Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

  • Article
  • Published:

Functional organization of the transcriptome in human brain

Abstract

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.

This is a preview of subscription content, access via your institution

Access options

Rent or buy this article

Prices vary by article type

from$1.95

to$39.95

Prices may be subject to local taxes which are calculated during checkout

Figure 1: Network analysis of gene expression in human cerebral cortex, caudate nucleus and cerebellum identifies distinct modules of coexpressed genes.
Figure 2: Many gene coexpression modules are present in multiple human brain networks.
Figure 3: Module membership is highly correlated in multiple human brain networks.
Figure 4: Module membership identifies groups of genes that are consistently coexpressed in the human brain.
Figure 5: Overlap and functional characterizations reveal a meta-network of gene coexpression modules in the human brain.
Figure 6: M13C identifies genes that are coexpressed in the adult subventricular neurogenic niche.

Similar content being viewed by others

References

  1. Nelson, S.B., Hempel, C. & Sugino, K. Probing the transcriptome of neuronal cell types. Curr. Opin. Neurobiol. 16, 571–576 (2006).

    Article  CAS  Google Scholar 

  2. Khaitovich, P. et al. Regional patterns of gene expression in human and chimpanzee brains. Genome Res. 14, 1462–1473 (2004).

    Article  CAS  Google Scholar 

  3. Roth, R.B. et al. Gene expression analyses reveal molecular relationships among 20 regions of the human CNS. Neurogenetics 7, 67–80 (2006).

    Article  CAS  Google Scholar 

  4. Iwamoto, K., Bundo, M. & Kato, T. Altered expression of mitochondria-related genes in postmortem brains of patients with bipolar disorder or schizophrenia, as revealed by large-scale DNA microarray analysis. Hum. Mol. Genet. 14, 241–253 (2005).

    Article  CAS  Google Scholar 

  5. Iwamoto, K., Kakiuchi, C., Bundo, M., Ikeda, K. & Kato, T. Molecular characterization of bipolar disorder by comparing gene expression profiles of postmortem brains of major mental disorders. Mol. Psychiatry 9, 406–416 (2004).

    Article  CAS  Google Scholar 

  6. Hodges, A. et al. Regional and cellular gene expression changes in human Huntington's disease brain. Hum. Mol. Genet. 15, 965–977 (2006).

    Article  CAS  Google Scholar 

  7. Ryan, M.M. et al. Gene expression analysis of bipolar disorder reveals downregulation of the ubiquitin cycle and alterations in synaptic genes. Mol. Psychiatry 11, 965–978 (2006).

    Article  CAS  Google Scholar 

  8. Barabasi, A.L. & Oltvai, Z.N. Network biology: understanding the cell's functional organization. Nat. Rev. Genet. 5, 101–113 (2004).

    Article  CAS  Google Scholar 

  9. Chen, Y. et al. Variations in DNA elucidate molecular networks that cause disease. Nature 452, 429–435 (2008).

    Article  CAS  Google Scholar 

  10. Horvath, S. et al. Analysis of oncogenic signaling networks in glioblastoma identifies ASPM as a molecular target. Proc. Natl. Acad. Sci. USA 103, 17402–17407 (2006).

    Article  CAS  Google Scholar 

  11. Huang, Y. et al. Systematic discovery of functional modules and context-specific functional annotation of human genome. Bioinformatics 23, i222–i229 (2007).

    Article  CAS  Google Scholar 

  12. Ihmels, J., Bergmann, S. & Barkai, N. Defining transcription modules using large-scale gene expression data. Bioinformatics 20, 1993–2003 (2004).

    Article  CAS  Google Scholar 

  13. Jordan, I.K., Marino-Ramirez, L., Wolf, Y.I. & Koonin, E.V. Conservation and coevolution in the scale-free human gene coexpression network. Mol. Biol. Evol. 21, 2058–2070 (2004).

    Article  CAS  Google Scholar 

  14. Lee, H.K., Hsu, A.K., Sajdak, J., Qin, J. & Pavlidis, P. Coexpression analysis of human genes across many microarray data sets. Genome Res. 14, 1085–1094 (2004).

    Article  CAS  Google Scholar 

  15. Oldham, M.C., Horvath, S. & Geschwind, D.H. Conservation and evolution of gene coexpression networks in human and chimpanzee brains. Proc. Natl. Acad. Sci. USA 103, 17973–17978 (2006).

    Article  CAS  Google Scholar 

  16. Stuart, J.M., Segal, E., Koller, D. & Kim, S.K. A gene-coexpression network for global discovery of conserved genetic modules. Science 302, 249–255 (2003).

