Cell type resolved co-expression networks of core clock genes in brain development
Surbhi Sharma, Asgar Hussain Ansari, Soundhar Ramasamy
Author Information

1CSIRInstitute of Genomics and Integrative Biology (IGIB), Mathura Road, New Delhi 110025,India.

2Academyof Scientific and Innovative Research (AcSIR), CSIR Human Resource DevelopmentCentre (CSIR-HRDC), Campus Sector 19, Kamla Nehru Nagar, Ghaziabad, UttarPradesh 201 002, India.


Citation Information
Sharma et al. JoLS, J Life Sci. Vol. 3, No. 1, March 2021:40-58 https://doi.org/f3m7


The circadian clock regulates vital cellular processes by adjusting the physiology of the organism to daily changes in the environment. Rhythmic transcription of core Clock Genes (CGs) and their targets regulate these processes at the cellular level. Circadian clock disruption has been observed in people with neurodegenerative disorders like Alzheimer’s and Parkinson’s. Also, ablation of CGs during development has been shown to affect neurogenesis in both in vivo and in vitro models. Previous studies on the function of CGs in the brain have used knock-out models of a few CGs. However, a complete catalogue of CGs in different cell types of the developing brain is not available and it is also tedious to obtain. Recent advancements in single-cell RNA sequencing (scRNA-seq) have revealed novel cell types and elusive dynamic cell states of the developing brain. In this study, by using publicly available single-cell transcriptome datasets we systematically explored CGs co-expressing networks (CGs-CNs) during embryonic and adult neurogenesis. Our meta-analysis reveals CGs-CNs in human embryonic radial glia, neurons, and other lesser studied non-neuronal cell types of the developing brain.

Keywords: Circadian clock; neurogenesis; single-cell transcriptome; SCENIC; TSCAN.


Self -sustained oscillatory expression of a core set of genes is known to drive circadian rhythms in organisms ranging from fungi and archaebacteria to humans. At the molecular level, these rhythms arise from the waxing and waning of transcription factors which gives rise to rhythmicity in gene expression of their targets. In recent years, several groups have shown the association of key components of the molecular clock with neuronal function and diseases. Specifically, CLOCK protein, a part of the positive arm of the transcription-translation feedback loop has been shown to affect migration of neuronal cells(1,2,3). The reduced expression of CLOCK has been observed in epileptogenic tissues in humans(4). Also, knock-out of BMAL1 the binding partner of CLOCK, in mice has been shown to lower the seizure threshold(5). Circadian disruption has also been observed in major depressive disorder (MDD), as the rhythmicity of CGs like BMAL1, PER1-2-3, NR1D1, BHLHE40-41, and DBP was disrupted in the post-mortem brains derived from MDD patients(6).

The development and function of the brain are maintained in large part by embryonic and adult neurogenesis. During cortical development, heterogeneous populations of neural stem cells are present in the subventricular zone (SVZ) and ventricular zone while differentiating neurons form cortical plate. Previous studies have shown the involvement of CGs in neurogenesis; BMAL1 and PER2 are involved in cell cycle regulation in adult neurogenesis(7). Also, BMAL1 knockout mice exhibit symptoms of aging and perturbed ocular parameters(8). Similarly, conditional mutant mice of Clock or Bmal1 show delayed maturation of inhibitory parvalbumin neurons in the visual cortex(9). The above studies clearly establish the role of CGs in brain development, through genetic manipulation of selected CGs. Systematically extending the above strategies to all CGs is both laborious and time consuming. In the case of the developing human brain, it is further complicated due to ethical reasons. In this study, we analysed scRNA-seq datasets of embryonic and adult neurogenic niches to comprehensively explore the cell type identity



Figure 1: Schematic depicting workflow of (a) pseudotime & (b) co-expression analysis using TSCAN and pySCENIC respectively.

