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GABA Networks Destabilize Genetic Oscillationsin the Circadian PacemakerG. Mark Freeman, Jr.,1 Rebecca M. Krock,1 Sara J. Aton,1 Paul Thaben,2 and Erik D. Herzog1,*1Department of Biology, Washington University, St. Louis, MO 63130, USA2Institute for Theoretical Biology, Humboldt University, 10115 Berlin, Germany

*Correspondence: [email protected]://dx.doi.org/10.1016/j.neuron.2013.04.003

SUMMARY

Systems of coupled oscillators abound in nature.How they establish stable phase relationships underdiverse conditions is fundamentally important. Themammalian suprachiasmatic nucleus (SCN) is aself-sustained, synchronized network of circadianoscillators that coordinates daily rhythms in physi-ology and behavior. To elucidate the underlyingtopology and signaling mechanisms that modulatecircadian synchrony, we discriminated the firing ofhundreds of SCN neurons continuously over days.Using an analysis method to identify functionalinteractions between neurons based on changes intheir firing, we characterized a GABAergic networkcomprised of fast, excitatory, and inhibitory connec-tions that is both stable over days and changes instrength with time of day. By monitoring PERIOD2protein expression, we provide the first evidencethat these millisecond-level interactions activelyoppose circadian synchrony and inject jitter into dailyrhythms. These results provide a mechanism bywhich circadian oscillators can tune their phase rela-tionships under different environmental conditions.

INTRODUCTION

The study of coupled oscillators is part of a broader movement

toward understanding complex systems (Strogatz, 2000). In

biology, intercellular communication can modulate the precision

and synchronization of single-cell oscillations including glycol-

ysis, somitogenesis, respiration, and daily cycling (Jiang et al.,

2000; Herzog, 2007). In many cases, coupled systems are inher-

ently difficult to understand because the interactions are diverse

and dynamic. The suprachiasmatic nucleus (SCN) of the

mammalian brain provides an exceptional opportunity to reveal

the topology, types, stability, and function of diverse connec-

tions in a defined network of neural oscillators.

Neurons within the SCN express near 24 hr (circadian)

oscillations in electrical activity and gene expression and entrain

to regulate daily rhythms including metabolism, hormone

release, and sleep-wake cycles. These cells depend on an intra-

cellular transcription-translation feedback loop to generate daily

rhythms and intercellular signaling for both synchronization and

reliable rhythmicity (Yamaguchi et al., 2003; Webb et al., 2009).

Importantly, it is unknown how intercellular signaling modulates

the cycle-to-cycle precision of circadian rhythms.

Neural communication in the SCN includes gap junctions,

neurotransmitters and neuropeptides. Of these, loss of vasoac-

tive intestinal polypeptide (VIP) dramatically impairs circadian

rhythms in the SCN and in behavior (Aton et al., 2005). Recent

links between VIP signaling and schizophrenia highlight the

possibility that VIP determines the development of the circuits

underlying circadian synchrony (Vacic et al., 2011). To test

whether VIP is required to maintain network topology in the

SCN, we established a novel method to reliably map the func-

tional connections between SCN neurons.

Within the central nervous system, g-amino-butyric acid

(GABA) serves as the principal inhibitory neurotransmitter.

Nearly every neuron within the SCN synthesizes GABA (Moore

and Speh, 1993; Belenky et al., 2008) and exhibits inhibitory

postsynaptic currents (IPSCs) that depend on GABA signaling

and vary in frequency over the day (Itri et al., 2004). In spite of

its predominance, however, the function of GABAergic signaling

in the SCN remains unresolved. GABA has been reported to be

inhibitory at all times (Aton et al., 2006; Liu and Reppert, 2000),

mainly inhibitory during the day and excitatory during the night

(Albus et al., 2005; Choi et al., 2008; De Jeu and Pennartz,

2002) and inhibitory during the night, excitatory during the day

(Wagner et al., 1997). Furthermore, daily administration of exog-

enous GABA suffices to coordinate SCN neurons (Liu and

Reppert, 2000), and GABA can transmit phase information

between SCN populations (Albus et al., 2005); however, syn-

chrony among SCN cells can persist during chronic blockade

of intrinsic GABAergic signaling (Aton et al., 2006). To resolve

these apparent contradictions, we discriminated the discharge

patterns of large numbers of individual neurons over multiple

days and identified the stability and polarity of GABA-dependent

interactions in the SCN. Using real-time bioluminescence

imaging, we discovered a role for these synapses in circadian

timekeeping.

RESULTS

Spontaneous Firing Reveals Fast ConnectivityTo assess functional communication between SCN neurons, we

monitored gene expression and firing rates of individual SCN

neurons in vitro. We found that in explants and dispersals,

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Figure 1. Detection and Mapping of GABA-Dependent interactions in SCN Networks

(A) SCN neurons were recorded on a MEA. Colored shapes show the locations where four representative neurons were recorded. (B) The firing patterns of these

neurons differed in their time of peak firing over the day (left) and in their spike patterns (right). (C) Spike trains from one recorded hour (gray box in B) cross-

correlated positively, negatively, or not at all between each pair of neurons. These predictable changes in firing were illustrated as excitatory (red arrow) or

inhibitory (blue arrow), directed connections on amicrograph of the SCN culture. Dashed lines represent significance thresholds as determined by BSAC and the

magnitude of each Z-score is color coded (graded red and blue bars on the right). In these examples, the probability of neuron 2 firing increased by 233% after

neuron 1 fired and the probability of neuron 4 firing decreased by 17% after neuron 3 fired. (D) A map of interactions detected from a representative 24 hr

recording. The 103 firing neurons (nodes) made 542 directed connections (arrows). (E) The Z score of each interaction as a function of the latency to the peak of its

cross-correlogram. Note from the cumulative distribution of these excitatory (red line) and inhibitory (blue line) interactions that most peaked between 2.5 and

15 ms, consistent with monosynaptic communication. (F) Treatment of SCN cultures with GABAA receptor antagonists (200 mM bicuculline or 100 mM gabazine)

significantly decreased the number of excitatory and inhibitory connections. The remaining correlations were relatively weak and accounted for less than 7% of

the connections found in vehicle-treated cultures. (G) Degree distributions of three representative SCN networks were not linear on log-log plots and therefore

deviate from the power law predicted for scale-free networks. In these representative networks, the likelihood P(k) of finding a neuronwith k outgoing connections

decreased exponentially with increasing connections and the averaged degree distribution across networks was best fit by a first-order exponential decay

function (r2 = 0.95, red line). Scale bars, 200 mm.

