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The Journal of Neuroscience, September 3, 2003, 23(22):8070-8076
 Previous Article | Next Article 
BRIEF COMMUNICATION
The Biological Clock Nucleus: A Multiphasic Oscillator Network Regulated by Light
Jorge E. Quintero, *
Sandra J. Kuhlman, * and
Douglas G. McMahon
Department of Physiology, University of Kentucky, Lexington, Kentucky
40536-0084
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Abstract
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The circadian clock nucleus of the mammalian brain is composed of thousands
of oscillator neurons, each driven by the cell-autonomous action of a defined
set of circadian clock genes. A critical question is how these individual
oscillators are organized into an internal clock that times behavior and
physiology. We examined the neural organization of the suprachiasmatic nucleus
(SCN) through time-lapse imaging of a short-half-life green fluorescent
protein (GFP) reporter of the circadian clock gene Period 1
(Per1). Using brain slice preparations, Per1 promoter
rhythms were resolved at the level of the SCN, and in individual neurons
within the SCN, to determine the temporal patterns of rhythmicity resulting
from exposure of mice to light/dark cycle (LD) and constant darkness (DD)
conditions. Quantitative imaging and patch-clamp electrophysiology were used
to define the relationship of Per1 gene expression to
neurophysiological output on an individual neuron basis. We found that in both
LD and DD, the overall rhythm of the clock nucleus is composed of individual
cellular rhythms that peak in distinct phase groups at 3-4 hr intervals.
However, the phase relationships of Per1 oscillations to locomotor
activity and the phase relationships among individual neuronal oscillators
within the SCN are different in LD and DD. There was a positive, linear
correlation of Per1 transcription with neuronal spike frequency
output, thus Per1::GFP rhythms are representative of physiological
rhythmicity. Our results reveal multiple phase groupings of SCN oscillators
and suggest that light regulation of oscillator interactions within the SCN
underlies entrainment to the photoperiod.
Key words: suprachiasmatic nucleus; circadian rhythms; GFP; transgenic mice; confocal microscopy; electrophysiology; time-lapse imaging; gene expression; Period 1; entrainment
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Introduction
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In mammals, the principal circadian pacemakers driving daily behavioral
rhythms are the bilateral suprachiasmatic nuclei (SCNs) of the hypothalamus.
Each nucleus contains  10,000 neurons, many of which are individually
competent circadian oscillators (Welsh et
al., 1995 ). A defined set of genes, including the Period
1 gene (Per1), constitutes the fundamental circadian oscillation
mechanism (Albrecht, 2002). It
is clear that these "clock genes" produce 24 hr rhythms in
individual cells through interlocked transcription-translation feedback loops
(Albrecht, 2002). However, the
organization of these individual molecular oscillators into a
"biological clock" controlling behavior and physiology remains to
be fully elucidated.
A prevailing view of SCN organization is that individual SCN neurons
oscillate in synchrony, and the resulting physiological output is a monophasic
waveform (Liu et al., 1997).
In such models, the SCN pacemaker is suggested to produce a single, coherent
output representing the mean period of its many individual oscillators
(Liu et al., 1997;
Herzog et al., 1998;
Low Zeddies and Takahashi,
2001). An alternative view is that oscillators of the SCN are
organized in a multiphasic manner, and that phase relationships among
oscillators can be modulated by environmental input or experience
(Pittendrigh and Daan, 1976;
Jagota et al., 2000;
Mrugala et al., 2000). To
experimentally distinguish these two models of SCN organization, we have
monitored Per1 gene rhythmicity of individual neurons in acute brain
slices of the SCN in vitro using a dynamic green fluorescent protein
(GFP) reporter (Kuhlman et al.,
2000) and have tested the effects of cyclic light input on the
temporal organization of the clock nucleus.
