Sehgal Circadian Rhythms and Biological Clocks Part B

1. Auflage 2015
ISBN: 978-0-12-803381-4
Verlag: Elsevier Science & Techn.
Format: EPUB
Kopierschutz: 6 - ePub Watermark

E-Book, Englisch, Band Volume 552, 414 Seiten

Reihe: Methods in Enzymology

Two new volumes of Methods in Enzymology continue the legacy of this premier serial with quality chapters authored by leaders in the field. Circadian Rhythms and Biological Clocks Part A and Part B is an exceptional resource for anybody interested in the general area of circadian rhythms. As key elements of timekeeping are conserved in organisms across the phylogenetic tree, and our understanding of circadian biology has benefited tremendously from work done in many species, the volume provides a wide range of assays for different biological systems.ÿ Protocols are provided to assess clock function, entrainment of the clock to stimuli such as light and food, and output rhythms of behavior and physiology.ÿ This volume also delves into the impact of circadian disruption on human health.ÿ Contributions are from leaders in the field who have made major discoveries using the methodsÿ presented here. - Continues the legacy of this premier serial with quality chapters authored by leaders in the field - Covers research methods in biomineralization science - Keeping with the interdisciplinary nature of the circadian rhythm field, the volume includes diverse approaches towards the study of rhythms, from assays of biochemical reactions in unicellular organisms to monitoring of behavior in humans.

Autoren/Hrsg.

Sehgal, Amita

Inhaltsverzeichnis

1;Front Cover;1
2;Circadian Rhythms and Biological Clocks, Part B;4
3;Copyright;5
4;Contents;6
5;Contributors;12
6;Preface;18
7;Part I: Dissecting the Central Clock Circuit;20
7.1;Chapter One: Measuring Synchrony in the Mammalian Central Circadian Circuit;22
7.1.1;1. Introduction;23
7.1.1.1;1.1. What is synchrony?;23
7.1.1.2;1.2. What is circadian synchrony?;23
7.1.1.3;1.3. Goals of this review;24
7.1.2;2. Monitoring SCN Rhythms with Cellular Resolution;25
7.1.3;3. Isolating Data from Single Cells;25
7.1.4;4. Defining a Rhythm;28
7.1.4.1;4.1. Plotting rhythmic data for visual inspection;28
7.1.5;5. Period Synchrony: Methods to Extract and Compare Periods Between Cells;29
7.1.5.1;5.1. Do cells share the same period?;34
7.1.6;6. Phase Synchrony: Methods to Extract and Compare Phase Relationships Between Cells;35
7.1.7;7. Perturbations Reveal Synchronization Mechanisms;36
7.1.8;8. Methods Awaiting Application in Circadian Biology;36
7.1.9;9. Step-by-Step Instructions for Measuring Synchrony in SCN Slice;37
7.1.9.1;9.1. Bioluminescence recordings using a charge-coupled device camera;37
7.1.9.2;9.2. Image processing;37
7.1.9.3;9.3. Single-cell tracking;37
7.1.9.4;9.4. Data presentation;38
7.1.10;Acknowledgments;38
7.1.11;References;38
7.2;Chapter Two: Patch-Clamp Electrophysiology in Drosophila Circadian Pacemaker Neurons;42
7.2.1;1. The Drosophila Circadian Network;42
7.2.2;2. Circadian Control of Neuronal Activity;46
7.2.2.1;2.1. Intrinsic currents;47
7.2.2.2;2.2. Synaptic currents;47
7.2.3;3. Methods for Patch-Clamp Electrophysiology;49
7.2.3.1;3.1. Equipment;50
7.2.3.2;3.2. Solutions;51
7.2.3.3;3.3. Drosophila brain dissection;52
7.2.3.4;3.4. Recordings;52
7.2.3.5;3.5. Assessing the quality of the recording;53
7.2.3.6;3.6. Relevant data in current cl55
7.2.3.7;3.7. Relevant data in voltage cl56
7.2.4;4. Conclusion;58
7.2.5;Acknowledgments;58
7.2.6;References;58
7.3;Chapter Three: Glial Cell Regulation of Rhythmic Behavior;64
7.3.1;1. Introduction;65
7.3.2;2. Studies of Glial Cell Function in Circadian Behavior and Sleep;66
