the effect of spatially patterned light on the

THE EFFECT OF SPATIALLY PATTERNED LIGHT ON THE

SUPRACHIASMATIC NUCLEUS

A thesis submitted to the University of Manchester for the degree of

Doctor of Philosophy (PhD)

in the Faculty of Biology, Medicine and Health

2016

JOSH MOULAND

SCHOOL OF BIOLOGICAL SCIENCES/ Division of

Neuroscience and Experimental Psychology

Circadian Rhythms: Introduction

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Table of Contents

1 Chapter 1: Background Literature .................................................. 17

1.1 Circadian Rhythms .............................................................................................. 17

1.1.1 Introduction .................................................................................................................... 17

1.1.2 Clock Genes ...................................................................................................................... 18

1.1.3 Tau ...................................................................................................................................... 20

1.1.4 Entrainment .................................................................................................................... 21

1.1.5 Masking ............................................................................................................................. 23

1.1.6 Phase Shifting and Phase Response Curves ........................................................ 24

1.1.6.1 Circadian Time..................................................................................................... 24

1.1.6.2 Number of Photons ............................................................................................. 25

1.1.6.3 Wavelength ......................................................................................................... 25

1.1.7 Phase Angle of Entrainment (Psi, ψ) ..................................................................... 26

1.1.8 Cell Autonomous Oscillators ..................................................................................... 26

1.1.9 Central and Peripheral Oscillators ......................................................................... 27

1.2 The Suprachiasmatic Nuclei ............................................................................ 28

1.2.1 Evidence for its Position as the Central Oscillator ........................................... 28

1.2.2 Anatomy ............................................................................................................................ 29

1.2.2.1 Core–Shell Distinction ......................................................................................... 29

1.2.3 Neuropeptides ................................................................................................................ 30

1.2.4 Synchronicity .................................................................................................................. 32

1.2.5 Light Response ............................................................................................................... 34

1.2.6 Projections from the SCN ........................................................................................... 39

1.2.7 Projections to the SCN ................................................................................................. 40

1.3 Retina ...................................................................................................................... 42

1.3.1 Importance of the Retina for the SCN .................................................................... 42

1.3.2 Overview of the Classical Retinal Pathways ....................................................... 44

1.3.3 Rods and Cones .............................................................................................................. 44

1.3.3.1 Phototransduction Cascade ................................................................................ 44

1.3.3.2 Response to light ................................................................................................. 46

1.3.3.3 Photopigments and their Spectral Sensitivity ..................................................... 46

1.3.3.4 Sensitivity ............................................................................................................ 47

1.3.3.5 Light Adaptation .................................................................................................. 47


2| Chapter 1: Background Literature

1.3.4 Bipolar Cells .................................................................................................................... 48

1.3.5 Retinal Ganglion Cells ................................................................................................. 48

1.3.6 The Horizontal Pathway ............................................................................................ 48

1.3.7 Horizontal Cells ............................................................................................................. 49

1.3.8 Amacrine cells ................................................................................................................ 49

1.3.9 Rod Signalling Pathways ............................................................................................ 51

1.4 Intrinsically Photoreceptive RGCs (ipRGCs) .............................................. 52

1.4.1 Discovery ......................................................................................................................... 52

1.4.2 Phototransduction........................................................................................................ 53

1.4.3 Response to light ........................................................................................................... 54

1.4.4 Subtypes of ipRGCs ...................................................................................................... 57

1.4.5 Projections ....................................................................................................................... 58

1.4.6 Synaptic Inputs to ipRGCs ......................................................................................... 59

1.4.7 Photoreceptor Contribution to Light-Evoked Activity in the SCN ............. 60

1.5 Tools and Techniques ........................................................................................ 64

1.5.1 Electrophysiology ......................................................................................................... 64

1.5.2 Mouse model .................................................................................................................. 65

1.5.3 Murine Visual System.................................................................................................. 65

1.5.4 Silent Substitution ........................................................................................................ 68

1.5.5 Red Cone Mice ................................................................................................................ 70

1.6 Objectives ............................................................................................................... 72

2 Chapter 2: General Methods .............................................................. 73

2.1 Light Calibration .................................................................................................. 73

2.2 Electrophysiology. ............................................................................................... 74

2.2.1 Mice .................................................................................................................................... 74

2.2.2 Surgical Procedures ..................................................................................................... 74

2.2.3 Data Acquisition ............................................................................................................ 76

2.2.4 Histology .......................................................................................................................... 77

2.2.5 Spike Sorting ................................................................................................................... 77

2.2.6 Light Sources .................................................................................................................. 80

2.2.7 Light Stimuli .................................................................................................................... 81

2.2.8 Correcting for Visual Angle ....................................................................................... 81


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2.2.9 Test stimulus ................................................................................................................... 81

2.2.10 Receptive Field Mapping Stimulus ...................................................................... 83

2.2.11 Analysis .......................................................................................................................... 84

2.2.11.1 Test Pulse .......................................................................................................... 84

2.2.11.2 Receptive Field Mapping ................................................................................... 84

2.2.11.3 Power Spectrum Analysis .................................................................................. 85

2.3 Behavioural ........................................................................................................... 87

2.3.1 Animals.............................................................................................................................. 87

2.3.2 Protocol ............................................................................................................................. 87

2.3.3 Light pulse ........................................................................................................................ 88

2.3.4 Light Stimuli .................................................................................................................... 89

2.3.5 Data Collection ............................................................................................................... 91

2.3.6 Analysis ............................................................................................................................. 91

3 Chapter 3: Receptive Field Properties of Neurons within the

SCN. .................................................................................................................... 93

3.1 Introduction .......................................................................................................... 93

3.2 Experimental Procedures ................................................................................ 95

3.2.1 Animals.............................................................................................................................. 95

3.2.2 Surgical procedures ..................................................................................................... 95

3.2.3 Data Acquisition and Spike Sorting ........................................................................ 96

3.2.4 Generic Light Stimuli ................................................................................................... 96

3.2.4.1 Protocol ............................................................................................................... 97

3.2.4.2 Analysis ............................................................................................................... 97

3.2.5 Receptive Field Mapping ............................................................................................ 97

3.2.5.1 Protocol ............................................................................................................... 97

3.2.5.2 Analysis ............................................................................................................... 99

3.2.6 Histology ........................................................................................................................ 100

3.3 Results ................................................................................................................... 101

3.3.1 Individual SCN Cells have a Diverse Selection of Receptive Fields ......... 101

3.3.2 Full Field Units ............................................................................................................ 101

3.3.3 Centre-Surround Units ............................................................................................. 104

Discrete Units ......................................................................................................................... 108



3.4 Discussion............................................................................................................ 110

3.4.1 Anatomy ......................................................................................................................... 110

3.4.1.1 Individual SCN Cells are Innervated by Single M1 Cells. .................................... 110

3.4.1.2 The Surround Antagonism is an Emergent Property of the SCN ....................... 112

3.4.2 Physiology ..................................................................................................................... 114

3.4.2.1 Some Cells Receive a True Irradiance Signal ..................................................... 114

3.4.2.2 Centre-Surround Antagonism could Reduce the Effect of Spatial Contrast ..... 114

3.5 References ........................................................................................................... 116

4 Chapter 4: The Impact of Spatial Patterns on the Light

Response of the Mouse Suprachiasmatic Nucleus. ......................... 119

4.1 Introduction........................................................................................................ 119

4.2 Experimental Procedures .............................................................................. 121

4.2.1 Animals ........................................................................................................................... 121

4.2.2 Light Stimuli .................................................................................................................. 121

4.2.3 In vivo Electrophysiology ......................................................................................... 123

4.2.3.1 Surgical Procedures ........................................................................................... 123

4.2.3.2 Stimuli ................................................................................................................ 124

4.2.3.2.1 Inverting Gratings .................................................................................... 124

4.2.3.2.2 Drifting Gratings ...................................................................................... 124

4.2.3.2.3 Irradiance Matched Comparison ............................................................. 124

4.2.3.3 Spike Sorting ...................................................................................................... 125

4.2.3.4 Analysis .............................................................................................................. 125

4.2.3.4.1 Receptive Field Mapping ......................................................................... 125

4.2.3.4.2 Inverting Gratings .................................................................................... 125

4.2.3.4.2.1 Power Spectrum Analysis ................................................................. 125

4.2.3.4.2.2 Amplitude of Modulation ................................................................. 126


4.2.3.4.3.1 Power Spectrum Analysis ................................................................. 126

4.2.3.4.3.2 Amplitude of Modulation ................................................................. 126

4.2.3.4.4 Irradiance Matched Comparison ............................................................. 126

4.2.3.5 Histology ............................................................................................................ 127

4.2.4 Behavioural ................................................................................................................... 127


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4.2.4.1 Protocol ............................................................................................................. 127

4.2.4.2 Stimuli ............................................................................................................... 128


4.2.4.2.2 Static Gratings ......................................................................................... 128

4.2.4.3 Analysis ............................................................................................................. 128

4.3 Results ................................................................................................................... 129

4.3.1 Spatial patterns impact SCN firing ...................................................................... 129

4.3.2 Spatiotemporal Frequency Tuning of SCN Cells ............................................ 133

4.3.3 Time Averaged Firing Rate in the SCN is Modulated by Spatial Patterns.

...................................................................................................................................................... 134

4.3.4 Phase Resetting is not Altered by the Inclusion of Spatial Patterns. ...... 137

4.4 Discussion ............................................................................................................ 139

4.5 References ........................................................................................................... 143

5 Chapter 5: Melanopsin Contribution to Time Averaged Firing

in the SCN under Naturalistic Viewing Conditions. ......................... 145

5.1 Introduction ........................................................................................................ 145

5.2 Methods ................................................................................................................ 148

5.2.1 Animals........................................................................................................................... 148

5.2.2 Surgery ........................................................................................................................... 148

5.2.3 Light Calibration ......................................................................................................... 149

5.2.4 Light Source .................................................................................................................. 149

5.2.5 Stimuli ............................................................................................................................. 150

5.2.6 Analysis .......................................................................................................................... 152

5.3 Results ................................................................................................................... 154

5.3.1 Single Unit Responses under Natural Viewing Conditions ........................ 155

5.3.2 Multi-Unit Activity under Natural Viewing Conditions ............................... 155

5.3.3 Time Averaged Firing under Natural Viewing Conditions. ........................ 158

5.4 Discussion ............................................................................................................ 162

5.5 Reference ............................................................................................................. 168

6 Chapter 6: General Discussion ........................................................ 169

6.1 Overview .............................................................................................................. 169

6.2 Future Directions .............................................................................................. 170



6.2.1 Formation of the Full Field Inhibition. ............................................................... 170

6.2.2 Complete Photoreceptor Contribution Breakdown ...................................... 170

6.2.3 Connectivity between ipRGCs and SCN Cells ................................................... 172

6.2.3.1 Brainbow............................................................................................................ 173

6.2.3.2 Tracer Studies .................................................................................................... 174

6.2.4 Do Different ipRGC Subtypes Create the Diversity of Responses Observed

in the SCN? ................................................................................................................................ 174

6.2.5 Are Light Responsive Characteristics Correlated to Neuropeptide

Expression? .............................................................................................................................. 175

6.2.6 Which SCN Cells Receive Direct and Indirect Light Input? ......................... 175

6.2.7 Further Receptive Field Properties and Visuotopic Order ......................... 176

6.3 Novel Techniques.............................................................................................. 177

6.3.1 Chronic Recordings .................................................................................................... 177

6.3.2 Retinally Attached SCN Slices ................................................................................ 178

6.3.3 Novel Animal Models ................................................................................................ 179

6.3.4 CRISPR ............................................................................................................................ 179

7 References ............................................................................................. 181

Word count 54,519


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Table of Figures

Chapter 1

Figure 1.1: The Molecular Clock .................................................................................................. 19

Figure 1.2: Phase Response Curves. ........................................................................................... 22

Figure 1.3: Neuropeptide Expression in the Murine SCN. ................................................. 31

Figure 1.4: Typical Responses from within the Suprachiasmatic Nucleus. ................ 37

Figure 1.5: Light’s Effect on the Molecular Clock. ................................................................. 38

Figure 1.6: The Basic Structure of the Mammalian Retina. ............................................... 43

Figure 1.7: The Phototransduction Cascade and Light Responses. ................................ 45

Figure 1.8: Lateral Inhibition and Centre-Surround Antagonism. ................................. 50

Figure 1.9: A Typical Melanopsin Irradiance Response Curve. ....................................... 56

Figure 1.10: Melanopsin Light Response Profile. .................................................................. 56

Figure 1.11: Photoreceptor Contribution to the Suprachiasmatic Nuclei. .................. 63

Figure 1.12: Opsin Distribution in the Murine Retina. ........................................................ 67

Figure 1.13: Metamers. .................................................................................................................... 69

Figure 1.14: The Relative Opsin Specific Photon capture. ................................................. 71

Chapter 2

Figure 2.1: Single unit recordings from the SCN. .................................................................. 79

Figure 2.2: Correcting for Visual Angle. .................................................................................... 82

Figure 2.3: Generating the Receptive Field Mapping Stimulus. ....................................... 82

Figure 2.4: Running Wheel Schematic. ...................................................................................... 90



Chapter 3

Figure 3.1: Isolation of an example single unit used for receptive field mapping. .. 98

Figure 3.2: Properties of cells which respond to light anywhere in the visual scene.

............................................................................................................................................................. 1023

Figure 3.3: Properties of cells exhibiting centre surround antagonism within the

SCN. ....................................................................................................................................................... 106

Figure 3.4: Properties of SCN cells with a discrete receptive field within the SCN.

................................................................................................................................................................ 108

Chapter 4

Figure 4.1: SCN Single Unit Responses to Inverting Gratings. ....................................... 131

Figure 4.2: Spatiotemporal Tuning of SCN Units. .............................................................. 132

Figure 4.3: The Effect of Spatial Patterns on Time Averaged Firing Rate. ............... 134

Figure 4.4: The Effect of Spatially Patterned Stimuli on Phase Shifting. ................... 138

Chapter 5

Figure 5.1: Properties of the Light Stimuli. ........................................................................... 151

Figure 5.2: Single Unit Extraction and Validation. ............................................................. 153

Figure 5.3: Single Unit Responses to Melanopsin and Energy Steps........................... 156

Figure 5.4: Multi-Unit Activity in Response to a Melanopsin or Energy Step. ......... 158

Figure 5.5: Photoreceptor Contribution to the Time Averaged SCN Light Response.

................................................................................................................................................................ 161

Figure 5.6: The Spectral Radiance of 3 Electronic Devices for Comparison with the

Stimuli used in this Experiment. ............................................................................................... 167


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Table of Tables

Table 2.1: P-value Adjustments for Multiple Tests. .............................................................. 86

Table 4.1: Relative Photon Capture. ........................................................................................ 122

Table 5.1: Photoreceptor Specific Stimuli. ............................................................................ 151

Table 5.2: Relative Photon Capture for Different e-Devices. ........................................ 166



Table of Abbreviations

AII Angiotensin II AMPA α-amino-3-hydroxy-5-methyl-4-isoxazolepropionic acid ATP Adenosine Triphosphate ASPD Advanced Sleep Phase Disorder AVP Arginine Vasopressin Ca2+ Calcium Ion CalR Calretinin CBT Core Body Temperature cAMP Cyclic Adenosine Monophosphate cGMP Cyclic Guanosine Monophosphate CHO cells Chinese Hamster Ovary Cells CK1ε Casein Kinase 1 epsilon CLOCK/Clock Circadian Locomotor Output Cycles Kaput Protein/Gene CRE cAMP response elements CREB cAMP response element-binding protein CRISPR Clustered Regularly Interspaced Short Palindromic Repeats CRY/Cry Cryptochrome Protein/Gene CSF Cerebral Spinal Fluid CT Circadian Time DAG Diacylglycerol DD Constant Darkness DMLO Dim Light Melatonin Onset DOG Di-octanoyl-s-n-gly DSPD Delayed Sleep Phase Disorder F1 First Harmonic F2 Second Harmonic FRD Free-Running Disorder GABA γ-Aminobutyric acid DBP D Site of Albumin Promoter Binding Protein GFP Green Fluorescent Protein GHT Geniculohypothalamic Tract GRP Gastrin Releasing Peptide HEK cells Human Embryonic Kidney HLF Hepatic Leukemia Factor IGL Intergeniculate Leaflet ipRGCs Intrinsically Photoreceptive Retinal Ganglion Cells IP3 Inositol 1,4,5-trisphosphate IR Infrared ISWR Irregular Sleep Wake Rhythm λmax. Wavelength of Light LD Light Dark Cycle L cones Long Wavelength Sensitive Cones mENK met-Enkephalin M cones Medium Wavelength Sensitive Cones mRGCs Melanopsin Expressing retinal Ganglion Cells Na+ Sodium Ion ND Neutral Density


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NKCC Na-K-Cl-Cotransporters NT Neurotensin OAG 1-oleoyl-2-acetyl-sn-glycerol OPN4/Opn4 Melanopsin Protein/Gene Opn1mwR Human Long Wave Sensitive Gene PACAP Pituitary Adenylate Cyclase-Activating Peptide PB Phosphate Buffer PCA Principle Component Analysis PDE Photodiesterase PIP2 Phosphatidylinositol 4,5-bisphosphate PLC Phospholipase C PRC Phase Response Curve Psi (ψ) Phase Angle PWM Pulse Width Modulation rd/rd Retinal Degeneration (mouse model) PER/ Per Period Protein/ Gene mPER1::LUC mouse PER1::LUCIFERASE fusion protein mPER2::LUC mouse PER2::LUCIFERASE fusion protein RGCs Retinal Ganglion Cells. RHT Retinohypothalamic Tract S cones Short Wavelength Sensitive Cones SCN Suprachiasmatic Nucleus SPVZ Subparaventricular Zone Tau (τ) Period Length TEF Thyrotroph Embryonic Factor TTX Tetrodotoxin TRP Transient Receptor Potential TRP-L TRP-like channels TRPC Canonical TRP UV light Ultra Violet Light VDU Visual Display Unit VIP Vasoactive Intestinal Protein WT Wild Type YFP Yellow Fluorescent Protein



Abstract

The daily variation in background light intensity (irradiance) can entrain the

endogenous clock in the suprachiasmatic nucleus (SCN) to the external

environment. The only source of this photic information in mammals is the eye,

which is primarily a visual organ. It is therefore highly specialised to detect high

frequency spatiotemporal modulations. This together with the adaptation which

occurs within the retina could be present difficulties when encoding global

irradiance. This raises the question of whether spatial patterns, which are present

in our everyday viewing, might affect the ability of the SCN to receive ‘true

irradiance’ signals and entrain to the external environment.

My first approach was to determine whether individual SCN cells might receive a

‘true irradiance’ signal. To this end I mapped and characterised the receptive field

properties of SCN neurons using in vivo electrophysiology. Indeed a handful of

neurons (full field cells) responded to light anywhere in the visual scene and thus

may act as ‘irradiance detectors’. However, the vast majority of cells only sampled

local radiance from a limited area of the visual scene.

Having mapped the receptive field properties it became clear that cells which

sampled from a limited area of the visual scene would be sensitive to spatial

contrast (patterns). To examine the effect of spatiotemporal contrast on the SCN I

examined two SCN outputs: locomotor activity and neuronal firing rates. Although

spatiotemporal modulation in light intensity could induce large amplitude

oscillations in neuronal activity; the time averaged firing rate and locomotor

activity, which are believed to be determined by irradiance, were largely

unaffected by spatial patterns. This led to the conclusion that the SCN can

multiplex photic information into information regarding irradiance, and spatial

information by encoding them under different timescales.

Melanopsin has been heralded as the key photopigment for encoding irradiance

and entraining the SCN. However such experiments have been only performed

using diffuse light stimuli. Here I investigated the role of melanopsin under natural

viewing conditions which incorporated spatial patterns. Under such stimuli the

SCN response can be almost entirely accounted for by the melanopic irradiance of

the stimuli.


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Declaration

I Josh Mouland declare that no portion of the work referred to in the thesis has

been submitted in support of an application for another degree or qualification of

this or any other university or other institute of learning.



Copyright Statement

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policy on Presentation of Theses


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Acknowledgements

This PhD thesis would not have been possible without the help and support of

many individuals; to whom I am extremely grateful.

First and foremost I must express my sincere gratitude to my PhD supervisor, Rob

Lucas. It has been a great privilege to work under Rob’s scientific philosophy. He is

a patient and supportive supervisor and I would like to thank him for always being

willing to stay that extra hour to help me to overcome a problem. I greatly

appreciate the time and energy that Rob has invested in me and without his help I

would not be where I am now. I would also like to thank my co-supervisors

(Rasmus Petersen and Marcelo Montemurro) and advisor (John Gigg) for their role

in mentoring me and shaping my thesis.

It is also important that I recognise my unofficial co-supervisor, Tim Brown, who

has been a great support in teaching me the tools of the trade. I would also like to

thank the Lucas, Brown and Piggins’ Lab members past and present who have been

invaluable with their knowledge and advice. In addition a special thanks to all the

technicians who have worked tirelessly in the background, especially Jon Wynne.

Ben Henshaw, Gary Mills and co-workers also deserve my thanks for a wonderful

career placement that was required as part of my PhD.

Lastly I would like to thank my family who have always supported me, especially

through the recent times. I could not have finished without them.

At this point I would like to acknowledge the Biotechnology and Biological

Sciences Research Council, who generously provided funding for this PhD.


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1 Chapter 1: Background Literature

1.1 Circadian Rhythms

1.1.1 Introduction

The rotation of the Earth on its axis produces the periodic changes in light

intensity associated with the day-night cycle. Almost all organisms have evolved to

predict these periodic changes by developing an internal chronometer, allowing

them to regulate biological processes efficiently with regard to the predicable

changes in the environment. Where the time period is about 24 hours, these

oscillations are referred to as circadian rhythms (from the latin: circa=about,

dian=day).

When a person’s behaviour is out of phase with their endogenous clock, it is known

as circadian misalignment. Both shift work and long transmeridian travel are man-

made constructs that can cause circadian misalignment. There is a vast body of

literature on both animal models and humans regarding the adverse effects of

circadian misalignment (For review see: Golombek et al., 2013). Short term effects

often involve reduced cognitive performance, tiredness during the day and

insomnia at bed time. Long term effects of chronic circadian misalignment are

more serious. Associations and increased risk have been observed with metabolic

disorder, cardiovascular disease, cancer, type two diabetes (Pan et al. 2011) and

gastric and duodenal ulcers. In 2014, a report by the World Health Organization

stated that: ‘’shift-work that involves circadian disruption is probably carcinogenic

to humans’’ (World Health Organization 2014).

With our increasing knowledge regarding circadian rhythms we are now detecting

disorders related to disruption of the endogenous clock, such as advanced sleep

phase disorder (ASPD), delayed sleep phase disorder (DSPD), free-running

disorder (FRD) and irregular sleep wake rhythm (ISWR) (ICSD 2001). These

disorders predominantly affect the sleep-wake cycle, an output of the endogenous

clock, and may lead to chronic circadian misalignment through social jet-lag. Social

jet-lag is where the constraints of society, such as a 9-5 working day, can oppose

the endogenous clock of these patients.

Circadian Rhythms: Clock Genes


Light is an important time cue that helps synchronise the endogenous clock to the

environment. In countries such as Canada light therapy is the first line treatment

against certain circadian related disorders (Lam & Levitt 1999).

In this increasingly 24 hour society the prevalence of circadian disorders and

circadian related diseases are on the rise. By understanding more about how light

affects the internal body clock, we hope to aid treatment of the disorders and

prevent their occurrences in the first place.

1.1.2 Clock Genes

The circadian rhythm is the product of a negative feedback loop (Albrecht 2002;

Hastings et al. 2008; King & Takahashi 2000). There are two arms to this process: a

positive arm that drives production of certain genes, and a negative arm that then

inhibits the positive arm (Figure 1.1A). The genes that comprise this negative

feedback loop and control the period of the clock were first observed in Drosophila

(Konopka & Benzer 1971) and Neurospora (Feldman & Hoyle 1973). Mammalian

homologues of the clock genes (Vitaterna & Takahashi 1994) were discovered

later, although the precise mechanisms differ slightly between organisms

(Hastings et al. 2008).

In mammals, the positive arm is formed of two proteins (CLOCK and BMAL), which

form a heteromeric transcription factor (Figure 1.1B) (Hoganesch et al. 1998;

Gekakis et al. 1998). This transcription factor binds to E-box elements and

facilitates the transcription of Per and Cry genes, the negative arm (Gekakis et al.

1998; Hoganesch et al. 1998). The PER and CRY proteins form a heterodimer and

translocate back into the nucleus, where it inhibits CLOCK:BMAL1 transcription

(Kume et al. 1999). This inhibition prevents further transcription of Per and Cry

genes and PER and CRY levels begin to fall through protein degradation. This

releases the inhibition and CLOCK:BMAL1 induced transcription can occur again.

The result is cyclical expression of both the PER and CRY proteins as well as the

resultant rhythmic inhibition of CLOCK:BMAL1 induced transcription. These

oscillations have a period of about 24 hours, thus creating the circadian rhythm.

This cyclical gating of the CLOCK:BMAL1 induced transcription allows a wide

selection of genes to be rhythmically expressed. Especially as CLOCK:BMAL1

Circadian Rhythms: Clock Genes

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Figure 1.1: The Molecular Clock A. The basic mechanism that produces periodic oscillations. B. The proteins involved in the mammalian molecular clock. CLOCK and BMAL1 proteins form a heterodimer that activates transcription of Per and Cry as well as many other clock regulated genes. PER and CRY proteins form a heterodimer and translocate to the nucleus where they inhibit CLOCK:BMAL1 mediated transcription. This will then halt the production of PER and CRY proteins and thus limit the PER:CRY inhibition, allowing CLOCK:BMAL1 to activate transcription again.

Circadian Rhythms: Tau


appears to promote the transcription of a host of other transcription factors such

as REV-ERBα, DBP ,TEF and HLF (Ripperger & Schibler 2006; Oishi et al. 2003;

Gachon et al. 2006; Ripperger et al. 2000). There are thus thousands of genes,

known as clock-controlled genes that are regulated on a circadian basis.

1.1.3 Tau

Circadian rhythms have by definition a period (Tau, τ) of about 24 hours. Longer

rhythmic oscillations are termed infradian rhythms, such as monthly or yearly

cycles. Shorter oscillations are called ultradian rhythms, for example tidal rhythms.

Tau varies both within and between species (Pittendrigh & Daan 1976). Tau is

rarely 24 hours, humans for example have a mean period of 24.2 hours (Czeisler et

al. 1999; Duffy et al. 2011) and a range of 23.5-24.6 (from 150 subjects Duffy et al.,

2011). Common laboratory animals have a wide range of τ’s (Pittendrigh & Daan

1976). The common mammals used for circadian biology have τ’s both above and

below 24 hours. Golden hamsters have a period of about 24.1 hours (Daan &

Pittendrigh 1976; Ralph & Menaker 1988; Liu et al. 1997; Johnson et al. 1988;

Pittendrigh & Daan 1976), ground squirrels about 24.7 hours (Nelson & Zucker

1981), rats about 24.2 hours (Honma et al. 1998) and mice have a circadian

rhythm of about 23.5 hours (Kriegsfeld et al. 2008; Panda et al. 2003; Panda et al.

2002; Güler et al. 2008; Sujino et al. 2003; Van Der Horst et al. 1999; Zheng et al.

1999; Hatori et al. 2008; Vitaterna & Takahashi 1994; Nelson & Zucker 1981;

Pittendrigh & Daan 1976; Daan & Pittendrigh 1976).

Tau is defined as the endogenous period of the clock. This period is determined by

the kinetics of the clock gene interactions. For example, Tau mutant hamsters have

a single point mutation in the gene that encodes Casein Kinase 1 epsilon (CK1ε)

(Lowrey et al. 2000). This reduces the ability of CK1ε to phosphorylate Per

(Lowrey et al. 2000): altering the kinetics of the clock by accelerating the removal

of nuclear PER protein (Dey et al. 2005). This increases the rate of the oscillation,

resulting in a shorter period. Heterozygotes of the tau mutation have a period of

22 hours, whilst homozygous mutants have an even shorter period of 20 hours

(Ralph & Menaker 1988; Dey et al. 2005; Liu et al. 1997; Ralph et al. 1990; Silver et

al. 1996).

Circadian Rhythms: Entrainment

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There are many ways to record tau in vivo. For lab animals, locomotor activity,

usually through wheel running, is probably the most commonly used technique

(Figure 1.2A). In contrast, melatonin onset under dim lighting conditions (Dim

light melatonin onset DLMO) or core body temperature (CBT) are the most

commonly used indicators for human studies (Sack et al. 2007). Another common

method for determining tau in animals is to record from individual cells. Whilst

there is good correlation between behavioural and single cell recordings of tau,

single cell recordings are more variable (Liu et al. 1997; Honma et al. 1998; Herzog

et al. 2004).

1.1.4 Entrainment

Given that tau is rarely 24 hours, the endogenous clock should ‘free run’ slowly

shifting in and out of phase with the environment (Figure 1.2A). In order to

prevent this, the clock is regularly reset. This process of entraining the clock,

maintains the synchronicity between the endogenous clock and the environment.

As circadian misalignment would result in biological functions occurring at

suboptimal time points, the circadian system is very sensitive to the external

environment with many different sources acting as time cues (zeitgebers).

Numerous studies have shown entrainment to a broad spectrum of zeitgebers such

as light (For review, in humans see: Duffy & Wright, 2005) food (Aschoff, Goetz, &

Honma, 1983; for reviews see: Boulos & Terman, 1980; Mistlberger, 1994),

benzodiazepines (Turek & Losee-Olson 1986; Van Reeth & Turek 1989), cage

cleaning (Mrosovsky 1988), physical activity (Reebs & Mrosovsky 1989),

exogenous melatonin (Redman et al. 1983; Lewy et al. 1992), social cues

(Marimuthu & Chandrashekaran 1983; Marimuthu et al. 1981; Mrosovsky 1988)

and temperature (Refinetti 2010; Tokura & Aschoff 1983). Of all these light is by

far the most potent zeitgeber.

Circadian Rhythms: Entrainment


Figure 1.2: Phase Response Curves. A. Double plotted locomotor activity of a mouse. Initially entrained to the 12:12 light dark cycle and then left to free run at their endogenous period (<24hrs) in constant conditions. Halfway through the constant conditions the mouse was subjected to a 15 minute light pulse early in their subjective night (CT16). This pulse phase delayed the animal as is shown by the red line marking the activity onsets. B. Cartoon of the murine phase response curves (PRCs) for photic (blue) and non-photic (gold) stimuli. The magnitude of the phase shift is determined by the circadian time (CT) of administration. The flat portions of the PRCs are where no phase shifts are observed and are termed the ‘dead zone’.

Circadian Rhythms: Masking

|23

Broadly speaking the majority of these zeitgebers can be split into two groups,

photic and non-photic. Non-photic zeitgebers encompass: dark pulses,

benzodiazepines, cage cleaning, physical activity and social cues (Mrosovsky et al.

1989). These non-photic zeitgebers are believed to act via a common mechanism,

as they produce the same phase response curve which is distinct from the photic

phase response curve (Figure 1.2B) (Mrosovsky et al. 1989). Whilst the exact

mechanism for non-photic entrainment is unknown , there is evidence linking it to

either an increased state of arousal or activity (Mrosovsky et al. 1989; Van Reeth &

Turek 1989).

1.1.5 Masking

Masking is a phenomenon that can be mistaken for entrainment. Masking is when

induction (positive masking) or inhibition (negative masking) of a behaviour by an

environmental stimulus over-rides the influence of the circadian clock. For

instance, nocturnal mammals will often reduce locomotor activity, i.e. negative

masking, when a light is turned on at night (for review see: Mrosovsky, 1999). This

can be clearly demonstrated through the experiments of van der Horst et al. on

Cry1 and Cry2 double knockouts (Van Der Horst et al. 1999). These mice lack the

core clock machinery and are thus arrhythmic. In constant darkness (DD) the

animal does not free run at its internal tau as there is no tau. Thus there is no

pattern or rhythm to the animal’s locomotor activity. However when the mice are

placed in a light dark (LD) cycle they show activity only during the dark phase.

Here the animal is not entrained but rather the locomotor activity is masked

during the light phase, thus creating a rhythmic activity profile even though the

animal is not entrained.

There are several useful techniques to allow one to differentiate between masking

and entrainment. One technique is to minimise the duration of the potential

masking stimuli. This can be done through the use of skeleton photoperiods

whereby only two brief light pulses are given to signal ‘dawn’ and ‘dusk’. If the

animal is entrained then activity will occur only in one of the two dark periods. If

the animal was merely displaying negative masking, activity will be present in both

dark periods. Another common method for determining entrainment or phase

shifting behaviour compared with masking is to compare the transition from the

Circadian Rhythms: Phase Shifting and Phase Response Curves


‘entrained’ situation to the ‘free running’ set up (e.g. from an LD cycle to DD).

Entrained animals will free run from the entrained period whilst masked animals

will show a fragmented transition as they return to their unmasked activity

rhythm.

1.1.6 Phase Shifting and Phase Response Curves

The magnitude and direction of environmentally induced changes in circadian

phase (phase shifts) are dependent on three main factors: the duration of the

stimuli, the intensity of the stimuli, and the circadian time (CT) at which the stimuli

are presented, where CT is the time with respect to the endogenous clock. This is

true for both photic and non-photic stimuli.

1.1.6.1 Circadian Time

Keeping all other parameters equal the direction and magnitude of the phase shift

varies with the phase of the endogenous clock. Plotting the magnitude and

direction of the phase shifts at different CTs produces a phase response curve

(PRC) (Figure 1.2B).

A PRC is not a fixed entity but is dependent on a range of parameters. Different

species have different PRCs. Photic entrainment has a very different PRC to non-

photic entrainment and PRCs also vary as either of the other parameters (duration

or intensity) of the stimuli change.

The general profile of photic PRCs are relatively consistent. Light applied early

during the subjective night (CT 12-18) results in a phase delay (Figure 1.2A).

