Email updates

Keep up to date with the latest news and content from BMC Genomics and BioMed Central.

Open Access Research article

Cell population-specific expression analysis of human cerebellum

Alexandre Kuhn12*, Azad Kumar1, Alexandra Beilina1, Allissa Dillman1, Mark R Cookson1 and Andrew B Singleton1

Author affiliations

1 Laboratory of Neurogenetics, National Institute on Aging, National Institutes of Health, Bethesda, MD, USA

2 Current address: Microfluidics Systems Biology, Institute for Materials Research and Engineering, A*STAR, 3 Research Link, Singapore, Singapore, 117602

For all author emails, please log on.

Citation and License

BMC Genomics 2012, 13:610  doi:10.1186/1471-2164-13-610

Published: 12 November 2012

Abstract

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.

Keywords:
Genomics; Computational biology; Cerebellum; Gene expression; Aging; Astrocyte