosphatase (PLAP) and its specific substrate. 3. FRB-A23-FKBP, which is a column-purified fusion protein that is designed to recognize rapamycin and exert intramolecular conformation changes (see Note 1). 4. pcDNA3.1(+) vector encoding ERLBD-RLuc8-SH2 [5]. 5. COS-7 cells derived from African green monkey kidney fibroblast (ATCC). 6. Culture medium: Dulbecco’s Modified Eagle Medium (DMEM) with 10% (v/v) fetal bovine serum (FBS) and 1% (v/v) penicillin/ streptomycin (P/S). 7. TransIT-LT1 (Mirus), a lipofection reagent.

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8. Renilla luciferase assay kit (Promega), which comprises native coelenterazine (CTZ) stock solution (100
), cell lysis buffer (5
), and assay buffer (1
). 9. CTZ solution: A working solution prepared by dissolving the 100
CTZ stock solution in 1
PBS buffer. 10. Trypsin/EDTA solution. 11. Multichannel micropipette. 12. 0.1% (v/v) ethanol. 13. 6-well microplate. 14. 6-channel microslide (μ-slide VI0.4, ibidi). 15. 0.1% (v/v) DMSO. 16. 8-lane PCR tube. 17. 4-hydroxytamoxifen (OHT) as an estrogen antagonist. 2.2 Instrumental Parts

1. Aluminum mirror cap, where holes are slantingly bored at the side. The distance between the holes should be fit to the channel distance of the disposable microslide and 8-lane PCR tube (pitch: 9 mm) (see Notes 2 and 3). 2. Top slide deck, which is designed to contain the mirror cap and 8-lane PCR tube. 3. Middle slide deck, which is railroad likely grooved in the interval of 9 mm. 4. Bottom slide deck, which is carved to carry three optical filters (see Note 4). 5. Slide holder, which is designed to embed the above slide decks. 6. Stander, which is designed to support the sample stage. The inside is hollowed to embed a thick optical fiber as a light path. 7. Fiber optic taper: This fiber optic taper is shaped of a broad inlet (18 mm) and narrow outlet (8 mm) for a lens-focusing effect (catalog number: 55–134, Edmund Optics). 8. Light-proof black plastic case with upper and lower stories, where the upper story is a space for the sample stage, and the lower story is for containing the photon counter assembly. 9. Shutter (Fujita Electric Works Ltd.) (see Note 5). 10. Discriminator (Texas Instruments Japan Ltd). 11. Amplifier (Analog Devices KK). 12. Pulse sharpener (Toshiba Electronic Devices and Storage Co.). 13. Counter (Texas Instruments Japan Ltd). 14. Microcomputer (Microchip Technology Japan K.K). 15. Single-channel photomultipliers (PMT) (Hamamatsu Photonics, Japan) (see Note 6).

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16. Mechanical fan for air cooling the electric board. 17. USB interface cable. 2.3

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1. IVIS imaging system (PerkinElmer). 2. One-channel (1-ch) microsliding luminometer. 3. 5% (v/v) CO2 incubator. 4. Centrifuge.

2.4

Software

1. Living Image Ver. 4.5 (PerkinElmer). 2. Controlling software (IM1 V00-00C, Nishihara Co., Kashiwa, Japan). 3. Prism Ver. 8.1 (GraphPad). 4. Excel (Microsoft).

3

Methods

3.1 Assembly of Mechanical, Optical, and Electric Parts for 1-ch Microsliding Luminometer (See Fig. 1)

1. Install a single-channel photomultiplier tube (PMT) inside the black plastic case shaped like the picture (see Fig. 1). 2. Mount a shutter unit on the light-sensing window of the PMT, and further connect it to the outlet terminal of the fiber optic taper. 3. Connect the PMT to the other photon counter assembly units (amplifier, discriminator, pulse shaper, and counter). 4. Attach the mechanical fan to the black plastic case for cooling down the electric circuit. 5. Connect the USB slot of the microcomputer to a USB port of a PC, and check the power supply. 6. For the system control, develop a software of the following specification: (a) Functions of the power supply to PMT and PMT signal acquisition. (b) Function to turn on and off PMT (c) Signal acquisition time range: 0.5–600 s (d) Signal acquisition interval: 0.5–100 s (e) Signal unit: photon count in relative luminescence unit (RLU) (f) File format for data saving: binary or CSV format (g) Operation system: Microsoft Windows 7. Finally, determine the fidelity of the 1-ch microsliding luminometer using the specific software in PC.

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Fig. 2 (a) Experimental setup for the measurement of the optical intensities of PLAP samples. The 1-ch microsliding luminometer is deployed inside the chamber of the IVIS imaging system. The same optical intensities were determined with both of the 1-ch microsliding luminometer and the IVIS system. Inset a illustrates the sample preparation. (b) The correlation of the measurement values from the 1-ch microsliding luminometer and the IVIS system. As the 1-ch microsliding luminometer provides low- and high-sensitivity modes, the correlations are graphed differently

3.2 Confirmation of the Fidelity of the 1-ch Microsliding Luminometer Through Determining PLAP Activities (See Fig. 2)

1. Before measurements, deploy the 1-ch microsliding luminometer inside the chamber of the IVIS system, and then connect the microsliding luminometer to the outer controlling PC via a USB cable (see Fig. 2a). 2. Warm up and initialize the IVIS system and the 1-ch microsliding luminometer 5 min before the start of experiments. 3. Dilute the PLAP standard stock solution (100 μg/mL) from the Great EscAPe SEAP Chemiluminescence Kit 2.0 with the provided dilution buffer to three final concentrations of 20, 5, and 1 μg/mL. 4. Transfer 10 μL of the diluted PLAP solutions to PCR tubes. 5. Inject-10 μL of the provided substrate solution to a PCR tube using a multichannel micropipette, and immediately insert the tube into the mirror cap (see Note 7). 6. Mount the mirror cap on the sample stage of the 1-ch microsliding luminometer inside the chamber of the IVIS system.

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7. Simultaneously measure the BL intensities using both controlling software of the 1-ch microsliding luminometer and the IVIS system. 8. Repeat the measurement for different concentrations of PLAP solutions. 9. Analyze the statistical parameters correlating the data sets from the 1-ch microsliding luminometer and the IVIS system using Prism Ver. 8.1 software. 1. Warm up and initialize the 1-ch microsliding luminometer 5 mins before the start of experiments.

3.3 Determination of Rapamycin-Activated Optical Intensities of FRB-A23-FKBP Using the 1-ch Microsliding Luminometer (See Fig. 3)

2. Dissolve the stock solution of FRB-A23-FKBP to a concentration of 0.5 μg/mL and 500 μL in total using PBS buffer, and transfer 200 μL each of the diluted sample to two microtubes. 3. Add 1 μL of the rapamycin solution (final concentration: 1 μM106 M) or the vehicle (0.1% (v/v) ethanol) to the microtubes, and incubate them in room temperature (RT) for 1 min (see Note 8). 4. Label them as rapamycin plus or minus solutions. 5. Transfer 40 μL of the rapamycin plus or minus solutions to each tube of an 8-lane PCR tube (n ¼ 4). 6. Separately prepare a CTZ assay solution by dissolving the 100
CTZ stock solution with the assay solution provided from the Renilla Luciferase assay kit. 7. Simultaneously inject 20 μL of the CTZ assay solution into the prepared 8-lane PCR tube (see step 5 in this section) using an 8-channel micropipette.

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8. Immediately insert the 8-lane PCR tube into the mirror cap, and mount on the top slide of the sample stage of the 1-ch microsliding luminometer. 9. Determine the optical intensities of each tube one by one with conveying the top and bottom slides using the adjuster bars (see Note 9). One may determine the optical intensities with lowor high-sensitivity modes of the 1-ch microsliding luminometer (see Note 10). 10. Analyze the data set using Excel. 1. Grow COS-7 cells in a 6-well microplate in culture medium to reach 70% confluency.

3.4 Determination of Estrogen AntagonistActivated Optical Intensities of a Molecular Strain Probe Using the 1-ch Microsliding Luminometer (See Fig. 4)

2. Transiently transfect the cells with a mammalian expression vector, pcDNA3.1(+), encoding ERLBD-RLuc8-SH2, using a Lipofectamine transfection reagent, TransIT-LT1, according to the manufacturer’s instruction. 3. Incubate the cells overnight in the 6-well microplate in a humidified 5% (v/v) CO2 incubator at 37  C. 4. Wash the transfected cells with PBS once and trypsinize the cells by adding 500 μL of Trypsin/EDTA solution per well. Incubate the cells in a humidified 5% (v/v) CO2 incubator at 37  C, and suspend with a micropipette. 5. Transfer the cell suspension to a microtube and centrifuge at 1,000
g for 5 min. 6. Remove the supernatant, and resuspend the cell pellet with the culture medium to be approximately 106 cells/mL through gently pipetting up and down.

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7. Seed 70 μL of the cell suspension into each channel of a 6-channel microslide (ibidi), and incubate overnight in a humidified 5% (v/v) CO2 incubator at 37  C (see Note 11). 8. Replace the culture media with fresh culture media-carrying vehicle (0.1% DMSO) for the left three channels whereas the culture media in the right three channels with fresh culture media carrying 1 μM of OHT. 9. Incubate the microslide in a humidified 5% (v/v) CO2 incubator for 30 min at 37  C (see Note 12). 10. Completely remove all the culture media from the 6-channel microslide. Inject 60 μL of the lysis buffer per each channel and incubate 15 min in RT. 11. Simultaneously add 60 μL of the CTZ solution per channel to the microslide using an 8-channel micropipette. 12. Immediately transfer the microslide into the sample stage of the 1-ch microsliding luminometer, and cover the microslide with the mirror cap. 13. Finally, determine the BL intensity of each channel one by one with conveying the top and bottom slides using the adjuster bars. 14. (Optional) Transfer the prepared microslide into the chamber of the LAS-4000 or IVIS imaging system, and determine the BL images as a reference (see Note 13).

4

Notes 1. The authors recommend that the purified FRB-A23-FKBP in a high concentration (1 mg/mL) should be stocked in separate tubes and stored at 80  C deep freezer for a long-time preservation. 2. The mirror cap is beneficial for both grip of the sample tubes (or microslide) and focusing of weak light. Fixation of the microslide on the deck allows a precise determination of the samples in the system. The internal surface of the mirror cap is grinded to reflect BL to the bottom slit (7
24 mm). The BL signal passes the optical filter beneath the slit and finally reaches the PMT detector at the bottom. The optical filter beneath the bottom slit is designed to be replaceable according to experimental preference. 3. The mirror cap comprises honeycomb-like internal barriers, which efficiently blockade interference light from the other channels. BL emitted from each channel is reflected by the interior surface of the mirror cap and reach to the inlet of the optical fiber.

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4. The emission filters should be optimally adapted to the BL reporters. 5. The optical shutter is attached at the light-sensing window of the PMT to protect it from strong current. 6. The PMT works by USB3.0 bus power and generates photon counts (relative luminescence unit, RLU) in response to light. 7. Simultaneous injection of the substrate with a multichannel pipette is an important tip to determine the BL intensities with high precision with minimal intersample variations. The decay of BL intensities can be rapid according to light samples. The decay errors are minimized by such a simultaneous injection of the substrate. 8. The incubation time greatly depends on the materials. In the case that live animal cells are incubated with rapamycin, we recommend incubating them longer than 5 h. It is because rapamycin is poor in the plasma membrane permeability of live cells. 9. The present 1-ch microsliding luminometer allows to rapidly determine BL intensities from eight kinds of samples without the replacement of the sample tubes, in contrast to conventional ones. 10. The reported 1-ch microsliding luminometer provides lowand high-sensitivity modes, which are different in the basal voltages applying to the PMT. The authors see that the high sensitivity mode provides a better linearity to light samples. 11. The medium evaporation per channel is very rapid. The authors recommend replacing the medium once in every 12 h. This unique microslide helps minimize the autoluminescence derived from thick medium levels by the virtue of the minimal dead volume of the culture medium per channel. 12. The membrane permeability of steroid and its analogues are generally good. According to the authors’ precedent studies, more than 20 min of incubation time is sufficient to reach the plateau in the optical intensities. 13. The limitation of the LAS-4000 imaging system (FujiFilm) is that it does not provide information on absolute photon counts in contrast to IVIS imaging systems. The authors recommend converting the RLU values of LAS-4000 imaging system into the corresponding absolute photon counts (p/s/ mm2) using an LED standard light device (Hamamatsu). Once we determine the conversion ratio, we can conveniently convert the RLU values to the corresponding absolute photon counts. In the case of the authors, 1 RLU/s in Tray Level 1 area of LAS-4000 is converted to 3486.8 photons/s/mm2.