7.3.2.1;2.1. Glia and circadian behavior;66
7.3.2.2;2.2. Mammalian astrocytes and the sleep homeostat;69
7.3.2.3;2.3. Glia-neuron communication regulates Drosophila sleep;70
7.3.2.4;2.4. Microglial clocks, synaptic strength, and sleep;72
7.3.3;3. Potential Circadian Glia-Neuron Signaling Molecules;73
7.3.3.1;3.1. Glial clocks and ATP rhythms;73
7.3.3.2;3.2. Glial neurotrophic factors and cytokines in circadian behavior;75
7.3.3.3;3.3. Secreted molecules mediating glia-neuron communication in Drosophila;77
7.3.4;4. Molecular Genetic Strategies for Studying the Glial Regulation of Drosophila Rhythms;78
7.3.4.1;4.1. Glia-selective genetic perturbation methods;78
7.3.4.2;4.2. Glial expression profiling and cell-specific targeted genetic screens;79
7.3.4.3;4.3. Glial microRNAs as regulators of rhythmicity;80
7.3.5;Appendix A. Protocol for TRAP Profiling of Fly Glial Cells;82
7.3.5.1;A.1. Bead preparation;83
7.3.5.2;A.2. Sample preparation;84
7.3.5.3;A.3. Immunoprecipitation;84
7.3.5.4;A.4. RNA extraction for RNA-seq library preparation;84
7.3.5.5;A.5. Reagents;85
7.3.6;References;86
7.4;Chapter Four: Neurophysiological Analysis of the Suprachiasmatic Nucleus: A Challenge at Multiple Levels;94
7.4.1;1. Introduction;95
7.4.2;2. Part I: Clock Mechanisms at the Cellular Level;96
7.4.2.1;2.1. The molecular feedback loops and beyond;96
7.4.2.2;2.2. Cytosolic oscillators;97
7.4.2.3;2.3. Role of neuropeptides;98
7.4.2.4;2.4. Circadian modulation of excitability;99
7.4.2.5;2.5. The road back: Neuronal activity impacts clock gene expression;100
7.4.3;3. Part II: The SCN as a Multi-Oscillator;101
7.4.3.1;3.1. Using acute slice preparations to study phase resetting;101
7.4.3.2;3.2. Using acute slices to study photoperiod-encoding mechanisms;104
7.4.3.3;3.3. Using acute slices to study synchronization within the SCN;106
7.4.3.4;3.4. Rescue of single-cell deficiencies by the neuronal network;107
7.4.4;4. Part III: In Vivo Electrophysiology Recordings from the SCN in Anesthetized and Freely Moving Animals;107
7.4.4.1;4.1. Example1: In vivo electrophysiology studies of photic entrainment;109
7.4.4.2;4.2. Example2: In vivo studies indicate influence of behavioral activity and sleep stage on the SCN;110
7.4.5;5. Conclusions;112
7.4.6;Acknowledgments;114
7.4.7;References;114
8;Part II: Entrainment of Central and Peripheral Clocks;122
8.1;Chapter Five: Photic Entrainment in Drosophila Assessed by Locomotor Activity Recordings;124
8.1.1;1. Introduction;125
8.1.2;2. Different Light Regimes Used to Entrain Locomotor Activity of Fruit Flies;126
8.1.2.1;2.1. Rectangular light-dark cycles;126
8.1.2.1.1;2.1.1. 12h light and 12h darkness;127
8.1.2.1.2;2.1.2. Long and short photoperiods;128
8.1.2.1.3;2.1.3. Varying light intensity or wavelength;128
8.1.2.1.4;2.1.4. Light-moonlight cycles;129
8.1.2.2;2.2. Simulating gradual changes in light intensity;130
8.1.2.2.1;2.2.1. Simulating dawn and dusk;130
8.1.2.2.2;2.2.2. Simulating more natural conditions;130
8.1.3;3. Methods to Measure Locomotor Activity;131
8.1.3.1;3.1. Home-made recording systems;131
8.1.3.2;3.2. Commercially available Trikinetics system;132
8.1.3.3;3.3. Camera-based recording system;132
8.1.4;4. Data Analysis and System Comparison;134
8.1.4.1;4.1. Designing actograms;134
8.1.4.2;4.2. Creating average activity profiles;135
8.1.4.3;4.3. Further analyses based on average days of individual flies;137
8.1.4.3.1;4.3.1. Calculating activity levels;137
8.1.4.3.2;4.3.2. Analysis of morning anticipation;139
8.1.4.3.3;4.3.3. Determining M and E peaks;139
8.1.4.3.4;4.3.4. Siesta determination;140
8.1.5;References;140
8.2;Chapter Six: Photic Regulation of Clock Systems;144
8.2.1;1. Introduction;145
8.2.2;2. The Suprachiasmatic Nuclei;145
