Functional organization of the transcriptome in human brain
Open accessAbstract
The enormous complexity of the human brain ultimately derives from a finite set of molecular instructions encoded in the human genome. These instructions can be directly studied by exploring the organization of the brain's transcriptome through systematic analysis of gene coexpression relationships. We analyzed gene coexpression relationships in microarray data generated from specific human brain regions and identified modules of coexpressed genes that correspond to neurons, oligodendrocytes, astrocytes and microglia. These modules provide an initial description of the transcriptional programs that distinguish the major cell classes of the human brain and indicate that cell type-specific information can be obtained from whole brain tissue without isolating homogeneous populations of cells. Other modules corresponded to additional cell types, organelles, synaptic function, gender differences and the subventricular neurogenic niche. We found that subventricular zone astrocytes, which are thought to function as neural stem cells in adults, have a distinct gene expression pattern relative to protoplasmic astrocytes. Our findings provide a new foundation for neurogenetic inquiries by revealing a robust and previously unrecognized organization to the human brain transcriptome.
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The hubs of M12 were A2BP1, APBA2, SCAMP5, CNTNAP1, KLC2, CHRM1.
“The hubs of M12, i.e. the genes with the highest rank of M12 membership 8 , were A2BP1, APBA2, SCAMP5, CNTNAP1, KLC2, CHRM1 (Supplementary Data).”
From Transcriptomic Analysis of Autistic Brain Reveals Convergent Molecular Pathology (2011) · cites this paper as [8]
The control brain network showed high similarity with previously described human brain co-expression networks.
“The control brain network showed high similarity with the previously described human brain co-expression networks (Supplementary Table 7), consistent with the existence of robust modules of co-expressed genes related to specific cell types and biological functions 8 .”
From Transcriptomic Analysis of Autistic Brain Reveals Convergent Molecular Pathology (2011) · cites this paper as [8]
M12 showed high overlap with a module of genes involved in synaptic function previously identified as part of the human brain transcriptional network.
“and high overlap with two neuronal modules previously identified as part of the human brain transcriptional network 8 : a PVALB+ interneuron module and a module of genes involved in synaptic function.”
From Transcriptomic Analysis of Autistic Brain Reveals Convergent Molecular Pathology (2011) · cites this paper as [8]
M16 showed significant similarity to a microglial module identified in previous studies of normal human brain.
“showed significant similarity to two modules identified in previous studies of normal human brain 8 : an astrocyte module and a microglial module.”
From Transcriptomic Analysis of Autistic Brain Reveals Convergent Molecular Pathology (2011) · cites this paper as [8]
M16 showed significant similarity to an astrocyte module identified in previous studies of normal human brain.
“showed significant similarity to two modules identified in previous studies of normal human brain 8 : an astrocyte module and a microglial module.”
From Transcriptomic Analysis of Autistic Brain Reveals Convergent Molecular Pathology (2011) · cites this paper as [8]
M12 showed high overlap with a PVALB+ interneuron module previously identified as part of the human brain transcriptional network.
“and high overlap with two neuronal modules previously identified as part of the human brain transcriptional network 8 : a PVALB+ interneuron module and a module of genes involved in synaptic function.”
From Transcriptomic Analysis of Autistic Brain Reveals Convergent Molecular Pathology (2011) · cites this paper as [8]
Gene products involved in an unknown cell type or biological process can be annotated based on their proximity to marker genes of known function.
“Thus, gene products that are involved in an unknown cell type or biological process can be annotated on the basis of their proximity to marker genes of known function (‘guilt by association’)26,33,34.”
From Systems biology and gene networks in neurodevelopmental and neurodegenerative disorders (2015) · cites this paper as [26]
Network analysis can identify multiple levels of molecular organization within the hierarchy of brain region, cell type, organelle and molecular pathways using only transcriptional data, and can enable integration with other information.
“A major advantage of network analysis over DGE analysis is that it can identify multiple levels of molecular organization within the hierarchy of brain region, cell type, organelle and molecular pathways using only transcriptional data, and can thus enable integration with other information, such as known pathway annotations, protein interactions and other molecular profiling data11,12,26,27”
From Systems biology and gene networks in neurodevelopmental and neurodegenerative disorders (2015) · cites this paper as [26]
Network approaches are particularly useful for relating whole-tissue-level changes to high-resolution experiments.
“Network approaches are particularly useful for relating whole-tissue-level changes to data from these high-resolution experiments11–13,26 (FIG. 2a).”
From Systems biology and gene networks in neurodevelopmental and neurodegenerative disorders (2015) · cites this paper as [26]
When evaluating co-expression across brain regions, hubs in modules associated with specific regions, such as the cerebellum, are usually markers for predominant cell types, such as granule cells.
“For example, when evaluating co-expression across brain regions, hubs in modules that are associated with specific regions, such as the cerebellum, are usually markers for predominant cell types, such as granule cells11,12,26,32.”
From Systems biology and gene networks in neurodevelopmental and neurodegenerative disorders (2015) · cites this paper as [26]
The human brain transcriptome has a reproducible structure.
“Given that the human brain transcriptome has a reproducible structure12,26”
From Systems biology and gene networks in neurodevelopmental and neurodegenerative disorders (2015) · cites this paper as [26]
Several modules (M1-M2) identify genes with enriched expression in neurons, based on overlap with neural-cell-type-enriched gene sets from previous studies.
“Several modules identify genes with enriched expression in neurons (M1–M2), based on overlap with neural-cell-type-enriched gene sets identified in previous studies18”
From An anatomically comprehensive atlas of the adult human brain transcriptome (2012) · cites this paper as [18]
Applied to the entire 911 sample set from Brain 1, genes were grouped into well-defined co-expression modules with specific anatomical distributions, consistent with previous studies.
“Applied to the entire 911 sample set from Brain 1, genes were grouped into well-defined co-expression modules with specific anatomical distributions (Fig. 3a, b), consistent with previous studies in brain tissues18, 19.”
From An anatomically comprehensive atlas of the adult human brain transcriptome (2012) · cites this paper as [18]
WGCNA can identify gene expression patterns related to specific cell types such as neurons and glia from heterogeneous samples such as whole human cortex, due to distinct transcriptional profiles and variation in relative proportions.
