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Quantitative approaches of live single-cell transcriptomics

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If you have a question about this talk, please contact Dr. Adrien Hallou.

Transcriptional dynamics and cellular decision making – Professor Nancy Papalopulu

In recent years, our understanding of how cells make cell state transitions has been transformed by discovery of short-time scale protein expression oscillatory dynamics. Ultradian oscillations are exemplified by the expression of HES /Her transcription factors in neural progenitors, but it is not known whether oscillations occur in vivo and what is the role of noise. This talk will describe a quantitative and dynamic single cell live imaging approach to analyse ultradian oscillations and noise during cell state transitions during mouse and zebrafish neural development. With a combination of mathematical modeling and experimentation we report that in mouse ex-vivo slices Hes5 protein expression noise has an oscillatory “priming” function, where stochastic conversions between dynamics are enabled, correlating with a transition to differentiation. However, CRISPR /Cas9 mutagenesis of a miR-9 binding site in a knock-in Her6 reporter in zebrafish shows that increased high frequency noise has the opposite effect in that it interferes with oscillations and at the same time locks cells in a progenitor state. These findings show that a transient oscillatory state appears during cell state transitions and that noise optimization at this stage is necessary for decoding to occur.

Transcriptional dynamics and cellular decision making – Professor Jonathan Chubb

To allow a deeper understanding of cell decision making, and the generation of more useful conceptual frameworks, we have developed and implemented a range of technologies- with the view that to understand the decision of cell requires monitoring a broad range of regulatory features of single cells, as they differentiate.  These approaches encompass both imaging and transcriptomic methods for monitoring the signalling and gene expression dynamics of single cells. In particular, we use imaging to directly observe the bursts of activity of individual genes in living cells (see movie below). This approach provides a real time readout of the gene expression decisions of the cell, in parallel with other features of the cells and their environment.   We combine these techniques with detailed quantitative analysis of the data, and use modelling and molecular genetics to refine our hypotheses. 

This talk is part of the Theory of Living Matter Group series.

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