Whilst light applied late in the subjective night (CT 19-0) results in a phase

advance. During the subjective day there is usually very little or no phase shifting

capabilities. This is referred to as the ‘dead zone’.

In this thesis, all of the experiments were performed using mice. They have a

characteristic PRC where the phase delays are larger than the phase advances

(Comas et al. 2006; Daan & Pittendrigh 1976) with the maximum magnitude phase

shift occurring at CT16 (Daan & Pittendrigh 1976).

Circadian Rhythms: Phase Shifting and Phase Response Curves

|25

1.1.6.2 Number of Photons

With light, the duration and intensity of the stimuli can be combined into a single

parameter; the number of photons. Increasing either irradiance or duration results

in an increase in the magnitude of the phase shift. In 1991 Nelson and Takahashi

discovered that keeping the total number of photons constant, by inversely

altering irradiance and duration, produced phase shifts of comparable magnitude

(Nelson & Takahashi 1991). This was observed over a range of durations (4-

45mins), suggesting that the circadian system integrates the number of photons

over these timescales. Additional studies further support this claim that the

circadian system can integrate over time (Nelson & Takahashi 1999; Gooley et al.

2012; Zeitzer et al. 2011; Rimmer et al. 2000; Meijer et al. 1992; Van Den Pol et al.

1998; Zeitzer et al. 1997; Gronfier et al. 2004; Lall et al. 2010). However, as the

duration of the light step increases, the stimuli will act over an increased range of

CT points. Because CT affects both the magnitude and the direction of the phase

shift, the effect of increasing duration becomes more complex on large timescales

(Comas et al. 2006).

The circadian system responds in a dose-dependent manner to the number of

photons. There is a threshold below which no phase shifts are observed. Above this

threshold the circadian system responds linearly to the log of the number of

photons until it reaches saturation where the phase shift is maximal.

Unfortunately, photons are not all identical. Photons can differ in energy which is

characterised by the photon’s wavelength. Thus photic phase shifts are reliant on

an additional parameter; wavelength.

1.1.6.3 Wavelength

The wavelength of a photon affects its ability to be detected (further explanation in

the retina section see Chapter1: 1.3.3.3 Photopigments and their Spectral

Sensitivity). Using a series of monochromatic light sources it has been shown that

the circadian system is responsive to a wide range of visible wavelengths (400-600

nm in retinal degenerate murine models: Hattar et al., 2003; Yoshimura & Ebihara,

1996) but is optimally sensitive to blue light at about 480nm (Hattar et al. 2003;

Circadian Rhythms: Phase Angle of Entrainment (Psi, ψ)


Thapan et al. 2001; Yoshimura & Ebihara 1996; Brainard et al. 1985; Brainard et al.

2001; Takahashi et al. 1984).

1.1.7 Phase Angle of Entrainment (Psi, ψ)

Circadian time is determined by the period of the endogenous clock. CT12 is

considered the start of the subjective night and CT0, the start of the subjective day.

For a nocturnal animal CT12 is defined as the activity onset. Under an LD cycle,

especially under dim light levels, the activity onset might not align with the switch

into darkness (night). The difference in phase between the endogenous clock and

the environment is denoted as the phase angle (psi, ψ).

During circadian misalignment ψ can be very large. In humans the common

indicators for determining circadian phase are core body temperature and salivary

DLMO (Sack et al. 2007). Other phase markers for humans are: skin temperature,

salivary melatonin, urinary melatonin, salivary cortisol, urinary cortisol, plasma

cortisol, plasma growth hormone and plasma thyroid-stimulating hormone (Sack

et al. 2007).

In addition ψ can be used to characterise the phase between two circadian

oscillations. Numerous biological processes have circadian rhythms, many of

which differ in their phase. This phase difference can occur naturally as different

functions operate best at different times. However phase differences can also occur

pathologically due to circadian disruption.

1.1.8 Cell Autonomous Oscillators

The core clock genes have been observed in practically every mammalian cell (Yoo

et al. 2004). Using circadian gene reporter techniques these genes have been

shown to oscillate ex vivo (Yoo et al. 2004). Original studies using either the

mPer1Luciferase mouse model, or dissociated cells showed that cells were able to

oscillate but that these oscillations were not robust (Abe et al. 2002). After only a

couple of days the oscillations in most of these tissues would dampen to nothing

(Abe et al. 2002). Further studies have since shown that the dampening effect was

due to methodology; in the case of the dissociated cells, individual cells were still

rhythmic but over the course of a few days they had become desynchronous with

Circadian Rhythms: Central and Peripheral Oscillators

|27

one another and thus the population oscillation appeared to dampen (Welsh et al.

2004). Studies using the mPer2Luciferase reporter now show that these oscillations

can persist ex vivo for over 20 days (Yoo et al. 2004). Thus practically every

mammalian cell expresses the core clock machinery and can generate circadian

rhythms independently over an extended period of time.

1.1.9 Central and Peripheral Oscillators

Whilst almost every cell is an autonomous oscillator, most of them (at least for

mammals) do not receive any direct environmental zeitgebers. Thus they need to

be both entrained to the external world as well as to one another. This has led to

the idea of a dual system in which self-autonomous peripheral oscillators are

orchestrated by a central oscillator.

The phase of tissue specific circadian rhythms naturally varies between tissues

(Yoo et al. 2004). This phase difference is regulated by the central oscillator (Yoo et

al. 2004). It is believed that the peripheral oscillators are entrained by a range of

internal zeitgebers (for review see: Mohawk et al. 2012). These internal zeitgebers

are under the control of the central oscillator, which is itself entrained to the

external environment.

The Suprachiasmatic Nuclei: Evidence for its Position as the Central Oscillator


1.2 The Suprachiasmatic Nuclei

1.2.1 Evidence for its Position as the Central Oscillator

Historical studies have shown that lesioning the suprachiasmatic nucleus (SCN)

abolishes circadian rhythms such as those seen in locomotor activity (Sujino et al.

2003; Mosko & Moore 1978; Stephan & Zucker 1972; Moore & Eichler 1972).

These rhythms could be recovered by transplanting donor SCN tissue into the

lesioned animals (Sujino et al. 2003; Drucker-Colín et al. 1984; Sawaki et al. 1984;

Lehman et al. 1987; DeCoursey & Buggy 1989). Indeed, functional SCN tissue was

shown to be sufficient to drive rhythmic locomotor behaviour, as wild type (WT)

SCN tissue transplanted into mice lacking a functional endogenous clock

(mCry1/mCry2 deficient mice) restored rhythmicity (Sujino et al. 2003). As

mentioned earlier, Tau mutant hamsters have a shortened period of ~20hrs (Dey

et al. 2005; Ralph & Menaker 1988; Liu et al. 1997; Ralph et al. 1990; Silver et al.

1996). When SCN lesioned hamsters were transplanted with SCNs from a Tau

mutant, the period of the restored rhythm was that of the Tau mutant donor

(Ralph et al. 1990; Silver et al. 1996). Likewise, SCN lesioned Tau mutants that

were transplanted with the SCN from WT hamsters expressed a circadian rhythm

with a period consistent with the WT donor (Ralph et al. 1990; Silver et al. 1996).

Equivalent reciprocal transplants have been performed in mice with comparable

results (Sujino et al. 2003). Together these studies conclusively showed that the

SCN determines the circadian period of the rest of the animal.

Many brain regions show a circadian rhythm in firing rate (Inouye & Kawamura

1979). When the SCN is isolated from these areas by cutting around the

hypothalamus to create a hypothalamic ‘island’, the circadian rhythmicity in firing

rate was abolished in all brain regions bar the SCN (Inouye & Kawamura 1979).

This robust circadian rhythm in SCN neuronal activity can persist ex vivo for

extended periods of time (Herzog et al. 1997). Indeed, even when SCN neurons are

dissociated they still maintain a circadian rhythm in firing rate (Welsh et al. 1995).

Further studies using a PERIOD1::LUCIFERASE (mPER1::LUC) fusion protein

showed that only the SCN exhibited prominent periodic rhythmicity in vivo

(Yamaguchi et al. 2000). Real time studies in vitro showed that other tissues also

exhibited oscillations in mPER1::LUC abundance but that these oscillations quickly

The Suprachiasmatic Nuclei: Anatomy

|29

dampened within a few days; whilst SCN explants could sustain this rhythm for

much longer (Abe et al. 2002). This strongly reinforced the idea that the SCN is the

master oscillator and was responsible for generating the circadian rhythms seen in

other tissues. This view has since changed with the advent of the PERIOD2::

LUCIFERASE (mPER2::LUC) fusion protein (Yoo et al. 2004). Practically all

mammalian cells have now been shown to be able to maintain rhythmic

oscillations ex vivo and for extended periods of time (Yoo et al. 2004). These

oscillations often present different phases in different tissues. It is generally

believed that the SCN is responsible for co-ordinating the phase of the peripheral

oscillators as without the SCN, these set phase differences between tissues are not

maintained (Yoo et al. 2004). So while the SCN might not be required to drive the

peripheral oscillators, it appears to be necessary for coordinating the phases of the

different peripheral oscillators and entraining them to the LD cycle.

1.2.2 Anatomy

The SCN is a dense collection of small neurons (~10μm) located both ventrally and

medially in the anterior hypothalamus, just superior to the optic chiasm (Paxinos &

Franklin 2001; Abrahamson & Moore 2001; Hofman et al. 1996). The two lung-

shaped nuclei are separated by the third ventricle (Paxinos & Franklin 2001;

Abrahamson & Moore 2001; Hofman et al. 1996). Humans are reported to have

about 42,000 neurons per nucleus (Hofman et al. 1996); whilst in mice there are

approximately 10,500 neurons per nucleus (Abrahamson & Moore 2001). Each

murine nucleus extends ~300μm laterally, ~350μm dorsally and ~600μm caudally

(Abrahamson & Moore 2001).

1.2.2.1 Core–Shell Distinction

In the literature the SCN is commonly divided into two regions delineated by their

neuropeptide expression. The inner region is termed the ‘core’ and is surrounded

by the outer ‘shell’ region. The location of the ‘core’ and ‘shell’ regions can vary

between species. In mice the core-shell distinction is less well defined but there is

still a strong neuropeptide divide (Abrahamson & Moore 2001). In the mouse, the

core region is located ventrally, whilst the shell region extends ventrolaterally

from the dorsal aspect (Figure 1.3A). There is some contention over the core-shell

The Suprachiasmatic Nuclei: Neuropeptides


nomenclature as being an oversimplification of the SCN structure and as such an

inappropriate model (Morin et al. 2006). Regarding models, I am a firm believer in

the words of George Box: ‘’All models are wrong, but some are useful’’(Box 1954).

Whilst the core-shell divide might be an oversimplification, it can still be a very

practical and useful distinction.

1.2.3 Neuropeptides

Whilst the majority of the neurons in the SCN produce GABA, they are far from a

homogeneous population. Currently, SCN neurons are classified based on

neuropeptide expression. These neuropeptides are expressed in distinct

overlapping areas (Figure 1.3B) and may be co-expressed in some neurons

(Abrahamson & Moore 2001).

The most prominent are the arginine vasopressin (AVP) expressing cells which

contribute ~20% of the neurons in the mouse SCN (Abrahamson & Moore 2001).

AVP has been linked to the robustness of the clock to perturbations such as those

that induce clock resetting (Yamaguchi et al. 2013). The second most extensive

populations are the vasoactive intestinal protein (VIP) expressing neurons and the

angiotensin II (AII) expressing neurons which each constitute ~10% of the SCN

(Abrahamson & Moore 2001). VIP signalling is considered pivotal for maintaining

synchrony with the SCN (Maywood et al. 2006). Other neuropeptides that are

expressed are Calretinin (CalR) ~10%, Neurotensin (NT) ~7.5%, Gastrin releasing

peptide (GRP) ~5% and met-Enkephalin (mENK) ~5% (Abrahamson & Moore

2001). Cells that express VIP, CalR, GRP and NT are found within the core region

whilst cells that express AVP, mENK and AII are located within the shell region

(Abrahamson & Moore 2001) (Figure 1.3B).

Interestingly VIP (Reed et al. 2001; Piggins et al. 1995), GRP (Maywood et al. 2006;

Piggins et al. 1995) and NT (Meyer-Spasche et al. 2002) which are all found in the

core have been shown to be able to phase shift the SCN suggesting a potential

functional segregation between the core and the shell. Whilst VIP and GRP both

depolarise SCN neurons and produce phase shifts similar to light. NT elicits phase

advance during the day (Meyer-Spasche et al. 2002), comparable to that of the

non-photic response curve.

The Suprachiasmatic Nuclei: Neuropeptides

|31

Figure 1.3: Neuropeptide Expression in the Murine SCN. Core regions are depicted in green and shell regions in lilac. A. Cartoon depicting the Core-Shell delineation in the mouse SCN. B. Expression of the main core (left) and shell (right) neuropeptides in the mouse SCN. Based on the immunohistochemistry study by Abrahamson & Moore (Abrahamson & Moore 2001).

The Suprachiasmatic Nuclei: Synchronicity


In addition to the above neuropeptides, GABA, an inhibitory neurotransmitter, has

also been shown to phase shift the endogenous clock (Liu & Reppert 2000). This

phase shift can be induced by muscimol (a GABAA Receptor agonist) but not

Baclofen (a GABAB Receptor agonist), conferring the importance of GABAA

receptors (Liu & Reppert 2000). When GABAA receptors are active, chloride ions

can flow into the neuron, hyperpolarising it and inhibiting neuronal firing.

However there is some evidence to suggest that GABA can also be excitatory within

the SCN (Wagner et al. 1997; Choi et al. 2008; Irwin & Allen 2009). The exact

percentage of cells that are excitatory and whether this phenomenon occurs only

at a specific circadian time remains a contentious issue (For review see: Albers et

al. 2017). The mechanism behind excitatory GABA appears to involve Na-K-2Cl

(NKCC) and K-Cl (KCC) cotransporters (Choi et al. 2008; Irwin & Allen 2009).

These ion transporters help regulate the ionic gradients across the plasma

membrane, in particular they help maintain the intracellular chloride ion

concentration. If the influx of chloride ions through the NKCC cotransporters is

greater than the efflux of chloride ions through the KCC cotransporters then the

intracellular concentration of chloride ions will be higher than normal. Thus when

GABAA receptors are activated a net efflux of chloride ions is observed which will

depolarise the cell, leading to its excitatory effect (For review see: Benarroch 2013

and Albers et al. 2017).

As mentioned at the start of this section, practically all SCN neurons express GABA

(Abrahamson & Moore 2001) and/or GABA receptors suggesting that GABA plays

an important role in the SCN. One key function is maintaining synchrony between

SCN neurons. Although GABA has been implicated in aiding synchronicity within

the SCN (Liu & Reppert 2000, Evans et al. 2013) there are a few studies that

suggest that GABA may actually reduce synchrony (Freeman et al. 2013, Aton et al.

2006).

1.2.4 Synchronicity

The spontaneous firing rate of the SCN, as a whole, exhibits circadian variation.

The firing rate is lowest during the night and increases to its peak activity during

the day (Brown et al. 2011; Meijer et al. 1998). This is true for both diurnal (Sato &

Kawamura 1984) and nocturnal (Deboer et al. 2003) mammals suggesting the

The Suprachiasmatic Nuclei: Synchronicity

|33

temporal niche is specified downstream of the SCN activity (Smale et al. 2003;

Dardente et al. 2004). Single SCN cells also show the characteristic circadian

oscillation in spontaneous firing rate. This has been shown both in vivo (Brown &

Piggins 2007) and even when dispersed in vitro (Welsh et al. 1995; Liu et al. 1997;

Honma et al. 1998). Whilst the majority of cells show a similar phase there is

evidence for subpopulations expressing a different phase (Welsh et al. 1995;

Honma et al. 1998; Schaap et al. 2003; Brown & Piggins 2009; Yamaguchi et al.

2003). For instance the firing rate of cells in the dorsal SCN peak later in the day

and have a much wider distribution of phases (Brown & Piggins 2009). However,

in brain slices, oscillations in PER1 expression tend to peak earlier in the dorsal

SCN (Yamaguchi et al. 2003). These phase differences appear to be an intrinsic

property of the SCN network (Yamaguchi et al. 2003). Prolonged addition of

cycloheximide to SCN slices resets the phase of all neurons (Yamaguchi et al.

2003). Over time the SCN neurons re-establish the phase differences that were

present prior to cycloheximide treatment (Yamaguchi et al. 2003).

In order for the SCN as a whole to express robust circadian oscillations the

majority of cells must be in phase (Welsh et al. 2004). Dissociation of cells

increases the variation in phase suggesting that there is some sort of intercellular

coupling (Herzog et al. 2004). An ingenious study using SCN grafts to restore

rhythmicity in a host bioluminescent SCN uncovered some potential synchronising

agents (Maywood et al. 2011). SCNs lacking VIP (VIP-/-) are arrhythmic as different

cells have different phases (Maywood et al. 2011). Addition of a WT donor graft

was able to restore rhythms in the host tissue when the tissues were separated by

a 10 kDa semi-permeable membrane but not a 2kDa membrane (Maywood et al.

2011). They proposed that VIP (3.3kDa) is important for maintaining intercellular

synchrony (Maywood et al. 2011). Through addition of specific receptor

antagonists it was also shown that AVP and GRP can also regulate synchrony in the

SCN (Maywood et al. 2011). In addition to this paracrine signalling, synaptic

mechanisms have been implicated in maintaining synchrony. Separating the dorsal

and ventral halves of the SCN, through a knife cut, creates desynchrony in the

dorsal SCN (Yamaguchi et al. 2003). Furthermore tetrodotoxin (TTX), which blocks

Na+ channels, preventing the formation of action potentials, has been shown to

The Suprachiasmatic Nuclei: Light Response


desynchronise SCN neurons (Yamaguchi et al. 2003). This suggests that electrical

activity might also be required for synchronisation of the SCN.

Overall the circadian rhythm in electrical activity and VIP release helps to maintain

synchrony within the SCN. The proposed mechanism is via an increase in

intracellular calcium resulting in cAMP response element-binding protein (CREB)

activation. CREB binds to cAMP response elements (CRE) and promotes CRE

mediated gene expression. CRE sites have been observed in the vicinity of both

mPer1 (Travnickova-Bendova et al. 2002; Yamaguchi et al. 2000) and mPer2

(Travnickova-Bendova et al. 2002) genes. Furthermore there is evidence to

suggest that CRE mediated gene expression does itself show circadian variation

(Obrietan et al. 1999). This in turn might help synchronise the SCN.

The internal synchrony of the SCN appears to be malleable. The amount of

variation in phase between cells has been shown to vary on a circannual basis

(VanderLeest et al. 2007). In addition the synchronicity of the SCN has been shown

to decrease with age, thus reducing the amplitude of the overall circadian

oscillation of the SCN (Farajnia et al. 2012). This reduction in synchronicity may

reflect the decrease in VIP expressing neurons with age, which has been observed

in males (Hofman et al. 1996). This reduction in synchrony has a knock on effect as

the ability to phase shift is also decreased (VanderLeest et al. 2007) .

1.2.5 Light Response

Further to the evidence of the SCN as the central oscillator is its

electrophysiological response to light, which closely mirrors the light induced

clock resetting. In response to light, the firing rate of the SCN increases. The

magnitude of this increase is proportional to the log of irradiance (Meijer et al.

1986; Brown et al. 2011). In addition the increase in firing rate is maintained for

the duration of the light stimulus. Therefore the total number of spikes within a

given time window is a rudimentary measure of the total number of photons

experienced (irradiance x duration), one of the principle parameters that

determines the magnitude of a phase shift (see Chapter1: 1.1.6 Phase Shifting

and Phase Response Curves).


|35

Whilst there has not been an in depth analysis of the sensitivity of the SCN to

different wavelengths, there are a few murine studies that compare neural activity

in the SCN under illumination of either blue versus red light (Brown et al. 2011) or

blue versus UV light (Van Diepen et al. 2013). In both cases the SCN response was

largest to for blue light 460-500nm at moderate to bright irradiances. This is again

consistent with the wavelength sensitivity for clock resetting (see Chapter1: 1.1.6

Phase Shifting and Phase Response Curves).

It has also been shown that the SCN’s response to light is highly dependent on CT

(Brown et al. 2011; Meijer et al. 1998). The SCN is most sensitive to light when its

spontaneous firing rate is low, during early subjective night (Brown et al. 2011).

This is equivalent to when one observes the largest phase delays. Altogether, the

parameters that determine the magnitude of a phase shift are identical to the light

induced increase in SCN activity. It has therefore been hypothesised that during a

light pulse, the time averaged firing of the SCN determines the magnitude of the

phase shift.

Current electrophysiological recordings from the SCN are predominantly from

anaesthetised mice. The threshold and saturation of the SCN light response are

typically around 1012 and 1015 melanopic photons.cm-2.s-1 respectively (Brown et

al. 2011). This is much higher than that observed in behavioural studies (Meijer et

al. 1992), where threshold is ~109 melanopic photons.cm-2.s-1 and saturation

~1011 melanopic photons.cm-2.s-1 (Lall et al. 2010). This has been put down to the

anaesthesia. Indeed urethane, like other anaesthetics, has been shown to reduce

the magnitude of light induced phase shifts in hamsters (Colwell et al. 1993). SCN

light responses have been recorded in awake animals, but without a full

description of the light source it is hard to compare across studies with

anaesthetised animals (Meijer et al. 1998).

Not all the cells within the SCN are light responsive. Predictions for several species

have it as ~70% of SCN neurons are not light responsive (Groos & Mason 1980;

Meijer et al. 1986). Those cells that are light responsive vary in their response,

however the vast majority have a distinctive profile.



The characteristic SCN response to light has an initial transient increase in firing

rate that decreases to a stable raised firing rate that is sustained for the duration of

the light pulse (Figure 1.4A). There is a small proportion of cells that respond with

a transient increase in firing but lack any sustained component (Figure 1.4B)

(Brown et al. 2011). Finally there are a small subset of cells (20-30%) that are

inhibited by light (Figure 1.4C) (Groos & Mason 1980; Groos & Mason 1978;

Meijer et al. 1986). There is some recent evidence to suggest that cells in this

inhibitory subtype are outside of the SCN (Brown et al. 2011). However one must

bear in mind that in order to observe these inhibitory units the spontaneous firing

rate must be sufficiently high. Thus they may be more prevalent during the day

when spontaneous firing is higher. Our lab performs experiments during the early

subjective night as this is when the SCN is most excited by light. However, at this

circadian time the spontaneous firing in the SCN is relatively low and thus we may

observe fewer of these inhibitory cells.

Finally light also has an effect on the core clock machinery. Light during the

subjective night can induce CRE mediated gene expression (Obrietan et al. 1999),

providing a potential mechanism for light induced phase shifting. Further evidence

for the role of CREB in phase shifting has been shown by the fact that: pituitary

adenylate cyclase-activating peptide (PACAP) and glutamate (both excitatory

neurotransmitters released by the retinohypothalamic tract (RHT) into the SCN)

can increase CREB activation (Gall et al. 1998); phase shifts are accompanied by

CREB activation (Gall et al. 1998); and that repressing CREB reduces the

magnitude of phase shifts (Lee et al. 2010).


|37

Figure 1.4: Typical Responses from within the Suprachiasmatic Nucleus. A. A sustained response. B. A transient response. C. An inhibitory response. The red line denotes the 99% confidence limits for each unit based upon the 5 seconds preceding the light pulse.



Figure 1.5: Light’s Effect on the Molecular Clock. Cartoon depicting Per expression (black trace) over a 36 hour period. A. Normal Per expression. B. Per expression following a light pulse late in the subjective night. Light induces a CRE mediated increase in Per expression during the rising phase. The molecular clock is thus phase advanced. C. Per expression following a light pulse early in the subjective night. Light induces a CRE mediated increase in Per expression during the falling phase, thus phase delaying the molecular clock.

The Suprachiasmatic Nuclei: Projections from the SCN

|39

Per expression mirrors the light phase of the LD cycle (Figure 1.5A); rising during

early subjective day, peaking, and then declining during early subjective night

(Belle et al. 2009; Dey et al. 2005; Zheng et al. 2001; Zylka et al. 1998). Light pulses

during the night result in CREB induced Per expression. This increase in PER

protein will alter the dynamics of the negative feedback loop and shift the phase of

the clock (Figure 1.5). Increasing PER during its rising phase will result in earlier

inhibition of CLOCK:BMAL1 induced transcription, and therefore the circadian

oscillator is advanced (Figure 1.5B). By contrast, increasing PER during the falling

phase results in more PER to degrade in order to release CLOCK:BMAL1 from

inhibition. Therefore CLOCK:BMAL1 transcription is inhibited for longer and so the

phase of the clock is delayed (Figure 1.5C). The prediction from the CREB induced

phase delays and advances corresponds nicely with the photic phase response

curve.

1.2.6 Projections from the SCN

Neurons from the SCN have been shown to project to several brain areas, mostly

comprising of areas in the hypothalamus and the midline thalamus (Abrahamson &

Moore 2001; Watts et al. 1987). Two prominent projections from the SCN are to

the subparaventricular zone (SPVZ) and the paraventricular nucleus (Abrahamson

& Moore 2001). One particular projection, of interest to this thesis, is the

projection to the intergeniculate leaflet (IGL) which separates the dorsal and

ventral halves of the lateral geniculate nucleus (Card & Moore 1989). This is

because the IGL is known to project to the SCN providing photic information. It

could be hypothesised that these SCN projections to the IGL might be modulatory.

In addition to the neuronal projections from the SCN, there are diffusible factors

that are important outputs of the SCN. In a study by Silver et al., the SCN was

lesioned in both Tau mutant and WT hamsters. Fetal SCN tissue was then

transplanted into these animals and locomotor activity was restored, with a period

matching that of the donors. However, the fetal SCN was encapsulated in a

semipermeable membrane which allowed only diffusion of small molecules and

not axonal growth (Silver et al. 1996). This study thus proposed that some rhythms

were driven by diffusible factors.

The Suprachiasmatic Nuclei: Projections to the SCN


Further evidence for diffusible factors comes from analysis of the neuropeptides

released by the SCN (Kramer et al. 2005; Hatcher et al. 2008). Many of these

peptides are released with a circadian profile. In addition electrical stimulation of

the RHT that innervates the SCN produces a marked change in many of these

peptides profiles (Hatcher et al. 2008). As the SCN is located either side of the third

ventricle it has been hypothesised that these diffusible factors act via secretion

from the SCN into the cerebral spinal fluid (CSF) in the third ventricle. Indeed

arginine vasopressin (expressed by ~20% of murine SCN cells) is observed in the

CSF with a circadian profile (Reppert et al. 1981).

Through fetal transplants many circadian rhythms can be restored, however

neuroendocrine rhythms remain abolished (Meyer-Bernstein et al. 1999). This

may reflect that different circadian rhythms are regulated through different

pathways; some requiring diffusible factors and others synaptic connections.

1.2.7 Projections to the SCN

There are three main inputs to the SCN, two provide photic information and the

third provides serotonergic input from the median raphe nuclei. The predominant

input is via the RHT. This photic information comes directly from the retina and

innervates predominantly the ventral/core SCN (Morin et al. 2006; Abrahamson &

Moore 2001; Dai et al. 1998; Moore & Lenn 1972; Lokshin et al. 2015). Many

studies report RHT projections throughout the entire SCN (Morin et al. 2006;

Abrahamson & Moore 2001; Hattar et al. 2010; Fernandez et al. 2016; Moore &

Lenn 1972). However, a recent murine study which used sagittal rather than

coronal sections, found a lack of RHT projections in the most rostral areas of the

SCN (Lokshin et al. 2015).

The SCN receives input from both ipsilateral and contralateral retinas (Morin et al.

2006; Dai et al. 1998; Abrahamson & Moore 2001; Magnin et al. 1989; Moore &

Lenn 1972; Hattar et al. 2010); the proportion of which varies between species

(Dai et al. 1998; Magnin et al. 1989). Mice receive approximately equal proportions

of innervation from the two eyes (Abrahamson & Moore 2001; Magnin et al. 1989;

Hattar et al. 2010), which is unlike the rest of their visual system. Whilst previous

studies in the cat observed only monocular input to SCN neurons (Groos & Mason

The Suprachiasmatic Nuclei: Projections to the SCN

|41

1980), a recent publication has shown that individual SCN cells can receive

binocular input in the mouse (Walmsley & Brown 2015).

The Newton-Muller-Godden law states that the proportion of decussation in the

optic chiasm is related to the laterality of the eyes. Animals that have lateral eyes

with minimal binocular overlap have almost all fibres projecting to the

contralateral side of the brain. Animals that have forward facing eyes and a large

binocular zone have a larger degree of decussation at the optic chiasm. In these

animals the retinal innervation from each eye to each half of the brain is almost

equal. Unlike the rest of the visual system the SCN does not follow this law. In

primates the SCN receives predominantly ipsilateral innervation (Magnin et al.

1989). It is proposed that humans have comparable RHT projections to the SCN.

Work done by Dai et al. (1988) showed there was a large ipsilateral innervation to

the SCN. Contralateral innervation, however, was not recorded so proportional

input can’t be confirmed. The RHT co-expresses PACAP and glutamate (Hannibal et

al. 2000) which are both excitatory neurotransmitters.

The second input from the retina is indirect input as the information is initially

sent to the IGL. This photic information then reaches the SCN via the

geniculohypothalamic tract (GHT). The GHT may innervate the entire SCN (Morin

et al. 2006) but predominantly innervates the ventral/core SCN neurons (Morin et

al. 2006; Abrahamson & Moore 2001). In rats, it has been shown that a single IGL

innervates both suprachiasmatic nuclei, however most input is from the ipsilateral

IGL (Card & Moore 1989). The GHT expresses Neuropeptide Y (Card & Moore

1989) which has an inhibitory effect on the SCN (Van Den Pol et al. 1996).

Retina: Importance of the Retina for the SCN


1.3 Retina

1.3.1 Importance of the Retina for the SCN

A retinorecipient SCN has been observed in all vertebrate classes: birds (Cassone &

Moore 1987; Cooper et al. 1983), reptiles (Ebbesson & Karten 1981; Janik et al.

1994), Amphibians (Tuinhof et al. 1994), fish (Burrill & Easter 1994) and

mammals (Morin et al. 2006; Abrahamson & Moore 2001; Dai et al. 1998; Moore &

Lenn 1972). Yet despite this retinal input, via the RHT, most vertebrates do not

require their eyes to entrain to a light dark cycle. Extraocular photoreceptors such

as the deep brain photoreceptors in the pineal organ have been linked with

photoperiodic responses in birds (Foster et al. 1985; for review see: Benoit 1964),

reptiles (Underwood & Menaker 1970; Underwood & Menaker 1976), fish (Dodt

1963) and amphibians (Fraile et al. 1989). Unlike those vertebrates, mammals do

require their eyes for photoentrainment (Nelson & Zucker 1981; Yamazaki et al.

1999; Mosko & Moore 1978; Groos & van der Kooy 1981; Pohl & Gibbs 1978) as

bilateral enucleation (the removal of the eyes) abolishes photoentrainment

(Nelson & Zucker 1981; Yamazaki et al. 1999). One potential caveat of these

studies is that laboratory lighting is much dimmer than natural lighting. In order to

finally show that any potential extraocular photoreceptors in mammals do not

contribute to photoentrainment, Nelson and Zucker repeated those enucleation

studies in an outdoor environment. Despite the increased irradiance, the

enucleated animals failed to entrain (Nelson & Zucker 1981). One study has since

claimed to have observed extraocular photoentrainment in humans through light

exposure to the popliteal fossa, the area behind the knee (Campbell & Murphy

1998). Experiments to repeat this finding under more rigorous and light controlled

environments have failed to replicate the results (Lockley et al. 1998; Wright &

Czeisler 2002). Thus it has been concluded that the eyes are necessary for

photoentrainment in mammals.

Retina: Importance of the Retina for the SCN

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Figure 1.6: The Basic Structure of the Mammalian Retina. Light passes from the bottom of this figure to the top, where it is detected by the rod (purple) and cone (green) photoreceptors. These cells synapse with bipolar cells (blue) which in turn relay the information to the retinal ganglion cells (brown or red). In the case of rod bipolar cells this is via the AII amacrine cells. The axons from the retinal ganglion cells make up the optic nerve and project to various brain regions. This is the vertical pathway. In addition there is the horizontal pathway which is involved with modulating the signal from the vertical pathway. This is comprised of horizontal cells (maroon) and amacrine cells (yellow). A small subset of retinal ganglion cells (red) are intrinsically photoreceptive and are able to detect light even in the absence of rods and cones.

Retina: Overview of the Classical Retinal Pathways


1.3.2 Overview of the Classical Retinal Pathways

Light enters the eye through the pupil and is focused by the lens onto the retina.

The retina is comprised of 6 main cell types that are responsible for the detection,

pre-processing and transmission of photic information to the brain (Figure 1.6).

The so-called vertical pathway (photoreceptors->bipolar cells-> retinal gangion

cells) detects photons and sends the information to the brain, whilst the horizontal

pathways (amacrine and horizontal cells) are involved with the pre-processing of

the photic information. In the vertical pathway photons are detected in the outer

nuclear layer by rod and cone photoreceptors. These cells transform this

information into an analogue signal and relay the information through bipolar cells

and on to retinal ganglion cells (RGCs). It is here that the signal is converted from

an analogue to a digital signal (in the form of action potentials) and relayed to the

brain. The horizontal pathway consists of the horizontal and amacrine cells. These

cells pool the information from multiple cells to allow pre-processing of the signal.

1.3.3 Rods and Cones

Rods and cones are the two types of photoreceptive cell within the mammalian

retina. They are situated in the outer nuclear layer and in dark conditions they

continuously release glutamate onto bipolar cells. Both photoreceptors are highly

specialised to detect photons of visible light which results in a decrease in their

glutamate release. To maximise photon detection, rods and cones have specialised

disk-like structures that are densely packed with photopigment, ~25,000

photopigments per μm (Liebman et al. 1987; Nickell et al. 2007). These disks are

layered upon one another which further increase the likelihood of photon capture.

