Cell Type Specific Analysis of Human Brain Transcriptome Data to Predict Alterations in Cellular Composition.

The central nervous system (CNS) is composed of hundreds of distinct cell types, each expressing different subsets of genes from the genome. High throughput gene expression analysis of the CNS from patients and controls is a common method to screen for potentially pathological molecular mechanisms of psychiatric disease. One mechanism by which gene expression might be seen to vary across samples would be alterations in the cellular composition of the tissue. While the expressions of gene 'markers' for each cell type can provide certain information of cellularity, for many rare cell types markers are not well characterized. Moreover, if only small sets of markers are known, any substantial variation of a marker's expression pattern due to experiment conditions would result in poor sensitivity and specificity. Here, our proposed method combines prior information from mice cell-specific transcriptome profiling experiments with co-expression network analysis, to select large sets of potential cell type-specific gene markers in a systematic and unbiased manner. The method is efficient and robust, and identifies sufficient markers for further cellularity analysis. We then employ the markers to analytically detect changing cellular composition in human brain. Application of our method to temporal human brain microarray data successfully detects changes in cellularity over time that roughly correspond to known epochs of human brain development. Furthermore, application of our method to human brain samples with the neurodevelopmental disorder of autism supports the interpretation that the changes in astrocytes and neurons might contribute to the disorder.