8.2.3;3. The Molecular Circadian Clock;146
8.2.4;4. Peripheral Clocks;146
8.2.5;5. Photoentrainment and Melanopsin;148
8.2.6;6. Entrainment of the Molecular Clock;150
8.2.7;7. Molecular Photoentrainment;152
8.2.8;8. Studying the Effects of Light on the Circadian Clock;154
8.2.8.1;8.1. Locomotor activity;154
8.2.8.1.1;8.1.1. Entrainment to light-dark cycle;154
8.2.8.1.2;8.1.2. Period (tau) in constant dark or constant ligh;154
8.2.8.1.3;8.1.3. Phase shifting;155
8.2.8.1.4;8.1.4. Negative masking;156
8.2.8.2;8.2. SCN gene induction;156
8.2.8.3;8.3. Clock gene reporter transgenics;156
8.2.8.4;8.4. In vivo electrophysiology;157
8.2.9;9. Conclusions;157
8.2.10;Acknowledgments;158
8.2.11;References;158
8.3;Chapter Seven: Response of Peripheral Rhythms to the Timing of Food Intake;164
8.3.1;1. Introduction;165
8.3.2;2. Animal Strain and Age;168
8.3.3;3. Animal Room and Equipment;168
8.3.4;4. Facilities to Accommodate Feeding Schedule;169
8.3.5;5. Diet;170
8.3.6;6. Monitoring Eating Pattern;171
8.3.7;7. Physiological Readout of Eating Pattern;172
8.3.8;8. Feeding Paradigms;173
8.3.8.1;8.1. Assessing the pace of resetting of peripheral clock to a change in eating pattern;173
8.3.8.2;8.2. Assessing the contribution of circadian clock and feeding pattern on peripheral molecular rhythms;174
8.3.8.3;8.3. Amplitude and phase of expression of peripheral clock under timed feeding or ad libitum condition;175
8.3.9;9. Mouse Tissue Collection;176
8.3.10;10. Transcript, Protein, and Metabolome Expression Analysis;177
8.3.11;11. Conclusion;177
8.3.12;References;178
9;Part III: Clocks and Metabolic Physiology;182
9.1;Chapter Eight: Circadian Regulation of Cellular Physiology;184
9.1.1;1. Introduction;185
9.1.2;2. Materials;186
9.1.2.1;2.1. Circadian in vitro studies (in synchronized C2C12 myotubes);186
9.1.2.2;2.2. Circadian in vivo studies (48h constant darkness experiments in mice);187
9.1.3;3. Methods;188
9.1.3.1;3.1. Circadian in vitro cell-based methods (in synchronized C2C12 myotubes);188
9.1.3.1.1;3.1.1. Advantages of cell-based models to test circadian control of metabolism;188
9.1.3.1.2;3.1.2. Circadian control of mitochondrial bioenergetics;189
9.1.3.1.3;3.1.3. In vitro bioenergetics measurements in synchronized cells;190
9.1.3.2;3.2. In vivo bioenergetics measurements-48h DD mouse collection;196
9.1.4;4. Notes;199
9.1.4.1;4.1. Circadian in vitro cell-based methods;199
9.1.4.2;4.2. In vivo bioenergetics measurements-48h DD mouse collection;200
9.1.5;Acknowledgments;201
9.1.6;References;202
9.2;Chapter Nine: Analysis of the Redox Oscillations in the Circadian Clockwork;204
9.2.1;1. Introduction: Circadian and Redox Coupling in the Cell;205
9.2.2;2. The Biochemical Properties of the Peroxiredoxin System;208
9.2.2.1;2.1. The catalytic cycle of peroxiredoxins;208
9.2.2.2;2.2. Oxidative inactivation and oligomerization;211
9.2.3;3. Analysis of PRX Redox Oscillations;214
9.2.3.1;3.1. Methodology and important considerations;214
9.2.3.2;3.2. Protocol;217
9.2.3.2.1;3.2.1. Sample preparation;217
9.2.3.2.1.1;3.2.1.1. Cultured cells;217
9.2.3.2.2;3.2.2. Gel electrophoresis and protein transfer to nitrocellulose membranes for blotting;220
9.2.3.2.3;3.2.3. Immunoblotting;221
9.2.3.2.4;3.2.4. Normalization and controls;224
9.2.3.2.5;3.2.5. Alternative methods for the measuring of PRX overoxidation;225
9.2.3.3;3.3. Example: Circadian cycles of PRX oxidation in fruit flies;226
9.2.4;Acknowledgments;226
9.2.5;References;226
9.3;Chapter Ten: Clocks and Cardiovascular Function;230
9.3.1;1. Introduction;231
9.3.2;2. Circadian Analysis of Physiological Parameters with Radiotelemetry;233
9.3.3;3. Primary Cell Culture of Macrophages;234
