Abstract
The assembly of neural circuits involves multiple sequential steps such
as the specification of cell-types, their migration to proper brain
locations, morphological and physiological differentiation, and the
formation and maturation of synaptic connections. This intricate and
often prolonged process is guided by elaborate genetic mechanisms that
regulate each step. Evidence from numerous systems suggests that each
cell-type, once specified, is endowed with a genetic program that
unfolds in response to, and is regulated by, extrinsic signals,
including cell–cell and synaptic interactions. To a large extent, the
execution of this intrinsic program is achieved by the expression of
specific sets of genes that support distinct developmental processes.
Therefore, a comprehensive analysis of the developmental progression of
gene expression in synaptic partners of neurons may provide a basis for
exploring the genetic mechanisms regulating circuit assembly. Here we
examined the developmental gene expression profiles of well-defined
cell-types in a stereotyped microcircuit of the cerebellar cortex. We
found that the transcriptomes of Purkinje cell and stellate/basket
cells are highly dynamic throughout postnatal development. We revealed
“phasic expression” of transcription factors, ion channels, receptors,
cell adhesion molecules, gap junction proteins, and identified distinct
molecular pathways that might contribute to sequential steps of
cerebellar inhibitory circuit formation. We further revealed a
correlation between genomic clustering and developmental co-expression
of hundreds of transcripts, suggesting the involvement of chromatin
level gene regulation during circuit formation.
Keywords: cerebellum, circuit assembly, development, GABAergic, gene
expression, microcircuit, Purkinje cell, stellate basket cell
Introduction
The cerebellum, a primary center for motor coordination, is an
excellent system to study neural circuit assembly in the CNS due to its
highly stereotyped cytoarchitecture. For example, the cerebellar cortex
is organized as a near lattice-like circuit architecture with a
protracted period of circuit formation. At the focal position in
cerebellar cortex and as its sole output are the Purkinje neurons,
which receive multiple sets of excitatory and inhibitory inputs. The
glutamatergic parallel fibers synapse onto the slender spines of the
distal dendrite, while the climbing fibers prefer the stubby spines of
the more proximal dendrite. In addition, the GABAergic basket
interneurons target Purkinje cell (PC) soma and axon initial segments
(AIS), whereas the stellate interneurons innervate the dendritic
shafts. The integration of these excitatory and inhibitory inputs
shapes PC outputs, which are transmitted to deep cerebellar nuclei and
regulate motor coordination (D’Angelo et al., [30]2011). While
significant progress have been made in understanding the development of
excitatory connectivity (i.e., parallel fibers and climbing fibers),
the development of inhibitory circuitry remains poorly understood.
All cerebellar neurons derive from progenitors that proliferate in two
germinal neuroepithelia: the ventricular zone (VZ) generates GABAergic
neurons, whereas the rhombic lip is the origin of glutamatergic
neurons. Among VZ-derivatives, GABAergic projection neurons and
interneurons are generated according to different strategies. PC are
produced at the onset of cerebellar neurogenesis by discrete progenitor
pools located in distinct VZ microdomains; they are specified within
the VZ and acquire mature phenotypes largely according to
cell-autonomous programs. On the other hand, the different classes of
inhibitory interneurons, including basket (BkC) and stellate (StC)
cells, derive from a single population of precursors that delaminate
into the prospective white matter (PWM), where they continue to divide
until the end of the first postnatal week (Altman and Bayer, [31]1997;
Leto et al., [32]2012).
Following their generation in the PWM, basket, and stellate cells
migrate toward cerebellar cortex and reach Purkinje cell layer (PCL) by
approximately P7 and P10, respectively. During the subsequent weeks,
PC, BkC, and StC undergo profound morphological and physiological
differentiation while establishing specific synaptic connectivity
(Cameron et al., [33]2009). For example, the PCs elaborate their
dendrites with characteristic branching patterns while the BskCs extend
their axon branches onto PCs. Each BskC innervates 7–10 PCs at their
soma and AIS (Figure [34]1A). The maturation of BskC axon arbor and
pinceau synapses continue into the fourth postnatal week (Ango et al.,
[35]2004, [36]2008). The development of StCs lags behind that of BskCs
by a few days. StC axons extend along the Bergmann glia fibers with
characteristic trajectories to innervate PC dendrites. Using a
candidate gene approach, we have previously demonstrated that members
of the L1 family immunoglobulin cell adhesion molecules (IgCAMs),
neurofascin, and CHL1, contribute to the subcellular organization of
BskC and StC innervation, respectively (Ango et al., [37]2004,
[38]2008). However, the genetic mechanisms that control the concerted
differentiation and synaptic connectivity during inhibitory circuit
formation remain poorly understood.
Figure 1.
[39]Figure 1
[40]Open in a new tab
Cell-type and developmental stage-specific gene expression profiles in
PC and S/BC. (A) Schematic representation of the anatomical changes
during cerebellar development with emphasis on the postnatal stages.
(B-D) PCA on all samples. Transcripts that changed significantly
(SD > 1 of mean, p < 0.05) across all time points can clearly segregate
PCs (blue) and S/BCs (red) (B). PCA based on all postsynaptic protein
transcripts (C), encoding membrane proteins such as ion channels, cell
adhesion molecules, and cell-surface ligand receptors; GABAergic
transcripts (D) can segregate PCs and S/BCs, Non-specific B-cell
activation transcripts (E), Blood glycolipid biosynthesis (F), and Glia
expressed transcripts (G) fails to segregate the samples according to
cell-types. (H–J) Cross-correlation analysis across all samples
captures several developmental epochs in each cell-type. Transcription
factor transcripts parse the PC into two distinct P3–P7 and P14–P56
epochs. S/BCs are also subdivided into P14–35 and P56 (H). Cell
adhesion molecules divides the PC into two broad groups P3–P7 and
P14–P56 and further subdivides the later into 3 epochs (P14–P21,
P28–P35, and P56). S/BCs are also subdivided into 3 epochs P14–P21,
P28–P35, and P56 (I). GABAergic transcripts subdivide the PCs into
three epochs P3–P7, P14–P21, and P28–P56. S/BCs are roughly split into
2 classes P14–P21 and P28–P56. GABAergic transcripts comprised of all
murine GABA receptors and transporters (J).
