Gonin-Giraud, S. | bioRxiv Science

bioRxiv2018

Drugs modulating stochastic gene expression affect the erythroid differentiation process

BackgroundTo understand how a metazoan cell makes the decision to differentiate, we assessed the role of stochastic gene expression (SGE) during the erythroid differentiation process. Our hypothesis is that stochastic gene expression has a role in single-cell decision-making. In agreement with this hypothesis, we and others recently showed that SGE significantly increased during differentiation. However, evidence for the causative role of SGE is still lacking. Such demonstration would require being able to experimentally manipulate SGE levels and analyze the resulting impact of these variations on cell differentiation.\n\nResultWe identified three drugs that modulate SGE in primary erythroid progenitor cells. Artemisinin and Indomethacin simultaneously decreased SGE and reduced the amount of differentiated cells. Inversely, -methylene-{gamma}-butyrolactone-3 (MB-3) simultaneously increased the level of SGE and the amount of differentiated cells. We then used a dynamical modelling approach which confirmed that differentiation rates were indeed affected by the drug treatment.\n\nConclusionUsing single-cell analysis and modeling tools, we provide experimental evidence that in a physiologically relevant cellular system, control of SGE can directly modify differentiation, supporting a causal link between the two.

systems biology↗

bioRxiv2017

Automated cell cycle and cell size measurements for single-cell gene expression studies

Recent rise of single-cell studies revealed the importance of understanding the role of cell-to-cell variability, especially at the transcriptomic level. One of the numerous sources of cell-to-cell variation in gene expression is the heterogeneity in cell proliferation state. How cell cycle and cell size influences gene expression variability at single-cell level is not yet clearly understood. To deconvolute such influences, most of the single-cell studies used dedicated methods that could include some bias. Here, we provide a universal and automatic toxic-free label method, compatible with single-cell high-throughput RT-qPCR. This led to an unbiased gene expression analysis and could be also used for improving single-cell tracking and imaging when combined with cell isolation. As an application for this technique, we showed that cell-to-cell variability in chicken erythroid progenitors was negligibly influenced by cell size nor cell cycle.

cell biology↗