IEG mapping identified numerous brain regions differentially affected by behavioral state
In order to identify brain regions specifically activated by SD and RS, IEG expression was analyzed using a standardized high-throughput ISH platform (Lein et al., 2007
). IEG expression provides a cellular resolution “readout” of recent neuronal activity (Sagar et al., 1988
; Morgan and Curran, 1991
). Altogether, nine IEGs were analyzed by colorimetric and/or fluorescent ISH over the five experimental conditions with three replicates each (Arc
; all colorimetric data are available at http://sleep.alleninstitute.org
). These IEG were selected because there were multiple reports that each gene changed in response to SD (Table S2
in Supplementary Material).
were particularly robust examples of condition-specific expression patterns, shown in Figure . Arc
mRNA expression was highest in SD in the neocortex, hippocampus, piriform cortex and amygdala (Figure A). Mice allowed to sleep for 4
h following SD (RS mice) showed a similar pattern to SD, although with reduced signal intensity, suggesting a “recovery” from the sleep-deprived state. Sleeping controls (SDC, RSC) showed dramatically lower Arc
expression overall. Finally, W showed robust IEG induction relative to sleeping controls, although the W pattern was quite different from SD.
Figure 1 IEG mapping of neuronal activation in response to behavioral state. (A) Fluorescent Arc ISH (white) shows robust induction in SD, RS, and W relative to sleeping controls SDC and RSC, most notably in forebrain structures including discrete neocortical (more ...)
Strikingly, in the neocortex and caudate putamen, SD had the effect of both inducing and repressing IEG expression in distinct complementary anatomic subcompartments as compared to SDC, with virtually no part of these brain regions remaining static (Figure B). Arc and Nr4a1 expression was elevated in SD in primary visual cortex and, to a lesser extent, in the neighboring caudal somatosensory cortex, agranular insular cortex and orbitofrontal cortex (not shown). Conversely, Arc and Nr4a1 in SDC mice were typically highest in motor and rostral somatosensory cortices. Similarly, IEG induction was seen selectively in the caudal dorsolateral caudate putamen of SD mice, while the converse pattern (highest in rostral and ventral caudate putamen) was present in SDC mice. Arc and Nr4a1 showed highly similar patterns, at both an anatomical and cellular level, as shown by extensive co-labeling using double fluorescent ISH (yellow cells in Figure B).
In order to perform quantification and statistical analyses of the ISH image data, colorimetric ISH data for each gene were algorithmically quantified and mapped onto a common 3D anatomical coordinate framework using methods developed as part of the Allen Brain Atlas project (Lein et al., 2007
). This methodology enabled automated quantification of gene expression in each ISH experiment for several hundred brain regions, as delineated in the Allen Reference Atlas (Dong, 2008
), and is demonstrated in Figure using Arc
as an example. Signal detection algorithms were used to detect expressing cells (represented as an “expression mask” in Figures C,F) and register expression information to the Allen Reference Atlas (Dong, 2008
), shown in Figures D,G as a projection of the reference atlas volume back on to the tissue sections. These algorithms allowed statistical analysis of expression summarized by classically annotated brain structures, or analyzed in a grid volume with (200
voxels overlaid on the 3D reference atlas volume. Using the grid volume, differential gene expression could be computed at a voxel level between pairs of conditions (e.g., SD versus SDC), with presentation of the results in both 2D and 3D formats. Briefly, triplicate data for each gene within a behavioral condition were averaged at the voxel level in 3D and used to generate a smoothed “difference grid” representing subtractive or fold change comparisons between conditions. As shown in Figures H–L, averaged Arc
expression in SD and SDC mice was used to calculate an SD/SDC ratio in 3D coordinate space (Figure J). Difference grids could also be calculated as a subtraction (SD-SDC) and projected in 2D (Figure L). For example, the SD-induced Arc
expression pattern illustrated in Figure , with induction in specific neocortical areas, caudate putamen and piriform cortex, is clearly visible on a smoothed 2D projection of the 3D difference grid (Figure L).
Figure 2 Automated quantification and mapping of ISH data. (A–L) Quantification and registration of ISH data to an anatomical coordinate framework allowing statistical analysis of differential image-based gene expression across conditions, illustrated (more ...)