To explore the intrinsic genetic program that direct the
differentiation and connectivity between PC and BskC/StC(S/BC) cells,
here we examined the developmental dynamics of their specific gene
expression profiles. We found (i) phasic developmental expression of
transcription factors (TFs), ion channels, receptors, cell adhesion
molecules (CAMs), gap junction proteins, and novel cell-type specific
transcripts, (ii) distinct molecular pathways that may contribute to
different developmental stages for these cell-types; (iii) correlation
between genomic clustering and developmental co-expression of hundreds
of transcripts, suggesting the involvement of chromatin level gene
regulation during circuit formation. Our dataset also provides a
valuable resource for further studying the molecular and developmental
genetic mechanisms underlying cerebellar circuit formation.
Materials and Methods
Manual sorting
Amplified and labeled mRNA were prepared from 50 to 75 PC or between
125 and 150 S/BC cells that were manually sorted from each
developmental stage. While PCs are 40 μm in diameter and lie along the
PCL the StC and the BskC are both 8–10 μm in diameter and are
distributed in the molecular layer. Although the BskC cell bodies lie
relatively closer to the PC it is not always apparent since StC and
BskC are not uniformly distinct in layer separation. Also there is no
molecular marker that distinguishes Bsk from StC. This prevented us
from distinguishing StC from BskC during manual sorting. We had
previously demonstrated the sensitivity and accuracy of the manual
sorting method and that ∼30 cells were enough to detect rare
transcripts (Sugino et al., [41]2006). The amplified cDNA were
hybridized to Affymetrix MOE430.2 chips containing 45,101 probes sets.
Each cell-type at each time point had at least three biological
replicates from different male mice. The results from each were highly
reproducible with average correlation coefficient between each
replicate pair for all probe sets being 0.97211 ± 0.01827 (Mean, SD).
RNA amplification
RNA was isolated using Arcturus PicoPure (Applied biosystems, USA
Cat#KIT0202) spin columns according to manufacturers protocol with the
additional step of in-column DNase digestion. RNA was converted to cDNA
by oligo-dT primers and then amplified by two rounds of linear
isothermal amplification steps using the MessageAmpII aRNA
amplification kit (Ambion, USA Cat#AM1751) followed by biotin labeling.
Gene expression
Labeled aRNA were hybridized to Affymetrix mouse 3′ expression array
MOE430.2 according to manufacturers fluid handling, hybridization, and
scanning protocols. CEL files from each hybridization was converted to
normalized expression values using Bioconductor package[42]^1 in
R[43]^2 using GCRMA method. Further differential analysis, Principal
Component Analysis (PCA), cross-correlation analysis, and downstream
visualization was done in dCHIP software[44]^3. Microarray data
submitted to GEO; accession [45]GSE37055.
In situ hybridization
To generate in situ probes Trizol extracted total mouse brain RNA was
used to perform RT-PCR using gene specific primers (Superscript III,
Invitrogen, USA). RT product was subjected to nested PCR with T3 tagged
forward and T7 tagged reverse primers (see primer list in Table [46]A2
in Appendix). In vitro transcription using with T7 and T3 driven RNA
polymerase and DIG-labeled rNTPs generate the probes that was run on
Bioanalyzer to ensure single RNA product of expected size. T7 produced
antisense probes and T3 generated the control sense probes. In situ
hybridization was performed at 61°C on 15 μm thick sagittal
cryo-sectioned brains from C57B6 male animals. Detection was done using
anti-DIG antibody and VectaRed detection reagent (Vector Labs, USA
Cat#SK-5100).
Permutation test
Permutation test was performed on normalized expression values of PC
and S/BC cells to find genes that are differentially expressed across
different time points. For each probe, a t-statistic T[obs] was
computed as
[MATH: Tobs=Xi-XjSi2ni
+Sj2
nj :MATH]
Where is the mean of expression values of replicates at the ith
time point, is the mean of replicates from all other time points
except the ith time point, s[i] is the standard deviation of expression
values of replicates at the ith time points and s[j] is the standard
deviation of expression values of replicates at other time points n[i]
denotes number of replicates at the ith time point and n[j] denotes
number of replicates at other time points.
Then random permutations were performed across all time points and
replicates. In such test, for one probe, at one time point, we
calculated T[obs] (see above formula), then shuffled the expression
values for this probe 10,000 times and calculated the T[m] (also use
the same formula as calculating T[obs]), for m = 1, … M, where M is
number of random permutations, here we set M equal to 10,000. Then the
permutation p-value was calculated as the following formula.
[MATH: p=#b:Tm>Tob
s/M :MATH]
The above procedures were applied to all probes at each time point to
calculate p-values. Raw p-values were adjusted by false discovery rate
(FDR) approach to account for multiple hypotheses testing by
controlling the proportion of false positive (Storey and Storey,
[47]2002; Storey, [48]2003). This approach determines a q-value for
each test. It controls the number of false discoveries in those test
that result in a discovery (i.e., a significant result). In our
analysis, any probes with q-values <0.05 were considered as
significantly differentially expressed. Then IDs of these probe set
were converted to gene symbols.
Principal Component Analysis
Principal Component Analysis projects multivariate data objects onto a
lower dimensional space while retaining as much of the original
variance as possible. Replicates were averaged to obtain one vector of
expression values for each time point in development and regeneration.