“This method can identify gene expression patterns related to specific cell types such as neurons and glia from heterogeneous samples such as whole human cortex18, due to the highly distinct transcriptional profiles of these cell types and variation in their relative proportions across samples.”
From An anatomically comprehensive atlas of the adult human brain transcriptome (2012) · cites this paper as [18]
Gene co-expression was organized into modules of distinct cellular and functional categories, consistent with prior brain transcriptome studies.
“Consistent with prior studies of the brain transcriptome15,47–50, we find gene co-expression to be organized into modules of distinct cellular and functional categories (Supplementary data file 7).”
From Gene expression elucidates functional impact of polygenic risk for schizophrenia (2016) · cites this paper as [49]
Gene co-expression is organized into modules of distinct cellular and functional categories.
“Consistent with prior studies of the brain transcriptome15,47–50, we find gene co-expression to be organized into modules of distinct cellular and functional categories (Supplementary data file 7).”
From Gene expression elucidates functional impact of polygenic risk for schizophrenia (2016) · cites this paper as [49]
Protein co-expression analysis is a powerful tool for understanding biological network, pathway, and cell type changes in human tissue.
“Protein co-expression analysis is a powerful tool to understand biological network, pathway, and cell type changes in human tissue3,4.”
Some of these networks appear to represent co-expression in broad cell classes such as telencephalic neurons, oligodendrocytes, astrocytes and vasculature.
“Although the anatomical sampling in this study was not cell type-specific, some of these networks appear to represent co-expression in broad cell classes such as telencephalic neurons, oligodendrocytes, astrocytes and vasculature, as observed previously3, 5.”
From Canonical genetic signatures of the adult human brain (2015) · cites this paper as [5]
Systems-based approaches such as WGCNA can be used to analyze biochemical and cellular changes in brain and are useful to capture the complexity of perturbations in biological networks related to disease.
“Systems-based approaches such as weighted gene co-expression network analysis (WGCNA) can be used to analyze biochemical and cellular changes in brain, and are useful to help capture the complexity of perturbations in biological networks that are related to disease [2–4].”
From Deep proteomic network analysis of Alzheimer’s disease brain reveals alterations in RNA binding proteins and RNA splicing associated with disease (2018) · cites this paper as [4]
Transcriptomes from complex tissues such as human brain may be organized into networks of co-expressed genes that better reflect the biological functions and organization of the tissue.
“Transcriptomes from complex tissues, such as human brain, may be organized into networks of co-expressed genes that better reflect the biological functions and organization of the tissue7–14.”
From Analysis of whole genome-transcriptomic organization in brain to identify genes associated with alcoholism (2019) · cites this paper as [10]
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WGCNA: an R package for weighted correlation network analysis
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“WGCNA has been used to analyze gene expression data from brain cancer [10], yeast cell cycle [13], mouse genetics [14-17], primate brain tissue [18-20], diabetes [21], chronic fatigue patients [22] and plants [23].”
MethodSelf-citationAn anatomically comprehensive atlas of the adult human brain transcriptome
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BackgroundTranscriptomic Analysis of Autistic Brain Reveals Convergent Molecular Pathology
Voineagu, I., Wang, X., Johnston, P., Lowe, J. K., Tian, Y., Horvath, S., Mill, J., Cantor, R. M., Blencowe, B. J., & Geschwind, D. H. (2011) · Nature · doi:10.1038/nature10110 · Cited by 1,874 · see citing works
“We next applied weighted-gene co-expression network analysis (WGCNA) 8; 9 to integrate the expression differences observed between autistic and control cerebral cortex into a higher order, systems level context.”
InfluentialSelf-citationIntegrated Systems Approach Identifies Genetic Nodes and Networks in Late-Onset Alzheimer’s Disease
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“…gene signatures, from the Allen Brain Atlas (http://www.brain-map.org/), were enriched in distinct network modules as previously observed (Oldham et al., 2008): neurons in the synaptic transmission modules (11-fold, p = 3.7 3 10 24), astrocytes in the GABA biosynthesis module (22 fold, p…”
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Transcriptional landscape of the prenatal human brain
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“Modules are likely to represent biological pathways in a way that they are usually coregulated or interact (Oldham et al., 2008).”
Self-citationComprehensive functional genomic resource and integrative model for the human brain
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Self-citationGene expression elucidates functional impact of polygenic risk for schizophrenia
Fromer, M., Roussos, P., Sieberts, S. K., Johnson, J. S., Kavanagh, D. H., Perumal, T. M., Ruderfer, D. M., Oh, E. C., Topol, A., Shah, H. R., et al. (2016) · Nature Neuroscience · doi:10.1038/nn.4399 · Cited by 1,091 · see citing works
“440 441 Consistent with prior studies of the brain transcriptome (27, 67-70), we find gene co442 expression to be organized into modules of distinct cellular and functional categories (data file 443 7).”
Shared molecular neuropathology across major psychiatric disorders parallels polygenic overlap
Gandal, M. J., Haney, J. R., Parikshak, N. N., Leppa, V., Ramaswami, G., Hartl, C., Schork, A. J., Appadurai, V., Buil, A., Werge, T. M., et al. (2018) · Science · doi:10.1126/science.aad6469 · Cited by 1,095 · see citing works
Self-citationIs My Network Module Preserved and Reproducible?
Langfelder, P., Luo, R., Oldham, M. C., & Horvath, S. (2011) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1001057 · Cited by 1,012 · see citing works
“We used microarray data from a recent publication [30]”
BackgroundMethodInfluentialSelf-citationJohnson, E. C. B., Dammer, E. B., Duong, D. M., Ping, L., Zhou, M., Yin, L., Higginbotham, L. A., Guajardo, A., White, B., Troncoso, J. C., et al. (2020) · Nature Medicine · doi:10.1038/s41591-020-0815-6 · Cited by 1,001 · see citing works
Phylogenetic Molecular Ecological Network of Soil Microbial Communities in Response to Elevated CO 2
Zhou, J. Z., Deng, Y., Luo, F., He, Z., & Yang, Y. (2011) · mBio · doi:10.1128/mbio.00122-11 · Cited by 943 · see citing works
“Based on the oftinvoked principle of guilt by association (26), the abundance changes in the microbial populations with strong module memberships are probably driven by the same underlying factors.”