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Materials and Methods
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Gene expression imaging of SCN. Coronal hypothalamic slices (300
µm) containing the SCN were harvested from mice hemizygous for the mouse
Per1 (mPer1)::d2EGFP transgene
(Kuhlman et al., 2000) and
were imaged according to previously published methods
(LeSauter et al., 2003). For
SCN from light/dark cycles (LD), animals 1-3 weeks old were maintained on a
14/10 hr LD. Zeitgeber time (ZT) 12 was defined as the time of lights off,
whereas ZT22 was the time of lights on. SCNs from constant darkness (DD)
animals were taken from adult mice individually monitored for wheel-running
activity. They were first entrained to a 12 hr LD cycle and then released into
constant darkness for at least 4 d before slices were harvested. Wheel-running
activity was monitored using Chronobiology Kit software (Stanford Scientific,
Santa Cruz, CA). Slices were harvested using infrared goggles. Circadian Time
(CT) was predicted for the cycle during which the SCN was harvested by
extrapolating a regression line through the activity onsets for the previous 4
d. CT12 was defined as the time of predicted locomotor activity onset. The
time scales for in vitro recordings of LD slices are shown in ZT to
indicate their reference to the previous light/dark cycle, whereas the time
scales for DD slices are indicated in CT to indicate their reference to the
previous wheel-running activity. For comparison of phases across LD and DD
conditions, the time scales were aligned at the time of lights off/wheel
running activity onset (ZT12/CT12). The timing of locomotor activity onsets on
the first cycle in DD from animals entrained to 14/10 hr LD did not exhibit
significant masking effects, suggesting that this time scale alignment
reliably indicates pacemaker phase in the two conditions.
Gene expression imaging of individual neurons. For imaging of
individual cells, animals 16-21 d old were used because their SCNs were found
to be highly suitable for confocal imaging. At this age, the photic input to
the mouse SCN is fully developed (Llamosas
et al., 2000), whereas, in the absence of a light cycle, the
activity rhythms of the pups are tightly grouped around the phase of the
mother (Viswanathan, 1999).
For LD, mothers and pups were maintained in 14/10 hr LD. For DD, mothers and
pups were placed in constant darkness for at least 6 d before slice harvesting
from pups, and the wheel-running activity of the mother was monitored to
predict CT12 for the pups. SCNs were imaged using a laser-scanning confocal
microscope (Leica, Bannockburn, IL). The 488 nm laser line was used for
excitation; emission was detected between 500 and 525 nm. Every 15-30 min, six
to eight optical z-plane sections were acquired through 50-70 µm.
For analysis, individual cells were delimited by a region of interest tool,
and their fluorescence intensity was tracked over time with either NIH Image
or IP Lab software (Scanalytics, Fairfax, VA). The time of peak was defined as
the time of maximum fluorescence, where the values for the preceding and
following time points were at least 95% of the maximum. Cells that exhibited a
1.4-fold difference in intensity between peak and nadir, the empirically
determined threshold for reliable rhythm detection from our conventional
imaging experiments, were considered rhythmic. Cells in which the fluorescence
intensity was sufficiently elevated above background so that we could reliably
detect and accurately quantify the Per1::GFP signal (i.e., at least
1.2-fold above the lowest cell nadir in the same slice) but that exhibited
time-dependent changes that were <1.4-fold in amplitude were classified as
nonrhythmic. Those cells in which the fluorescence intensity was not
sufficient for accurate quantification over time (i.e., <1.2-fold above the
lowest cell nadir) were not included in the data set. Phase analysis of
individual cell rhythms was performed in the first circadian cycle in
vitro.
To test the extent to which cells losing their viability contributed to the
GFP signals from the SCN, propidium iodide (PI; 3-5 µm), a stain
for cells with compromised membrane integrity, was added at different
intervals in vitro. We then assayed for overlap between the GFP and
PI fluorescence signals. At the start of recording, 3 of 64 GFP +
cells were also PI +; after 10 hr, 2 of 75 GFP + were PI
+; and after 24 hr, 1 of 12 GFP + cells were PI
+ (n = 3 slices). Thus the proportion of GFP
+/PI + cells remained at <10% across 24 hr, and the
preponderance of GFP signals we recorded were from healthy, viable
neurons.