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Acknowledgments This work was supported in part by JSPS KAKENHI Grants: Numbers 15KK0029, 17H01215, 20 K21851, and 21H04948. We thank Dr. Simon Miller, Dr. Toshiya Senda, and Dr. Mikio Tanabe (Structural Biology Research Center, Institute of Materials Structure Science, High-Energy Accelerator Research Organization (KEK)) for preparation of purified FRB-A23-FKBP sample. This work was also partly supported by Basis for Supporting Innovative Drug Discovery and Life Science Research (BINDS) from the Japan Agency for Medical Research and Development (AMED) under grant numbers JP21am0101071. References 1. Mirzoyan R, Goebel F, Hose J, Hsu CC, Ninkovic J, Paneque D, Rudert A, Teshima M (2007) Enhanced quantum efficiency bialkali photo multiplier tubes. Nucl Instrum Meth A 572(1):449–453. https://doi.org/10.1016/j. nima.2006.11.047 2. Conti M, Eriksson L (2016) Physics of pure and non-pure positron emitters for PET: a review and a discussion. EJNMMI Phys 3(1):8. https://doi.org/10.1186/s40658-016-0144-5 3. Partovi S, Kohan A, Rubbert C, VercherConejero JL, Gaeta C, Yuh R, Zipp L, Herrmann KA, Robbin MR, Lee Z, Muzic RF Jr,

Faulhaber P, Ros PR (2014) Clinical oncologic applications of PET/MRI: a new horizon. Am J Nucl Med Mol Imaging 4(2):202–212 4. Andrade RAN, Andrade SIE, Martins VL, Moreira PNT, Costa DJE, Lyra WS, Araujo MCU (2013) A flow-batch luminometer. Microchem J 108:151–155 5. Kim SB, Sato M, Tao H (2009) Molecular tension-indexed bioluminescent probe for determining protein-protein interactions. Bioconjugate Chem 20(12):2324–2330. https://doi. org/10.1021/Bc900330w

Chapter 29 Compact Eight-Channel Light-Sensing System for Bioassays Sung-Bae Kim, Sharon Seiko Hori, Negar Sadeghipour, Uday Kumar Sukumar, and Ramasamy Paulmurugan Abstract The present protocol introduces a new instrumental setup as a luminometer to simultaneously measure eight light samples with high sensitivity. The system consists of 8-channel photomultiplier tubes (8-PMTs) with different sensitivities to light. Therefore, it is critical to normalize the sensitivities of PMTs to light samples and integrate them as a system. We first introduce how to normalize the diverse light sensitivity among the PMTs using placental alkaline phosphatase (PLAP) as a model chemiluminescence light source. The normalized BBI system shows a statistically strong linear correlation graph to photon counts. The biomedical utility of this system is exemplified by (i) determining the alkaline phosphatase (AP) activities in mouse plasma samples as a cancer biomarker and (ii) diagnosing metastatic tissues during cancer progression using bioluminescent reporter. Key words Eight-channel, Luminescence, Chemiluminescence, Photomultiplier tubes (PMTs), Onsite analysis, Black Box I (BBI)

1

Introduction As photomultiplier tube (PMT) is a powerful device to measure weak light signals [1], it has been utilized in a wide range of instrumentations such as luminometers, positron emission tomography (PET), gamma cameras, and others [2–4]. Many biomedical imaging modalities have degrees of health risk when using radioisotopes (e.g., PET, SPECT) or strong magnetic fields (e.g., MRI, MRS) [5]. In addition, most of these instruments are large, expensive, and not affordable for most in vitro medical biopsy applications, including cancer biopsies, and preclinical small animal studies. The unique advantages of PMTs in optical instrumentations over other modalities include low cost, high sensitivity to weak light, portability, and fast sample throughput.

Sung-Bae Kim (ed.), Bioluminescence: Methods and Protocols, Volume 2, Methods in Molecular Biology, vol. 2525, https://doi.org/10.1007/978-1-0716-2473-9_29, © The Author(s), under exclusive license to Springer Science+Business Media, LLC, part of Springer Nature 2022

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Fig. 1 The schematic illustration of the overall electronics of the Black Box I (BBI) for simultaneously determining multiple light samples. The mirror cap is designed to cover an 8-strip PCR tube and mirrorfocus the light onto the sample stage. The photons from each sample are reflected by the mirror cap and collected to the light-conduction window of the sample stage. Inset a shows the images of mirror caps that are designed to accommodate 8-lane PCR tubes or 6-channel microslides (part number 1–3). Inset b shows the pictures of a plastic light-conduction path (part number 4), PMTs (part number 5), and a circuit board for amplifier and counter (part number 6). After all assembly, the BBI appears as shown (part number 7). (Reproduced from Kim et al. with permission from RCS, Ref. [8])

The present protocol introduces a new 8-channel light-sensing platform system for determining many chemiluminescence (CL) samples with low intensity, termed Black Box I (BBI), which consists of (i) an 8-channel sample stage with a mirror cap, (ii) a light-conduction path and shutter, (iii) a photon counter assembly, (iv) a power supply, and (v) a controlling software with a personal computer (PC). The 8-channel sample stage is designed to be covered with a mirror cap which is designed with slantingly bored holes at the side (see Fig. 1), where the pitches between the eight channels are designed to exactly fit to the ones of multichannel pipettes and the light detection window on the sample stage. The system is controlled with a specific software (IM8 V00-00C) in a PC via a Universal Serial Bus (USB) interface cable. In this protocol, we introduce a methodology that is useful for normalizing the eight PMTs in the system using placental alkaline phosphatase (PLAP) as a control light source, where the light intensities of PLAP are simultaneously determined with both BBI and an IVIS imaging system. We show that the two datasets from BBI and the IVIS imaging system show a very strong correlation in logarithmic scales, that is, log10 [RLU/s of BBI] ¼ m  log10 [p/s of IVIS system] + b, where the constants m and b represent the unique photon sensitivity of each PMT (see Fig. 2).

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Fig. 2 Linear correlation between the datasets from the BBI systems and from other conventional systems. (a) Experimental setup for the determination of the correlation between the systems. BBI system is deployed inside the chamber of the IVIS imaging system for simultaneous determination of BL intensities from samples. Inset a shows the optical images of 8-lane PCR tubes with three different sample concentrations. (b) Normalized light sensitivities of the BBI to 168 standard placental alkaline phosphatase (PLAP) samples. The responses of each PMT of the BBI system to the samples are plotted rigorously. (c) Correlation of the BL intensities to SEAP samples by BBI and the other instrumentations. The datasets from BBI system and singlechannel luminometer (Turner Biosystems 20/20n) are plotted on the y- and x-axis, respectively. The number by each data point indicates the photon counts (p/s) of each plasma sample, as determined by the IVIS system (n ¼ 3 replicates per sample). (Reproduced from Kim et al. with permission from RCS, Ref. [8])

The biomedical utility of the BBI system is exemplified through simultaneous determination of (i) eight secreted alkaline phosphatase (SEAP) samples from mouse plasma samples as a cancer biomarker and (ii) imaging the tissue metastases in mice (see Fig. 3). The present protocol is an important addition to biomedical instrumentation platforms that are of low cost and high sensitivity to very fast and weak light, have high sample throughput, and are compact and suitable for on-site analysis.

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Fig. 3 Metastasis analysis of mouse tissue xenografts. (a) Mouse images bearing tumor xenografts. (b) Simultaneous determination of metastasis sites in mouse organ tissues. The organ metastases were simultaneously determined with the BBI and IVIS systems. (c) Correlation of the BL intensities obtained by the BBI and IVIS systems. The photon counts were normalized by integration time (s). Inset a illustrates the heterogeneous metastasis features of the xenografts in organs. Abbreviations: M1, M2, and M3 indicate Mouse number 1, 2, and 3; r2 means the correlation coefficient. (Reproduced from Kim et al. with permission from RCS, Ref. [8])

2

Materials

2.1 Chemicals and Labware

1. Phosphate-buffered saline (PBS). 2. Great EscAPe SEAP CL assay Kit 2.0 (Takara Bio, Mountain View, CA), which includes 100 μg/mL placental alkaline phosphatase (PLAP) solution and its specific substrate solution (1). 3. Eight-lane PCR tube (200 μL volume). 4. Eight-channel micropipette. 5. BALB/c female nude mice (nu/nu) of 4–6 weeks old. 6. D-Luciferin solution, 3 mg of which is dissolved in 100 μL PBS. 7. 4 T1 triple negative breast cancer cells engineered to stably expressing firefly luciferase (FLuc) reporter gene developed from our previous study [6]. 8. Eight-well microstrip (350 μL volume per each well).

2.2

Electronic Parts

1. Light conduction path (TOKYO BOX Co. Ltd.). 2. Shutter (Fujita Electric Works Ltd.). 3. Discriminator (Texas Instruments Japan Ltd.).

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4. Amplifier (Analog Devices KK). 5. Pulse sharpener (Toshiba Electronic Devices and Storage Co.). 6. Counter (Texas Instruments Japan Ltd.). 7. Microcomputer (Microchip Technology Japan KK). 8. Photomultiplier Japan). 2.3

Instrumentation

1. IVIS Lumina II optical imaging system (simplified to IVIS imaging system hereafter, PerkinElmer). 2. Single-channel Sunnyvale, USA).

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tubes (PMTs) (Hamamatsu Photonics,

luminometer

(Turner

20/20,

1. Custom-made BBI controlling software (e.g., the specific software (IM8 V00-00C)). 2. Prism Ver. 8.1 (GraphPad) software.

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Methods

3.1 Assembly of Optical and Electric Parts for Constructing Black Box I (BBI)

One may design and make in-house all the metal parts including the sample stage and aluminum mirror cap according to their own preference. 1. Carve 9 mm-pitch channels on a rectangular aluminum piece for covering the samples in an 8-strip PCR tube and lightfocusing windows on the sample stage (named the mirror cap). 2. Separately prepare a light-proof black plastic case with a lid for containing the electric parts and light paths. 3. Assemble and connect all the following electric parts according to the schematic diagram of the electronics (see Fig. 1): (i) light conduction paths, (ii) shutters, (iii) discriminators, (iv) amplifiers, (v) pulse sharpeners, (vi) counters, (vii) microcomputer, and (viii) PMTs. 4. Connect the microcomputer to a PC via a USB interface cable. 5. Develop and test the controlling software of the system (e.g., IM8 V00-00C for our case) that may be custom developed by outsourcing. 6. Confirm the systematic fidelity with the specific software in the PC.

3.2 Normalization of the Light Sensitivities of the Integrated Eight PMT Channels in BBI

We compared the light sensitivities of the BBI system and a conventional IVIS imaging system to the same light samples (see Fig. 2b) by simultaneous imaging. 1. Before measurements, place the BBI system inside the chamber of the IVIS imaging system, and connect it to the outer controlling PC via a USB cable as shown in Fig. 2a.

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2. Dilute the 100 μg/mL PLAP solution from the Great EscAPe SEAP CL Kit 2.0 in the kit-provided dilution buffer to three final concentrations of 20, 5, and 1 μg/mL. 3. Transfer 10 μL of the diluted PLAP solutions to each tube in an 8-lane PCR strip (200 μL volume), simultaneously inject 10 μL of the provided 1 substrate solution from the kit into the strip using an 8-channel micropipette, and immediately mount the strip on the sample stage of the BBI system inside the chamber of the IVIS imaging system. 4. Simultaneously determine the corresponding CL intensities using both BBI and IVIS imaging systems at the same time with the same integration time of 1 min. 5. As the intensities decay by time, keep the measurement in the same experimental setup until an enough number of data points are obtained, for example, 168 data points in total (3 PLAP concentrations  8 channels  7 replicates). 6. Deploy each corresponding data point from the two datasets of BBI and IVIS systems in the Y- and X-axis of a graph, respectively, to plot a correlation curve. 7. Analyze the statistical parameters correlating the two datasets using Prism Ver. 8.1 (see Notes 1 and 2). 3.3 Determination of SEAP Activities in Mouse Plasma Using BBI

We exemplify how to determine SEAP levels in mouse plasma using three instruments: a single-channel luminometer, BBI, and IVIS imaging systems. 1. Inject mice with two different concentrations of SEAP (high, 36 μg/kg, or low, 200 ng/kg) intravenously (i.v.), and collect the plasma samples by submandibular bleeding at various time points starting 1 min postinjection for up to 10 days. 2. Store the plasma samples at 80  C until assayed. 3. Prior to measurement, thaw on ice and vortex the plasma samples (n ¼ 8). 4. Mix 20 μL of each plasma sample with 80 μL of the dilution buffer from the kit, and heat-inactivate the mixture by incubating at 65  C for 30 min to inactivate endogenous AP of the plasma. 5. Cool down the samples on ice for 2–3 min and equilibrate to room temperature (RT). 6. Place the main body of the BBI system in the dark chamber of the IVIS imaging system as illustrated in Fig.2, and connect it to the outer controlling PC via a USB interface cable. 7. Transfer 10 μL of the samples to the 8-lane PCR tubes, where each tube contains a different concentration of plasma sample in 10 μL volume.