9.3.3.1;3.1. Peritoneal macrophage culture;234
9.3.3.2;3.2. Bone marrow-derived macrophages;235
9.3.4;4. Circadian Variation in Thrombogenesis;237
9.3.4.1;4.1. Time to vessel occlusion;237
9.3.4.2;4.2. Real-time fluorescence intravital microscopy quantification of thrombosis;239
9.3.5;5. Atherosclerosis and Vascular Integrity in Models of Clock Disruption;240
9.3.5.1;5.1. En face analysis of aortas;241
9.3.5.2;5.2. Aortic root sectioning and staining;241
9.3.5.3;5.3. Wire-mediated vascular injury;242
9.3.6;6. Clocks and Myocardial Dysfunction;243
9.3.6.1;6.1. Mouse echocardiography;243
9.3.6.2;6.2. Isolation of adult ventricular mouse cardiomyocytes;244
9.3.7;7. Conclusion;245
9.3.8;References;245
10;Part IV: Circadian Rhythms in Humans;248
10.1;Chapter Eleven: Measuring Circadian Clock Function in Human Cells;250
10.1.1;1. Introduction;251
10.1.2;2. Studies of Circadian Clock Properties Using Reporters;252
10.1.3;3. Ex Vivo and In Vitro Studies of Human Circadian Clocks;255
10.1.4;4. Similar Technologies to Study Other Major Signaling Pathways;256
10.1.5;5. Cell-Based Approaches to Study Gene Expression Variation and Human Interindividual Differences in Drug Responses;258
10.1.6;6. Promise of In Vitro Gene Expression Profiling;261
10.1.7;7. Specific Protocols;263
10.1.7.1;7.1. Cell lines for the measurement of human circadian properties;263
10.1.7.2;7.2. Generation and production of lentiviral vectors;264
10.1.7.3;7.3. Transduction of primary skin fibroblasts and real-time bioluminescence measurement of human circadian properties in ...;264
10.1.7.4;7.4. Bioluminescence measurement of the transcriptional activation of different signaling pathways;265
10.1.7.5;7.5. Use of lentiviral reporters in longitudinal in vivo imaging of signaling pathways within a growing tumor;266
10.1.8;8. Perspectives;267
10.1.9;Acknowledgments;268
10.1.10;References;268
10.2;Chapter Twelve: Human Activity and Rest In Situ;276
10.2.1;1. Introduction;277
10.2.2;2. Probing Activity and Sleep by Questionnaires;278
10.2.2.1;2.1. The Munich ChronoType Questionnaire;278
10.2.2.2;2.2. The power of big numbers;279
10.2.2.2.1;2.2.1. Chronotype and development;281
10.2.2.2.2;2.2.2. Chronotype and light exposure;282
10.2.2.3;2.3. The concept of social jetlag;284
10.2.3;3. Measuring Activity and Sleep by Actimetry;286
10.2.3.1;3.1. The method of actimetry;286
10.2.3.2;3.2. Specialized software;287
10.2.3.3;3.3. Viewing activity time series;289
10.2.3.4;3.4. Trends and smoothing;291
10.2.3.5;3.5. Applying cosine fits;291
10.2.3.6;3.6. Averaged daily profiles;293
10.2.3.7;3.7. Detecting sleep in activity;295
10.2.4;4. Concluding Remarks;297
10.2.5;References;300
10.3;Chapter Thirteen: Phenotyping of Neurobehavioral Vulnerability to Circadian Phase During Sleep Loss;304
10.3.1;1. Prevalence and Consequences of Sleep Loss;305
10.3.2;2. Sleep-Wake and Circadian Regulation: Two-Process Model;305
10.3.3;3. Subjective and Objective Measures for Circadian Variation in Performance;310
10.3.4;4. Circadian Variation Assessment in Neurobehavioral Functions;311
10.3.5;5. Sleep Deprivation and Performance;313
10.3.6;6. Cumulative Effects on Performance from Chronic Sleep Restriction;314
10.3.7;7. Phenotypic Individual Differences in Response to Sleep Deprivation;316
10.3.8;8. The PVT: Example of a Behavioral Assay for Phenotyping Responses to Sleep Loss;319
10.3.9;9. Conclusions;320
10.3.10;Acknowledgments;322
10.3.11;References;322
10.4;Chapter Fourteen: Genetics of Human Sleep Behavioral Phenotypes;328
10.4.1;1. Introduction;328
10.4.2;2. Clinical Phenotyping;330
10.4.2.1;2.1. Overview;330
10.4.2.2;2.2. Methods;330
10.4.2.2.1;2.2.1. Recruitment and sampling of potential affected subjects;330