FigureS1. Schematics of TSCAN workflow on mouse embryonic cortex (E15.5) (a) PCA plot on the dataset showing first two PCs. (b) TSCAN MST and its cell state ordering (8,1,2,6,5,7). Cell state 2 and 4 were omitted by TSCAN. (c)  Expression of markers genes by manually including all cell states. (d) Marker gene expression based manual re-ordering of pseudotime (2,4,6,5,7,3,1,8).




and expression dynamics of CGs in neurogenesis using pseudotime analysis. Since most of the CGs function as transcription factors (TFs) with a wide range of genomic targets(10), we also identified their co-expressing networks (CNs). For pseudotime and co-expression analysis, we used TSCAN (pseudo-Time reconstruction in Single-Cell RNA-seq Analysis)(11) and SCENIC (Single-Cell rEgulatory Network Inference and Clustering)(12) respectively, for more description refer to methods section (Figure1, S1). Our analysis reveals CGs-CNs enrichment in distinct cell types of the neurogenic niches thereby providing a framework for future studies exploring clock genes in brain development.


For the list of CGs that form the focus of this study refer to supplementary TableS1. We followed the recommendation of HUGO Gene Nomenclature Committee for gene names. Hence gene names are in upper case (e.g., CLOCK) for humans and the first letter in upper case for mouse (e.g., Clock).

Expression dynamics of CGs-CNs in mouse embryonic cortex E15.5

Embryonic neurogenesis is characterized by the rapid proliferation of neural stem cells. These cells divide symmetrically and expand the pool of neurogenic stem cells during the initial stages of neurogenesis. Later stages of embryonic neurogenesis are characterized by asymmetric divisions to give rise to neurons via intermediate progenitor cells(13). In contrast to embryonic neurogenesis, adult neurogenesis is restricted to specialized regions like SVZ and subgranular zone (SGZ)(14). Apart from giving rise to neurons and glia, embryonic neural stem cells also serve as the source of adult neural stem cells. We intended to analyze the expression of CGs with cell type identity and expression dynamics in mouse embryonic and adult neurogenesis. To study the CGs expression dynamics during embryonic neurogenesis we have used a single-cell transcriptome dataset of mice generated by Yuzwa et. al., which encompass embryonic timepoints: E11.5, E13.5, E15.5 and E17.5 days(15). Using (TSCAN), neural progenitors and differentiating neuron clusters were identified from the above datasets. Pax6 and Tubb3 marker genes were used to identify neural progenitors and differentiating neurons, respectively. Further, these cells were pseudotime aligned with neural progenitors at the start and differentiating neurons at the end. Upon this neurogenesis trajectory aligned by pseudotime, the statistical significance of expression dynamics of CGs was calculated. We applied TSCAN workflow to all the developmental time points (data not shown), only E15.5 showed statistically significant expression dynamics for three CGs – Rorb, Nfil3 and Csnk1e (Figure 2a). Except for Csnk1e, the two CGs expressions were high along the pseudotime associated with differentiated neurons. The lack of expression dynamics of CGs in other three times points (E11.5, E13.5 and E17.5) may be due to high drop-out rates observed in scRNA-Seq or true CGs expression trend, addressing the above issue is beyond the scope of this study.

Applying the co-expression analysis workflow on the E15.5 dataset revealed two gene modules: Hlf(21g) and Rorb(51g) (Figure 2b). Hitzler et al. earlier hypothesized the role of Hlf in neuronal differentiation based on its expression pattern in diverse regions of developing mouse brain(16), and also experimental models of epilepsy show downregulation of Hlf(17). Rorb shows expression in the L4 region of the mouse and human brain(18). It also plays a vital role in the development and organization of the whisker barrel in rodent brain(19). These studies provide hints for the involvement of Hlf and Rorb in brain development. Additionally, our analysis discloses their underlying gene regulatory network (Figure 2c & S3). Out of 21 genes in the Hlf module (Figure 2c), Ntrk2 was previously reported in the GeneMANIA co-expression database.

Figure 2: Expression dynamics of CGs-CNs in mouse embryonic and adult neurogenesis. a&d) Heatmap showing CGs (row) expression over pseudotime ordered cells (column) in neurogenesis trajectory of (a) mouse E15.5 cortex, (d) mouse SVZ), top annotation denotes cell clusters assigned by TSCAN, bottom annotation denotes TSCAN calculated pseudotime in ascending order. In heatmaps, M denoting marker genes, S and NS denoting statistically significant and non-significant pseudotime expression dynamics, respectively. (b&e) Binary activity heatmap of CG modules over pseudotime aligned neurogenesis trajectory of (b) mouse E15.5 cortex (e) mouse SVZ. Top annotation denotes cell clusters assigned by TSCAN, bottom annotation denotes TSCAN calculated pseudotime in ascending order. c&f) Network plot showing CGs and its target genes, the edges of the target genes placed from highest importance score (red) to lowest (yellow). GeneMANIA reported co-expression gene pairs are indicated in blue color.