For example rods contain ~1000 photoreceptor disks per cell (Young 1971; Liang

et al. 2004).

1.3.3.1 Phototransduction Cascade

Photoreceptors contain a light sensitive pigment (chromophore) which is bound

within a G-Protein coupled receptor (opsin). The chromophore present in rods and

cones is retinal. The phototransduction cascade begins when photons are absorbed

by the photopigment located in retinal photoreceptors (Figure 1.7A). Upon

absorbing a photon, 11 –cis retinal undergoes isomerisation into its all-trans state.

Retina: Rods and Cones

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Figure 1.7: The Phototransduction Cascade and Light Responses. A. Phototransduction cascade for the classical mammalian photoreceptors: rods and cones. Here we shall discus the process as occurs in rods. Light induces photoisomerisation of 11-cis retinal, which causes a conformational change in rhodopsin (maroon), thus turning it into its active form metarhodopsin (maroon). Metarhodopsin activates the G protein, transducin (light blue) causing dissociation of the α subunit. The α subunit in turn activates phosphodiesterase (PDE, dark blue). PDE hydrolyses cGMP thereby resulting in a reduction in cGMP (pink). cGMP binds to cGMP gated channels (orange) allowing an influx of cations such as Na+ and Ca2+. The reduction in cGMP results in the closure of the cGMP gated channels and hyperpolarisation of the cell. B. A cartoon depicting the change in membrane voltage in response to a light flash for rods (top) and cones (bottom). As the intensity of the flash increases, the magnitude of the response increases and the latency decreases. C. The phototransduction cascade for melanopsin. Light induces photoisomerisation of 11-cis retinal, which causes a conformational change in melanopsin (maroon). This results in the activation of a Gq type G protein (light blue) causing dissociation of the α subunit. The α subunit in turn activates phospholipase C (PLC, dark blue). PLC hydrolyses PIP2 resulting in the formation of DAG and IP3 (lilac). Through currently unknown mechanisms, this results in the opening of TRPC channels (orange). This allows an influx of Ca2+, which depolarises the cell. D. Melanopsin has a high threshold and long integration time and is thus best explained using a light pulse as opposed to a light flash. Unlike rods and cones, melanopsin depolarises in response to light. As the light intensity increases, the magnitude of the response increases and the latency decreases.



This creates a conformational change in the opsin resulting in the activation of the

G protein transducin. The Gα subunit of transducin dissociates and activates

phosphodiesterase (PDE). PDE hydrolyses cGMP and thus reduces the

concentration of cGMP. This reduction in cGMP results in the closure of cGMP

gated cation channels. As a result the cell hyperpolarises and the tonic release of

glutamate is reduced.

1.3.3.2 Response to light

An increase in light intensity hyperpolarises both rods and cones; resulting in a

decrease in neurotransmitter release (Figure 1.7B). Increasing the magnitude of

the light step both increases the magnitude of the response and decreases the

latency to peak (Schneeweis & Schnapf 1995). Cones are better suited to high

frequency temporal modulation as they have a shorter latency compared to rods

(Schneeweis & Schnapf 1995).

1.3.3.3 Photopigments and their Spectral Sensitivity

When a photon is absorbed by a chromophore, initiating the phototransduction

cascade, the downstream response is the same irrespective of the wavelength of

the photon that was absorbed. This is the fundamental principle of univarience

(Rushton 1972). However the ability for the chromophore to absorb the photon in

the first place is dependent on the wavelength of the photon. The probability that a

photon of a given wavelength will be absorbed is described by the absorption

spectrum of the chromophore. Different chromophores will exhibit a different

absorption spectra profile. The wavelength, where a photon has the maximum

probability of being absorbed is called the peak absorbance, λmax. All mammalian

photoreceptors contain retinal as their chromophore and so have the same basic

absorption profile (Govardovskii et al. 2000). However, as a chromophore is bound

to an opsin, the energy required for photoisomerisation is altered. This results in a

shift of the absorption spectrum. Thus, as different photoreceptors express

different opsins, they are maximally sensitive to different wavelengths of light.

Both the fact that photopigments do not absorb all wavelengths of light equally and

that each photopigment has a different absorption profile, creates a problem when

trying to quantify light. This problem can be resolved using the α-opic irradiance


|47

concept (Lucas et al. 2014). This concept quantifies light as the effective photon

flux for each photopigment of interest. The details of which can be found in the

general methods section (see Chapter2: 2.1 Light Calibration).

Rods contain rod opsin which is most sensitive to blue light, ~500nm (Bowmaker

& Dartnall 1980; Dartnall et al. 1983). Cones can express a range of different

opsins, each with its own peak absorbance. Cones are divided into types based on

the opsin that they express. These distinct populations are responsible for colour

vision. Humans are trichromates and express three types of cone cell: short (S),

medium (M) and long (L). These are so named due to the portion of the visual

spectrum that they are most sensitive to. The corresponding cone opsins are as

follows: S cones contain cyanolabe (λmax : 420nm), M cones expresses chlorolabe

(λmax: 530nm) and L cones which express erythrolabe (λmax: 560 nm) (Schnapf et

al. 1987; Merbs & Nathans 1992; Bowmaker & Dartnall 1980; Dartnall et al. 1983).

1.3.3.4 Sensitivity

Rods are very sensitive and can produce robust responses to single photons

(Baylor et al. 1979; Schneeweis & Schnapf 1995). Cones produce a smaller single

photon response that is indistinguishable from background noise (Schneeweis &

Schnapf 1995). This smaller response at dim flashes may contribute to the

observation that cones can continue to respond to much brighter flashes before

saturating. Thus rods dominate the visual response under low (scotopic) light

levels whilst cones dominate under higher (photopic) levels of light intensity. The

range of both rods and cones is increased through light adaptation.

1.3.3.5 Light Adaptation

Both rods and cones, respond to changes in relative light intensity (contrast) over

space and time. Light adaptation allows contrast to be encoded over a wide range

of light intensities. As the background light intensity increases the magnitude of

the photoreceptor response to a single photon absorption decreases i.e. the gain of

the cellular response to light is reduced thus extending the operating range. This

can be seen in the response to an extended light pulse: after the initial rise, the

amplitude of the response drops. This decrease in amplitude is more prominent in

cones.

Retina: Bipolar Cells


1.3.4 Bipolar Cells

Bipolar cells are glutamatergic neurons that receive input from photoreceptors

and output on to RGCs. There are over 10 types of mammalian bipolar cells,

however here we shall just mention one main distinction (Ghosh et al. 2004;

Masland 2012; Boycott & Wässle 1991; Kolb et al. 1992; Kolb et al. 1981). Bipolar

cells express either ionotropic or metabotropic glutamate receptors (for review:

Brandstätter & Hack, 2001). Those bipolar cells that express the ionotropic

glutamate receptors, AMPA or Kainate, hyperpolaralise in response to light (Puller

et al. 2013) and are thus termed OFF bipolar cells. By contrast, those bipolar cells

that express the metabotropic glutamate receptor mGluR6, depolarise in response

to light (Vardi & Morigiwa 1997; Nakajima et al. 1993) and are termed ON bipolar

cells. Both classes of bipolar cell form synapses with RGCs within the inner

plexiform layer (IPL). However the IPL can be divided into two distinct

sublaminae: an ‘On’ layer and an ‘Off’ layer (Figure 1.6) (Nelson et al. 1978; Peichl

& Wässle 1981; Famiglietti & Kolb 1976). The On and Off layers are where the ON

and OFF bipolar cells synapse respectively.

1.3.5 Retinal Ganglion Cells

The final units of the vertical pathway are the RGCs. These cells are located in the

ganglion cell layer and synapse with bipolar cells in the IPL. Like bipolar cells,

RGCs can be divided into ON and OFF subtypes. This corresponds to both their

response to light and the IPL sublamina upon which they synapse with bipolar

cells (Nelson et al. 1978; Peichl & Wässle 1981; Famiglietti & Kolb 1976). There are

over a dozen types of RGCs which can be identified based on their morphology,

response, projections and gene expression (Kolb et al. 1992; Kolb et al. 1981) (For

general reviews see: Masland, 2001, 2012).

1.3.6 The Horizontal Pathway

In addition to the vertical pathway, photic signals propagate and are processed

horizontally across the retina via the horizontal pathway. There are two cells that

contribute to the horizontal pathway, horizontal cells, which will be discuss first,

and amacrine cells. Together these cells help modulate the photic signal from the

vertical pathway.

Retina: Horizontal Cells

|49

1.3.7 Horizontal Cells

Horizontal cells are found within the inner nuclear layer. Their dendrites project

over a large area in the outer plexiform layer where they receive input from many

rods and cones. The integrated signal is then fed back onto both the

photoreceptors themselves and onto the bipolar cells. This feedback is known as

lateral inhibition. Lateral inhibition acts to reduce redundant information and

emphasises differences (Figure 1.8A). Lateral inhibition is pivotal in providing the

antagonistic centre-surround receptive fields that are observed in both the bipolar

cells and downstream RGCs.

As described in the vertical pathway section, bipolar cells receive direct input from

either rods or cones. Depending on the glutamate receptors expressed on the

bipolar cells, the bipolar cell will either be classified as an ON or an OFF cell. This

description characterises the centre of the bipolar cell’s receptive field. However,

bipolar cells also receive light input from a much wider area thanks to the

horizontal cells. Because of the sign inversion of the horizontal cell’s input, this

produces an antagonistic surround. Thus in an ON centre bipolar, the surround

would be inhibitory. Due to this inhibitory surround, the response to a diffuse light

is relatively small because it is dampened by the surround. To maximally excite or

inhibit these cells either only the centre or the surround must be illuminated

(Figure 1.8B).

Ultimately centre-surround enhances the cell’s response to spatial contrast.

Depending on the photoreceptor contribution this could be a contrast in irradiance

or in colour. Thus, centre-surround antagonism allows cells to encode additional

information such as colour and edges.

1.3.8 Amacrine cells

Amacrine cells are located in the inner nuclear layer and form inhibitory synapses

with both RGCs and bipolar cells in the IPL. They are a diverse population in both

morphology and function. AII amacrine cells pool information to aid light detection

in dim light environments at the cost of spatial acuity. Starburst amacrine cells

have asymmetric dendrites and are direction selective, responding to movement in

a particular direction. Dopaminergic amacrine cells control dopamine levels in the

Retina: Amacrine cells


Figure 1.8: Lateral Inhibition and Centre-Surround Antagonism. A. A cartoon depicting lateral inhibition which acts like data compression, to highlight changes such as edges. Top panel is a light stimulus falling onto many photoreceptors. Middle panel is the output of these cells without any lateral inhibition. Bottom panel is the output of these cells with lateral inhibition. The lateral inhibition here is the average of the two immediately neighbouring cells. B. A cell exhibiting ON centre, centre-surround antagonism. The cell is maximally excited when only the centre of the cell is illuminated. When the whole cell is illuminated the response is reduced. The response can be further reduced if only the surround annulus is illuminated.

Retina: Rod Signalling Pathways

|51

retina. Dopamine plays a role in light adaptation, effecting rod transmission via AII

amacrines (For review see: Witkovsky, 2004).

1.3.9 Rod Signalling Pathways

The vertical pathway outlined above describes predominantly the cone signalling

pathway. There are two, possibly three pathways whereby photic information

from rods is relayed to RGCs. Here we shall mention how these pathways differ

from the aforementioned vertical pathway above.

The principal pathway consists of rods synapsing onto ON rod bipolars. Unlike the

vertical pathway, these bipolar cells do not synapse onto RGC but rather synapse

onto AII amacrine cells. These amacrine cells form both sign conserving

connections (gap junctions) with ON cone bipolar cells and sign inverting

connections (synaptic release of glycine) with OFF cone bipolar cells. These ON

and OFF cone bipolar cells then synapse onto their respective RGCs.

In the second pathway, rods relay photic information through cones themselves.

This signal eventually reaches the RGCs through the conventional vertical pathway

described above. Rods are able to influence cone activity though rod::cone gap

junctions (Ribelayga et al. 2008; Raviola & Gilula 1973; Völgyi et al. 2004;

Schneeweis & Schnapf 1995). These gap junctions are circadian regulated by the

release of dopamine.

Lastly there is some evidence that in some mammalian species some cone bipolar

cells receive direct rod innervation (Protti et al. 2005; Li et al. 2004; Tsukamoto et

al. 2007). In this situation photic information from rods would proceed through

the conventional vertical pathway, described above, to RGCs.

Intrinsically Photoreceptive RGCs (ipRGCs): Discovery


1.4 Intrinsically Photoreceptive RGCs (ipRGCs)

1.4.1 Discovery

In the late 90’s the classical model of mammalian vision was unable to explain

several novel observations. Firstly non-image forming responses to light such as

entrainment and melatonin suppression were observed in clinically blind

individuals (Czeisler et al. 1995; Klerman et al. 2002). Further evidence

accumulated through murine models of retinal degeneration; despite the lack of

rod and cone photoreceptors these retinally degenerate mice exhibited non-image

forming responses such as photoentrainment (Foster et al. 1991; Yoshimura &

Ebihara 1996; Yoshimura et al. 1994; Freedman et al. 1999) and a pupillary light

reflex (Lucas et al. 2001). These non-image forming responses were most sensitive

to blue light, ~480 nm (Lucas et al. 2001; Yoshimura & Ebihara 1996; Hankins &

Lucas 2002), which was spectrally distinct from the λmax of the retinal

photoreceptors known at that time. Since mammalian photoentrainment requires

the eyes (Nelson & Zucker 1981), it was proposed that there must be an additional

undiscovered photoreceptor in the eyes.

In 2000 a novel opsin, melanopsin, was discovered in the ganglion cell layer of the

mammalian retina (Provencio et al. 2000). Two years later it was shown that

melanopsin was expressed in a subset of RGCs (mRGCs) that projected to the SCN

(Berson et al. 2002; Hattar et al. 2002). These mRGCs were shown to respond

directly to light in the absence of synaptic transmission from rods and cones

(Berson et al. 2002; Hattar et al. 2002). Hence these mRGCs are known as

intrinsically photoreceptive RGCs (ipRGCs).

Further studies using triple knockouts, affecting all three classes of photoreceptor,

abolished both image and non-image forming responses including

photoentrainment (Hattar et al. 2003; Panda et al. 2003). These studies suggest

that these three types of photoreceptors are responsible for all the image and non-

image forming responses in mammals. In addition, the ablation of ipRGCs

abolished non-image forming response, suggesting that ipRGCs are required for

these non-image forming responses such as photoentrainment (Güler et al. 2008;

Hatori et al. 2008).

Intrinsically Photoreceptive RGCs (ipRGCs): Phototransduction

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1.4.2 Phototransduction

Unlike the traditional mammalian photoreceptors, ipRGCs depolarise to light. This

implies that they possess a different phototransduction cascade. Whilst many of

the specifics of the pathway remain to be uncovered, the general mechanism is

remarkable similar to that of the well-studied phototranduction cascade of

Drosophila (Graham et al. 2008). Here we shall first present the Drosophila

phototransduction cascade (For review see: Hardie & Raghu, 2001; Hardie, 2012)

and then provide evidence for shared components in the melanopsin

phototransduction pathway (Figure 1.7C).

Drosophila rhodopsin expresses 11-cis-retinal, which can undergo

photoisomerisation. This conformational change transforms the rhodopsin into its

active state of metarhodopsin. The Gq-type G protein which is bound to the opsin is

then activated by the conformational change of the opsin. This results in the

release of the α subunit of the G protein which then binds to phospholipase C (PLC)

and activates it. PLC hydrolyses the membrane bound phosphatidylinositol 4,5-

bisphosphate (PIP2) to produce membrane bound diacylglycerol (DAG), inositol

1,4,5-trisphosphate (IP3) and protons. The hydrolysis of PIP2 ultimately leads to

the opening of transient receptor potential (TRP) and TRP-like channels resulting

in an influx of primarily Ca2+. The mechanism leading to the opening of the TRP

channels is unknown, but it has been linked to PIP2, the release of protons and

there is some evidence for DAG too. One hypothesis suggests that the TRP channels

are mechanosensitive and that the hydrolyses of PIP2 causes contraction of the

lipid bilayer which triggers their opening (Hardie & Franze 2012).

The main difference in the melanopsin pathway is the difference in opsin, from

rhodopsin to melanopsin, and therefore the shift in wavelength sensitivity.

Evidence suggests that melanopsin is most likely bound to a Gq/11 type G-protein

(Hughes et al. 2014; Graham et al. 2008). However, there is also evidence

suggesting that Gq/11 type G-proteins are not required for melanopsin based

phototransduction (Chew et al. 2014). There is strong evidence for the

involvement of PLC in melanopsin transduction as the β4 isoform of PLC is present

in all ipRGCs (Graham et al. 2008). In addition, ipRGCs from mice lacking PLC β4

(Plcb4-/-) showed no detectable light response (Xue et al. 2011). Finally a PLC

Intrinsically Photoreceptive RGCs (ipRGCs): Response to light


antagonist (U73122) can abolish the light response in ipRGCs (Graham et al. 2008).

In Xue’s paper where the PLC antagonist did not block melanopsin

phototransduction the authors claim that their result was due to poor drug

penetration (Xue et al. 2011). Evidence for TRP channels in melanopsin

phototransduction has also been observed. The mammalian homolog of the

Drosophila TRP and TRP-like channels are the canonical TRP (TRPC) channels.

Generic TRPC channel inhibition using drugs prevents the ipRGC light response

(Sekaran et al. 2007; Hartwick et al. 2007; Warren et al. 2006). Mice lacking TRPC6

or TRPC7 showed responses comparable to WT animals (Xue et al. 2011). However

mice lacking both TRPC6 and TRPC7 did not possess functioning ipRGCs (Xue et al.

2011). Both TRPC6 (Sekaran et al. 2007; Warren et al. 2006) and TRPC7(Sekaran

et al. 2007; Hartwick et al. 2007) have been observed in mRGCs.

The mechanism underlying the opening of the TRP channels is still unknown,

however inside out membrane patches were still light responsive, suggesting that

neither intracellular Ca2+ nor IP3 are required for the melanopsin light response

(Graham et al. 2008). In addition, application of OAG and DOG (analogues of DAG)

was unable to evoke a response, implying a DAG independent pathway (Graham et

al. 2008; Warren et al. 2006). Support for the mechanosensitive mechanism in

ipRGCs comes from a study showing that TRPC6 can act as a mechanosensitve

channel when expressed in HEK and CHO cells (Spassova et al. 2006).

1.4.3 Response to light

Melanopsin expressing RGCs express the same chromophore as rods and cones -

11-cis retinal (Lucas et al. 2001; Melyan et al. 2005). Thus melanopsin has the

same characteristic vitamin A based absorption profile (Govardovskii et al. 2000).

For melanopsin the peak sensitivity is in the blue region of the visual spectrum,

~480nm (Berson et al. 2002; Hattar et al. 2002; Dacey et al. 2005; Tu et al. 2005).

Unlike rods and cones, ipRGCs do not have specialised structures for increasing

photon capture. In fact, as ipRGCs are located in the ganglion cell layer they

specifically want a low probability for photon capture as light must pass through

the ganglion cell layer to reach the rod and cone photoreceptors underneath. If

ipRGCs contained a high concentration of photopigment then they would cast a


|55

shadow over the photoreceptors beneath them. Instead melanopsin is expressed

uniformly throughout the cells soma and dendrites (Hattar et al. 2002; Do et al.

2009) at a low concentration of ~3 photopigments per μm, which is ~107 x less

than in rods (Do et al. 2009). This compromise, of a large area for photon capture

but low concentration, increases the likelihood of photon capture whilst still

allowing the majority of photons to pass through the ipRGC to the photoreceptive

layer underneath. This low photon capture, results in ipRGCs having a relatively

high threshold compared with rods and cones. The ipRGC threshold was originally

reported at about 5 x 1011 melanopic photons.cm-2.s-1 (Berson et al. 2002; Dacey et

al. 2005). However a recent study using a long duration light step, showed that

ipRGCs can respond robustly to stimuli as dim as 4 x 1010 melanopic photons.cm-

2.s-1 (Wong 2012). Like rods and cones, increasing light intensity results in an

increase in the amplitude of the ipRGC response and a decrease in its latency to

peak. (Figure 1.7D)

The ipRGC response to light (both depolarisation and firing rate; Figure 1.9)

shows a typical dose response relationship (Dacey et al. 2005; Wong 2012; Tu et al.

2005). Above threshold the ipRGC response increases linearly with respect to the

log10 of irradiance (Dacey et al. 2005; Wong 2012; Tu et al. 2005) until the

response saturates at ~ 5 x 1014 melanopic photons.cm-2.s-1 (Berson et al. 2002;

Dacey et al. 2005; Schmidt & Kofuji 2009; Schmidt & Kofuji 2011). The melanopsin-

driven response is very slow, taking 2s to peak at saturating light levels and up to

30minutes at threshold levels (Berson et al. 2002; Schmidt & Kofuji 2009; Wong

2012). This is several orders of magnitude slower than the rod and cone responses,

but allows a greater temporal integration of photons.

Despite this high threshold, melanopsin is responsive to single photons (Do et al.

2009). The ipRGC single photon response produces a current of about 1pA (Do et

al. 2009) which is about double that of murine rods (Chen et al. 1999). The

membrane potential of the ipRGC is kept near spike firing threshold in dark such

that a single photon response can induce a change in firing rate (Do et al. 2009).



Figure 1.9: A Typical Melanopsin Irradiance Response Curve. Melanopsin shows a dose response relationship to light. Over the majority of the working range the response is linear with respect to the log of irradiance shown here but the red line.

Figure 1.10: Melanopsin Light Response Profile. A cartoon depicting the characteristic profile for the melanopsin component of an ipRGC light response. Such a response is observed when ipRGCs are recorded under synaptic blockade to remove rod and cone input. The response slowly rises to reach a peak firing rate and then decays to a steady sustained firing rate. This decay to a sustained response is characteristic of light adaptation. The light response often continues even after light offset as shown here. Below is the profile of the light stimulus.

Intrinsically Photoreceptive RGCs (ipRGCs): Subtypes of ipRGCs

|57

This single photon current is relatively large, consisting of ~0.5% of the maximal

current observed by ipRGCs under a saturating light stimulation (Do et al. 2009).

In addition the response duration to a single photon is 20 times longer than that of

murine rods and 100 times longer than rodent cones(Do et al. 2009). The result is

a much longer integration time. Thus the response threshold to a light step is lower

than for a flash (Do et al. 2009).

In response to light the firing rate of ipRGCs increases. For a sustained pulse of

light, the ipRGCs responds for the duration of the light pulse (Berson et al. 2002;

Schmidt & Kofuji 2009; Schmidt & Kofuji 2010; Schmidt & Kofuji 2011; Wong

2012; Tu et al. 2005). Even after the light offset the ipRGC will maintain a raised

firing rate for some time, before slowly returning to baseline (Figure 1.10)

(Schmidt & Kofuji 2009; Schmidt & Kofuji 2011; Schmidt & Kofuji 2010; Wong

2012; Tu et al. 2005). Typically the response will peak and then decline to a raised

stable and sustained firing rate (Figure 1.10) (Warren et al. 2006; Wong 2012).

This is typical of light adaptation. This sustained response has been shown to be

stable for the duration of the light pulse for up to 10 hours (Wong 2012). In

addition the time averaged firing responds linearly with respect to the log of the

irradiance (Dacey et al. 2005; Wong 2012). Hence, whilst rods and cones are

efficient at detecting changes in irradiance (contrast), ipRGCs act more as a photon

counter and encode irradiance as opposed to contrast.

1.4.4 Subtypes of ipRGCs

There are currently 5 known subtypes of ipRGC, termed M1-M5. Subtypes were

sequentially discovered as evermore sensitive techniques for detecting melanopsin

expression were developed. M1 cells, discovered first, express the most

melanopsin (Schmidt & Kofuji 2009), whilst the latest discoveries, M4 and M5 cells

express the least. The different subtypes are distributed in a dorsal-ventral

gradient in the murine retina. M1 and M2 cells are more densely distributed in the

dorsal retina, whilst M4-M5 seem to be more densely distributed in the ventral

retina (Hughes et al. 2013).

All ipRGCs respond to light with a sustained excitation. The M2-M5 subtypes also

express centre-surround antagonism, which is not observed in M1 cells (Zhao et al.

Intrinsically Photoreceptive RGCs (ipRGCs): Projections


2014). The centres correspond to roughly the size of the ipRGC’s dendritic arbour,

whilst the surround is much larger. M1 cells have been proposed to encode

irradiance. The lack of an observable centre-surround is beneficial for an

irradiance detector. There are many morphological and electrophysiological

differences between subtypes (Schmidt & Kofuji 2009; Schmidt & Kofuji 2010;

Schmidt & Kofuji 2011; Tu et al. 2005), however individual ipRGCs are most

commonly classified into subtypes based on dendritic projections and size.

1.4.5 Projections

IpRGCs project to a variety of brain regions such as; the SCN (Baver et al. 2008;

Hattar et al. 2010; Hattar et al. 2002), the intergeniculate leaflet (Hattar et al. 2010;

Hattar et al. 2002), the lateral geniculate nucleus (Hattar et al. 2010), the olivary

pretectal nucleus (Baver et al. 2008; Hattar et al. 2010; Hattar et al. 2002), the

superior colliculus (Hattar et al. 2010; Baver et al. 2008; Zhao et al. 2014) and even

the ventral SPVZ (Hattar et al. 2010), a brain region that receives direct SCN input

from VIP expressing cells (Abrahamson & Moore 2001). Each area receives input

from a different composition of ipRGC subtypes (Baver et al. 2008; Estevez et al.

2012).

The retinal innervation of the SCN consists almost entirely of ipRGCs. Evidence for

this comes from targeted ablation studies of the ipRGCs (using diphtheria toxin)

which abolished photoentrainment (Hatori et al. 2008; Güler et al. 2008) and from

immunohistochemistry (Baver et al. 2008). This latter study reported that 80% of

the ipRGC innervation was from M1 cells and the rest M2 (Baver et al. 2008).

Most ipRGCs express the transcription factor Brn-3b (Jain et al. 2012). These

include all non-M1 ipRGCs as well as some M1 cells (Jain et al. 2012). M1 cells that

express Brn-3b project to different brain regions than those that do not express

Brn-3b (Chen et al. 2011). Brn-3b –ve M1 cells are sufficient for photoentrainment

but not for other non-image forming responses like the pupillary light response

(Chen et al. 2011). It has not yet been shown if Brn-3b positive cells are sufficient

for photoentrainment. To conclude, the SCN receives photic input from a subset of

ipRGCs. These include both Brn-3b positive and negative M1 cells as well as M2

ipRGCs.

Intrinsically Photoreceptive RGCs (ipRGCs): Synaptic Inputs to ipRGCs

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1.4.6 Synaptic Inputs to ipRGCs

Despite being intrinsically photosensitive, ipRGCs are first and foremost retinal

ganglion cells. As such, they form synapses with bipolar cells in the IPL and

therefore receive photic input from both rods and cones. The different ipRGC types

synapse at different IPL sublaminae. The main distinction between M1-M3 cells are

the IPL projections. M1 cells synapse in the OFF sublamina, M2 cells synapse in the

ON sublamina and M3 cells synapse in both. Despite this segregation, all 3

subtypes are ON cells, even in the absence of any intrinsic signal (Schmidt & Kofuji

2010; Dacey et al. 2005). The mechanism by which M1 cells receive ON responses

is through en-passent synapses. These are synapses that are formed as the M1

cell’s dendrites pass the ON bipolars’ as they project into the OFF sublamina

(Dumitrescu et al. 2009). However in the presence of L-AP4, which blocks cone ON

bipolar signalling, an ON inhibition driven by OFF bipolar cells can be observed

(Wong, Dunn, et al. 2007).

To investigate the photoreceptor contribution on ipRGCs subtypes, ipRGCs were

recorded, in vitro, from mice lacking the gene encoding melanopsin (OPN4-/-). In

the absence of melanopsin, M1 cells showed a weak synaptically driven light

response, whilst the light response of M2 cells were comparable with their WT

counterparts. Furthermore, addition of synaptic blockers (i.e. L-AP4), had little

effect on WT M1 cell’s responses but significantly reduced the response of M2 cells

(Schmidt & Kofuji 2010). This implies that M2 cells are predominantly driven by

synaptic input from the other photoreceptors whilst M1 cells are predominantly

intrinsically driven by melanopsin (Schmidt & Kofuji 2010).

Through this extrinsic response (from rods and cones), the photoreceptive range

of ipRGCs is extended. Using a broad spectrum tungsten light, ipRGCs were

observed to respond to light 6 orders of magnitude dimmer (Wong, Dunn, et al.

2007). In addition the characteristic sustained response was observed at

intensities four orders of magnitude lower suggesting that this might be an

intrinsic property of the ipRGC (Zhao et al. 2014; Wong, Dunn, et al. 2007). This

was further backed up by the presence of sustained responses to light in ipRGCs

from OPN4-/- mice (Schmidt & Kofuji 2010). The slow return to baseline firing was

also observed at dimmer intensities but only those intensities a tenth as bright,

Intrinsically Photoreceptive RGCs (ipRGCs): Photoreceptor Contribution to Light-Evoked Activity in the SCN


suggesting that it might be melanopsin driven (Wong, Dunn, et al. 2007). Finally in

the absence of extrinsic signals the latency to respond is greatly increased (Dacey

et al. 2005; Wong, Dunn, et al. 2007). Thus both rods and cones help to alter the

dynamics and the sensitivity of ipRGCs (Dacey et al. 2005; Zhao et al. 2014; Wong,

Dunn, et al. 2007).

1.4.7 Photoreceptor Contribution to Light-Evoked Activity in the SCN

Photic information from rods, cones and melanopsin are all encoded by the

ipRGCs. This raises the question of whether such information reaches and is

encoded by the SCN. A battery of evidence suggest that not only do rods and cones

contribute to the SCN firing rate but that they also contribute to its outputs such as

behavioural activity and melatonin suppression.

Firstly melanopsin is not necessary as experiments using mice lacking melanopsin

(OPN4-/-) show that the SCN still increases firing in response to light (Van Diepen

et al. 2013; Van Oosterhout et al. 2012; Mure et al. 2007), and that these mice can

photoentrain and phase shift (Panda et al. 2002; Panda et al. 2003; Hatori et al.

2008).

Secondly the circadian clock in rats (McCormack & Sontag 1980) and humans

(Zeitzer et al. 1997) has been reset using red light, too dim for melanopsin

activation. Mice lacking M cone opsin (TRβ1-/- and TRβ2-/-) show reduced phase

shifts to relatively brief (<5mins) light pulses but not to longer durations (Dkhissi-

Benyahya et al. 2007). In addition, repeated one minute light pulses could enhance

the phase shift magnitude compared to a continuous pulse only at longer

wavelengths (Lall et al. 2010). Together these studies suggest that long wavelength

sensitive cones contribute to photoentrainment. Further experiments have also

implicated short wavelength sensitive cones in contributing to the SCN’s light

response (Van Oosterhout et al. 2012). Recently, it has shown that colour may play

an important role in photoentrainment, and that there are colour opponent cells

within the SCN (Walmsley et al. 2015). Colour opponency has been shown in

primate ipRGCs but they seem to project to other brain structures (Dacey et al.

2005). As yet, murine ipRGCs have not been observed to be colour opponent.


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Evidence for rod contribution to photoentrainment has come from comparing a

murine model for retinal degeneration (rd/rd) with WT mice. By 8 weeks of age

rd/rd mice lack any rod photoreceptors (Carter-Dawson et al. 1978). Although

these rd/rd mice showed photoentrainment, the ability to entrain and phase shift

was severely reduced at lower irradiances (Mrosovsky 2003; Yoshimura & Ebihara

1996; Yoshimura et al. 1994). In addition Lall et al. (2010) compared two circadian

outputs (phase shifting and tau lengthening) under different wavelengths of light.

They found that in both cases normalising to rhodopic irradiance produced a

better correction than any other photoreceptor normalisation (Lall et al. 2010).

The corresponding irradiances were mostly subthreshold for melanopsin.

Further evidence towards rod and cone contribution comes from experiments

where a train of millisecond long flashes were used to phase shift mice (Van Den

Pol et al. 1998), hamsters (Vidal & Morin 2007) and humans (Zeitzer et al. 2011;

Zeitzer et al. 2014). Such transient stimuli are strongly biased towards cone

stimulation.

It is now generally considered that the classic SCN response, with its sustained and

transient components is driven by multiple photoreceptor types (Figure 1.11)

(Brown et al. 2011). Evidence for this comes from an experiment where the

wavelength of light was varied to bias the response to one or the other

photoreceptors (Brown et al. 2011). In this study both light stimuli were matched

in M conopic irradiance, however one light stimulus was 5x103 fold greater in

melanopic irradiance. Under the low melanopic conditions the SCN response was

less sustained. This has led to the interpretation that cones are important for

driving the transient component whilst the sustained response is predominantly

driven by melanopsin (Brown et al. 2011). One hypothesis is that all the

photoreceptors help to encode irradiance over the wide range of natural light

levels we experience. Under bright conditions, irradiance is predominantly

encoded by melanopsin with the help of cones. To increase the range of sensitivity,

rods can encode irradiances that are below the threshold for melanopsin.

Altogether there is strong evidence that all of the photoreceptor types contribute

to the encoding of irradiance in the SCN. These photoreceptors have different



properties and light response dynamics. One particular aspect is that as the eye is

predominantly an organ of visual perception, it is therefore highly specialised to

detect high frequency spatial and temporal contrasts. Fast dynamics and adapting

contrast responses are disadvantageous for encoding the global irradiance of the

visual scene. Current experimental and light therapy methodologies ignore the

spatial component by using diffuse light stimuli. This is uncharacteristic of our

experience of natural light exposure.

There is ample literature characterising the effect of light on either SCN firing rate

or clock resetting (Nelson & Takahashi 1991; Nelson & Takahashi 1999; Lall et al.

2010; Brown et al. 2011; Groos & Meijer 1985; Meijer et al. 1986; Meijer et al.