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8. Simultaneously inject 10 μL of the substrate solution into the 8-lane PCR tubes using an 8-channel micropipette, and immediately mount the PCR tube on the sample stage of the BBI system. 9. Simultaneously determine corresponding BL intensities over time (0 and 40 min) using both BBI and IVIS imaging systems (datasets 1 and 2) (see Notes 3 and 4). 10. Separately, determine the SEAP activities of all the plasma samples, that is prepared according to steps 1–7, at 0 and 40 min after substrate addition with a conventional singlechannel luminometer (Turner Biosystems 20/20n) (dataset 3). 11. As we need to know the linear correlation between luminometer readings (from step 10) and SEAP concentrations, and to convert signal intensities (relative light units, RLU) to concentrations (ng/mL), conduct the experimental steps 12–15. 12. Firstly, serially dilute the stock solution from the kit using a SEAP positive control, and transfer 10 μL of sample from each dilution to a fresh 1.5-mL microtube (see Note 5). 13. Secondly, transfer 10 μL of each diluted and heat-inactivated serum sample (of steps 3–4) to a fresh 1.5-mL microtube. 14. Add 10 μL of the SEAP substrate solution to the 1.5-mL microtube prepared at step 12 or step 13, and briefly vortex them. 15. Immediately measure the light intensity with a 10 s integration time in a single-channel luminometer at two time points: 0 and 36 min (dataset 3). 16. Draw a dose-response curve based on the correlation between the RLU values of the luminometer and SEAP concentrations. 17. On the basis of the dose-response curve, calculate the SEAP concentrations from the plasma samples. 3.4 Diagnosis of Metastases in MiceBearing Xenografts of Triple Negative Syngeneic Breast Cancer Using the BBI System

For metastasis diagnosis, we exemplify four mice carrying 4 T1 triple negative breast cancer cells in the flanks of nude mice (see Fig. 3b, c). 1. Seed 4 T1 cells stably expressing firefly luciferase (FLuc) reporter gene to 10 cm culture dishes, and incubate them in a CO2 incubator until it reaches 90% confluence. 2. Trypsinize and harvest the cells, and subcutaneously (s.c.) implant two million 4 T1 cells on either flank of the hind limbs of living nude mice (nu/nu) on day 0. 3. Keep the mice for more than 2 weeks until the tumors metastasized, that is, when the primary tumor size had reached 500–750 mm3.

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4. Before sacrificing the mice, anesthetize all the mice, inject 100 μL of the substrate D-Luciferin solution per each mouse, and determine the BL images using the IVIS imaging system. 5. Then sacrifice the three mice, and extract their organs (brain, kidney, thymus, heart, spleen, liver, and lung tissues). 6. Immerse the organ tissues in PBS for more than 1 h in cold room, and then measure their weights for reference (see Note 6). 7. Transfer the tissues to an 8-well microstrip (see Note 7). 8. Simultaneously inject 100 μL of the D-luciferin substrate solution (30 mg/mL) to the 8-well microstrip using an 8-channel micropipette. 9. Immediately mount the 8-well microstrip on the sample stage of the BBI inside IVIS system, and determine the corresponding BL images using both BBI and IVIS imaging systems (see Notes 8, 9, and 10).

4

Notes 1. The datasets from the BBI and IVIS systems demonstrate excellent linear correlation (r2 ¼ 0.998). The excellent linear correlation suggests that the RLU counts per second of the BBI system can be converted into more common units of light flux (photons/s) on the basis of the linear correlation function established for each channel. 2. The analysis shows that the correlation between the two datasets can be expressed as log10 [RLU/s of BBI] ¼ m  log10 [photon/s of IVIS] + b, where the constants m and b represent the fitted slope (m) and y-intercept (b) parameters of each PMT. 3. One can compare the sensorial properties of the instruments, BBI and IVIS imaging systems, simply starting their measurements at the same time scale and measurement intervals. 4. The optical intensities of SEAP gradually grow by time and reach the plateau at around 36 min after substrate injection and are very stable by 40 min. 5. As described before [7], no background SEAP activity is expected ([SEAP] ¼ 0) in the healthy mouse plasma. The linear range of the luminometer signal vs. SEAP concentration in plasma can be used to determine the linear correlation between luminometer readings and SEAP concentrations and to convert signal intensities (relative light units, RLU) to concentrations (ng/mL).

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6. An excess amount of blood in the tissues causes high background BL intensities. Immersing the tissues in PBS reduces the background BL intensities. 7. In the case of oversized organ tissues, the samples may be dissected in an appropriate size using a surgical knife to accommodate them into the wells of the microstrip. 8. The correlation coefficient using the BBI and IVIS imaging systems is r2 ¼ 0.70 for 23 tissue samples analyzed, which is much lower than those observed with SEAP plasma samples. This is possibly owing to the heterogeneity of the biopsy tissue samples, which show variable depth of metastasis within the organs. This may cause confounding variability owing to distinctive light attenuation levels and variability upon photon acquisition, as illustrated in Fig. 3c, Inset a. 9. Considering metastasis is a challenging clinical problem and the cause of most cancer-related deaths, this example supports the notion that the BBI system provides a technological advance, allowing sensitive and high-throughput analysis of organ tissue biopsies, especially for fast low-level light optical signals that have been even below the detection limits of conventional biomedical imaging systems available for small animal models currently. 10. This example shows that the BBI system is a solution to determine low-level light signals with high sensitivity, high sample throughput, low cost, and rapid on-site analysis. The simultaneous light-sensing modality of the BBI system enables many other applications including lab-on-chip and flow-batch analysis of nonradioactive optical readouts in biomedical samples.

Acknowledgments This work was supported in part by JSPS KAKENHI Grants: Numbers 15KK0029, 17H01215, 20 K21851, and 21H04948 (SBK). This work was also supported in part by the Department of Defense through the Breast Cancer Research Program under Award No. W81XWH-18-1-0342 (SH). References 1. Mirzoyan R, Goebel F, Hose J, Hsu CC, Ninkovic J, Paneque D, Rudert A, Teshima M (2007) Enhanced quantum efficiency bialkali photo multiplier tubes. Nucl Instrum Meth A 572(1):449–453. https://doi.org/10.1016/j. nima.2006.11.047 2. Conti M, Eriksson L (2016) Physics of pure and non-pure positron emitters for PET: a review

and a discussion. EJNMMI Phys 3(1):8. https://doi.org/10.1186/s40658-016-0144-5 3. Partovi S, Kohan A, Rubbert C, VercherConejero JL, Gaeta C, Yuh R, Zipp L, Herrmann KA, Robbin MR, Lee Z, Muzic RF Jr, Faulhaber P, Ros PR (2014) Clinical oncologic applications of PET/MRI: a new horizon. Am J Nucl Med Mol Imaging 4(2):202–212

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4. Andrade RAN, Andrade SIE, Martins VL, Moreira PNT, Costa DJE, Lyra WS, Araujo MCU (2013) A flow-batch luminometer. Microchem J 108:151–155 5. Massoud TF, Gambhir SS (2003) Molecular imaging in living subjects: seeing fundamental biological processes in a new light. Genes Dev 17(5):545–580 6. Bose RJC, Kumar SU, Zeng YT, Afjei R, Robinson E, Lau K, Bermudez A, Habte F, Pitteri SJ, Sinclair R, Willmann JK, Massoud TF, Gambhir SS, Paulmurugan R (2018) Tumor cell-derived extracellular vesicle-coated nanocarriers: an efficient theranostic platform for the

cancer-specific delivery of anti-miR-21 and imaging agents. ACS Nano 12(11): 10817–10832 7. Hori SS, Lutz AM, Paulmurugan R, Gambhir SS (2017) A model-based personalized cancer screening strategy for detecting early-stage tumors using blood-borne biomarkers. Cancer Res 77(10):2570–2584 8. Kim SB, Hori SS, Sadeghipour N, Sukumar UK, Fujii R, Massoud TF, Paulmurugan R (2020) Highly sensitive eight-channel light sensing system for biomedical applications. Photochem Photobiol Sci 19(4):524–529

Chapter 30 Characterization of Firefly Flashes at Various Temperatures in Different Wavelength Regions Anurup Gohain Barua and Angana Goswami Abstract Chemiluminescence reaction efficiently produces light, and that is where its scientific importance lies. For investigating the reaction in live fireflies, we introduce a few protocols that exert light emission at different temperatures and in different color sectors. From the changes in the peak position and the duration of flashes, the light can be characterized. As the firefly emits in green, yellow, and red color sectors, three colorfilters are used for getting emissions in these three regions. Emission spectra are recorded in a highresolution spectrometer, and flashes are obtained in an oscilloscope, after amplification in a photo multiplier tube, in the range of temperature 20–40  C. Key words Firefly, Optical filter, High-resolution spectrometer, Photomultiplier tube, Digital storage oscilloscope

1

Introduction The light of the firefly is produced from the catalyzed oxidation of the substrate molecule luciferin by the enzyme luciferase. This reaction could be summed up as follows. In the first step, the luciferase converts the firefly D-luciferin into the corresponding enzyme-bound luciferyl adenylate. In the next step, luciferase amino acid residues are recruited to promote addition of molecular oxygen to the luciferin. In presence of ATP and Mg2+, an unstable dioxetanone intermediate is produced, which decomposes to form an electronic excited-state oxyluciferin and carbon dioxide. While decaying to its ground state, oxyluciferin emits a photon in the visible part of the electromagnetic spectrum. Analysis of the light of the firefly under the influence of ethyl acetate reveals that a set of three pulses of duration of a couple of microseconds is a mirror image of another set of three similar pulses, prompting the conclusion that this light is the manifestation of an oscillating chemical reaction, like the Belousov-Zhabotinsky reaction [1].

Sung-Bae Kim (ed.), Bioluminescence: Methods and Protocols, Volume 2, Methods in Molecular Biology, vol. 2525, https://doi.org/10.1007/978-1-0716-2473-9_30, © The Author(s), under exclusive license to Springer Science+Business Media, LLC, part of Springer Nature 2022

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There are more than 2,000 species of fireflies in the world, especially in the tropics. It has been suggested that different species of fireflies emit in slightly different spectral regions due to slight differences in their enzyme structures [2]. The duration of a single flash has been found to vary from about 70 ms to 2 s at normal temperatures [3–8]. Emission spectra of the Indian species of firefly Luciola praeusta (L. praeusta) have been recorded on color films, and those have revealed that the emitted light consists of three color sectors: green, yellow, and red [9, 10]. In the recent past, numerous studies have been carried out on the emission properties of different chemical forms of the oxyluciferin under various laboratory conditions. In comparison, the number of studies carried out on firefly bioluminescence (BL) in vivo has been quite small. But the real light-emitting system is the one existing inside the live firefly. So it has become a necessity to carry out such experiments to understand in a greater detail how the emission characteristics of oxyluciferin are influenced by protein microenvironments of living organisms. In this chapter, we describe the method that is useful for the measurements of wavelength peaks and lifetimes at different temperatures. The results have been published recently [11]. Obtaining the necessary clearance from the institutional ethical committee is a prerequisite of starting the experiment.

2

Materials 1. L. praeusta fireflies: Specimens collected in the campus of Gauhati University (see Notes 1 and 2). 2. A piece of sponge with cotton and Sellotape for fixing the specimen (see Note 3). 3. A high-resolution spectrometer (Ocean Optics; HR4000) for recording the emission spectrum. 4. Photomultiplier tube (PMT) (Hamamatsu; H10722–20 with power supply C10709) for converting the optical signal to an electrical one. 5. Digital storage oscilloscope (DSO) (Tektronix; TDS 2022C) for recording the flashes. 6. Three optical filters: green (Edmund Optics; peak 560 nm, FWHM 43 nm), yellow (Edmund Optics; peak 584 nm, FWHM 10 nm), and red (Johnson V/4–5; peak 623 nm, FWHM 63 nm), which are for allowing light to pass in the three respective color sectors.

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Methods Carry out the experiments in a dark room, such as a typical spectroscopy research laboratory.

3.1 Steady-State Recording

1. Place the light-emitting organ of the firefly specimen in front of the end of the fiber of the high-resolution spectrometer (see Fig. 1). 2. Set the integration time at a particular value which produces spectra of suitable intensity, such as 0.4–1.0 s for the no-filter case of the experiment. 3. Place three color filters one by one in front of the lantern, keeping the distance between the light organ and a filter as close as possible so that light of usable intensity could pass through the filter. Position the fiber-end appropriately (see Note 4). 4. Do not set the integration time at a very high value as then the system noise would appear making the spectra noisy. Employ a trial-and-error-based measurement for obtaining reasonably “good-looking” spectra (see Note 5). Keep the integration time in the spectrometer for the green-passed, yellow-passed, and red-passed light for a species like L. praeusta at approximately 2 s. Set the integration time at an appropriate lower value for a species emitting light at a higher intensity, such as the Japanese species Luciola cruciata.

