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• W4313254454 abstract "•Cell type-specific spatiotemporal co-expression modules are interpreted•Astrocyte, microglia, and endothelial cell associated genes are upregulated in the DS•Neuron and oligodendrocyte-associated genes are downregulated in the DS brain•The most related module with DS was enriched in endothelial cell-associated genes Down syndrome (DS) is the most common genetic cause of intellectual disability and increases the risk of other brain-related dysfunctions, like seizures, early-onset Alzheimer’s disease, and autism. To reveal the molecular profiles of DS-associated brain phenotypes, we performed a meta-data analysis of the developmental DS brain transcriptome at cell type and co-expression module levels. In the DS brain, astrocyte-, microglia-, and endothelial cell-associated genes show upregulated patterns, whereas neuron- and oligodendrocyte-associated genes show downregulated patterns. Weighted gene co-expression network analysis identified cell type-enriched co-expressed gene modules. We present eight representative cell-type modules for neurons, astrocytes, oligodendrocytes, and microglia. We classified the neuron modules into glutamatergic and GABAergic neurons and associated them with detailed subtypes. Cell type modules were interpreted by analyzing spatiotemporal expression patterns, functional annotations, and co-expression networks of the modules. This study provides insight into the mechanisms underlying brain abnormalities in DS and related disorders. Down syndrome (DS) is the most common genetic cause of intellectual disability and increases the risk of other brain-related dysfunctions, like seizures, early-onset Alzheimer’s disease, and autism. To reveal the molecular profiles of DS-associated brain phenotypes, we performed a meta-data analysis of the developmental DS brain transcriptome at cell type and co-expression module levels. In the DS brain, astrocyte-, microglia-, and endothelial cell-associated genes show upregulated patterns, whereas neuron- and oligodendrocyte-associated genes show downregulated patterns. Weighted gene co-expression network analysis identified cell type-enriched co-expressed gene modules. We present eight representative cell-type modules for neurons, astrocytes, oligodendrocytes, and microglia. We classified the neuron modules into glutamatergic and GABAergic neurons and associated them with detailed subtypes. Cell type modules were interpreted by analyzing spatiotemporal expression patterns, functional annotations, and co-expression networks of the modules. This study provides insight into the mechanisms underlying brain abnormalities in DS and related disorders. Down syndrome (DS) is the most common genetic disorder causing intellectual disability; it occurs in approximately 1 per 800−1,200 live births.1Presson A.P. Partyka G. Jensen K.M. Devine O.J. Rasmussen S.A. McCabe L.L. McCabe E.R.B. Current estimate of Down Syndrome population prevalence in the United States.J. Pediatr. 2013; 163: 1163-1168https://doi.org/10.1016/j.jpeds.2013.06.013Abstract Full Text Full Text PDF PubMed Scopus (260) Google Scholar,2de Graaf G. Buckley F. Skotko B.G. Estimates of the live births, natural losses, and elective terminations with Down syndrome in the United States.Am. J. Med. Genet. 2015; 167A: 756-767https://doi.org/10.1002/ajmg.a.37001Crossref PubMed Scopus (148) Google Scholar DS results from trisomy of human chromosome 21 (HSA21) and affects a wide range of phenotypes in many organ systems. Representative characteristics of DS include physical appearance, neurological symptoms, heart disease, cancer, and gastrointestinal problems.3Agarwal Gupta N. Kabra M. Diagnosis and management of Down syndrome.Indian J. Pediatr. 2014; 81: 560-567https://doi.org/10.1007/s12098-013-1249-7Crossref PubMed Scopus (30) Google Scholar,4Asim A. Kumar A. Muthuswamy S. Jain S. Agarwal S. Down syndrome: an insight of the disease.J. Biomed. Sci. 2015; 22: 41https://doi.org/10.1186/s12929-015-0138-yCrossref PubMed Scopus (150) Google Scholar Among them, intellectual disability is the most common phenotype in individuals with DS, implying that almost all individuals with DS have abnormal brain phenotypes. Because of the sustained influence of the extra copy of HSA21, the prenatal brain with DS develops abnormally.5Haydar T.F. Reeves R.H. Trisomy 21 and early brain development.Trends Neurosci. 