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GABA Networks Destabilize the Circadian Pacemaker

SCN neurons maintained synchronized circadian rhythms for as

long as we recorded, demonstrating that the network mecha-

nisms underlying coordinated circadian rhythmicity are intrinsic

to these cultures (see Figure S1 available online).We took advan-

tage of this self-sustained neural circuit to test the role and sta-

bility of specific connections in circadian rhythms. We recorded

spontaneous action potentials frommany SCN neurons simulta-

neously and continuously over days with 40 ms resolution on

multi-electrode arrays (MEAs) (Figure 1A). Consistent with previ-

ous reports (Welsh et al., 1995), circadian neurons fired daily for

9.8 ± 0.3 hr (mean ±SEM)withmaximal firing frequencies of 4.7 ±

0.3 Hz (n = 101 neurons from 3 cultures). From the time-stamped

spikes, we found spike trains that cross-correlated either posi-

800 Neuron 78, 799–806, June 5, 2013 ª2013 Elsevier Inc.

tively or negatively between neurons (Figures 1B and 1C). These

cross-correlations indicated that when one neuron fired, there

was a low, but real, probability that its correlated partner

increased or decreased its discharge ratewith a short time delay.

Between-Network Validation of ConnectivityCorrelated activity can reflect functional neural connectivity

(Bialek et al., 1991; Gerstein and Perkel, 1969) but may also arise

coincidentally. To determine the likelihood of detecting spurious

versus functional connections, we developed a method (BSAC;

see Experimental Procedures) that generated an empirical distri-

bution of Z scores for false-positive connections in SCN circuits.

Iterative pair-wise analysis of spike trains from 610 neurons

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recorded on 10MEAs yielded cross-correlograms of the 185,745

possible pairwise comparisons. Of these, 161,101 were impos-

sible interactions because the neurons were in physically distinct

MEAs (Figure S2). These false connections were more prevalent

than would be expected based on the standard prediction inter-

vals associated with their Z scores. This indicates that studies

that use Z scores to determine the significance of correlated

neural activity (e.g., functional neuroimaging or neural circuit

analyses) can overestimate the number of connections. By

including nodes (neurons) from independent networks (SCN cul-

tures), we were able to set an empirically derived false discovery

rate (FDR) to 0.001 (1 in every 1,000 correlations could be incor-

rect) and define functional connections as inter-neuronal firing

correlations with either jZj > 5.6 (positive cross-correlations) or

jZj > 4.68 (negative cross-correlations). Importantly, iterative

comparisons across 3–10 cultures yielded similar Z score

thresholds (p > 0.05, one-way ANOVAs for positive and negative

correlations, respectively) indicating that connection detection

was highly reproducible from culture to culture. For all SCN re-

cordings, we calculated the frequency of detecting true neuronal

interactions (hit rate) to be 96.0% ± 1.2% (mean ± SEM). Thus,

BSAC recognizes functional connections with exceptionally

high hit rates (96%) and low false-alarm rates (0.1%).

Sparse GABAergic Connectivity in the SCNWenext sought to identify the signalingmechanism(s) underlying

the identified communication between SCN neurons. Using the

significantly cross-correlated firing patterns from 330 SCN neu-

rons recorded in three cultures over 24 hr (n = 103–121 neurons/

culture), we generated spatial maps of connectivity with neurons

represented as nodes and their interactions as directed edges

(Figure 1D). We found interactions within cultures that were

inhibitory (58% ± 4%, mean ± SEM of n = 3 cultures), excitatory

(42%± 4%), or switched polarity (10%±1%) over the day (where

the proportions of the three types of interactions summed to

100% within each culture). The median lag times to maximal

cross-correlation for inhibitory and excitatory connections

were 15.4ms and 12.5ms, respectively, with 71%of correlations

peaking within 20ms and 93%within 50ms (Figure 1E). Distribu-

tions of lag times did not differ significantly between inhibitory

and excitatory connections (p = 0.24, Student’s t test). Taking

into account delays due to action potential propagation (0.1 to

6.5 ms; Figure S3) and postsynaptic response (15.4 ms as

measured by Itri et al. [2004]), these results suggest that a major-

ity of the deduced connections represent direct, fast synaptic

interactions and a minority (i.e., those with longer lag times)

may arise from polysynaptic interactions, common inputs, or

postinhibitory rebound.

Because most, if not all, SCN neurons are GABAergic and

express GABAA receptors, we tested whether the mapped

interactions depend on GABA-mediated signaling using gaba-

zine (Gbz, 100 mM) or bicuculline (200 mM). These GABAA

receptor antagonists decreased the number of significant

connections by 90% ± 2% (mean ± SEM) compared to vehicle

(p = 0.03; Figure 1F; comparing connections from 120 randomly

selected neurons in three cultures during vehicle and drug appli-

cation). This loss in functional connections was not due to a

decrease in firing since discharge rates actually increased

slightly under GABA blockade (vehicle = 4.06 ± 0.31 Hz;

mean ± SEM; GABAA-R antagonists = 5.41 ± 0.38 Hz;

p < 0.05). Those few cross-correlations that remained had low

Z-scores, suggesting that they could have been weakened by

the blockers or could depend on an alternate, weak intercellular

signal. Given the 96% hit rate of BSAC, we conclude that at least

93% of all detected interactions in these SCN cultures, both

inhibitory and excitatory, depend on GABAA receptor signaling.