BackgroundCorrelating transcriptional networks to breast cancer survival: a large-scale coexpression analysis
Clarke, C., Madden, S. F., Doolan, P., Aherne, S. T., Joyce, H., O’Driscoll, L., Gallagher, W. M., Hennessy, B. T., Moriarty, M. J., Crown, J. P., et al. (2013) · Carcinogenesis · doi:10.1093/carcin/bgt208 · Cited by 849 · see citing works
Sex-specific transcriptional signatures in human depression
Labonté, B., Engmann, O., Purushothaman, I., Menard, C., Wang, J., Tan, C., Scarpa, J. R., Moy, G., Loh, Y.-H. E., Cahill, M., et al. (2017) · Nature Medicine · doi:10.1038/nm.4386 · Cited by 748 · see citing works
Fried, E. I., Epskamp, S., Nesse, R. M., Tuerlinckx, F., & Borsboom, D. (2015) · Journal of Affective Disorders · doi:10.1016/j.jad.2015.09.005 · Cited by 751 · see citing works
Temporal dynamics and genetic control of transcription in the human prefrontal cortex
Colantuoni, C., Lipska, B. K., Ye, T., Hyde, T. M., Tao, R., Leek, J. T., Colantuoni, E. A., Elkahloun, A. G., Herman, M. M., Weinberger, D. R., et al. (2011) · Nature · doi:10.1038/nature10524 · Cited by 685 · see citing works
Neuroimaging Support for Discrete Neural Correlates of Basic Emotions: A Voxel-based Meta-analysis
Vytal, K., & Hamann, S. (2010) · Journal of Cognitive Neuroscience · doi:10.1162/jocn.2009.21366 · Cited by 680 · see citing works
Hierarchical modularity in human brain functional networks
David Meunier, A. (2009) · Frontiers in Neuroinformatics · doi:10.3389/neuro.11.037.2009 · Cited by 660 · see citing works
“…partition has been shown to reveal important network community structures in a variety of contexts, e.g. the global air transportation network (Guimerà et al., 2005) and gene expression interactomes (Oldham et al., 2008) are two diverse examples of complex systems with topological modularity.”
Canonical genetic signatures of the adult human brain
Hawrylycz, M., Miller, J. A., Menon, V., Feng, D., Dolbeare, T., Guillozet-Bongaarts, A. L., Jegga, A. G., Aronow, B. J., Lee, C.-K., Bernard, A., et al. (2015) · Nature Neuroscience · doi:10.1038/nn.4171 · Cited by 663 · see citing works
“Studies using these methods suggest that, largely because of their nonparametric statistically robust nature, conserved differential expression relationships may be more descriptive of transcriptome organization than absolute magnitude of expression level 3, 5, 11–13 .”
InfluentialEstablishing Cerebral Organoids as Models of Human-Specific Brain Evolution
Pollen, A. A., Bhaduri, A., Andrews, M. G., Nowakowski, T. J., Meyerson, O. S., Mostajo-Radji, M. A., Di Lullo, E., Alvarado, B., Bedolli, M., Dougherty, M. L., et al. (2019) · Cell · doi:10.1016/j.cell.2019.01.017 · Cited by 600 · see citing works
Johnson, M. B., Kawasawa, Y. I., Mason, C. E., Krsnik, Ž., Coppola, G., Bogdanović, D., Geschwind, D. H., Mane, S. M., State, M. W., & Šestan, N. (2009) · Neuron · doi:10.1016/j.neuron.2009.03.027 · Cited by 603 · see citing works
“We therefore performed weighted gene coexpression network analysis to identify groups of coregulated genes, or ‘‘modules,’’ with similar patterns of connectivity (high topological overlap) (Oldham et al., 2008).”
MethodSelf-citationThe modular and integrative functional architecture of the human brain
Bertolero, M. A., Yeo, B. T. T., & D’Esposito, M. (2015) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1510619112 · Cited by 552 · see citing works
“For example, gene expression in the brain is modular; the transcriptomes of human brain regions are robustly organized into modules of coexpressed genes that reflect the underlying cellular composition of brain tissue (8), and the spatial topography of cortex is also strongly reflected in its genetic topography—the cl…”
BackgroundJohnson, E. C. B., Carter, E. K., Dammer, E. B., Duong, D. M., Gerasimov, E. S., Liu, Y., Liu, J., Betarbet, R., Ping, L., Yin, L., et al. (2022) · Nature Neuroscience · doi:10.1038/s41593-021-00999-y · Cited by 561 · see citing works
“We removed outliers detected by network connectivity Z -transformed metric for a sample, using a pre-specified threshold of |Z.k | > 3 standard deviations from the mean Z.k, iteratively until no further detection, as previously described using the ‘SampleNetworks’ R script (version 1.06) 7,40,57 .”
InfluentialStandardizing workflows in imaging transcriptomics with the abagen toolbox
Markello, R. D., Arnatkevičiūtė, A., Poline, J., Fulcher, B. D., Fornito, A., & Mišić, B. (2021) · eLife · doi:10.7554/elife.72129 · Cited by 541 · see citing works
Seyfried, N. T., Dammer, E. B., Swarup, V., Nandakumar, D., Duong, D. M., Yin, L., Deng, Q., Nguyen, T., Hales, C. M., Wingo, T., et al. (2017) · Cell Systems · doi:10.1016/j.cels.2016.11.006 · Cited by 477 · see citing works
“Thus, as seen in transcriptome level network analyses (Miller et al., 2008; Oldham et al., 2008a), certain modules of co-expressed proteins enrich with markers of specific brain cell types, and likely reflect changes in the abundance, activation, and/or changing phenotype(s) of these cells in AD.”
InfluentialSelf-citationDoering, T. A., Crawford, A., Angelosanto, J., Paley, M., Ziegler, C. G. K., & Wherry, E. J. (2012) · Immunity · doi:10.1016/j.immuni.2012.08.021 · Cited by 542 · see citing works
“Outlier arrays were identified and excluded using an interarray correlation threshold (Oldham et al., 2008).”