10.4.2.2.2;2.2.2. Self-reports and interviews;331
10.4.2.2.3;2.2.3. Physiological measurements for circadian rhythm;333
10.4.3;3. Identification of Associated Genetic Variants;334
10.4.3.1;3.1. Overview;334
10.4.3.2;3.2. Methods;335
10.4.3.2.1;3.2.1. Collecting DNA samples;335
10.4.3.2.2;3.2.2. Mapping the locations of the associated genetic variants by linkage analysis;335
10.4.3.2.3;3.2.3. Identify the associated genetic variants;336
10.4.4;4. Modeling Human Sleep Phenotypes in Rodents;337
10.4.4.1;4.1. Overview;337
10.4.4.2;4.2. Methods;337
10.4.4.2.1;4.2.1. Generation of mouse models;337
10.4.4.2.2;4.2.2. Sleep phenotyping of mouse models;338
10.4.5;5. Concluding Remarks;340
10.4.6;Acknowledgments;341
10.4.7;References;341
10.5;Chapter Fifteen: Sleep and Circadian Rhythm Disruption and Recognition Memory in Schizophrenia;344
10.5.1;1. Introduction;345
10.5.2;2. Sleep and Circadian Rhythm Disruption in Schizophrenia;346
10.5.2.1;2.1. Patients;346
10.5.2.2;2.2. Schizophrenia-relevant mouse models;346
10.5.2.3;2.3. Using running wheels to assess rest–activity rhythms: A cautionary note;348
10.5.3;3. Recognition Memory Deficits in Schizophrenia;348
10.5.3.1;3.1. Patients;348
10.5.3.2;3.2. Schizophrenia-relevant mouse models;350
10.5.4;4. Recognition Memory Deficits After the Direct Manipulation of Sleep and Circadian Rhythms;353
10.5.4.1;4.1. Sleep deprivation;353
10.5.4.2;4.2. Abnormal photic input;353
10.5.4.3;4.3. Core clock gene manipulation;354
10.5.5;5. Dual-Process Theory of Recognition;355
10.5.5.1;5.1. Hippocampus-dependent, recollection-like mechanism;356
10.5.5.2;5.2. Perirhinal cortex-dependent, familiarity-based mechanism;356
10.5.6;6. Which is Impaired, Recollection or Familiarity?;357
10.5.6.1;6.1. Recognition memory deficits in schizophrenia-relevant mouse models;357
10.5.6.2;6.2. Recognition memory deficits after the direct manipulation of sleep and circadian rhythms;358
10.5.7;7. Is There an Association Between Sleep and Circadian Function and Recognition Memory in Schizophrenia?;358
10.5.7.1;7.1. Human studies;358
10.5.7.2;7.2. Animal studies;359
10.5.8;8. Summary of the Chapter and Some Unresolved Issues;360
10.5.9;Acknowledgments;361
10.5.10;References;361
11;Author Index;370
12;Subject Index;404
13;Color Plate;415

Leseproben

Chapter One Measuring Synchrony in the Mammalian Central Circadian Circuit
Erik D. Herzog*,1; István Z. Kiss†; Cristina Mazuski*    * Department of Biology, Washington University, St. Louis, Missouri, USA
† Department of Chemistry, Saint Louis University, St. Louis, Missouri, USA
1 Corresponding author: email address: [e-mail] Abstract
Circadian clocks control daily rhythms in physiology and behavior across all phyla. These rhythms are intrinsic to individual cells that must synchronize to their environment and to each other to anticipate daily events. Recent advances in recording from large numbers of cells for many circadian cycles have enabled researchers to begin to evaluate the mechanisms and consequences of intercellular circadian synchrony. Consequently, methods have been adapted to estimate the period, phase, and amplitude of individual circadian cells and calculate synchrony between cells. Stable synchronization requires that the cells share a common period. As a result, synchronized cells maintain constant phase relationships to each (e.g., with cell 1 peaking an hour before cell 2 each cycle). This chapter reviews how circadian rhythms are recorded from single mammalian cells and details methods for measuring their period and phase synchrony. These methods have been useful, for example, in showing that specific neuropeptides are essential to maintain synchrony among circadian cells. Keywords Circadian Fourier transform Period gene Vasoactive intestinal polypeptide Rayleigh plot Synchronization Index 1 Introduction