Fig. 1 Experimental arrangement for recording steady-state spectra. (a) The high-resolution spectrometer connected to a laptop computer. The free end of the optical fiber is kept very close to an optical filter, which, in turn, is placed close to the light organ of the firefly, not shown because of the small size. (b) A schematic diagram of the arrangement of the experimental components

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Fig. 2 Experimental arrangement for recording time-resolved spectra. Emissions from the firefly passing through the optical filter are amplified by the photomultiplier tube (PMT), and the waveforms are captured by the digital storage oscilloscope (DSO). In this photograph, with lights on, the distance between the firefly and the filter is shown in a little exaggerated manner for clarity. In the experiment, this distance is kept as small as possible, and the LM35 temperature sensor needs to be placed on top of the PMT for most of the time so that distances from the window of the ac to their locations are equal—thus making the temperatures equal 3.2 Time-Resolved Recording

1. Connect the DSO to the PMT (see Fig. 2). 2. Vary the control voltage applied to the PMT for having good intensity of the pulses. With no filter, this voltage variation is from 0.2 to 0.4 V in our experiment (see Fig. 3 for the control voltage of 0.261 V). 3. Carefully fix an optical color filter with an adhesive tape covering the PMT window so that only the light filtered through it reaches the PMT. 4. As the intensity of the light diminishes greatly after passing through the filter, for having flashes of acceptable appearance, increase the controlling voltage of the PMT to an appropriate value, such as to the range of 0.6–0.8 V in our experiment (see Note 6). 5. Vary the temperature in the location of the firefly to a low value such as 20  C with the help of a window air conditioner (see Note 7) and to a high value like 40  C with a heater. 6. Since temperatures are various at different distances from the AC or the heater, mark the positions at temperature intervals of 5  C. Notice the fluctuations in temperatures at those steps,

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Fig. 3 A snapshot from the video of the experiment. Two flashes from a specimen of the firefly are observed on the screen of the DSO. The value of the controlling voltage of the PMT appears in red color as 0.261 V. On the left-hand side, in between the top and bottom of the screen, the temperature which the sensor gives appears faintly in a greenish background in the multimeter as 19.8  C. The experiment is designed to be carried out in a dark lab room

which should be kept as small as possible, about 0.1 or  0.2  C in about 10 minutes’ time. 7. Use a thermo-sensor, such as IC LM35 (accuracy 0.1  C), and connect it to a digital multimeter. Place this IC adjacent to the location of the firefly. The reading in the multimeter gives the temperature in that locality. As the effect of temperature on the firefly flashes can be observed to be almost instantaneous, the changes appearing in the flash duration should accurately reflect this effect. 8. Use a curved reflector to focus the air from the window ac to a small region around the location of the firefly if low temperatures such as 10  C or lower than this by a couple of degree centigrade are to be realized [12]. 9. Save the data in an external drive in .csv file extensions, and analyze these in a scientific analysis software like Origin.

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Notes 1. Catch the flying males or lying females either with soft hands or with a mosquito net attached to a circular frame (of diameter about a foot and a half) joined at the end of a 12-m long stick. 2. Select the specimen flashing the brightest for the experiment. 3. Do not fix the firefly too tightly as in that case, it may die or may not emit flashes. Even it does, the flashes are unlikely to be uniform ones. 4. Keep the end of the fiber very close to, but not touching, the filter in the side opposite to the one of the firefly to ensure good intensity of the spectra. 5. Set the integration time of the spectrometer as per the intensity of the emitted light as is suggested already. Setting of the integration time at a low value—such as 400 ms or lower for emissions through the filters in the experiment with specimens of L. praeusta—makes the spectra very weak in intensity. 6. Set the value of the control voltage of the PMT in a trial-anderror manner (see the flashes in Fig. 3 in the no-filter case). The flash-intensity obviously becomes considerably weaker after passing through the filters. It also decreases at high temperatures such as 40  C. Accordingly, increase the control voltage for obtaining flashes of good intensity. 7. Choose a window-type air conditioner over the split AC (see Fig. 2) as different distances from the window can then be marked for different temperatures. A temperature-controlled closed container may be more robust, but for an experiment of this type, it is not appropriate as the firefly is likely to feel more uncomfortable in that surrounding.

Acknowledgments Angana Goswami acknowledges the support of Department of Science and Technology, Government of India, under Women Scientist Scheme (WOS-A), vide no. SR/WOS-A/PM-25/2018. References 1. Gohain Barua A, Rajbongshi S (2010) The light of the firefly under the influence of ethyl acetate. J Biosci 35:183–186 2. Seliger HH, Buck JB, Fastie WG, McElroy WD (1964) The spectral distribution of firefly light. J Gen Physiol 48:95–104

3. Branham MA, Greenfield MD (1996) Flashing males win mate success. Nature (London) 381: 745–746 4. Buck J, Case JF, Hansen FE (1963) Control of flashing in fireflies III. Peripheral excitation. Biol Bull 125:251–269

Firefly Flashes in Different Wavelength Regions 5. Barry JD, Heitman JM, Lane CR (1979) Timeresolved spectrometry of in vivo firefly bioluminescence emissions. J Appl Phys 50:7181– 7184 6. Saikia J, Changmai R, Baruah GD (2001) Bioluminescence of fireflies and evaluation of firefly pulses in light of oscillatory chemical reactions. Indian J Pure Appl Phys 39:825–828 7. Gohain Barua A, Hazarika S, Saikia NM, Baruah GD (2009) Bioluminescence emissions of the firefly Luciola praeusta Kiesenwetter 1874 (Coleoptera: Lampyridae: Luciolinae). J Biosci 34:287–292 8. Gohain Barua A, Iwasaka M, Miyashita Y, Kurita Y, Owada N (2012) Firefly flashing under strong static magnetic field. Photochem Photobiol Sci 11:345–350

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9. Dehingia N, Baruah D, Siam C, Gohain Barua A, Baruah GD (2010) Purkinje effect and bioluminescence of fireflies. Curr Sci 99: 1425–1427 10. Gohain Barua A, Sharma U, Phukan M, Hazarika S (2014) Sharp intense line in the bioluminescence emission of the firefly. J Biol Phys 40: 267–274 11. Goswami A, Gohain Barua A (2019) In vivo lifetimes of the three color-sectors in bioluminescence emissions of the firefly Luciola praeusta at different temperatures. Spectrosc Lett 52:253–260 12. Goswami A, Phukan P, Gohain Barua A (2019) Manifestation of peaks in the live firefly flash. J Fluoresc 29:505–513

Chapter 31 Bioluminescent Monitoring of Circadian Rhythms in Isolated Mesophyll Cells of Arabidopsis at Single-Cell Level Shunji Nakamura and Tokitaka Oyama Abstract A bioluminescent monitoring system is used to detect the circadian rhythms of individual plant cells. Transgenic Arabidopsis carrying the firefly luciferase (FLuc) gene driven by a circadian-regulated promoter is used as the material for protoplast isolation. The bioluminescence of these protoplasts in the culture medium is separately captured using a highly sensitive camera system. The time-series data of the bioluminescent imaging reveals the circadian rhythms of these isolated cells, enabling the native properties of the cellular circadian clocks to become elucidated. Key words Bioluminescence (BL), Circadian rhythm, Mesophyll protoplast, Arabidopsis thaliana, Firefly luciferase (FLuc), Single cell

1

Introduction Plants are sessile organisms that adapt to dramatic daily changes, such as day-night cycles. Plant circadian clocks, which generate intrinsic rhythms with a periodicity of approximately 24 h, enable organisms to anticipate environmental changes and coordinate multiple physiological processes [1]. A microarray analysis of rhythmic gene expression in Arabidopsis thaliana revealed that approximately 90% of transcripts oscillate under various light and temperature cycles, and approximately 30% of transcripts exhibit circadian oscillation [2]. Approximately 20 clock genes, which are responsible for generating the circadian rhythm, are known in Arabidopsis. These genes form transcriptional/translational feedback loops, and the expression of many of them represents circadian rhythms [3]. Circadian clock associated 1 (CCA1) is a typical clock gene showing circadian expression that peaks around dawn. Thus, the dynamics of clock gene expression represent the state of the

Sung-Bae Kim (ed.), Bioluminescence: Methods and Protocols, Volume 2, Methods in Molecular Biology, vol. 2525, https://doi.org/10.1007/978-1-0716-2473-9_31, © The Author(s), under exclusive license to Springer Science+Business Media, LLC, part of Springer Nature 2022

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circadian clock, such as its time. Therefore, monitoring clock gene expression is a powerful tool for studying plant circadian clocks. Firefly luciferase (FLuc) is a versatile bioluminescent (BL) reporter used to analyze gene expression. A BL monitoring system with a circadian promoter and FLuc fusion gene has greatly contributed to the progress in the field of chronobiology [4]. In 1992, a BL monitoring system for transgenic tobacco and Arabidopsis plants carrying LHCB1*1 (CAB2)::LUC was introduced to analyze circadian rhythms [5, 6]. Transgenic Arabidopsis with a BL circadian rhythm was used as a “wild type” for the genetic screening of clock mutants, such as short
/long-period ones; such screening led to the discovery of important clock genes, timing of cab expression (toc1), and zeitlupe (ztl) [7, 8]. A BL monitoring system with sufficient spatial resolution was also used to analyze the tissueorgan-specific behavior of circadian rhythms in plants [9– 14]. Through these analyses, the local cell-cell coupling of circadian rhythms was generally observed in the plant body. The BL monitoring system has also been used to analyze the circadian rhythms of cultured cells and protoplasts of Arabidopsis [15–18], revealing the cellular nature of the plant circadian system on the basis of the BL behavior of the cell population (ensemble behavior). However, it remains unclear how much the ensemble behavior reflects the native properties of the cellular circadian clock, which would be represented in the circadian behavior of individual cells without influence from other cells. To overcome this problem, we established a monitoring system for BL circadian rhythms of isolated cells at the single-cell level. On this basis, we built an automated imaging system composed of a cooled electron-multiplying charge-coupled device (EM-CCD) camera under a macro zoom microscope. Using this system, we successfully detected the circadian rhythms of individual protoplasts isolated from transgenic Arabidopsis carrying a circadian BL reporter [19]. This system enables us to monitor dozens of cellular BL spots in an experiment and to statistically approach the basic and intrinsic properties of cellular circadian rhythms [19]. This BL monitoring system is also useful for studying the inherent properties of various plant cellular behaviors.

2

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1. Transgenic Arabidopsis thaliana (Col-0) carrying a CCA1:: LUC reporter (see Note 1).

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Reagents

1. 0.5  Murashige and Skoog (MS) agar plate: A mixture of 0.5  Murashige and Skoog salts (0.5 MS), 0.8% (w/v) agar, 1% (w/v) sucrose, 0.2% (v/v) vitamin stock, and 2.35 mM 2-(N-morpholino)ethanesulfonic acid (MES) is prepared in

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Milli-Q water and sterilized by autoclaving. The pH is adjusted to 5.7. 2. Vitamin stock: 150 g of thiamine hydrochloride, 250 mg of nicotinic acid, 25 mg of pyridoxine hydrochloride, 100 mg of glycine, and 5 mg of Myo-inositol are added per 100 mL of the stock. 3. Bleach solution: 95 mL Milli-Q water, 5 mL commercial bleach, and 50μL Triton X-100. Stored at 4  C in the dark. 4. Enzyme buffer: 400 mM mannitol, 20 mM KCl, and 20 mM MES are prepared in Milli-Q water and sterilized by autoclaving. The pH is adjusted to 5.7. 5. W5 solution: 154 mM NaCl, 125 mM CaCl2, 5 mM KCl, and 1.5 mM MES are prepared in Milli-Q water and sterilized by autoclaving. The pH is adjusted to 5.7 and then stored at 4  C. 6. Cellulase, “Onozuka” R10 (Yakult). 7. Macerozyme, R10 (Yakult). 8. Agarose-LE. 9. 1 M CaCl2 sterilized by autoclaving. 10. BSA (bovine serum albumin: pH 5.2). 11. Filter-sterilized 0.1 M D-luciferin, potassium salt. Stored at
20  C. 12. Filter-sterilized fetal bovine serum (FBS). Stored at
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Labware

1. Petri dish (90Ф  20 mm). 2. Tissue culture dish, 35-mm diameter (Iwaki). 3. Plastic dish, 60-mm diameter (Iwaki). 4. Surgical tape. 5. 50-mL centrifuge tube. 6. 20-mL syringe 7. Syringe filter (cellulose acetate 0.20 μm, GVS) 8. Nylon mesh (70 μm) sterilized by autoclaving. 9. 1.5-mL microcentrifuge tube sterilized by autoclaving. 10. Sterilized razor double edge).

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1. EM-CCD camera Photonics).