Overall, different IEGs had highly concordant condition-specific expression patterns. For example, Arc, Egr1, and Nr4a1 (Figure M) as well as Fos, Egr3, and Fosl2 (not shown), showed robust induction in visual and caudal somatosensory cortex, piriform cortex, cortical amygdala, and dorsolateral portions of the caudate putamen in SD mice (Figure M). The effect of SD is particularly robust, and is evident in both the SD-SDC and SD-W comparisons. W-SDC showed consistent induction of IEG mRNAs across many forebrain regions. Gene-specific patterns were also observed, including elevated Fos and Nr4a1 expression in the cerebellum (Figure M) and Arc in the pons following SD, as well as decreased Fos expression in part of the ventral posteromedial nucleus of the thalamus (VPM) following SD (Figure ). Lastly, the algorithmic quantification enabled a complete characterization of gene expression across annotated anatomic regions, shown in Figure N as a heat map for the gene Arc, which plots the expression level of Arc across 209 brain regions (y-axis, with the key to structure colors in Figure O) for each of the five conditions. The highest expression level (yellow on heatmap) demonstrates that SD induced widespread expression of Arc across much of the forebrain and was most prominent in cortical, hippocampal and olfactory (green), and striatal (blue) regions (Figure N).
Figure 3 Difference grid analysis identified decreased Fos expression in the ventral posteromedial nucleus of the thalamus during SD. Difference grid representation of upregulated Fos expression in SDC versus SD, showing higher expression (orange) in SDC than (more ...)
Molecular signatures of sleep/wake and sleep deprivation identified by genetic profiling
To identify additional genes regulated by behavioral state or time-of-day, DNA microarrays were used to profile seven brain regions. IEG mapping was used to select three subregions of the neocortex, amygdala and hippocampus, structures linked to known detrimental effects of SD on cognitive, emotional and mnemonic abilities. Orbitofrontal cortex (ORB) showed robust SD-induced IEG expression as measured by both standard manual densitometry and the automated quantification (e.g.,Arc
in Figure A). Although relatively modest changes in IEG expression were seen in the hippocampus proper (Ammon's horn and dentate gyrus), robust SD induction was observed in the entorhinal cortex (ENT) that provides the major input to the dentate gyrus (e.g., Egr3
in Figure B). In the amygdala, the deep nuclei (lateral, basal, and accessory basal) provide the majority of the input to the hippocampus for the emotional association with memory (Pikkarainen et al., 1999
; Pikkarainen and Pitkanen, 2001
); however, the most robust changes were in the neighboring posteromedial cortical amygdala (PMCO; e.g., Egr3
in Figure C). These three regions also exhibited statistically significant widespread induction of other IEGs in response to SD as measured by densitometry (data not shown). Although the visual cortex was a site of strong Arc
induction after SD, the activation of this area was likely a reflection of the recent activation of visual pathways by light rather than a phenomenon related to the known detrimental effects of SD on cognition, emotion, or memory, and thus was not selected for further study.
Figure 4 Regions assayed by laser microdissection and microarray. IEG mapping identified three forebrain regions affected by SD, including orbitofrontal cortex [ORB, (A)], entorhinal cortex [ENT, (B)] and posteromedial cortical amygdala [PMCo, (C)]. Left panels (more ...)
Based on these observations, ORB, PMCO, and ENT were isolated by laser microdissection across all five conditions for microarray analysis. Four regions known to be involved in the neural control of behavioral states and circadian rhythms were also analyzed in parallel: the LC, TMN, tuberal hypothalamus (location of the Hcrt neurons) and SCN (Figure D). High quality RNA was isolated from four biological replicates for each brain region and condition, and a small sample RNA amplification technique was used for DNA microarray analyses on the GE Codelink platform.
After normalization of the microarray data, differentially expressed genes were identified for each brain region (one-way ANOVA, p
0.05). In order to visualize the dynamics of these genes across the five experimental conditions, microarray expression values were plotted as “trends.” The averaged replicate microarray values of each gene were z-transformed to create an individual gene trend across the five conditions, and then the genes for each brain region were clustered into 25 distinct trend clusters. To identify similar expression dynamics occurring across different brain regions, the trend clusters from all brain regions were then hierarchically clustered. Figures A–I shows nine of the trend types (rows) observed across the seven brain regions (columns), where each trend is plotted as five points corresponding to SD, SDC, RS, RSC, and W, respectively.
Figure 5 Gene expression showed a diversity of responses to time-of-day and treatment condition. Differentially expressed genes were identified by one-way ANOVA with a p-value cutoff of 0.05. For each identified gene, the expression values of four replicates were (more ...)