We used PCA to project the seven PC and five S/BC samples of the
development time series, each consisting of 45,101 variables (probe
sets), into a two-dimensional plane. PCA operation was performed using
dCHIP and the first two principal components are plotted. The class
labels are used to color the samples but do not enter the PCA analysis.
Each principal component is a linear transformation of the expression
values of all genes in a gene list. So in effect PCA maps samples in
high N dimension (N is the number of genes) to two dimension,
maximizing the space among the samples.
Pathway enrichment analysis
Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment
analyses for significant genes at each time point were carried out
using the DAVID tool[49]^4. Pathways with enrichment p-values <0.05
were considered as significantly enriched. Pathway enrichment analyses
were performed separately for genes with the different expression
pattern (upregulation or down-regulation). Genes were considered as
upregulated at each time point if the mean of expression values of
replicates at one time point is larger than the mean of replicates from
all other time points, vice versa for down-regulation. The p-values of
enriched pathways for both upregulated and down-regulated genes were
combined together to form a p-value matrix. Each element of this matrix
represented the p-values of an enriched pathway at a certain time
point. The transformed p-value matrix was used to construct a heat map
using a Pearson correlation metric with hierarchical clustering using
R[50]^5. Two colors were utilized specifically for pathways containing
upregulated and down-regulated genes.
Genomic co-localization and co-expression
A set of co-regulated genes on the same chromosome in the genome can be
defined to be co-localized at a certain genomic loci, though they are
not necessarily consecutive. In our analysis, a co-localized gene
cluster can be detected computationally if some genes that are
significantly differentially expressed at a time point are within three
ORF distances of each other and on the same chromosome. We used our
algorithm to scan the chromosomes of Mus musculus using window lengths
of three genes, advancing one gene between two instances of the window
so that all possible three-gene windows were tested. Consecutive
statistically significant windows were merged up in only one cluster.
Then a permutation test was used to evaluate whether these
significantly differentially expressed genes impose a stronger
clustering tendency than would be expected by chance. In such a test,
we counted how many clusters can be identified among our co-regulated
genes at a time point, then repeated the clustering analysis on 10,000
sets of genes that were randomly selected in the genome to find out how
many clusters could be obtained by chance. In the entire distribution
of the number of clusters for 10,000 random gene sets, the p-value of
clustering tendency can be defined by the portion of cluster numbers
that are larger than the cluster number in significantly expressed
genes. It can be expressed as following formula, for m = 1, … M, where
M is number of random permutations. In this study, we set M equal to
10,000, T[obs] is the number of cluster observed in our data, and T[m]
is that number of cluster in random gene sets. Then the permutation
p-value was calculated as
[MATH: p=#ofTm>Tob
s/M :MATH]
For visualization of co-expressed and co-localized regions using UCSC
genome browser co-ordinates have been provided as individual text files
in BED format in the Supplementary Material.
Promoter scanning
PSCAN[51]^6 Ver1.2.1 was used for promoter scanning selecting a region
−450 to +50 of the input genes using motif descriptors from JASPAR
database[52]^7. The JASPAR CORE database contains a curated,
non-redundant set of profiles, derived from published collections of
experimentally defined TF binding sites for eukaryotes. For promoter
scanning algorithm refer to Zambelli et al. ([53]2009).
Results
Gene expression profiles distinguishes different GABAergic cell populations
The G42 transgenic GFP reporter line labels PCs, BskCs, and StCs
throughout their postnatal development (Ango et al., [54]2004). We used
G42 and a manual sorting procedure (Sugino et al., [55]2006) to purify
BskC and StCs together (S/BCs, see [56]Materials and Methods for
details) and PCs from the cerebellar cortex from the first to the
eighth postnatal week. The first time point for PC is P3, soon after
the PCL is formed and the first time point for S/BCis P14 because they
do not complete their migration until the second postnatal week. Total
RNA from ∼50 PC and ∼150 S/BC were amplified by two rounds of linear
amplifications followed by hybridization to Affymetrix mouse expression
array 430.2. At least three biological replicates were analyzed for
each developmental time point for each cell-type.
We performed a PCA (Raychaudhuri et al., [57]2000) on all postsynaptic
protein transcripts, GABAergic transcripts as well as the top 1500
highly altered probesets (1943 probesets, 4.2% of total that showed
changes in expression greater than 1 standard deviation of mean) across
all samples. PCA clearly distinguished PCs and S/BCs between ages
P14–56, whereas P3 and P7 PCs formed a separate cluster (Figures
[58]1B–D). Such distinction was abolished when unrelated transcripts
such as all B-cell activation, blood glycolipid biosynthesis, or glial
expressed transcripts were used (Figures [59]1E–G). Gene expression
profiles were compared between all stages to derive a correlation
matrix that indicate the overall degree of similarity or relatedness
between any two samples during circuit development. Gene-wise
standardized expression values of TFs, CAMs, and GABA transmission
transcripts were used to compute the Pearson correlation coefficients
between two samples and the matrix containing all the values were
plotted as a heatmap where the X and Y axis denotes the individual
samples from each developmental stage. Developmentally PC and S/BC can
each be segregated into two broad groups based on their expression of
TFs and CAMs (Figures [60]1H,I) by cross-correlation analysis.
Furthermore, the GABAergic transcripts parsed the developmental
trajectory of PCs into three distinct epochs: P3–7, P14–21, and P28–56;
the same analysis parsed S/BC trajectory into two epochs: P14–21 and
P28–56 (Figure [61]1J). Compared to PC, the S/BC developmental profile
are less robust probably due to the less homogeneous nature of this
population. Our cell-type specific gene expression profiles could
readily distinguish the PCs and S/BCs and further capture the distinct
developmental epochs as they engage in circuit formation.