Specific network topologies have been postulated to underlie

coordinated activity in a variety of biological systems including

neural networks (Grinstein and Linsker, 2005; Harris et al.,

2003; Bonifazi et al., 2009). These topologies are considered

scale-free if their degree distributions follow a power law. To

assess the architecture of the GABA-dependent network in the

SCN, we measured the number of connections from and to

each neuron (out and in node degrees, respectively). As indi-

cated by spatial network maps (e.g., Figures 1D and S2), levels

of single-cell connectivity were heterogeneous. Neurons

received connections from a median of 4.4% of the network

and sent connections to 4.5% of the network; strikingly, some

cells (15 of 330 cells) were directly connected to greater than

25% of the whole population and only 1.8% of nodes remained

unconnected. Out and in degree distributions did not differ

significantly across cultures (p > 0.05 respectively, one-way

ANOVAs) and were fit better by first-order exponential decay

functions (r2 = 0.95 and 0.87, respectively) than power functions

(out: r2 = 0.82, F(1,20) = 51.83, p < 0.0001; in: r2 = 0.81, F(1,15) =

8.13, p = 0.012, extra-sum-of-squares F test). That is, most cells

sent and received approximately 1–4 connections with exponen-

tially fewer sending or receiving progressively more connections

(Figure 1G). In these cultures (n = 3), the proportion of possible

interactions identified as functional (i.e., the network density)

ranged from 0.047 to 0.061. Together, these data suggest that

SCN neurons reliably form networks of fast neurotransmission

comprising 5% to 6% of the possible connections with patterns

that are not purely scale-free.

Because we were concerned that the density of recording

electrodes might affect the deduced topology of neural

networks, we subsampled known networks to model the effects

of undersampling and hidden nodes. We found that network

density, clustering coefficient and path length were unaffected

by including as little as 70% of the recorded neurons (Figure S4).

These results suggest that BSAC accurately revealed network

properties from recordings of 50–100 SCN neurons.

To determine if physiologically identifiable subgroups of SCN

cells were more or less connected, we linearly correlated node

degree (sending, receiving, and total interactions) with measures

of each neuron’s firing pattern at its daily peak of firing. Interest-

ingly, no metric of the interspike interval distribution (i.e., the

coefficient of variation, mode, median or mean) predicted the

degree of connectivity of single neurons. We conclude that fast

neurotransmission between SCN neurons has no apparent

preference for neurons with specific firing patterns.

VIP Signaling Does Not Alter Fast Network ArchitectureBecause VIP has been implicated in both synchronization of

circadian neurons in the SCN (Aton et al., 2005) and neural devel-

opment (Muller et al., 1995), we testedwhether VIP is required for


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normal GABA-dependent communication. We mapped connec-

tions within high-density, VIP null SCN cultures and found they

did not differ from wild-type cultures in network density

(0.057 ± 0.015 versus 0.045 ± 0.009, respectively; p = 0.50, n =

7 cultures per genotype), average path length (2.84 ± 0.32 nodes

versus 3.30 ± 0.23; p = 0.27), mean node degree (0.11 ± 0.03

versus 0.09 ± 0.01; p = 0.49) or mean clustering coefficient

(0.18 ± 0.03 versus 0.23 ± 0.03, respectively; p = 0.41). Together,

these data indicate that VIP signaling is not required to determine

the topology of the fast connections in the SCN. We conclude

that VIP provides a synchronizing, not a trophic, signal to coordi-

nate circadian cells within the SCN.

Connectivity Fluctuates in Circadian TimeChanges in functional connectivity over milliseconds to hours

can be critical for experience-dependent plasticity, synchroniza-

tion, or metastability in the nervous system (Harris et al., 2003).

To date, it is not known if reliable changes in functional connec-

tivity are inherent to specific synapses. To examine the dynamics

of specific connections, we monitored the strength of correlated

electrical activity from identified pairs of SCN cells over a circa-

dian cycle (Figure 2A). Iterative pair-wise analysis yielded heat

maps that convey the temporal dynamics of all connections

found within representative synchronized networks (Figure 2B).

Importantly, when both neurons fired during the day, changes

in connection strength occurred independently of circadian

changes in firing frequencies (r2 = 0.006, p = 0.27). We found

that the strength of SCN connections fluctuated (coefficient of

variation = 0.24 ± 0.01, mean ± SEM, n = 189 pairwise interac-

tions from 3 cultures), with a majority of identified connections

increasing, decreasing or oscillating in strength over the day as

opposed to varying randomly (Figure 2C).

To assess the relative stability of SCN connections, we

tracked individual, fast connections over multiple days from

three cultures. We found millisecond-level connectivity between

identified pairs of neurons that persisted over multiple days

regardless of whether their circadian patterns were in phase or

antiphase (Figures 2D and 2E, respectively). Together, these

data strongly suggest that sparse GABAergic connections can

change strength over hours, persist over multiple days within

synchronized SCN networks, and do not define a unique phase

relationship between circadian SCN neurons.

GABA Makes Daily Rhythms Less PreciseGiven that we found significant GABAA receptor-mediated inter-

actions within SCN networks that can change in strength over

time, we tested the role of these connections in modulating

circadian rhythmicity. Using a CCD camera, we monitored

single-cell rhythms in Period2::Luciferase (PER2::LUC) expres-

sion from SCN explants over 12 days with 1 to 15 min resolution

(Figure 3A). Period measurements for single cells were derived

by continuous wavelet transform analysis (CWT) and period pre-

cision was calculated based upon the variance of the continuous

period time series. Consistent with a prior report (Aton et al.,

2006), we found that GABAA receptor antagonism with 100 mM

gabazine did not alter the level (Figure S5) or average period

(23.63 ± 0.10 hr; mean ± SEM, n = 122 neurons in three SCN

explants) of cellular PER2 rhythms compared to baseline


(23.42 ± 0.12 hr; p > 0.05). Importantly, GABA blockade signifi-

cantly decreased period variability of individual cells (0.70 ±

0.04 hr, mean ± SEM) compared to vehicle (0.96 ± 0.07 hr; p =

0.002; n = 3 SCN explants per treatment, 218 total cells; Fig-

ure 3B). The variability of the interpeak intervals was also

decreased during GABA blockade (0.76 ± 0.05 hr) compared

to vehicle (0.98 ± 0.08 hr, p = 0.02). Together these data show

that endogenous GABAA signaling decreases precision of circa-

dian oscillations in networked SCN neurons.