Two trends could be generally characterized as “wake-associated” or possibly activity-associated, with upregulation of gene expression in SD and W, either with or without a “recovery” during RS (Figures A,B). Numerous IEG, markers of neuronal activity, exhibited wake-associated expression in the microarray data (Arc
). Consistent with previous reports (Cirelli et al., 2004
; Mackiewicz et al., 2007
), gene ontology (GO) analysis of these trends identifies “response to stress” (Figure A ORB and PMCO trends and Figure B MENT trend; Table S3
in Supplementary Material), “response to unfolded protein,” and “inflammatory response” (Figure A) as enriched functional categories. These trends are exemplified by Cdkn1a
, also known as p21WAF1/CIP1
, a cell cycle gene regulated in response to DNA damage that has also been shown to be a circadian clock-controlled gene in some tissues (Grechez-Cassiau et al., 2008
). Many genes from this trend type were later validated by ISH. Two other trends could be characterized as “sleep-associated” (Figures C,D), with or without a recovery during RS. GO analysis suggested that genes involved in vasculogenesis and epigenetic regulation of gene expression were enriched in sleep-associated gene clusters. Genes involved in the “regulation of transcription” were enriched in both wake- and sleep-associated trends (Figures A,D).
The trends shown in Figures E,F are likely driven by time-of-day or light-dark cycle, with the defining feature being either an up- (Figure F) or down- (Figure E) regulation during ZT18/W (dark phase). The SCN trend illustrated in Figure E includes genes associated with the GO term “rhythmic process” (p
0.016), appropriate for a time-of-day trend occurring in the master circadian clock, and includes the well-known clock gene Per2
. Two other GO terms were associated with both time-of-day and sleep/wake regulation. Ubiquitin-mediated proteolysis is important for tightly regulating the levels of some of the key circadian clock proteins (such as Per2
). Genes involved in the “ubiquitin cycle” were down-regulated in the SCN in wake, consistent with a previous report that these genes exhibit circadian oscillation in the SCN (Panda et al., 2002
). However, these genes also exhibited wake-associated expression in the SCN and Hcrt (Figures A,B). Similarly, “chromatin modification” has previously only been implicated in the control of circadian transcription (Etchegaray et al., 2003
), but in the present study, genes associated with chromatin modification were affected by both time-of-day in the SCN (Figure F) as well as sleep/wake in the Hcrt and TMN, respectively (Figures A,D).
Only 48 of 175 trends are shown in Figure . The majority of the remaining trends show patterns of gene expression that reflect the combined influence of time-of-day, sleep/wake, SD, and RS-specific patterns. The last three trend types demonstrate some of the diversity of expression patterns. In Figures G,H, the only feature in common is upregulation in SD that is distinct from expression in W, while the trends in Figure I all exhibit downregulation of expression in SD. Genes differentially expressed between SD and SDC were identified (t
-test, fold change >
0.05). Out of 34,967 unique probes available on the microarray, approximately 4–6% of genes were significantly changed by SD for each brain region. Altogether across the seven brain regions, ~22.6% of the probes were affected by SD in at least one brain region.
Anatomic signatures of sleep deprivation using high-throughput in situ hybridization
In order to place the molecular characterization of SD into a cellular/anatomical context, high-throughput colorimetric ISH was used to screen 222 candidate genes (including the IEGs described above; gene list and primers in Table S1
in Supplementary Material) chosen to provide a broad survey of state-, condition-, and region-specific gene expression. Candidate selection was based on a combination of previously published studies documenting dynamic gene regulation across behavioral states or in response to SD (Table S2
in Supplementary Material) as well as the microarray experiments in the present study. ANOVA (across five conditions) p
-values and t
-test (SD versus SDC) p
-values and fold change are provided for ISH candidates in Table S4
in Supplementary Material. The full ISH data set is available at http://sleep.alleninstitute.org
. The screen was used to select 53 genes (including IEGs) showing the most robust qualitative changes across behavioral conditions, and these genes were processed on an additional two sets of animals to obtain triplicate data. Generally, the effects of SD elicited the most robust changes in gene expression, and thus the triplicate dataset heavily represents activation by SD. These dynamic genes included the following gene ontologies: circadian rhythm and rhythmic process (Per1
), response to stress (Vwf
), and regulation of apoptosis and progression through cell cycle (Prok2
A brain-wide (Figure ) and gene-specific (Figure ) signature of the effects of SD on gene expression was generated using an algorithmic process. The set of 53 genes with triplicate ISH data were mapped to the reference atlas framework (Figure ). The effects of SD for this gene set were widespread across the brain, with 65 of 209 brain regions showing statistically significant (p
0.05) differential expression between SD and SDC for at least 6 and up to 24 genes in each region (Figure ). Forebrain regions typically exhibited the most robust SD-associated changes across the most genes (Figure A; blue histogram bars represent striatum/pallidum; green histogram bars represent cerebral cortex), including olfactory structures (anterior olfactory nucleus, piriform cortex and main olfactory bulb), cerebral cortex, striatum, nucleus accumbens and hippocampus. In contrast, the hypothalamus and brainstem were notably unresponsive at a regional level to the effects of SD.