Electrophysiology. Fluorescence intensity and spike frequency were
determined for individual SCN neurons as described by Kuhlman et al.
(2003). Electrophysiological
methods for N2A cells were similar to those of Wagner et al.
(1998). For transfection of
N2A cells, 5 µg of the P1PG plasmid carrying the Per1::GFP
construct used to generate the reporter mice
(Kuhlman et al., 2000) was
transfected into cultures by the calcium phosphate method. Experiments were
performed 24-72 hr after transfection and reproduced on two separate rounds of
transfection.
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Results
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Per1-driven GFP rhythms in the in vitro SCN
The Per1-driven GFP transgene faithfully reports the endogenous
circadian rhythm of mPer1 promoter activity in the SCN in
vivo and in vitro (Kuhlman
et al., 2000, LeSauter et al.,
2003). Coronal brain slices containing the SCN, removed from
animals kept on 14/10 hr LD cycles, showed robust rhythms of
Per1-driven GFP fluorescence (Fig.
1A,B). SCN fluorescence peaked 2 hr before the time of
lights off on the preceding LD cycle (ZT, 10.0 ± 2.5 hr) on the first
day in vitro, regardless of whether the slices were prepared early in
the day (ZT, 22 ± 2 hr; n = 5) or late in the day (ZT, 3
± 7 hr; n = 4), indicating that slice preparation itself did
not perturb the endogenous clock. The interval between first and second cycles
was 23.55 ± 2.18 hr (n = 9), which is identical to the
free-running period of behavioral activity rhythms in this transgenic line
(Kuhlman et al., 2000). The
peak of fluorescence intensity averaged 2.1-and 1.5-fold higher than the nadir
on the first and second days, respectively. Slices from animals maintained in
DD (Fig. 1C) also
exhibited Per1-driven GFP cycles
(Fig. 1D), with the
mean time of peak fluorescence synchronous with predicted locomotor activity
onset (CT, 11.8 ± 1.1 hr; n = 4; slices made between CT1.5 and
CT3.5). The interval between first and second cycle peaks for SCN from DD
animals was 23.2 ± 0.5 hr (n = 4). Non-SCN hypothalamic areas
from transgenic mice and the SCN of nontransgenic mice did not show rhythms in
fluorescence intensity (data not shown).
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Figure 1. Time-lapse imaging of SCN Per1-driven GFP rhythms. A,
Individual pseudocolored fluorescence images from a time-lapse recording of
Per1-driven GFP fluorescence rhythms from an SCN brain slice in
vitro. B, Fluorescence rhythms in the in vitro SCN from an
animal housed in a 14/10 hr LD cycle. Images were captured every 30 min. The
open and filled bars indicate the previous light/dark cycle for the animal.
C, Wheel-running activity, double-plotted, from an animal housed in
constant darkness. Vertical marks show times when the animal was active. The
filled arrow marks the time the SCN slice was prepared for imaging.
D, GFP fluorescence recording of the SCN from the animal in
C. Images were captured every 30 min for 2 d.
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Spike activity correlates with Per1 promoter activity
To assess the relationship between Per1 promoter activity and
neurophysiological activity, we performed loose patch electro-physiology while
quantifying the Per1-driven GFP fluorescence of individual neurons.
When examined in the midday (ZT4-7), a 13-fold range in neuronal fluorescence
intensities was highly correlated with a 10 Hz range in spike frequency
(r = 0.91; p < 0.001;
Fig. 2). There was a positive,
linear correlation of Per1 promoter activity with the spike frequency
output of circadian clock neurons. Neurons with high levels of
Per1-driven gene expression were more active than those with low
levels; thus, the gene expression activity we observed in our imaging
experiments is also reflected in neural activity during the day phase.
