Input-Output Relationships Underlying Transcriptional Bursting at the Genome-Wide Level
The emergence of new single cell analysis techniques has allowed to measure gene expression at the single cell level. These methods have been decisive in demonstrating that transcription is a fundamentally stochastic process. In particular our groups and others showed that transcription in mammalian cells is not continuous in time, but occurs in short bursts followed by longer silent intervals. It has been difficult to make progress on the upstream mechanisms leading to transcriptional bursting. One limitation for making progress on these issues has been the challenge to simultaneously monitor the temporal transcriptional inputs (regulators) and outputs (bursting) of a gene in single cells with sufficient sensitivity and at a large number of genomic loci.
In this IPhD project, we will develop an experimental system to overcome this limitation by exploiting the possibility to combine fluorescence (to mark transcriptional activators) and bioluminescence (to monitor transcriptional bursting) in individual mammalian cells. This will enable us to address two interrelated questions. The first will be understanding the relationship between transcription factor concentration (input) and transcriptional bursting kinetics (output). This will allow us to analyze how the absolute levels of transcription factor affect parameters such as promoter switching rates, transcription rates, burst sizes and frequency. The second question will take advantage of a large panel of cell lines with different genomic insertions of the synthetic gene reporter to determine how bursting kinetics, notably the number of rate-limiting steps, transcription rates, burst sizes and frequency depend on the location of the reporter.
These experiments, combined with mathematical analysis, will be of great interest towards quantitatively understanding stochastic gene transcription, with potential biotechnology applications such as recombinant protein production or guidance for future gene therapy strategies.
Keywords: Single-Cell Dynamics, Stochastic Gene Expression, Computational Modelingback