MAY 13, 2015 12:00 PM PDT

What can we learn from large gene expression data sets?

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  • Scientist, Allen Institute for Brain Science
      Jeremy Miller joined the Allen Institute in 2011 to help with computational data analysis of the Allen Human Brain Atlas project, and is currently extending his study to transcriptional atlases of the developing human and non-human primate brain. He is interested in using large-scale gene expression data to identify essential components (i.e., cell types) of the mammalian brain, as well as molecular pathways unique to human. Miller received a B.A. in mathematics and a B.S. in astronomy from the University of Maryland, and was subsequently a researcher at Los Alamos National Laboratory, where he used computer models to study the mammalian retina. Miller received his Ph.D. in neuroscience from UCLA, where he studied gene expression changes in the brain in Alzheimer's disease and normal aging.
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    The Allen Institute for Brain Science provides several brain atlases that are freely available to the public at A common use for these atlases is to study expression patterns for specific genes of interest in the developing or adult mouse, rhesus monkey, or human brain. In addition, we and others have performed transcriptome-wide analyses on these data to address particular questions of brain development and anatomy. Here, I will describe a few of the Allen Brain Atlases and then present three short vignettes. First, we find that most genes with consistent expression patterns between adult human brains are involved in brain function and dysfunction. Second, we find that, while most genes show consistent expression patterns between species, many differences exist, which could potentially provide insight into the efficacy of some mouse models of disease. Finally, we show how gene expression patterns in different layers of cortex can change dramatically with age while retaining discrete laminar identities, suggesting that it is important both ‘where' in the brain you look as well as ‘when'. Learning objectives: 1. Provide examples for how the Allen Brain Atlases can be applied to study brain development and disease using whole-transcriptome data. 2. Describe how complex data sets can provide both unique analytical challenges as well as novel biological insights.

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