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Figure 2. Per1-driven fluorescence intensity correlates with spike frequency
in SCN neurons. Right, Fluorescence intensity, measured as percent above
background, versus action potential (AP) frequency plotted for SCN neurons
(n = 24, 3 slices). r = 0.91; p < 0.001. Left,
Example of spike output from neighboring neurons; top arrow, low fluorescence
intensity (6.9% above background, 0.03 Hz); bottom arrow, high fluorescence
intensity (49.5% above background, 9.14 Hz).
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The expression of GFP has been previously demonstrated to have no
significant effects on neuronal properties
(Smith et al., 1997). However,
as a direct control for the possibility that GFP levels per se could alter
electrophysiological parameters, we transfected mouse N2A neuroblastoma cells
with the GFP construct and then performed combined patch-clamp and
quantitative imaging experiments similar to those we had performed on SCN
neurons. Low-GFP-expressing cells (dim) and high-GFP-expressing cells (bright)
within the same transfections were selected, and current-voltage relationships
from -120 through +75 mV were determined (see Materials and Methods). On
average, the high-expressing cells exhibited sevenfold higher fluorescence
intensity, yet the current-voltage curves for the two populations were
identical (supplemental Fig. 1,
available at
www.jneurosci.org),
with both peak inward and outward currents being indistinguishable in
amplitude (low-intensity cells, -44.0 ± 7.3 and 397.6 ± 57.0 pA;
high-intensity cells, -49.6 ± 7.0 and 364.9 ± 27.4 pA). These
results indicate that cellular GFP concentrations per se do not influence
electrophysiological properties, and, thus, the correlation of
Per1::GFP expression with spike rate in SCN neurons is likely
attributable to the action of this reporter gene as a faithful indicator of
underlying circadian rhythms in mPer1 gene expression.
Individual neuronal rhythms in SCN in vitro
Using time-lapse confocal microscopy, we recorded the gene expression
rhythms of individual SCN neurons within SCN slices from animals housed in
either LD or DD conditions. Imaging was directed to one SCN from each animal
to assess intra-SCN organization. Figure
3A shows single-cell gene expression rhythms from two
cells in a DD SCN in which the Per1 promoter activity was tracked as
GFP fluorescence intensity. Individual SCN neurons in both LD and DD SCN
exhibited circadian cycles of Per1-driven gene expression that
averaged approximately threefold in amplitude and 21.4 ± 0.5 hr in
period (n = 27). We did not identify any oscillations in individual
cells having multiple peaks during a circadian cycle. Eleven percent of imaged
neurons in LD SCN and 26% of neurons in DD SCN exhibited nonrhythmic
Per1::GFP expression (see Materials and Methods).
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Figure 3. Individual neuron gene expression rhythms in the SCN. A,
Per1-driven GFP rhythms from two individual SCN neurons in an in
vitro DD SCN obtained by confocal time-lapse imaging. B, C,
Example gene expression rhythms from four individual neurons in an in
vitro SCN from a LD animal. Each set of symbols represents the measured
fluorescence for an individual cell during the circadian cycle. The black
solid line indicates the integrated overall fluorescence rhythms of the SCN as
a whole.
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The individual neuronal rhythms within each SCN exhibited a wide range of
peak times (Fig. 3B,C;
also see time-lapse video in supplemental
Fig. 2). In SCN from both LD
and DD animals, approximately half of the individual cell rhythms peaked in
synchrony, forming a principal phase group that gave rise to the overall peak
in Per1-driven GFP fluorescence from the nucleus as a whole
(Fig. 3B,C, black
line). These rhythms were typified by the cell plotted in in
Figure 3B, filled
circles (52% of rhythmic cells at ZT8 of LD and 63% of rhythmic cells at CT12
of DD; n = 126 cells for LD; n = 131 cells for DD;
n = 5 SCNs each). The peak times of the remaining 48 or 37% of
rhythmic cells in LD and DD SCN were not aligned with the principal phase
group (Fig. 3C, open
circles, filled triangles) and included neurons that cycled in antiphase to
the principal phase peak (Fig.