1998). Not surprisingly both outputs are highly correlated. For example an

increase in either light intensity or duration will increase SCN activity and the

magnitude of the phase shift in a dose dependant manner. Likewise both show a

similar phase response curve showing maximal amplitude phase shifts when the

SCN is most light responsive (early subjective night). Despite this strong

correlation there is little evidence to show that there is a causal link between the

two outputs. A recent paper showed that by using optogenetic techniques they

were able to reset the clock though exciting or inhibiting SCN neurons (Jones et al.

2015). An important question to therefore consider is what aspect of neuronal

firing might be important for clock resetting.

In this thesis I aim to probe the extent to which spatial structure might affect the

outputs of the SCN in response to light. The first question would therefore be: how

spatial structure influences the firing rate of SCN neurons? To this end I wish to

record from within the SCN whilst presenting light stimuli which incorporate

spatial structure (patterns).


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Figure 1.11: Photoreceptor Contribution to the Suprachiasmatic Nuclei. The cells involved in producing the SCNs response to light. To the right of each cell is the composite response as it slowly makes its way to the SCN. The response from rods (purple) and cones (green) are relatively transient and predominantly contribute to the transient component of the SCN response. The ON bipolar (blue) inverts the sign of the composite response. The intrinsic melanopsin response from ipRGC (red) is relatively slow and predominantly contributes to the sustained response of the SCN neuron.

Tools and Techniques: Electrophysiology


1.5 Tools and Techniques

1.5.1 Electrophysiology

Neurons transmit information across long distances using action potentials. These

are a series of electrochemical chain reactions that allow the signal to propagate

along an axon. Through various ion transporters, a neuron creates and maintains a

difference in ionic composition between the intra- and extra-cellular

environments. Of particular importance are the sodium/potassium pumps. These

transmembrane proteins use adenosine triphosphate (ATP) to transport sodium

out of the cell and potassium into the cell. This creates both a chemical gradient

and an electrical gradient created by the charges on the ions. The result is that

there is a potential difference (voltage) across the cell membrane, known as the

resting membrane potential. It is this electrochemical gradient that is pivotal to the

generation of action potentials.

A neuron contains two types of voltage gated ion channels that create an action

potential. There is the voltage gated sodium channel and the voltage gated

potassium channel. When the resting membrane potential is perturbed making the

cell more depolarised these voltage gated channels begin to open. The sodium

channel opens first and allows sodium ions to enter the cell down the

electrochemical gradient. This further depolarises the cell increasing the

probability of sodium channels being in the open state and further fuelling

depolarisation. However as the cell further depolarises two things happen. The

sodium channels begin to inactivate, preventing any further entry of sodium ions

into the cell until the cell is repolarised again. In addition the voltage gated

potassium channels begin to open allowing potassium ions to leave the cell. This

results in the cell repolarising and returning to resting membrane potential. As the

neuron repolarises the voltage drops and thus fewer of the potassium channels

remain in the open state. This is the generation of an action potential. However the

whole cell is relatively large and acts as a buffer to small perturbations in ion flux.

Rather than the whole cell depolarising as we have described above, it is really

only a localised patch of the membrane. The deviation, from resting membrane

potential, decays with distance from the site of the action potential. However the

production of an action potential causes such a large localised change in

Tools and Techniques: Mouse model

|65

membrane potential that the neighbouring areas begin to become depolarised

initiating the opening of voltage gated ion channels as outlined above and

propagating the action potential. It is in this way that the electrical signal can

travel along an axon. In theory each action potential could cause further action

potentials in both directions. However because of the inactivation of sodium

channels until that patch of membrane has repolarised again, the action potential

can only travel forward from the soma to synapse.

By using electrodes one can record these small changes in electrical potential in

the brain. This can be achieve using either intra- or extra-cellular electrodes. Here

we use a multi-electrode probe to record extracellular activity from multiple

locations within the SCN at once. One can then filter the signal to detect large

(≥35μV) transient (~500 μs) deviations in the signal that correspond to action

potentials. This allows us to determine the electrical activity of neurons within the

SCN.

1.5.2 Mouse model

In order to record extracellular activity from the SCN we require a well validated

mammalian model. Hamsters, rats and mice are well validated mammalian models

for circadian research. All of our studies are conducted in mice as our lab has a

long expertise using this model as well as the wide range of genetically engineered

mouse lines that we could use to perturb the circadian or visual systems.

1.5.3 Murine Visual System

Several aspects of the mouse (Mus musculus) retina substantially differ from our

own. First and foremost, these mice are nocturnal and their retinas are thus rod

dominant (Carter-Dawson & LaVail 1979). Unlike humans, they do not possess a

highly concentrated area of cones specialised for high acuity vision such as the

macula, or a visual streak found in other mammals.

Furthermore, like most mammals, but unlike humans, mice are dichromats,

possessing only two types of cone opsin, a short wave length sensitive opsin (S)

and a long wavelength sensitive opsin. The S cone opsin is maximally sensitive to

UV light (~360 nm) (Jacobs et al. 1991). Unlike humans and several diurnal

Tools and Techniques: Murine Visual System


mammals the mouse eye allows a significant amount of UVA (315-400nm)

transmission (Douglas & Jeffery 2014). The long wavelength sensitive cone opsin

has a peak (~510 nm) (Jacobs et al. 1991) more similar to the human M cone than

the L cone opsin. Thus for simplicity the murine long wavelength sensitive cone

will be referred to as the M cone.

Both cone types are distributed across the entire retina (Figure 1.12) with M

cones being more numerous (Haverkamp 2005). However, unlike most mammals,

murine M cones can also co-express the S cone opsin (Röhlich et al. 1994). The

amount of co-expression is dependent on retinal location (Figure 1.12C) (Röhlich

et al. 1994). The S cone opsin is increasingly expressed as one progresses ventrally

across the retina. Thus the murine retina can be divided into three areas; the

dorsal area where the M cones almost exclusively express M cone opsin, the

transitional zone where S cone opsin is increasing and the ventral area where S

cone opsin is predominant in the M cones (Chang et al. 2013; Röhlich et al. 1994).

Thus the dorsal retina expresses predominantly M cone opsin and is sensitive to

long wavelengths of light, whilst the ventral retina expresses mostly S opsin and so

is sensitive to short wavelengths of light (Calderone & Jacobs 1995; Szél et al.

1992; Röhlich et al. 1994; Haverkamp 2005). The transitional zone is interesting as

it allows a non-traditional mechanism for generating weakly colour opponent cells

(Chang et al. 2013). Cells near the transitional zone may have a centre that is

predominantly responsive to one or the other opsin. However the inhibitory

surround will encompass near equal levels of both opsins, resulting in weak colour

opponency.

Tools and Techniques: Murine Visual System

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Figure 1.12: Opsin Distribution in the Murine Retina. A-C Schematic depicting distribution and density of the murine cones and cone opsins. A. True S cones are distributed at low density across the entire retina. B. M cones are distributed at much higher density across the entire retina. C. M cones co-express M cone opsin and S cone opsin in a gradient dependant manner. This splits the M cone population into three categories. In the dorsal retina M cone is predominantly expressed. In the ventral retina S cone opsin is predominantly expressed, in the transitional zone (yellow) M and S cone opsin are co-expressed in a dorsal-ventral gradient dependant manner. M cone opsin is depicted (green) and S cone opsin (blue).

Tools and Techniques: Silent Substitution


1.5.4 Silent Substitution

In order to characterise photoreceptor contribution without resorting to retinally

degenerate models, which may contain compensatory mechanisms, we turned to a

sophisticated technique known as silent substitution.

Televisions can reproduce our experience of the world with just three colours. If

one was to compare the spectrum of light reflected off a banana in real life to that

off a banana displayed by a television there would be a large discrepancy in their

spectral profiles despite the perceptual experience of them being the same.

Different spectral profiles of light that produce the same physiological response

‘colour’ are known as metamers (Figure 1.13).

Metamers and silent substitution are based on the principle of univariance

(Rushton 1972). The output of a photoreceptor is dependent on the amount of

photons absorbed irrespective of their wavelength. Instead the wavelength

correlates with the probability of a photon being detected. Using different light

spectra at different intensities one can keep the excitation of these photoreceptors

constant. When two spectrally distinct stimuli produce identical activation for all

of the cone photoreceptors, then one can call those two stimuli metamers. If you

step from one metamer to another, by definition, there will be no change in cone

activation. However other photoreceptors, i.e. rods and ipRGCs, may detect a

change as you alter the spectrum. In this case the stimuli is said to be silent for

cones. This technique can be applied to all photoreceptors, not only cones. By

applying this process to several photoreceptors at once one can selectively

modulate the activity of specific photoreceptors.

Tools and Techniques: Silent Substitution

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Figure 1.13: Metamers. An example of two metamers. The banana (right) reflects light in the yellow spectrum, 570-590nm (yellow trace). However when reproduced on an RGB monitor (left) the colour is still perceived to be the same despite a vastly different spectral composition. Indeed there is no ‘yellow light at all’, but a combination of predominantly green and red light (multicolour trace).

Tools and Techniques: Red Cone Mice


1.5.5 Red Cone Mice

In mice, the peak absorption for M cone opsin, ~510 nm (Jacobs et al. 1991;

Smallwood et al. 2003; Sun et al. 1997), rod opsin, ~500 nm (Bridges 1959), and

melanopsin, ~480 nm (Lucas et al. 2001; Yoshimura et al. 1994) are all very

similar (Figure 1.14). Thus it is technically difficult to create silent stimuli for one

photoreceptor whilst creating a large enough contrast in another. To overcome

this problem we have used a transgenic mouse in which the native M cone opsin is

replaced with the human L cone opsin. Therefore the absorption profile is shifted

to longer wavelengths, ~560 nm (Figure 1.14) (Smallwood et al. 2003). This

transgenic mouse-line (Opn1mwR) has been well validated by several labs (Lall et

al. 2010; Smallwood et al. 2003; Walmsley et al. 2015; Brown et al. 2011). In our

experiments the mouse was bred using a C57 line, which is the strain used for my

other experiments. For the remainder of this thesis I shall refer to the Opn1mwR

mice as red cone mice.

Tools and Techniques: Red Cone Mice

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Figure 1.14: The Relative Opsin Specific Photon capture. Plotted is the Govardovskii nonogram (Govardovskii et al. 2000) for retinal, shifted to the λmax of each opsin based on the literature: S cone opsin: 360nm (Jacobs et al. 1991) M cone opsin: 508nm (Jacobs et al. 1991; Smallwood et al. 2003; Sun et al. 1997), Rod opsin: 498nm (Bridges 1959). Melanopsin: 480nm (Lucas et al. 2001; Yoshimura et al. 1994). As M cone opsin, melanopsin and rod opsin all have similar profiles, we used mice where the M cone opsin was replaced with the human L cone opsin: 556nm (Smallwood et al. 2003) (red trace). This shifts the spectral absorption profile allowing greater manipulation for silent substitution.

Objectives:

72|

1.6 Objectives

The principle objective of this thesis is to determine the extent to which the SCN

response to light is affected by spatial patterns which are present in natural

viewing conditions. To address this question we have proposed the following

objectives:

Do individual SCN cells show a true ‘irradiance’ response; being equally

sensitive to light wherever it originates in the visual space?

How are the firing patterns of the SCN neurones and circadian shifts

influenced by spatial structure (patterns) in the visual scene?

Under natural viewing conditions do cones or melanopsin determine the

time averaged SCN light response?

This thesis is presented in the alternative format. The following chapters are

presented in the form of three published papers, with each paper addressing one

of the objectives mentioned above. A general methods chapter is included to

provide more specific detail on techniques (which due to limitations associated

with published papers, are often too brief). A final chapter draws this thesis to a

conclusion. I discuss how far I have addressed my principle objective (to

determine the extent to which the SCN response to light is affected by spatial

patterns which are present in natural viewing conditions) and consider future

perspectives.

Light Calibration:

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2 Chapter 2: General Methods

2.1 Light Calibration

For each light source (two RGB monitors and a custom-made light system) the

light intensity was measured in 1nm intervals between 380-750nm using a

spectroradiometer (Chapters 3 & 4: Bentham Instruments Ltd, UK, Chapter 5:

SpectroCal, Cambridge Research Systems, UK). This was performed independently

on each of the R, G and B channels in the monitors and for each LED in the light

system. Measurements were performed across the range of output intensities

(OFF-Max) in order to confirm linearity and spectral consistency. Using these

spectra we were able to calculate the effective photon flux for each of the murine

opsins.

∑ 𝐴𝜆.

𝜆=780

𝜆=380

𝑛𝜆. 𝑡𝜆

Where 𝑛𝜆 is the number of photons at a given wavelength, tλ is the lens

transmission (Jacobs & Williams 2007) and Aλ is the absorbance factor. The

absorbance factor was derived from the vitamin A based photopigment template

(Govardovskii et al. 2000) and shifted to the appropriate λmax for each opsin: S cone

opsin: 360nm (Jacobs et al. 1991), Melanopsin: 480nm (Lucas et al. 2001;

Yoshimura et al. 1994), Rod opsin: 498nm (Bridges 1959), M cone opsin: 508nm

(Smallwood et al. 2003; Jacobs et al. 1991; Sun et al. 1997), L cone opsin: 556nm

(Smallwood et al. 2003).

To create the stimuli for silent substitution I calculated the effective photon flux for

each photopigment for each LED at its maximum output. Using these values I was

able to predict the relative output of each LED required to make two stimuli that

were irradiance matched (within ±2%) for both cone opsins but produced a large

contrast for melanopsin. I then re-measured the irradiance of these stimuli,

calculated the effective photon flux for each photopigment and fine-tuned the LED

parameters to produce the optimum silent spectra.

Electrophysiology.: Mice

74| Chapter 2: General Methods

2.2 Electrophysiology.

2.2.1 Mice

All electrophysiological experiments were performed on male mice from a

C57BL/6J background. Males were exclusively used to prevent gender based

variation. The majority of experiments were performed on wild type C57BL/6J

mice, purchased from Harlan Industries (Harlan Industries, Bicester, UK). The

second set of mice used was bred at the University of Manchester. These were male

Opn1mwR mice, progeny of female Opn1mwR and male C57BL/6J mice. These are

the ‘red cone’ mice as mentioned in the introduction.

Mice were group housed in custom made light tight cabinets under a reversed

12:12 light/dark cycle using fluorescent lighting (Light onset occurring at 00:00,

midnight, local time. Cage level irradiance: 8x1013 photons.cm-2.s-1). This light cycle

allowed us to record SCN activity from mice during their late day–early night

transition, which is when the SCN is most responsive to light (Brown et al. 2011).

Mice were taken for electrophysiological experiments aged between 6 to 18 weeks.

Animal care and experimentation received institutional ethics committee and UK

Home Office approval and was in accordance with regulations laid out in the UK

Animals (Scientific Procedures) Act 1986, and the European Directive 2010/63/EU

on the protection of animals used for scientific purposes. Food and water was

available ad libitum. The room was regulated to maintain a constant temperature

of ~22°C. The health and welfare of the mice were checked on a daily basis and

cages (incl. bedding) were changed fortnightly.

2.2.2 Surgical Procedures

Mice were anaesthetised with an intraperitoneal injection of 1.55g/kg urethane

(20%, w/v), with additional urethane (20%, w/v) injected subcutaneously if

required. Urethane was used as it can induce a stable level of anaesthesia for long

durations, which can be achieved with a single dose. Compared with other

anaesthetics urethane has little effect on GABAergic signalling and subcortical

regions which makes it a good candidate for recording from the SCN (Maggi & Meli

1986). In addition, compared with many other common anaesthetics, small eye

Electrophysiology.: Surgical Procedures

|75

movements are reduced under urethane anaesthesia (Nair et al. 2011). Reducing

any such eye movements is crucial for mapping visual receptive fields.

Once anaesthetised, mice were placed in a custom made stereotaxic frame

(Narishige, Japan) and the skull was fixed in position using both ear bars and a bite

bar with nose clamp. A sagittal incision was made to expose both lambda and

bregma on the scull. The position of the head was normalised by setting lambda

and bregma to the same height using the micromanipulator. Because the superior

sagittal sinus and the azygos pericallosal artery lie just beneath the sagittal suture

we entered the brain at an angle of 9° to prevent damage to those structures

(Figure 2.1A). Thus to position the recording sites of the probe within the

stereotaxic coordinates for the SCN, a hole was drilled in the skull, 0.30mm

posterior and 0.98mm lateral of bregma. The dura is then removed to expose the

brain. At this point the ipsilateral eye was covered so that only responses from the

eye receiving controlled light stimuli were recorded (the contralateral eye).

For all recordings I used a Buzsaki32L multi-electrode silicon probe (NeuroNexus

Technologies, MI) which consisted of four strengthened shanks (10mm length,

50μm thick) spaced 200 µm apart. Each shank contained 8 staggered recording

sites (160μm2, iridium) spanning a total of 150 µm vertically (Figure 2.1C). Such

close proximity of recording sites allowed me to a) increase the number of

recording sites within the SCN b) to combine the signal from neighbouring sites to

increase our ability to isolate individual units. Single units are waveforms that are

all similar and suggestive of a single cell or a small group of cells with identical

properties. Multi-unit activity however is the neural activity from a numerous

population of cells.

The electrode was dipped in fluorescent dye (CM-DiI; Invitrogen, Paisley, UK) and

inserted at an angle of 9° from the midline about the sagittal axis. This dye is

lipophilic and is incorporated by the cell’s membrane. There are currently no

studies probing the effect this dye might have on neuronal physiology, although it

is widely used. The electrode was lowered using a fluid filled micromanipulator

(M0-10; Narishige) until slight flection was observed and then raised between 50-

150µm. This led to an average depth of ~5.5mm, which corresponds to the location

Electrophysiology.: Data Acquisition


of the SCN according to a stereotaxic mouse atlas (Paxinos & Franklin 2001).

Location was confirmed based on light responsive recordings. A 5 second light

pulse was presented every 15 seconds for 5 mins to determine if the brain region

was light responsive. If no light responses were detected the multi-electrode probe

was repositioned and then tested again. Once light responses were detected, mice

were left for 30 minutes in darkness to allow the brain activity to settle from

possible trauma from the electrode placement.

Throughout the experiment, core body temperature was monitored and regulated

using a homeothermic heat mat (Harvard Apparatus, UK), and fluid replenished

with a 0.1ml subcutaneous injection of Hartsmann’s solution approximately every

couple of hours.

2.2.3 Data Acquisition

Data were recorded using Recorder64 (Plexon, TX). Signals were amplified by a

20x gain AC-coupled headstage (Plexon, TX) followed by pre-amplifier

conditioning, providing a total gain of 3500x. The measured potential varies across

multiple timescales. To increase the detection of spikes the data were filtered using

a Butterworth high-pass filter set at 300Hz. This removed the slow oscillations

such as local field potential, leaving just high frequency signals such as action

potentials. Signals passing a 5% threshold, which corresponds to a 35µV change in

voltage, were timestamped and their waveforms digitized at a rate of 40 KHz

across all 32 channels. The 5% threshold was chosen as it produced a good signal

to noise ratio. The raw signal was also digitized and later used to create virtual

tetrodes. In addition to the neuronal activity, event triggers were produced to

occur every time the stimulus changed. Event triggers were created in Matlab and

sent via a serial port to an Arduino which converted the trigger stimuli into a pulse

that was recorded by Recorder64 (Plexon, TX). All data were stored on a hard

drive for offline analysis. Files were processed and merged using PlexUtil [ver. 3]

(Plexon, Tx).

Electrophysiology.: Histology

|77

2.2.4 Histology

Histology was used to verify the location of the recording probe in each

experiment (Figure 2.1B). This is why the multi-electrode probe was dipped in

fluorescent dye prior to insertion into the brain. At the end of the experiment the

brain was harvested and fixed with paraformaldehyde (4%). After 24 hours the

brains were transferred to sucrose (30%) for cryoprotection. Coronal slices

(100μm) were taken of the SCN and surrounding tissue using a sledge microtome.

Slices were stored free-floating in 0.1M PB-S solution (0.9% NaCl in 0.1M

phosphate buffer). Sections were mounted onto glass slides and coverslips were

applied using either Vectashield (Vector Laboratories Inc.,Uk) or DPX mountant

(VWR International). Sections were viewed using an Olympus BX51 upright

microscope with a 4x/0.13 objective and captured using a Coolsnap HQ camera

(Photometrics) through MetaVue Software (Molecular Devices). Images were

overlaid with the Mouse Atlas (Paxinos & Franklin 2001) to verify probe location.

2.2.5 Spike Sorting

Spike sorting is the process of grouping similar waveforms. The assumption being

that a single cell will produce a characteristic waveform based on the cells unique

cellular properties and spatial location with respect to the probe. Extracellular

recordings will detect signals from multiple cells. If two cells produce similar

waveforms, they cannot be separated using spike sorting. For this reason grouped

waveforms are referred to as single units as opposed to single cells. A single unit

could therefore be comprised of any number of cells which have similar

waveforms. Another assumption of this methodology is that the waveform that a

cell produces is robust and will remain the same over time. However the waveform

of a cell can change depend on the health of the cell. This is one of the reasons why

30 minutes is left between insertion of the electrode and the start of the recording

protocol.

All spike sorting was performed manually using Offline Sorter [ver. 3] (Plexon, TX).

Waveforms appearing on more than half of the channels were considered cross

channel artefacts as the distance between shanks should prevent cells from being

recorded across multiple shanks. These cross channel artefacts were removed

Electrophysiology.: Spike Sorting


from the data set. For multiunit activity, additional noise and artefacts were

removed manually from each channel using principle component analysis (PCA)

and other waveform parameters. The remaining waveforms were grouped as

multi-unit activity.

Single units were isolated using the notion that the contacts on each shank of the

Buszaki32L probe are in close proximity. This means that single cells could

potentially be detected on multiple recording sites. However the magnitude of the

waveform will vary based on the location of the cell with respect to each electrode.

This will produce a specific waveform amplitude ratio unique to that cells location

(Figure 2.1D). This should allow for better isolation of single units, as two cells

with a similar waveform on one electrode can be differentiated based on the

relative magnitude of the response across the neighbouring electrodes. To this end

virtual-tetrodes were created by stitching the raw traces of four neighbouring

recording sites using Matlab. The virtual-tetrode was then analysed for spikes. A

spike was recorded if the electrical activity on any of the 4 traces exceeded the

threshold (typically -35μV). A 1ms waveform was extracted, starting 300μs before

the threshold was crossed (the mean tetrode-waveform for a single unit is shown

in Figure 2.1D). Thus for any spike the waveform at each of the 3 neighbouring

sites were also excised. All of the above was created using a custom-made Matlab

script produced by Dr Tim Brown, (Described in more detail in: Howarth,

Walmsley, & Brown, 2014). Using Offline Sorter, these spikes were manually

grouped into single units via PCA and other waveform characteristics (An example

tetrode where multiple single units were observed, Figure 1E). Cross-

correlograms (Figure 2.1F) and the inter-spike interval (Figure 2.1G) were used

to validate the isolation of units. Cross-correlograms show the relationship

between the spiking of two proposed units. Two independent units should have no

correlation between each other. Meanwhile the inter-spike interval can be used to

determine how well isolated a single unit is. This is because cells have a refractory

period and so spikes that are within a ms tend to be from different cells. Likewise if

a cell shows a very rhythmic firing rate, then oscillations are observed in the inter-

spike interval histogram.

Electrophysiology.: Spike Sorting

|79

Figure 2.1: Single unit recordings from the SCN. A. Location of the SCN in the mouse brain and the angle at which the multi-electrode probe was inserted to reach it. B. Histology from 3 separate experiments, showing the electrode placement within the SCN. Bottom panel shows an experiment where the electrode traversed the third ventricle and penetrated the ipsilateral SCN. C. A schematic of a Buzsaki32L probe. Illustrated are the recording sites that make each of the 3 ‘virtual tetrodes’ per shank. D-G. Two isolated single units from a single tetrode, coloured red and green respectively. D. ‘Virtual tetrode’ waveforms (mean ± 3 SD) for each unit. E-G. Screenshots from Offline Sorter. E. Waveforms from both units plotted as their first two principle components. F. Cross correlogram of the two units (0.2ms bins). G. Interstimulus interval (0.1ms bins) for each unit. Less than 0.1 % of spikes fell within 1ms (dashed red line) of one another within the same unit.

Electrophysiology.: Light Sources


2.2.6 Light Sources

We used two light sources (an RGB monitor: Dynascan DS46LO4 and a custom-

made light system) for the electrophysiological experiments. The monitor was

positioned 35.5cm away from the mouse and incorporated 70x110 degrees of

visual angle. The monitor had three channels (red: λmax =615 nm, green: λmax =540

nm, blue: λmax =445 nm) which were controlled using Matlab on a 0-255 RGB scale.

Even using the lowest input (0,0,0) the monitor still produced a dim light. The

maximum (255,255,255) and minimum (0,0,0) irradiance from the monitor were

2x1015 and 3x1012 photons.cm-2.s-1 respectively.

The second light source used was a custom-made light system. This was composed

of four LEDs (Bandwidth < 25nm, red: λmax =630 nm, green: λmax =525 nm, blue:

λmax =455 nm, violet: λmax =405 nm, Phlatlight PT-120 Series, Luminus Inc.,

Sunnyvale, California, USA) which fed into the projector (DLP LightCommander;

Texas Instruments Inc, Dallas, Texas, USA) via light guides. Each LED can be

controlled independently using a ChipKIT Uno32 board (Digilent, Washington,

USA) with a pulse width modulation (PWM) at 32 bit resolution. The Uno32 was

controlled using MPIDE software (Digilent, Washington, USA). At the lowest input

(RGBV=0,0,0,0) there was no output. The projector contains multiple microscopic

mirrors that shift to create an image with a resolution of up to a resolution

608x684. The image is rear projected through a semi translucent screen upon

which all light measurements are made from. The screen is positioned 13.5cm

away from the mouse and incorporates 40x60 degrees of visual angle. The

irradiance range of the projector system is 0 to 6x1013 photons.cm-2.s-1.

The experiments in Chapter 3 were all performed using the Dynascan monitor. In

order to perform the silent substitution we turned to the projector system. Thus all

the experiments in Chapter 5 used the projector system. In Chapter 4, both the

monitor and the projector were used, but for different electrophysiological

experiments.

Electrophysiology.: Light Stimuli

|81

2.2.7 Light Stimuli

Here I shall describe aspects of the light stimuli that are common between

chapters. Chapter specific stimuli are described in detail in the chapters in which

they are used. The following statements apply to all stimuli unless otherwise

stated. Firstly, stimuli were produced using psychophysics toolbox [ver.3]

(Brainard, 1997; Pelli, 1997; Kleiner et al. 2007) in Matlab [ver. R2012a]

(MathWorks Inc., MA, USA). Secondly, to maximise the response stimuli were

presented using maximum contrast (black vs white). Finally, visual stimuli were

corrected for visual angle.

2.2.8 Correcting for Visual Angle

When a mouse is positioned close to a VDU so that the screen subtends a large

portion of the visual scene one must consider correcting for visual angle. This is

because if a square wave grating was presented on the screen, the bars that are

furthest away will appear smaller (Figure 2.2A). Therefore the perceived spatial

frequency will vary depending on the location of the bars on the screen. This can

be rectified using basic trigonometry to increase the width of the bars that are

further away from the mouse. The width of each bar is increased so that it

subtends the same visual angle (Figure 2.2B). For all gratings this correction was

applied in both vertical and horizontal directions simultaneously. However for the

bars used in the receptive field mapping protocol the correction was only made in

one dimension and then accounted for in the offline analysis.

2.2.9 Test stimulus

In order to confirm the location of the probe prior to the histological analysis, a

series of test pulses to confirm that the region of interest was light responsive. This

protocol consisted of a 5 second step from darkness presented every 15 seconds.

The stimulus was the largest possible contrast for the given light source. This test

pulse was performed at the beginning of every experiment and often between

protocols to examine the stability of the light response over the duration of the

experiment.

Electrophysiology.: Test stimulus


Figure 2.2: Correcting for Visual Angle. Top panel depicts a grating presented to the mouse. The perceived grating is depicted below on the bottom panel. A. A grating with a specific spatial frequency is presented to the mouse. As the mouse is very close to the screen, the visual angle occupied by each bar varies. The mouse thus experiences a grating that has a varying spatial frequency. B. A grating where each bar subtends the same degrees of visual angle. The mouse now experiences a grating with a constant spatial frequency.

Figure 2.3: Generating the Receptive Field Mapping Stimulus. Red lines denote the vertical and horizontal meridians which mark the centre of vision. A. A screen divided into a grid system. B. A grid system where visual angle is corrected for along the vertical and horizontal meridians. This grid system was used in Chapters 3 and 4. C. Although this grid system corrects for the width of the bar at the meridian. What the mouse will actually perceive is a thinning of the bars towards the ends of the screen. D. The grid system in B mapped corrected in 2D, so as perceived by the mouse. This was achieved by translating each pixel on the screen to a vertical and horizontal angle from the viewing axis. Calculations for the receptive field size used this correction to account for effect of position on the receptive field size.

Electrophysiology.: Receptive Field Mapping Stimulus

|83

2.2.10 Receptive Field Mapping Stimulus

The stimulus consisted of randomly presenting a series of either horizontal or

vertical bars at various positions in the visual scene. These white bars were

presented for 250ms against a black background. There was a 250 ms

interstimulus interval between bar presentations. The screen was divided up into a

grid system (Figures 2.3A and B). This grid system was corrected for visual angle

along the meridians (Figure 2.3B) Horizontal and vertical bars were presented

against a black background according to this grid system. Bars were 6 grid cells

thick and extended the length/height of the screen. This produced bars which

would overlap with one another giving a finer spatial resolution. As the bars were

only corrected for visual angle with respect to the meridian, the perceived width of

the bar would decrease towards the end of the screen (Figure 2.3C). Therefore,

further corrections were applied post hoc in the analysis. To achieve this, the

position of each pixel in visual angle was recalculated in 2D (Figure 2.3D). The

receptive field mapping stimuli was presented for Chapters 3 and 4 using different

light sources and parameters in each.

For Chapter 3 the receptive field mapping protocol was presented with the bright

Dynascan monitor, which covers 70 x 110° of the visual scene. Bars were

presented on a 76 x 82 cell grid, each cell ~1 degree square. Horizontal bars had a

width of ~6° whilst vertical bars with a width of ~8°.

For Chapter 4 the receptive field mapping protocol was presented with the

dimmer custom-made light system which covered only 40 x 60° of the visual scene.

Bars were presented on a 36 x 27 cell grid, each cell ~1.5 degree square. Both

horizontal and vertical bars had a width of ~9.5°.

Electrophysiology.: Analysis


2.2.11 Analysis

2.2.11.1 Test Pulse

The 5 second test pulse was used to determine light responsiveness of both single

units and multi-unit activity. A perievent histogram (from -5 to 10 seconds, 0.25s

bins) was produced in NeuroExplorer. The initial 5 seconds preceding the light

pulse were taken as the background response. From this background response,

99% confidence limits were calculated and applied to the perievent histogram. If

the activity crossed either the upper or lower confidence limits at any time during

the light pulse or immediately after, then the unit was classified as light responsive.

2.2.11.2 Receptive Field Mapping

To determine the receptive field of single cells I first produced a perievent

histogram (10ms bins, 5 bin boxcar smoothing) for every bar position. This was

normalised (baseline subtracted) to the background firing rate as determined by

the 100ms preceding the bar position. The change in firing rate could then be

mapped across both time and space.

The time point where the maximum change in firing rate occurred (tmax) was used

to determine the receptive field size. At this time point the change in firing rate

was mapped against horizontal bars and again for vertical bars. Each data set was

fitted using GraphPad, with either a Gaussian or a difference of Gaussians. The

mean of the Gaussian determined the centre of the receptive field. The horizontal

and vertical bar positions were then used to locate the position of the receptive

field and correct for visual angle in 2 dimensions (Figure 2.3D). From these

corrected fitted distributions the size of the receptive fields were calculated. The

size of the receptive field was determined as the width at half maximum amplitude,

using the following formula.

𝑅𝑒𝑐𝑒𝑝𝑡𝑖𝑣𝑒 𝐹𝑖𝑒𝑙𝑑 𝑆𝑖𝑧𝑒 = 2√2𝑙𝑛2. σ

Where σ is the standard deviation of the fitted distribution. This was calculated for

both horizontal and vertical bars. As the receptive field sizes in either direction

were comparable, the average receptive field size was used as the metric for each

cell. To increase the number of receptive fields detected the angular position of the


|85

monitor relative to the mouse varied both within and between experiments.

Therefore the locations of the receptive fields were corrected for screen position

so that the location of the receptive field centre was characterised with respect to

the mouse (0° being directly in front of the mouse with no elevation).

2.2.11.3 Power Spectrum Analysis

To determine whether single units were able to detect a drifting or an inverting

grating I turned to power spectrum analysis. If the unit showed an oscillation in

firing rate that had the same period as the grating then it was classified as being

able to track the grating. A custom made Matlab script courtesy of Dr Dan Elijah

was used to analyse the data.

The program ran a fast Fourier transform on both the spike times and a shuffled

version of those spikes. The shuffled power spectrum was then subtracted from

the power spectrum of the real data set. This was repeated 1000 times, averaged

and then normalised (z-score normalisation). Narrow bins were used to maximise

sensitivity when measuring the peak power at the predicted F1 and F2 frequencies

(allowing ±0.025Hz tolerance). As multiple gratings were tested, and the maximal

power was taken from within a window, the selection criteria needed to be

adjusted in order to minimise false positives. Thus for each cell the number of tests

were calculated (number of gratings x selection window x frequencies examining)

and sigma (p-value) was adjusted to give a false positive rate below 5% (Table

2.1). Thus the predicted probability of a cell being falsely classified as being able to

track a drifting grating was <0.05.



Table 2.1: P-value Adjustments for Multiple Tests. For each type of grating (drifting and inverting), the stringency for tracking the grating was adjusted so that the false positive rate. (F.P.R.) per cell was <5%. This was achieved by raising the detection threshold as determined by a set number of standard deviations (SD) above the mean.