    Article  CAS  Google Scholar 

  17. Snel, B., van Noort, V. & Huynen, M.A. Gene co-regulation is highly conserved in the evolution of eukaryotes and prokaryotes. Nucleic Acids Res. 32, 4725–4731 (2004).

    Article  CAS  Google Scholar 

  18. Zhang, B. & Horvath, S. A general framework for weighted gene co-expression network analysis. Stat. Appl. Genet. Mol. Biol. 4, 17 (2005).

    Article  Google Scholar 

  19. Caceres, M. et al. Elevated gene expression levels distinguish human from non-human primate brains. Proc. Natl. Acad. Sci. USA 100, 13030–13035 (2003).

    Article  CAS  Google Scholar 

  20. Enard, W. et al. Intra- and interspecific variation in primate gene expression patterns. Science 296, 340–343 (2002).

    Article  CAS  Google Scholar 

  21. Khaitovich, P. et al. A neutral model of transcriptome evolution. PLoS Biol. 2, e132 (2004).

    Article  Google Scholar 

  22. Lu, T. et al. Gene regulation and DNA damage in the ageing human brain. Nature 429, 883–891 (2004).

    Article  CAS  Google Scholar 

  23. Zhang, J., Finney, R.P., Clifford, R.J., Derr, L.K. & Buetow, K.H. Detecting false expression signals in high-density oligonucleotide arrays by an in silico approach. Genomics 85, 297–308 (2005).

    Article  CAS  Google Scholar 

  24. Johnson, W.E., Li, C. & Rabinovic, A. Adjusting batch effects in microarray expression data using empirical Bayes methods. Biostatistics 8, 118–127 (2007).

    Article  Google Scholar 

  25. Ravasz, E., Somera, A.L., Mongru, D.A., Oltvai, Z.N. & Barabasi, A.L. Hierarchical organization of modularity in metabolic networks. Science 297, 1551–1555 (2002).

    Article  CAS  Google Scholar 

  26. Kerrien, S. et al. IntAct—open source resource for molecular interaction data. Nucleic Acids Res. 35, D561–D565 (2007).

    Article  CAS  Google Scholar 

  27. Ge, H., Liu, Z., Church, G.M. & Vidal, M. Correlation between transcriptome and interactome mapping data from Saccharomyces cerevisiae. Nat. Genet. 29, 482–486 (2001).

    Article  CAS  Google Scholar 

  28. Myers, A.J. et al. A survey of genetic human cortical gene expression. Nat. Genet. 39, 1494–1499 (2007).

    Article  CAS  Google Scholar 

  29. Horvath, S. & Dong, J. Geometric interpretation of gene coexpression network analysis. PLoS Comput. Biol. 4, e1000117 (2008).

    Article  Google Scholar 

  30. Cahoy, J.D. et al. A transcriptome database for astrocytes, neurons, and oligodendrocytes: a new resource for understanding brain development and function. J. Neurosci. 28, 264–278 (2008).

    Article  CAS  Google Scholar 

  31. Lein, E.S. et al. Genome-wide atlas of gene expression in the adult mouse brain. Nature 445, 168–176 (2007).

    Article  CAS  Google Scholar 

  32. Nielsen, J.A., Maric, D., Lau, P., Barker, J.L. & Hudson, L.D. Identification of a novel oligodendrocyte cell adhesion protein using gene expression profiling. J. Neurosci. 26, 9881–9891 (2006).

    Article  CAS  Google Scholar 

  33. Bachoo, R.M. et al. Molecular diversity of astrocytes with implications for neurological disorders. Proc. Natl. Acad. Sci. USA 101, 8384–8389 (2004).

    Article  CAS  Google Scholar 

  34. Morciano, M. et al. Immunoisolation of two synaptic vesicle pools from synaptosomes: a proteomics analysis. J. Neurochem. 95, 1732–1745 (2005).

    Article  CAS  Google Scholar 

  35. Rong, Y., Wang, T. & Morgan, J.I. Identification of candidate Purkinje cell–specific markers by gene expression profiling in wild-type and pcd(3J) mice. Brain Res. Mol. Brain Res. 132, 128–145 (2004).

    Article  CAS  Google Scholar 

  36. Sugino, K. et al. Molecular taxonomy of major neuronal classes in the adult mouse forebrain. Nat. Neurosci. 9, 99–107 (2006).

    Article  CAS  Google Scholar 

  37. Nagata, T. et al. Profiling of genes associated with transcriptional responses in mouse hippocampus after transient forebrain ischemia using high-density oligonucleotide DNA array. Brain Res. Mol. Brain Res. 121, 1–11 (2004).

