Quantitative Gene Expression

“We investigate how variability in RNA and protein molecule numbers impact traits such as cell size”

Living cells have to generate sufficient amounts of biomolecules to reach and maintain an optimal size. Within an organism, cell size is related to function and can span orders of magnitudes from tiny lymphocytes to giant neurones. Accurate size control requires a careful coordination of growth and division. Surprisingly, while regulation of progression through the cell-cycle has been extensively studied, much less attention has been devoted to the mechanisms behind cell growth and cell size control. Yet, this is a very important problem because understanding how cells generate mass is indispensable to understand how cells function. Moreover, deregulated growth and size control can lead to pathologies such as cancer.Our group is interested in this problem, in particular, the interplay between cell size, growth rate, and regulation of gene expression. As a model system, we use environmental and genetic perturbations affecting growth and cell size in the fission yeast Schizosaccharomyces pombe. We apply experimental and computational approaches, combining population level measurements of RNA and protein numbers, with information on phenotypic variability and noise in gene expression acquired from single cells. Ultimately, our work will bring us closer to understanding how eukaryotic cells grow and maintain their size, and consequently how malfunctions in cell physiology can lead to diseases.

Selected Publications

Bertaux F, Von Kügelgen J, Marguerat S, Shahrezaei V. (2020). A bacterial size law revealed by a coarse-grained model of cell physiology. BioRxiv, doi: http://dx.doi.org/10.1101/078998

Sun XM, Bowman A, Priestman M, Bertaux F, Martinez-Segura A, Tang W, Whilding C, Dormann D, Shahrezaei V, Marguerat S. (2020). Size-dependent increase in RNA Polymerase II initiation rates mediates gene expression scaling to cell size. Curr Biol. 30:1217-1230.

Tang W, Bertaux F, Thomas P, Stefanelli C, Saint M, Marguerat S, Shahrezaei V. (2020). bayNorm: Bayesian gene expression recovery, imputation and normalisation for single cell RNA-sequencing data. Bioinformatics, 36:1174-1181

Software available from: https://bioconductor.org/packages/release/bioc/html/bayNorm.html

Marguerat S, Schmidt A, Codlin S, Chen W, Aebersold R, Bähler J. (2012). Quantitative analysis of fission yeast transcriptomes and proteomes in proliferating and quiescent cells. Cell, 151:671-83.