Stimuli Gratings (per cell)

F1 + F2 bins (per grating)

Total P-value (SD)

Predicted F.P.R (per cell)

Inverting Gratings

16 8 128 p=0.00023 (3.5)

2.98%

Drifting Gratings

25 26 650 p=0.000032 (4.0)

2.06%

Irradiance Matched Comparison

1 8 8 P=0.0062 (2.5) 4.94%

Behavioural: Animals

|87

2.3 Behavioural

2.3.1 Animals

Male C57BL/6J mice (Harlan Industries, Bicester, UK) aged 8-10 weeks were singly

housed in a cage (300 x 130 x 115 mm) equipped with a custom made running

wheel (Vettech) fitted with upright disposable cardboard wheels, where the mice

could run (82 mm diameter, depth 57 mm). Cages were kept at ~22°C in a light

tight cabinet (Technoplast) containing four shelves, each fitted with a

programmable LED based light source (cage level irradiance: 7x1014 photons.cm-

2.s-1). Food and water was available ad libitum. The health and welfare of the mice

were checked on a daily basis and cages (incl. bedding) were changed fortnightly.

For some portions of the experiment the animals were kept in constant darkness.

This was to allow the animal to free run at their endogenous period. When the

animals were in constant darkness all observations were conducted using night

vision goggles (Pulsar Edge GS 1x20 NVG, model: 75095, dimensions:

203x122x65) equipped with an infrared (IR) light and sensor. In addition

observations were made at different times each day, preventing the mice from

potentially entraining to the regular disruption.

Animal care and experimentation received institutional ethics committee and UK

Home Office approval and was in accordance with regulations laid out in the UK

Animals (Scientific Procedures) Act 1986, and the European Directive 2010/63/EU

on the protection of animals used for scientific purposes.

2.3.2 Protocol

Mice were entrained to a 12:12 LD cycle for two weeks. During this time more

hands-on maintenance was used where appropriate (e.g. repairing wheels, moving

bedding from near wheels). Mice were then simultaneously plunged into constant

darkness for 4 weeks with a light pulse presented after ~2 weeks. During this time

maintenance was minimal and only used if vital (e.g. if the wires had become

disconnected). This was to reduce the possibility of non-photic clock resetting

during the free running phase of the experiment which would thus complicate both

the predictions of Tau and scoring the data (Mrosovsky 1988). For the same

reason, immediately following the light pulse the animal was returned to a clean

Behavioural: Light pulse


cage. This prevents the requirement to clean the cage during constant darkness

which can induce unwarranted phase shifts (Mrosovsky 1988). In addition all

maintenance and general animal welfare were done in constant darkness using IR

irradiation and night vision goggles as mentioned above. This is because, whilst

mice have no long wave length cones, thus making them relatively insensitive to

red light; there is still some evidence that the circadian rhythm is sensitive to dim

red light commonly used in animal husbandry (Hofstetter et al. 2005).

After 4 weeks of constant darkness the protocol was repeated, however the animal

would be presented with a different light pulse stimulus. The protocol was

repeated from the very beginning by entraining the mice back on to a 12:12 LD

cycle. This re-entrainment was introduced as the magnitude of phase shifts has

been reported to increase with prolonged exposure to constant darkness

(Shimomura & Menaker 1994). Behavioural data was only collected for the 4th

Chapter.

2.3.3 Light pulse

Mice were pulsed between 11-17 days into constant darkness, depending on the

strength of their actograms. Activity was scored using KitAnalyze (Stanford

Software Systems, Santa Cruz, California, USA) and their approximate CT16 was

determined as 4 hours post predicted activity onset. CT 16 was chosen as this is

the circadian time that produces the largest phase shift in mice (see Chapter 1:

1.1.6 Phase Shifting and Phase Response Curve). Using IR googles mice were

transferred in their cages to the testing room in constant darkness. Mice were

placed into a glass arena (diametre: 12.8 cm, height: 38cm) in the centre of 4

monitors (Viewsonic VA2037). This glass arena prevented the animal from getting

too close to the monitors and thus altering the perceived spatial frequency of the

gratings. After 15 minutes the stimulus would return to a black screen. The mouse

was placed into a new home cage and transferred back to the light tight cabinets,

where the cage was reconnected to the data logger. The glass arena was cleaned

between mice.

Behavioural: Light Stimuli

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2.3.4 Light Stimuli

All stimuli were created in Matlab, and sent via a serial port to a VGA splitter

(ATEN: VS84) which duplicated the stimulus onto all four monitors

simultaneously. The laptop which produced the stimuli was covered by a thick

light tight sheet to prevent any light from the monitor interfering with the

experiment.

Three stimuli were presented during these sets of experiment: a sinusoidal drifting

grating (0.03 cpd, 4Hz), a static grating (square wave, 0.03 cpd) and a spatially

uniform stimulus. Both gratings were corrected for visual angle using the methods

described in the electrophysiolgical experiments. Corrections were made using the

centre of the arena, as the viewpoint for the mouse. The drifting grating and

irradiance matched spatially uniform stimulus were presented at a range of

different irradiances to allow the production of an irradiance response curve. The

irradiance was reduced by overlaying the monitors with neutral density gels. This

created a range of irradiances from 2.7x109 to 9x1013 photons.cm-2.s-1.

After the initial experiment (to determine the irradiance at which clock resetting

was most sensitive to perturbations in light intensity) the grating stimulus was

changed. The reason behind this was to maximise the spatial contrast of the visual

scene. As the mouse was awake and freely moving the stimuli would naturally

traverse across the retina and thus a static grating could be used instead. As the

stimulus was no longer moving a square wave grating was now feasible which

would increase the contrast in irradiance.

Behavioural: Light Stimuli


Figure 2.4: Running Wheel Schematic. A fixed magnet in the wheel housing induces an electrical current in the magnet within the stand with every revolution.

Behavioural: Data Collection

|91

2.3.5 Data Collection

Data from the wheel running were collected through electric induction. Each wheel

contained a magnet within the frame and in the base plate of the wheel itself

(Figure 2.4). Every revolution brought these two magnets in close proximity,

inducing an electric current. This current was detected via a DIO3264 board and

relayed to the computer. Data were collected using KitCollect and KitMonitor

(Stanford Software Systems, Santa Cruz, California, USA) data were then analysed

using KitAnalyze.

2.3.6 Analysis

Percentile distribution actograms were single plotted on a 24 hour x-axis. Three

individuals scored the data blind. The only annotation was a star to indicate the

day on which a light pulse was present. If any of the three individuals thought an

actogram was unscorable (e.g. too few data points, missing data) the actogram was

excluded from the data set. Actograms were scored by eye by drawing lines of best

fit on the free running activity before and after the light pulse. Both lines were

extrapolated to the day immediately following the light pulse and the time

difference between the two lines was measured.

Behavioural: Analysis


Introduction: Analysis

|93

3 Chapter 3: Receptive Field Properties of Neurons within

the SCN.

Abstract

Background light intensity (irradiance) is an important time of day cue for

regulating circadian rhythms. Thus we investigated whether individual SCN neurons

might receive a ‘true irradiance’ signal, sampling light intensity across the entire

visual scene. To accomplish this we used in vivo electrophysiology to map and

characterise the receptive field properties of SCN neurons from anaesthetised mice.

Whilst some neurons did respond to light presented anywhere in the visual scene, the

majority were only excited by light presented in a specific area of the visual scene.

The size of these small receptive fields is consistent with the size of the melanopsin

expressing retinal ganglion cells (mRGCs) that innervate the SCN. We thus propose

that most SCN cells that receive input from mRGCs receive input from only a single

mRGC.

3.1 Introduction

Measuring background light intensity (irradiance) is important for the regulation

of our circadian (~24 hour) rhythms, as the daily change in irradiance is a good

time cue for synchronising our internal circadian rhythm to that of the

environment. Irradiance is encoded by a subset of retinal ganglion cells (RGC) that

express the photopigment melanopsin and are thus intrinsically photoreceptive. In

addition to their intrinsic response, intrinsically photoreceptive RGCs (ipRGCs)

also receive photic information from rod and cone photoreceptors via bipolar cells.

There are currently 5 subtypes of ipRGCs, M1-M5 (Zhao et al. 2014). M1 and M2

cells innervate the suprachiasmatic nuclei (SCN) located in the anterior

hypothalamus (Baver et al. 2008; Hattar et al. 2002; Hattar et al. 2010), with the

predominant input (~80%) arising from the M1 subtype (Baver et al. 2008). The

SCN is the central oscillator and controls the circadian rhythms of peripheral

oscillators throughout the body. A subpopulation of SCN neurones are

electrophysiologically light responsive, exhibiting a change in firing rate in

Introduction: Analysis

94| Chapter 3: Receptive Field Properties of Neurons within the SCN.

response to light (Groos & Mason 1980; Brown et al. 2011; Groos & Mason 1978;

Meijer et al. 1986) and may have a role in entraining the SCN to the light dark

cycle.

M1 cells typically have a dendritic field of 300-350 μm (Schmidt & Kofuji 2011;

Schmidt & Kofuji 2009; Estevez et al. 2012). As 1 degree of visual angle

corresponds to ~31 μm on the murine retina (Remtulla & Hallett 1985), M1

ipRGCs should have a receptive field of ~10° in mouse. The extrinsic signal from

rods and cones does not appear to significantly increase the size of the intrinsically

mediated receptive field (Wong, Dunn, et al. 2007). In order for SCN neurons to

receive a true irradiance signal they must therefore summate over the entire M1

cell population. Due to the large number of both M1 cells and light responsive SCN

neurons it is inconceivable that each light responsive SCN neuron receives direct

input from all the M1 cells in order for it to receive a true irradiance signal. There

is however another mechanism by which individual SCN cells can receive a true

irradiance signal. The SCN is a dense collection of neurons that are highly

interconnected. Through this network it is plausible that M1 cell input is pooled

and thus each light responsive SCN neuron is able to receive a true irradiance

signal. One problem with that is that the predominant neurotransmitter of the SCN

is GABA, which is generally considered inhibitory. However, a subset of SCN

neurones show excitatory responses to GABA thanks to expression of the NKCC

chloride importer and could form the basis of an excitatory network allowing the

SCN to integrate inputs over visual space (Wagner et al. 1997; Choi et al. 2008;

Irwin & Allen 2009).

In order to investigate this question, we set out to characterise the receptive field

properties of individual SCN neurons. To this end we used in vivo

electrophysiology to record from individual SCN neurons whilst we presented

flashing bars to map their receptive fields.

Experimental Procedures: Animals

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3.2 Experimental Procedures

3.2.1 Animals

All animal care and experimentation received institutional ethics committee and

UK Home Office approval and was in accordance with regulations laid out in the

UK Animals (Scientific Procedures) Act 1986, and the European Directive

2010/63/EU on the protection of animals used for scientific purposes. Male

C57BL/6J mice (Harlan Industries, Bicester, UK) were group housed under a 12:12

light/dark cycle, with food and water available ad libitum. Data were collected

from mice aged between 9 to 17 weeks old.

3.2.2 Surgical procedures

Surgical procedures were conducted as previously described (Brown et al. 2011).



required. When fully anaesthetised, the mice were placed in a custom made

stereotaxic frame (Narishige, Japan). The skull was fixed in position using ear and

bite bars. The skull was exposed by a sagittal incision. A hole was drilled in the

skull, 0.30mm posterior and 0.98mm lateral of bregma. The dura was removed to

expose the brain.

We used a Buzsaki32L electrode (NeuroNexus Technologies, MI) which consisted

of 4 shanks, 200µm apart; each shank contained 8 staggered recording sites

spanning a total of 150µm vertically. The electrode was coated in fluorescent dye

(CM-DiI; Invitrogen, Paisley, UK) and inserted at an angle of 9° from the midline

about the sagittal axis. The electrode was lowered using a micromanipulator (M0-

10; Narishige) until slight flection was observed and then raised 50µm. This led to

an average depth of 5.5mm, which corresponds to the location of the SCN

according to a stereotaxic mouse atlas (Paxinos & Franklin 2001). A test stimulus

was presented to check for light responses. If no light pulses were detected the

electrode was repositioned. After detecting light responses mice were left for 30

minutes to let the brain activity to settle from possible trauma from the electrode

placement.

Experimental Procedures: Data Acquisition and Spike Sorting


Throughout the experiment, core body temperature was regulated using a

homeothermic heat mat (Harvard Apparatus, UK), and fluid replenished with a

0.1ml subcutaneous injection of Hartsmann’s solution every two hours. The

ipsilateral eye was covered.

3.2.3 Data Acquisition and Spike Sorting



conditioning providing a total gain of 3500x. Data were filtered by Butterworth

high-pass filter (300Hz). Signals passing a 5% threshold were timestamped and

their waveforms digitized at a rate of 40 KHz across all 32 channels. The raw signal

was also digitalized. All data were stored on a hard drive for offline analysis.

Spike sorting was performed using methods previously described (Howarth et al.

2014). Raw data were combined to form ‘virtual’-tetrode waveforms using custom

made Matlab scripts (Howarth et al. 2014). Single units were extracted through

principle component analysis using Offline Sorter (Figure 3.1A). Cross-

correlograms (Figure 3.1B) and the interspike interval (Figure 3.1C) were used to

validate single units. The single unit data were then analysed using a combination

of NeuroExplorer [ver. 4] (Nex Technologies, MA), Matlab and GraphPad Prism

(ver. 6, GraphPad Software).

3.2.4 Generic Light Stimuli

All Stimuli were presented using an RGB monitor (DS46LO4; Dynascan). The light

intensity at each wavelength between 350-800nm was measured using a

spectroradiometer (Bentham Instruments Ltd, UK) and used to calculate the

photon capture for each class of photoreceptor. The maximum and minimum

irradiances were generated using the full field test stimulus. These irradiances

were 1.86 x 1015 and 3.08 x 1012 photons.cm-2.s-1 respectively (Cyanopic: 3x109 – 4

x 1011, Choloropic: 1 x 1012 – 2x1015, Rhodopic: 2 x 1012 – 7 x 1014, Melanopic: 2 x

1012 – 6 x 1014 photons.cm-2.s-1).

Experimental Procedures: Receptive Field Mapping

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3.2.4.1 Protocol

A full field test stimulus was used to determine light responsive units. For this

stimulus the entire screen (70x110°) was used to create a light pulse. The stimulus

consisted of 20 repeats of a 5 second light pulse with a 10 second interstimulus

interval.

3.2.4.2 Analysis

Perievent histograms for the test stimulus were produced in NeuroExplorer. The 5

seconds preceding the light pulse, were used to draw 99% confidence limits (0.25s

bins; Example unit shown in Figure 3.1D). Units that exceeded the confidence

limit, after either light onset or offset, were considered light responsive.

3.2.5 Receptive Field Mapping

3.2.5.1 Protocol

The receptive field protocol presented a series of white bars against a black

background. The monitor was split using a grid system where each grid square

subtended about a single degree of visual angle (Figure 3.1E). A bar with a width

of 6 grid squares extended across the entire screen in either the horizontal or

vertical direction (Horizontal bar width: 5.6 - 6.1°, Vertical bar width: 8.0 - 8.3°).

Every bar was presented at a random azimuth/elevation for 250ms, with an

interstimulus interval of 250ms (Figure 3.1F). A single trial consisted of all the

possible bar positions for that orientation. Each recording consisted of between 10

- 30 repeats of a trial with the same randomised order of bar position.



Figure 3.1: Isolation of an example single unit used for receptive field mapping. All panels are showing the same unit. A. Two isolated units separated by the first principle component on electrode 2 and 4 respectively. The unit shown in green is used for the following analysis. B. Cross correlogram (bin size: 0.1ms) between the green and red unit. C. Interspike interval (bin size: 0.1ms) for the green unit. Red line denotes 1ms, 0.5% of spikes fell below this cutoff. D. The perievent histogram (0.25 second bins) and raster plot of this unit to a 5 second test flash. E. cartoon of the grid system (black lines) used to create the horizontal and vertical bars. Each grid cell corresponded to ~1 degree visual angle as measured from the meridian lines (red). The meridian lines are centred on the mouse’s optical axis. F. An example run of the first few bar presentations. Note: The grid system was not presented. G. Perievent histogram (mean±SEM, 10 ms bins, 5 bin boxcar smoothing) depicting the response to vertical (left) and horizontal (right) bars that passed through the centre of this unit’s receptive field. H. A simple one dimensional receptive field map relating the change in firing rate over time (10 ms bins with 5 bin boxcar smoothing) to the position in visual space (left: Azimuth, right: Elevation) . The time point where the maximum change in firing occurs was denoted as tmax. I. Firing rate (mean ± SEM) at tmax was fitted using either a Gaussian or difference of Gaussians. J. Simple 2D plot depicting the receptive field in visual space at tmax. K. Histology, showing location of the probe within the SCN.


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3.2.5.2 Analysis

Matlab scripts were produced to analyse the data. The data were analysed in 10ms

bins smoothed using a 50ms boxcar average. A perievent histogram (mean±SEM)

was produced for every vertical and horizontal bar position (Example unit shown

in Figure 3.1G). These perievent histograms were then baseline subtracted and

combined to produce one dimensional receptive field maps, relating the change in

firing rate over time as a function of position on either the azimuth (Figure 3.1H

left) or elevation (Figure 3.1H right) for all bar positions. The time point that

evoked the maximum change in firing rate (tmax) was used to determine the

receptive field size of the unit. The change in firing rate at tmax was plotted as a

function of either azimuth (Figure 3.1I left) or elevation (Figure 3.1I right). These

curves were then fitted using either a Gaussian or Difference of Gaussians using

GraphPad Prism. The receptive field diameter in each direction was taken as full

width at half maximum (2√(2ln2)σ). As the receptive field diameter was

comparable in both vertical and horizontal directions the receptive field for a unit

was determined as the mean receptive field diameter of the horizontal and vertical

bars (Figures 3.3P and 3.4L). The centre of the receptive field was determined by

the location of the peak of the fitted data. To produce the 2D representation of the

receptive field we calculated the average change in firing rate for each grid cell

when it was illuminated (irrespective of the bar orientation; Example unit shown

in Figure 3.1 J). The grid cells were corrected for visual angle.

Where possible, the latency of a unit to respond to the flashing bars was measured

from the centre of the receptive field. A baseline subtracted perievent histogram

response (2.5 ms bins, smoothed with a box car average over 20 bins), was used to

measure the latency. The latency to respond was determined as the time at which

the response crossed a confidence limit of ±3 standard deviations from the

background firing rate in the direction of the response. The peak response was the

time-point of the maximum response during the light pulse. For units that

responded to light at all bar positions the response was averaged across spatial

location.

Experimental Procedures: Histology


For centre-surround cells we characterised the relative strength of the surround

component was characterised by comparing the centre (C) and surround (S)

amplitude (A) for horizontal (H) and vertical (V) bars respectively. This is best

described in the following formula:

𝑆𝑢𝑟𝑟𝑜𝑢𝑛𝑑 𝑆𝑡𝑟𝑒𝑛𝑔𝑡ℎ =𝐴𝐻𝑆 + 𝐴𝑉𝑆

𝐴𝐻𝐶 + 𝐴𝑉𝐶

3.2.6 Histology

To confirm the location of the electrode within the brain, the electrode was dipped

in fluorescent dye (Cell Tracker CM-DiI; Invitrogen, UK) prior to insertion. Post

experiment the brain was retrieved and fixed in PFA (4%). After several days the

brains were transferred to sucrose (30%). Coronal slices (100μm) were taken of

the SCN and surrounding tissue using a sledge microtome. Slices were stored free-

floating in 0.1M PB-S solution (0.9% NaCl in 0.1M phosphate buffer (PB)). Sections

were mounted onto glass slides, coverslips were applied using Vectashield (Vector

Laboratories Inc.,Uk) or DPX mountant (VWR International). Sections were viewed

with an Olympus BX51 upright microscope equipped with a 4x/0.13 objective and

captured using a Coolsnap HQ camera (Photometrics) through MetaVue Software

(Molecular Devices). Anatomical location was determined by overlaying the

captured images with the Mouse Brain Atlas (Paxinos & Franklin 2001) of the SCN

(example unit shown in Figure 1K).

Results: Individual SCN Cells have a Diverse Selection of Receptive Fields

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3.3 Results

Using in vivo electrophysiology, we recorded extracellular activity from the SCN of

13 mice. This data were used to create virtual tetrodes that were then spike sorted

to isolate single units (Example unit shown in Figure 3.1A-C). We obtained 69

single units, of which 27 (40%) showed a reliable response to the appearance of a

white bar presented against a black background (250ms stimulus, 250ms

interstimulus interval; see methods) in at least one portion of visual space (Figure

3.1G).

3.3.1 Individual SCN Cells have a Diverse Selection of Receptive Fields

We proceeded to map the receptive field properties of these units (as described

fully in the methods). Our first observation was that whilst some single units

responded to stimuli only when presented in discrete locations within the visual

field, others responded to light presented anywhere within the visual scene (visual

area tested: 70 x 110°). We thus classified these units as either having a discrete

(30% of units) or continuous receptive field (70% of units). A large subset of the

units that displayed continuous receptive fields exhibited centre-surround

antagonism. Based on these characteristics we termed these three subtypes as full

field (33% of the population; example units Figure 3.2), centre-surround (37% of

the population; example units Figure 3.3), and discrete units (30% of the

population; example units Figure 3.4).

3.3.2 Full Field Units

About a third of the light responsive units responded to every bar position

presented and thus have a receptive field diameter ≥ 110°. These cells were thus

considered to respond to light anywhere in the visual scene. Approximately half of

these cells (n=5) increased their firing rate (An example single unit is shown in

Figure 3.2A-E) whilst the remainder (n=4) decreased their firing rate (an example

single unit shown in Figure 3.2F-J) in response to light anywhere in the visual

scene. There was no significant difference in either the latency to respond (t-test,

p=0.77) or peak (t-test, p= 0.65) in either sub-category. We were unable to

measure the latencies of two units (one that was excited and another that was

inhibited by light) that showed very variable background firing rate. One

Results: Full Field Units


excitatory unit showed a delayed response (latency to peak: 315ms), whilst the

remaining full field units (n=6, Figure 3.2L) responded much faster (latency to

respond: 65 ± 5ms; latency to peak: 139 ± 7ms). The full field inhibition occurred

for the duration of the light pulse whereas the duration of the full field excitation

varied from unit to unit.

Cells that responded to light anywhere in the visual scene were observed in both

the ipsi- and contra-lateral SCN irrespective of whether they showed excitation or

inhibition (Figure 3.2L).

Figure 3.2: Properties of cells which respond to light anywhere in the visual scene. Two examples of single units exhibiting a full field response. Top and middle panels, respectively show a unit with either an excitatory response or inhibitory response to light. A,F. Perievent histograms for the vertical bar position producing the largest change in firing rate, during a 250 ms flash from darkness (20 repeats, bin size: 10 ms, ISI: 250 ms, mean ± SEM shown). B, G. Heatmap showing the firing rate over time (bar appears 0-0.25s) as a function of position on azimuth for all vertical bar positions. The change in firing rate (mean ± SEM, difference between the time point at response peak (tmax) for that unit and the 100 ms preceding the stimulus appearance) as a function of location on azimuth or elevation for vertical bars (C,H) and horizontal bars (D,I). Red line depicts best fit for a Gaussian model. E, J. Two dimensional representation of spatial receptive field showing derived response magnitude (mean difference between the time point at response peak (tmax) for that unit and the 100 ms preceding the stimulus appearance) as a function of both azimuth and elevation. K Latency for full field units to peak/respond to a bar presentation (Mean ± SEM for combined data). L Anatomical location of the recording site within the SCN at which units that responded to light anywhere in their receptive field were detected (filled circles; open circles depict location of units with other response properties for comparison). Percentage of cells exhibiting excitatory and inhibitory full field receptive fields are: 18.5% and 14.8% respectively, n= 27 cells from 11 mice.

Results: Full Field Units

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Results: Centre-Surround Units


3.3.3 Centre-Surround Units

The most prevalent single units were those that could be both excited or inhibited

depending on the position of the bar. These cells exhibited centre-surround

antagonism and were fit using a difference of Gaussians model (Figure 3.3C,D,I,J).

These units showed very little variation across the population (two example units

are shown in Figure 3.3). They were all comprised of a small excitatory centre of

~10° (mean 9.7, range 8.0 - 15.2; Figure 3.3N) and an inhibitory surround which

was greater than 110°. The excitatory centre would typically respond to a bar

within 42 ± 9ms and peak at 89 ± 11ms (mean ± SEM of 8 units, we were unable to

measure the latency from 2 units which possessed weak centres). The firing rate

would then relax to a lower level of excitation for the remainder of the light pulse

(Figure 3.3A, G red trace). Following light offset the firing rate would undershoot

on its return to baseline producing a transient off inhibition (latency to respond:

67 ± 6 ms; latency to ‘peak’: 129 ± 6 ms). This OFF inhibition could be created if the

surround inhibition had slower response dynamics than the centre. To investigate

this we also measured the response of the surround (Latency to respond: 67 ±

5ms, 9 units). One unit was excluded due to low signal to noise. For 7 of the units

the baseline firing rate was high enough to observe a noticeable initial decrease in

firing rate following lights on that rose to a stable but still reduced firing rate

(Latency to peak inhibition: 127 ± 11ms. Figure 3.3G). Upon light offset, the firing

rate returned to baseline with a latency of about 153 ± 6ms.

Both the surround component and the OFF inhibition were significantly slower to

both respond (Repeated measures ANOVA: p=0.0003. Tukey’s multiple

comparison: ON vs OFF: p<0.01, ON vs Surround: p<0.01, OFF vs Surround: ns;

Figure 3.3M) and to peak (Repeated measures ANOVA: p=0.0003. Tukey’s

multiple comparison: ON vs OFF: p<0.01, ON vs Surround: p<0.001, OFF vs

Surround: ns; Figure 3.3N) when compared to the ON centre response. However

there was no significant difference between the latencies of the surround

component and the OFF inhibition, which is consistent with the hypothesis that the

OFF inhibition is created by the dynamics of the surround inhibition.

As the magnitude of the surround component compared to the centre component

varied between units we wondered if this would affect the response to a full field


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light pulse. We therefore presented a 5 second light pulse to these units. Most units

(n=6) responded with an increase in firing rate (an example unit is shown in

Figure 3.3F), however some units decreased their firing rate (n=3: an example

unit is shown in Figure 3.3L). The firing rate of one unit was too low to determine

light responsiveness to the test stimuli. In this case the unit was tested several

hours before the receptive field mapping protocol which could explain this

observation. Those that decreased their firing rate in response to the light pulse

had stronger comparative surrounds (t-Test=0.026, Figure 3.3O).

The receptive field centres were observed across a range of azimuths and

elevation, showing no prominent spatial localisation (Figure 3.3P). Units

exhibiting centre-surround receptive field properties were observed in both the

ipsi- and contra-lateral SCN (Figure 3.3Q).



Figure 3.3: Properties of cells exhibiting centre surround antagonism within the SCN. Two examples of single units exhibiting centre surround antagonism. Top and middle panels, respectively show a unit with either an excitatory response or inhibitory response to a full field light pulse. A,G. Perievent histograms for the vertical bar position producing the largest increase (red) and decrease (blue) in firing rate, during a 250 ms flash from darkness (15 and 10 repeats respectively, bin size: 10 ms, ISI: 250 ms, mean ± SEM traces shown). B,H Heatmap showing the firing rate over time (bar appears 0-0.25s) as a function of position on azimuth for all vertical bar positions. The change in firing rate (mean±SEM, difference between the time point at response peak (tmax) for that unit and the 100 ms preceding the stimulus appearance) as a function of location on azimuth or elevation for vertical bars (C,I) and horizontal bars (D,J). Red line depicts best fit for a difference of Gaussians model. E,K Two dimensional representation of spatial receptive field showing derived response magnitude (mean difference between the time point at response peak (tmax) for that unit and the 100 ms preceding the stimulus appearance) as a function of both azimuth and elevation. F, L Perievent histogram for a full field flash from darkness (20 and 40 repeats respectively, Flash 5 s, bin size: 0.25 s, ISI: 10 s). The red lines depict the 99% upper and lower confidence levels. Bottom panel: population data from all centre-surround units. M. Time taken for the ON excitation, OFF inhibition, and the surround inhibition response components to reach threshold (3 SD above/below the baseline excitation, in the direction of the response). N. The time taken for the ON excitation, OFF inhibition, and the surround inhibition to reach their maximal response. O. The surround strength (Amplitudecentre/Amplitudesurround) of units that are either inhibited or excited to a full field light pulse (t-test: p=0.03). P. The location of the receptive field centres for units showing centre-surround antagonism in azimuth and elevation. Origin corresponds to directly in front of the mouse. Bars depict receptive field diameter (width at half maximum to difference of Gaussians fit) of the centre component. Q. Anatomical location of the recording site within the SCN at which units displaying centre-surround antagonism were detected (filled circles; open circles depict location of units with other response properties for comparison). Percentage of cells exhibiting centre surround antagonism: 37.0%, n=27 cells from 11 mice.


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Results: Discrete Units


Discrete Units

Finally we observed 8 units that responded to the horizontal and vertical bars in

some but not all positions. The vast majority of the discrete units (7 of 8) increased

their firing rate in response to a light stimulus (An example unit is shown in Figure

3.4A-E). However, we did observe a unit with a large receptive field (63.4°) that

decreased its firing rate in response to a light stimulus (Figure 3.4J). The

excitatory units typically responded to the bars within 59±7 ms, reaching a peak

(tmax) at 117±13 ms (mean±SEM, n=7; Figure 3.4K). A Gaussian was fit to the

response profile across both azimuth (Figure 3.2C and 3.2H) and elevation

(Figure 3.4D and 3.4I) at tmax for each unit. The receptive field size of these

excitatory discrete units ranged from 6.6° to 43.1° with the majority of the units

(n=4) clustered between 9-16° (Figure 3.4L). From one unit, we were only able to

measure a light response in 1 dimension; this unit had smallest receptive field

(6.6°). The receptive field centres were observed at various locations in the visual

field, with no obvious spatial localisation (Figure 3.4L). The excitatoy units were

observed in both the ipsi- and contra-lateral SCN (Figure 3.4M).

Figure 3.4: Properties of SCN cells with a discrete receptive field within the SCN. Two examples of single units exhibiting a discrete receptive field. Top and middle panels, respectively show a unit with either an excitatory response or inhibitory response to light. A,F. Perievent histograms for the vertical bar producing the largest increase change in firing rate, during a 250 ms flash from darkness (30 and 20 repeats respectively, bin size: 10 ms, ISI: 250 ms, mean ± SEM shown). B,G Heatmap showing the firing rate over time (bar presents 0-0.25s) as a function of position on azimuth for all vertical bar positions. The change in firing rate (mean±SEM, difference between the time point at response peak (tmax) for that unit and the 100 ms preceding the stimulus appearance) as a function of location on azimuth or elevation for vertical bars (C,H) and horizontal bars (D,I). Red line depicts best fit for a Gaussian model. E,J. Two dimensional representation of spatial receptive field showing derived response magnitude (mean difference between the time point at response peak (tmax) for that unit and the 100 ms preceding the stimulus appearance) as a function of both azimuth and elevation. Bottom panel, population data from all discrete units. K. Latency to respond and to peak response for each excitatory unit. L. The location of the receptive fields for units with discrete receptive fields in azimuth and elevation. Origin represents directly in front of the mouse. Bars depict receptive field diameter (width at half maximum of Gaussian fit) in the two dimensions. M. Anatomical location of the recording site within the SCN at which units with discrete receptive fields were detected (filled circles; open circles depict location of units with other response properties for comparison). Percentage of cells exhibiting excitatory and inhibitory discrete receptive fields are: 25.9% and 3.7% respectively, n= 27 cells from 11 mice.

Results: Discrete Units

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Discussion: Anatomy


3.4 Discussion

In this study we have provided a comprehensive account on the receptive field

properties of SCN neurones. We found that single units could be categorised into 3

equally numerous populations. A third of units responded to light anywhere in the

visual scene (full field). A third sampled light from a limited area of the visual scene

(discrete) and the final type exhibited a small excitatory receptive field with an

antagonistic surround that encompassed the entire visual scene (centre-surround).

These units were observed in both the ipsi- and contra-lateral SCN with respect to

the eye receiving light stimuli and showed no observable spatial localisation within

the SCN (Figures 3.2L, 3.3Q, and 3.4M). We also questioned whether there might

be a distinction between excitatory and inhibitory cells. Overall excitatory and

inhibitory units were observed throughout the SCN. There were too few units to

compare excitatory and inhibitory responses within each receptive field category.

To date we have yet to encounter equivalent receptive fields in the literature such

as units that respond to light anywhere in the visual field. A simple explanation for

this would be that eye was moving during the experiment and thus light anywhere

in the scene could provoke a response. However this would cause a blurring of the

receptive field for all the cells in the experiment. During our recordings we were

often able to record multiple types of receptive fields at the same time thus the full

field receptive fields are not due to eye movements. In addition very little eye

movement occurs under urethane anaesthesia (Nair et al. 2011). Altogether the

variety of receptive fields characteristics that we observed, provide an insight into

both the anatomy and physiology of the SCN.

3.4.1 Anatomy

3.4.1.1 Individual SCN Cells are Innervated by Single M1 Cells.

Excluding the one cell in which we were able to only map the receptive field in one

direction, we noticed that there appeared to be a lower limit to the receptive field

size. A majority of the cells seemed to be clustered around this lower limit. All of

the centres of the centre-surround cells, and the majority of the discrete cells had a

receptive field size between 8-16°. The SCN receives photic information from

predominantly M1 ipRGCs and potentially some M2 ipRGCs as well (Baver et al.

Discussion: Anatomy

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2008). The optics of the murine eye means that 1° of visual angle corresponds to

~31μm on the retina (Remtulla & Hallett 1985). Based on the dendritic field sizes

of M1 and M2 cells we can calculate the corresponding receptive field size in

degrees of visual angle. Based on the literature the majority of M1 cells have

dendritic fields about 300-350 μm (Schmidt & Kofuji 2011; Schmidt & Kofuji 2009;

Estevez et al. 2012; Ecker et al. 2010) and M2 cells about 425 μm (Schmidt et al.

