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Statistical expression deconvolution from mixed tissue samples.

ABSTRACT: Global expression patterns within cells are used for purposes ranging from the identification of disease biomarkers to basic understanding of cellular processes. Unfortunately, tissue samples used in cancer studies are usually composed of multiple cell types and the non-cancerous portions can significantly affect expression profiles. This severely limits the conclusions that can be made about the specificity of gene expression in the cell-type of interest. However, statistical analysis can be used to identify differentially expressed genes that are related to the biological question being studied.We propose a statistical approach to expression deconvolution from mixed tissue samples in which the proportion of each component cell type is unknown. Our method estimates the proportion of each component in a mixed tissue sample; this estimate can be used to provide estimates of gene expression from each component. We demonstrate our technique on xenograft samples from breast cancer research and publicly available experimental datasets found in the National Center for Biotechnology Information Gene Expression Omnibus repository.R code (http://www.r-project.org/) for estimating sample proportions is freely available to non-commercial users and available at http://www.med.miami.edu/medicine/x2691.xml.

SUBMITTER: Clarke J

PROVIDER: S-EPMC2853690 | biostudies-literature | 2010 Apr

REPOSITORIES: biostudies-literature

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Statistical expression deconvolution from mixed tissue samples.

Clarke Jennifer J Seo Pearl P Clarke Bertrand B

Bioinformatics (Oxford, England) 20100304 8

<h4>Motivation</h4>Global expression patterns within cells are used for purposes ranging from the identification of disease biomarkers to basic understanding of cellular processes. Unfortunately, tissue samples used in cancer studies are usually composed of multiple cell types and the non-cancerous portions can significantly affect expression profiles. This severely limits the conclusions that can be made about the specificity of gene expression in the cell-type of interest. However, statistical ...[more]

PMID: 20202973

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Project description:Tissues are often made up of multiple cell-types. Blood, for example, contains many different cell-types, each with its own functional attributes and molecular signature. In humans, because of its accessibility and immune functionality, blood cells have been used as a source for RNA-based biomarkers for many diseases. Yet, the proportions of any given cell-type in the blood can vary markedly, even between normal individuals. This results in a significant loss of sensitivity in gene expression studies of blood cells and great difficulty in identifying the cellular source of any perturbations. Ideally, one would like to perform differential expression analysis between patient groups for each of the cell-types within a tissue but this is impractical and prohibitively expensive. To test the relationship between measured gene expression in mixed samples and the expression of genes in the isolated pure subsets, we begin with a situation in which all factors are known. Tissue samples from the brain, liver and lung of a single rat were analyzed using expression arrays (Affymetrix) in triplicate. Homogenates of those three tissues were then mixed together at the cRNA level. We then measured the gene expression pattern of each mixed sample. Such mixtures mimic the common scenario in which biological samples in a dataset are heterogeneous and vary in the relative frequency of the component subsets from one another. We mixed rat brain, liver and lung biospecimens derived from one animal at the cRNA homogenate level in different proportions. 3 technical replicates each. Snap frozen rat liver and brain was kept frozen while cutting it into pieces. cDNA synthesis and labeling was done with a starting amount of 1 Î¼g, using the Affymetrix Eukaryotic One-Cycle Target Hybridization. Washing, Staining and scanning protocol for Eukaryotic Cartridge Arrays with User-Prepared Buffers and Sloutions according to the technical manuals (Affymetrix GeneChip Expression, Analysis for Cartridge Arrays using the GCAS version 1.4, Affymetrix GeneChip Expression Analysis (P/N 701021,Rev. 5) , Affymetrix GeneChip Expression Wash, Stain and Scan (P/N 702731, Rev. 3) , following the manufacturer/s instructions. Data was RMA normalized.

Dataset Information

Statistical expression deconvolution from mixed tissue samples.

Publications

Statistical expression deconvolution from mixed tissue samples.

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