Deciphering the regulation of fission yeast phenotypic heterogeneity by external conditions

Phenotypic cell-to-cell variability is a fundamental determinant of microbial fitness that contributes to stress adaptation and drug resistance. Gene-expression heterogeneity underpins this variability, but is challenging to study genome-wide. We recently developed a novel approach, which combines imaging of individual cells with single-cell RNA sequencing (scRNA-seq). We analysed the heterogeneity and dynamics of gene expression during adaptive and acute responses to changing environments. Interestingly, we found that entry into stationary phase in response to diminishing amounts of nutrients is preceded by a gradual, synchronised adaptation in gene regulation, followed by highly variable gene expression when growth decreases. Conversely, a sudden and acute heat-shock leads to a stronger, coordinated response and adaptation across cells. This analysis revealed that the extent and dynamics of global gene-expression heterogeneity is regulated in response to different physiological conditions within cell populations. This open the intriguing possibility that different degrees of heterogeneity can arise depending on the nature and strength of environmental changes. It also suggests that cells may respond differently to their environment depending on the state they are in giving rise to heterogeneity at the population level.

This project is about studying those two hypotheses. In particular, using scRNA-seq, genetic and computational approaches, we will aim at discovering: 1) the impact of the intensity and timing of external conditions on heterogeneity; 2) the importance of the cell-cycle, size and growth rate of the cell on heterogeneity. Moreover, we will exploit the ability of scRNA-seq to identify single cells genotypes and use libraries of genetically diverse strains to identify genetic determinants regulating heterogeneity. This project will reveal the environmental, phenotypic, and genetic determinants of transcriptional heterogeneity in populations of unicellular organisms and lead to an improved understanding of evolution and drug resistance.