MethodCortical Evolution: Judge the Brain by Its Cover
Geschwind, D. H., & Rakic, P. (2013) · Neuron · doi:10.1016/j.neuron.2013.10.045 · Cited by 519 · see citing works
“Increased Molecular Complexity in Human Frontal Lobe and Neoteny By organizing genes into coexpression modules, network analysis provides a functional context from which to assess the significance of expression changes and can further help to prioritize individual genes from long lists of differential expressed genes…”
Self-citationAltered proliferation and networks in neural cells derived from idiopathic autistic individuals
Marchetto, M. C., Belinson, H., Tian, Y., Freitas, B. C., Fu, C., Vadodaria, K. C., Beltrao-Braga, P. C., Trujillo, C. A., Mendes, A. P. D., Padmanabhan, K., et al. (2017) · Molecular Psychiatry · doi:10.1038/mp.2016.95 · Cited by 466 · see citing works
Self-citationDivergence of human and mouse brain transcriptome highlights Alzheimer disease pathways
Miller, J. A., Horvath, S., & Geschwind, D. H. (2010) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0914257107 · Cited by 480 · see citing works
“These markerswere comparedwith publishedmouse (20–22) and human (3, 23) marker genes for each cell type, and validated by showing highly significant overlap (P < 10 for each cell type).”
BackgroundMethodResultInfluentialSelf-citationDeng, Y., Zhang, P., Qin, Y., Tu, Q., Yang, Y., He, Z., Schadt, C. W., & Zhou, J. Z. (2015) · Environmental Microbiology · doi:10.1111/1462-2920.12981 · Cited by 466 · see citing works
“To reveal higher order organization of fMENs, the eigengene analysis (Langfelder and Horvath, 2007; Horvath and Dong, 2008; Oldham et al., 2008) was implemented.”
MethodComparison of co-expression measures: mutual information, correlation, and model based indices
Song, L., Langfelder, P., & Horvath, S. (2012) · BMC Bioinformatics · doi:10.1186/1471-2105-13-328 · Cited by 460 · see citing works
“unsigned networks depends on the application; both signed [22] and unsigned [30,61,65] weighted gene networks have been successfully used in gene expression analysis.”
BackgroundSelf-citationSystems biology and gene networks in neurodevelopmental and neurodegenerative disorders
Parikshak, N. N., Gandal, M. J., & Geschwind, D. H. (2015) · Nature Reviews Genetics · doi:10.1038/nrg3934 · Cited by 430 · see citing works
“…organization within the hierarchy of brain region, cell type, organelle and molecular pathways using only transcriptional data, and can thus enable integration with other information, such as known pathway annotations, protein interactions and other molecular profiling data 11,12,26,27 FIG.”
BackgroundInfluentialSelf-citationZeng, H., Shen, E. H., Hohmann, J. G., Oh, S. W., Bernard, A., Royall, J. J., Glattfelder, K. J., Sunkin, S. M., Morris, J. A., Guillozet-Bongaarts, A. L., et al. (2012) · Cell · doi:10.1016/j.cell.2012.02.052 · Cited by 426 · see citing works
Higginbotham, L., Ping, L., Dammer, E. B., Duong, D. M., Zhou, M., Gearing, M., Hurst, C., Glass, J. D., Factor, S. A., Johnson, E. C. B., et al. (2020) · Science Advances · doi:10.1126/sciadv.aaz9360 · Cited by 367 · see citing works
“Due to notable differences in total protein abundance levels, one control sample was determined to be an outlier using statistical methods previously described [10, 17] and was not included in subsequent analyses.”
BackgroundMethodBridging the Gap between Connectome and Transcriptome
Fornito, A., Arnatkevičiūtė, A., & Fulcher, B. (2018) · Trends in Cognitive Sciences · doi:10.1016/j.tics.2018.10.005 · Cited by 380 · see citing works
Weighted Gene Coexpression Network Analysis: State of the Art
Zhao, W., Langfelder, P., Fuller, T. F., Dong, J., Li, A., & Hovarth, S. (2010) · Journal of Biopharmaceutical Statistics · doi:10.1080/10543400903572753 · Cited by 393 · see citing works
“( 2008 ) applied WGCNA to four microarray data sets generated from 160 human brain control samples.”
MethodSelf-citationHuman-specific transcriptional regulation of CNS development genes by FOXP2
Konopka, G., Bomar, J. M., Winden, K., Coppola, G., Jonsson, Z. O., Gao, F., Peng, S., Preuss, T. M., Wohlschlegel, J. A., & Geschwind, D. H. (2009) · Nature · doi:10.1038/nature08549 · Cited by 390 · see citing works
Gene Coexpression Networks in Human Brain Identify Epigenetic Modifications in Alcohol Dependence
Ponomarev, I., Wang, S., Zhang, L., Harris, R. A., & Mayfield, R. D. (2012) · Journal of Neuroscience · doi:10.1523/jneurosci.3136-11.2012 · Cited by 377 · see citing works
“Module comparison between brain region networks was done following the approach described by Oldham et al. (2008).”
BackgroundMethodResultInfluentialWhen is hub gene selection better than standard meta-analysis?
Langfelder, P., Mischel, P. S., & Horvath, S. (2013) · PLoS ONE · doi:10.1371/journal.pone.0061505 · Cited by 313 · see citing works
“unsigned networks depends on the application; both signed [27,84] and unsigned [6,22,24] weighted gene networks have been successfully used in gene expression analysis.”
Self-citationGene expression in human brain implicates sexually dimorphic pathways in autism spectrum disorders
Werling, D. M., Parikshak, N. N., & Geschwind, D. H. (2016) · Nature Communications · doi:10.1038/ncomms10717 · Cited by 296 · see citing works
Self-citationMunji, R. N., Soung, A., Weiner, G., Sohet, F., Semple, B. D., Trivedi, A. A., Gimlin, K., Kotoda, M., Korai, M., Aydin, S., et al. (2019) · Nature Neuroscience · doi:10.1038/s41593-019-0497-x · Cited by 332 · see citing works
Self-citationThe Allen Brain Atlas: 5 years and beyond
Jones, A. R., Overly, C. C., & Sunkin, S. M. (2009) · Nature reviews. Neuroscience · doi:10.1038/nrn2722 · Cited by 322 · see citing works
Horvath, S. (2011) · Springer eBooks · doi:10.1007/978-1-4419-8819-5 · Cited by 315 · see citing works
Self-citationStrategies for aggregating gene expression data: The collapseRows R function
Miller, J. A., Cai, C., Langfelder, P., Geschwind, D. H., Kurian, S. M., Salomon, D. R., & Horvath, S. (2011) · BMC Bioinformatics · doi:10.1186/1471-2105-12-322 · Cited by 300 · see citing works
“For example, in the case of transcriptional networks, genes showing positive correlation are more likely than uncorrelated or negatively correlated genes to have known protein-protein interactions [2-4].”
