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A DNA microarray survey of gene expression in normal human tissues

Radha Shyamsundar12, Young H Kim1, John P Higgins1, Kelli Montgomery1, Michelle Jorden1, Anand Sethuraman3, Matt van de Rijn1, David Botstein35, Patrick O Brown24* and Jonathan R Pollack1*

Author affiliations

1 Department of Pathology, Stanford University School of Medicine, 269 Campus Drive, CCSR 3245A, Stanford, CA 94305-5176, USA

2 Department of Biochemistry, Stanford University School of Medicine, 279 Campus Drive, Stanford, CA 94305-5307, USA

3 Department of Genetics, Stanford University, Stanford, CA 94305, USA

4 Howard Hughes Medical Institute, Stanford University School of Medicine, 279 Campus Drive, Stanford, CA 94305-5307, USA

5 Lewis-Sigler Institute for Integrative Genomics, Princeton University, Princeton, NJ 80544, USA

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Citation and License

Genome Biology 2005, 6:R22  doi:10.1186/gb-2005-6-3-r22

Published: 14 February 2005

Abstract

Background

Numerous studies have used DNA microarrays to survey gene expression in cancer and other disease states. Comparatively little is known about the genes expressed across the gamut of normal human tissues. Systematic studies of global gene-expression patterns, by linking variation in the expression of specific genes to phenotypic variation in the cells or tissues in which they are expressed, provide clues to the molecular organization of diverse cells and to the potential roles of the genes.

Results

Here we describe a systematic survey of gene expression in 115 human tissue samples representing 35 different tissue types, using cDNA microarrays representing approximately 26,000 different human genes. Unsupervised hierarchical cluster analysis of the gene-expression patterns in these tissues identified clusters of genes with related biological functions and grouped the tissue specimens in a pattern that reflected their anatomic locations, cellular compositions or physiologic functions. In unsupervised and supervised analyses, tissue-specific patterns of gene expression were readily discernable. By comparative hybridization to normal genomic DNA, we were also able to estimate transcript abundances for expressed genes.

Conclusions

Our dataset provides a baseline for comparison to diseased tissues, and will aid in the identification of tissue-specific functions. In addition, our analysis identifies potential molecular markers for detection of injury to specific organs and tissues, and provides a foundation for selection of potential targets for selective anticancer therapy.