Temporal expression profiles capture elevated biological pathways at
different developmental stages
We conducted pathway enrichment analysis using KEGG database based on
developmental gene expression of PCs and S/BCs (Figure [62]2A).
Interestingly, a number of common biological pathways were elevated in
both cell populations, but with different developmental timing that
appears to correlate with their different maturation profiles. For
example, between P3 and P7 in PCs pathways of “axon guidance,”
“regulation of actin cytoskeleton,” “gap junction,” and “tight
junctions” is highly upregulated indicating the early steps of circuit
integration by PC. This is accompanied by an upregulation of other
pathways (Figure [63]2A) such as insulin signaling, TGF-beta, Hedgehog,
and Wnt which are important for axon guidance (Song et al., [64]2003;
Charron and Tessier-Lavigne, [65]2007; Scolnick et al., [66]2008).
Upregulation of GnRH signaling that has an modulatory effect on
cerebellar neurons (Albertson et al., [67]2008) and P53 signaling which
is important for PC survival (Gavino and Richard, [68]2011) was also
observed during this time. In P14PCs, pathways related to “long-term
potentiation,” “long-term depression,” “JAK/STAT,” “VEGF,” and “mTOR
signaling” were elevated, which correlate to the development of
parallel fiber synapses. The JAK/STAT, VGEF pathway is in general
associated with postnatal brain development, differentiation, and
survival but recently are also implicated in NMDAR-LTD (De-Fraja et
al., [69]1998; Storkebaum et al., [70]2005; Nicolas et al., [71]2012)
whereas mTOR signaling is involved in local protein synthesis in LTP
(Kelleher et al., [72]2004). The apoptosis pathway was upregulated at
P14 perhaps reflecting the known developmental death of a subset of PC
cells (Dusart et al., [73]2006). Between P28 and 56, the upregulation
of pathways related to “CAMs,” “chondroitin sulfate biosynthesis,”
“focal adhesion,” “cytokine–cytokine receptor interaction,” and
“extracellular matrix receptor interaction” (ECM-interaction) correlate
with the maturation and stabilization of PC connectivity.
Figure 2.
[74]Figure 2
[75]Open in a new tab
Pathway enrichment analysis of developmentally co-regulated transcripts
in PC and S/BCs. (A) Pathways enriched at specific developmental stages
in PCs (left) and S/BCs (right). Each colored box represents
significant p-values of the associated pathway across the developmental
stages that are either upregulated (red), down-regulated (blue), or
non-significant enrichment (gray). (B) Comparison between PC and S/BCs
shows delayed elevation of pathways involved in early postnatal
development in S/BCs.
In S/BCs a number of similar pathways are also activated but delayed
compared to PC (Figures [76]2A,B). For example, “axon guidance,” “tight
junction,” “adherens junction,” “insulin signaling,” “ErbB,” and
“spliceosome” pathways were upregulated in P14S/BCs, reflecting the
delayed axogenesis of BskC and StC after they enter the ML during the
second postnatal week. However between P28 and 35, similar to PC cells,
pathways of “ECM-receptor interaction,” “CAMs,” “cytokine–cytokine
receptor interaction,” “neuroactive ligand receptor interactions,” and
“regulation of cytoskeleton” were activated; these correlate with the
synaptic maturation and consolidation when BskCs elaborate and maintain
pinceau synapses to the AIS of PC (Ango et al., [77]2004) and StC make
multiple synaptic connections to PC dendrites (Ango et al., [78]2008).
Correlation between co-expressed genes and their chromosome co-localization
Several studies have shown that genes that are co-regulated tend to
localize as “clusters” along the chromosome (Boutanaev et al.,
[79]2002; Blanco et al., [80]2008). We therefore examined whether there
is a correlation between developmental co-regulation and genomic
co-localization in our dataset in PCs and S/BCs. We define a cluster as
a set of co-regulated transcripts (upregulated, down-regulated, and up-
and down-regulated with the same developmental profile) located within
three ORF distances of each other. The number of the clusters detected
in co-regulated genes was significantly higher than that would be
expected by chance. In total we detected 4640 clusters in PCs across
seven developmental stages and 1657 in S/BCs across five stages (Figure
[81]3A). The clusters varied from 2 to13 genes with a size range from
194 Kb to 1.37 Mb, with an average of 279 (±79) Kb for PCs and 219
(±56) for S/BCs (Figures [82]3B,C). Surprisingly, more clusters were
identified in the early postnatal ages (combined 2229 clusters in P3
and P7 PC; 1160 clusters in P14 S/BC) and at adult stage P56 (1896 and
440 clusters in PC and S/BC respectively) compared to intermediate
stages (P21–P35; Figure [83]3A). Despite the highly correlated
expression of TFs, CAMs, and GABAergic transcripts seen in PCs an S/BCs
between P21 and 35 (Figures [84]1E–G) we found the co-transcribing
genes were not significantly genomically co-localized.
Figure 3.
[85]Figure 3
[86]Open in a new tab
Co-expressed transcripts are often co-localized on the genome. (A)
Distribution of total number of co-expressed and co-localized
transcript between PC and S/BCs during development. (B) Frequency
distribution of the different co-localized clusters. (C) Size
distribution of different co-localized clusters in PC and S/BCs.
Average size of all clusters across all time points for PC and S/BCs
shown in inset. (D–G) Representative chromosomal distribution of
co-localized and co-expressed transcripts at specific developmental
stages. The region harboring co-expressed and co-localized clusters are
depicted as black bar spanning across the starting and ending ORFs in
UCSC genome browser (see UCSC track files in BED format Table S1 in
Supplementary Material for PC and Table S2 in Supplementary Material
for S/BC). Certain cluster groups are stable between developmental
stages P3 and P7 in PCs [(D), see also Table [87]A1 in Appendix].