Figure 6 Signatures of sleep deprivation by anatomic region. (A) The histogram shows the number of genes exhibiting differential expression between SD and SDC (ANOVA p <0.05) per anatomic region based upon automated quantification of triplicate (more ...)
Figure 7 Signatures of sleep deprivation by gene. (A) The histogram shows the number of the 209 brain regions examined that showed statistically different expression in SD versus SDC (ANOVA p <0.05) for the same ISH data set used in Figure (more ...)
The neuroanatomical distribution of changes in gene expression could indicate the associated behavioral functions affected by SD. Therefore, the brain regions shown in Figure A were manually annotated for their involvement in different functional pathways (Figure A, bottom). SD-affected gene expression in many brain regions involved in sensory pathways (visual, auditory, and somatosensory), as previously observed with IEG mapping. Brain regions associated with reward and addiction as well as learning and memory also exhibited numerous SD-associated changes. To further determine how these genes and brain regions were responding across all five behavioral conditions, we plotted gene expression for the associated brain regions for two functional pathways (learning and memory and emotional behaviors), including additional brain regions not part of the original 209 structures assessed in the algorithmic quantification pipeline (Figures B,C).
Most of the brain regions involved in learning and memory exhibited gene activation in SD, with some genes also being activated in either RS or W (but not both; Figure B). Interestingly, the three genes which exhibited activation in SD and RS but not W included two genes from the “response to stress” GO category (Sgk
), suggesting that SD specifically (but not W) may function as a stressor that induces gene expression which does not completely recover following 4
h of RS. Genes activated in SD and W, and thus presumably associated more generally with wakefulness and consequently neuronal activity, include two IEGs, Homer1
, and Junb
, which are commonly interpreted as markers of neuronal activity. While most of the brain regions in learning and memory behaved similarly, the dentate gyrus showed the greatest deviation from the group, exhibiting less SD- or W-induced gene expression.
Thirteen brain regions associated with emotional behaviors, including amygdalar regions and bed nucleus of stria terminalis, were also assessed for changes in gene expression (Figure C). Most of the genes shown in Figure C exhibit broad activation by SD across all of the brain regions associated with emotional behaviors. Similar to the learning and memory group, most genes exhibited activation either in SD and W or SD and RS across the majority of brain regions. Again, the genes which exhibited induction in SD and RS (but not W) included genes from the GO category “response to stress” (Sgk, Crh, Cfp), as well as Dusp14, which is in the stress-activated MAPK signaling pathway (note that Sgk was also upregulated in RSC). And once again, the genes represented in the awake and active groups SD and W (not RS), include two classical IEGs: Fosl2 and Junb.
Overall, there was a significant effect of SD on 50 of the 53 genes analyzed by ISH (Figure A). Some genes exhibited SD-associated changes in gene expression in very restricted brain regions (minimum of one brain region) and ranged to SD-associated changes in gene expression broadly across the brain (up to 115 brain structures for Sgk1). Within this 50 gene set, the median was 11 brain structures affected/gene. Genes that were significantly affected by SD in at least 20 brain regions were heavily selective for nuclear (green bars) and extracellular or secreted (orange bars) genes such as Bdnf or Scg2, suggesting that SD or its associated wakefulness resulted in changes in transcriptional regulatory events and or cell-cell signaling. Correspondingly, GO terms for “transcription factor activity” (Figure B) were well-represented in the set of 53 genes. The genes exhibiting the broadest range of changes across the brain also tended to belong to related gene ontology categories cell death, stress response and MAPK signaling. Serum/glucocorticoid-regulated kinase 1 (Sgk1) exhibited the broadest response to SD, with significant differential expression across nearly twice as many reference atlas structures as the most responsive IEGs. The expression heatmap for Sgk1 (Figure C) showed a generalized upregulation by SD as well as regulation by time-of-day with peak expression at the two ZT10 timepoints (RS and RSC). Examination of the primary ISH data explained the anatomical breadth of this gene regulation, in that Sgk1 appeared to be induced in oligodendrocytes across the entire brain (Figures D–F), based upon the distribution and density of labeled cells in white matter tracts. Expression in other cell types, including hippocampal CA3 pyramidal cells and the choroid plexus, appeared relatively unchanged across all conditions.
