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Human Molecular Genetics 2007 16(R2):R209-R219; doi:10.1093/hmg/ddm183
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© 2007 The Author(s)
This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/2.0/uk/) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.

Insights from spatially mapped gene expression in the mouse brain

Susan M. Sunkin* and John G. Hohmann

Allen Institute for Brain Science, 551 N. 34th Street, Seattle, WA 98103, USA

* To whom correspondence should be addressed. Tel: +206 548 7000; Fax: +206 548 7071; Email: susans{at}alleninstitute.org

Received June 1, 2007; Revised June 1, 2007; Accepted July 6, 2007

The growing number of publicly available databases of murine gene expression arising from genomic-scale transcriptome/proteome profiling projects allows open access to information about genes potentially involved in diseases and disorders of the brain. The use of various methodologies by myriad projects provides complementary types of information, ranging from easily quantifiable microarray data for gross brain regions, to transcript tag analysis and proteomic characterization. One mode of gene expression analysis that has recently been widely adopted is the utilization of colorimetric in situ hybridization. This approach is adaptable for high throughput production, and provides a reproducible, scaleable platform for large datasets. The Allen Brain Atlas in particular has utilized this technology to produce a genomic-scale anatomical digital atlas of gene expression in the adult male mouse brain. The availability of global datasets with cellular level spatial resolution, which can be easily parsed due to accessible informatics-derived image analysis tools, can provide both high level and detailed insights into gene regulation. This article reviews various gene expression profiling projects in the mouse brain, how these data sets are increasingly used to complement other studies and applications of these datasets to further understanding of neurological disease.


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