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Fig. 1 Overview (a) and close-up (b) of the imaging system

3. Windows desktop PC. Install a CameraLink interface board (Hamamatsu Photonics) and RS232C serial interface board (I-O DATA). 4. Imaging software (HoKaWo, Hamamatsu Photonics). 5. Macro zoom microscope (MVX-10, Olympus Optical) 6. Microscope objective lens (MVPLAPO 2  C lens, Olympus Optical) 7. Short-pass filter to cut delayed autofluorescence from chloroplasts (SV630, Asahi Spectra). 8. Light-emitting diode (LED) device for plant illumination (RFB2-20SW, CCS Inc.). Connect to the HoKaWo-installed PC via an RS232C cable. 9. Optical fiber cable (FCB-W, CCS Inc.) to guide illumination from the LED device. 10. A custom-made lightproof box (size: depth of 540 mm, width of 565 mm, and height of 870 mm). 11. Incubator (KCLP-1000I, NK-system) (see Note 2). 2.5

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1. ImageJ Software.

Methods

3.1 Preparation of Arabidopsis Plants

1. Aliquot approximately microcentrifuge tube.

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3. After discarding the ethanol, add 1 mL bleach solution to the tube and vortex for 10 min. Wash the seeds several times (5–6 times) with sterilized Milli-Q water. 4. Keep the tube at 4  C for 2 days for stratification. 5. Resuspend the seeds in 0.1% (w/v) agarose solution. 6. Sow eight seeds onto a 0.5  MS agar plate using a pipette at equal spaces. 7. Place the agar plates in an incubator of a culture room and aseptically grow plants for 3–4 weeks (see Note 3). 3.2 Preparation of Enzyme Solution

1. Mix 200 mg cellulase (R10) and 54 mg macerozyme (R10) into the enzyme buffer (19.8 mL) (see Note 4). 2. Warm the solution at 55  C for 10 min in a water bath to completely dissolve the enzymes. 3. Cool down the mixture to room temperature (25  C), and add 200μL of 1 M CaCl2, 20 mg of BSA, and 20μL of 0.1 M D-luciferin to the mixture. 4. Sterilize the enzyme solution by filtering through a syringe filter device into a Petri dish.

3.3 Protoplast Isolation from Arabidopsis Leaves (See Fig. 2)

1. Harvest 50 healthy leaves from 3- to 4-week-old plants (see Fig. 2a, Note 5). Perform the following procedures on a clean bench (see Note 6). 2. Cut leaf strips (less than 1 mm) using a sterilized razor blade without crushing tissues at the cutting site (see Note 7). 3. Rapidly transfer leaf strips onto a Petri dish containing the enzyme solution (see Subheading. 3.2) and loosen them with a pair of tweezers.

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Fig. 2 Arabidopsis protoplast isolation. (a) A detached leaf of healthy 4-week-old Arabidopsis plant. (b) Isolated protoplasts. (c) A mesophyll protoplast

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4. Shake the dish reciprocally at 57 min
1 on a shaker for 3 h in dim light at 25  C (see Note 8). 5. Add 25 mL of W5 solution to a 50-mL centrifuge tube, to which a sterile handmade strainer (70-μm nylon mesh) is installed (see Note 9). 6. Add the enzyme solution to the strainer, and then remove residual tissues by filtration through a nylon mesh. 7. Harvest protoplasts by centrifugation at 500  g for 2 min at 22  C (see Note 10). 8. Resuspend the protoplast pellet in 45 mL of W5 solution, and harvest the protoplasts by centrifugation at 500  g for 3 min at 22  C. 9. Resuspend the protoplast pellet in 4 mL of W5 solution, of which 1 mL is aliquoted into sterile 1.5-mL microcentrifuge tubes. 10. Centrifuge the harvested protoplasts at 500  g for 3 min at 22  C, and resuspend in a 1 mL W5 solution supplemented with 5% (v/v) FBS and 0.1 mM D-luciferin (W5 + FBS/D-luciferin medium) (see Note 11). 11. Check the protoplasts under a microscope (see Note 12). 12. Add 10μL of protoplast suspension to a 35 mm culture dish filled with 4 mL W5 + FBS/D-luciferin medium and incubate protoplasts at 22  C. 3.4 Bioluminescent Imaging (BLI) (See Fig. 1 for the Imaging System)

In the following procedures, the EM-CCD camera and the LED device are operated using HoKaWo imaging software. 1. Cool the EM-CCD to
80  C. 2. Place the 35-mm culture dish with protoplasts into a 60-mm plastic dish to prevent medium evaporation. 3. Place the 60-mm plastic dish under the lens of the macro zoom microscope in the lightproof box (see Note 13). 4. Observe the protoplasts via live imaging in the bright field and adjust the focus (see Note 14). 5. Set an area in the dish with an appropriate density of protoplasts for long-term BL monitoring (see Note 15). 6. Capture a bright-field image of protoplasts (see Fig. 3a). 7. Turn off the LED device, and close the lightproof box door. 8. Wait 4 min for autofluorescence decay. 9. Set the EM gain to 1,200 to capture a BL image with an appropriate exposure time to confirm the focus. 10. Start a script for the operation of the EM-CCD camera for time-lapse imaging (see Note 16). Capturing two sequential images at a time is recommended to obtain reliable data.

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Fig. 3 BL circadian rhythms of individual mesophyll cells under constant dark conditions. (a) A bright-field image of protoplast culture and (b) its BL image. (c) Examples of BL changes in cells with circadian rhythm [sequential images (1 h intervals) of cell 1 and 2 in panel (b)]. (d) BL circadian rhythms of the two cells (3 h moving average)

3.5

BL Quantification

The following image analysis is conducted using ImageJ software. 1. Remove signals of cosmic ray spikes in each image (see Note 17). 2. Manually set a region of interest (ROI) for each BL spot in the stack of images (see Fig. 3b, c, Note 18). 3. Measure the signal intensity of each BL spot in individual images as the integrated density of the ROI. 4. Calculate the background signal of the pixels in each image as the median of the signal intensities within the ROI (10  10 pixels) that is set at an area showing no BL spots.

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Raw data

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Fig. 4 Comparison of BL circadian rhythms between different moving average periods. Time series of raw data of a luminous cell (top) and moving averages with the periods of 1, 3, 5, 7, and 9 h. Note that the second peak almost disappears when long moving average periods are applied

5. Quantify the BL intensity of each BL spot using the following formula: Number of photons ¼ (signal intensity - background intensity)/conversion factor/(analog gain  EM gain  conversion efficiency) (see Note 19). 6. Calculate the moving average for the time-series analysis (see Figs. 3d and 4) (see Note 20).

4

Notes 1. The CCA1::LUC transgenic Arabidopsis plant was first reported by Nakamichi et al. [20, 21].

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2. To prevent the effects of compressor vibration in the incubator, place a lightproof box on a shelf directly set on the floor of the room (see Fig. 1). 3. The growth conditions are 22  C under constant light conditions with fluorescent lamps (30–36μmol/m2/s). 4. The enzyme solution should be prepared immediately before use. 5. Digest 50 leaves in 20 mL of enzyme solution producing 8  106 protoplasts. 6. Aseptic techniques are required for the long-term monitoring of the BL circadian rhythms of the isolated cells. 7. The thinner the leaf strips, the more protoplasts collected. 8. In general, digesting leaf strips in the dark is recommended [22]. As circadian rhythms are affected by a dark period, leaf strips are digested in dim light to reduce perturbations caused by changes in light conditions. 9. To reduce potential damage to the protoplasts by the filtration process, the mesh needs to be immersed in the W5 solution beforehand. 10. Harvest protoplasts using either a swinging-bucket rotor or fixed-angle rotor. Herein, we exemplify a fixed-angle rotor to harvest the protoplasts. 11. Adding FBS to the W5 solution effectively prolongs the protoplast BL [15]. 12. Spherical mesophyll protoplasts contain many chloroplasts (see Figs. 2b, c). 13. We set the zoom magnification at 1.25. 14. Focus the microscope on the bottom of the dish, where most protoplasts settled. 15. An appropriate area in which protoplasts are scattered without contact with each other is preferable for detecting the BL of individual cells. If a high cell-density condition is required for the experiment, the addition of a larger number of nonluminous cells is recommended [19]. 16. For time-lapse imaging under constant dark conditions, we set the exposure time at 4 min with a 20 min interval. When BL images are captured during the light period, 4 min of darkness before exposure can be used for autofluorescence decay. Controlling both the camera and light device is required to capture an image during the light period [23]. 17. Simultaneously capture two sequential images to remove the cosmic ray spikes. The minimum value for each pixel between

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the two images is used to obtain the representative light intensity. 18. Check the following before proceeding to the next step: (1) overlap of BL of neighboring cells and (2) standstill of the cell in the ROI during monitoring. 19. Under the conditions, calculate the number of photons using the following formula: (signal intensity - background intensity)  5.8/(1  1200  0.9). 20. Moving averages are a common smoothing strategy used in the time-series analysis of circadian rhythms and noise reduction. However, a wide smoothing range may result in the loss of important information (see Fig. 4). References 1. Creux N, Harmer S (2019) Circadian rhythms in plants. Cold Spring Harb Perspec Biol 11: a034611 2. Michael TP, Mockler TC, Breton G et al (2008) Network discovery pipeline elucidates conserved time-of-day-specific cis-regulatory modules. PLoS Genet 4:e14 3. Nohales MA, Kay SA (2016) Molecular mechanisms at the core of the plant circadian oscillator. Nat Struct Mol Biol 23:1061–1069 4. Welsh DK, Imaizumi T, Kay SA (2005) Realtime reporting of circadian-regulated gene expression by luciferase imaging in plants and mammalian cells. Methods Enzymol 393:269– 288 5. Millar AJ, Short SR, Chua NH et al (1992) A novel circadian phenotype based on firefly luciferase expression in transgenic plants. Plant Cell 4:1075–1087 6. Millar AJ, Short SR, Hiratsuka K et al (1992) Firefly luciferase as a reporter of regulated gene expression in higher plants. Plant Mol Biol Rep 10:324–337 7. Millar AJ, Carre´ IA, Strayer CA et al (1995) Circadian clock mutants in Arabidopsis identified by luciferase imaging. Science 267:1161– 1163 8. Somers DE, Schultz TF, Milnamow M et al (2000) ZEITLUPE encodes a novel clockassociated PAS protein from Arabidopsis. Cell 101:319–329 9. Wenden B, Toner DLK, Hodge SK et al (2012) Spontaneous spatiotemporal waves of gene expression from biological clocks in the leaf. Proc Natl Acad Sci U S A 109:6757–6762 10. Fukuda H, Ukai K, Oyama T (2012) Selfarrangement of cellular circadian rhythms

through phase-resetting in plant roots. Phys Rev E 86:041917 11. Fukuda H, Nakamichi N, Hisatsune M et al (2007) Synchronization of plant circadian oscillators with a phase delay effect of the vein network. Phys Rev Lett 99:098102 12. Muranaka T, Oyama T (2016) Heterogeneity of cellular circadian clocks in intact plants and its correction under light-dark cycles. Sci Adv 2:e1600500 13. Greenwood M, Domijan M, Gould PD et al (2019) Coordinated circadian timing through the integration of local inputs in Arabidopsis thaliana. PLoS Biol 17:e3000407 14. Thain SC, Hall A, Millar AJ (2000) Functional independence of circadian clocks that regulate plant gene expression. Curr Biol 10:951–956 15. Kim J, Somers DE (2010) Rapid assessment of gene function in the circadian clock using artificial microRNA in Arabidopsis mesophyll protoplasts. Plant Physiol 154:611–621 16. Takahashi N, Hirata Y, Aihara K et al (2015) A hierarchical multi-oscillator network orchestrates the Arabidopsis circadian system. Cell 163:148–159 17. Hansen LL, van Ooijen G (2016) Rapid analysis of circadian phenotypes in Arabidopsis protoplasts transfected with a luminescent clock reporter. J Vis Exp 115:e54586 18. Nakamura S, Oyama T (2018) Long-term monitoring of bioluminescence circadian rhythms of cells in a transgenic Arabidopsis mesophyll protoplast culture. Plant Biotech 35:291–295 19. Nakamura S, Oyama T (2022) Adaptive diversification in the cellular circadian behavior of Arabidopsis leaf- and root-derived cells. Plant Cell Physiol 63:421–432

Bioluminescent Monitoring of Isolated Plant Cells 20. Nakamichi N, Matsushika A, Yamashino T et al (2003) Cell autonomous circadian waves of the APRR1/TOC1 quintet in an established cell line of Arabidopsis thaliana. Plant Cell Physiol 44:360–365 21. Nakamichi N, Ito S, Oyama T et al (2004) Characterization of plant circadian rhythms by employing Arabidopsis cultured cells with bioluminescence reporters. Plant Cell Physiol 45: 57–67