Independent modes of gene regulation in SCN and neocortex
The molecular components of the circadian clock are expressed nearly ubiquitously, with the existence of an autonomous circadian clock in each cell that is then synchronized via various mechanisms to the overall rhythm of the tissue or organism (Antle and Silver, 2005
). Thus, while the SCN is considered the site of the master circadian clock that synchronizes the other tissues, circadian rhythms are observed in most other tissues and cell types, although the phase of these rhythms may lag behind the SCN (Yoo et al., 2004
). The SCN was a target of analysis by both high-throughput ISH and microarray. Based upon our ISH analysis, differential gene expression in the SCN was overwhelmingly dependent upon time-of-day, and only a single gene, Gfap
, was determined to be robustly induced by SD in the SCN as shown by ISH (Figure D). A number of genes were identified with differential gene expression in both SCN and ORB but, surprisingly, circadian regulation in the SCN was not predictive of gene regulation in other regions such as the neocortex. For example, in Figure , the microarray trends and ISH data of four genes (Snf1lk
) are shown for ORB and SCN. Each of these genes were predominantly regulated by time-of-day in the SCN, with either peak expression during the day (Snf1lk
) or peak expression at night (Rasd1
). In contrast, the expression of Snf1lk
, and Nr4a1
was elevated in the ORB during SD in both the microarray data and in the ISH. Furthermore, Dbp
displayed an inverse time-of-day rhythm in the ORB compared to SCN, with peak expression in ORB at ZT10 and ZT18 as opposed to the trough expression at ZT18 in the SCN. Dbp
expression in the ORB was reduced by SD.
Figure 8 Differential effects of behavioral condition on the SCN and orbital cortex. (A) Microarray values shown as z scores for four genes, Snf1lk, Dbp, Rasd1, and Nr4a1, for both orbital cortex (blue) and SCN (red), demonstrating the different trends shown for (more ...)
Region and cell type-specificity of SD-induced gene expression in neocortex and caudate putamen
The neocortex can be divided into functional areas (visual, somatosensory, motor, agranular insular, and orbitofrontal) as well as into classically described layers easily separated by histological characteristics (layers 1–6). Furthermore, within each of the layers exist numerous cell types including subsets of both inhibitory and excitatory neurons. Colorimetric ISH can be used to detect both areal- and laminar-specificity of gene expression. In the neocortex, mRNA induction by SD was observed in visual cortex, somatosensory cortex, agranular insular cortex and orbitofrontal cortex (Figure ). Some genes were induced in all of these cortical areas (e.g., Arc, Scg2, Nptx2, Rgs20, Fos, Rasd1; Figures A–E,G), while induction of other genes exhibited areal specificity and were limited to visual and somatosensory cortex (e.g., Bdnf, Ccrn4l; Figures F,H), or just to visual cortex (Crh, Figure J). Crispld1 showed induction in somatosensory and agranular insular cortex but not visual cortex (Figure I). Laminar distribution of SD-induced gene expression in the neocortex also ranged broadly, from widespread induction across all layers (e.g., Arc, Scg2; Figures A,B) to highly layer-specific (layer 2/3 for Rasd1, Crispld1, Crh; layer 2 for Ccrn4l; Figures G–J). Genes with the highest specificity after SD were observed in superficial cortical layers and induction was limited or (most pronounced in some cases) to an extremely sparse and specific population of neurons in layer 2 (e.g., Nptx2, Ccrn4l; Figures C,H).
Figure 9 Heterogeneous cellular patterns of cortical gene upregulation by SD. (A–J) Colorimetric ISH for genes that were upregulated in SD (upper panels) relative to SDC (lower panels), shown at low magnification on a sagittal section through the lateral (more ...)