1.1 What is synchrony?
When a good marching band enters the field, the players step at exactly the same moment. The drummers keep time so that each band member synchronizes their paces to their neighbors’. The musicians perform with the same period. As they march across the field, the line of trumpeters might arrive at midfield first followed by, perhaps, the trombonists. The trombonists share the same period as the trumpeters, but are phase delayed in their time of arrival. In this way, they synchronize their periodicity while assuming unique phase relationships. Period synchrony (also called frequency entrainment) does not require oscillators to peak together. Instead, synchronized oscillators can establish unique, and stable, phase relationships with other oscillators in the population (phase synchrony or phase locking). In nature, noise (internal and external to the oscillators) introduces a small, bounded variation in the phase differences. Many studies of mechanical, electrical, chemical, and biological oscillators have focused on mechanisms that can produce period synchrony and conditions that can alter phase synchrony (Pikovsky, Rosenblum, & Kurths, 2003; Strogatz, 2003). 1.2 What is circadian synchrony?
Daily changes at both cellular and systemic levels arise from biological oscillators that keep near 24-h rhythms and entrain to the 24-h cues associated with day and night. These self-sustained circadian rhythms are intrinsic to individual cells. The period of the individual cells depends predominantly on their genetics and light–dark history, and less so on the ambient temperature (i.e., their period is temperature compensated) or other environmental inputs. These cells must synchronize to each other and the environment to coordinate daily rhythms including feeding-fasting, waking–sleeping, hormone levels, metabolism, and gene expression. Circadian synchrony describes when cells (or organisms) express the same, near 24-h period (Bloch, Herzog, Levine, & Schwartz, 2013). Much like the synchronized marching of a band of musicians, circadian clocks are often comprises populations of cells that share the same daily period, but with some cells leading (by up to 12 h) other cells. Critically, oscillators may share the same period and a constant phase relationship for one of three reasons: (1) they communicate with each other, (2) they both receive the same synchronizing signal from other cells or the environment, or (3) coincidence. By measuring circadian synchrony following a perturbation, we can distinguish whether cells are entraining each other, to their environment, or simply express the same near 24-h period by chance. Synchrony among circadian cells has been described in single-celled organisms like cyanobacteria and dinoflagellates and metazoans including plants, fungi, flies, and rodents. In a few cases, there is evidence that the synchrony arises primarily due to environmental inputs (e.g., cyanobacteria, dinoflagellates, and plants) while cells in other systems appear to have evolved the ability to synchronize to each other (e.g., fungi, flies, and rodents). To illustrate how to measure and use synchrony in a circadian system, this chapter will focus on the mammalian suprachiasmatic nucleus (SCN). The SCN of mice and humans contains approximately 20,000 cells with many of them functioning as individual self-sustained circadian oscillators. SCN cells receive information about local day–night changes indirectly from other cell types. For