Co-localized clusters can re-emerge in late-developmental stages as
shown in PC cell by comparing the P3 and P7 time points with P56 (E).
Representative common clusters across cell-types during early postnatal
developmental stage (F). Few such clusters are common between
cell-types in late-adult stages suggesting the maintenance of a
“locked-in” differentiated state (G).
Within the same cell-type such as PC, 418 gene clusters maintained
their co-expression between P3 and P7 (Figure [88]3D). KEGG pathway
analysis of these stable gene pairs identified “regulation of actin
cytoskeleton,” “pentose phosphate pathway,” and “ribosome” to be
enriched (Table [89]A1 in Appendix). Interestingly, in both PCs and
S/BCs from P21 to P35 few or no such stable clusters were detected, but
these clusters re-emerge at later ages. For example in PC, 234 gene
pairs were conserved between P3 and P56 stages and 311 between P7 and
P56 (Figure [90]3E). Furthermore, we also identified conserved and
co-expressed gene clusters between cell-types, such as 212 common gene
pairs between P14 S/BCs and P3 PCs as well as157 pairs between P14
S/BCs and P7 PCs (Figure [91]3F). However, 119 common gene pairs were
found between mature PCs and S/BCs at P56 (Figure [92]3G). It is
possible that these co-expressed genomic clusters may constitute common
“regulatory modules” across cell-types that encode components of
functional pathways and are subjected to chromatin regulation during
neural development and the relatively lower common clusters between
cell-type in adults reflects the post-maturation state.
Molecular signatures of developing PCs and S/BCs
We examined whether different classes of genes are coordinately
expressed in PCs and S/BCs that correlate with their developmental and
physiological phenotypes.
GABA transmission transcripts
In PC cells, we detected the known sharp changes in GABA(A) receptor
subunits (a shift from α2 to α1) between P7 and P14 (Figures [93]4A1
and [94]A2 in Appendix), with a simultaneous upregulation of α2 at P14
followed by increase in α4 transcript. These temporal patterns
correlate with the increase of GABAergic synaptic innervation and
transmission from BskCs and StCs during this period. Gabra4 is known to
be responsive to positive GABA modulatory neurosteroids (Zhou and
Smith, [95]2009), mediate extra-synaptic inhibition (Chandra et al.,
[96]2006), and has been implicated in regulating inhibitory tone (Smith
and Gong, [97]2005). In S/BCs, the Gabrb3 subunit is upregulated at P21
followed by Gabra3 and the extra-synaptic receptors Gabre and Gabrq at
P35. The high-affinity Gabrb3 (GABA-A b3) is also extra-synaptic and is
involved in slower tonic inhibition, affecting neuronal excitability,
and network activity (Huntsman et al., [98]1999; Nusser et al.,
[99]2001; Hentschke et al., [100]2009). Finally at P56 Gabrb1, Gabra4,
Gabrg1 are upregulated (Figure [101]4A3).
Figure 4.
[102]Figure 4
[103]Open in a new tab
Cell-type specific phasic temporal profiles of several classes of
transcripts. (A1) Cell-type specific GABAergic transcripts between
cell-types, (A2,A3) PC and SB individual temporal profiles are shown.
(B1) Cell-type specific cell adhesion molecules (CAMs) transcripts,
(B2,B3) PC and SB specific individual profiles are shown. (C) Cell-type
specific expression of NMDA receptors, (D) AMPA receptors, (E) Calcium
channels, and (F) Gap junction molecules.
Cell adhesion molecules
CAMs are expressed in two broad groups in PCs and S/BCs, respectively
(Figure [104]4B1). In PC, Cdh13, and Cdh4 are expressed during the
early postnatal stages; Col18a1, Bcar1, Cntnap2, Aatf, Adam23 continue
expression up until P56. Nid1, Cd9, Mfged8, and Mia1 are restricted to
S/BC. In situ data on eight transcripts in the Allen Brain Atlas (ABA)
developmental mouse ISH database[105]^8 matched our findings (Figures
[106]A1 and [107]A2 in Appendix). Distinct developmental expression of
CAMs in PCs and S/BCs might contribute to the formation, maturation,
and maintenance of pre- and post-synaptic components (Figures
[108]4B2,B3). In S/BCAstn1, implicated in neuronal migration (Zheng et
al., [109]1996), peaked at P14 which correlates with their migration
during this period. Interestingly cadherins and protocadherins showed
interesting temporal patterns as well and we also found developmental
co-expression of the same cadherins (e.g., Cdh10 and Cdh22) and
protocadherins (e.g., Pcdh20, Pcdh7, and Pcdh9) between PC and S/BC.
Glutamate receptors
Past studies suggested the absence of functional NMDA receptors in PC
cells (Farrant and Cull-Candy, [110]1991; Llano et al., [111]1991),
however in agreement with recent observations we detect the expression
of Grin2b between P3 and 7, Grin2a and Grin2c between P14 and 35 and
the obligatory Grin1 from P14 onwards in PC (Casado et al., [112]2002;
Piochon et al., [113]2007, [114]2010; Renzi et al., [115]2007; Bidoret
et al., [116]2009; Figure [117]4C). In S/BCs we further detected
specific expression of Grin3a and Grin3b subunits, which are known to
have atypical channel properties (Sucher et al., [118]1996; Matsuda et
al., [119]2002; Figure [120]A3 in Appendix). Metabotropic glutamate
receptorsGrm1 and Grm7 are expressed at high levels in PC whereas S/BC
expressed Grm3 between P14 and 56 (Figure [121]4D), which has a
relatively weaker expression in StC compared to Golgi cells (Ohishi et
al., [122]1993; Figure [123]A3 in Appendix). The functional
significance of these expression patterns should be examined by
electrophysiology experiments.