Figure S2. Heatmap showing CGs (row) expression over pseudotime ordered cells (column) in mouse SGZ, M denoting marker genes, S and NS depicting genes showing statistically significant and non-significant pseudotime analysis.

Figure S3. Rorb co-expression module in mouse embryonic cortex E15.5. Network plot showing Rorb with its target genes. The edges of the target genes with


Expression dynamics of CGs-CNs in the neurogenic niches of adult mouse brain

In the adult brain, neurogenesis occurs in restricted niches: SGZ of the hippocampus and SVZ near the lateral ventricles(20,21). The periodic activation of quiescent neural stem cells (qNSCs) in SGZ/SVZ produces mature neurons migrating to deep layers of dentate gyrus and olfactory bulb respectively, whose homeostasis plays a vital role in learning, memory formation and emotion regulation(22,23). SGZ and SVZ regions are shown to have expressions of CGs(7). Using publicly available scRNA-seq data and pseudotime analysis, we studied the CGs expression dynamics during adult neurogenesis.

For pseudotime ordering of SVZ(24) we used known marker genes like Id3 and Egfr for quiescent and activated NSCs respectively and Dcx for differentiated neurons (Figure 2d). Most of the CGs analyzed showed significant enrichment in the initial stages of pseudotime which corresponds to the transition from quiescent to activated neural stem cells. Though most of the CGs analyzed showed enrichment during the early stages of adult neurogenesis, Csnk1e showed reverse trend of enrichment, with higher expression in the later stages of pseudotime corresponding to the birth of differentiated neurons.

Besides SVZ, we also analyzed SGZ, the other prominent adult neurogenic niche using scRNA-seq data(25). It also showed enrichment of CGs expression during the early stages of neurogenesis (Figure S2), though the expression dynamics were not as evident as in SVZ. Both SVZ and SGZ showed enrichment of Csnk1e during later stages of adult neurogenesis. Our co-expression analysis on SVZ and SGZ, revealed CGs-CNs only in SVZ datasets as none of the predicted CNs of SGZ datasets passed the AUCell scoring threshold. In SVZ, the co-expression analysis revealed two distinct CGs-CNs: Dbp(31g) and Rorb(28g) active during early stages of adult neurogenesis (Figure 2e). The detailed analysis of the Dbp module (Figure 2f) revealed 5 of its target genes (Paip1, Eci1, Ssbp2, Cst3 and Ndrg2) to be documented in the GeneMANIA. Among the above genes, Ndrg2 expression was previously reported in SVZ(26).


Expression dynamics of CGs-CNs in developing human brain

The development of the human brain and maturation lasts upto to 20 years(27), while embryonic and fetal developmental changes range from 0-8 and 8-24 post-conception week (pcw) respectively(28). To identify CGs-CNs in early human brain development, we analyzed the scRNA-seq dataset generated by Nowakowski et al.(29). The dataset includes prefrontal cortex (PFC) and primary visual cortex (V1), with ages ranging from 5 to 37pcw. In total, from 48 samples they broadly identified 11 major cell types. Their dataset is also available as an interactive browser (https://cells.ucsc.edu). We applied co-expression analysis workflow on their dataset and used the cell cluster information provided by authors (Table S3). Our co-expression analysis revealed four CGs-CNs: BHLHE41(193g), BHLHE40(57g). RORB(12g) and NR1D2(14g) (Figure 3a).


Interestingly, the BHLHE41(193g)/BHLHE40(57g) module enriched predominantly in non-neuronal cell types like mural, microglial and truncated radial glia (tRG) cluster (Figure 3a). Few topmost genes co-expressing with BHLHE41 (Figure 3c) included P2RY12, CLEC7A, ATP8B4 and FYB. P2RY12 functions as a receptor for nucleotides which are released in response to CNS(30,31) to assist chemotaxis of microglia to the site of injury. CLEC7A has been shown to be linked with beta amyloid disease progression(32). Genes including CLEC7A, P2RY12 and FYB serve as a marker of microglia and we reveal their co-expression with BHLHE41 in our analysis. In BHLHE40(57g) module (Figure 3d), S100A4 was one of the co-expressing target genes, which was also reported in GeneMANIA. It also acts as a biomarker of glioblastoma(32,33). Among the top connected genes in the same module, we observed HPGDS, which is known to localize to  microglia and upregulated in Alzheimer’s(34). The RORB(12g) module (Figure 3e) showed  enrichment in medial ganglionic eminence-radial glia cluster 1 (MGE-RG1) and excitatory neurons-PFC 1 (EN-PFC1) cluster, the RORB module was also captured in the developing cortex and SVZ dataset of the mouse (Figure S3). Comparison of the target genes of the RORB module in mouse and humans revealed MEF2C-RORB gene pair co-expression in mouse and human embryonic cortical neurogenesis.

Figure3: Expression dynamics of CGs-CNs in developing human brain.

(a) Binary activity heatmap of CGs modules over cell clusters of the developing human brain, cell clusters as annotated by Nowakowski et al. for cluster description refer table S3. (b) Heatmap showing gene expression of CGs over cell clusters, which showed significant enrichment score in the co-expression analysis. Cell clusters as annotated by Nowakoski et al. for cluster description refer table S3. (c, d, e & f) Network plots showing CGs and its targets in the developing human brain, the edges are placed from highest (red) to lowest (yellow) importance score of target genes. GeneMANIA reported co-expression gene pairs are highlighted in blue color. (c) BHLHE41, (d) BHLHE40, (e) RORB and (f) NR1D2 only top15 target genes in the BHLHE40/41 module are shown, target genes with known diurnal expression are underlined. (g) Binary activity heatmap of upstream TFs co-expressing with BHLHE41 over cell clusters of the developing human brain, cell clusters as annotated by Nowakowski et al. for cluster description refer table S3. (h) Heatmap showing gene expression of upstream TFs co-expressing with BHLHE41, which showed significant enrichment score in the co-expression analysis. Cell clusters as annotated by Nowakowski et al. for cluster description refer table S3.

Knockdown of NR1D2 in adult murine neural stem cells has been shown to affect neural differentiation(37). The topmost target genes of the NR1D2 module included SLC35F2, ELF1, PON2, KPNA2 and EEF1B2 (Figure 3f). These genes are involved in vital neuronal functions like SLC35F2 in transport across blood brain barrier(38), ELF1 in neurite growth(39) and PON2 imparts neuroprotection by scavenging superoxides(38,40). The enrichment of four CGs-CNs in diverse cell types of the developing human brain prompted us to look for upstream regulators of CGs. Our search for upstream TFs co-expressing with CGs revealed only one significant upstream module associated with BHLHE41, (Figure 3g). We found four TFs namely TAL1, IRF8, IKZF1 and ZBTB24 which are documented to have a regulatory role in microglia activation in response to injury(41), aging(42) with TAL1 acting as a specific marker of human microglia(43).

Expression dynamics of CGs-CNs during astrocyte genesis

Previously, considered to be the support system of neurons in the brain, glial cells are being recognized as vital players in brain function. By promoting neuronal survival and modulating synapse function these cells actively participate in neuronal signal transduction. In particular, the participation of astrocytes in circadian regulation has been recognized recently(44). The various functions of astrocytes like modulation of intracellular calcium levels or neurotransmitter release occur rhythmically. A study(45) has shown that astrocyte specific genetic deletion of Bmal1 and behavioral disruption of rhythms in mice induces astrocyte activation. This effect is augmented when Bmal1 is knocked out in both neurons and astrocytes. These studies point to an important role for astrocytes in maintaining circadian function in the brain.

We analyzed the total transcriptome of human fetal and adult brain-derived astrocytes(46) . In contrast to neurons, astrocytes showed statistically significant upregulation of CGs namely CLOCK, CRY2, PER3, RORA, RORB, HLF, NR1D1, BHLHE40 and BHLHE41 upon maturation (Figure 4a). To strengthen this observation, we analyzed single-cell transcriptomic data of astrocytes derived from cortical spheroids(47) . Similar to neuronal differentiation analysis, we first constructed pseudotime trajectory of astrocyte differentiation and classified cell clusters using cellular markers of early and late stages of astrocyte development. Overall, we found an increased expression of all CGs except RORB and RORC in differentiated astrocytes (Figure 4b). This was in concordance with the trend seen in total RNA-seq of fetal and adult brain-derived astrocytes.

Figure 4: Expression dynamics of CGs-CNs during astrocyte genesis

a) Heatmap showing CGs expression in fetal and adult brain derived astrocytes profiled by total RNA-seq. TOP2A and AQP4 represent marker genes associated with early and late stages of astrocyte development, respectively. Asterisks indicate the differential expression with statistical significance calculated using DEseq2.b) Heatmap showing CGs (row) expression over pseudotime ordered cells (column) derived from astrocytic spheroid, M denoting marker genes, S and NS depicting genes showing statistically significant and non-significant pseudotime expression dynamics respectively. Top annotation denotes cell clusters assigned by TSCAN, bottom annotation denotes TSCAN calculated pseudotime in ascending order.

c) Binary activity heatmap of CGs modules over pseudotime aligned trajectory of astrocyte genesis. Top annotation denotes cell clusters assigned by TSCAN, bottom annotation denotes TSCAN calculated pseudotime in ascending order. d) Network plot of BHLHE41 module in astrocytes, the edges are placed from highest (red) to lowest (yellow) importance score of target genes. GeneMANIA reported co-expression gene pairs are highlighted in blue color, only top15 target genes are shown

The co-expression analysis of astrocyte scRNA-Seq revealed the BHLHE41 associated module with 97genes to be the prominent module that showed enrichment in the later stages of astrocyte genesis (Figure 4c). BHLHE41 acts as a transcriptional repressor in the circadian regulation of gene expression. Previous studies have reported the association of mutations in BHLHE41 with short sleep timing in both humans and experimental models(48,49). Also, it shows high abundance in astrocytes as compared to neurons(50). Few of the topmost target genes in the BHLHE41 module included CLU, PSAP, BCL6, CRYAB, TPP1, TUBA4A and ADD3 (Figure 4d). The co-expression of a few genes like ITPKB, SSPN and CRYAB have been previously reported in the GeneMANIA. Additionally, we report various novel genes, specifically GFAP, a well-known marker of astrocytes and also shown to be associated with the etiology of Alexander disease(51). We also observed CD44 co-expression with BHLHE41, CD44 acts as a vital adhesion protein during astrocyte development(52) and an increase in CD44 positive astrocytes have been shown in Alzheimer’s brain(53). Our approach highlights the potential regulation of these vital genes involved in astrocyte function by BHLHE41.


We combined pseudotime/co-expression analysis and explored potential co-regulatory modules of CGs in the developing brain. Our workflow reveals CGs-CNs with cell type and cell state identity, a feat not possible from bulk RNA-seq based co-expression analysis. Also, high gene drop-out rates pertaining to scRNA-seq greatly impact the pseudotime analysis of low expressing genes like CGs. Therefore, we looked at co-expression modules that can accurately reflect variations in gene expression trends related to underlying biological processes (neurogenesis).

Our analysis of the murine and human embryonic brain scRNA-seq datasets reveals CGs-CNs of RORB, whose expression and role in cortical development is well studied in murine models. Further, many of the identified co-expression pairs are also reported in GeneMANIA with few of the target genes showing oscillatory expression in the adult brain(54). These validations highlight the biological relevance of the CGs-CNs captured through our analysis.

Many recent studies reveal the regulatory role of non-neuronal cell types in brain physiology. Our analysis also identified four CGs-CNs in the non-neuronal population of the developing human brain. Among the four modules, BHLHE41 strongly co-express with genes having a crucial function in microglia such as P2RY12 and CLEC7A. Further, our co-expression analysis for TFs upstream of CGs reveal BHLHE41 as a target of microglia associated TFs -TAL1, IRF8, IKZF1 and ZBTB24. In addition, astrocytes derived from cortical spheroids show high expression of the BHLHE41 module in mature astrocytes. Above results clearly favour BHLHE41 as a prioritized module for future biological validation in the context of glial biology.

Our meta-analysis uncovers CGs-CNs in rare cell types of the developing brain- radial glia, microglia, and astrocytes. Similar analysis workflow on scRNA-seq datasets on pre-sorted rare neuronal populations, with high sequencing depth in future can reveal more CGs-CNs in the brain. Further, a rapid increase in public availability of human scRNA-Seq dataset can be utilized to construct CGs-CN map of all cell types in human body. Additively, integrating emerging single cell chromatin accessibility maps with scRNA-seq will improve confidence of co-expression analysis in general. We also want to underlie that through this analysis we only prioritized CGs based on pseudotime and co-expression analysis in developing brain cells, understanding their functional consequence need experimental validations. Thus, studies involving genetic perturbation of these CGs in organoid models of human brain development is needed to fully appreciate the intricate role of circadian transcription factors in human brain development. Considering the known dysregulation of clock genes in psychiatric disorders it will also be interesting to extend similar analysis into scRNA-seq datasets derived from diseased conditions.


Pseudotime analysis

TSCAN was used for pseudotime analysis (https://github.com/zji90/TSCAN). Each single-cell gene expression matrix was subjected to TSCAN default workflow (Figure 1a), the workflow included preprocessing, dimensionality reduction followed by clustering. The genes with zero count were removed and scRNA-seq dropouts were handled by clustering genes having similar expression patterns using euclidean distance and complete linkage. The above pre-processing method was applied to all the datasets. Next, PCA was used for dimension reduction followed by clustering, yielding a minimum spanning tree. The identification of clusters was performed using marker genes. p-value and FDR (False discover rate) were calculated on the TSCAN ordered cells. TSCAN assigned cell clusters, were inspected for a panel of marker genes from the publications of the respective datasets, while a single marker gene was used for visualization purpose. If required, TSCAN cluster ordering was further manually corrected to place neural stem cell clusters as the starting point of the pseudotime. The TSCAN employs generalized additive model (GAM) for fitting the individual gene expression dynamics over pseudotime. GAM fitting was compared to null-model by assuming gene expression was constant over pseudotime. P-value was computed over the above comparison using likelihood ratio test and then converted to false discovery rate (FDR) using Benjamini-Hochberg Procedure. An FDR of <0.05, was used as a cut-off to score for genes with statistically significant expression dynamics over calculated pseudotime. The accession number of the datasets are provided in the supplementary table 2.

Co-expression analysis

Two steps in the pySCENIC (V0.10.3)(11)and SCENIC (Single-Cell rEgulatory Network Inference and Clustering)(12) pipeline were implemented in our co-expression analysis. First the co-expressing gene modules were identified using GRNBoost (https://github.com/aertslab/GRNBoost). Input for GRNBoost included a list of annotated transcription factors (https://github.com/aertslab/pySCENIC/tree/master/resources) and scRNA-seq expression matrix. In return GRNBoost outputs adjacency matrix with TFs, target genes and importance score. All the CGs (TFs) and its associated targets genes with importance scores greater than 99th percentile were subsetted as CGs-CNs.

Next AUCell (V1.11.0) was used for scoring the activity of above CGs-CNs. AUCell uses “Area under curve” to score the activity of a given geneset (CGs-CNs) within expressed genes of scRNA-seq dataset. AUCell was implemented using R (4.0.2) using default parameters. The CGs-CNs which crossed the default threshold of AUCell geneset activity parameters are shown as binary CGs-CNs activity heatmap.

ScRNA-seq dataset description

Mouse Cortex

Yuzwa et al. (GSE107122) performed drop-seq of CD1 mouse embryos at E11.5, 13.5, 15.5 and 17.5 stage. For E15.5 embryonic stage, 13 embryos were used and their brains dissected and dissociated for Drop-seq droplet collection. Following droplet collection single-cell libraries were prepared and sequenced on Illumina NextSeq500 platform.

 Mouse Subventricular zone (SVZ)

Babodilla et al (GSE67833) performed microdissection of SVZ from C57BL/6 adult mice. GLAST+ Prominin1+ and PSA-NCAM+ were used as marker to sort neural stem cells and neuroblasts from SVZ region. scRNA-seq libraries were prepared using Smart-seq2 and sequenced on HiSeq2000 platform. Trimmed reads were mapped to the mouse genome (ENSEMBL Release 78) using STAR_2.4.0g followed by read quantification as FPKM.

Mouse Subgranular zone (SGZ)

Shin et al. (GSE71485) used transgenic mice expressing nuclear localized CFP under the control of nestin (Nes-CFPnuc). The CFP containing cells marking quiescent-NSCs and their progenies were sorted under a fluorescent microscope using glass pipette. The single-cell libraries were prepared by SMART protocol and sequenced by HiSeq2500 followed by read mapping and quantification to give TPM matrix.