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22. Yoo SD, Cho YH, Sheen J (2007) Arabidopsis mesophyll protoplasts: a versatile cell system for transient gene expression analysis. Nat Protoc 2:1565–1572 23. Muranaka T, Oyama T (2020) Application of single-cell bioluminescent imaging to monitor circadian rhythms of individual plant cells. In: Ripp S (ed) Bioluminescent imaging: methods and protocols. Springer US, New York, NY, pp 231–242Methods in molecular biology

Part V Software

Chapter 32 Exploring Phylogenetic Relationships and Divergence Times of Bioluminescent Species Using Genomic and Transcriptomic Data Danilo T. Amaral, Monique Romeiro-Brito, and Isabel A. S. Bonatelli Abstract Next-generation sequencing (NGS) has dominated the scene of genomics and evolutionary biology as a great amount of genomic data have been accumulated for a diverse set of species. At the same time, phylogenetic approaches and programs are in development to allow better use of such large-size datasets. Phylogenomics appears as a promising field to accommodate and explore all the information of NGS data in phylogenetic methods, being an important approach to investigate the evolution of bioluminescence in different organisms. To guarantee accurate results in phylogenomic studies, it is mandatory to correctly identify orthologous genes in phylogenetic reconstruction. Here, we show a simplified step-by-step framework to perform phylogenetic analysis along with divergence time estimation, beginning with an orthologous search. As empirical data, we exemplify transcriptome sequences of six species of the Elateroidea superfamily (Coleoptera). We introduce several bioinformatics tools for handling genomic data, especially those available in the software OrthoFinder, IQTREE, BEAST2, and TreePL. Key words Bioluminescence, Computational biology, Elateroidea, Phylogeny, Species tree, Calibration

1

Introduction The exponential growth observed for genomic data in the last three past decades was favored by advances in sequencing technologies. Great efforts have been made in terms of the volume of genetic data obtained, accuracy, and cost-effectiveness, giving rise to a set of next-generation sequencing (NGS) technologies [1–3]. The broad range of applications for NGS technologies addresses distinct biological questions from genome structure variation to species biogeographic history. This kind of data has shown statistical

Supplementary Information The online version contains supplementary material available at [https://doi.org/ 10.1007/978-1-0716-2473-9_32]. Sung-Bae Kim (ed.), Bioluminescence: Methods and Protocols, Volume 2, Methods in Molecular Biology, vol. 2525, https://doi.org/10.1007/978-1-0716-2473-9_32, © The Author(s), under exclusive license to Springer Science+Business Media, LLC, part of Springer Nature 2022

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power to solve phylogenetic relationships and estimate divergence time and trait evolution [4, 5]. NGS data applied in molecular phylogenetic methods have contributed to the emergence of a new field in evolutionary biology: phylogenomics [6, 7]. Phylogenomic methods use a large number (and different sources) of genomic data, such as genes, transcripts, amino acid sequences, noncoding regions, or SNPs (Single Nucleotide Polymorphism), to reconstruct a robust phylogeny [8]. The most recent progress in the reconstruction of the Tree of Life of several organisms, such as superfamily Elateroidea (Coleoptera), is related to the expansion from a few molecular markers, which failed to resolve relationships in both deep and shallow node levels, to several hundred or thousand loci. Thus, we can better explore the totality of the genomic data and recover even more phylogenetic informativeness of a clade [9]. However, the unprecedented amount of genomic and transcriptome data also includes noninformative sequences in multilevel taxonomic categories [10]. The identification of orthologous genes among different species is fundamental to reconstruct well-resolved phylogenies [11]. For this purpose, the bioinformatic approach may help to discern between orthologs and paralogs, fundamentally distinct types of homology [4, 5]. Currently, several programs are available to assess and identify orthologous/paralogous genes, such as OrthoMCL, JustOrthology, OrthoFinder, among others, displaying its informativeness and performing phylogenetic inference with thousands of genes [12]. This chapter describes how to conduct the orthology search and downstream analyses in computational biology to perform phylogenetic inferences using IQtree and ASTRAL and estimate the divergence time using BEAST and TreePL programs. For that, we show a real-life example of these methods by identifying a set of single-copy orthologs of six Elateroidea (Coleoptera) species, including distinct bioluminescent and non-bioluminescent families.

2 2.1

Materials Instrumentation

1. Recommended computer configuration: Unix Operating System (Linux or Mac OS), Intel Core i7/AMD Ryzen 3 or upper, 8 Gb RAM; 4 cores; 1 TB HDD (hard drive). 2. For the demonstration, we used the following machine: Mac OS Catalina laptop, 8Gb RAM, 4 cores, 2.9GHz Intel Core i7 Dual-Core processor, 1 TB HDD (for more details, see Note 1).

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Table 1 Useful software recommendation Software

Application

URL

Ref.

Anaconda

Data Science Platform

https://www.anaconda.com

[14]

LinuxBrew/ HomeBrew

Manager packages

https://docs.brew.sh/

--

TransDecoder

Nucleotide translation

https://github.com/TransDecoder/ TransDecoder

[15]

OrthoFinder

Ortholog assignment

https://github.com/davidemms/ OrthoFinder

[16]

MAFFT

Amino acid sequence alignment

https://mafft.cbrc.jp/alignment/ software/

[17]

ASTRAL

Summary species tree method

https://github.com/smirarab/ASTRAL [18]

IQTREE

Tree topologies reconstruction

https://github.com/iqtree/iqtree2

[19]

BEAST2

Divergence time estimation

https://www.beast2.org/

[20]

TRACER

Evaluation of BEAST output convergence

http://tree.bio.ed.ac.uk/software/ tracer/

[21]

TreePL

Divergence time estimation

https://github.com/blackrim/treePL

[22]

2.2 Software and Dependencies

In this section, we provide a list of recommended software for ortholog searches, phylogenetic and species tree reconstruction, and divergence time estimation (see Table 1). All programs and packages are free of charge for academic use. Notes about the main programs used in this guide: 1. OrthoFinder: An ortholog assignment software that is easy to install/configure, fast, accurate, and user-friendly. 2. MAFFT: An alignment tool for sequence alignment. 3. IQTREE: A software for reconstructing the phylogenies. 4. BEAST 2.6: A software for estimating the divergence time, which is dependent on a set of priors, such as substitution model and rate and tree model (not optimized to deal with large datasets). 5. TreePL: A software for estimating the divergence time; however, it deals better with a high amount of genomic data.

2.3

Input/Dataset

1. Genome data: Input file containing the gene prediction per species 2. Transcriptome data: Input file containing transcript assembly per species (see Note 2)

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The input dataset should contain only translated amino acid sequences. To this protocol, we exemplify the input file of transcriptome data for six Elateroidea species used for phylogenomics analyses by Amaral et al. [13].

3

Methods

3.1 Transcript and Gene Prediction Adjustment

1. (Optional) Shorten the fasta header to minimizing potential issues in downstream analyses: ##Use as an example the Pyrearinus fragilis transcriptome ##fasta file (Pyrearinus.fasta). You should replace the bold ##words with your abbreviation and input/output file names awk ’/^>/{print "> Pyre|" ++i; next}{print}’ < Pyrearinus. fasta > less

Pyrearinus_out.fasta

Pyrearinus_out.fasta

The output format would be (example to Pyrearinus fragilis transcriptome): >Pyre|1 NNNNNNNNNNNNNN >Pyre|2 NNNNNNNNNNNNNN . .

2. Create a directory and copy all transcript data to it: mkdir Transcript cd Transcript cp /PATH_to_the_fasta_files/*.fasta .

If you performed step 1, do not forget to copy only the files with the abbreviation headers. 3. Translate the nucleotide to amino acids sequence using the software TransDecoder: TransDecoder.LongOrfs -t Pyrearinus_out.fasta

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Repeat this step for the other fasta sequences or apply the command below: for i in *.fasta; do TransDecoder.LongOrfs -t “$i”; done (see Note 3)

The TransDecoder will create a new file containing the following outputs: longest_orfs.pep: amino acids sequences longest_orfs.cds: coding sequences longest_orfs.gff3: general feature format, containing gene range, mRNA position, etc. base_freqs.data: frequency of each nucleotide base We will use the output file longest_orfs.pep for the downstream analyses. 4. Create a new directory to copy the amino acids sequences. mkdir seqs cd seqs cp /PATH_to_the_TransDecoder_output_files/*.pep species_name. fasta (see Note 4)

3.2 Ortholog Search Using OrthoFinder (See Note 5)

1. With all amino acids data, separated per species, within the seqs folder, we can use the following command: ./orthofinder.py -t 4 -S diamond -T iqtree -f seqs

The OrthoFinder displays several options to be included during the run command. We show some of them (for more details, see ref. [16]): -t: number of parallel sequence search threads -S: a method for sequence search. The options are blast (and its variations) and diamond (and its variations). The diamond algorithm is faster than blast, with quite similar results (see ref. [23]). -T: a method for tree reconstruction. We chose iqtree based on time and computational resources. -op, -og, -oa, -os, or -ot: workflow stopping options (for more details, see Note 6).4

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2. Confirm that the OrthoFinder creates several folders within the Results directory, which contains information about the search and the sequences recovered. Each one recovers distinct information that can be used for different downstream analyses. In our case, the directories and files that will be used in the phylogenomic approaches are as follows: Comparative_Genomics_Statistics/Statistic_Overall.tsv: This file contains the main metrics used and summary information of the ortholog search. This information is crucial to determine the quality of the search. Single_Copy_Orthologue_Sequences: This folder contains all the orthogroups, in fasta format, that display single copy for each species, which is fundamental for phylogenomic reconstruction. The filename of the single copy may also be recovered in the file Orthogroups/Orthogroups_SingleCopyOrthologues.txt. 3.3 Species Tree and Concatenated Phylogenomic Reconstruction

This topic will explore two distinct approaches to reconstruct the possible phylogenetic relationship among the species: the species tree and supermatrix (concatenated genes). Both methods display distinct approaches and, consequently, distinct steps to the input data preparation.

3.3.1 Species Tree Using the Software ASTRAL

1. Create a new directory to run the species tree analysis using the software ASTRAL [18], which summarizes the results of each gene tree and recovers a species tree. cd .. mkdir sp_tree cd sp_tree cat /PATH_to_the_fasta_files/*.treefile > concat_sp_tree.tre #input file containing all gene tree java -jar astral.5.7.7.jar -i concat_sp_tree.tre

2. Check the tree relationship and the support values, which are discriminated using the quartet method (see Note 7). 3.3.2 Concatenated Tree (Supergene and Supertree)

1. Here, we will access the folder named Single_Copy_Orthologue_Sequences (within OrthoFinder results directory), which contains the single copy orthogroups, aligns the fasta files separately, and then concatenates the files. For that: cd .. mkdir single_copy_genes cd single_copy_genes cp /PATH_to_the_fasta_files/*.fas . for i in *.fas; do mafft --auto "$i" > "$i.fasta"; done ##The catfasta2phyml.pl concatenates the genes/transcript based

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##on the sequence headers. In this case, the sequence headers are ##similar to ">Pyre|200". To standardize all sequence header among ##fasta files we applied the following bash command sed -i ’s/|.*//’ *.fasta ##run catfasta2phyml.pl catfasta2phyml.pl -f -s -c *.fasta > supergene.fas cd .. mkdir concatenated_tree cd concatenated_tree cp ../single_copy_genes/supergene.fas . iqtree -s supergene.fas -m TEST -o Zop -bb 1000

2. Check the tree relationship and the bootstrap support values. 3.4 Divergence Time Estimation Using BEAST2 and TreePL 3.4.1 BEAUti

The BEAST2 (see Note 8) packages consist of some algorithms. The first algorithm employed here is the BEAUti, which is introduced above. 1. In the BEAST directory, double-click in BEAUti, which is a GUI interface that generates the .xml input file for the BEAST algorithm. In this interface, we also include the important parameters for the time divergence analysis. 2. Load the alignment: File > import alignment > /PATH_to_the_fasta_files/supergene.fas (amino acid; Supplementary file) 3. Choose site model: Click in site model (central top panel). Select “Gamma Site Model”; set the gamma category count as 4 and “LG” as the substitution model. 4. Clock model: Choose “Relaxed Clock Log Normal” 5. Prior (dating time) l

“Tree.t::supergene” choose “Birth Death Model.”

l

Click in “+ Add Prior,” and choose MRCA prior and click in “OK.”

l

In “Taxon Set Label,” add a name to the ingroup (such as “Elateridae”). Select all taxa except “Zop,” click in “>>,” and click in “OK.”

l

Set as “monophyletic” and select “Normal” (see Note 9).

l

Click in the left arrow in front of the term “Elateroidea. prior.”

l

In the “Mean,” add 130 (130 Ma), set “Sigma” as 4.5, and “offset” as 2 (95% HPD 123–141 Ma) (see Note 10).