3B, open diamonds).
Many neurons outside the main phase group also clustered their peak times
at distinct circadian phases. In both individual and group data
(Fig. 4A-C) peak time
histograms exhibited clear multiple-phase peaks. Peak times could not be
described by a single normal distribution (Kolmogorov-Smirnov test, p
> 0.05) but were, in fact, best fit by three-peak Gaussian functions. For
SCNs from animals experiencing an LD cycle, groupings of cell rhythms
consistently occurred before and after the main phase group, at ZT5 and ZT11
(13% each; Fig. 4B).
In the absence of a light cycle, groupings of cell rhythms consistently
occurred after the main peak at CT15 and CT18 (19 and 10%, respectively;
Fig. 4C). Thus, in LD
SCN, 78% of neuronal peak times were contained in three distinct phase groups
at ZT5, ZT8, and ZT11, whereas in DD SCN, 92% of neuronal peak times were
contained in phase groups at CT12, CT15, and CT18. Twenty-two percent of
rhythmic SCN neurons in LD slices and 8% of rhythmic neurons in DD slices
peaked at times outside the three organized phase clusters, their peak times
being primarily distributed throughout the late night and into the early day
(Fig. 4B,C, points not
fit by smooth curves).
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Figure 4. Neuronal phase relationships are dependent on light history. A,
Histograms of individual neuron fluorescence peak times in an SCN slice from
an LD animal (gray bars) and an SCN slice from a DD animal (black bars). Cell
peak times were binned at 1 hr intervals; n = 26 neurons for the LD
slice; n = 37 neurons for the DD slice. B, Histogram of
individual neuron fluorescence peak times summed from SCN slices from five LD
animals. Peak times are in 1 hr bins. The black line indicates the best fit
curve showing phase group peak times of ZT5, ZT8, and ZT11. C,
Histogram of individual neuron fluorescence peak times summed from SCN slices
from five DD animals. The black line indicates the best fit curve showing
phase group peak times of CT12, CT15, and CT18. D, Cumulative
probability plot of peak times from animals housed in the LD cycle (filled
circle, gray line) and peak times from DD (filled diamond, black line). The
median time of cellular peaks for each animal is plotted as an open symbol
(circle, LD; diamond, DD).
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Although the temporal gene expression patterns of SCN networks from LD and
DD animals were both characterized by three phase groups at 3-4 hr intervals,
they were distinct in terms of the phase relationship of Per1 rhythms
to locomotor activity and the phase relationships among individual SCN
oscillator neurons. The cumulative probability distribution of cell peak times
in vitro, referenced to the time of lights off/activity onset of the
slice donor animal on previous circadian cycles, were significantly earlier in
the LD SCN compared with the DD SCN (Fig.
4D; Kolmogorov-Smirnov test, p < 0.001). In
addition, as described above, in LD SCN, one accessory phase group preceded
the main peak and the other followed it, whereas in DD SCN, both accessory
phase groups followed the main phase group.
In addition to the different temporal patterns of cellular gene expression
rhythms, LD and DD SCN slices also exhibited distinct anatomical distributions
of rhythmic cells. When the position of rhythmic cells
(Fig. 5A) relative to
the lateral-medial axis of the SCN was examined, LD slices exhibited nearly
equal proportions of lateral and medial rhythmic cells (54% lateral vs 46%
medial), whereas DD slices had twice as many rhythmic cells in the medial half
of the SCN (66% medial vs 34% lateral). In the dorsal-ventral axis, LD slices
had approximately twice as many rhythmic cells in the ventral region (64%
ventral vs 36% dorsal), whereas DD slices had more dorsal rhythmic cells,
resulting in a more equal distribution (57% dorsal vs 43% ventral). With
regard to the spatial distribution of cells in the various phase groups, there
was a lateral-to-medial gradient that correlated with the peak time of phase
groups (Fig. 5B). The
earliest phase group, ZT5 of LD slices, had the highest proportion of
laterally located cells, whereas the latest phase group, CT18 of DD slices,
had the highest proportion of medial cells. Intermediate phase groups had
intermediate proportions that followed a general trend of increasing medial
and decreasing lateral cells with later peak times. The small numbers of
"antiphase" rhythmic neurons and nonrhythmic neurons precluded
meaningful analysis of their spatial distributions.
