Single-cell-based analysis highlights a surge in cell-tocell molecular variability preceding irreversible commitment in a differentiation process

Orateur : Olivier GANDRILLON (LBMC, Lyon)

Models of cell differentiation have been proposed in which cells switch from one differentiation state to another through stochastic dynamics characterized by a peak in gene expression variability at the point of fate commitment (Kupiec, 1997 ; Huang, 2011 ; Moris et al., 2016). We have tested this hypothesis at the single-cell level by measuring the expression level of selected genes by RT-qPCR at six sequential time-points after induction of differentiation of primary chicken erythroid progenitors (Gandrillon et al., 1999). We then used a multivariate statistical analysis for exploring this dataset. Cell-to-cell heterogeneity, hidden behind the averaging effect in populations was clearly revealed at the single-cell level. This cell-to-cell variability, as measured by entropy value, showed in a significant increase during the first hours of the differentiation process, peaking at 24h, and decreasing at 72h. Distribution-based gene correlation networks were also computed for each time-point, showing a sudden drop in correlation at 8 hours. Interestingly, such increase in cell-to-cell variability and gene correlation decrease preceded an increase in cell size variability and an irreversible commitment to differentiation. In this work (Richard et al., 2016), we obtained strong evidence to support the idea that differentiation is not a "simple" program that all cells execute identically, but results from the dynamical behavior of the underlying molecular network, in agreement with another recent work (Mojtahedi et al., 2016).

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