Double-labeling with marker genes for inhibitory (Gad1) and excitatory (Slc17a7) neurons demonstrated that the vast majority of gene upregulation was restricted to excitatory neurons. For example, there was no co-labeling of Arc (Figures A–C), Nr4a1 or Nptx2 (Figures H–K) with Gad1, the major synthetic enzyme for GABA that marks inhibitory neurons. Unlike Arc, Nr4a1 and Nptx2, Fos co-labeled a very small percentage of Gad1-positive neurons (Figures D,E), although the vast majority of Fos-positive neurons co-labeled with the excitatory neuron-specific vesicular glutamate transporter 1 (Slc17a7; Figures F,G). Expression of Rasd1 in layer 2/3 partially overlapped with Gad1 (Figures L,M), although there was also expression of Rasd1 in SDC (not shown), so it is unclear whether the specific neurons showing SD induction of Rasd1 were inhibitory neurons.
Figure 10 SD-induced transcripts in the neocortex label overlapping populations of excitatory neurons. (A–C) Double fluorescent ISH for Arc (green) and Gad1 (red) at low magnification (A) and high magnification in agranular insular cortex (B) and visual (more ...)
Double-labeling was also used to determine whether genes were induced by SD in the same cell populations. Arc, Nr4a1 and Fos labeled largely overlapping cellular populations, with some single-gene labeled neurons in each comparison in visual and agranular insular cortices (Figures N–S). On the other hand, the robust induction of Ccrn4l in a sparse population of neurons in layer 2 of somatosensory cortex, which was almost entirely excitatory based on limited co-labeling with Gad1 (Figure T), was entirely a subset of Nr4a1-expressing neurons (Figure U). Ccrn4l- expressing neurons was also entirely a subset of Nptx2, another gene displaying robust layer 2 induction (Figure V). Although Nptx2 showed partial overlap with Fos in layer 2 (Figure W), Ccrn4l and Fos expression were surprisingly mutually exclusive (Figure X), indicating that Nptx2 induction by SD in layer 2 labeled two separate cell populations.
The use of ISH to confirm differential gene expression identified from the microarray dataset provided a characterization of these genes across the brain, far more comprehensive than the seven brain regions specifically targeted for microarray. For instance, ISH revealed that the caudate putamen was a frequent site of robust SD-induced gene expression, although this region was not included in the microarray experiments. Robust gene induction by SD was observed across many genes in the caudal dorsolateral caudate putamen and, in several cases (e.g., Nts
), the effect of SD was nearly restricted to this region (Figures C,E). The numbers of labeled cells in the caudate putamen varied widely from gene to gene. For example, Arc
labeled large numbers of striatal neurons (Figures A,B), Nts
labeled smaller numbers (Figures C,D) and Arr3
labeled only a handful of neurons per brain section (Figure E). In general, the different SD-induced genes labeled partially overlapping subsets of neurons in SD, with the sparser populations generally labeling subsets of the more broadly expressing genes. For example, Nts
is entirely a subset of Nr4a1
-positive cells (Figure F), while Ccrn4l
labeled partially overlapping cell populations (Figure I). To identify the striatal neuron subtypes labeled by SD-induced genes, co-labeling was performed against two neuropeptides which are non-overlapping markers for the two subcategories of medium spiny neurons in the caudate putamen, accounting for 95% of striatal neurons in rodents (Tepper and Bolam, 2004
). Proenkephalin 1 (Penk1
) is expressed in neurons in the indirect striatopallidal pathway, and prodynorphin (Pdyn
) is expressed in neurons in the direct striatonigral pathway (Kawaguchi, 1997
). Expression of Nr4a1
in SD partially overlapped with Pdyn
(Figure G) and Ccrn4l
partially overlapped with Penk1
(Figure J), suggesting that these genes are expressed in both subsets of medium spiny neurons. However, Nts
expression was completely exclusive of Pdyn
expression (Figure H), indicating that Nts
induction by SD is exclusive of the direct striatonigral pathway, and is mostly likely occurring in neurons of the indirect striatopallidal pathway. Overall, these data show that SD affects gene expression reproducibly across several gross brain regions (e.g., neocortex and caudate putamen), within each of these regions there are complicated cell type-specific responses that likely reflect some combination of differential timecourses of induction and gene-specific induction in specific neuronal subtypes.
Figure 11 Molecular phenotyping neuronal populations exhibiting SD-induced gene expression in the dorsolateral caudate putamen. (A–E) Fluorescent ISH images showing selective induction of Arc, Nr4a1, Nts, Ccrn4l, and Arr3 in the caudal dorsolateral caudate (more ...)