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Figure 5. Rhythmic neurons by SCN region. Split cylinders shown in a
three-dimensional perspective rendering represent the percentage of rhythmic
neurons in the SCN mapped by SCN region. The height of each half-cylinder
indicates the percentage of rhythmic SCN neurons in that region. Scale bars
indicate the percentage scale for the lateral-medial axis (vertical) and the
dorsal medial axis (horizontal). Total cells are n = 126 cells for LD
slices and 131 cells for DD slices. A, Overall distributions of
rhythmic neurons. Top row, Percentage of rhythmic SCN neurons mapped in the
lateral-medial axis for LD (left) and DD (right) slices. Bottom row,
Percentage of rhythmic SCN neurons mapped in the dorsal-medial axis for LD
(left) and DD (right) slices. B, Distributions of rhythmic neurons by
phase group. Top row, Percentage of rhythmic SCN neurons from the three LD
phase groups mapped in the lateral-medial axis. Bottom row, Percentage of
rhythmic SCN neurons from the three DD phase groups mapped in the
lateral-medial axis.
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Discussion
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A prevailing model of SCN tissue level organization is a
"monophasic" view in which SCN neurons form a single coherent
phase population (Liu et al.,
1997; Herzog et al.,
1998). Recent findings of light cycle-dependent biphasic SCN
electrical output suggested a more multiphasic SCN organization but could not
distinguish between light-induced changes in the waveform of coherent cellular
oscillators vs changes in the relative phase of individual oscillators
(Jagota et al., 2000;
Mrugala et al., 2000). Our
time-lapse imaging studies of Per1 gene dynamics directly demonstrate
that the individual neuronal molecular rhythms comprising the SCN clock
waveform are organized as multiple-phase groups of neurons with distinct peak
times. Simultaneous patch recording and quantitative imaging revealed that
heterogeneity in the phase of molecular oscillations is reflected in the
neurophysiological output of Per1-expressing neurons. In addition, we
demonstrated that entrainment of animals to a light cycle redefines the phase
relationships among oscillator ensembles within the SCN. These data suggest
that the SCN is organized as multiphasic oscillator ensembles, and that
light-driven changes in the phase relationships between constituent
oscillators underlie pacemaker entrainment to the photoperiod.
The SCN clock comprises discrete phase groups
The SCN comprises multiple individual cellular oscillators, with a bare
majority of neurons (50-60%) forming a main phase group. A consistent finding
of our studies was that, in addition to this main activity peak, the remaining
neuronal rhythms in an SCN were clustered into two additional well defined
phase groups, each of which contained 10-20% of the rhythmic neurons. The
three phase groups peaked at 3-4 hr intervals. Interestingly, previous in
vivo studies have documented similar accessory peaks in SCN multiunit
spike frequency at 3-4 hr intervals
(Meijer et al., 1997).
Together, the evidence strongly suggests that discrete phase groups, now
experimentally defined by both molecular and electrical activity, are
characteristics of the mammalian circadian pacemaker.
In addition to the robust accessory peaks, a less numerous group of neurons
cycled with peak times primarily in the night phase. The presence of cells
cycling in approximate antiphase is not entirely unexpected because PER1
immunocytochemistry has demonstrated a subpopulation of SCN neurons that peak
in antiphase to most of the nucleus (Bae et
al., 2001; King et al.,
2003). Furthermore, spike frequency rhythms of individual units
within organotypic cultures of SCN also exhibit antiphase rhythmicity
(Herzog et al., 1997;
Nakamura et al., 2001).