BackgroundSelf-citationNetwork Organization of the Huntingtin Proteomic Interactome in Mammalian Brain
Shirasaki, D. I., Greiner, E. R., Al-Ramahi, I., Gray, M., Boontheung, P., Geschwind, D. H., Botas, J., Coppola, G., Horvath, S., Loo, J. A., et al. (2012) · Neuron · doi:10.1016/j.neuron.2012.05.024 · Cited by 297 · see citing works
Self-citationGenes and pathways underlying regional and cell type changes in Alzheimer’s disease
Miller, J. A., Woltjer, R. L., Goodenbour, J. M., Horvath, S., & Geschwind, D. H. (2013) · Genome Medicine · doi:10.1186/gm452 · Cited by 275 · see citing works
“For computational reasons, initial module formation was performed only on the approximately 15,000 genes with the highest overall connectivity, as previously described [14].”
BackgroundMethodResultInfluentialSelf-citationGaiteri, C., Ding, Y., French, B., Tseng, G. C., & Sibille, E. (2014) · Genes, Brain and Behavior · doi:10.1111/gbb.12106 · Cited by 289 · see citing works
“This may create cell-type specific modules associated with oligodendrocytes, microglia and several classes of neurons (Hawrylycz et al. 2012; Oldham et al. 2008), as coexpression links within these modules are often driven by the covariance of cell-type markers.”
BackgroundXu, X., Wells, A., O’Brien, D. R., Nehorai, A., & Dougherty, J. D. (2014) · Journal of Neuroscience · doi:10.1523/jneurosci.4488-13.2014 · Cited by 271 · see citing works
“For human expression data, there are other methods, notably weighted gene co-expression network analysis (WGCNA), that have been used to identify what might be the signatures of particular cell types (Langfelder and Horvath, 2008; Oldham et al., 2008).”
MethodMason, M. J., Fan, G., Plath, K., Zhou, Q., & Horvath, S. (2009) · BMC Genomics · doi:10.1186/1471-2164-10-327 · Cited by 243 · see citing works
“Gene co-expression network methods have been successfully applied in a variety of different settings [18,19,21,22,24-32].”
BackgroundMethodSelf-citationBroad transcriptomic dysregulation occurs across the cerebral cortex in ASD
Gandal, M. J., Haney, J. R., Wamsley, B., Yap, C. X., Parhami, S., Emani, P. S., Chang, N., Chen, G. T., Hoftman, G. D., de Alba, D., et al. (2022) · Nature · doi:10.1038/s41586-022-05377-7 · Cited by 281 · see citing works
Self-citationHuman-Specific Transcriptional Networks in the Brain
Konopka, G., Friedrich, T., Davis‐Turak, J., Winden, K. D., Oldham, M. C., Gao, F., Chen, L., Wang, G.-Z., Luo, R., Preuss, T. M., et al. (2012) · Neuron · doi:10.1016/j.neuron.2012.05.034 · Cited by 264 · see citing works
“We apply WGCNA, which permits in silico dissection of whole tissue into cell-level expression patterns (Oldham et al., 2008).”
BackgroundMethodInfluentialSelf-citationLu, L., Yin, S., Liu, X., Zhang, W., Gu, T., Shen, Q., & Qiu, H. (2013) · Soil Biology and Biochemistry · doi:10.1016/j.soilbio.2013.05.025 · Cited by 267 · see citing works
Ecker, J. R., Geschwind, D. H., Kriegstein, A. R., Ngai, J., Osten, P., Polioudakis, D., Regev, A., Šestan, N., Wickersham, I. R., & Zeng, H. (2017) · Neuron · doi:10.1016/j.neuron.2017.10.007 · Cited by 254 · see citing works
“…cell classes by brain region, cortical cell layers or developmental stages (Abrahams et al., 2007; Arlotta et al., 2005; Belgard et al., 2011; Bernard et al., 2012; Chen et al., 2005; Doyle et al., 2008; Fertuzinhos et al., 2014; Hawrylycz et al., 2012; Oldham et al., 2008; Sugino et al., 2006).”
Self-citationAccelerating axonal growth promotes motor recovery after peripheral nerve injury in mice
Ma, C. H. E., Omura, T., Cobos, E. J., Latrémolière, A., Ghasemlou, N., Brenner, G. J., van Veen, J. E., Barrett, L. B., Sawada, T., Gao, F., et al. (2011) · Journal of Clinical Investigation · doi:10.1172/jci58675 · Cited by 259 · see citing works
“A WGCNA was performed as described previously (25, 64).”
BackgroundMethodSelf-citationWöhr, M., Orduz, D., Gregory, P., Moreno, H., Khan, U., Vörckel, K. J., Wolfer, D. P., Welzl, H., Gall, D., Schiffmann, S. N., et al. (2015) · Translational Psychiatry · doi:10.1038/tp.2015.19 · Cited by 258 · see citing works
“7) strongly overlaps with the PVALB+ interneuron module 9 and most transcripts, including PVALB and DLX1 , are downregulated in ASD brains, these two genes and/or gene products were proposed as ASD-related candidates.”