Calcium channels
We found high level expression of Cacna1a (Westenbroek et al.,
[124]1995), Cacna1g (Talley et al., [125]1999), Cacna1h, and Cacnb4
transcripts in PCs (Figure [126]4E; Figure [127]A4 in Appendix). While
the P/Q type calcium channel Cacna1a (Cav2.1) is characteristic of PC
(Hashimoto et al., [128]2011), the T-type channels (such as Cacna1g or,
Cav3.1 and Cacna1h or, Cav3.2) are expressed in cells with pacemaker
activity (Yunker et al., [129]2003; Engbers et al., [130]2012), can
play a key role at the AIS in generating spike bursts (Bender and
Trussell, [131]2009) and implicated in rebound potentiation of
inhibitory synaptic signals in cerebellar PCs (Kano et al., [132]1992;
Tempia and Konnerth, [133]1994; Cueni et al., [134]2009). On the other
hand, the expression of L-type calcium channel subunits Cacna1c,
Cacna1d, and associated gamma subunit Cacng4 appear restricted to
S/BCs. Cacna1c (Cav1.2) and Cacna1d (Cav1.3) are often co-expressed in
the same cell on dendrites and dendritic spines with slow activation
kinetics and large conductance (Lipscombe et al., [135]2004). They
regulate a multitude of neuronal processes including gene expression,
neuronal survival, synaptic efficacy, suppressing spontaneous
intracellular calcium oscillations, and slow rhythmic firing.
Gap junction proteins
Gap junctions create a direct cytoplasmic connection between neurons
and form electrical synapses that contribute to various network
rhythmic activities (LeBeau et al., [136]2003; Middleton et al.,
[137]2008). We found gap junction transcripts Sgsm3 (Rutbc3) and Gja8
(CX50) in PCs, and Tjp2 and Gjc3 in S/BC at adult stages (Figure
[138]4F; Figure [139]A4 in Appendix). Gjd2 (CX36), Gjc1 (CX45), Gjb3,
and Panx1 are expressed in PCs in the first week and in S/BCs from P14
onward. CX36 and CX45 shape the spontaneous firing during retina
development (Blankenship et al., [140]2011) and might play a similar
role in cerebellar circuit formation.
We found co-expression of GABA-B receptors, mGluR1, P/Q type and T-type
calcium channels, and cytohesins in PC. Interestingly postsynaptic
GABA-B receptors, P/Q and T-type can functionally interact with
mGluR1-mediated excitatory transmission at PF-PC synapses (Hirono et
al., [141]2001; Gugger et al., [142]2012), where as cytohesins, a group
of guanine nucleotide exchange factors (GEFs), also forms a
postsynaptic complex with mGluR1 (Kitano et al., [143]2003).
Differential expression of transcription factors between cell-types
We queried 1266 out of 1675 non-redundant TFs (∼75.5%) from the Riken
Transcription Factor Database (TFdb; Kanamori et al., [144]2004) in our
expression dataset and found 79 to be differentially expressed between
PCs and S/BCs and during development (Figure [145]5A). Twenty-nine TFs
were highly expressed in PCs, such as Ebf1 (Olf1), Ebf2, Ebf3
(Malgaretti et al., [146]1997; Croci et al., [147]2006), Lhx1 (Zhao et
al., [148]2007), FoxP2 (Fujita and Sugihara, [149]2012), and Plagl1
(Zac1; Chung et al., [150]2011). Other PC restricted TFs are novel such
as Fem1c, Sh3d19, Aatf, Ankrd6, Ank1, Otf1 (Pou2f1), Gas7, Nfatc2
(Nfat1), Nptxr (Npr), Gtf2h4, and Bcll2a (Ctip1). NFATs are best
studied in immune system and chondrocytes differentiation (Horsley and
Pavlath, [151]2002) but their role in neuronal differentiation is less
clear. On the other hand at least five TFs appear S/BC specific, Sox1,
Sox2, Klf4, Epas1, and Rorb. The postmitotic expression of Sox1, Sox2,
Myst4, and Klf4 transcripts in S/BC is surprising as they maintain
neuronal progenitor identity and neurogenesis in early development
(Graham et al., [152]2003). However recent evidence suggest that
continued postmitotic expression of Sox1 is necessary for maintaining
cell-fate (Ekonomou et al., [153]2005). We also detected 35 TFs that
had early (P3–P7) expression in PC but later became specifically
enriched in S/BC from P14 to P56; these includeTcfap2b, Btbd11, Esrrg,
Klf12, and Egr1 which showed high level and tightly restricted
expression.
Figure 5.
[154]Figure 5
[155]Open in a new tab
Cell-type specific expression of TFs. (A) Phasic expression of TFs
between PC and S/BCs. (B,C) Identification of cell-type specific TFs by
promoter scanning of co-regulated transcripts during development. (B)
Observed cell-type specific co-regulated transcripts at each time
points in both PC and S/BCs. (C) Promoter scanning for binding motifs
of temporally co-regulated transcripts correctly identifies S/BC and PC
TFs that match with cell-type specific microarray data.
We noticed co-expressed transcripts in virtually all-developmental
stages of PC and S/BC, suggesting the possibility that these
transcripts may be regulated by common TFs (Figure [156]5B). We
reasoned that by scanning promoters of co-expressed genes for known TF
binding motifs we could identify the relevant TFs in PCs and S/BCs. We
investigated upstream (−450 bp) and immediate downstream (+50 bp)
regions relative to the transcription start site for statistically
significant enrichment of TF binding sites using PSCAN (Zambelli et
al., [157]2009) that looks for over-represented binding motifs from
public JASPAR database (Sandelin et al., [158]2004; Bryne et al.,
[159]2008). In S/BCs, we found 13 TFs at P14 and 6 TFs at P56. In PCs,
we found two TF at P14 and four TFs at P56 (Table [160]1). We were
limited by motif database (130 TF for mouse), which is a fraction of
the currently known 1675 mouse TFs from the Riken-TFdb. Cross
comparison against our microarray data revealed that seven TFs
predicted by PSCAN were also detected in microarray to be S/BC
specific:Egr1 at both P14 and 56; Elk4 at P14; Zfx, Sp1, Klf4, and
Tcfap2a at P56 (Figure [161]5C). In PCs, Gabpa expression was in
agreement with P56 microarray data.