Desynchronization of Rhythmicity by GABABecause GABAA receptor signaling decreased precision of

circadian gene expression in the presence of VIP, we postulated

that GABAA receptor activation opposes the synchronizing

effects of VIP in the SCN. We monitored PER2::LUC expression

from VIP null SCN explants (Vip�/�;PER2::LUC). Consistent with

previous reports of Per1 transcription and PER2 protein in VIP-

deficient SCN (Maywood et al., 2011), circadian biolumines-

cence damped over 6 days of baseline recording (relative

amplitude error [RAE] = 0.044 ± 0.0003). SCN were then placed

in fresh media with either 100 mM gabazine or vehicle and moni-

tored for an additional 6 days (Figure 4A). Remarkably, blockade

of GABAA signaling prevented significant damping in VIP-

deficient SCN slices (for vehicle versus gabazine, RAEtreated/

RAEbaseline = 1.13 ± 0.26 versus 0.51 ± 0.10, respectively; n =

10 SCN explants per treatment; p < 0.05). CWT analysis of the

same data provided an independent quantification of the

increased amplitude of circadian rhythmicity during GABA

blockade (Figure 4B). We conclude that GABA is critical for the

loss of circadian rhythmicity in VIP-deficient SCN.

To further determine how GABAA receptor signaling impacts

circadian rhythms in single cells, we recorded bioluminescence

using a cooled-CCD camera from SCN explants. We found

that over 7 days of baseline treatment, Vip�/� cells exhibited

low amplitude PER2::LUC oscillations (RAE = 0.116 ± 0.001; Fig-

ures 4C and 4D) and progressively desynchronized (Figure 4E).

Upon medium change and addition of gabazine (100 mM), single

cell PER2 expression (Figure S6) and rhythmicity dramatically

increased (RAE = 0.004 ± 0.000, p < 0.000001 versus baseline;

n = 23 cells) and failed to damp or desynchronize for the duration

of the recording. Together these results indicate that GABAA

receptor-mediated signaling induces cycle-to-cycle jitter,

weakly opposing the stabilizing and synchronizing effects of

VIP on circadian rhythms in the SCN.

DISCUSSION

Fast Connectivity of Coupled Circadian OscillatorsIntercellular communication is necessary for proper SCN time-

keeping and regulation of daily behaviors (Yamaguchi et al.,

2003). By iteratively analyzing spike trains, we have produced

the first maps of the functional, fast connections in the SCN

network. Based on their probability to change firing within

50 ms, sensitivity to antagonists, and reproducibility across

cultures and days, we posit that at least 93% of these connec-

tions represent direct, GABAA receptor-mediated interactions

between SCN neurons. This is consistent with numerous studies

that found most, if not all, SCN neurons receive GABAergic

Figure 2. Functional Connectivity Is Dynamic over Circadian Time

(A) Circadian firing patterns with firing rate (FR) measured in Hertz (Hz) of two representative SCN neurons and the strength (S, blue circles) of their pairwise spike-

for-spike interaction every hour over 1 day. Note that the strength of this inhibitory communication increased over the day. (B) A temporal heat map demonstrates

how pairwise interactions between neurons in one SCN network changed in number and strength over 24 hr. Each row represents the strength of the connection

between a different pair of neurons every hour. Note that most interactions were inhibitory (blue) or excitatory (red) throughout the day while some switched

polarity (red and blue). (C) We categorized these representative neuron-neuron interactions as inhibitory (top) or excitatory (middle) and, using least-squares

fitting, determined whether their strength increased, decreased, oscillated, or varied randomly over the day. Pie charts (bottom) show the percentages of each

type of interaction within SCN networks (n = 189 pairwise interactions that had significant interactions for at least 4 consecutive hours from 3 cultures). Note that

the majority of inhibitory and excitatory connections changed with time of day with approximately 25% increasing in strength after the neurons started firing.

Smaller fractions of neurons either consistently decreased or increased and then decreased their interaction strength. (D and E) Connectivity persists over days

between identified neurons. Here, four representative pairs of neurons with in-phase (D) or antiphase (E) circadian firing patterns had significant communication

over multiple days. Note that an absence of strength measurements does not signify a lack of anatomic connectivity, but rather insufficient spikes to assess

activity-dependent functional connectivity.

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inputs (Moore and Speh, 1993; Belenky et al., 2008). The remain-

ing 7% of connections we detected were relatively weak and

could reflect GABAergic communication incompletely blocked

by the concentrations of the antagonists used or weak signaling

via other pathways (e.g., glycine neurotransmission [Mordel

et al., 2011] or gap junctions [Long et al., 2005]). We conclude

that GABAmediates nearly all interactions capable of influencing

the millisecond firing patterns among SCN neurons.

Electron microscopic studies have found that individual SCN

neurons receive between 300–1,200 synaptic contacts (Guldner,

1976), a relatively low number compared to many brain areas.

Whole-cell recordings in acute slices report typical SCN neurons

fire during the day at 5 Hz with spontaneous IPSC frequencies at

3–12 Hz (Itri et al., 2004). Consistent with this evidence for inputs

from small numbers of neurons, we found that functional

GABAergic connectivity is sparse within SCN networks with

approximately 55% of neurons responding to inputs from 1–4

neurons. We also found that nearly every SCN neuron sends at

least one GABA-dependent output, such that most functionally

connect to approximately 5% of the network and some connect


Figure 3. GABAA Receptor Signaling

Decreases Precision of Single-Cell Circa-

dian Rhythmicity

(A) Blockade of GABAA signaling with gabazine

(Gbz) reduced the variability of circadian periodi-

city in SCN cells. Traces (top) show PER2::LUC

bioluminescence from two representative cells in

SCN explants (insets, red circle shows cell loca-

tion) treated with either vehicle (left) or 100 mMGbz

(right). Heat maps (bottom) indicate the instanta-

neous amplitude of the CWT and show the stability

of the dominant period (black line) for each cell over

12 days. (B) Blockade of GABAA-dependent

signaling significantly decreased period variability

compared to vehicle (mean ± SEM; n = 120 and 98

cells, respectively; *p < 0.01).

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to over 25% of the network. Taken together, these results sug-

gest GABA provides sparse, weak and fast connectivity among

SCN neurons.