Cho, S.-H., Chen, J. A., Sayed, F., Ward, M. E., Gao, F., Nguyen, T. A., Krabbe, G., Sohn, P. D., Lo, I., Minami, S., et al. (2015) · The Journal of Neuroscience · doi:10.1523/jneurosci.2939-14.2015 · Cited by 275 · see citing works
“Network analysis was performed as described previously (Oldham et al., 2008), using the WGCNA package in R. Briefly, correlation coefficients were constructed between expression levels of genes, and a connectivity measure (topological overlap; TO) was calculated for each gene by summing the…”
MethodMolyneaux, B. J., Goff, L. A., Brettler, A. C., Chen, H.-H., Brown, J. R., Hrvatin, S., Rinn, J. L., & Arlotta, P. (2014) · Neuron · doi:10.1016/j.neuron.2014.12.024 · Cited by 247 · see citing works
Multiple chronic pain states are associated with a common amino acid–changing allele in KCNS1
Costigan, M., Belfer, I., Griffin, R. S., Dai, F., Barrett, L. B., Coppola, G., Wu, T., Kiselycznyk, C., Poddar, M., Lu, Y., et al. (2010) · Brain · doi:10.1093/brain/awq195 · Cited by 253 · see citing works
“2) (Oldham et al., 2006, 2008).”
MethodSelf-citationRamos, A. D., Diaz, A. A., Nellore, A., Delgado, R. N., Park, K.-Y., Gonzales-Roybal, G., Oldham, M. C., Song, J. S., & Lim, D. A. (2013) · Cell stem cell · doi:10.1016/j.stem.2013.03.003 · Cited by 240 · see citing works
“For mRNAs, module membership distinguishes sets of genes that correspond to specific cell types and biological processes (Oldham et al., 2008), and a similar ‘‘guilt-by-association’’ approach has been used to assign putative functions to lncRNAs based on their coexpression with protein-coding genes…”
BackgroundSelf-citationFormaldehyde in brain: an overlooked player in neurodegeneration?
Tulpule, K., & Dringen, R. (2013) · Journal of Neurochemistry · doi:10.1111/jnc.12356 · Cited by 238 · see citing works
A Quantitative Framework to Evaluate Modeling of Cortical Development by Neural Stem Cells
Stein, J. L., de la Torre-Ubieta, L., Tian, Y., Parikshak, N. N., Hernández, I. A., Marchetto, M. C., Baker, D. K., Lu, D., Hinman, C. R., Lowe, J. K., et al. (2014) · Neuron · doi:10.1016/j.neuron.2014.05.035 · Cited by 201 · see citing works
Self-citationJohnson, E. C. B., Dammer, E. B., Duong, D. M., Yin, L., Thambisetty, M., Troncoso, J. C., Lah, J. J., Levey, A. I., & Seyfried, N. T. (2018) · Molecular Neurodegeneration · doi:10.1186/s13024-018-0282-4 · Cited by 225 · see citing works
Koenning, M., Jackson, S., Hay, C., Faux, C. H., Kilpatrick, T., Willingham, M. M., & Emery, B. (2012) · Journal of Neuroscience · doi:10.1523/jneurosci.1069-12.2012 · Cited by 211 · see citing works
“…have not yet been formally established, it is known that the human ortholog C11Orf9 is enriched in myelin-rich regions of the human brain (Oldham et al., 2008), downregulated in human fetuses with a lethal motor neuron/oligodendrocyte disease (Pakkasjarvi et al., 2005), and upregulated…”
BackgroundTranscriptional Architecture of the Primate Neocortex
Bernard, A., Lubbers, L. S., Tanis, K. Q., Luo, R., Podtelezhnikov, A. A., Finney, E. M., McWhorter, M. M. E., Serikawa, K., Lemon, T., Morgan, R., et al. (2012) · Neuron · doi:10.1016/j.neuron.2012.03.002 · Cited by 224 · see citing works
“The Module Eigengene (ME), defined as the first principle component of a given module, was used to represent the characteristic anatomical expression pattern of individual modules (Oldham et al., 2008).”
BackgroundMethodResultInfluentialSelf-citationNeuroscience in the era of functional genomics and systems biology
Geschwind, D. H., & Konopka, G. (2009) · Nature · doi:10.1038/nature08537 · Cited by 213 · see citing works
Self-citationRBFOX1 regulates both splicing and transcriptional networks in human neuronal development
Fogel, B. L., Wexler, E., Wahnich, A., Friedrich, T., Vijayendran, C., Gao, F., Parikshak, N., Konopka, G., & Geschwind, D. H. (2012) · Human Molecular Genetics · doi:10.1093/hmg/dds240 · Cited by 216 · see citing works
“related to RBFOX1 function, we assessed the module eigengene relationship with RBFOX1 knockdown status (47).”
MethodSelf-citationIdentification of differentially expressed microRNAs across the developing human brain
Ziats, M. N., & Rennert, O. M. (2014) · Molecular Psychiatry · doi:10.1038/mp.2013.93 · Cited by 214 · see citing works
“neurodevelopmental and psychiatric disorders (1).”
A Molecular Signature of Depression in the Amygdala
Sibille, E. L., Wang, Y., Joeyen-Waldorf, J., Gaiteri, C., Surget, A., Oh, S., Belzung, C., Tseng, G. C., & Lewis, D. A. (2009) · American Journal of Psychiatry · doi:10.1176/appi.ajp.2009.08121760 · Cited by 208 · see citing works
““Log2(WM/GM)” indicates relative glial (high LogR, bottom rows) to neuronal (low LogR, top rows) enrichment of gene transcript (see Method section and reference 23), consistent with other large-scale categorization of cellular origin approaches (32).”
Romero-García, R., Whitaker, K. J., Váša, F., Seidlitz, J., Shinn, M., Fonagy, P., Dolan, R. J., Jones, P. B., Goodyer, I. M., Bullmore, E. T., et al. (2017) · NeuroImage · doi:10.1016/j.neuroimage.2017.12.060 · Cited by 188 · see citing works
“In humans, the grouping of genes according to their co-expression pattern also reveals a modular structure that distinguishes the major cell classes of the underlying brain tissue (Oldham et al. 2009; Eising et al. 2016).”
