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Functional genomics of the brain: uncovering networks in the CNS using a systems approach

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Abstract The central nervous system (CNS) is undoubtedly the most complex human organ system in terms of its diverse functions, cellular composition, and connections. Attempts to capture this diversity experimentally were the foundation on which the field of neurobiology was built. Until now though, techniques were either painstakingly slow or insufficient in capturing this heterogeneity. In addition, the combination of multiple layers of information needed for a complete picture of neuronal diversity from the epigenome to the proteome requires an even more complex compilation of data. In this era of high‐throughput genomics though, the ability to isolate and profile neurons and brain tissue has increased tremendously and now requires less effort. Both microarrays and next‐generation sequencing have identified neuronal transcriptomes and signaling networks involved in normal brain development, as well as in disease. However, the expertise needed to organize and prioritize the resultant data remains substantial. A combination of supervised organization and unsupervised analyses are needed to fully appreciate the underlying structure in these datasets. When utilized effectively, these analyses have yielded striking insights into a number of fundamental questions in neuroscience on topics ranging from the evolution of the human brain to neuropsychiatric and neurodegenerative disorders. Future studies will incorporate these analyses with behavioral and physiological data from patients to more efficiently move toward personalized therapeutics. WIREs Syst Biol Med 2011 3 628–648 DOI: 10.1002/wsbm.139 This article is categorized under: Laboratory Methods and Technologies > Genetic/Genomic Methods

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Cell‐specific gene expression in the brain. Cellular heterogeneity in the brain makes analyzing gene expression data challenging. In addition to the myriad of differential expression of transcripts depicted here in different amounts and colors, the corresponding protein products can vary in ratio from cell to cell. Not only can there be differences based on cell types within the cortex, but cells from the same laminar location but also from different areas of the brain can have very different expression profiles.

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A combinatorial approach is needed to achieve a systems level understanding of the CNS. Combining imaging and behavioral data from patients with expression data from blood and post‐mortem tissues will result in a nearly complete picture of human CNS disease networks.

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A network approach can parse brain data at multiple levels. Using network analysis, datasets from tissue, regions, or cells can be used to identify coexpressed genes. However, a network approach can also be appropriated to ascertain regional or cellular networks from whole tissue data.

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Workflow for analyzing gene expression data. A classical approach to gene expression analysis uses differential expression analysis followed up by confirmation studies in the tissue or model system of interest. Further refinement of target genes can be obtained by analyzing gene lists using GO or pathway analysis tools. In addition, data can be analyzed for differential connectivity using various network approaches.

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