2011; Schmidt & Kofuji 2009) which give an average receptive field size of about

10° and 14° respectively. The cluster of receptive field sizes that we observe are

consistent with the notion that many light responsive SCN neurons receive input

from a single ipRGC. Our data is unable to inform us whether this is a one to one or

a one to many relationship between the ipRGCs and SCN cells. A recent study

showed that individual ipRGCs project deep and wide into the SCN and thus

potentially forming multiple functional synapses with many SCN neurons

(Fernandez et al. 2016).

The limit imposed by the dendritic field size of ganglion cells in the retina may not

be unique to the SCN. Indeed the receptive field properties of other retinorecipient

brain areas within the mouse have been well documented (Superior Colliculus: 10-

15° (Wang et al. 2010); dorsal Lateral Geniculate Nucleus: 9-11° (Grubb &

Thompson 2003; Piscopo et al. 2013); V1: range:4-37°, mean 14° (Metin et al.

1988); V1 layers 2-4: 10-14° (Niell & Stryker 2008); V1 layer 6: 30° (Niell &

Stryker 2008)). In many cases the receptive field diameter reaches a lower limit

similar to ours.

To date there has only been one other paper that has broached on the subject of

characterising the receptive field properties of SCN neurones. In a seminal paper in

1980, Groos and Mason characterised the light response of SCN neurones (Groos &

Mason 1980). In a section of this study they briefly described the receptive field

properties of SCN neurones in the cat. They remarked that cells responded to a 3°

spot across the majority of the visual scene and came to the conclusion that cells in

SCN had large receptive fields (>20°) that did not exhibit any centre surround

antagonism. These receptive fields were observed in both cells that were excited

and cells that were inhibited by light. Unfortunately, neither the stimuli nor the

characterisation of the receptive fields were described in great detail making it

Discussion: Anatomy


difficult to make a direct comparison between our two studies. However their

description does appear to be consistent with the full field units that we observed.

This raises the question of whether, under different stimuli conditions, they might

have observed the other subtypes in the cat as well.

The receptive field diameter of SCN projecting RGCs in the cat are much smaller

than those in the mouse, being between only 2-5° (Pu 2000). This is most likely

due the fact that one degree subtends a much larger retinal area in the cat (Holden

et al. 1987). Pu inferred from his data and the work of Groos and Mason that

individual SCN neurones must receive input from multiple RGCs, implying that

these SCN neurones are spatial integrators. If Groos and Mason’s receptive fields

are indeed full field units this would require integrating photic information from

across the entire retina. It is inconceivable that multiple SCN cells can receive such

dense direct retinal innervation, which raises the question of how does this spatial

integration arise?

3.4.1.2 The Surround Antagonism is an Emergent Property of the SCN

The most commonly observed subtype were the units that exhibited centre-

surround antagonism. However there are a couple of reasons to suggest that this

subtype might have been under-represented in our study. Firstly, inhibitory full

field units are remarkable similar to the surround component of the centre-

surround cells. As we were unable to map the entire visual scene it is plausible that

the inhibitory full field units are in fact the surround component of centre

surround units that had a centre outside of the visual area tested (70 x 110°). To

determine if the inhibitory units could be centre-surround units, we calculated the

expected number of centre-surround units we could have recorded from, whose

centre was outside our sampling space. In several experiments we rotated the

mouse, enabling us to map the receptive field across a large area (~70 x 140°).

Assuming that a single mouse eye can detect light anywhere in a 180 x 180°

hemisphere, we would expect that we have been able to record ~1/3 of the visual

scene available to the mouse. This would imply that we should observe twice as

many inhibitory full field units than centre-surround units. This more than

accounts for the number of inhibitory full field units that we recorded. However

one might then ask why are inhibitory full field units so rare? Could this indicate

Discussion: Anatomy

|113

that the centres of the centre-surround units are not uniformly distributed across

the visual scene; occurring more rostral and inferiorly? In addition to the

inhibitory full field units that could represent centre-surround units, some of the

discrete units we recorded from may also be from a centre-surround origin. Many

of the discrete cells (5 of 7) had a receptive field size comparable to that of the

centre of the centre-surround cells. For two of these units the background firing

rate was negligible. Under such conditions one cannot determine any surround

inhibition. Therefore these cells might be centre surround cells exhibiting a ‘silent’

surround.

Centre-surround antagonism is a common feature of receptive fields in the visual

system, observed both in the murine retina and brain (superior colliculus: Wang et

al. 2010; lateral geniculate nucleus: Grubb & Thompson 2003; Piscopo et al. 2013;

and V1 Metin et al. 1988). However the centre-surround units we observed were

most singular in appearance. The surround component in the retina is much

smaller than the full field surround we have observed here. Indeed the M1 cells

which provide the majority of the retinal innervation to the SCN (Baver et al. 2008)

do not exhibit any centre-surround antagonism (Zhao et al. 2014). The only other

direct retinal input to the SCN is from M2 cells which make up 20% of the SCN

innervation (Baver et al. 2008). Whilst M2 cells do show centre surround

antagonism, at a conservative estimate, the diameter of the surround component is

less than 2000µm(Zhao et al. 2014), which corresponds to ~65° of visual angle.

Therefore the large inhibitory surrounds we observe in the SCN likely do not

originate in the retina. We thus propose that the inhibitory surround is an

emergent property of the SCN.

There are two main sources of inhibition in the SCN: GABA and neuropeptide Y.

Neuropeptide Y is released by the geniculohypothalamic tract (GHT) which relays

photic information from the retina, via the intergeniculate leaflet (IGL), to the SCN.

As the photic information from the GHT has an inhibitory effect on the SCN, it is

plausible that neuropeptide Y may be the source of the surround component of the

centre-surround. Indeed the surround component was shown to have a longer

latency (Figure 3.4M&N) than the central component of the centre-surround

units, indicative of polysynaptic input. The other potential source of the surround

Discussion: Physiology


inhibition could be from the GABAergic signalling within the SCN. GABA is widely

expressed in the SCN (Abrahamson & Moore 2001) and would be capable of

producing a large inhibitory effect. One particular feature of this hypothesis is that

GABA has been shown to have an excitatory effect on some neurons in the SCN

(Wagner et al. 1997; Choi et al. 2008; Irwin & Allen 2009). Whilst this is still a

contentious issue and reports of excitatory GABA vary in their estimates, a small

amount of excitatory GABA would account for the full field excitation that were

also observe in our experiments.

3.4.2 Physiology

3.4.2.1 Some Cells Receive a True Irradiance Signal

The daily change in irradiance can be an important time cue for the circadian clock.

We thus set out to determine whether single SCN neurones received a true

irradiance signal. Whilst the vast majority of units sampled from a limited area of

the visual scene, a small population had an excitatory response to light anywhere

in the visual scene. These full field excitatory cells are a good candidate for

receiving a global irradiance signal and thus might be an important subset for

entraining the circadian rhythm. However, these cells contribute to only about one

sixth of the light response population that we recorded from. It is thus import to

understand the role of the other light responsive units. Do they have a separate

visual function or are they part of the process the helps produce the full field

excitatory cells?

3.4.2.2 Centre-Surround Antagonism could Reduce the Effect of Spatial

Contrast

The prevalence of the centre-surround units suggests that these cells might also

have an important role in the SCN. Typically centre-surround antagonism acts to

enhance responses to local spatial contrast. The maximal response is observed

when radiance in the visual field centre is higher than in neighbouring parts of the

visual space. However, the centre-surround units we’ve observed are atypical in

that the surround spans the entire visual scene.

In the natural environment, high frequency spatiotemporal contrast detracts from

the irradiance signal. A particularly salient bright patch in the visual scene could

Discussion: Physiology

|115

greatly perturb the irradiance code. If we take one of these SCN neurons with a 10°

receptive field, this bright patch would result in a large increase in SCN firing. Now

due to both our movement in the environment and eye movements such as

saccades, images are rarely fixed on the retina but are rather constantly traversing

across it. Thus over time this salient bright patch will drift in an out of the SCN

neuron’s receptive field. As the receptive field is small the bright patch will spend

most of its time outside of the SCN neurons receptive field. If there was no

surround inhibition, as is observed with some discrete units, then there would be

periodic bursts of high SCN firing rate, detracting from the true irradiance signal.

However if we now incorporate an inhibitory surround that encompasses the

entire visual field then over time the excitatory effect of the bright patch will be

inhibited by lengthy time the patch is spent in the surround inhibition. We thus

propose that the centre-surround units may act as a way of dampening the effect of

spatiotemporal contrast, over time.

References: Physiology


3.5 References

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neuroscience, 27(7), pp.1763–70.

Brown, T.M. et al., 2011. Multiple hypothalamic cell populations encoding distinct visual

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structural study of human and animal eyes. Vision Research, 27(8), pp.1229–1235.

Howarth, M., Walmsley, L. & Brown, T.M., 2014. Binocular integration in the mouse lateral

geniculate nuclei. Current Biology, 24(11), pp.1241–1247.

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the suprachiasmatic nucleus network. European Journal of Neuroscience, 30(8), pp.1462–1475.

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suprachiasmatic nucleus of the rat and the hamster. Brain research, 382, pp.109–118.

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properties and functional organization. Experimental Brain Research, 69, pp.594–612.

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Niell, C.M. & Stryker, M.P., 2008. Highly Selective Receptive Fields in Mouse Visual Cortex. The

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Pu, M., 2000. Physiological response properties of cat retinal ganglion cells projecting to

suprachiasmatic nucleus. Journal of biological rhythms, 15(1), pp.31–36.

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Schmidt, T.M., Chen, S.-K. & Hattar, S., 2011. Intrinsically photosensitive retinal ganglion cells: many

subtypes, diverse functions. Trends in neurosciences, 34(11), pp.572–80.

Schmidt, T.M. & Kofuji, P., 2009. Functional and morphological differences among intrinsically

photosensitive retinal ganglion cells. Journal of neuroscience, 29(2), pp.476–82.

Schmidt, T.M. & Kofuji, P., 2011. Structure and function of bistratified intrinsically photosensitive

retinal ganglion cells in the mouse. Journal of comparative neurology, 519(8), pp.1492–504.

Wagner, S. et al., 1997. GABA in the mammalian suprachiasmatic nucleus and its role in diurnal

rhythmicity. Nature, 387(6633), pp.598–603.

Wang, L. et al., 2010. Visual receptive field properties of neurons in the superficial superior

colliculus of the mouse. The Journal of neuroscience : the official journal of the Society for

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118|

Introduction: Physiology

|119

4 Chapter 4: The Impact of Spatial Patterns on the Light

Response of the Mouse Suprachiasmatic Nucleus.

Abstract

Background light intensity (irradiance) is often thought as a potent time of day cue

for the suprachiasmatic nucleus (SCN). However the world we live in is full of spatial

patterns making irradiance encoding difficult. Using in vivo electrophysiology we

observed that SCN neurons displayed nonlinear spatial summation across the visual

scene, making the SCN susceptible to spatiotemporal modulation of light. Using

drifting gratings we were able to determine the spatiotemporal tuning of the SCN. In

the absence of any change in background light intensity the SCN was able to track

the drifting gratings over a wide range of spatiotemporal frequencies. Whilst spatial

patterns were able to modulate the SCN firing rate on a fine temporal scale, inclusion

of spatial patterns produced only a minimal change in time averaged firing rate. To

investigate which of these temporal codes might be important for clock resetting we

turned to behaviour. Irradiance matched stimuli that either contained patterns or

was spatially uniform produced similar magnitude phase delays. Suggesting that it is

the time averaged firing rate that is important for determining irradiance. Together

this work shows that the SCN is able to multiplex both information regarding

irradiance and spatiotemporal patterns by encoding the information on different

timescales.

4.1 Introduction

In mammals, an endogenous clock in the suprachiasmatic nucleus (SCN) drives 24

hour rhythms in biological functions. This clock must be regularly reset to local

time (‘entrained’) in order to maintain synchronicity with the external

environment. The most reliable environmental representations of time of day are

the highly reproducible changes in the total amount of light (irradiance) and its

spectral composition that occurs across twilight transitions (Walmsley et al. 2015).

A direct projection from the retina (the retinohypothalamic tract or RHT) brings

such photic information to the SCN.

Experimental Procedures: Physiology

120| Chapter 4: The Impact of Spatial Patterns on the Light Response of the Mouse Suprachiasmatic Nucleus.

While features of ambient illumination (irradiance and colour) show obvious 24hr

variations, there is no such anticipation that spatial patterns could be used to tell

time of day. Accordingly, the spatial distribution of light has largely been ignored in

studies of the circadian light response.

There is a great body of literature, which shows that diffuse illumination is able to

reset the phase of the clock and also to increase the firing rate of SCN neurones.

Meanwhile, clinical guidelines for light therapy for circadian related disorders

make no mention of spatial patterns, but rather recommend diffuse illumination

with light boxes. Nevertheless, spatial patterns are an unavoidable feature of our

visual experience.

In the companion paper, Chapter 3, we show that many individual SCN neurones

did not respond uniformly to light across the visual scene but rather sample light

from particular regions of visual space. This raises the question of how spatio-

temporal modulations in light intensity might impact the firing pattern of SCN

neurones, and/or the circadian phase resetting effects of light.

Experimental Procedures: Animals

|121

4.2 Experimental Procedures

4.2.1 Animals




2010/63/EU on the protection of animals used for scientific purposes. Unless

otherwise stated male C57BL/6J mice (Harlan Industries, Bicester, UK) were

group housed under a 12:12 light/dark cycle, with food and water available ad

libitum. Data were collected from mice aged between 8 to 17 weeks old.

Additional experiments were performed with mice that had the murine long wave

length opsin gene replaced by the human red cone opsin gene Opn1mwR

(Smallwood et al. 2003; Lall et al. 2010). These are henceforth referred to as red

cone mice.

4.2.2 Light Stimuli

Light intensity for each wavelength between 350-800nm was measured using a

spectroradiometer (Bentham Instruments Ltd, UK). For each light system, all the

light channels were measured at a range of intensities. These data were used to

calculate the photon capture (photons.cm-2.s-1) for each of the photopigments

Table 4.1.

We used three light systems: an RGB monitor (DS46LO4; Dynascan); 4 x RGB

monitors (Viewsonic; VA2037-LED); and a custom made light system. The custom

made light source consisted of four independently controlled LEDs (λmax 405, 455,

525, and 630 nm; Phlatlight PT-120 Series, Luminus Inc., Sunnyvale, California,

USA), presented through a digital mirror device projector (DLP LightCommander;

Texas Instruments Inc, Dallas, Texas, USA).

Unless otherwise stated, the Dynascan monitor was used for all

electrophysiological experiments, and the Viewsonic monitors were used for all

behavioural experiments. For the electrophysiological experiments the monitor

was always on and thus there was a background irradiance of 3 x 1012 photons.cm-

2.s-1. This background irradiance was not present when using the custom made

light system.

Experimental Procedures: Light Stimuli


Table 4.1: Relative Photon Capture. For each of the three light sources the main irradiances used and the corresponding photopigment excitation. Most mice were wild type C57BL/6J. Mice used with the projector system were Red cone mice and thus the corresponding photopigment has been included. Values where irradiance levels were too low are described by NA. B= Behavioural, E= Electrophysiological.

Photopigment Specific Photon Capture: photons.cm-2.s-1

Total Photons

S Cone opsin

M Cone opsin

Human L Cone opsin

Rod opsin Melanopsin

Ele

ctro

ph

ysi

olo

gy

Pro

ject

or Background (Min)

Exp E1

NA NA NA NA NA

Full Field Flash (Max) 6 x1013 9 x 1011 2 x 1013 2 x 1013 2 x 1013

Dy

na

sca

n Background (Min)

Exp E2-3

3 x1012 3 x 109 1x 1012 2 x 1012 2 x 1012

Full Field Flash (Max) 2 x1015 4 x 1011 8 x 1014 7 x 1014 6 x 1014

Grey Screen 9 x1014 2 x 1011 4 x 1014 4 x 1014 3 x 1014

Be

ha

vio

ur

Vie

wso

nic

Drifting vs Grey Maximum Irradiance

Exp B1

9 x1013 1 x 1010 3 x 1013 3 x 1013 3 x 1013

Static vs Grey Maximum Irradiance

Exp B3

6 x1013 6 x 109 2 x 1013 2 x 1013 2 x 1013

Static vs Grey Half Maximum Response

Exp B2

9 x1011 NA 2 x 1011 2 x 1011 1 x 1011

Experimental Procedures: In vivo Electrophysiology

|123

4.2.3 In vivo Electrophysiology

4.2.3.1 Surgical Procedures

Surgical procedures were conducted as previously described (Brown et al. 2011).



required. When fully anaesthetised, the mice were placed in a custom made

stereotaxic frame (Narishige, Japan). The skull was fixed in position using ear and

bite bars. The skull was exposed by a sagittal incision. A hole was drilled in the

skull, 0.30mm lateral and 0.98mm posterior of bregma. The dura was removed to

expose the brain.

We used a Buzsaki32L electrode (NeuroNexus Technologies, MI) which consisted

of 4 shanks, 200 µm apart; each shank contained 8 staggered recording sites

spanning a total of 150 µm vertically. The electrode was coated in fluorescent dye

(CM-DiI; Invitrogen, Paisley, UK) and inserted at an angle of 9° from the midline

about the sagittal axis. The electrode was lowered using a micromanipulator (M0-

10; Narishige) until slight flection was observed and then raised 50µm. This led to

an average depth of 5.5mm, which corresponds to the location of the SCN

according to a stereotaxic mouse atlas (Paxinos & Franklin 2001). A test stimulus

was presented to check for light responses. The test stimulus was a 5 second light

pulse (2 x 1015 photons.cm-2.s-1) every 15 seconds for 5-10mins. If no light pulses

were detected the electrode was repositioned. After detecting light responses mice

were left for 30 minutes to let the brain activity settle from possible trauma from

the electrode placement.

Throughout the experiment, core body temperature was regulated using a

homeothermic heat mat (Harvard Apparatus, UK), and fluid replenished with a

0.1ml subcutaneous injection of Hartsmann’s solution every two hours. The

ipsilateral eye was covered.



conditioning providing a total gain of 3500x. Data were filtered using a

Butterworth high-pass filter set at 300Hz. Signals passing a 5% threshold were



timestamped and their waveforms digitized at a rate of 40 KHz across all 32

channels. The raw signal was also digitalized. All data were stored on a hard drive

for offline analysis.

4.2.3.2 Stimuli

All gratings were created and displayed using Matlab [ver. R2012a] (MathWorks

Inc., MA, USA). All gratings were corrected for visual angle. Receptive fields were

mapped for each experiment using methods described in our companion paper,

Chapter 3.

4.2.3.2.1 Inverting Gratings

Black and white square-wave gratings were created with a range of spatial

frequencies (1, 0.5, 0.2, 0.1, 0.05, 0.033, 0.025, 0.017 cycles per degree (cpd)). We

presented a 1.88 or 3.75 Hz inverting grating for 50 sec followed by the same

inverting grating shifted by a quarter of a cycle. The presentation order of spatial

frequencies was randomised. These experiments were performed on red cone

mice and with the custom-made light source.

4.2.3.2.2 Drifting Gratings

Drifting gratings (left to right) were designed for presentation on the Dynascan

monitor. Black and white sinusoidal gratings were created with a range of spatial

(1, 0.2, 0.1, 0.05, 0.033 cpd) and temporal (0.5, 1, 4, 12, 20 Hz) frequencies. All 25

spatiotemporal combinations were presented for 5 minutes each. The stimuli were

semi randomised. For a given spatial frequency, we sequentially presented each

temporal frequency in ascending order. The presentation order for the different

spatial frequencies was randomised.

4.2.3.2.3 Irradiance Matched Comparison

A grey screen was irradiance matched (9.1 x1014 photons.cm-2.s-1) to a black and

white drifting sinusoidal grating (0.1 cpd, 3Hz). These two stimuli were

interleaved, with the order of presentation randomised. Each stimulus was

presented for 5 minutes.


|125

4.2.3.3 Spike Sorting


2014). Neural waveforms were processed using PlexUtil and Offline Sorter [ver.3]

(Plexon, TX). Cross-channel artefacts and noise were removed, leaving multi-unit

data. Raw data was combined to form a ‘virtual’-tetrode waveforms using custom

made Matlab scripts (Howarth et al. 2014). Tetrodes were processed using Offline

Sorter. Single units were extracted using principle component analysis.

4.2.3.4 Analysis

Single and multi-unit data were then analysed using; NeuroExplorer [ver. 4] (Nex

Technologies, MA); GraphPad Prism (ver. 6, GraphPad Software) and Matlab.

Perievent histograms for the test stimulus were produced in NeuroExplorer. The 5

seconds preceding the light pulse, were used to draw 99% confidence limits (0.25s

bins). Units exceeding the confidence limits, after light onset or offset, were

considered to be light responsive.

4.2.3.4.1 Receptive Field Mapping

Protocols are as described in the comparison paper, Chapter 3.

4.2.3.4.2 Inverting Gratings

4.2.3.4.2.1 Power Spectrum Analysis

Power spectrum analysis was performed using a custom made Matlab script on

both the spike times and the shuffled spike times. The ‘shuffled’ power spectrum

was subtracted from the ‘original’ power spectrum and then normalised to the

standard deviation of the new spectrum. The peak power was chosen within 2

bins of both the predicted F1 and F2 frequencies. The normalised power was

averaged over 1000 repeats. F1 or F2 values greater than 3.5 standard deviations

above the mean were considered significant. This corresponds to a ~3% in chance

of detecting one or more false positives per cell.

Significant oscillations where the F2 power was greater than F1 power were

considered to show frequency doubling.



4.2.3.4.2.2 Amplitude of Modulation

Perievent histograms, with 26.7ms bins and a 5 bin moving average were used to

determine the maximum and minimum instantaneous firing rates. Amplitude was

defined as the peak to trough modulation (Firing ratemax – Firing ratemin). If a cell

tracked a spatial frequency at both phases, then the maximum of the two

amplitudes was used. For each cell amplitudes of the significant oscillations were

normalised to the maximum amplitude of that cell.


4.2.3.4.3.1 Power Spectrum Analysis

We used the same protocol as with the inverting gratings. However as different

frequencies were used the peak power was chosen within 6.5 bins of both the

predicted F1 and F2 frequencies. Due to the increased window for selecting a peak

value and the increased number of trials for a cell; cells with an F1 or F2 value

greater than 4 standard deviations above the mean were considered significant.

This corresponds to a ~2% in chance of detecting one or more false positives per

cell.

4.2.3.4.3.2 Amplitude of Modulation

Perievent histograms, with 10 bins were used to determine the maximum and

minimum instantaneous firing rates. Amplitude was defined as the peak to trough

modulation. For each cell the significantly tracking spatiotemporal frequency with

the maximum amplitude was denoted the preferred spatiotemporal frequency of

that cell. Amplitude of oscillations within that cell were then normalised to that

preferred spatiotemporal frequency.

4.2.3.4.4 Irradiance Matched Comparison

GraphPad was used for analysis. The time averaged firing rate for each stimulus

was compared using parametric two tailed paired t-test. Cells were considered

significant if p<0.05. Average perievent histograms were produced by aligning the

peaks of each cell, then normalising by subtracting the mean uniform firing rate

and dividing by the amplitude of the oscillation. A two tailed Wilcoxon test was

Experimental Procedures: Behavioural

|127

performed for the multi-unit comparison. Multi-units were from electrodes that

were light responsive within or around the SCN.

4.2.3.5 Histology

To confirm the location of the electrode within the brain, the electrode was dipped

in fluorescent dye (Cell Tracker CM-DiI; Invitrogen, UK) prior to insertion. Post

experiment the brain was retrieved and fixed in PFA (4%). After several days the

brains were transferred to sucrose (30%). Coronal slices (100μm) were taken of

the SCN and surrounding tissue using a sledge microtome. Slices were stored free-

floating in 0.1M PB-S solution (0.9% NaCl in 0.1M phosphate buffer (PB)). Sections

were mounted onto glass slides, coverslips were applied using Vectashield (Vector

Laboratories Inc.,Uk) or DPX mountant (VWR International). Sections were viewed

using Olympus BX51 upright microscope using a 4x/0.13 objective and captured

using a Coolsnap HQ camera (Photometrics) through MetaVue Software (Molecular

Devices).

4.2.4 Behavioural

4.2.4.1 Protocol

C57BL/6J mice (Harlan Industries, Bicester, UK) were individually housed at 8

weeks under a 12:12 light/dark cycle, with food and water available ad libitum.

Their activity was monitored with a running wheel , data logging and analysis

software (The Chronobiology Kit, Stanford Software Systems, Santa Cruz CA). Mice

were housed for 2 weeks in a 12:12 light/dark cycle before entering continuous

darkness (DD). After 12 days of DD, mice were placed within cylindrical glass

arena (12.8cm diameter). Four RGB monitors (Viewsonic VA2037-LED)

surrounded the arena in a square (48 x 48 cm). The mouse was presented with a

15 minute light presentation then returned to its home cage for another 12 days.

The process was then repeated using the alternative stimulus.

Experimental Procedures: Behavioural


4.2.4.2 Stimuli


The light presentation was either a spatially uniform grey screen or a drifting

sinusoidal grating 0.033 cpd, 4Hz, corrected for visual angle. The experiment was

performed at a range of different light intensities using ND gels. Without filters, the

corresponding irradiance was 8.7 x 1013 photons.cm-2.s-1. The range of irradiances

covered ~ 5 orders of magnitude.

4.2.4.2.2 Static Gratings

A static square wave grating, 0.033 cpd, corrected for visual angle, or an

irradiance-matched spatially uniform stimulus were presented at two different

irradiances: The maximum possible irradiance under the experimental design (6.4

x 1013 photons.cm-2.s-1) and the irradiance that produced the half maximum

response as interpolated from the irradiance response curve (8.6 x 1011

photons.cm-2.s-1).

4.2.4.3 Analysis

Actiograms were scored blind by three individuals and the mean taken. A

parametric two tailed paired t-test was used to analysis the shifts in the two

paradigms. All analysis was performed using GraphPad Prism

Results: Spatial patterns impact SCN firing

|129

4.3 Results

4.3.1 Spatial patterns impact SCN firing

We first set out to determine whether the firing pattern of SCN neurones was

impacted by the appearance of spatial patterns in the absence of any change in

global light intensity (irradiance). To this end, we presented inverting vertical

black and white bars (100% Michelson contrast, Figure 4.1A) over a range of

spatial frequencies (0.017 to 1 cpd; two phases at each), and at two temporal

frequencies (~4 and 2Hz) to urethane anaesthetised mice and recorded

extracellular activity in the SCN. 24 of 47 single units recorded in the SCN of 6 mice

were designated as ‘light responsive’ because they showed a statistically

significant change in firing to a full field flash. 20 of these light responsive units

showed a significant modulation in firing rate associated with one or more of the

gratings tested (based upon power spectrum analysis; see methods). The

likelihood of observing a significant modulation for each unit (shown for

representative unit in Figure 4.1B-C) and across the population (Figure 4.1D)

was inversely related to spatial frequency. Similarly, the peak to trough amplitude

of any oscillation in firing rate detected reduced as spatial frequency increased

(Figure 4.1E).

In our companion paper, we report that spatial receptive fields of SCN units fall

into two broad categories. Approximately 2/3rds have discrete receptive fields,

while the remainder respond to light wherever it appears in the visual scene.

There is a clear expectation that the former should respond to inverting gratings of

the appropriate size and location. It is less clear that this should occur in full field

units. We next mapped receptive fields for all SCN units for which we had recorded

grating responses. We found that most units of both types (87.5% discrete RF;

75% full field) showed a significant modulation to one or more of the low spatial

frequency gratings. Indeed, there was no clear difference in the range of spatial

frequencies over which these cells responded (Figure 4.1F). This indicates that the

receptive field size of the unit does not define the range of gratings it can track.

This in turn implies that there is non-linear spatial summation with SCN receptive

fields. To explore this further we returned to the power spectrum analysis to

determine whether there were examples of frequency doubling (instances in



which the power at the second harmonic frequency (F2) was greater than that at

the first (F1); shown for two representative units in Figure 4.1G). This has

previously been reported in the visual system for units that are excited by both

radiance increments and decrements (ON-OFF units), and for units with non-linear

spatial summation within their receptive field. Frequency doubling should be

apparent for all stimuli to which ON-OFF units can respond (irrespective of their

size and including full field flashes). By contrast, in units with non-linear spatial

summation, frequency doubling should be most commonly encountered at higher

spatial frequencies, under which the activity of individual elements contributing to

the composite receptive field of the unit can be modulated in antiphase. We found

7 units with frequency doubling and in all cases this was restricted to the higher

spatial frequencies to which that cell responded (Figure 4.1H). This is consistent

with the accepted lack of ON-OFF responses in the SCN, and implies non-linear

spatial summation is indeed a feature of SCN receptive fields.

The peak to trough amplitude of the oscillation in firing rate induced by the grating

could be large compared to the time averaged firing of that unit. Indeed, the

median peak amplitude observed for each unit was 9.6 spikes/sec (Interquartile

range: 3.8 - 30.4 spikes/sec), which corresponded to 189% ± 12% (mean±SEM) of

the time averaged firing under that condition. For comparison, sustained

illumination with bright light increases SCN firing rates by only 4-5 spikes/s

compared to activity in the dark in anaesthetised (Brown et al. 2011).

Continued: spatial frequencies that the cell was able to track are indicated by the thin black line. The red bar indicates the spatial frequencies that frequency doubling occurred (F2>F1).


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Figure 4.1: SCN Single Unit Responses to Inverting Gratings. Single unit data, n=24 from 6 mice. A Cartoon of the stimulus: Inverting grating presented at either ~2 or 4 Hz and at various spatial frequencies (1-0.017 cpd). B-C An example full field cell. B Raster and perievent histogram responses to inverting gratings (~4 Hz) at various spatial frequencies. Red lines indicate the 99% confidence levels. C Normalised power (at F1 or F2) against spatial frequency. Power normalised to its standard deviation. Cells were considered able to track inversion if the power was >3.5 SD, dotted line. Each inverting grating was repeated 90 degrees out of phase along the horizontal axis. Window shows power spectrum analysis (0-10 Hz, peak at F1 and F2 frequencies). D Number of cells able to track an inverting grating at each spatial frequency tested. E Mean and SEM. of cell-normalised amplitude of modulation for each spatial frequency. (Kruskal-Wallis test, p<0.0001, Dunn’s multiple comparisons (p<0.01, 15 comparisons)). F Proportion of each receptive field type that is able to show a significant response at each spatial frequency. Each data set is fitted with an exponential decay curve. Full Field n=8, Finite n=16. G Perievent histograms and rasters of two example units exhibiting frequency doubling. Arrows are positioned at the inversion of the gratings. H For all 7 cells that show frequency doubling,



Figure 4.2: Spatiotemporal Tuning of SCN Units. Single unit data of cells responsive to at least one grating (n=23 from 6 experiments). A and B are data from the same single unit. A. Raster and perievent histogram depicting firing rate across a cycle of the drifting grating. Oscillation amplitude was defined as the maximal difference in firing rate across the cycle. B. Perievent histograms showing how the amplitude of the oscillation varies as you Continued: alter either spatial frequency left or temporal frequency right. C. The

Results: Spatiotemporal Frequency Tuning of SCN Cells

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proportion of cells that can track the drifting grating at each spatiotemporal frequency tested. Significance was determined using power spectrum analysis: F1 or F2 peak > 4 SD above the mean. D. A heat map showing the population response to different spatiotemporal frequencies, warmer colours represent increased oscillation amplitude. Population data from all the cells responsive to at least one grating. Each unit was normalised to the maximum oscillation amplitude within that unit. E and F are data from the same single unit which shows both frequency doubling and spatial band pass filtering. This is a cell with centre-surround antagonism. E. Perievent histogram and raster showing frequency doubling. F. Example of a spatial band pass filter. G. Perievent histograms and raster plots for three example units that are able to track a drifting grating with a spatial frequency of 0.2 cpd.

4.3.2 Spatiotemporal Frequency Tuning of SCN Cells

To examine the spatiotemporal tuning of SCN neurones in more detail we turned

to drifting gratings, in which a sinusoidal grating (amplitude=100% Michelson

contrast) was presented at a range of spatial (0.03-1cpd) and temporal (0.5 to

20Hz) frequencies without an associated change in irradiance. Of 55 SCN units (36

light responsive) from 8 mice, 23 showed a significant modulation in firing rate at

the appropriate frequency for at least one of the gratings presented (spectral

power analysis (see methods); The peak to trough amplitude at the preferred

spatiotemporal frequency for each cell varied from approximately 1-100wide

range of spatiotemporal frequencies (shown for a representative example in

Figure 4.2A-B). In the temporal domain, at all frequencies ≤12Hz more than half of

the units showed a significant modulation in firing rate (Figure 4.2C). The number

of units responding peaked at 4Hz, which was also the frequency at which the

response amplitude was highest (Figure 4.2D). Eight units showed frequency

doubling to at least one grating, consistent with the conclusion drawn from the

inverting gratings that non-linear spatial summation is a common feature of SCN

receptive fields (Figure 4.2E-F).

At the appropriate temporal frequency, responses were most commonly observed

for the lowest spatial frequency (0.03cpd, Figure 4.2C). However, we did find

examples of cells with strong preference for higher spatial frequencies, as

predicted for units with strong centre-surround antagonism, and indicative of

spatial band pass filter behaviour (Figure 4.2F). Although response amplitude fell

away at higher spatial frequencies for all cells, there were clear examples of units

that could track 0.2cpd gratings (representative units in Figure 4.2G).

Results: Time Averaged Firing Rate in the SCN is Modulated by Spatial Patterns.


