Aleksandra Mandic
Quantitative studies of gene expression in have shown widespread variability in transcript and protein product abundance at the single-cell level. Such fluctuations in gene expression have been shown to lead to heterogeneous cellular responses and phenotypes, acting in a range of systems, from immunity, cancer to development. To better understand heterogeneity in cellular populations, we developed a novel method for the precise quantification of gene expression in single living cells. Furthermore, we focused on whether and to which extent transcriptional expression profiles are transmitted during cell division.
In the first section, we described a new approach for quantitative measurements of gene expression, based on internal tagging of endogenous proteins with a reporter allowing luminescence and fluorescence time-lapse imaging. Using the currently brightest luminescent reporter, fluctuations of protein expression levels could be monitored in single living cells with high sensitivity and temporal resolution over extended time periods. Moreover, our system allowed measurement of single-cell protein degradation rates in the absence of protein synthesis inhibitors, as well as calculation of absolute synthesis rates over the cell cycle. Finally, the internal tag could be excised by inducible expression of Cre recombinase, which enabled estimation of endogenous mRNA half-lives by monitoring the decay of luminescent signal.
Second, to monitor transcriptional activity and quantify memory across multiple endogenous genes in mouse embryonic stem cells, we generated cell lines in which a short-lived luciferase reporter allowed sensitive monitoring of transcriptional kinetics by luminescence imaging at high time resolution over long periods of time. By coupling live single-cell monitoring of transcriptional activity of 9 different promoters with mathematical modelling, we found that different levels of expression fluctuations shape transcriptional memory. Long fluctuations (1-10 generations) and short fluctuations (1-3 h), as well as their amplitudes, vary widely between across different genes and together shape transcriptional memory. Surprisingly, we have shown that there was a large level of gene-specific cell-to-cell synchronicity in transcriptional profiles. Overall, we uncovered that noise and timescales of slow and fast fluctuations, as well as their synchronisation over the cell cycle, defined how transcriptional dynamics were correlated within a lineage of related cells.
In conclusion, this thesis described an innovative method that allows measurement of absolute protein expression levels, protein turnover, as well as mRNA half-lives for endogenously expressed genes. Our approach opens new avenues in quantitative studies of gene expression in single living cells. Furthermore, we revealed different levels of transcriptional fluctuations that shape gene specific transcriptional memory and its timescales in mouse embryonic stem cells.
EPFL2018