Table 1.
PSCAN results for PC and S/BC showing TF enriched among the
co-expressed transcripts at P14 and P56 time points.
Cell-type and age TF name Z-score P-value Bonferonni P-value
PCs at P14 MIZF 5.65792 6.11E−09 7.94E−07
HIF1A:ARNT 4.79104 7.31E−07 9.50E−05
PCs at P56 ELK1 8.90068 1.37E−19 1.79E−17
GABPA 8.77248 4.42E−19 5.74E−17
MIZF 5.97238 8.54E−10 1.11E−07
ARNT:AHR 4.52425 2.61E−06 3.40E−04
S/BCs at P14 EGR1 12.8264 2.19E−44 2.85E−42
KLF4 11.5272 9.02E−36 1.17E−33
NFKB1 8.13968 4.59E−19 5.96E−17
PAX5 7.95113 2.31E−18 3.00E−16
CTCF 7.437 2.19E−16 2.85E−14
E2F1 7.29049 8.92E−16 1.16E−13
ARNT 6.35681 1.29E−12 1.68E−10
NHLH1 6.05526 1.98E−11 2.57E−09
TCFCP2L1 5.85053 8.88E−11 1.15E−08
RREB1 4.72247 1.15E−07 1.49E−05
MAFB 4.37757 8.97E−07 1.17E−04
MYF 4.24664 1.78E−06 2.32E−04
ELK4 4.21708 2.00E−06 2.59E−04
S/BCs at P56 KLF4 5.64019 6.24E−09 8.12E−07
SP1 5.46909 1.77E−08 2.30E−06
HIF1A:ARNT 4.79591 6.37E−07 8.28E−05
EGR1 4.28222 7.55E−06 9.81E−04
TFAP2A 4.60855 1.73E−06 2.25E−04
ZFX 4.44252 3.70E−06 4.82E−04
[162]Open in a new tab
Novel subtype specific transcripts in stellate/basket cells
We detected not only genes previously shown to be expressed in PCs
and/or S/BCs (Figure [163]6A), but also novel S/BC specific
transcripts. For example, while the Kit ligand (Kit-l) was known to be
expressed in PCs, we found that the transcript for its receptor, the
Kit oncogene (c-Kit), was restricted to S/BCs (Morii et al.,
[164]1992), suggesting the involvement of this ligand receptor system
in PC-S/BC interactions. In S/BCs, we found 38 new transcripts, of
which 19 are not expressed in PCs (Figure [165]6A). Eight of these were
validated (Figures [166]6B–J) by in situ hybridization. These appear to
be primarily expressed in basket cells and could potentially serve as
specific markers. Acam and Arhgap21 signal was not appreciably
down-regulated at P56 in S/BC compared to earlier stages which could be
due to the chromogenic detection method that causes a loss in dynamic
range.
Figure 6.
[167]Figure 6
[168]Open in a new tab
Expression of PC and S/BCs specific transcripts. (A) Heatmap showing
novel cell-type specific transcripts and their developmental profiles.
(B) Schematic diagram of the cerebellar and nissl stained cerebellum
section indicating respective layers. (C–J) In situ hybridization
panels of the novel S/BC transcripts validating the microarray data.
Each panel shows two time points P21, P56, and control probes.
Abbreviations: gcl, granule cell layer; ml, molecular layer; pcl,
Purkinje cell layer; wm, white matter; M, medial; L, lateral.
Discussion
Previous studies have assayed gene expression of whole cerebellum using
tissue homogenates (Kagami and Furuichi, [169]2001; Kanamori et al.,
[170]2004; Sato et al., [171]2008). Due to cellular heterogeneity,
these results are difficult to interpret in the context of cerebellar
circuit organization and development. Individual neuron types are both
the building blocks of neural circuits and the basic units of gene
regulation. Here we purified two major GABAergic cell-types of the
cerebellar cortex and measured the developmental progression in their
transcriptomes from neonatal to mature ages.
Several methods have been used to achieve cell-type-based gene
expression profiling in mice through cell specific expression of
fluorescence reporters (e.g., FACS, manual sorting) or molecular tags
(e.g., TRAP, Ribo-Tag; Okaty et al., [172]2011). The TRAP and Ribo-Tag
methods allow direct immunoprecipitation of cell specific mRNAs from
tissue homogenates using GFP-tagged ribosomal proteins but often
require careful optimization of specificity and pooling of multiple
tissue samples (Heiman et al., [173]2008; Sanz et al., [174]2009).
Manual sorting has the advantage that even a small number or fraction
of fluorescence-labeled neurons among dissociated cells can be visually
identified and purified. Compared with FACS, manual sorting exerts less
physical manipulation of dissociated neurons and allows direct
evaluation of the sorting process by the experimenter. For example,
although there are no specific reporters that differentially label PCs,
BskCs, and StCs, we were able to readily distinguish PCs and S/BCs
simply by their cell sizes using the G42 line that labels both. A
caveat of the cell sorting method, whether FACS or manual, is that
certain aspect of gene expression might be altered by the dissociation
procedure (see Okaty et al., [175]2011 for a discussion).