Developing human fetal cortex

Nowakowski et al. performed stereotaxic based microdissection of brain regions like prefrontal cortex (PFC), primary visual cortex (V1) and MGE, the brain region aged from 5-37pcw. The single-cell suspension was prepared using FluidigmC1 followed by sequencing of single-cell libraries on the Illumina Hiseq2500 platform. The sequencing reads were aligned to GRCh38 and quantified to reveal transcript count as CPM.

Astrocyte spheroid

Astrocyte lineage cells were purified from cortical spheroids derived from human iPSC line by Sloan et al (GSE99951). The immunopanning method was used to isolate astrocytes using HepaCAM as an astrocyte marker. The sequencing libraries were prepared from astrocytes derived using immunopan method at 100, 130, 175 and 450 days of in vitro differentiation. Single-cell libraries were prepared using SMART-seq protocol and sequenced on NextSeq500 sequencing platform. The raw reads were aligned to hg19 human genome followed by transcript quantification as FPKM values.

Total RNA-seq of human astrocytes

Zhang et al. (GSE73721) performed RNA-sequencing of astrocytes derived from juvenile (8-18years old) and adult (21-63 years old) human brain. The astrocytes from the brain tissue were purified in culture using immunopanning. Briefly, single-cell suspension of donor tissues was subjected to dishes coated with antibodies against various cell types, Hepa-CAM was used as a marker for astrocytes. The cDNA libraries from the two samples were sequenced on illumina NextSeq sequencer as 150bp paired end reads. The RNA-seq reads were mapped using TopHat2 to hg19 as the reference genome and expression level was estimated as FPKM.

Acknowledgements The authors acknowledge Dr. Beena Pillai and Dr. Souvik Maiti for their help in writing the manuscript and discussions. We are grateful to them for allowing us to communicate the manuscript. We also acknowledge the high-performance-computing (HPC) facility of CSIR-IGIB.

Author contributionsSR and SS contributed equally. SR and SS conceptualized the work, SR and AHA performed the analysis, SR and SS prepared the manuscript.

Supplementary tables

Table S1. List of core clock genes analyzed in this study.






















Table S2. List of datasets used in the analysis.


Accession number

RNA-seq platform

Single-cell platform

Enrichment of cell types

Mouse embryonic cortex (E15.5)


Yuzwa et al. 2017




Sub Ventricular Zone (SVZ)


Llorens-Bobadilla et al. 2015



FACS sorting

Sub Granular Zone (SGZ)


Shin et al. 2015




Astrocyte bulk RNA-seq


Ye Zhang et al. 2016




Astrocyte spheroid scRNA-seq


Sloan et al. 2017




Developing human brain cortex


dbGaP: phs000989.v3

Nowakowski et al. 2017



Stereotaxic microdissection


TableS3. Identities of cell clusters in developing human brain (derived from Nowakowski et al. 2017).

Cluster Name

Cluster Interpretation




CGE/LGE-derived inhibitory neurons


CGE/LGE-derived inhibitory neurons




Dividing Intermediate Progenitor Cells RG-like


dividing MGE Progenitors


Dividing Radial Glia (G2/M-phase)


Dividing Radial Glia (S-phase)


Early and Late Born Excitatory Neuron PFC


Early and Late Born Excitatory Neuron PFC


Early and Late Born Excitatory Neuron V1


Early Born Deep Layer/subplate Excitatory Neuron PFC


Early Born Deep Layer/subplate Excitatory Neuron V1






Excitatory Neuron V1 - late born




Intermediate Progenitor Cells EN-like


Intermediate Progenitor Cells EN-like


Intermediate Progenitor Cells EN-like


Intermediate Progenitor Cells RG-like


MGE newborn neurons


MGE newborn neurons


MGE newborn neurons


MGE newborn neurons


MGE newborn neurons


MGE Progenitors


MGE Progenitors


MGE Progenitors


MGE Radial Glia 1


MGE Radial Glia 2


MGE-derived Ctx inhibitory neuron, Cortical Plate-enriched


MGE-derived Ctx inhibitory neuron, Germinal Zone Enriched






Newborn Excitatory Neuron - early born


Newborn Excitatory Neuron - early born


Newborn Excitatory Neuron - late born


Oligodendrocyte progenitor cell


Outer Radial Glia


Striatal neurons


Truncated Radial Glia










Ventricular Radial Glia


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