Deciphering genetic determinants of cell growth by single-cell RNA sequencing

Deregulated growth is a hallmark of human diseases such as cancer. A wide range of therapeutic approaches target proliferating cells effectively through inhibition of pathways related to growth and division. However, cells can often escape arrest due to the genetic and phenotypic heterogeneity present in cancer cell populations leading to relapse of the disease. Likewise, heterogeneity in gene expression makes treatment of persistent microbial infections an important challenge for human health. Identifying genetic and non-genetic features that can drive individual cells to overcome growth inhibition is therefore required to understand disease progression and resistance. Recent experimental protocols made it possible to characterise the transcriptomes and genotypes of single cells simultaneously. These opened the door to investigate the dynamics and heterogeneity of gene expression in population of cells with variable genotypes. These methods are technically challenging, generate large amounts of complex data, and require the development of state-of-the-art computational methods for their analysis. Yet, when coupled with mathematical modelling of cell physiology, these single cells approaches have an unprecedented potential to unravel complex genetic and gene expression programmes that underlie growth dynamics of cell populations. This project aims at: i) understanding the genetic basis of cell growth under conditions were gene expression or proliferation are compromised by associating high fitness genotypes to transcriptome signatures in single cells, ii) develop computational tools to integrate single-cell and population level genotyping and gene expression data, iii) improve selected aspect of the laboratory single-cell RNA-seq protocol to improve data quality. To this end we will grow populations of cells with mixed genotypes in tightly controlled conditions where transcription, translation, or genome stability are impaired. Genotypes and transcriptome data will be acquired at different time points for individual cells and for the population as a whole. These data will be combined with statistical approaches to identify genes that provide growth advantage under limiting conditions and link them to specific genotypes. The single cell resolution will allow identifying rare or transient variants fluctuation during the time-frame of the experiment. Coupled with stochastic coarse-grained modelling of cell physiology these rich datasets with help understanding the complex dynamics of growth in genetically diverse cell populations, and how cells can overcome arrest induced by compounds that target growth and proliferation. This project will be supervised by Dr Sam Marguerat (LMS, ICL) and Dr Vahid Shahrezaei (Dept of Mathematics, ICL).