2012; 35: 81-91https://doi.org/10.1016/j.tins.2011.11.001Abstract Full Text Full Text PDF PubMed Scopus (149) Google Scholar Its structural characteristics, such as the size of several brain regions, their connectivity, and the number or morphology of specific cell populations, differ from those of the normal brain.6Dierssen M. Down syndrome: the brain in trisomic mode.Nat. Rev. Neurosci. 2012; 13: 844-858https://doi.org/10.1038/nrn3314Crossref PubMed Scopus (182) Google Scholar These developmental and structural differences and associated changes in gene expression patterns caused by a third copy of HSA21 affect various functions of the brain, leading to many brain-related symptoms. Reports have shown that individuals with DS have a higher incidence of seizures than euploid individuals and are more likely to develop early-onset Alzheimer’s disease in their 40s.7Malt E.A. Dahl R.C. Haugsand T.M. Ulvestad I.H. Emilsen N.M. Hansen B. Cardenas Y.E.G. Skøld R.O. Thorsen A.T.B. Davidsen E.M.M. Health and disease in adults with Down syndrome.Tidsskr. Nor. Laegeforen. 2013; 133: 290-294https://doi.org/10.4045/tidsskr.12.0390Crossref PubMed Scopus (80) Google Scholar In addition, associations between DS and psychiatric disorders, particularly autism spectrum disorders, have been reported.8DiGuiseppi C. Hepburn S. Davis J.M. Fidler D.J. Hartway S. Lee N.R. Miller L. Ruttenber M. Robinson C. Screening for autism spectrum disorders in children with Down syndrome: population prevalence and screening test characteristics.J. Dev. Behav. Pediatr. 2010; 31: 181-191https://doi.org/10.1097/DBP.0b013e3181d5aa6dCrossref PubMed Scopus (150) Google Scholar,9Santoro J.D. Pagarkar D. Chu D.T. Rosso M. Paulsen K.C. Levitt P. Rafii M.S. Neurologic complications of Down syndrome: a systematic review.J. Neurol. 2021; 268: 4495-4509https://doi.org/10.1007/s00415-020-10179-wCrossref PubMed Scopus (13) Google Scholar,10Richards C. Jones C. Groves L. Moss J. Oliver C. Prevalence of autism spectrum disorder phenomenology in genetic disorders: a systematic review and meta-analysis.Lancet Psychiatr. 2015; 2: 909-916https://doi.org/10.1016/S2215-0366(15)00376-4Abstract Full Text Full Text PDF PubMed Scopus (228) Google Scholar Although trisomy of HSA21 is the definitive underlying cause of DS and its associated symptoms, the mechanisms by which supernumerary HSA21 triggers these symptoms remain unclear. Substantial efforts have been made to identify and elucidate the roles of candidate genes on HSA21, but further work is needed to clarify the genotype−phenotype relationships. With the development of functional genomics, questions about the roles of the additional HSA21-coding and-non-coding sequences on the whole transcriptome have arisen.6Dierssen M. Down syndrome: the brain in trisomic mode.Nat. Rev. Neurosci. 2012; 13: 844-858https://doi.org/10.1038/nrn3314Crossref PubMed Scopus (182) Google Scholar In a previous study that characterized gene expression in varied regions of postmortem human brains of DS and euploid controls ranging in age from 14 weeks post-conception to 42 years old, approximately 5% of differentially expressed genes (DEGs) were on HSA21, and the rest were on the other chromosomes.11Olmos-Serrano J.L. Kang H.J. Tyler W.A. Silbereis J.C. Cheng F. Zhu Y. Pletikos M. Jankovic-Rapan L. Cramer N.P. Galdzicki Z. et al.Down syndrome developmental brain transcriptome reveals defective oligodendrocyte differentiation and myelination.Neuron. 2016; 89: 1208-1222https://doi.org/10.1016/j.neuron.2016.01.042Abstract Full Text Full Text PDF PubMed Scopus (139) Google Scholar Therefore, to decipher the exact mechanisms of DS phenotypes, an investigation of the integrated features of gene expression in all chromosomes is required. Tissue transcriptome data consist of aggregate gene expression data from heterogeneous cell types; nevertheless, the gene expression signatures are quite different across various cell types.12Darmanis S. Sloan S.A. Zhang Y. Enge M. Caneda C. Shuer L.M. Hayden Gephart M.G. Barres B.A. Quake S.R. A survey of human brain transcriptome diversity at the single cell level.Proc. Natl. Acad. Sci. USA. 2015; 112: 7285-7290https://doi.org/10.1073/pnas.1507125112Crossref PubMed Scopus (767) Google Scholar,13Lake B.B. Chen S. Sos B.C. Fan J. Kaeser G.E. Yung Y.C. Duong T.E. Gao D. Chun J. Kharchenko P.V. Zhang K. Integrative single-cell analysis of transcriptional and epigenetic states in the human adult brain.Nat. Biotechnol. 