Many biological networks have been hypothesized to be scale

free and follow a power-law distribution. Within the SCN, recent

computational models have predicted that a small-world scale-

free network would be the most efficient topology for circadian

synchrony (Vasalou et al., 2009; Hafner et al., 2012); however,

here we provide clear evidence that signaling through GABAA

receptors is not strictly patterned as a small-world network

and, rather than enhance synchrony, decreases the precision

and synchrony of neuronal rhythms. This is distinct from the

well-described role of GABAergic signaling in coordinating

higher-frequency (e.g., gamma [40–80 Hz]) neocortical oscilla-

tions (Ermentrout et al., 2008), but is consistent with the previous

report that GABA is not required for SCN neurons to maintain

circadian synchrony (Aton et al., 2006). In contrast to an exten-

sive literature predicting GABA-induced synchrony (Liu and

Reppert, 2000; Diekman and Forger, 2009), only a few

theoretical studies have predicted that GABA could lead to


reduced precision and synchrony in neural networks (Kopell

and Ermentrout, 2004). We posit that GABAA signaling may

accelerate re-entrainment of the SCN to phase-shifted timing

cues by increasing cycle-to-cycle jitter so that the cells are

more easily phase-shifted by another signal (e.g., VIP). Our re-

sults lead us to predict that drugs that enhance GABAA signaling

(e.g., benzodiazepines) in the SCN could reduce the precision of

circadian rhythms but enhance their adjustment to new sched-

ules. Indeed, benzodiazepine administration speeds entrain-

ment of human behavioral and hormonal circadian rhythms after

simulated travel across time zones (Buxton et al., 2000).

Although it is highly unusual for GABA to be excitatory in the

adult mammalian nervous system, previous studies have pro-

vided apparently contradictory evidence that within the SCN,

GABA can be excitatory, inhibitory, or both. We found that

approximately 60% of all GABA-dependent connections were

inhibitory and 40% were excitatory throughout the circadian

day. A small fraction of these connections switched polarity for

at least one hour during the day. The differential role of inhibitory

and excitatory currents in the SCN remains unresolved.

Figure 4. Endogenous GABA Desychro-

nizes Circadian Cells in the SCN

(A) Representative traces of raw bioluminescence

from two Vip�/�;PER2::LUC SCN explants show

that gabazine (Gbz, 100 mM) rescued circadian

rhythmicity compared to vehicle.

(B) In the absence of VIP-signaling, gabazine

increased the amplitude of the dominant period

compared to baseline while vehicle treatment did

not (mean ± SEM; n = 10 explants in each condi-

tion; *p < 0.05). Total explant bioluminescence (C)

and synchrony among cells (D and E) imaged from

a Vip�/�;PER2::LUC explant (inset) in (C) quickly

damped over 6 days of baseline recording while

gabazine prevented damping of the biolumines-

cence rhythm andmaintained circadian synchrony

of the population.

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Desynchronizers Opposing Synchronizers within theCircadian ClockA central focus of biological timing research has been to under-

stand the processes that promote synchronization. It is

currently appreciated that activation of Gas and Gai/o protein-

coupled receptors is vital to maintain synchrony among SCN

cells (Aton et al., 2005, 2006; Doi et al., 2011). VIP and, to a

lesser extent, vasopressin and gastrin releasing peptide have

been shown to mediate rhythm stability in and synchrony

among circadian cells (Maywood et al., 2011). In contrast, we

have found GABA to destabilize and desynchronize circadian

cells. Synchronization and desynchronization are thus both

active processes that can be differentially modulated. It is inter-

esting to speculate that developmental and seasonal changes

may alter the balance between fast neurotransmission and

slower neuropeptide signaling to adjust the timing among

SCN neurons. It is possible that GABA plays an important role

within this setting to actively drive networks of oscillators to

new phase relationships.

Additionally, recent work suggests that a hierarchy of neuro-

peptidergic signals may differentially promote or sustain rhyth-

micity and synchrony among SCN cells (Maywood et al.,

2011). In light of our results, we must place GABAA signaling

within this hierarchy and classify potential synchronizing agents

by their ability to overcome the destabilizing effects of GABA.

Our data suggest that VIP may be the only agent capable of

overcoming this destabilizing effect since it is only after VIP

signaling is eliminated that the desynchronizing effects of

GABA are unmasked. Given that VIP signaling diminishes during

aging (Cayetanot et al., 2005), increasingly unopposed

GABAergic signaling may weaken SCN neuronal synchrony

and contribute to sleep/wake cycle fragmentation in the elderly.

EXPERIMENTAL PROCEDURES

See a detailed description in the Supplemental Experimental Procedures.

Connectivity and Network Analysis

All animal procedures complied with National Institutes of Health (NIH)

guidelines and were approved by the Washington University Animal Care

and Use Committee. Spike trains from SCN neurons were recorded using

MEAs and the Z score and strength of each interaction was measured. We

graphed functional connections with GUESS software and analyzed network

architecture with NodeXL software.

BSAC (Between-Sample Analysis of Connectivity)

We developed an empirical method to discriminate correlated activity that

derived from real versus coincidental neuronal interactions. We reasoned

that correlations between spike trains recorded from neurons in physically

distinct culture dishes do not signify connectivity. Using this logic, we

iteratively cross-correlated spike trains from all neurons across 10 cultures

(samples) over 1 hr to determine the distribution of Z scores associated with

inherently false across-sample correlations. Using the full distribution of false

across-sample correlations, we determined Z scoremagnitude thresholds that

corresponded with likelihoods of discovering false-positive across-array

correlations.

Temporal Changes in Connection Strength

To determine if connection strength systematically changed with time of day,

we fit the strength versus time data with linear and cosine functions and

estimated the resultant p value using the F test.

Bioluminescence Recording and Analysis

PER2::LUC expression from SCN explants was measured using photo-

multiplier tubes or a CCD camera. Circadian period, amplitude and period

variability were assessed using FFT-NLLS andCWT. Additional statistical tests

were done with Origin 7 software. Mean periods were compared by two-tailed

t tests. Distributions of periods were compared using both Levene’s and

Brown-Forsythe’s tests for equal variance.

Drug Treatments

Bicuculline methiodide (Sigma) and SR95531 (gabazine; Tocris) were diluted

in water or DMSO. Drugs or vehicle controls were added at less than 0.5%

of total volume).

SUPPLEMENTAL INFORMATION

Supplemental Information includes seven figures and Supplemental Experi-

mental Procedures and can be found with this article online at http://dx.doi.

org/10.1016/j.neuron.2013.04.003.