There are three principal mechanisms by which the phase heterogeneity in
SCN neuronal rhythms could arise: differences in the periods of neuronal
rhythms, differences in the amplitude neuronal rhythms, and differences in the
coupling interactions among neurons. Variations in the period and amplitude of
SCN neuron rhythms have indeed been documented
(Liu et al., 1997;
Herzog et al., 1998;
Honma et al., 1998; this
paper). Although more detailed studies are warranted, the data, at present,
suggest that period and amplitude vary in continuous distributions; thus,
although they could engender monophasic distributions in neuronal phase, they
are not likely to cause the multiphasic clustering we observed. Therefore, we
suggest that differential coupling interactions between SCN neurons, perhaps
expressed as differential responsiveness to intra-SCN coupling signals, such
as neuropeptides, or GABA, likely play key roles in establishing the
multiphasic nature of the SCN clock. The organized phase groups we have
defined could represent specific outputs of the SCN or SCN subnetworks tuned
to specific inputs. Additional studies are needed to determine the precise
relationship between phase diversity and cell type heterogeneity within the
SCN.
The patterns of phase heterogeneity among SCN neurons could also be stable
or dynamic. We have used acute SCN slice preparations and have primarily
analyzed neuronal rhythms on the initial circadian cycle in vitro to
assess the effects of animal light experience during pacemaker organization.
Our data set of neuronal rhythms from the second cycle in vitro is
more limited but shows that the general patterns of phase relationships among
neurons are maintained, and that individual neurons are not rigidly
phase-locked to each other (Fig.
3A). Although we have demonstrated that the pattern of
neuronal gene rhythms on the initial cycle reflects the light history of the
donor animal, the patterns on subsequent cycles also likely reflect
reorganization of the SCN slice attributable to deafferentation and
maintenance in in vitro conditions. Thus, our experiments provide a
unique "snapshot" of experience-dependent SCN organization but are
less well suited for determining the stability of individual neuron phase
relationships. Although this issue should ultimately be addressed with in
vivo experiments, electrophysiological neuronal rhythms in long-term
organotypic cultures of SCN show significant cycle-to-cycle variations in
relative phase (Nakamura et al.,
2001), suggesting that dynamic phase relationships among SCN
neurons may be the norm.
Light-driven changes in oscillator ensembles
Two defining hallmarks of circadian pacemakers are that they generate
rhythms endogenously and that they are synchronized to the external
environment. We tested directly the hypothesis that entrainment of the SCN
pacemaker occurs through light-driven changes in the relative phase of
individual oscillators. By examining the effects of the animal's previous
light history on SCN network dynamics, resolved at the level of individual
neurons, we determined that the presence of a light cycle induced a
reorganization of individual neuron peak times. Importantly, light changed the
phases but not waveforms of individual neuronal rhythms. This is a direct
demonstration that light input to the SCN indeed influences the relative phase
relationships of its constituent oscillators, as previously suggested by
behavioral observations (Pittendrigh and
Daan, 1976) and in vitro electrical recordings
(Jagota et al., 2000;
Mrugala et al., 2000).
We found that light input has two distinct effects on SCN neuronal rhythms:
(1) altering the absolute phase relationship between SCN rhythmic activity and
locomotor activity onset (Fig.
4D) and (2) reorganizing the relative phases of
constituent oscillators within the SCN
(Fig. 4B,C).
Comparison of the absolute phase relationships of SCN rhythms and activity
rests, in part, on the assumption that in DD pups, activity rhythms are
synchronized to their mother's. Previous results from mice, rats, and hamsters
indicate the widespread nature of maternal entrainment of neonatal rodents,
both for locomotor behavior (Takahashi and
Deguchi, 1983; Davis and
Gorski, 1985; Viswanathan,
1999) and for SCN rhythms
(Reppert and Schwartz, 1983).
The fact that in our experiments the patterns of phase peaks observed in an
individual SCN (Fig.