4.3.3 Time Averaged Firing Rate in the SCN is Modulated by Spatial Patterns.

The high amplitude modulation in firing induced by drifting and inverting gratings

described here indicates that, at a fine temporal scale, the firing rate of SCN

neurones can be strongly modulated by spatial patterns. We next asked whether

this might disrupt the relationship between background light intensity (irradiance)

and firing rate that has previously been described for SCN neurones. To this end,

we recorded SCN activity in anaesthetised mice presented with interleaved 5min

epochs of spatially uniform or drifting grating (0.1 cpd, 3 Hz) stimuli matched for

irradiance (5 repeats). Paired t-test comparisons revealed a statistically significant

(p<0.05; across 5 repeats) difference in time averaged firing between uniform and

grating conditions (Representative examples in Figure 4.3A) in 50% (16/32) of

light responsive single units. Of these, 81% (13 of 16 units) showed higher firing

rate under gratings (Figure 4.3B) and the remainder (19%, 3 of 16) higher firing

under uniform illumination. We next addressed the question of whether this

diversity in response was related to variability among the units in their ability to

Figure 4.3: The Effect of Spatial Patterns on Time Averaged Firing Rate. Randomly interleaved 5 minute presentations of either a spatially uniform screen, or a drifting grating (0.10 cpd, 3 Hz, irradiance matched; 9 x 1014 photons.cm-2.s-1) were presented to 5 mice (5 repeats). A Data from two single units. Left: A cell where drifting gratings increased the mean firing rate, Paired t-test P<0.0001. Right: A cell where drifting gratings decreased mean firing rate, Paired t-test P<0.05. B. Number of cells showing significant (p<0.05) changes in firing rate between spatially uniform and spatially structured stimuli n=32. For cells which showed a

significant increase in firing rate to drifting gratings, mean change in firing rate = 1.69 ± 0.82 spikes/sec (mean ± SEM), corresponding to a percentage increase of 198.0 ± 85.1%. For cells which show a significant decrease in firing rate to drifting gratings the mean change in firing rate = 4.35 ± 2.97 spikes/sec (mean ± SEM) which corresponds to a percentage decrease of 24.13 ± 1.54%. Cells that were considered to track the 3 Hz grating (power spectrum analysis; see methods) are shown in green. C. Perievent histogram depicting the response to a grating (blue) compared to that of the spatially uniform stimuli over the course of one cycle of the grating. Left and centre panels are of the corresponding units in figure 3A. Right panel shows the population average of cells that tracked the grating but did not show a significant change in firing rate (n = 8). D. Multiunit data showing the percentage change in firing rate between stimuli 100% x (FRGrating-FRUniform)/FRUniform. Median and interquartile range (7.8% increase. IQR: -2.5-22%). Each point represents multiunit data from a single recording site. Multiunit data was included if shanks were in the SCN region and were light responsive to the 5 second light pulse, n=122.

Results: Time Averaged Firing Rate in the SCN is Modulated by Spatial Patterns.

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Results:


track drifting gratings. In fact, many units showing changes in time averaged firing

did not meet our criteria for tracking the grating with a significant modulation in

firing rate (Figure 4.3B). In addition, numerous units (8/16) showing no

significant change in time averaged firing had significant 3Hz modulations in firing

rate (Power spectrum analyses; p<0.05) indicating an ability to track the drifting

gratings. These units showed a modulation in firing when exposed to the gratings

that was fairly symmetrical around the mean under the uniform stimulus (Figure

4.3C right).

To provide the most comprehensive overview of the SCN’s response to these

stimuli we turned to the multiunit data. This revealed a small, but statistically

significant, increase in time averaged firing rate under exposure to drifting

gratings (n= 122 from 5 mice, Wilcoxon test; p<0.0001, Figure 4.3D). The relative

change in firing rate was quite variable across multiunit records (Figure 4.3E;

median increase in firing rate (spikes/s) in gratings vs uniform=7.8% interquartile

range -2.5 – 22%).

Results: Phase Resetting is not Altered by the Inclusion of Spatial Patterns.

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4.3.4 Phase Resetting is not Altered by the Inclusion of Spatial Patterns.

The electrophysiological data confirm that the firing pattern of SCN neurones

differs in the presence of spatial patterns. We finally set out to determine what

impact, if any, this had on the circadian phase resetting effects of light. To this end,

we measured phase shifts in locomotor activity rhythms of mice free-running in

constant darkness and exposed for 15 mins to an array of visual display units

presenting either spatially uniform (grey screen) or structured (grating) stimuli

(Figure 4.4A & B). We first used this apparatus to construct irradiance response

curves for uniform grey and drifting sinusoidal gratings (4 Hz, 0.03 cpd). In both

cases, the magnitude of the phase delays was positively correlated with irradiance

(Figure 4.4C), both datasets could be fit with linear or sigmoidal functions and in

neither case was there a statistically significant difference in fit parameters

between uniform and spatially structured stimuli (Linear regression p=0.89;

Sigmoidal fit p= 0.99). We used this curve to identify irradiances driving maximal

and half-maximal phase shifts and explored the impact of spatial structure at these

irradiances in more detail by presenting spatially uniform and square wave grating

(to maximise local contrast) stimuli to 12 mice in a random order. Paired

comparisons confirmed that at neither irradiance did the inclusion of gratings

significantly impact phase shift magnitude (Paired t-test; p = 1.00, and p = 0.78

respectively Figure 4.4D).

Results: Phase Resetting is not Altered by the Inclusion of Spatial Patterns.


Figure 4.4: The Effect of Spatially Patterned Stimuli on Phase Shifting. A. Double plotted actograms and phase shift of the same mouse to different light pulse stimuli. Mice were pulsed at CT 16 with either a grating or an irradiance matched spatially uniform grey screen. B. Stimulus set up for the light pulse. Mice were placed in a glass area surrounded by 4 monitors. C. Irradiance response curve for both spatially uniform stimuli (red filled circles) and drifting sinusoidal gratings (0.03 cpd, 4 Hz, blue open circles). Maximum irradiance was 8.7 x1013 total photons.cm-2.s-1. No significance observed between curves (linear p = 0.89; sigmoidal p = 0.99; graph plotted with sigmoidal function of pooled data). D. Paired T-test for intra individual responses between a static square wave grating (0.03 cpd) and a uniform stimuli, at both the irradiance that produced the half maximal response (8.6 x1011 total photons.cm-2.s-1) and the maximum response (6.4 x1013 total photons.cm-2.s-1). No difference between the spatially patterned and the uniform stimuli was detected for either the irradiance at half maximum (p = 1.00) or maximal irradiance (p = 0.78)

Discussion: Phase Resetting is not Altered by the Inclusion of Spatial Patterns.

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4.4 Discussion

The current view of the function of the retinohypothalamic tract is to bring

information about background light intensity (irradiance), and its spectral

composition (colour) to the circadian clock. Both of those parameters convey

information about time of day and thus have clear relevance as a means of

ensuring entrainment to local time. The same is not true for spatial patterns, and

yet we find here that two spatially-structured visual stimuli (drifting and inverting

gratings) can substantially modulate the firing of SCN neurones even when

carefully calibrated to avoid any change in the overall amount of light in the scene.

The magnitude of this effect is large compared to that of changes in irradiance,

with firing rates changing by 10 spikes/s across phases of the grating, which is

larger than that induced by even large increases in irradiance in anaesthetised

mice (4-5 spikes/s; Brown et al., 2011) Thus, an unbiased assessment of the visual

response properties of SCN neurones would identify spatial patterns as at least as

large an influence on their activity as background light intensity.

The SCN response to visual gratings is, in part, predicted by the spatial receptive

fields of SCN neurones. In our previous chapter, we show that many SCN neurones

have discrete receptive fields, being excited by visual stimuli over only a limited

area of visual space. Such neurones respond to local radiance and are thus

predicted to be able to track the appearance and disappearance of bright bars

within their receptive field, even when the total amount of light in the scene

(irradiance) is invariant. As an extension of this idea, a subset of SCN neurones

have receptive fields with centre-surround antagonism. This arrangement should

result in strongest activation by stimuli that maximise the difference in radiance

between centre and surround elements of the receptive field. For visual gratings

this occurs at spatial frequencies at which the bright phase of the grating just

covers the receptive field centre. The result is spatial frequency band-pass

behaviour in which maximal modulations in firing occur at an intermediate grating

frequency. We do indeed see examples of SCN neurones showing strong variations

in firing under moderate frequency gratings, but little response to the fatter bars

(Figure 4.2F).



The receptive field architecture of SCN neurones, however, cannot explain all

aspects of the grating response. Thus, many units whose receptive field covers the

whole visual scene still respond to spatial patterns. Moreover, several units

exhibited evidence of so-called ‘frequency doubling’, in which firing peaks twice

per cycle for drifting and/or inverting gratings. Frequency doubling can be

observed for ON-OFF cells that are excited by both light increments and

decrements, but here it was only observed at finer spatial scales, indicative of non-

linear spatial summation within SCN receptive fields. These aspects of the grating

response thus imply that the unit of linear spatial summation for SCN units is

smaller than their receptive field. The spatial frequency tuning curve for SCN

neurones produced with drifting grating stimuli indicates that, on the average, SCN

neurones prefer low spatial frequencies (<0.05 cpd). The M1 ipRGCs that dominate

the RHT have dendritic fields covering around 9-14° of visual space. The variation

in radiance across this dendritic arbour is therefore predicted to fall away for

gratings >0.04cpd (at which each light/dark phase occupies 12.5°). In this way, the

drifting grating data are consistent with the view that the unit of linear spatial

summation for SCN neurones is an M1 ipRGC.

We thus propose that the response to spatial patterns can be traced back to the

array of RGCs innervating the SCN. Implicit in this explanation is the assumption

that the M1 ipRGCs that dominate this projection do not themselves provide a

simple representation of local radiance. If they did, then the input to an SCN

neuron would reflect the total amount of light falling across the array ipRGCs

comprising its receptive field irrespective of its spatial distribution within the that

field. In fact, we know that ipRGCs are disproportionately excited by abrupt

increases in radiance, with light steps eliciting a transient hyper-excitation that

relaxes to a steady state response under extended exposure. Thus, for every cycle

of an inverting or drifting grating, there will be a point where the radiance

increases within the ipRGC receptive field, which in turn should elicit a transient

increase in firing. This explains the modulation in SCN firing under drifting or

inverting gratings. However, it also implies that the total number of spikes

propagated along the RHT would be enhanced in conditions in which ipRGCs

experience numerous increases in radiance. This appears to be the case (Vartanian


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et al. 2015), raising the possibility that high spatial contrast environments could

induce what might be termed ‘circadian visual illusions’ in which the excitatory

input to the SCN is equivalent to that experienced at higher irradiance in low

contrast environments. In fact, we find that time averaged firing of SCN neurones is

barely altered by visual gratings. Some units show an increase of up to 150% but

on average, the time averaged firing increases only by 8% (median, Figure 4.3D;

geometric mean = 11.6%).

Our data thus reveal that SCN neurones multiplex separate types of visual

information. At fine time scales, their firing pattern conveys information about

spatial patterns. On the other hand, time averaged firing rate encodes background

light intensity (irradiance). As irradiance is thought to convey most information

about time of day, one might expect clock resetting to correlate more with time

averaged firing. That indeed appears to be the case, with phase shift amplitude

being defined by stimulus irradiance irrespective of the inclusion of spatial

patterns (Figure 4.4C & D).

The fact that phase shift magnitude is unaffected by the inclusion of spatial

contrast implies that, at least to a first approximation, the fine scale temporal

modulation in SCN firing induced by such spatial patterns is not relevant for clock

resetting. There has recently been renewed interest in the mechanistic

relationship between SCN neurone spiking and clock resetting. Our data imply that

phase shifting mechanisms are substantially unaffected by stimuli that are

predicted to strongly alter the fine timing of spikes. This could be because the SCN

neurones that are important for phase shifting are those that do not show

significant sensitivity to spatial patterns, or because the phase shifting mechanism

responds equally to spikes irrespective of their timing over the msec to sec range.

Optogenetic interventions could represent an attractive method to address these

alternatives. A more practical implication of the finding that clock resetting is not

impacted by spatial patterns is that it suggests a reconsideration of the methods of

light therapy for circadian and circadian related conditions. Current clinical

guidelines recommend extended viewing of high intensity light boxes (Lam &

Levitt 1999). Our results imply that a visual display unit presenting spatial

patterns could equally elicit circadian effects, while improving compliance by



allowing subjects to simultaneously engage in more entertaining activities e.g.

watch a movie or access the internet.

If the SCN response to spatial patterns is not relevant for resetting the clock could

it perform any other function? One possibility is that the SCN’s sensitivity to spatial

patterns has no functional significance but is merely a consequence the fact that

each SCN neurone receives excitatory input from a limited subset of ipRGCs, each

of which has a defined spatial receptive field. One argument against this is that we

show in the companion paper that SCN receptive fields are always continuous.

Thus, ipRGC inputs to individual SCN neurones are not assigned randomly, but

rather arranged in order to ensure that each neurone monitors radiance in

particular coherent patch of visual space. In this way, the RHT projection is

specifically designed to retain spatial information. The neurones of the SCN project

to more ventral regions of the hypothalamus, where they are able to influence a

very wide array of hypothalamic systems. The accepted function of this projection

is to provide time of day and/or irradiance regulation of body systems. Our data

raise the possibility that the SCN output could also allow these processes to be

modulated according to appearance and/or movement of objects in the

environment. Although, to our knowledge, there is no evidence of visuotopic order

to the RHT, that need not preclude such conventional visual features being

available from the SCN. Indeed, visuotopic order is not a requirement for visual

processing (Avitan et al. 2016). Future work will be required to determine whether

spatial information is relevant for any of the SCN outputs.

References: Phase Resetting is not Altered by the Inclusion of Spatial Patterns.

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4.5 References

Avitan, L. et al., 2016. Limitations of Neural Map Topography for Decoding Spatial Information. The






Lall, G.S. et al., 2010. Distinct contributions of rod, cone, and melanopsin photoreceptors to

encoding irradiance. Neuron, 66(3), pp.417–28.

Lam, R.W. & Levitt, A.J., 1999. Canadian Consensus Guidelines for the Treatment of Seasonal

Affective Disorder


Smallwood, P.M. et al., 2003. Genetically engineered mice with an additional class of cone

photoreceptors: implications for the evolution of color vision. PNAS, 100(20), pp.11706–11.

Vartanian, G. V, Zhao, X. & Wong, K.Y., 2015. Using Flickering Light to Enhance Nonimage-Forming

Visual Stimulation in Humans. Investigative Opthalmology & Visual Science, 56(8), p.4680.

Walmsley, L. et al., 2015. Colour As a Signal for Entraining the Mammalian Circadian Clock. PLoS

biology, 13(4), p.e1002127.

References: Phase Resetting is not Altered by the Inclusion of Spatial Patterns.


Introduction: Phase Resetting is not Altered by the Inclusion of Spatial Patterns.

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5 Chapter 5: Melanopsin Contribution to Time Averaged

Firing in the SCN under Naturalistic Viewing Conditions.

Abstract

Both cones and melanopsin expressing retinal ganglion cells (mRGCs) relay photic

information to the SCN allowing photoentrainment of the circadian system. The

relative importance of each type of photoreceptor has so far only been studied using

diffuse light stimuli. Such stimuli are uncharacteristic of natural viewing conditions,

in which light is rich in both spatial and temporal contrast. We therefore used in vivo

electrophysiology to determine the relative importance of melanopsin under more

natural viewing conditions. Under such conditions the time averaged firing within

the SCN could be accounted for by the melanopic irradiance.

5.1 Introduction

The axial rotation of the Earth creates environmental changes with a 24 hour

period. The predictable nature of these events allows organisms to optimise their

biological functions by creating their own 24 hour rhythms, known as circadian

rhythms. In mammals, circadian rhythms are controlled by the suprachiasmatic

nucleus (SCN), located in the anterior hypothalamus. However, in order to

maintain a correct phase, the endogenous clock must entrain itself to the external

environment.

The change in background light intensity (irradiance) is the most prominent 24

hour environmental oscillation. Therefore, the SCN could utilise irradiance as a

source of time of day information. Indeed there is a vast literature showing that

light does increase the neural firing within the SCN (Brown et al. 2011; Groos &

Mason 1980; Meijer et al. 1986; Meijer et al. 1992) and resets the endogenous

clock (Nelson & Takahashi 1999; Nelson & Takahashi 1991; Meijer et al. 1992).

The magnitude of both the phase shift and the increase in SCN firing rate is

correlated with the irradiance of a stimulus (Brown et al. 2011; Nelson &

Takahashi 1991; Nelson & Takahashi 1999; Meijer et al. 1992). The SCN receives

such photic information from the retina via the retinohypothalamic tract (RHT).

The retinal cells that relay this information are a subtype of retinal ganglion cells


146| Chapter 5: Melanopsin Contribution to Time Averaged Firing in the SCN under Naturalistic Viewing Conditions.

(RGCs) that contain the photopigment melanopsin and are thus intrinsically

photoreceptive (ipRGCs). However, like conventional RGCs, ipRGCs also receive

photic information from both rod and cone photoreceptors. Each photoreceptor

responds maximally to a different wavelength of light, with melanopsin being most

sensitive to blue light at ~480nm (Hattar et al. 2002; Berson et al. 2002). By using

either different wavelengths of light, or animals lacking specific photoreceptors

several studies have shown that each type of photoreceptor contributes to the SCN

response (Lall et al. 2010; Brown et al. 2011; Panda et al. 2002; Hatori et al. 2008).

The SCN response to a simple light step is quite complex and is typically comprised

of two components. The first component is a large but transient increase in firing

rate. The second component is a smaller but sustained increase in firing rate. The

result is that the SCN will rapidly increase its firing rate before decaying to a new

sustained level. Broadly speaking one can say that the transient component is

predominantly driven by cones (Brown et al. 2011), whilst the sustained

component is dominated by melanopsin (Brown et al. 2011). However it is

important to mention that at irradiances below the threshold for melanopsin rods

become increasingly important in the encoding of irradiance (Lall et al. 2010) and

that there is also evidence for UV cones contributing to the sustained response of

the SCN too (Van Diepen et al. 2013, Van Oosterhout et al. 2012). However simple

light steps are uncharacteristic of our everyday experience. We experience light for

much longer durations than those experienced in a typical light step. In addition

the stimuli we typically experience contain high frequency spatiotemporal

modulation. The cone and melanopsin components of a simple light step can be

more generalised for a naturalistic stimuli. The sustained melanopsin component

encodes stable background light intensity (irradiance) whilst rods and cones

encode more the changes in light intensity (contrast). An important question then

is what are the relative contributions of cones compared to melanopsin on the

electrophysiological action of the SCN, under natural viewing conditions?

One method to address this question is to present a common visual task whilst

producing a stepped increase in irradiance which is either visible to all

photoreceptors or visible just to melanopsin. Here we vary the spectral


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composition of a movie to elicit a melanopsin only response or a cone and

melanopsin response. Using in vivo electrophysiology to record multi-unit activity

we show that under normal viewing conditions, the SCN response to light is

predominantly driven by melanopsin.

Methods: Animals


5.2 Methods

5.2.1 Animals




2010/63/EU on the protection of animals used for scientific purposes. Male

Opn1mwR mice aged 6-8 weeks were group housed on a reverse 12:12 light:dark

cycles with food and water available ad libitum. These mice had the murine middle

wavelength cone opsin gene replaced by the human long wavelength cone opsin

gene (Smallwood et al. 2003; Lall et al. 2010; Walmsley et al. 2015). Data was

collected from mice aged between 2-5 months old.

5.2.2 Surgery

Surgery was performed as previously described (Brown et al. 2011). Mice were

anaesthetised with an intraperitoneal injection of 1.55g/kg urethane (20%, w/v),

with additional urethane injected subcutaneously if required. Once fully under

anaesthesia, the mouse was placed in a custom-made stereotaxic frame (Narishige,

Japan) and the skull was fixed in position using bite and ear bars. The skull was

exposed with a sagittal incision and a hole was drilled, 0.98mm lateral to bregma.

The dura was removed to expose the brain. The ipsilateral eye was covered.

A Buzsaki32L electrode (NeuroNexus Technologies, MI) was coated in fluorescent

dye (CM-DiI; Invitrogen, Paisley, UK) and inserted at an angle of 9° from the

midline about the sagittal axis. The electrode was lowered using a

micromanipulator (M0-10; Narishige) until slight flection was observed and then

raised 100 µm. This led to a depth between 5.5 - 6 mm, which corresponds to the

location of the SCN according to a stereotaxic mouse atlas (Paxinos & Franklin

2001). A test stimulus was presented to check for light responses. The test

stimulus was a 5 second light pulse (6 x 1013 photons.cm-2.s-1; 10 second

interstimulus interval) for 5-10mins. If no light pulses were detected the electrode

was repositioned. After detecting light responses mice were left for 30 minutes to

let the brain activity settle from possible trauma from the electrode placement.

Methods: Light Calibration

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The core body temperature of the mouse was monitored and regulated using a

homeothermic heat mat (Harvard Apparatus, UK), and fluid was replenished with a

0.1ml subcutaneous injection of Hartsmann’s solution approximately every two

hours.

Data were recorded using Recorder64 (Plexon, Tx). Signals were amplified by a

20x gain AC-coupled headstage (Plexon, Tx) followed by pre-amplifier

conditioning providing a total gain of 3500x. Data were filtered using a high-pass

Butterworth filter set at 300Hz. Signals passing a 5% threshold were timestamped

and their waveform digitized at a rate of 40 KHz. The raw signals from all 32

channels were also digitized. All data were stored on a hard drive for offline

analysis.

5.2.3 Light Calibration

Light intensity was measured in 1 nm intervals between 380-780 nm using a

spectroradiometer (SpectroCal, Cambridge Research Systems). Each light channel

was measured at a range of intensities to check for linearity. These data were used

to calculate the photon capture for each class of photoreceptor and the

corresponding spectral compositions required to produce photoreceptor specific

light steps. The spectral profiles of the 3 spectra, required to produce the

photoreceptor specific light step, are shown in Figure 5.1A. The corresponding

perceived irradiance for each photoreceptor is calculated, along with the

Michelson contrast of the light steps, in Table 5.1.

5.2.4 Light Source

The stimulus was played through a custom made projector previously described

by Allen et al. (Allen et al. 2014). The RGB inputs to the projector were calibrated

using MPIDE linked to an Arduino which controlled the respective intensities of

the projector’s 4 light sources. Thus the RGB channels were transformed into a

multispectral composition (Figure 5.1A). These spectral compositions were

produced such that the corresponding photoreceptor specific contrast between

two of the spectral compositions would be either large or minimal. Light intensity

was reduced by attaching a combination of glass neutral density filters to the

projector, each of which reduced the intensity by a factor of 10.

Methods: Stimuli


5.2.5 Stimuli

Three test pulses were used in this study. Each pulse consisted of a 5 second pulse

with a 10 second inter-stimulus interval. The first test pulse, which was used for

the movie, used a white light from darkness, whilst the second and third pulses,

used for the simple light step, were a melanopsin and energy steps respectively.

A five minute episode of web browsing was captured using Bandicam (Bandisoft,

Korea). This was manipulated using video editing software (VSDC: Free video

editor, Flash-Integro LLC) into a 40 minute film that consisted of interleaved 5

minute monochromatic segments that corresponded to the RGB output channels

(order of presentation: RGRBRGRB, see Figure 5.1B). The R G and B outputs were

transformed by the projector into the corresponding spectra: background

spectrum, spectrum 1 and spectrum 2 respectively. The global irradiance of the

stimulus varied across the duration of the movie (shown in Figure 5.1C, sample

rate: 10Hz). Over the entire course of the movie the range of irradiances produced

a Michelson contrast of 27.5% (Figure 5.1C top) ; with an instantaneous maximal

change in irradiance of 14.1% Michelson contrast (Figure 5.1C bottom, 10Hz

sampling rate). However, local changes in radiance would be both much larger in

magnitude and more frequent.

Methods: Analysis

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Figure 5.1: Properties of the Light Stimuli. The three shades: black, grey and cream correspond to the three spectra: Background, Spectrum 1 and Spectrum 2 respectively. A. The spectral power distribution of the two spectra used to produce a melanopsin step (top) or an energy step (bottom). B. Cartoon of the spectral presentation order for the movie (top). Traces depict the steps in irradiance that each photoreceptor experiences. C. Trace of how the irradiance (top) and change in irradiance (bottom) change over the course of the 5 minute movie.

Table 5.1: Photoreceptor Specific Stimuli. The relative photon absorption per photopigment for each of the three spectra used. Shaded rows denote the photopigment specific contrast produced by the two steps from background.

Methods: Analysis


5.2.6 Analysis


2014). Neural waveforms were processed using PlexUtil and Offline sorter Sorter

[ver.3] (Plexon, TX). Cross-channel artefacts and noise were removed, leaving

multi-unit data. Raw data was combined to form a ‘virtual’-tetrode waveforms

using custom made Matlab scripts (Howarth et al. 2014). Tetrodes were processed

using Offline Sorter. Single units were extracted using principle component

analysis and validated by comparing correlograms (Figure 5.2).

Single and multi-unit data were then analysed using; NeuroExplorer [ver. 4] (Nex

Technologies, MA) and GraphPad Prism (ver. 6, GraphPad Software). Perievent

histograms (bins = 0.5 sec) for the test stimuli were produced using

NeuroExplorer. The 99% confidence limits were determined from the 5 seconds

preceding the light pulse. Units that exceeded the confidence limits, during the

light pulse or immediately after light offset, were classified as light responsive. For

the simple light step paradigm we used the energy and melanopsin step to check

for light responsive units. For the movie paradigm we used the maximum contrast

available by the projector, this was to capture the maximum number of light

responsive units. As integration over the 5 minute stimulus would allow us to

detect responses that might not have been detected using a 5 sec melanopsin or

energy step.

Results: Analysis

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Figure 5.2: Single Unit Extraction and Validation. Two single units from a single tetrode extracted using Offline Sorter. A. Individual spikes plotted using the first two principle components. B. Cross correlogram of the two units first 10ms pre and post spike. C. Tetrode waveform for each unit (mean ± 3 SD). D. Inter-spike interval for each unit (first 10ms shown, <0.1% of spikes fall below the dashed red line at 1ms).

Results: Single Unit Responses under Natural Viewing Conditions


5.3 Results

The wavelength of light that is maximally absorbed (λmax) by melanopsin (480nm),

rod opsin (498nm) and middle wavelength sensitive cone opsin (508nm) are all

very similar in mice. Thus it is difficult to design stimuli that produce a change in

irradiance for one photoreceptor but not the others. In order to overcome this, we

have used a well validated transgenic murine model (Opn1mwR). In this mouse, the

gene encoding the human long wavelength sensitive opsin (556nm) replaces the

native long wavelength sensitive murine opsin (508nm) (Lall et al. 2010;

Smallwood et al. 2003). This shifts the peak wavelength sensitivity of the middle

wavelength sensitive cone away from that of rod opsin and melanopsin. This

allows one to design metameric stimuli such that the relative photon capture for

cones is kept constant.

We created three spectra: ‘spectrum 1’, ‘spectrum 2’, and a ‘background’ spectrum,

(Relative photon capture is shown in Table 5.1, and the spectra themselves are

shown in Figure 5.1A). Stepping from background to spectrum 1 (Figure 5.1A

top), produces an increase in irradiance for melanopsin (76-78% Michelson

contrast; Table 5.1) but not for cones. This step was termed the ‘melanopsin step’.

Stepping from background to spectrum 2 (Figure 5.1A bottom), produced an

equivalent increase in irradiance for both melanopsin and for cones (76-78%

Michelson contrast; Table 5.1).

In order to create a common visual experience that incorporates both spatial and

temporal contrast we created a movie consisting of 5 minutes of web browsing

footage. This movie was presented continuously for 40 minutes. The spectral

composition of the movie was changed to provide 5 minute melanopsin or energy

steps. These steps were interleaved with 5 minutes of background in-between

(Figure 5.1B).

Results: Single Unit Responses under Natural Viewing Conditions

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5.3.1 Single Unit Responses under Natural Viewing Conditions

We presented the movie at three irradiances (3x1012, 3x1011, 3x1010 photons.cm-

2.s-1) to urethane anaesthetised mice. From 8 mice we were able to isolate 23 single

units of which 20 were classified as light responsive as they significantly

responded to a full field light step (see methods). We observed that some single

units (n=6) increased their firing rate in response to both a melanopsin and an

energy step (An example unit is shown in Figure 5.3A top) whilst others

decreased (n=4) their firing rate in response to the stimuli (An example unit is

shown in Figure 5.3A bottom). For an individual unit, the direction of the response

was the same for both energy and melanopsin steps. The direction was also

consistent across irradiances too, although the magnitude of the response

decreased as irradiance fell (Figure 5.3A). Indeed, the proportion of light

responsive cells that responded with a significant increase or decrease in firing

rate (Paired t-test of the combined energy and melanopsin steps, p<0.05)

decreased with decreasing irradiance (Figure 5.3B).

We were unable to draw any conclusions for the SCN population as a whole from

these single unit recordings for two main reasons. Firstly, there was lack of single

units that showed a significant response to the movies, especially at lower

irradiances. Secondly, there was a comparable spread of excitatory and inhibitory

responses. This is uncharacteristic of the SCN, which typically has a lower

proportion of inhibitory cells, 20-30% (Groos & Mason 1980; Meijer et al. 1986),

and may be an artefact of our small sample size. Thus, in order to get a truer

reflection of the overall SCN response under naturalistic stimuli, we turned to the

multi-unit activity.

5.3.2 Multi-Unit Activity under Natural Viewing Conditions

From 8 mice we recorded multi-unit activity from 211 light responsive sites

(Showing a statistically significant change in multi-unit activity to a test light step:

see methods). Multi-unit activity was highly variable, spanning four orders of

magnitude. Therefore we compared the proportional change in firing rate induced

by the visual stimuli. Despite this normalisation, the time averaged firing rates

Results: Multi-Unit Activity under Natural Viewing Conditions


Figure 5.3: Single Unit Responses to Melanopsin and Energy Steps. A. Two example units showing the change in firing rate (mean ± SEM) over the course of the movie. A cell that increased firing (top) and a cell that decreased its firing (bottom) were presented with either an energy step (left) or a melanopsin step (right) at 3 different irradiances. Green and red denote periods where the firing was above and below the baseline rate respectively. B. The proportion of light responsive single units (n=20) that showed a significant increase (black) or decrease (grey) in firing at each irradiance.

Results: Multi-Unit Activity under Natural Viewing Conditions

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were not normally distributed, exhibiting a high kurtosis (0.5-5.1). One

explanation is that due to the proximity of the sites on the multi-electrode probe,

cells could be recorded at multiple sites. Therefore, the 211 sites are not

independent data points and would result in a distribution with a high kurtosis.

We thus decided to average the proportional change in firing within each mouse.

The resultant data represented the mean proportional change in firing rate per

mouse and was normally distributed (D’Agostino & Pearson omnibus normality

test p<0.05).

The presentation of either a melanopsin or an energy step resulted in an increase

in firing rate (Figure 5.4A). This increase in firing rate was observed for the

duration of the movie, but was most prevalent at the start. These observations

were apparent at the two highest irradiances. As the irradiance decreased so did

the magnitude of the response (Figure 5.4A). At each irradiance, the melanopsin

and energy steps produced similar responses over the course of the movie (Figure

5.4A).

The observation of a transient response in both the melanopsin step and the cone

step is at odds with the literature as the transient response is driven by cones

(Brown et al. 2011). To further investigate the nature of our transient we focused

in on the response to the initial 5 seconds of our stimuli (Figure 5.4B). At each

irradiance, we observed a slow increase in firing rate that peaked ~2 seconds after

the start of both the melanopsin and energy steps. This slow rise to peak is

characteristic of a melanopsin response. A cone response would be expected to

occur within the first few hundred milliseconds and would not be present in the

response to a melanopsin step. We were unable to observe a cone response to the

energy step at any of the irradiances tested. This prompted us to compare the

responses of our melanopsin and energy steps in the absence of spatial structure.

To this end, we presented a 5 second simple melanopsin or energy step to a subset

of the mice (n=3). As we were using shorter stimuli, only electrode sites that were

responsive to either the 5 second melanopsin or energy step were considered light

responsive (see methods). We recorded multi-unit activity from 38 sites at two

irradiances (3 x 1012 and 3 x 1011 photons.cm-2.s-1; an example site is shown in

Figure 5.4C). Across the population the firing rate increased within the first

Results: Time Averaged Firing under Natural Viewing Conditions.


500ms under all 4 conditions (Figure 5.4D). We therefore examined the initial

response under the first 500ms. In all cases there was a slow increase in firing,

however when the energy step was presented at the brightest irradiance there was

an additional peak occurring within 100 – 200 ms (Figure 5.4E). This short

latency response is consistent with the latency of cone responses in the SCN (see

chapter 3). Therefore the lack of an initial cone transient during the energy step is

specific to the movie.

5.3.3 Time Averaged Firing under Natural Viewing Conditions.

To explore the overall impact of the different stimuli on SCN activity, we averaged

the firing rate across the 5 minute movie. Both irradiance and stimuli had an effect

on the time averaged firing rate, although there was no interaction between these

effects (Two way repeated measures ANOVA: Irradiance 33.2% of variation,

p=0.0029; Stimuli 1.4% of variation, p=0.0265, Interaction < 0.2% of variation,

p=0.688). The magnitude of the SCN response increased with increasing irradiance

(Figure 5.5A). However, contrary to expectations, the melanopsin step produced a

larger proportional increase than the energy step (Figure 5.5A). A post hoc

Figure 5.4: Multi-Unit Activity in Response to a Melanopsin or Energy Step. A-B. Multi-unit activity in response to a melanopsin (blue) or an energy (red) step under naturalistic viewing conditions at 3 irradiances (3 x 1012, 3 x 1011,

3 x 1010 photons.cm-2.s-1). Multi-unit activity averaged for each mouse (n=8 except at the lowest irradiance where n=6). A. Perievent histogram (10 sec bins) depicting the proportional change in firing rate (mean ± SEM) over the course of the 5 minute movie. B. Data from A, focussing on the 5 seconds either side of the stimulus onset. Perievent histogram (0.05 sec bins) showing the mean proportional change in firing rate. C-E. Multi-unit activity in response to a simple melanopsin (blue) or energy (red) step at two irradiances (3 x 1012, 3 x 1011 photons.cm-2.s-1). Multi-unit activity averaged for each mouse (n=3). C. Raster and perievent histogram (0.5 sec bins) showing the responses of an example light responsive unit to an energy step (left) and a melanopsin step (right) at 3 x 1012 photons.cm-2.s-1.D. Perievent histogram (0.5 sec bins) depicting the proportional change in firing rate (mean ± SEM) over the course of the 5 second light pulse, across the 3 mice. E. Data from D. focussing on the 500 milliseconds either side of the stimulus onset. Perievent histogram (mean± SEM, bins 0.01s, box-car smoothed over 5 bins) showing the mean proportional change in firing rate.


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comparison showed that, for each irradiance, there was no significant difference in

firing rate between the melanopsin and energy steps (Sidik’s multiple comparisons

test, p>0.05). Together, these data imply that the majority of the SCN response can

be attributed to the increase in melanopic irradiance.

Electronic devices are commonly used for periods longer than 5 minutes. When we

examined the response to the melanopsin and energy steps over time we observed

that the response peaked at the start of the movie and then decayed to a lower

sustained level. The effect of this initial peak on the time averaged response will

decrease as you increase the duration of the stimuli. To gain an appreciation of the

photoreceptor contribution for longer durations we repeated the analysis on only

the sustained component. To this end we removed the peak by excluding the

response to the first minute of the movie.

Examining the sustained component of the response we observed that neither

irradiance nor stimuli had a significant effect on the firing rate (Two way repeated

measures ANOVA: Irradiance 15.7% of variation, p=0.099; Stimuli 1.0 % of

variation, p=0.416, Interaction 0.3% of variation, p=0.886). In addition, the post

hoc comparison showed that, for each irradiance, there was no observable

significance between the melanopsin and energy steps (Sidik’s multiple

comparisons test, p>0.05).

In summary, the response to an irradiance step for cones and melanopsin is not

significantly different from the response to an irradiance step for just melanopsin.

This implies that under naturalistic viewing conditions, the light induced increase

in the SCN firing rate is predominantly driven by melanopsin.


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Figure 5.5: Photoreceptor Contribution to the Time Averaged SCN Light Response. A&B. The Proportional change (mean ± SEM) in the time averaged firing for a melanopsin (blue) and energy (red) step under natural viewing conditions at three irradiances. N=8 mice. A. SCN activity averaged over the entire movie (0-5 mins). Two way repeated measures ANOVA: Interaction, ns; Irradiance, p= 0.0029; stimuli, p=0.0265, Sidak’s multiple comparisons test; Melanopsin vs Energy at each irradiance, ns. B. SCN activity averaged over the sustained component of the response (1-5 mins). Two way repeated measures ANOVA: Interaction, ns; Irradiance, ns; stimuli, ns, Sidak’s multiple comparisons test; Melanopsin vs Energy at each irradiance, ns.

Discussion: Time Averaged Firing under Natural Viewing Conditions.


5.4 Discussion

Background light intensity (irradiance) is a strong zeitgeber for the circadian clock.

The irradiance signal that is encoded by the RHT is comprised of photic

information from rods, cones and melanopsin. So far, studies have investigated the

photoreceptor contribution to the SCN light response by using a simple light step.

Such a stimulus does not capture the high spatiotemporal contrast that we

encounter in our everyday lives. Under such conditions, one can expect that the

optic nerve will encode information about spatiotemporal contrast alongside the

irradiance signal. With our current understanding of how cones are highly tuned to

detect contrast, whilst melanopsin is tuned more to track irradiance, we set out to

determine how these two photoreceptors contribute to the SCN light response

under naturalistic viewing conditions. We found that an irradiance step for only

melanopsin produced a SCN response that was as large as the response to an

irradiance step for all photoreceptors. This implies that the majority of the SCN

response to a change in irradiance (under naturalistic conditions) is down to

melanopsin.

There is a wealth of literature that shows that cones are able to affect the circadian

system (Gooley et al. 2010; Mien et al. 2014; Panda et al. 2002; Zeitzer et al. 1997;

Lall et al. 2010; McCormack & Sontag 1980; Brown et al. 2011; Walmsley et al.

2015). Cones have been shown to increase the SCN neuronal activity as well as to

induce: phase shifts, entrainment and melatonin suppression. In order to show

that the response was cone mediated, these studies used techniques to

minimize/remove the melanopsin component of the response (e.g. melanopsin

knockout (OPN4-/-) mice, silent substitution and narrowband red light outside of

the melanopsin sensitivity range). Our results are not inconsistent with these

studies. We show that the melanopsin contribution to the SCN’s light response is

much greater than that of cones. We propose that when a light stimulus fails to

adequately stimulate melanopsin, the contribution from cones may be revealed. To

directly address this hypothesis, we could repeat this experiment but keep

melanopsin silent whilst we compare the response to a cone step with that of an

energy step.


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In response to a simple light step, a typical SCN neurone shows a transient increase

in firing followed by a decay in the firing rate to a sustained raised rate that lasts

the duration of the pulse. It is generally considered that the transient and

sustained components are predominantly driven by cones and melanopsin

respectively. Upon close inspection of the start of our energy step under natural

conditions, we were unable to observe the expected cone-driven fast increase in

firing. However, a simple full-field presentation of this stimulus did reveal such a

cone mediated transient. What could explain this discrepancy? One possible

explanation that we can exclude is that the irradiance of the energy step in the

movie condition was too low to induce a measurable cone transient. In the full field

condition, the characteristic cone transient was only observed when the energy

step was presented at the highest irradiance, implying that we could be close to the

threshold for detecting this response. The spatiotemporal structure of the movie

would result in the irradiance being slightly lower. In theory this small reduction in

irradiance could render the movie sub-threshold for a cone response at the phase

in the movie in which the cone transient occurs. However, in practice, the

irradiance at this point in the movie (first 0.5 sec after start of energy step) was

very similar (>95%) of the irradiance for the full-field stimulus. The most

plausible remaining explanation is that the cone influence on the SCN is subject to

contrast adaptation. The visual system adapts its sensitivity to cover a range of

stimuli. In the presence of high contrast stimuli the visual system reduces its

sensitivity to contrast, allowing it to encode the range of contrasts present in

diverse visual scenes (Smirnakis et al. 1997). With the simple light step, there is no

underlying contrast so the energy step will produce a large contrast response.

Under the naturalistic light stimulus, there is a relatively large ongoing spatial and

temporal contrast. This will reduce the sensitivity of the system to contrast, which

could explain why the cone-driven response is small when the energy step is

presented under naturalistic viewing conditions.

Over the course of the movie, it was observed that firing rate was most prominent

at the start of the movie and would then decline to a lower sustained level. This is

indicative of light adaptation, which has been observed in all mammalian

photoreceptors. To investigate the nature of this light adaptation we investigated



the initial response of the movie (Figure 5.4B). We observed a slow latency to

peak firing (~2 sec) followed by a decline in firing rate. These slow kinetics along

with the observation that this adaptation occurred under both the melanopsin and

the energy step conditions, strongly suggests that this response is mediated by

melanopsin. Light adaptation with similar slow dynamics has previously been

reported in melanopsin expressing cells in the absence of rod and cone input

(Wong 2012).

One caveat of this study is that the absorption spectra of rods and melanopsin are

very similar. Thus in order to create a large melanopsin contrast, we decided not to

hold the melanopsin step silent for rods. In order to reduce contribution of rods to

the melanopsin step we performed the experiment under light adapted conditions

and used relatively bright stimuli. Under such conditions rods will be relatively

saturated and should contribute little to the melanopsin step. However, there is

growing evidence that rods can still signal contrast at high irradiances (Yin et al.

2006). Distinguishing between rod and melanopsin activity is interesting from a

theoretical perspective and in understanding the biology of photoentrainment, but

is less important for practical purposes. Light sources are described using

(photopic) lux and in some cases they are also described in terms of colour too.

Both of these parameters are derived from the human cone response and do not

take into account rods or melanopsin. We have shown in this study that the

current methodology of characterising light sources (by colour and brightness)

does not contain information relating to the non-visual effects of light e.g. circadian

effects. As such we, like those before us, propose that light should have a further

descriptor that characterises the melanopsin component (see article: Lucas et al.,

2014). In this case, the similarity in spectral sensitivity between rods and

melanopsin implies that approaches used to maximise (or minimise) irradiance for

one would, in most cases, have a similar effect on the other.

With the increased use of light at night, especially through electronic devices used

for evening reading, the circadian system has become misaligned; leading to

difficulty sleeping (Chang et al. 2015). Recently developed apps aim to manipulate

the non-image forming effects of light by altering the colour of the visual display


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unit (VDU). Whilst such alterations may affect the melanopic input, they also alter

the colour. Through the careful design of metamers one could achieve the same

manipulation of non-image forming light induced effects without altering the

colour or brightness of the VDU. This use of metamers to incorporate the non-

image forming effects of light into a light source without effecting the visual

properties (colour, brightness) could be used in more general lighting conditions

such as in a working environment to help increase alertness.

We compared the radiance of our stimuli to what one might experience when

reading from an electronic device at night (Table 5.2, Figure 5.6). To this end, we

recorded the radiance of the following VDUs: A laptop (Toshiba, satellite pro), a

tablet (iPadmini 2) and a phone (Sony, Xperia J ST26i). The melanopic radiance of

the brightest spectra (4.3x1012 photons. sr-1.cm-2.s-1) was dimmer than the

corresponding melanopic radiance from our range of VDUs, many even at their

dimmest setting (Table 5.2; Tablet: 2.0 x1012 photons.sr-1.cm-2.s-1, phone: 1.3x1013

photons. sr-1.cm-2.s-1, laptop: 5.6x1012 photons. sr-1.cm-2.s-1). Thus all of the

electronic devices tested should be able to increase the firing rate of the SCN even

at the dimmest setting. In addition, the irradiance required to record a light

responsive unit in an anaesthetised mouse (~1012 melanopic photons.cm-2.s-1:

Brown et al., 2011) is much higher than the threshold for inducing a phase shift in

the same awake animal (~1010 melanopic photons.cm-2.s-1 (Lall et al. 2010)).

Although humans may not be as sensitive as mice to light, it is likely that electronic

devices in their current melanopsin-unregulated form are able to have a significant

effect on our circadian systems (Chang et al. 2015; Grønli et al. 2016).



Table 5.2: Relative Photon Capture for Different e-Devices. The relative radiance for each murine photopigment (+ the human L cone photopigment used in this study) was calculated for three e-devices: A tablet (iPadmini 2), a phone (Sony, Xperia J ST26i) and a laptop (Toshiba, satellite pro) on their brightest and dimmest settings. We also included the radiance of the brightest spectrum we used for the movie as a comparison. Radiance was adjusted for corneal transmission and measured in photons.sr-1.cm-2.s-1.

For Mouse Total

Photons L cone opsin

M cone opsin

S cone opsin Rod opsin Melanopsin

Tablet bright 3.43 x1014 1.81 x1014 1.35 x1014 1.69 x1011 1.24 x1014 1.06 x1014

Tablet dim 6.22 x1012 3.30 x1012 2.48 x1012 2.46 x1009 2.28 x1012 1.95 x1012

Phone bright 3.29 x1014 1.64 x1014 1.31 x1014 1.55 x1011 1.22 x1014 1.08 x1014

Phone dim 4.07 x1013 2.04 x1013 1.62 x1013 1.83 x1010 1.51 x1013 1.33 x1013

Laptop bright 1.51 x1014 7.80 x1013 6.89 x1013 6.94 x1010 6.69 x1013 6.33 x1013

Laptop dim 1.36 x1013 7.07 x1012 6.10 x1012 6.22 x1009 5.89 x1012 5.55 x1012

Movie Spectrum 3 1.01 x1014 2.25 x1013 3.42 x1012 1.17 x1012 3.40 x1012 4.33 x1012


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Figure 5.6: The Spectral Radiance of 3 Electronic Devices for Comparison with the Stimuli used in this Experiment. Radiance (mW.sr-1.m-2.nm-1) of three e-devices: A tablet (iPadmini 2), a phone (Sony, Xperia J ST26i) and a laptop (Toshiba, satellite pro) recorded at their brightest settings. The spectral radiance of the energy spectrum (with no spatial contrast) is shown in the bottom right. The relative photon capture (photons.sr-1.cm-2.s-1) for each of the murine photopigments, in this study, is calculated for each stimulus.

Chapter 5: Melanopsin Contribution to Time Averaged Firing in the SCN under Naturalistic Viewing Conditions.:


5.5 Reference

Allen, A.E. et al., 2014. Melanopsin-driven light adaptation in mouse vision. Current Biology, 24(21),

pp.2481–2490.

Berson, D.M., Dunn, F.A. & Takao, M., 2002. Phototransduction by retinal ganglion cells that set the

circadian clock. Science, 295(5557), pp.1070–3.



Chang, A. et al., 2015. Evening use of light-emitting eReaders negatively affects sleep , circadian

timing , and next-morning alertness. PNAS, 112(4), pp.1232–7.

Gooley, J.J. et al., 2010. Spectral responses of the human circadian system depend on the irradiance

and duration of exposure to light. Science translational medicine, 2(31), p.31ra33.

Grønli, J. et al., 2016. Reading from an iPad or from a book in bed: the impact on human sleep. a

randomized controlled crossover trial. Sleep Medicine.

Chapter 6: General Discussion:

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6 Chapter 6: General Discussion

6.1 Overview

In this thesis I set out to determine the extent to which the SCN response to light is

affected by spatial patterns, which are present in natural viewing conditions. In

Chapter 3, I showed that whilst a select few SCN cells may detect a true global

irradiance signal, the majority of SCN cells sampled from a limited area of the

visual scene. I took this idea further in Chapter 4, and showed that even the cells

that responded to light anywhere in the visual scene were still affected by spatial

patterns. Indeed, spatial patterns were able to produce high frequency

modulations in the firing rate of practically all cells within the SCN. Despite these

large amplitude and high frequency modulations, the overall time averaged firing

rate showed only a small change, when spatial patterns were introduced to the

stimulus. Furthermore, this fine scale modulation induced by spatial patterns did

not seem to effect clock resetting. This is consistent with the mechanism of phase

shifting having a long integration time (Nelson & Takahashi 1991; Nelson &

Takahashi 1999; Meijer et al. 1992) so that it is the time averaged firing as opposed

to the high frequency modulations that are important for clock resetting. The high

temporal frequency related with the modulation most likely relies upon rod or

cone input as opposed to the slower acting melanopsin signalling. As time

averaged firing was most potent for clock resetting, I endeavoured to find out the

relative melanopsin contribution to the time averaged firing rate of SCN neurons

under a naturalistic viewing condition. Under such conditions, the increase in SCN

activity from a change in spectral power (increasing irradiance uniformly across

photoreceptors) can be almost entirely explained by the increase in melanopic

irradiance.


170| Chapter 6: General Discussion

6.2 Future Directions

Whilst I have made some fair progress towards the understanding of how spatial

patterns affect the circadian system, there are a lot of further questions that have

emerged from these studies as well as questions that still remain unanswered.

Here I shall outline a few questions that I find particularly interesting. I shall start

with the smaller questions specifically relevant for specific chapters and then

progress on to larger questions relating to thesis as a whole.

6.2.1 Formation of the Full Field Inhibition.

In Chapter 3 the large receptive field properties such as the full field surround,

were proposed to emerge from the GABAergic network within the SCN. The

observation that some SCN cells can be excited by GABA could account for the full

field excitatory cells. Some reports suggest that the excitatory response to GABA is

under circadian control (Choi et al. 2008). This would imply that the receptive field

properties would vary during the course of a day. To investigate this, collaborators

in Michigan have produced an in silico model using the receptive field data. One of

the outputs of the model should show how the receptive field properties vary with

circadian time. This could then be verified using in vivo electrophysiology to

examine the most interesting time points as predicted by the model.

Another potential mechanism for generating the full field inhibition was proposed

in Chapter 3. The GHT, which provides an indirect photic pathway via the IGL

releases inhibitory neurotransmitters such as neuropeptide Y. To investigate the

role of this indirect photic input on the SCN one could use either pharmacological

or optogenetic approaches to activate/inhibit the IGL. As the time course of the

light response is pivotal to the receptive field mapping, long term excitation or

inhibition using pharmacological agents would be better suited than optogenetics

which would require precision timing.

6.2.2 Complete Photoreceptor Contribution Breakdown

The overall conclusion in Chapter 5, was that for a step in irradiance (under

naturalistic viewing conditions) the increase in neuronal firing within the SCN

could be attributed almost entirely to melanopsin. However, several experiments


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have shown that cones also impact the SCN firing rate and downstream effects

such as phase shifting and melatonin suppression (McCormack & Sontag 1980;

Zeitzer et al. 1997; Walmsley et al. 2015; Van Oosterhout et al. 2012). As

mentioned in Chapter 5, it would be interesting to see if reversing the experiment,

so that a cone only step was compared with an energy step, would reveal the

relative cone contribution. There are two possible mechanisms for the observation

in Chapter 5. The simplest is that cone and melanopsin components are

independent of each other and are summed linearly to produce an irradiance

response. The results from Chapter 5 would therefore imply that cone component

contributes only a very small a fraction of the melanopsin component. An

alternative hypothesis is that the melanopsin and cone components are summed

sub-linearly. In this case either a cone-isolating or a melanopsin-isolating step in

irradiance could induce a significant increase in firing rate. However, the response

to the corresponding energy step would be smaller than the combined responses

of the melanopsin and cone steps. It is thus important to explicitly calculate the

cone response to determine how the different photoreceptive components are

integrated into the SCN output.

Furthermore, it has recently been shown that some cells within the SCN are colour

opponent (Walmsley et al. 2015). This begs the question of whether the different

cone opsins also have different influences on the overall SCN electrical activity.

This could also be achieved through silent substitution; however, as the number of

photopigments that are being held silent increases, it becomes increasingly

difficult to generate a large contrast response for the remaining photopigments.

Ultimately, it would be beneficial to determine the complete photoreceptor

contribution. That is to say, the effect on time averaged firing rate within the SCN

of each photopigment under naturalistic viewing conditions and across a wide

range of biologically significant irradiances. The question posed in Chapter 5 was

very much biased towards the current state of affairs with visual display units. In

this case, whether the increase in firing is due to rods or melanopsin is a moot

point. However from an academic and basic science point of view, it would be

interesting to tease apart these two components. Mice lines lacking either rods or

melanopsin are readily available. There is even an OPN1mwR Opn4-/- mouse line



which both lacks melanopsin and has murine long wavelength sensitive cone opsin

replaced by the human red cone opsin. The problem with this method is that

removing either rod opsin or melanopsin does not allow a direct comparison as

different genetic lines will be used with potential developmental compensations.

A direct comparison of rod and melanopsin contribution (whereby one is able to

record both responses in the same animal) could be achieved through silent

substitution. However, due to the proximity of their absorption spectra, it would

require very bright monochromatic light sources that have a very narrow

bandwidth. Even then, the contrast is limited due to the overlap in their absorption

profiles. To counter this, a transgenic mouse, akin to the red cone mouse, could be

developed whereby either melanopsin or rod opsin is replaced by another opsin,

with a different λmax but is also compatible with both the cell type and transduction

cascade, ideally with similar dynamics. A shift towards a λmax of ~420nm would be

ideal as this could then be combined with the red cone mouse line which would

give four equally spaced photopigments, allowing much more precision in silent

substitution.

6.2.3 Connectivity between ipRGCs and SCN Cells

A primary question that is still largely unknown is what is the connectivity

between ipRGCs and SCN cells? Labelling single ipRGCs and tracing their

projections has shown that ipRGCs project far and wide into the SCN (Fernandez et

al. 2016). This may imply that a single iPRGC can supply direct photic information

to numerous SCN cells, suggesting a one to many (or many to many) relationship.

In Chapter 3, the inverse relationship is indirectly addressed: How many ipRGCs

innervate a single SCN cell? Based on the small receptive fields of many SCN

neurons it is likely that the many SCN cells receive direct photic input from only a

single ipRGC. However another study, in cats, proposed that individual SCN, cells

receive input from numerous ipRGCs (Pu 2000). As discussed in Chapter 3, the

receptive fields in the SCN that Pu was using to infer connectivity were likely to be

taken from full field units. These units may not receive direct innervation from

ipRGCs but rather receive photic information indirectly. In Chapter 3, I discuss the

possibility of the full filed units arising from GABAergic SCN connectivity. To better

answer this question, anatomical data would strongly advance either hypothesis.


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Below, I discuss methods that could help to provide anatomical evidence too

determine how many ipRGCs synapse onto a single SCN neuron:

6.2.3.1 Brainbow

Brainbow is a method of labelling multiple cells, each with a unique colour ratio.

This is achieved through having multiple cassettes of the brainbow transgene. This

transgene consists of several (commonly 4) fluorescent protein genes integrated

with a Cre-loxP recombination system. For each cassette, Cre will induce a random

excision or inversion at one of the loxP sites resulting in expression of only one of

the fluorescent proteins. This Cre-loxP recombination occurs independently for

each cassette, resulting in a distinct combination of fluorescent protein expression

unique to that cell. Brainbow mice which encode these cassettes in the genome can

be commercially bought and crossed with desired Cre lines. Alternatively the

brainbow cassettes can be inserted into the genome using a viral vector, again into

a Cre line mouse. One benefit of the viral method is that each neuron can be

infected by a random and multiple number of viral particles. This can increase the

number of cassettes per cell and produce a larger array of potential colour ratios.

By using brainbow to label ipRGCs one can determine how many different ipRGCs

are in close proximity, to a single SCN neuron. This method could be then

combined with electron microscopy or immunolabelling for synaptic proteins to

determine the number of ipRGCs that can synapse onto an SCN cell (Hammer et al.

2015).

However there are several problems regarding this brainbow method. Firstly in

order to gain a true innervation ratio all ipRGCs innervating the SCN must be

labelled. However, viral uptake is rarely 100%. Secondly as more cells are labelled,

the more difficult it is to isolate individual ipRGCs. Finally there is currently a

sparsity of tools available for the isolation and analysis of brainbow cells. In light of

these limitations, brainbow could still offer an approximation (although probably

an underestimation) of the number of ipRGCs that innervate a single SCN neuron.



6.2.3.2 Tracer Studies

Another potential mechanism would be to inject a retrograde tracer into an

individual SCN cell. However, this is technically very difficult and there is no

guarantee that the neuron you are injecting is light responsive. This could be

overcome by measuring in vivo electrophysiological responses followed by a

retrograde dye.

6.2.4 Do Different ipRGC Subtypes Create the Diversity of Responses

Observed in the SCN?

With the potential of up to three different types of ipRGCs innervating the SCN

(M2, M1 Brn3b +ve, and M1 Brn3b –ve) it is interesting to propose whether they

might relate to the diversity of responses observed; in particular the receptive field

mapping properties. Whilst the anatomy and physiology of M1 and M2 cells have

been compared there has not yet been any such comparison between the two

types of M1 cell. If there are indeed differences between the Brn3b-positive and

Brn3b–negative M1 cells it would be interesting to examine what affect these

differences have on the ability to encode irradiance. For instance, M2 cells exhibit

centre-surround antagonism and their response to light is heavily influenced by

rod and cone input, both of which are factors that would make irradiance encoding

difficult. M1 cells on the other hand are much better suited to encode irradiance.

Selective ablation of the Brn3b-postive ipRGCs has shown that M1 Brn3b-negative

cells are sufficient for photoentrainment (Chen et al. 2011). However the

functional importance of M2 and M1 Brn3b-positive cells has yet to be determined.

As there is no current biomarker for differentiation between the M1 and M2 cells,

the M1 Brn3b-positive and the M2 cells would have to be examined together as

Brn3b-positive cells. Selective ablation of the Brn3b-negative cells could be

induced by having the gene to encode the diphtheria toxin receptor linked to Opn4,

so that all ipRGCs would express the diphtheria toxin receptor. If an additional

gene which excises the diphtheria toxin receptor is linked to the Brn3b gene then

only Brn3b-negative ipRGCs will express the diphtheria toxin receptor and can be

selectively ablated. By ablating either Brn3b-positive or Brn3b–negative cells, one


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could examine the effects of both types of ipRGCs on; encoding irradiance and,

receptive field properties of SCN neurones.

6.2.5 Are Light Responsive Characteristics Correlated to Neuropeptide

Expression?

For me, the heterogeneity of the SCN is an interesting and still largely unknown

area of research. It is generally believed that the RHT projects predominantly to

the core region of the SCN and thus innervates mostly the VIP, GRP and/or NT

expressing cells. However the RHT does project throughout the entire SCN (Morin

et al. 2006; Abrahamson & Moore 2001; Hattar et al. 2010; Fernandez et al. 2016;

Moore & Lenn 1972). Thus, potentially many different neuropeptide expressing

cells may receive direct input from the RHT. With the diversity observed in both

neuropeptide expression and electrophysiological light responses, it begs the

question: can the different responses to light be attributed with neuropeptide

expression within the SCN? These varied light responses could be the well

documented transient, sustained and inhibitory responses observed within the

SCN, or the novel receptive field properties such as centre-surround units

observed in this thesis.

One method of addressing this would be to record intracellularly using a glass

electrode filled with dye. This allows recorded cells in the SCN to be labelled and

thus readily identified post recording. Following the experiment cells can then be

stained for different neuropeptide expression. However one of the main problems

with this technique is the low throughput. In addition intracellular recording from

within the SCN, in vivo, is a very difficult task. Another potential method, though

equally technical, is discussed in the novel techniques below.

6.2.6 Which SCN Cells Receive Direct and Indirect Light Input?

Some cells in the SCN are electrophysiologically light responsive. One might

hypothesis that these cells receive direct innervation from the RHT and the non-

light responsive do not. Of those that are light responsive, many may receive input

directly from the RHT but some might also receive indirect input via intra-SCN

interactions. In Chapter 3, it was proposed that the ON centres of discrete and

centre-surround cells arise from direct RHT innervation, whilst responses of full



field units and the OFF surround component of the centre-surround units

represent indirect retinal input via connections within the SCN (in particular the

GABAergic network). If the SCN can be divided into three groups based on whether

the cells receive direct, indirect or no retinal input, it would be interesting to see if

there is a correlation between the type of innervation and the neuropeptide

expressed by those cells.

6.2.7 Further Receptive Field Properties and Visuotopic Order

Recent papers have shown both colour opponent (Walmsley et al. 2015) and

binocular cells (Walmsley & Brown 2015) within the SCN. The receptive field

properties from such neurons would supply a host of information. Are the

monocular receptive fields in binocular cells congruent? Are the centre-surround

units observed in Chapter 3 colour opponent? Are the colour opponent receptive

fields congruent or complementary? All of these questions suggest a layer of

visuotopic order within the SCN.

Binocular cells could be mapped by covering one eye at a time and recording the

corresponding receptive fields. Mapping the cone-mediated receptive fields to

assay colour is slightly trickier. Silent substitution could be used to map colour

receptive fields. In order to produce silent stimuli there needs to be a background

spectrum from which the M or S cone opsin only steps are produced. This reduces

the contrast of the stimuli and thus a smaller light response is observed. This can

be countered by increasing the light intensity and/or the duration of the light

pulse. However, for receptive field mapping, both options are difficult. Receptive

field mapping requires multiple presentations of the stimulus (e.g. bar) at different

spatial locations and thus if the duration of the stimulus increase by only a small

amount the duration of the entire protocol would rapidly increase. In addition, to

apply a stimulus with spatial structure the monitor must lie some distance from

the eye. This is in contrast to full field stimuli where the light source can be

positioned adjacent to the eye. The result of having the light source some distance

away is a further reduction in retinal illumination. This could be overcome by

using even brighter light sources. However, this is currently a limiting factor in our

lab.


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6.3 Novel Techniques

6.3.1 Chronic Recordings

All of the electrophysiology in this thesis was performed on urethane

anaesthetised mice. This allows the application of very precise and controlled

stimuli, but at the cost of the neural activity being affected by the anaesthesia.

Urethane was used, as compared to other anaesthetics it has relatively little effect

on: neuronal activity in deep brain regions such as the hypothalamus (Cross 1971);

GABAergic signalling; and eye movements (Nair et al. 2011; for a review see: Maggi

& Meli 1986). Although the effects on experiments are minimal there will still be

some effect on the system by using anaesthesia. One known example is that clock

resetting appears to be reduced (Colwell et al. 1993). To counter this, recovery

surgery can be performed so that one can perform electrophysiological recordings

from implanted electrodes in awake animals, in the absence of anaesthesia. This

could be in head fixed animals, where the stimuli can still be tightly controlled or in

freely moving animals. In this section I shall focus on the latter as it is the

behaviour that I am most interested in and this cannot be tested to the same extent

using a head fixed animal. With freely moving animals the electrode probe is

cemented onto the skull and the connecting wire leading to the recording software

is tethered to the ceiling of the cage. Multi-electrode probes are commercially

available for these chronic, free moving recordings. The key question I would like

to address with this method is to directly relate neural activity to phase shifting

behaviour, which cannot be compared directly under a urethane preparation.

Under these conditions circadian analysis can be performed on both the locomotor

activity and the neural activity in the SCN. This will allow a comparison between

these two outputs of the SCN. Some initial questions one may ask are how

correlated are behavioural outputs to the neural activity in the SCN under constant

conditions. What is the variability in τ? What is the phase difference between the

two outputs and how phase locked are they on a daily basis? One could then

investigate the relationship between the light induced increase in firing and the

magnitude of the phase shift. For instance how much does the firing rate need to

increase to produce a 30 minute phase shift and does this relationship vary over

circadian time. Following on from this, one can investigate the temporal dynamics



of phase shifting on the neural activity in the SCN. Furthermore it would be

interesting to compare the lag between the neural activity and locomotor activity

in a jet lag phase advance/delay paradigm.

6.3.2 Retinally Attached SCN Slices

In vitro electrophysiology from SCN slices is a common technique with many

advantages over in vivo recording. Not least the ability to observe the electrode

placement and record both extra- or intra-cellularly. The use of GFP or YFP

reporter lines allows targeting of specific cell types to record from, such as VIP

expressing neurons. Whilst this can be a very informative; a typical slice

preparation does not contain any retinal input and so the questions that I have

been asking cannot be examined. However there is a highly skilled technique has

been developed that could help answer several of the major questions listed above.

This preparation retains the retina and retinal innervation to the SCN slice (Wong,

et al. 2007). Obviously there will be some damage and some connections will be

lost in preparation but all in all it allows a unique way of perturbing the SCN with

light within a slice preparation. This technique has been performed in both mice

and rats, and can retain both retinas which would allow testing for binocular

innovation and mapping the receptive field properties of such cells.

Some of the other important questions mentioned above, that this technique could

answer are: Which neuropeptide expressing cells are electrophysiologically light

responsive? Which cell types are directly or indirectly electrophysiologically light

responsive? As well as, is there a correlation between the receptive field types that

were observed here and neuropeptide expression? Using stimuli similar to those in

this thesis, light responses and receptive field properties could be recorded and

associated with either spatial location within the SCN or neuropeptide expression.

In addition this technique may help with answering questions such as how many

ipRGCs innervate a single SCN neuron and if there is a difference in innervation

between the different ipRGC subtypes. This could be achieved by stimulating

individual ipRGCs one at a time whilst recording from the SCN. Initially recording

from the SCN using calcium imaging or multi-electrode arrays might highlight

areas of interest that could then be examined at the single cell level, intracellularly.


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6.3.3 Novel Animal Models

All of this work was conducted on Mus musculus which is well characterised and

has a breadth of genetic lines that allow us to manipulate the circadian clock and

the retina. This has given us the tools to tackle difficult problems and relate them

to the surrounding literature. However, there is still quite a physiological gap

between mice and humans. Without jumping straight to a primate model there are

a few options that may give a better approximation in relating the findings here to

humans. For me, the main difference is that humans have a high density area of

cones: - the macula. This area is pivotal for high contrast image forming

perception, but it also is an important consideration for circadian biology. For

example, when reading at night from an e-device, the macular will be centred on

the ebook and therefore this area will receive the brightest light intensities. If

photic information from the macula feeds into ipRGCs and ultimately into the SCN

then this will have an effect on both the ability of the SCN to detect spatial patterns,

and the effect of cones on the SCN. Using a small mammal that possesses an area

where cone density is high may help bridge the gap between mice and human.

Keeping with the murine model, our lab has recently established a colony of

Rhabdomys pumilo. This is a diurnal mouse, which has a much higher proportion of

cones than Mus musculus. Alternatively, one might prefer a species that is more

commonly used in both the retinal and circadian fields. For this reason the ground

squirrel may be useful as it too possesses a high density area of cones in its retina

(Long & Fisher 1983).

6.3.4 CRISPR

CRISPR is a novel technique that is revolutionising science. It allows a much

cheaper and more reliable way of both introducing and deleting genes of interest.

One of the reasons why mice are the model organism is the intense infrastructure

that already exists. The animals are well documented, well validated, and have an

arsenal of genetic lines. This wealth of pre-established lines helps produce

conditional knockouts too. CRISPR can help breakdown the genetic monopoly that

mice (and a few other organisms) have and open these techniques to pretty much

any animal.



One particular aspect of CRISPR is that it unlocks the use of genetically modified

organisms for other species, thus one is not confined to just zebrafish or mice. As

mentioned above, both Rhabdomys and ground squirrels offer an exciting prospect

for studying the effect of light on the SCN in a more cone dominated species like

humans. With the use of CRISPR it is much simpler to create a transgenic line of

interest. Therefore CRISPR might provide a feasible route to produce a ‘red cone’

transgenic line, equivalent to the Opn1mwR line that was used here, in either

Rhabdomys or the ground squirrel. This would allow one to ask whether; in a more

cone dominant retina, do cones have more effect on the SCN outputs?

As mentioned earlier a transgenic animal where the λmax of rod-opsin or

melanopsin are separated would be a very useful tool. The CRISPR method could

facilitate the generation of such a model.

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