2018; 36: 70-80https://doi.org/10.1038/nbt.4038Crossref PubMed Scopus (471) Google Scholar Most biological phenomena, including brain development, function, and disease onset, are defined by the complex interactions between different cell types.14Thion M.S. Ginhoux F. Garel S. Microglia and early brain development: an intimate journey.Science. 2018; 362: 185-189https://doi.org/10.1126/science.aat0474Crossref PubMed Scopus (184) Google Scholar,15Allen N.J. Lyons D.A. Glia as architects of central nervous system formation and function.Science. 2018; 362: 181-185https://doi.org/10.1126/science.aat0473Crossref PubMed Scopus (355) Google Scholar Therefore, when intact tissue transcriptome data is used to investigate the biological mechanisms, there is a need to establish a method for clarifying the relevant cell types of differentially expressed transcripts. Previous co-expression network analysis of DS, which is based on the human brain transcriptome, showed the important contributions of oligodendrocyte-lineage cells.11Olmos-Serrano J.L. Kang H.J. Tyler W.A. Silbereis J.C. Cheng F. Zhu Y. Pletikos M. Jankovic-Rapan L. Cramer N.P. Galdzicki Z. et al.Down syndrome developmental brain transcriptome reveals defective oligodendrocyte differentiation and myelination.Neuron. 2016; 89: 1208-1222https://doi.org/10.1016/j.neuron.2016.01.042Abstract Full Text Full Text PDF PubMed Scopus (139) Google Scholar Our aim in this study was to explore specific cell types associated with the abnormal brain development of DS. We first performed a co-expression network analysis to investigate the network of genes on all chromosomes across brain development without bias, and genes with spatiotemporally similar expression patterns were grouped together into gene modules. Cell-type enrichment analysis on the gene co-expression modules made it possible to identify gene modules related to specific brain cell types. The spatiotemporal expression patterns, associated cell types, and functional annotations of co-expression modules provided insights into the role of specific cell types in biological processes or mechanisms related to DS. In this study, we used DS human brain transcriptome data, GSE59630,11Olmos-Serrano J.L. Kang H.J. Tyler W.A. Silbereis J.C. Cheng F. Zhu Y. Pletikos M. Jankovic-Rapan L. Cramer N.P. Galdzicki Z. et al.Down syndrome developmental brain transcriptome reveals defective oligodendrocyte differentiation and myelination.Neuron. 2016; 89: 1208-1222https://doi.org/10.1016/j.neuron.2016.01.042Abstract Full Text Full Text PDF PubMed Scopus (139) Google Scholar from the Gene Expression Omnibus (GEO) repository. These data include DS and matched control samples from various brain regions, spanning many different developmental time points (Table S1). DEGs of these data were identified in the dorsolateral prefrontal cortex (DFC) and cerebellar cortex (CBC) in a previous study using the paired t-test (false discovery rate (FDR)-adjusted p-value <0.1).11Olmos-Serrano J.L. Kang H.J. Tyler W.A. Silbereis J.C. Cheng F. Zhu Y. Pletikos M. Jankovic-Rapan L. Cramer N.P. Galdzicki Z. et al.Down syndrome developmental brain transcriptome reveals defective oligodendrocyte differentiation and myelination.Neuron. 2016; 89: 1208-1222https://doi.org/10.1016/j.neuron.2016.01.042Abstract Full Text Full Text PDF PubMed Scopus (139) Google Scholar We focused on these two regions because they have the widest range of developmental periods and are associated with cognition and motor coordination in DS phenotypes.16Lott I.T. Neurological phenotypes for Down syndrome across the life span.Prog. Brain Res. 2012; 197: 101-121https://doi.org/10.1016/B978-0-444-54299-1.00006-6Crossref PubMed Scopus (164) Google Scholar,17Krinsky-McHale S.J. Silverman W. Gordon J. Devenny D.A. Oley N. Abramov I. Vision deficits in adults with Down syndrome.J. Appl. Res. Intellect. Disabil. 