A major motivation in analyzing the development progression of gene
expression in PCs and S/BCs is that StC and BskC interneurons establish
GABAergic synaptic transmission with PC cells during the postnatal
period. In this context our study may reveal coordinated expression
programs in postsynaptic PCs and presynaptic S/BCs that direct the
formation and transmission of these inhibitory synapses. We noted
developmental co-expression of members of cadherins and protocadherins
in PC and S/BC. Cdh10 and Cdh22 showed strikingly similar expression
pattern in PC and S/BC. Cdh10 expression rose gradually, peaked at P28,
and declined at P35. Cdh22 has two expression peaks: the early peak
(P3–P14) may correlate to the initial interactions between S/BC and PCs
and the late peak(P28) might contribute to GABAergic synapse maturation
and maintenance (Figure [176]4B2). The involvement of Pcdhα in Reelin
signaling is well-known (Senzaki et al., [177]1999). Here we found that
members of non-clustered Pcdh (Pcdhδ) family (Pcdh20, Pcdh7, and Pcdh9)
are comparably co-expressed in PC and S/BC (Figure [178]4B2). Pcdhs are
proteolytically cleaved by disintegrins, such as ADAMs with
metalloprotease activity (Reiss et al., [179]2006). We found that PCs
preferentially expressed Adam23, which might regulate co-expressed Pcdh
and modulate cell–cell adhesion. Finally, the CAMs Cntnap2 (Caspr2) is
repressed by the TF Foxp2 (Fujita and Sugihara, [180]2012) through
direct binding at the Cntnap2 promoter (Vernes et al., [181]2008). We
found that the rise ofFoxp2expression at P14 in PCs (Figure [182]5A)
precisely correlated to the abrupt drop in Cntnap2 transcript (Figure
[183]4B1).
During the characteristic shift of GABA-A receptor subunit from α2 to
α1 in PCs between P7 and 14, we found an upregulation in the Slc32a1
(VGAT) and Slc6a1 (GAT1) in the S/BCs (Figures [184]4A1 and [185]A2 in
Appendix), suggesting coordinated regulation of post- and pre-synaptic
components, respectively. On the other hand, PCs are also postsynaptic
to glutamatergic parallel fibers and climbing fibers; they are also
presynaptic to multiple neurons (especially those in the deep
cerebellar nuclei). In addition, S/BCs are postsynaptic to
glutamatergic and GABAergic inputs. Therefore, gene expression profiles
in PCs and S/BCs may contain multiple pathways, each contributing to
distinct pre- and post-synaptic development. Identifying the molecules
and relevant pathways in PCs and S/BCs profiles that contribute to
S/BC → PC GABAergic synapses would require careful validation of their
subcellular localization and function. Even though this study treats
S/BC as a group, there are known differences in morphology and
subcellular synapse targeting between BskC-PC and StC-PC, which could
be molecularly distinct. However such unique molecular correlates can
only be discovered upon sorting the two cell-types to purity.
Several studies have shown that genes that are co-regulated tend to
localize as clusters along the chromosome (Boutanaev et al., [186]2002;
Blanco et al., [187]2008). Here we provide evidence that, in developing
cerebellar GABAergic neurons, groups of co-expressed genes also tend
co-localize as clusters in the genome, ranging from two genes to large
stretches of 13 genes. We interpret these naturally arising
co-transcribing gene clusters during key developmental stages to be the
transcript level readout of epigenetic control. Interestingly, a
significant fraction of such clusters are co-expressed in both PCs and
S/BCs, although at different postnatal ages. In addition, many of the
clusters appear to be co-regulated during development: for example, in
PCs they are turned on in the first postnatal week, turned off in
subsequent weeks, and turned back on in the mature age (P56). Such
developmental co-expression in different cell-types suggest that
certain groups of genes which localize as genomic clusters may
constitute “chromosome regulatory modules” that encode components of
functional pathways; they are deployed in different cell-types at the
appropriate stage to support the relevant neural developmental events.
In this context the discovery of abundant levels of 5-methylcytosine,
an unusual nuclear DNA base in PC (Kriaucionis and Heintz, [188]2009)
and its correlation with gene expression (Ficz et al., [189]2011) is
intriguing and need to be further explored by informatics analysis and
by experimental validation.
In PCs a sharp change in expression profile was noted from P7 to P14,
which might reflect the abrupt changes in morphology, positioning,
physiology, and connectivity of the PCs during this period (Dusart and
Flamant, [190]2012). Pathway terms such as “LTD,” “LTP,” “VEGF
signaling,” “JAK/STAT signaling,” “mTOR signaling,” and “apoptosis” are
all sharply upregulated at P14 correlating to the elimination of PCs,
rapid dendritic maturation, beginning of inhibitory synaptogenesis and
the increased demand for local protein synthesis associated with LTP
and LTD events (Figure [191]2A). Concomitantly there is a decline in
pathway terms “chemokine signaling,” “Hedgehog signaling,” and “axon
guidance” (Figures [192]2A,B).
Although numerous PC specific genes have been identified, few such
genes have been found for BskC and StCs. In our study we grouped StC
and BskC due the lack of molecular markers but our microarray and in
situ data suggests candidate genes that can be exploited for such
purpose. Our results on S/BC specific transcripts present opportunities
for discovering subtype specific genes for generating Cre-driver mice
that can separately target StCs and BskCs, as have been implemented for
cortical interneurons (Taniguchi et al., [193]2011).
Author Contributions
Anirban Paul and Z. Josh Huang designed research; Anirban Paul
performed all experiments; Anirban Paul, Ying Cai, and Gurinder S.
Atwal analyzed data; Anirban Paul and Z. Josh Huang wrote the paper.
Conflict of Interest Statement
The authors declare that the research was conducted in the absence of
any commercial or financial relationships that could be construed as a
potential conflict of interest.
Acknowledgments