Extracting cell-type-specific gene expression differences from complex tissues (144.30)

Abstract

Abstract Blood is a complex tissue made up of many cell-types, each with its own functional attributes and molecular signature. 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. Here we present a statistical methodology which, given microarray data from two groups of biological samples and the relative cell-type frequencies of each sample, estimates in a virtual manner the gene expression data for each cell-type at a group level, and uses these to identify differentially expressed genes at a cell-type specific level between groups. To validate the utility of csSAM, we apply our method to whole blood gene expression measurements of stable and acute rejection renal transplant patients. csSAM was able to identify hundreds of genes that were upregulated in monocytes of the acute rejection patients, as well as in other cell-types, that were undetectable at the whole blood level. Our methodology is widely applicable and enables the analysis of gene expression in complex tissues at a much higher resolution than previously possible.