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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, 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 cited source says what the claim attributes to it.
In the citing paper
“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]
The cited source says what the claim attributes to it.
In the citing paper
“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]
The cited source says what the claim attributes to it.
In the citing paper
“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]
The cited source says what the claim attributes to it.
In the citing paper
“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]
The cited source says what the claim attributes to it.
In the citing paper
“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]
In the citing paper
“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]
In the citing paper
“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]
In the citing paper
“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]
In the citing paper
“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]
In the citing paper
“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]
In the citing paper
“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]
In the citing paper
“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]
In the citing paper
“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]
In the citing paper
“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]
In the citing paper
“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]
In the citing paper
“Protein co-expression analysis is a powerful tool to understand biological network, pathway, and cell type changes in human tissue3,4.”
In the citing paper
“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]
In the citing paper
“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]
In the citing paper
“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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Citations
Cites
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A General Framework for Weighted Gene Co-Expression Network Analysis
Zhang, B., & Horvath, S. (2005) · Statistical Applications in Genetics and Molecular Biology · doi:10.2202/1544-6115.1128 · Cited by 5,684
Self-citationGenome-wide atlas of gene expression in the adult mouse brain
Lein, E. S., Hawrylycz, M. J., Ao, N., Ayres, M., Bensinger, A., Bernard, A., Boe, A. F., Boguski, M. S., Brockway, K. S., Byrnes, E. J., et al. (2007) · Nature · doi:10.1038/nature05453 · Cited by 5,572
Hierarchical Organization of Modularity in Metabolic Networks
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Cahoy, J. D., Emery, B., Kaushal, A., Foo, L. C., Zamanian, J. L., Christopherson, K. S., Xing, Y., Lubischer, J. L., Krieg, P. A., Krupenko, S. A., et al. (2008) · The Journal of Neuroscience · doi:10.1523/jneurosci.4178-07.2008 · Cited by 3,129
A Gene-Coexpression Network for Global Discovery of Conserved Genetic Modules
Stuart, J. M., Segal, E., Koller, D., & Kim, S. K. (2003) · Science · doi:10.1126/science.1087447 · Cited by 2,274
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Hosack, D. A., Dennis, G., Sherman, B. T., Lane, H. C., & Lempicki, R. A. (2003) · Genome biology · doi:10.1186/gb-2003-4-10-r70 · Cited by 1,961
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Self-citation
Cited by
Sorted by citations
WGCNA: an R package for weighted correlation network analysis
Langfelder, P., & Horvath, S. (2008) · BMC Bioinformatics · doi:10.1186/1471-2105-9-559 · Cited by 27,252
“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-citationMolecular ecological network analyses
Deng, Y., Jiang, Y., Yang, Y., He, Z., Luo, F., & Zhou, J. Z. (2012) · BMC Bioinformatics · doi:10.1186/1471-2105-13-113 · Cited by 3,236
An anatomically comprehensive atlas of the adult human brain transcriptome
Hawrylycz, M. J., Lein, E. S., Guillozet-Bongaarts, A. L., Shen, E. H., Ng, L., Miller, J. A., van de Lagemaat, L. N., Smith, K. A., Ebbert, A., Riley, Z. L., et al. (2012) · Nature · doi:10.1038/nature11405 · Cited by 3,083
Self-citationThe Glial Nature of Embryonic and Adult Neural Stem Cells
Kriegstein, A., & Alvarez-Buylla, A. (2009) · Annual Review of Neuroscience · doi:10.1146/annurev.neuro.051508.135600 · Cited by 2,475
“Transcriptional analysis suggests that periventricular astrocytes in humans are distinct from parenchymal astrocytes (Oldham et al. 2008), which may lead to a better definition of the astrocytes in the human SVZ that, like B cells in rodents, continue to function as stem cells in the adult.”
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
“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-citation
Metadata
- 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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