ACKNOWLEDGMENTS

We thank B. Carlson, T. Holy, J. Huettner, P. Taghert, C. Zorumski, H. Herzel

and members of the Herzog lab for helpful discussions. This work was sup-

ported by NIH grants MH63104 (to E.D.H.) and F30NS070376 (to G.M.F.).

Accepted: April 2, 2013

Published: June 5, 2013

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Supplemental Information

GABA Networks Destabilize Genetic Oscillations

in the Circadian Pacemaker G. Mark Freeman Jr., Rebecca M. Krock, Sara J. Aton, Paul Thaben, and Erik D. Herzog

Supplemental Inventory

1. Supplemental Figure 1 and Legend

Demonstrates that dispersed neurons recorded on MEAs in Figure 1 maintain

similarly coordinated circadian rhythms as explant networks.


Demonstrates the method for empirical determination of significant, functional

neuronal connections using BSAC which are represented in Figure 1.


Demonstrates the conduction velocity of SCN neurons in vitro whose spike trains are

represented in Figure 1.


Models how properties of networks represented in Figure 1 are affected by hidden

nodes.


Demonstrates the effect of GABA receptor blockade on PER2::LUC expression in

SCN cells. This complements the effects of GABA receptor blockade on circadian

period as shown in Figure 3.


Demonstrates how GABAA receptor blockade affects peak amplitude of circadian

bioluminescence and how peak amplitude is rescued by GABA receptor blockade in

VIP-null SCN explants. This complements the effects of GABA receptor blockade on

period variability and synchrony as shown in Figures 3 and 4.


Illustrates how a separate analysis method validates the Z-score thresholds illustrated

in Figure 1.

8. Supplemental Experimental Procedures

9. Supplemental References

Supplemental Figure 1. Neurons in dispersed and explant networks maintain similarly

coordinated circadian rhythms. (A) Bioluminescence images from a high-density

200 µm 100 µm

dispersed SCN culture (left) and SCN explant (right). (B) Neurons within both networks

generated daily oscillations in Period2 (Per2) levels. Traces show PER2::LUC

bioluminescence from 40 randomly selected neurons in each culture. (C) The times when

SCN cells reached their peak bioluminescence (filled circles) on the fourth day of

recording were similarly distributed in these representative dispersed (left; n=174 cells)

and explant cultures (right; n = 122 cells). Arrows indicate the average phase of each

distribution. Arrow length reflects the r-value or synchronization index (SI) of each

distribution. Neurons in high-density cultures and explants are significantly synchronized

(Rayleigh test, P < 0.05, respectively). Of note, these high-density cultures had higher SI

values than what has been reported for low-density dispersed cultures in which oscillator

autonomy has been previously studied (Welsh et al., 1995; Liu et al., 2007). (D) SI values

of high-density (HD) and explant networks did not significantly differ (n = 3 cultures in

each condition, mean ± SEM; P>0.05). (E) Immunolabeling of the high-density dispersal

in S1A illustrates the network of SCN cells (blue, nuclear stained with DAPI) with

associated VIPergic somata and fibers (green).

Supplemental Figure 2. Empirical determination of significant, functional neuronal

connections using BSAC. (A) Representative examples of across-array analyses of

connectivity between 3 MEAs (left) and 7 MEAs (right). Significant correlations with

suprathreshold Z-scores (|Z| > 5.6 (positive cross-correlations) or |Z| > 4.68 (negative

cross-correlations), corresponding to a false discovery rate (FDR) of 0.001) were plotted

including within-array connections (black) and across-array connections (green). (B) The

distribution function for Z-scores of cross-correlations between SCN neurons in different

cultures determined the FDR for each Z-score. We chose a false discovery rate (FDR) of

0.001 (dashed vertical line) as our threshold to maximize the number of within-array

connections with suprathreshold Z-scores (96.0 ± 1.2%, mean ± SEM, n=10 cultures)

while minimizing the likelihood of identifying a false connection to 0.1% (1 in 1,000

connections). Importantly, at lower Z-score thresholds, the percentage of connections that

are likely true positives (hit rate, black line) decreased significantly. (C) Significant

within-array connections (black circles) have higher Z-scores and shorter latencies to the

peak of their correlation than the weaker connections with randomly distributed latencies

found among across-array connections (green circles). Z-score thresholds (dashed lines)

with a FDR=0.001 for positive (red, |Z| > 5.6) and negative (blue, |Z| > 4.68) correlations.

Sub-threshold Z-scores (grey circles) from this analysis of 10 networks (MEAs) had a

broad range of latencies. (D) We found that false connections were more frequent

(Empirical FDR) than would be predicted based on the standard prediction intervals

associated with the Z-scores (Theoretical FDR), indicating that raw Z-score statistics

(theory) can overestimate the number of connections by underestimating rates of spurious

cross-correlations. (E) Z score distributions were highly reproducible from network to

network. BSAC yielded similar Z-score thresholds for a FDR of 0.001 positive (red line)

and negative (blue) correlations regardless of whether 3 or more cultures were analyzed

(mean ± SEM).

Supplemental Figure 3. The conduction velocity of SCN neurons in vitro. (A) A

representative neuron was recorded on two electrodes 894 microns apart, i.e. somatic

signal on electrode 53 (red), and en passant recording on electrode 77 (green). Scale bar,

200 µm. (B) The extracellular potentials recorded at the two electrodes were reliably

discriminated so that (C) their spike trains were nearly identical, (D) with a 0.98 peak

probability of firing together. Based on the likelihood that these spikes arose from the

same neuron, we calculated its conduction velocity to be 0.40 m/s. These rare examples

of single neurons recorded on more than one electrode provided an estimate of the

average conduction velocity of SCN neurons (0.49 ± 0.28 m/s, mean ± SEM, n=3),

consistent with the propagation properties of unmyelinated fibers.

Supplemental Figure 4. Modeling the effects of hidden nodes on network properties.

Varying percentages of nodes from a known network (represented as 100% of the

recorded neurons) were randomly sampled 100 times, and analyzed to determine the

effects of undersampling. We found that network properties including (A) network

density, (B) average clustering coefficient and (C) average path length (mean ± SD)

changed little even when only 50% of the recorded neurons were included in the network

analysis.