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6. MCMC. Set “Chain Length” as 10,000,000. Keep the default values for “tracelog,” “screenlog,” and “treelog” (default value is 1000). Click File > Save as > “Elateroidea.xml” (do not forget to include the .xml extension) 3.4.2 BEAST

1. In the BEAST directory, double-click in BEAST. In “BEAST XML File:” choose “Elateroidea.xml,” keep the default settings, and click in “RUN.”

3.4.3 TRACER

1. Double-click in TRACER. File > Import Trace File > select “Elateroidea.log” Check the “ESS” values to evaluate the convergence (for more details, see ref. [21]).

3.4.4 TreeAnnotator

1. In the BEAST directory, double-click in TreeAnnotator: “Burnin percentage” as 25 (25%) “Target tree type” select “Maximum Clade Credibility Tree” “Node Heights” select “Mean Heights” “Input Tree File” > click in “Choose file ...” > select “Elateroidea.trees” “Output Tree File” > click in “Choose file ...” > add the name “Elateroidea.tree” Click in “RUN”

3.4.5 Figtree

1. Double-click in FigTree File > Open > select “Elateroidea.tree” Click in the left arrow in front of “Node Label” and in “Display” select “Node Ages.”

TreePL

The TreePL (see Note 11) software, in comparison to BEAST, requires a few input files (tree topology file and configuration file) and is most simple to run and much faster. We used here the tree generated by the IQTREE from the supergene matrix (supplementary material). Before running the analysis, it is necessary to create a text file containing information of the tree and the times used to calibrate the phylogeny (we demonstrated below how to create this file): mkdir DT_TreePL cd DT_TreePL cp /PATH_to_the_supergene_tree_file/supergene.fas.treefile . nano config.txt

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##all these lines are part of the configuration file treefile = supergene.fas.treefile ## the tree input file smooth = 100 ##number of probability replicate numsites = 8502 ##number of aligned sites mrca = root Zop Pyre #any_name taxon1 taxon2 min = root 123 #minimum TMRCA of the root node max = root 140 #maximmum TMRCA of the root node outfile = supergene.dated.tre ##output dated file thorough ##continues the analysis iterative until reaching the convergence prime ## to test different optimization possibilities ##We create a configure file for a time point calibration. ##However, more than one point can be used. For that, it is only necessary ##to replicate the lines, except for treefile and outfile lines. #run the analysis treePL config.txt

Check the tree node age using the Figtree algorithms. figtree supergene.dated.tre

3.5 Computational Resource and TimeConsumption

In this protocol, we exemplify a small dataset containing the transcriptome data of Elateroidea bioluminescent and non-bioluminescent species used in Amaral et al. [13]. If you are interested in practicing this protocol, the dataset is available in the figshare repository (DOI: 10.6084/m9.figshare.14877702). Based on our computational resources (see Subheading 2.1), we specify the running time for each analysis step in Table 2.

Table 2 Computational time-consumption Software

Application

Consumed time (in sec.)

OrthoFinder

Ortholog assignment

~40,000

ASTRAL

Summary species tree method

39

IQTREE

Reconstruct tree topologies

~1,500

BEAST Ver. 2.6

Divergence time estimation

~540,000

TreePL

Divergence time estimation

360

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Notes 1. The majority of bioinformatics programs are available especially to UNIX operating systems. Thus, to run the example proposed in this chapter, we used a UNIX-like operating system (such as Mac OS or Linux). In particular, running the ortholog search could take from moderate to large computational times, depending on the number of species and the number of genes/ transcripts of each species (biological issues), the number of cores and RAM available, and the program chosen. Currently, with the improvement of cloud computing, such as Amazon and Google servers, the dimensionality of computer resources may be better explored. However, it is perfectly possible to perform our example and similar sampling on a typical desktop computer, as recommended here. 2. With the advances of NGS technologies and the enormous number of NGS facilities widely spread around the world, the molecular biology workbench and experimental steps are often resumed to sample collection and shipment. These facilities usually offer a broad range of NGS-related services, including DNA/RNA extraction, library preparation (genome and transcriptome), and sequencing of custom libraries in distinct NGS platforms, such as Illumina, PacBio, and Oxford Nanopore. Some of them also offer bioinformatics data processing, such as genome and transcriptome assemblies, which are the basic input information for phylogenomics studies. However, if you are doing this by yourself, distinct programs are available to carry out gene prediction in prokaryotes and eukaryotes, such as AUGUSTUS [24] and MAKER [25], as well as to perform the de novo or guided transcriptome assemblies, such as Trinity [26] and TopHat [27]. The best program will depend on the studied organism, the computational resource, and bioinformatics knowledge. Please check the manual of each program to best evaluate the available options. 3. The command for is used to perform loop analysis for distinct input files. (For more details about this function, see ref. [28]). 4. All outputs generated by the TransDecoder will receive the same name (e.g., longest_orfs.pep). Do not forget to change it when you copy the file to the seqs directory. 5. Currently, several programs have been developed to identify orthologs among genomic and transcriptomic data. Most algorithms infer the orthologs using the comparison all-versus-all, which requires time and computational resources. We cited some software options; however, here, we used OrthoFinder [16]. Our choice is based on the time and computational

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Fig. 1 Examples of gene trees decomposed into possible quartets from a 4-taxon relationship (a) and a multitaxa relationship (b)

resources consumed in addition to the easy step-by-step installation manual and friendly use. 6. Workflow stopping options: -op, stop after preparing input file for sequence search; -og, stop after inferring orthogroups; -os, stop after writing sequence file for orthogroups; -oa, stop after inferring alignments for orthogroups; -ot, stop after inferring gene tree, which shortens the analysis time, based on the output of interest. It also provides the option to include new species from a start search against the precomputed data (-b). 7. The summary species tree implemented in ASTRAL [18] is based on unrooted gene trees. These gene trees are decomposed into quartets—four-taxa unrooted trees—and the most likely species tree is estimated based on the most frequent quartet (see Fig. 1). 8. The BEAST2 software is the most cited and used to estimate divergence times (see BEAST2 tutorial available at https:// www.beast2.org/features/). It applies the Bayesian approach associated with Markov Chain Monte Carlo, besides the implementation and use of substitution model, clock rate estimation, and distribution time of the most recent common ancestor (TMRCA). However, this method is computationally intensive, resulting in a limitation of the number of loci and taxa included in the analysis, which is restricted to 100 loci and taxa [29–31]. The implementation of a calibrated species tree takes an extra computational effort, considering the complexity of divergence time estimation. An alternative strategy has been implemented, including locus subsampling [32] or divide-andconquer methods [30, 33]. Another option is to reduce the number of genes based on the computational resources. In the case in which hundreds or thousands of single-copy genes were obtained, you may reduce the number of genes employing

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strategies based on gene clock-likeness or other features, such as those conducted by the SortaDate algorithm [32]. This algorithm filters the gene based on the clock-likeness, reasonable tree length, and least topological conflict with a focal species tree. 9. Prior distributions should be chosen carefully according to models selected for the molecular clock, tree prior, and date prior sources, such as topology constraints and calibration densities [34, 35]. The molecular-clock models account for the different patterns of evolution rates in molecular regions, which are closely related to branch lengths and divergence time estimation. Among the most well-known molecular-clock models are strict (i.e., constant evolution rates) and relaxed models (heterogeneity of evolution rates). Complex models were implemented on Bayesian molecular-clock methods, leading to the retirement of the strict-clock model [36], but it may be used considering fewer parametrization analyses. One way to avoid subjectivity in the choice of molecular-clock models is to apply a model selection fit by applying different molecularclock models and the available data, individually (Bayes Factor), applying model averaging of the data across all models or applying model adequacy, using DNA simulation data and a predictive approach (for more details, see ref. [37]). The divergence time estimates are obtained, for example, by substitution rates or node age estimation based on previous geological data (e.g., fossil data) available for taxa. This calibration prior (calibration densities) is applied to the origin or the diversification time of the most recent common ancestor (TMRCA). Fossil records or biogeographical events (less common) can be useful information for the calibration of prior data, and they are applied to a node age constraint considering its interval distribution density. This method is known as primary calibration. The most common distribution densities used for primary calibration are uniform (considering maximum and minimum boundaries) and log-normal distributions (applying a most likely age interval for fossil data; see ref. [38]). When fossil data are missing, a node age estimated in previous studies can be applied to the divergence time estimation. This method is known as secondary calibration and applies a normal distribution on node age constraints, according to confidence intervals of previous divergence time estimates (for more details on other parameters, see also ref. [34] and [36]). 10. The confidence interval is a measure that translates the accuracy and precision of the divergence time estimation, which is more accurate and precise when the confidence interval is small. A large HPD (High Posterior Density) interval may occur due to different sources of errors and uncertainties of prior model

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choice [39]. Another source of errors committing the secondary calibrations is the errors associated with the primary calibration which they are based on. Even though secondary calibration has been known to produce younger ages and narrow confidence intervals (e.g., see ref. [40]), recent studies have shown an overestimation of ages with larger confidence intervals [41]. This impact may also be associated with the uncertainty of primary calibration estimation, the choice of younger or older nodes from primary calibrations [41]. 11. TreePL is usually applied to estimate the divergence times in very large phylogenies, in which the dataset magnitude order is a limitation query to other software. This algorithm employs a penalized likelihood approach, combining the standard derivative-based algorithm and stochastic annealing to optimize the dating processes. Here, we will not discuss the advantages and disadvantages of applying one or the other software. If you are interested in this discussion, please check the original papers (also see ref. [42]).

Acknowledgments We thank the Coordenac¸˜ao de Aperfeic¸oamento de Pessoal de Nı´vel Superior - Brasil (CAPES) - Finance Code 001 (fellowship to D.T.A.) and the Fundac¸˜ao de Amparo a` Pesquisa do Estado de Sa˜o Paulo (FAPESP n. 2018/06937-8 fellowship to M.R.B.). We thank Dr. Sung-Bae Kim (AIST/Tsukuba-JP) for the cordial invitation. References 1. Morganti S, Tarantino P, Ferraro E, D’Amico P, Duso BA, Curigliano G (2019) Next generation sequencing (NGS): a revolutionary Technology in Pharmacogenomics and Personalized Medicine in cancer. Translational Research and Onco-Omics Applications in the Era of Cancer Personal Genomics:9–30 2. Rasheed A, Hao Y, Xia X, Khan A, Xu Y, Varshney RK, He Z (2017) Crop breeding chips and genotyping platforms: progress, challenges, and perspectives. Mol Plant 10(8):1047–1064 3. D’Adamo GL, Widdop JT, Giles EM (2021) The future is now? Clinical and translational aspects of “Omics” technologies. Immunol Cell Biol 99(2):168–176 4. McCormack JE, Hird SM, Zellmer AJ, Carstens BC, Brumfield RT (2013) Applications of next-generation sequencing to phylogeography and phylogenetics. Mol Phylogenet Evol 66(2):526–538

5. Young AD, Gillung JP (2020) Phylogenomics—principles, opportunities and pitfalls of big-data phylogenetics. Syst Entomol 45(2):225–247 6. Philippe H, Delsuc F, Brinkmann H, Lartillot N (2005) Phylogenomics. Annu Rev Ecol Evol Syst 36:541–562 7. Chan CX, Ragan MA (2013) Next-generation phylogenomics. Biol Direct 8(1):1–6 8. Zaharias P, Pante E, Gey D, Fedosov AE, Puillandre N (2020) Data, time and money: evaluating the best compromise for inferring molecular phylogenies of non-model animal taxa. Mol Phylogenet Evol 142:106660 9. Beutel RG, McKenna DD, Leschen RAB (2016) Systematic position, basal branching pattern and early evolution. Berlin Walter de Gruyter:1–12

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INDEX A Adenosine-50 -triphosphate (ATP) bioluminescence ....................................... 36, 297–306 sensing paper .................................................. 297–306 Alkaline phosphatase (AP) .......................... 367, 380, 382 Amyotrophic lateral sclerosis (ALS).................... 289, 290 Antibacterial activity.....................................36, 38, 43–45 Arabidopsis leaves ............................................................... 399, 400 plants............................................................... 399, 402 Arabidopsis thaliana ...........................260, 261, 395, 396 Artificial luciferase 16 (ALuc16) ................ 112, 113, 115

B

Circadian rhythms ........................................240, 395–404 Coelenterazine (CTZ) .........................47, 50, 51, 54, 55, 57, 58, 112, 116–121, 187–189, 191, 244, 247, 250, 253, 282, 290–292, 334, 337, 338, 341–345, 348–350, 353–360, 362, 368, 371, 373 Coelenterazine h (CTZh).......................... 67, 80, 84, 87, 175, 177, 178, 180–183, 198, 199, 201–204, 221, 223, 225, 262–264, 270, 275, 278, 283, 285, 350, 359 Coleoptera ..................................................................... 410 Complementation assay ...............................123–133, 155 Computational Biology ................................................ 410 Copper oxide (CuO) ...................................................... 36

