Understanding gene expression variability in its biological context using theoretical and experimental analyses of single cells

Traditional gene expression studies have largely ignored cell-to-cell variability in transcription. Current methods allow for single cell analyses and have shown considerable variability in gene expression, even in populations of isogenic cells exposed to the same growth environment. In this thesis, we assess the impact of various parameters of gene expression cell-to-cell variability using experimental systems that enable quantification of the gene expression status of single cells. Based on the obtained data we parameterized mathematical models of gene expression.
In the first chapters we focused on the effects of cellular volume growth on gene expression. We combined single-cell mRNA expression levels with cell volume measurements and observed that the number of mRNA molecules scales proportionally with cell volume. Besides concentration homeostasis over the course of the cell cycle, our data indicates that the local chromatin structure in which genes are embedded impacts expression dynamics. Next we analyzed transcription dynamics in real time when inducing transcription inactivation of an activated reporter gene. The data shows that transcription inactivation is accelerated after targeting a chromatin regulatory protein to the transcription site. In the last two chapters we show how individual cells react upon UV exposure or long-term exposure to drug treatments.
Overall, we analyzed dynamics of gene expression cell-to-cell variability when exposing cells to fluctuating conditions and we demonstrate how single cell techniques can be utilized to understand such gene expression cell-to-cell variability.