0

5

10

15

20

0.00.10.20.30.40.5

012345

Net

wor

k D

ensi

ty (%

)C

lust

erin

gC

oeffi

cien

tP

ath

Leng

th (n

ode)

A

B

C

100 95 90 80 70 60 50

% of nodes sampled

0

5

10

15

20

0.00.10.20.30.40.5

012345

Net

wor

k D

ensi

ty (%

)C

lust

erin

gC

oeffi

cien

tP

ath

Leng

th (n

ode)

A

B

C

100 95 90 80 70 60 50

% of nodes sampled

Supplemental Figure 5. The effect of GABA receptor blockade on PER2::LUC

expression in SCN cells. Average peak bioluminescence over three cycles did not differ

significantly between SCN cells treated with vehicle or 100 μM Gabazine (mean ± SEM,

n = 2 cultures per treatment, n= 62 and 84 cells for vehicle and Gabazine treatment

respectively, P=0.07).

10

5

0

Bio

lum

ines

cenc

eC

ount

s/20

min

x 1

05

Vehicle Gabazine

Supplemental Figure 6. (A) GABAA receptor blockade with Gabazine (Gbz, 100 μM)

increased daily peak amplitude bioluminescence (mean ± SEM) in cells recorded from a

representative VIP-/- PER2::LUC SCN slice (same cells as in Figures 4C, 4D, 4E). (B)

Gabazine rescued the 5-day average peak amplitude of PER2 rhythms in Vip-deficient

(VIP-/-) SCN to wild-type (WT) levels (n= 23 and 344 cells, respectively; mean ± SEM).

Asterisk indicates P<0.01.

Time (d)

Bio

lum

ines

cenc

eC

ount

s/30

min

Bio

lum

ines

cenc

eC

ount

s/30

min

WT VIP-/- VIP-/-+Gbz

Bio

lum

ines

cenc

eC

ount

s/30

min

WT VIP-/- VIP-/-+Gbz

GbzA B n.s.

* *

Supplemental Figure 7. To further validate the BSAC-selected thresholds for significant

interactions between neurons (vertical dashed lines), we independently generated the

density distributions of the maximum of Z-scored variables. We randomly sampled 2% of

within-culture Z-scores, identified the maximum Z-score and repeated this 200 times to

generate the kernel density distribution of the maximum Z score (red lines) for inhibitory

(left plot) and for excitatory interactions (right plot). Comparing this distribution to the

Z-score distribution for false across-array (i.e. between SCN culture) interactions (black),

confirms that the Z-score thresholds determined by BSAC (dashed vertical gray lines)

captured greater than 99.99% of the max within-array distributions, while omitting

99.89% all Z-scores associated with false across-array interactions. These conclusions do

not change if 5% (green) or 10% (blue) of within-culture Z-scores were randomly

sampled.

0

4

8

0

4

8

1 10 100 1 10 100

Den

sity

Den

sity

Z-scoreZ-score

Inhibitory Excitatory

2%5%10%

2%5%10%

SUPPLEMENTAL EXPERIMENTAL PROCEDURES

Animals.

Homozygous Period2::Luciferase (PER2::LUC) knock-in mice (founders generously

provided by J.S. Takahashi), Vip-/- mice (founders generously provided by C.S. Colwell)

and Vip-/-;PER2::LUC mice were housed in a 12 h:12 h light:dark cycle in the Danforth

Animal Facility at Washington University. All procedures were approved by the Animal

Care and Use Committee of Washington University and followed National Institutes of

Health guidelines.

Cell Culture.

SCN were prepared as slice or high-density cultures as described previously (Webb et al.,

2009; Aton et al., 2005). For SCN explant recordings, 300 μm coronal SCN slices from

PER2::LUC mice or Vip-/-;PER2::LUC mice (age 21-28 days) were cultured at 34°C, 5%

CO2/95% air on Millicell-CM inserts (Millipore, Billerica, MA) in culture medium

(Dulbecco’s Modified Eagles Medium, DMEM, supplemented with 10% fetal bovine

serum). After four days, each explant was inverted onto a poly-D-lysine/laminin-coated

glass coverslip for 1 day prior to recording. To disperse cells, neonatal SCN (age 7-9

days) were punched from 300 μm-thick coronal brain slices and cells were dissociated

using papain. Viable cells (~10,000 cells from 10-14 SCN) were plated on either poly-D-

lysine/laminin coated multi-electrode arrays or glass coverslips and maintained in culture

medium for 3 weeks prior to recording.

Electrophysiology.

Long-term recordings of firing rate from SCN neurons were made using multi-electrode

arrays (Multichannel Systems, Reutlingen, Germany). SCN neurons were dispersed at

approximately 10,000 cells/mm2 onto sixty 30 μm-diameter electrodes (200 μm spacing)

and maintained in CO2-buffered medium for 3 weeks prior to recording. Culture

chambers fixed to arrays were covered with a fluorinated ethylenepolypropylene

membrane before transfer to the recording incubator. The recording incubator was

maintained at 36°C with 5% CO2 throughout all experiments. Extracellular voltage

signals were recorded simultaneously from 60 electrodes with 20,000 Hz sampling.

Action potentials exceeding a defined voltage threshold were digitized into 2-ms time

stamped cutouts (MC-Rack software, Multichannel Systems). Spikes from individual

neurons were discriminated offline (up to 5 neurons per electrode) based on principal

component analysis and a refractory period of greater than 2 ms (Offline Sorter, Plexon),

and firing rates were calculated over 1-min recording epochs (NeuroExplorer, Plexon).

Spike Train Analysis.

We examined the spike timing of a given target neuron during the 1 s before and 1 s after

each spike of every recorded neuron (Neuroexplorer 3.0). These cross-correlograms

included all spikes recorded over 1 h windows with 5 ms bins and were subsequently

smoothed using a 5-point boxcar filter. We confined spike train analyses to times when

both neurons fired greater than 100 spikes/hour. The Z-score of each cross-correlogram,

an indicator that a neuron increased or decreased the firing rate of a target neuron, was

measured as:

Z= | (Y-μ) / μsd |

where Y was the maximum deflection of the positive (or minimum deflection of the

negative) cross-correlation, μ was the mean of the cross-correlogram, and μsd was the

standard deviation of μ. The polarity of the cross-correlation was determined by the

polarity of the significant deflection closest to zero. The strength, S, of each

interneuronal communication was measured as:

S= | (Y- μ) / μ |

We generated graphs of functional connections between neurons with GUESS Graphical

Exploration freeware (www.graphexploration.cond.org) and analyzed network

architecture with NodeXL freeware (Microsoft, Redmond, WA).