4A) are preserved in summed data from multiple SCNs
(Fig. 4B,C) indicates
that the pups were indeed synchronized to a common Zeitgeber time. However, we
cannot rigorously exclude the possibility that in our mouse line, there could
be a systematic phase difference between mother and pup that could contribute
to the overall phase differences between LD and DD slices. Thus, we must be
cautious in interpreting the apparent effect of light input on the absolute
phase relationship between SCN rhythms and locomotor activity, but this does
not affect our measurements of phase reorganization within the SCN.
Cyclic light input to the SCN could induce phase reorganization of SCN
oscillators by directly resetting the phase of SCN neurons, by increasing the
amplitude of neuronal oscillations (and thus affecting coupling angles), by
driving SCN neuron populations that are not self-sustained pacemakers, or by a
combination thereof. There is considerable heterogeneity in SCN neuron
responsiveness to light, including a specific subpopulation enriched for
vascoactive intestinal polypeptide-containing neurons that exhibits persistent
increases in neurophysiological activity after Per1 induction
(Kuhlman et al., 2003) and a
subpopulation of hamster SCN neurons that is arhythmic in Per1
expression in the absence of cyclic light input
(Hamada et al., 2002). We found
that the proportion of nonrhythmic Per1-expressing neurons was higher
in DD SCN than in LD SCN, suggesting the presence of a light-driven
subpopulation of neurons in the mouse SCN that can continue to oscillate on
the initial cycles in vitro. In addition, rhythmic neurons were more
prevalent in the ventral-lateral region of LD SCN than in DD SCN, similar to
the regionalization of rhythmic electrical activity in the presence and
absence of a light cycle in the rat SCN
(Shibata et al., 1984). Thus,
light-driven neurons are a general feature of mammalian SCN organization and
may play critical roles in entrainment.
Summary
On the basis of our findings, we propose a "multiphasic" view
of SCN organization in which the individual neurons participate in oscillator
ensembles that adopt varying phase angles to each other through coupling
processes influenced by light input. Reconciling variations in the
characteristics of individual oscillators with a multiphasic overall pacemaker
organization of phase clusters, rather than with strict monophasic
synchronization, provides a framework for understanding critical aspects of
SCN function. This multiphasic view better accounts for many properties of
circadian organization, including the ability of the SCN to produce
multiple-phase peak rhythms in SCN electrophysiology in vitro and
in vivo (Meijer et al.,
1997; Yamazaki et al.,
1998; Jagota et al.,
2000; Mrugala et al.,
2000) to time various physiological output rhythms, and to respond
to seasonal variations in the photoperiod by altering the coupling of its
constituent oscillators (Pittendrigh and
Daan, 1976).
|
Footnotes
|
|---|
Received March 6, 2003;
revised June 11, 2003;
accepted June 30, 2003.
This work was supported by National Institutes of Health Grant MH063341
(D.G.M.). We thank Dr. Carl Johnson, Dr.TerryPage, Dr.RaeSilver, and Dr.Shin
Yamazaki for comments on previous versions of this manuscript; Dr.Dao-Qi Zhang
for performing the N2A recordings; and Jeff Stone for excellent technical
assistance.
Correspondence should be addressed to Dr. Douglas G. McMahon, Department of
Biological Sciences, Vanderbilt University, 1210 MRBIII, VU Station B, Box
35-1634, Nashville, TN 37235-1634.
E-mail:douglas.g.mcmahon{at}vanderbilt.edu.
J. E. Quintero's present address: Center for Sensor Technology, University
of Kentucky Medical Center, Lexington, KY 40536-0098.
S. J. Kuhlman's present address: Beckman Neuroscience, Cold Spring Harbor
Laboratory, Cold Spring Harbor, NY 11724.
Copyright © 2003 Society for Neuroscience
0270-6474/03/238070-07$15.00/0
* J.E.Q. and S.J.K. contributed equally to this work. 
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Top
Abstract
Introduction