2014; 27: 247-263https://doi.org/10.1111/jar.12062Crossref PubMed Scopus (36) Google Scholar Cell-type enrichment analysis on DEGs was conducted using published cell type-enriched gene lists.18Allen M. Wang X. Burgess J.D. Watzlawik J. Serie D.J. Younkin C.S. Nguyen T. Malphrus K.G. Lincoln S. Carrasquillo M.M. et al.Conserved brain myelination networks are altered in Alzheimer's and other neurodegenerative diseases.Alzheimers Dement. 2018; 14: 352-366https://doi.org/10.1016/j.jalz.2017.09.012Crossref PubMed Scopus (73) Google Scholar We divided neuron-enriched genes into glutamatergic neuron- and GABAergic neuron-enriched genes and their subtypes (Table S2). This was based on the overlaps between cell type-enriched genes18Allen M. Wang X. Burgess J.D. Watzlawik J. Serie D.J. Younkin C.S. Nguyen T. Malphrus K.G. Lincoln S. Carrasquillo M.M. et al.Conserved brain myelination networks are altered in Alzheimer's and other neurodegenerative diseases.Alzheimers Dement. 2018; 14: 352-366https://doi.org/10.1016/j.jalz.2017.09.012Crossref PubMed Scopus (73) Google Scholar and genes that were co-expressed with marker genes of glutamatergic or GABAergic neurons and their subtypes.19Kang H.J. Kawasawa Y.I. Cheng F. Zhu Y. Xu X. Li M. Sousa A.M.M. Pletikos M. Meyer K.A. Sedmak G. et al.Spatio-temporal transcriptome of the human brain.Nature. 2011; 478: 483-489https://doi.org/10.1038/nature10523Crossref PubMed Scopus (1345) Google Scholar DEGs in the CBC were significantly enriched with astrocyte-enriched genes (Benjamini−Hochberg-adjusted (BHA) p = 1.8 × 10−14). Although the DEGs in the DFC were not significantly enriched with any cell type-enriched genes, the most enriched cell type was endothelial cell (BHA p = 3.0 × 10−1) (Figure S1). To determine the differential expression patterns of cell type-enriched genes (Table S2), we calculated the expression differences of these genes in each region at different developmental stages using a sliding window approach. Generally, genes associated with astrocyte, microglia, and endothelial cells were upregulated in DS, whereas oligodendrocyte- and neuron-enriched genes were downregulated (Figure 1). Expression differences usually increased with age, except for the microglia- and endothelial cell-enriched genes in the CBC data. Microglia- and endothelial cell-enriched genes were gradually more expressed in the DFC of DS than in control samples; however, this pattern was reversed and the expression differences gradually reduced with age in the CBC. Furthermore, microglia-enriched genes were less expressed in the CBC of DS in the last sliding window. Overall, glutamatergic and GABAergic neuron-enriched genes showed similar patterns. However, in the fetal and infancy stages (periods 5–9; human brain developmental periods were previously described in Kang et al.19Kang H.J. Kawasawa Y.I. Cheng F. Zhu Y. Xu X. Li M. Sousa A.M.M. Pletikos M. Meyer K.A. Sedmak G. et al.Spatio-temporal transcriptome of the human brain.Nature. 2011; 478: 483-489https://doi.org/10.1038/nature10523Crossref PubMed Scopus (1345) Google Scholar), the expression patterns of these neuron types were dissimilar. Glutamatergic neuron-enriched genes were similarly expressed in DS and control DFC, but GABAergic neuron-enriched genes were less expressed in the DS DFC than in the control DFC in this period. Genes enriched in subtypes of glutamatergic and GABAergic neurons showed similar differential expression patterns to whole glutamatergic and GABAergic neuronal genes (Figures S2 and S3). We performed WGCNA20Zhang B. Horvath S. A general framework for weighted gene co-expression network analysis.Stat. Appl. Genet. Mol. Biol. 2005; 4: Article17https://doi.org/10.2202/1544-6115.1128Crossref PubMed Scopus (3531) Google Scholar to compare and analyze the brain transcriptomes of DS individuals and matched controls at the system level from an unbiased perspective. Module detection by gene clustering resulted in 57 co-expression gene modules. To establish cleaner modules, we allocated genes with a module membership (kME) value >0.7 in each module. This gene list in each module was