example, the cells of the SCN normally entrain to input from the retina and other brain areas so that their peak metabolism and electrical activity occur during the day. For the SCN to function as a circadian pacemaker, individual SCN cells must synchronize to each other to coordinately drive rhythms in neural activity and transmitter release. Strikingly, the degree of phase synchrony among SCN cells can change with conditions. During short winter days, for example, SCN cells tend to peak together whereas, they spread out their times of peak activity during the long days of summer. 1.3 Goals of this review
This chapter aims to review how to measure synchrony between circadian cells with a focus on analyzing single-cell SCN slice bioluminescence recordings. Briefly, we summarize methods for discriminating circadian rhythms from single cells. We then discuss the strengths and weaknesses of independent methods that quantify period and phase synchrony among a population of oscillating cells. Finally, we provide examples of how perturbations affect cell–cell synchrony in the SCN. 2 Monitoring SCN Rhythms with Cellular Resolution
To study synchrony among SCN cells, researchers have used a variety of direct and indirect indicators of circadian physiology. The best methods share the following features: (1) relatively noninvasive monitoring of single-cell physiology, (2) high-frequency sampling for more than 4 days, (3) sensitive enough to detect circadian rhythms above background, (4) a dynamic range that allows recording of daily, biological changes without saturating, and (5) can be combined with genetic or pharmacological perturbations. To date, circadian synchrony has been assessed based on daily rhythms in cytosolic calcium, gene expression, firing rate, and cAMP activity (Table 1). Figure 1 illustrates a representative, long-term recording of PERIOD2 (PER2) protein levels from SCN neurons using the PER2-luciferase (PER2::LUC) knockin reporter. Table 1 Methods that have been used to monitor circadian rhythms with cellular resolution Intracellular calcium Fluorescent calcium-sensitive reporter Yellow Cameleon 2.1 or 3.6 or 6.0, or GCaMP3-WPRE 0.5 s every 60 min Brancaccio, Maywood, Chesham, Loudon, and Hastings (2013), Ikeda and Ikeda (2014), Ikeda et al. (2003), Enoki, Kuroda, et al. (2012), Enoki, Ono, Hasan, Honma, and Honma (2012), and Irwin and Allen (2013) Gene expression Bioluminescent (luciferase) or fluorescent (e.g., destabilized GFP) reporter of transcription or translation Per1::Luc, Per1:GFP, Per1-Venus, PER2::LUC, Per2-DsRED, or Bmal1::Luc Integrated over 15–60 min Day and Schaufele (2008), Hastings, Reddy, McMahon, and Maywood (2005), Herzog, Aton, Numano, Sakaki, and Tei (2004), Welsh, Imaizumi, and Kay (2005), Welsh and Kay (2005), Welsh and Noguchi (2012), Yoo et al. (2004), Cheng et al. (2009), Kuhlman, Quintero, and McMahon (2000), Yamaguchi et al. (2003), and Yamazaki et al. (2000) Firing rate Multielectrode array MEA 60 or MED 64 Sampled every 50 µs to report spikes per second Herzog (2007) and Honma et al. (2011) cAMP activity Bioluminescent (luciferase) reporter of CREB activity or fluorescent (e.g., destabilized GFP) reporter cAMP levels CRE::Luc, ELISA kit, or ICUE2 Integrated over 60 min Brancaccio et al. (2013), An, Irwin, Allen, Tsai, and Herzog (2011), and O'Neill, Maywood, Chesham, Takahashi, and Hastings (2008) Figure 1 Recording circadian rhythms in gene expression from a SCN slice culture. (A) An image of a SCN carrying the PER2::Luc reporter construct with two representative cells encircled with regions of interest (ROI). With single-cell resolution, at least 100 ROIs can be identified from...