D

Biased agonism.............................................................. 186 Bioluminescence (BL) imaging (BLI) .................................. 3–11, 15–19, 47, 309–319, 321–331, 337, 345 resonance energy transfer (BRET)....... 176, 242, 278 Bioluminescence-optogenetics (BL-OG) ...................334, 336–338, 347–350, 361 Bioluminescent indicator .............................219–225, 261 Biosensor .............................62, 124, 155–168, 185–193, 197–204, 230, 231, 233, 235, 298 Black Box I (BBI) ................................................ 378–385 Blood .................................. 32, 119, 219–225, 242, 243, 317, 319, 326, 385 Bone marrow stem cells....................................... 284, 285

C Calibration.................................... 99, 302–304, 420, 421 Camera................................ 32, 210–212, 216, 217, 220, 221, 223, 224, 262–264, 298, 299, 302, 303, 305, 310, 311, 313, 317, 318, 322, 323, 326, 344, 351, 355, 365, 366, 377, 396, 397, 400, 403 Cancer cell membrane ......................................................... 235 Cell internalization kinetics ....................................93–105 8-channel ........................... 120, 371, 373, 378, 382–384 Channelrhodopsins ......................................334–336, 362 Chemiluminescence imaging (CLI) .....................................................22, 26 Chemogenetics.............................................................. 334

Diagnosis .........................................................21, 93, 219, 268, 367, 383 Digital storage oscilloscope (DSO)............ 388, 390, 391 Disk diffusion method ..............................................36, 39 D-luciferin ..................................... 5, 7, 9, 11, 17–19, 47, 96, 97, 99, 102, 203, 244, 282, 290–292, 298, 299, 305, 310, 313, 318, 323, 325, 380, 384, 387, 397, 399, 400 DNA nanostructures ..................................................... 61–88 origami............................................. 61, 62, 65, 69–87 Donor ........................................ 22, 48, 86, 87, 173–175, 179–181, 187, 198–204, 207–210, 212–216, 240, 242, 244–255, 309 Drug discovery ...................... 123, 174, 186, 240, 281, 375 screening ........................................168, 174, 186, 187 Dual-colour molecular imaging ...............................47–58 Dynamics ................................ 87, 94, 97, 124, 140, 173, 174, 176, 181, 187, 193, 197, 199, 208, 236, 259–265, 301, 322, 337, 395

E Elateroidea ...........................................410, 412, 415–417 Electrophysiology.............................. 337, 342, 344, 349, 352, 360, 361 Embryonic stem (ES) cells ........................................... 323 Endoplasmic reticulum (ER)..... 120, 149, 197–204, 372

Sung-Bae Kim (ed.), Bioluminescence: Methods and Protocols, Volume 2, Methods in Molecular Biology, vol. 2525, https://doi.org/10.1007/978-1-0716-2473-9, © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Science+Business Media, LLC, part of Springer Nature 2022

425

BIOLUMINESCENCE: METHODS AND PROTOCOLS, VOLUME 2

426 Index

Enhanced green fluorescent protein (EGFP)............... 48, 49, 51, 53, 54, 56–58 Extracellular vesicles (EVs).................................. 227–236

F Firefly ......................................5, 203, 208, 298, 387–392 Firefly luciferase (FLuc) ......................... 4, 16–18, 94–99, 101, 105, 133, 156, 208, 215, 244, 268, 270, 277, 278, 282, 330, 380, 383, 396 FLuc reporter .................................................................. 18 Fluorescence ................................4, 8, 10, 40, 50, 77, 78, 94, 123, 173, 176, 187, 197, 198, 207, 228, 240, 262, 322, 339, 351, 358, 360 Fo¨rster principle ............................................................ 240 FRB-A23-FKBP ..................................367, 371, 373, 375 Freeze-dry technology ................................ 298, 302, 303 Functional assays ..................................... 125, 155, 186, 192, 249 selectivity ................................................................. 186

G Ghrelin..........................................................141, 146–149 Global DNA methylation .................................... 267–279 G protein-coupled receptor (GPCR)................. 141, 144, 185–193, 242 Gram-negative Escherichia coli bacteria ......................... 36 Gram-positive Staphylococcus aureus .............................. 36 Growth hormone secretagogue receptor type 1a (GHSR1a).............. 141, 142, 146, 148, 149, 151

H Halorhodopsin ..................................................... 335, 336 HaloTag ...................................... 129, 203, 208–215, 217 HaloTag JF525 ...................................208–212, 214, 216 Head and neck squamous cell carcinoma (HNSCC) ......................................................15–19 Hes7...................................................................... 321–331 Heterodimerization .....................................124–126, 242 High-resolution spectrometer............................. 388, 389 High throughput ................ 61, 165, 168, 199, 200, 385 Hippo pathway ....................................156, 157, 163–165 Histidine peptide tag (Histag) .................. 62–65, 74, 75, 80, 83, 85, 87 HN12-Luc2 cells ......................................................17, 18 Homovanillic acid (HVA)..........127, 129, 130, 132, 133 Hypoxia inducible factor-1 (HIF-1) ............................ 290 Hypoxic stress ...................................................... 289–292

I Imaging.......................................... 5, 8, 9, 11, 18, 21–33, 47, 48, 63, 76, 77, 111–121, 164, 207–217, 225, 227–237, 240, 244–248, 252–254, 259–265, 281–286, 289–291, 299, 303, 313, 322–330, 336, 337, 342, 348, 349, 351, 354–360, 365, 370, 373, 374, 377, 379, 381, 385, 396, 398, 400

Indicator ..................................... 198, 219–225, 242, 261 Inflammation ........................................... 21–33, 356, 361 Inhibitors ....................................... 4, 10, 16, 49, 53, 124, 126, 132, 133, 164, 165, 168, 179, 181, 182, 192, 246, 250, 251, 265 In vivo imaging .................................... 10, 16–18, 26, 29, 124, 139, 242, 282, 283, 285, 290, 291, 298

L Large NanoLuc fragment (LgBiT) ........... 124, 126, 140, 150, 156, 158, 160, 166 Large tumor suppressor (LATS) ......................... 155–168 Laser diffuser ..................................................................... 5 Live cell imaging ......................................... 121, 208, 326 Liver-expressed antimicrobial peptide 2 (LEAP2) .....141, 142, 147–149 Luciferase.....................................4, 7, 10, 16, 17, 44, 48, 63, 65, 67, 69, 73, 79–85, 87, 105, 112, 120, 123, 124, 132, 133, 139, 151, 155, 156, 158, 160, 162–168, 181, 198, 207, 208, 220, 240, 242, 244–247, 264, 268, 269, 271, 277, 278, 290, 297–299, 301, 303, 305, 322, 329, 334–336, 387 Luciferin ................................. 4, 9, 10, 47, 48, 104, 297, 313, 317, 318, 334, 343, 359, 387 Luminescence .......................................40, 50, 54, 55, 84, 145, 147, 150, 201, 202, 212, 310, 311, 313, 367, 369, 374 Luminopsins (LMOs) ................................ 334–339, 341, 343, 344, 347–349, 352–355, 359, 361 Lymphatic metastases ...............................................15–19 Lymph nodes.............................................................16–19 Lysates ................................ 53, 116, 120, 124, 163, 167, 168, 174, 175, 177, 181, 182, 250, 252, 271

M Machine perfusion ............................................... 309–319 Machine perfusion system ................................... 310–312 mCherry ........................................................................ 243 mCherry-Renilla luciferase (mCherry-RLuc)............. 282 MDA-MB-231 cells ............................112, 113, 115–120 Mesenchymal stem cells (MSCs)........244, 281–286, 310 Mesophyll protoplasts .......................................... 399, 403 Methyl-CpG binding domain (MBD) ....... 268, 269, 277 Mice .....................................4, 5, 8, 9, 11, 16–19, 23–33, 47, 48, 112, 114, 118–120, 186, 208, 220, 221, 223–225, 244–246, 248, 250, 252, 254, 281–286, 289–292, 321–324, 326, 327, 338, 342, 348, 349, 351–362, 379, 380, 382–384 Microfluidics.......................................229, 231–233, 235, 236, 298 Microsliding ......................................................... 365–374 Mitochondria ER contact sites (MERCs) ............................ 197–204 ER length indicator nanosensor (MERLIN) ............................................... 197–204

BIOLUMINESCENCE: METHODS Modulators ...................................................173–183, 336 Molecular imaging ....................................................47–58 Monoclonal antibody..................................................4, 50 Mouse embryo .............................................................. 321 Muscle pedicle flap .....................310, 311, 313, 316, 317

N NADPH oxidase .................................................. 124–126 NADPH oxidase 4 (NOX4)........................125–129, 132 Nanocapsules ........................................... 94, 97–101, 105 NanoLuc (NLuc) .............................. 124, 126, 128, 132, 139, 140, 142, 145, 147, 150, 156, 166–168, 203, 208, 220, 224, 234, 235, 242 NanoLuc Binary Technology (NanoBiT).......... 124–126, 128, 132, 133, 139, 141, 156, 157, 160, 166 Nanoparticles (NPs).......................15–19, 21–33, 35–46, 62, 63, 86, 227, 242 Near-infrared imaging ..................................................... 3–11, 47–58 photoimmunotherapy (NIR-PIT) ....................... 3–11 Nuclear erythroid 2-related factor 2 (Nrf2)................ 289

O One-channel microsliding luminometer............. 365–374 On-site analysis..................................................... 379, 385 Optical filters .............................. 366, 368, 373, 388–390 Optogenetics (OG) .............................298, 333, 334, 349 Organ preservation .................................................... 309–319 transplantation......................................................... 309 viability..................................................................... 310 The orthotopic model ..............................................16–18 Oscillations ..........................................321, 322, 329, 395 Oxidative stress...............................................22, 289–291 Oxyluciferin .........................................104, 105, 387, 388

P p22phox ........................................125, 126, 128, 129, 132 Peptide nucleic acids (PNA)....................... 64, 66, 75, 87 Photomultiplier tube (PMT) ............................. 298, 351, 358–360, 365–369, 373, 374, 377–379, 381, 384, 388, 390–392 Photosystem I (PSI)............................................. 259, 260 Photosystem II (PSII) ......................................... 259, 260 Phylogeny ............................................410, 411, 416, 421 Placental alkaline phosphatase (PLAP) .............. 367, 370, 371, 378–380, 382 Plant ........................................... 175, 240, 246, 259–265, 395, 396, 398, 399 Presomitic mesoderm (PSM) .................... 321, 322, 324, 326, 329 Primary neurons ................................................... 339–342 Protein kinase A (PKA).......................208, 212–214, 216 Protein–protein interaction (PPI) ...................... 112, 123, 125–127, 155, 156, 166, 175, 176, 239–244, 246

AND

PROTOCOLS, VOLUME 2 Index 427

Q Quantum dot (QD) .....................................47–58, 61–88

R Rats ............................................310, 311, 313, 316, 318, 338, 348, 352, 354 Reactive oxygen species (ROS) ....... 21, 22, 36, 124, 125 Real-time quantification ................................................. 95 Receptor pharmacology................................................ 193 Renilla luciferase (RLuc)......................... 48, 49, 53, 133, 156, 174, 175, 180, 187, 199, 202, 215, 242, 244, 282, 335, 336, 368, 371, 372 Retinoic acid receptor (RAR)............................................... 111, 113

S Saracatinib .................................................................16, 18 Secreted alkaline phosphatase (SEAP) ............... 367, 370, 379, 380, 382–385 Secretory LgBiT (sLgBiT)......................... 140, 141, 144, 145, 149–152 Segmentation clock....................................................... 321 Self-illuminating ........................................................21–33 Signalling .............................................156, 157, 164, 165 Signal transduction ....................................................... 186 Single cell.....................................45, 200, 208, 214, 327, 328, 337, 349, 361, 395–404 Single-chain probe ........................................................ 120 Smartphone ......................................... 219–225, 297–306 Smartphone detection .................................223, 297–306 SmBiT ........................................ 124, 126, 140–143, 145, 149, 150, 152, 156, 158, 160, 166 Species tree .................................411, 414, 417, 419, 420 Squamous cell carcinoma................................................ 15 Step-function........................................................ 334, 336 Superoxide dismutase (SOD1)............................ 289, 290

T Time-lapse imaging........................... 217, 323, 327, 329, 400, 403 Titanium dioxide (TiO2) ................................................ 36 Transgenic Lewis rats.................................................... 310 TRUPATH ........................................................... 187–194 Trypanosoma cruzi ............................................... 174, 176 Tumor ............................... 4, 8, 9, 11, 15–19, 21–33, 48, 112, 119, 156, 199, 244, 267, 380, 383

U Ulcerative colitis .............................................................. 29 Unconjugated bilirubin (UCBR).......220, 221, 223–225 Unmethyl-CpG binding domain (CXXC) 268, 269, 277

Z Zinc oxide (ZnO) ........................................................... 36