BackgroundA single-nuclei RNA sequencing study of Mendelian and sporadic AD in the human brain
Del-Aguila, J. L., Li, Z., Dube, U., Mihindukulasuriya, K. A., Budde, J. P., Fernandez, M. V., Ibanez, L., Bradley, J., Wang, F., Bergmann, K., et al. (2019) · Alzheimer's Research & Therapy · doi:10.1186/s13195-019-0524-x · Cited by 183 · see citing works
Kelley, K. W., Nakao-Inoue, H., Molofsky, A. V., & Oldham, M. C. (2018) · Nature Neuroscience · doi:10.1038/s41593-018-0216-z · Cited by 198 · see citing works
Self-citationInsights into TREM2 biology by network analysis of human brain gene expression data
Forabosco, P., Ramasamy, A., Trabzuni, D., Walker, R., Smith, C., Bras, J., Levine, A. P., Hardy, J., Pocock, J. M., Guerreiro, R., et al. (2013) · Neurobiology of Aging · doi:10.1016/j.neurobiolaging.2013.05.001 · Cited by 180 · see citing works
“This procedure allows the identification of modules of densely interconnected, coexpressed genes (Oldham et al., 2006; Ravasz and Barabasi, 2003; Zhang and Horvath, 2005), and has been shown to be placing functionally related genes into groups (Eisen et al., 1998).”
InfluentialGenome-wide, integrative analysis implicates microRNA dysregulation in autism spectrum disorder
Wu, Y. E., Parikshak, N. N., Belgard, T. G., & Geschwind, D. H. (2016) · Nature Neuroscience · doi:10.1038/nn.4373 · Cited by 195 · see citing works
Self-citationIwamoto, K., Bundo, M., Ueda, J., Oldham, M. C., Ukai, W., Hashimoto, E., Saito, T., Geschwind, D. H., & Kato, T. (2011) · Genome Research · doi:10.1101/gr.112755.110 · Cited by 189 · see citing works
“Average correlation between each module eigengene (Oldham et al. 2008) and the expression levels of genes associated with neuronal MR (NeuN+), non-neuronal MR (NeuN ), and all brain-expressed genes (all probes).”
BackgroundInfluentialSelf-citationAutism genetics: searching for specificity and convergence
Berg, J. M., & Geschwind, D. H. (2012) · Genome Biology · doi:10.1186/gb-2012-13-7-247 · Cited by 192 · see citing works
“The top autism associated WGCNA network, enriched for ASDasso ciated GWAS targets, showed high overlap with a previously identified interneuronrelated module [143].”
BackgroundSelf-citationKrienen, F. M., Yeo, B. T. T., Ge, T., Buckner, R. L., & Sherwood, C. C. (2016) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1510903113 · Cited by 190 · see citing works
Wei, Y. B., Melas, P. A., Wegener, G., Mathe, A. A., & Lavebratt, C. (2015) · International Journal of Neuropsychopharmacology · doi:10.1093/ijnp/pyu032 · Cited by 186 · see citing works
“The latter concept in drug design is also supported by findings showing that many genes are part of co-expression networks, meaning that their expression covaries (Oldham et al., 2008).”
BackgroundUnderwood, T., Hayden, A. L., Derouet, M. F., Garcia, E., Noble, F., White, M. J., Thirdborough, S., Mead, A., Clemons, N. J., Mellone, M., et al. (2014) · The Journal of Pathology · doi:10.1002/path.4467 · Cited by 186 · see citing works
“Data acquisition and processing for network analysis Weighted gene correlation network analysis (WGCNA) [34,35] was applied to microarray datasets generated on Affymetrix HG-U133A GeneChips from two independent studies (for full details of this analysis, see supplementary material, Supplementary materials and methods).”
MethodInfluentialSaris, C. G. J., Horvath, S., van Vught, P. W. J., van Es, M. A., Blauw, H. M., Fuller, T. F., Langfelder, P., DeYoung, J. A., Wokke, J. H. J., Veldink, J. H., et al. (2009) · BMC Genomics · doi:10.1186/1471-2164-10-405 · Cited by 182 · see citing works
“Module membership measures allow one to annotate all genes on the array and to screen for disease related intramodular hub genes [44,47,48].”
BackgroundSelf-citationLuo, R., Sanders, S. J., Tian, Y., Voineagu, I., Huang, N., Chu, S. H., Klei, L., Cai, C., Ou, J., Lowe, J. K., et al. (2012) · The American Journal of Human Genetics · doi:10.1016/j.ajhg.2012.05.011 · Cited by 167 · see citing works
“each array and compared to the resulting distribution of IACs for the dataset.(25) Samples with an average IAC < 2 standard deviations (SDs) below the mean IAC for the dataset were removed.”
MethodSelf-citationOldham, M. C., Langfelder, P., & Horvath, S. (2012) · BMC Systems Biology · doi:10.1186/1752-0509-6-63 · Cited by 154 · see citing works
“We have previously shown that the transcriptome of normal human CN is organized into modules of coexpressed genes, many of which relate to specific cell types and functional processes [21].”
BackgroundMethodInfluentialSelf-citationBiesemann, C., Grønborg, M., Luquet, E., Wichert, S. P., Bernard, V., Bungers, S. R., Cooper, B., Varoqueaux, F., Li, L., Byrne, J. A., et al. (2014) · The EMBO Journal · doi:10.1002/embj.201386120 · Cited by 156 · see citing works
“In line with our findings, Tpd52 mRNA and protein were detected in the brain and in neurons (Chew et al, 2008; Oldham et al, 2008), and yeast two-hybrid assays identified Tpd52 protein family members as binding partners of MAL2 (Wilson et al, 2001), a specific component of VGLUT1-containing SVs…”
ResultThe Role of Transposable Elements in Health and Diseases of the Central Nervous System
Reilly, M. T., Faulkner, G. J., Dubnau, J., Ponomarev, I., & Gage, F. H. (2013) · The Journal of Neuroscience · doi:10.1523/jneurosci.3369-13.2013 · Cited by 175 · see citing works
“An alternative is to take a systems genomics approach, focusing on the regulation of the transcriptome as a whole, including both coding and noncoding regions (Oldham et al., 2008; Ward and Kellis, 2012).”
BackgroundZheng, R., Li, M., Liang, Z., Wu, F., Pan, Y. F., & Wang, J. (2019) · Bioinformatics · doi:10.1093/bioinformatics/btz139 · Cited by 166 · see citing works
Radial glia require PDGFD-PDGFRβ signalling in human but not mouse neocortex
Lui, J. H., Nowakowski, T. J., Pollen, A. A., Javaherian, A., Kriegstein, A. R., & Oldham, M. C. (2014) · Nature · doi:10.1038/nature13973 · Cited by 166 · see citing works
Self-citationZhang, Q., Ma, C., Gearing, M., Wang, P. G., Chin, L.-S., & Li, L. (2018) · Acta Neuropathologica Communications · doi:10.1186/s40478-018-0524-2 · Cited by 165 · see citing works
“biologically meaningful modules of co-expressed genes [45, 62, 92].”