BSAC (Between-Sample Analysis of Connectivity).

We sought an empirical method to discriminate correlated activity that derived from real

versus coincidental neuronal interactions. We reasoned that correlations between spike

trains recorded from neurons in physically distinct culture dishes do not signify

connectivity. Using this logic, we iteratively cross-correlated spike trains from all

http://www.graphexploration.cond.org/�

neurons across 10 cultures (samples) over 1 h to determine the distribution of Z-scores

associated with inherently false across-sample correlations. This method has benefits

over standard techniques using scrambled or surrogate data in that no a priori

assumptions are required. Using the full distribution of false across-sample correlations,

we determined Z-score magnitude thresholds that corresponded with various likelihoods

of finding false-positive across-array correlations. Supplemental Figure 2B shows the

likelihood that a correlation of given Z-score magnitude would be found within our

across-culture dataset. For example, Z-scores above 6.0 (positive cross-correlations, red)

or 4.9 (negative cross-correlations, blue) were associated with an average of 1 connection

detected between cultures out of 2,000 comparisons, a false-discovery rate (FDR) of

0.0005. We calculated the frequency of detecting true neuronal interactions (hit rate,

black) as the difference between the number of cross-correlations with suprathreshold Z-

scores and the predicted number of false positive connections, normalized by the number

of cross-correlations with suprathreshold Z-scores. Importantly, as the Z-score thresholds

are lowered, the percentage of connections that are likely true positives (hit rate)

decreases significantly. Of note, these Z-score values did not vary significantly as a

function of the number or combination of cultures compared, indicating that these

thresholds of significance are likely valid for SCN networks generally (Supplemental

Figure 2E). We further compared the BSAC results to a published method using Granger

Causality for point processes (Kim et al., 2011) and found BSAC identified the same

connections in a set of 4 neurons, but was approximately 500 – 1000 times faster.

Additionally, comparison of the density distributions of the maximum of N Z-scored

variables to the Z-score thresholds as determined by BSAC indicate that the Z-score

thresholds as determined by BSAC capture greater than 99.99% of the max within-array

distributions, while omitting 99.89% all Z-scores associated with false across-array

interactions (Supplemental Figure 7).

Temporal change in connection strength

Traces of interaction strength were analyzed for time dependent behavior by applying

least squares fitting for a linear function

str(t) = a + b • t

and a cosine function

str(t) = m + a • cos(ω • t) + b • sin(ω • t)

For each fit, the p-value was calculated using the F-statistic (R software, www.R-

project.org). An interaction was classified as changing linearly if the p-value for the

linear fit was less than 0.05. Linear interactions were further classified as increasing or

decreasing based on the sign of the linear slope. Oscillating interactions were not

significantly fit by a linear function, and had a p-value for the cosine fit less than 0.05.

Random interactions include all connections not grouped into the above categories.

Bioluminescence recording and analysis.

SCN explants were placed in sealed 35-mm Petri dishes (BD Biosciences, San Jose, CA)

with pre-warmed, recording medium (DMEM supplemented with 10 mM HEPES and

100 μM beetle luciferin (Promega, Madison, WI). Bioluminescence was recorded at 36oC

in air using a photomultiplier tube (PMT) (HC135-11 MOD, Hamamatsu Corp.,

Shizuoka, Japan) as described (Aton et al., 2006). Bioluminescence counts were

integrated and stored at 1-min intervals for up to 12 days. For single-cell recordings, SCN

explants or high-density dispersals were incubated in recording medium and imaged

using a Stanford XR/Mega-10Z CCD (Stanford Photonics) or Versarray 1024 cooled-

CCD camera (Princeton Instruments) at 36oC in air. Photon counts were integrated every

one to fifteen minutes, respectively, processed using 2 x 2 spatial binning and quantified

using Image J software.

We assessed circadian period and amplitude of gene expression from SCN

explants and single-cells using FFT-NLLS (Plautz et al., 1997) and wavelet analysis

(Meeker et al., 2011) from at least 4 days of bioluminescence. We quantified period

variability by the relative amplitude error (RAE) of the FFT-NLLS, the standard

deviation of the continuous period measurement of the continuous wavelet analysis and

by peak-to-peak timing. Additional statistical tests were done with Origin 7 software

(OriginLab, Northampton, MA) and Prism 4 software (Graphpad , La Jolla, CA). Mean

periods were compared by two-tailed t-tests. Distributions of periods were compared

using both Levene's and Brown-Forsythe's tests for equal variance.

Immunostaining.

SCN dispersals on glass coverslips were treated with colchicine (Sigma; 50 g/ml)

overnight before staining. Cells were fixed using 4% paraformaldehyde, 4% sucrose and

immunolabeled for neuropeptides using standard methods (Webb et al., 2009; Aton et al.,

2005). Cultures were labeled with rabbit anti-VIP (Immunostar; 1:2,000) followed by

donkey anti–rabbit IgG conjugated to Cy2 (Jackson ImmunoResearch; 1:50). Nuclei were

visualized using DAPI staining.

Drug Treatments.

Bicuculline methiodide (Sigma, Saint Louis, MO) and SR95531 (gabazine; Tocris,

Ellisville, MO) were diluted in deionized water or DMSO. Drugs or vehicle controls were

added to the 1 ml of recording medium (<0.5% of total volume).

SUPPLEMENTAL REFERENCES

Kim, S., Putrino, D., Ghosh, S. and Brown, E.N. (2011). A Granger causality measure for

point process models of ensemble neural spiking activity. PLoS Comp. Biol. 7,

e1001110.

Meeker K., Harang R., Webb A.B., Welsh D.K., Doyle F.J. 3rd, Bonnet G., Herzog E.D.,

and Petzold L.R. (2011). Wavelet measurement suggests cause of period instability in

mammalian circadian neurons. J. Biol. Rhythms 26, 353-62.

Plautz J.D., Straume M., Stanewsky R., Jamison C.F., Brandes C., Dowse H.B., Hall J.C.,

and Kay S.A. (1997). Quantitative analysis of Drosophila period gene transcription in

living animals. J Biol Rhythms 12, 204-217.

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