Background: Interpreting gene expression profiles obtained from heterogeneous samples can be difficult because bulk gene expression measures are not resolved to individual cell populations. We have recently devised Population-Specific Expression Analysis (PSEA), a statistical method that identifies individual cell types expressing genes of interest and achieves quantitative estimates of cell type-specific expression levels. This procedure makes use of marker gene expression and circumvents the need for additional experimental information like tissue composition.
Results: To systematically assess the performance of statistical deconvolution, we applied PSEA to gene expression profiles from cerebellum tissue samples and compared with parallel, experimental separation methods. Owing to the particular histological organization of the cerebellum, we could obtain cellular expression data from in situ hybridization and laser-capture microdissection experiments and successfully validated computational predictions made with PSEA. Upon statistical deconvolution of whole tissue samples, we identified a set of transcripts showing age-related expression changes in the astrocyte population.
Conclusions: PSEA can predict cell-type specific expression levels from tissues homogenates on a genome-wide scale. It thus represents a computational alternative to experimental separation methods and allowed us to identify age-related expression changes in the astrocytes of the cerebellum. These molecular changes might underlie important physiological modifications previously observed in the aging brain.