Kadosh, K. C., Muhardi, L., Parikh, P., Basso, M., Mohamed, H. J. J., Prawitasari, T., Samuel, F. O., Ma, G., & Geurts, J. M. W. (2021) · Nutrients · doi:10.3390/nu13010199 · Cited by 186 · see citing works
Gene Networks and microRNAs Implicated in Aggressive Prostate Cancer
Wang, L., Tang, H., Thayanithy, V., Subramanian, S., Oberg, A. L., Cunningham, J. M., Cerhan, J. R., Steer, C. J., & Thibodeau, S. N. (2009) · Cancer Research · doi:10.1158/0008-5472.can-09-2183 · Cited by 163 · see citing works
Nowick, K., Gernat, T., Almaas, E., & Stubbs, L. J. (2009) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0911376106 · Cited by 162 · see citing works
“Several recent studies of gene coexpression networks have used a wTO measure, based on pairwise correlations of all expressed genes as input for clustering algorithms, to identify gene modules of interest (26, 27).”
MethodUmoh, M. E., Dammer, E. B., Dai, J., Duong, D. M., Lah, J. J., Levey, A. I., Gearing, M., Glass, J. D., & Seyfried, N. T. (2017) · EMBO Molecular Medicine · doi:10.15252/emmm.201708202 · Cited by 154 · see citing works
“…were enriched for microglial and astrocytic proteins (M2, M10, M6, M5, and M13), while others were enriched for neuronal proteins (M1 and M8), or oligodendrocyte-specific proteins (M12 and M4) (Fig 3B), consistent with previous studies (Oldham et al, 2008; Miller et al, 2013; Seyfried et al, 2017).”
Two gene co-expression modules differentiate psychotics and controls
Chen, C., Cheng, L., Grennan, K., Pibiri, F., Zhang, C., Badner, J. A., Members of the Bipolar Disorder Genome Study (BiGS) Consortium, Gershon, E. S., & Liu, C. (2013) · Molecular Psychiatry · doi:10.1038/mp.2012.146 · Cited by 157 · see citing works
Zhou, X., Huang, X., Liang, S., Tang, S., Wu, S., Huang, T., Mo, Z., & Wang, Q. (2018) · OncoTargets and Therapy · doi:10.2147/ott.s163891 · Cited by 154 · see citing works
“Genes with highly co-expressed relationships are grouped into the same module; thus, the genes in the same module may have similar functions or possibly share common biological regulatory roles.(12) The most central genes within a module are identified as hub genes, suggesting their important roles in the module.”
Kozlenkov, A., Roussos, P., Timashpolsky, A., Barbu, M., Rudchenko, S., Bibikova, M. V., Klotzle, B., Byne, W. M., Lyddon, R., Narzo, A. F. D., et al. (2013) · Nucleic Acids Research · doi:10.1093/nar/gkt838 · Cited by 154 · see citing works
“WGCNA resulted in well-defined gene co-expression modules (N=77) with specific anatomical distributions, consistent with previous studies in the brain tissues (64,68) (Table 2).”
BackgroundResultFetit, R., Hillary, R. F., Price, D. J., & Lawrie, S. M. (2021) · Neuroscience & Biobehavioral Reviews · doi:10.1016/j.neubiorev.2021.07.014 · Cited by 148 · see citing works
Biogeographic patterns of microbial co-occurrence ecological networks in six American forests
Tu, Q., Yan, Q., Deng, Y., Michaletz, S. T., Buzzard, V., Weiser, M. D., Waide, R. B., Ning, D., Wu, L., He, Z., et al. (2020) · Soil Biology and Biochemistry · doi:10.1016/j.soilbio.2020.107897 · Cited by 148 · see citing works
Networks of Neuronal Genes Affected by Common and Rare Variants in Autism Spectrum Disorders
Ben-David, E., & Shifman, S. (2012) · PLoS Genetics · doi:10.1371/journal.pgen.1002556 · Cited by 145 · see citing works
“[19], modules in the network corresponded to specific cell types.”
Background
- Authors
- Oldham, M. C., Konopka, G., Iwamoto, K., Langfelder, P., Kato, T., Horvath, S., & Geschwind, D. H.
- Affiliations
- University of California, Los Angeles (US)
- RIKEN Center for Brain Science (JP)
- Corresponding author
- Oldham, Michael C
- Geschwind, Daniel H
- ORCID
- Venue
- Nature Neuroscience
- Published
- 2008-10-12
- Volume
- 11
- Issue
- 11
- Pages
- 1271-1282
- ISSN
- 1097-6256 (ISSN-L)
- 1546-1726
- PMC
- PMC2756411
- OpenAlex
- W2081837126
- Cited by
- 752
- References
- 50
- Citations by year
- 2026: 17 · 2025: 19 · 2024: 26 · 2023: 18 · 2022: 40 · 2021: 70 · 2020: 43 · 2019: 59 · 2018: 53 · 2017: 51 · 2016: 42 · 2015: 54 · 2014: 53 · 2013: 53 · 2012: 53
- FWCI
- 13.73
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Licence
- www.springer.com/tdm (tdm)
- Locations
- Nature Neuroscience (publishedVersion)
- PubMed (publishedVersion)
- PubMed Central (acceptedVersion)
- Funders
- National Institute of Mental Health (R37MH060233)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Gene expression and cancer classification
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Single-cell and spatial transcriptomics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Bioinformatics and Genomic Networks
- Keywords
- human brain transcriptome, gene co-expression, gene co-expression modules, cell type-specific gene expression, neurons, oligodendrocytes, astrocytes, microglia, neural stem cells, synaptic function, gender differences, neurogenesis, transcriptional programs
- MeSH terms
- Brain, Caudate Nucleus, Cerebellum, Cerebral Cortex, Humans, Neuroglia, Neurons, Gene Expression, Gene Expression Profiling, Databases, Genetic, Microarray Analysis, Gene Regulatory Networks
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