used in subsequent analyses (Table S3). We calculated module−trait relationships using the module eigengene, which is the first principal component of each module,21Langfelder P. Horvath S. Eigengene networks for studying the relationships between co-expression modules.BMC Syst. Biol. 2007; 1: 54https://doi.org/10.1186/1752-0509-1-54Crossref PubMed Scopus (579) Google Scholar and filtered out some modules that were more correlated with other confounding factors (RIN, PMI, race, and sex) than main factors (brain region, developmental stage, and disease status) (Table S4). Finally, we obtained 43 modules significantly related to specific traits, like brain region, developmental stage, and disease status (Table S4). Cell-type enrichment analysis was conducted on all WGCNA modules (Figure 2A ) using the cell type-enriched genes (Table S2). Cell type-enriched modules (BHA p< 1 × 10−2) consisted of a large proportion of genes that were mainly expressed in those cell types and they did not usually have overlap between different cell types. Although glutamatergic and GABAergic neurons are neuron subclasses, enriched modules for these subtypes did not overlap. Oligodendrocyte- and microglia-enriched genes were exclusively enriched in one module for each cell type (module(M)14 and M41, respectively). However, there were many enriched modules for astrocytes and neurons. By contrast, endothelial cell-enriched genes were less enriched in modules than other cell type-enriched genes. We performed cell-type enrichment analysis using the genes of glutamatergic and GABAergic neuron subtypes (Figure 2B). M21 was enriched for layers (L) 2–4 and L6 glutamatergic neuronal genes. M24 and M26 were enriched for L2−4 and L4 glutamatergic neuronal genes. M24 was more enriched for L2−4 glutamatergic neuronal genes than for L4 glutamatergic neuronal genes; however, the opposite was the case in M26. L1 neuronal genes were less enriched in modules than other glutamatergic neuron subtypes but were significantly enriched in M17 and M18. M23 was mostly enriched for genes correlated with CALB1 and NOS1. Genes correlated with the pan-GABAergic markers, GAD1 and GAD2, and PVALB were mainly enriched in M32. Furthermore, M32 was enriched for genes correlated with CALB1 and NOS1. No module was significantly enriched for genes correlated with CALB2. Genes correlated with CCK and VIP were specifically enriched in M42. Next, we chose cell type-enriched modules for each cell type (astrocytes: M5 and M29; oligodendrocytes: M14; microglia: M41; glutamatergic neurons: M24 and M26; GABAergic neurons: M32 and M42) and characterized these modules. First, we identified the expression patterns of genes in cell type-enriched modules across developmental stages, brain regions, and disease statuses (Figures 3, 4, 5, and 6). Genes in the astrocyte modules, M5 and M29, were expressed at low levels during the fetal stages and were upregulated in DS brains, especially at later stages (Figure 3). M5 genes were expressed at similar levels for the DFC and CBC (Figures 3A and 3C), whereas M29 genes were more expressed in the DFC than in the CBC (Figures 3B and 3D). The expression of genes in the oligodendrocyte module, M14, gradually increased with age and was generally downregulated in DS (Figures 4A and 4C). These expression differences were more remarkable in the DFC than in the CBC. The microglia module (M41) genes were more expressed in postnatal DFC than fetal DFC and were specifically upregulated in the later stages of DS DFC (Figures 4B and 4D).Figure 4Gene expression patterns of oligodendrocyte and microglia modulesShow full caption(A–D) Gene expression patterns of oligodendrocyte and microglia modules (M14 and M41, respectively) are visualized in a line graph of module eigengenes (A and B) and a gene-expression heatmap (C and D). M14 genes became gradually more expressed with age and were downregulated in DS, especially in the DFC. M41 genes were highly expressed in the older DFC of DS.View Large Image Figure ViewerDownload Hi-res image Download (PPT)Figure 5Gene expression patterns of glutamatergic neuron modulesShow full caption(A–D) Gene expression patterns of glutamatergic neuron modules (M24 and M26) are visualized in a line graph of module eigengenes (A and B) and a gene expression heatmap (C and D). M24 and M26 genes showed high expression levels during fetal stages, unlike other modules. In addition, they were more expressed in the DFC than in the CBC and were downregulated in DS. M24 was more constrained by developmental stages and disease status than M26, which was more constrained by brain region.View Large Image Figure ViewerDownload Hi-res image Download (PPT)Figure 6Gene expression patterns of GABAergic neuron modulesShow full caption(A–D) Gene expression patterns of GABAergic neuron modules (M32 and M42) are visualized by a line graph of module eigengenes (A and B) and a gene expression heatmap (C and D). M32 and M42 genes were repressed during fetal stages and were downregulated in DS. Genes in M32 showed similar expression patterns between the DFC and CBC, but genes in M42 were substantially repressed in the CBC.View Large Image Figure ViewerDownload Hi-res image Download (PPT) (A–D) Gene expression patterns of oligodendrocyte and microglia modules (M14 and M41, respectively) are visualized in a line graph of module eigengenes (A and B) and a gene-expression heatmap (C and D). M14 genes became gradually more expressed with age and were downregulated in DS, especially in the DFC. M41 genes were highly expressed in the older DFC of DS. (A–D) Gene expression patterns of glutamatergic neuron modules (M24 and M26) are visualized in a line graph of module eigengenes (A and B) and a gene expression heatmap (C and D). M24 and M26 genes showed high expression levels during fetal stages, unlike other modules. In addition, they were more expressed in the DFC than in the CBC and were downregulated in DS. M24 was more constrained by developmental stages and disease status than M26, which was more constrained by brain region. (A–D) Gene expression patterns of GABAergic neuron modules (M32 and M42) are visualized by a line graph of module eigengenes (A and B) and a gene expression heatmap (C and D). M32 and M42 genes were repressed during fetal stages and were downregulated in DS. Genes in M32 showed similar expression patterns between the DFC and CBC, but genes in M42 were substantially repressed in the CBC. Genes of glutamatergic neuron modules (M24 and M26) were more expressed in the DFC than in the CBC and, unexpectedly, were highly expressed during fetal stages. This implies that they are related to development (Figure 5). These genes were downregulated in the brains of DS individuals. M24 genes were more affected by disease status and developmental stage than M26 (Figure 5). Genes in M32, one of the GABAergic modules, were repressed during fetal stages and more expressed during later stages (Figures 6A and 6C). They were downregulated in DS individuals and showed few regional differences. Genes in the other GABAergic module (M42) also showed low expression during the fetal stages (Figures 6B and 6D). With the exception of the prenatal stages, they were consistently expressed across all stages. The expression of M42 genes in the CBC was lower than in the DFC during the fetal stages. These genes showed few expression differences between control and DS brains. All selected cell-type modules showed unique gene expression patterns and, therefore, could affect different phenotypes of DS. To interpret the biological function of these cell-type modules, we conducted gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis (Figures 7 and 8, and Tables S5, S6, S7, S8, S9, S10, S11, and S12). M5 genes were enriched for growth-related terms like “regulation of cell proliferation” (BHA p = 1.1 × 10−3), “heart growth” (BHA p = 7.1 × 10−4), “growth factor binding” (p = 8.5 × 10−4), “fibroblast growth factor binding” (p = 3.0 × 10−4), and “signaling pathways regulating pluripotency of stem cells” (p = 1.3 × 10−2) (Figure 7A and Table S5). They were expected to be expressed outside of cells and be involved in cell–cell interactions, as they were enriched for the terms “extracellular vesicle” (BHA p = 1.6 × 10−3) and “cell junction” (BHA p = 4.7 × 10−2). The other astrocyte module, M29, was enriched for categories related to biomolecule transport and metabolism, such as “nitrogen compound transport” (p = 5.6 × 10−4), “organonitrogen compound catabolic process” (p = 2.4 × 10−4), “carboxylic acid transport” (p = 7.2 × 10−4), “carboxylic acid biosynthetic process” (p = 5.3 × 10−4), and “dicarboxylic acid metabolic process” (p = 3.5 × 10−4) (Figure 7B and Table S6). The GO and the KEGG pathway enrichment analysis results of M14 and M41 were consistent with the cell-type enrichment analysis results (Figures 7C and 7D, and Tables S7 and S8). The oligodendrocyte module, M14, was enriched for “axon ensheathment” (BHA p = 3.1 × 10−8), “myelin sheath” (p = 3.9 × 10−3), and “compact myelin” (p = 4.0 × 10−3) (Figure 7C and Table S7). The microglia module, M41, was enriched for many immune response-related terms, like “immune response” (BHA p = 9.3 × 10−30), “leukocyte activation” (BHA p = 1.5 × 10−17), “inflammatory response” (BHA p = 1.8 × 10−13), and “myeloid leukocyte activation” (BHA p = 1.8 × 10−12) (Figure 7D and Table S8).Figure 8Functional annotation of neuronal cell type-related modulesShow full caption(A–D) Functional enrichment analysis with genes related to GO terms and KEGG pathways on the glutamatergic (A and B) and GABAergic (C and D) neuron modules. The five most significant terms (p< 0.05) are shown in each category. GO: gene ontology, BP: biological process, CC: cellular component, MF: molecular function, KEGG: Kyoto Encyclopedia of Genes and Genomes pathway. M24 and M26 were enriched for neurodevelopment-related terms (“cell projection morphogenesis”, “muscle organ development”, and “growth cone” in M24, and “neuron development”, “neuron projection development”, “central nervous system neuron differentiation”, and “filopodium” in M26). M24 was enriched for a dendrite-related term (“dendritic shaft”), whereas M26 was enriched for axon-related terms (“axon”, “delayed rectifier potassium channel activity”, “ephrin receptor activity”, and “axon guidance”). M32 was enriched for synapse-related terms (“secretion by cell”, “regulation of neurotransmitter levels”, “presynapse”, “syntaxin-1 binding”, and “synaptic vesicle cycle”). M42 was enriched for calcium–calmodulin signaling-related terms (“cAMP metabolic process”, “calmodulin binding”, and “calcium signaling pathway”).View Large Image Figure ViewerDownload Hi-res image Download (PPT) (A–D) Functional enrichment analysis with genes related to GO terms and KEGG pathways on the glutamatergic (A and B) and GABAergic (C and D) neuron modules. The five most significant terms (p< 0.05) are shown in each category. GO: gene ontology, BP: biological process, CC: cellular component, MF: molecular function, KEGG: Kyoto Encyclopedia of Genes and Genomes pathway. M24 and M26 were enriched for neurodevelopment-related terms (“cell projection morphogenesis”, “muscle organ development”, and “growth cone” in M24, and “neuron development”, “neuron projection development”, “central nervous system neuron differentiation”, and “filopodium” in M26). M24 was enriched for a dendrite-related term (“dendritic shaft”), whereas M26 was enriched for axon-related terms (“axon”, “delayed rectifier potassium channel activity”, “ephrin receptor activity”, and “axon guidance”). M32 was enriched for synapse-related terms (“secretion by cell”, “regulation of neurotransmitter levels”, “presynapse”, “syntaxin-1 binding”, and “synaptic vesicle cycle”). M42 was enriched for calcium–calmodulin signaling-related terms (“cAMP metabolic process”, “calmodulin binding”, and “calcium signaling pathway”). All neuron modules were enriched for neuron-related terms, consistent with the cell-type enrichment analysis results (Figure 8 and Tables S9, S10, S11, and S12). M24 and M26 were enriched for development-related terms, including “cell projection morphogenesis” (p = 1.3 × 10−4), “muscle organ development” (p = 4.4 × 10−3), and “growth cone” (p = 1.0 × 10−2) in M24 and “neuron develop" @default.
• W4313254454 created "2023-01-06" @default.
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• W4313254454 date "2023-01-01" @default.
• W4313254454 modified "2023-10-14" @default.
• W4313254454 title "Cell type characterization of spatiotemporal gene co-expression modules in Down syndrome brain" @default.
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