Inferring steady state single-cell gene expression distributions from analysis of mesoscopic samples

Jessica C Mar¹ , Renee Rubio² and John Quackenbush¹^,2^,3

¹Department of Biostatistics, Harvard School of Public Health, Huntington Avenue, Boston, Massachusetts 02115, USA

²Department of Biostatistics and Computational Biology, Dana-Farber Cancer Institute, Binney St, Boston, Massachusetts 02115, USA

³Department of Cancer Biology, Dana-Farber Cancer Institute, Binney St, Boston, Massachusetts 02115, USA

author email corresponding author email

Genome Biology 2006, 7:R119doi:10.1186/gb-2006-7-12-r119


Published:	14 December 2006

Subject areas: Bioinformatics, Genetics

Abstract

Background

A great deal of interest has been generated by systems biology approaches that attempt to develop quantitative, predictive models of cellular processes. However, the starting point for all cellular gene expression, the transcription of RNA, has not been described and measured in a population of living cells.

Results

Here we present a simple model for transcript levels based on Poisson statistics and provide supporting experimental evidence for genes known to be expressed at high, moderate, and low levels.

Conclusion

Although the model describes a microscopic process occurring at the level of an individual cell, the supporting data we provide uses a small number of cells where the echoes of the underlying stochastic processes can be seen. Not only do these data confirm our model, but this general strategy opens up a potential new approach, Mesoscopic Biology, that can be used to assess the natural variability of processes occurring at the cellular level in biological systems.