    Article  CAS  Google Scholar 

  38. Hosack, D.A., Dennis, G. Jr . Sherman, B.T., Lane, H.C. & Lempicki, R.A. Identifying biological themes within lists of genes with EASE. Genome Biol. 4, R70 (2003).

    Article  Google Scholar 

  39. Ihrie, R.A. & Alvarez-Buylla, A. Cells in the astroglial lineage are neural stem cells. Cell Tissue Res. 331, 179–191 (2008).

    Article  Google Scholar 

  40. Merkle, F.T. & Alvarez-Buylla, A. Neural stem cells in mammalian development. Curr. Opin. Cell Biol. 18, 704–709 (2006).

    Article  CAS  Google Scholar 

  41. Langfelder, P. & Horvath, S. Eigengene networks for studying the relationships between coexpression modules. BMC Syst. Biol. 1, 54 (2007).

    Article  Google Scholar 

  42. Calaora, V., Chazal, G., Nielsen, P.J., Rougon, G. & Moreau, H. mCD24 expression in the developing mouse brain and in zones of secondary neurogenesis in the adult. Neuroscience 73, 581–594 (1996).

    Article  CAS  Google Scholar 

  43. Quinn, C.C., Gray, G.E. & Hockfield, S. A family of proteins implicated in axon guidance and outgrowth. J. Neurobiol. 41, 158–164 (1999).

    Article  CAS  Google Scholar 

  44. Nieto, M., Schuurmans, C., Britz, O. & Guillemot, F. Neural bHLH genes control the neuronal versus glial fate decision in cortical progenitors. Neuron 29, 401–413 (2001).

    Article  CAS  Google Scholar 

  45. Sanai, N. et al. Unique astrocyte ribbon in adult human brain contains neural stem cells but lacks chain migration. Nature 427, 740–744 (2004).

    Article  CAS  Google Scholar 

  46. Quinones-Hinojosa, A. et al. Cellular composition and cytoarchitecture of the adult human subventricular zone: a niche of neural stem cells. J. Comp. Neurol. 494, 415–434 (2006).

    Article  Google Scholar 

  47. Curtis, M.A. et al. Human neuroblasts migrate to the olfactory bulb via a lateral ventricular extension. Science 315, 1243–1249 (2007).

    Article  CAS  Google Scholar 

  48. Li, J.Z. et al. Sample matching by inferred agonal stress in gene expression analyses of the brain. BMC Genomics 8, 336 (2007).

    Article  Google Scholar 

  49. Ashburner, M. et al. Gene ontology: tool for the unification of biology. The Gene Ontology Consortium. Nat. Genet. 25, 25–29 (2000).

    Article  CAS  Google Scholar 

  50. Langfelder, P., Zhang, B. & Horvath, S. Defining clusters from a hierarchical cluster tree: the Dynamic Tree Cut package for R. Bioinformatics 24, 719–720 (2007).

    Article  Google Scholar 

Download references

Acknowledgements

The authors would like to express their gratitude to the individuals who produced and made available to the scientific community the raw data analyzed in this study. Post mortem brain tissue was donated by The Stanley Research Institute's brain collection courtesy of M.B. Knable, E.F. Torrey, M.J. Webster, S. Weis and R.H. Yolken. We are grateful to our colleagues N. Khanlou, J. Pomakian and H. Vinters for their help in obtaining additional post mortem human brain tissue. We also thank C. Wiita for technical support and L. Kawaguchi for administrative support and lab management. This work was supported by the James S. McDonnell Foundation and a Method to Extend Research in Time (MERIT) Award 5R37MH060233 (D.H.G.), funded by the US National Institute of Mental Health.

Author information

Authors and Affiliations

Authors

Contributions

M.C.O. designed the study, analyzed the data and wrote the manuscript. S.H. guided data analysis and aided in manuscript preparation. G.K. designed and carried out wet bench experiments. P.L. performed module eigengene network comparisons. K.I. and T.K. provided microarray data. D.H.G. supervised the study, provided funding and aided in manuscript preparation. All authors discussed results and commented on the manuscript.

Corresponding authors

Correspondence to Michael C Oldham or Daniel H Geschwind.

Supplementary information

Supplementary Text and Figures

Supplementary Figures 1–7, Supplementary Note, Supplementary Network Analysis and Supplementary Methods (PDF 27594 kb)

Supplementary Tables

Supplementary Tables 1–12 (ZIP 75564 kb)

Rights and permissions

Reprints and permissions

About this article

Cite this article

Oldham, M., Konopka, G., Iwamoto, K. et al. Functional organization of the transcriptome in human brain. Nat Neurosci 11, 1271–1282 (2008). https://doi.org/10.1038/nn.2207

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/nn.2207

This article is cited by

Search

Quick links

Nature Briefing

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing