Stochasticity in the miR-9/Hes1 oscillatory network can account for clonal heterogeneity in the timing of differentiation

Abstract

Recent studies suggest that cells make stochastic choices with respect to differentiation or division. However, the molecular mechanism underlying such stochasticity is unknown. We previously proposed that the timing of vertebrate neuronal differentiation is regulated by molecular oscillations of a transcriptional repressor, HES1, tuned by a post-transcriptional repressor, miR-9. Here, we computationally model the effects of intrinsic noise on the Hes1/miR-9 oscillator as a consequence of low molecular numbers of interacting species, determined experimentally. We report that increased stochasticity spreads the timing of differentiation in a population, such that initially equivalent cells differentiate over a period of time. Surprisingly, inherent stochasticity also increases the robustness of the progenitor state and lessens the impact of unequal, random distribution of molecules at cell division on the temporal spread of differentiation at the population level. This advantageous use of biological noise contrasts with the view that noise needs to be counteracted.DOI: http://dx.doi.org/10.7554/eLife.16118.001

The influence of nuclear compartmentalisation on stochastic dynamics of self-repressing gene expression

“…Hence, it may be the case that the Hes1 mRNA and protein molecules are in the fast mRNA export, slow protein import regime we have identified which produces highly noisy gene expression. To test this idea further, we found a more recent study of Hes1 by Phillips et al (2016) which contains time series data for Hes1 luminescence levels for 15 cells. Using these time series, we applied the same criteria for determining whether or not stochastic oscillations are observed as we did in section 3.6 and the results are shown in Figure 9.…”

smiFISH and embryo segmentation for single-cell multi-gene RNA quantification in arthropods

“…In contrast, NV was effective at highlighting individual cells that were markedly different numerically in mRNA from their neighbours. NV is therefore a relevant measure for questions where the absolute RNA number is important, for example when a threshold expression level is required for a particular process to occur 29,30 , or post-transcriptional buffering mechanisms that maintain constant mRNA levels 31,32 . PV also effectively highlighted individual cells that differed from their neighbours, but in proportional expression rather than actual.…”

Quantitative single-cell live imaging links HES5 dynamics with cell-state and fate in murine neurogenesis

“…To understand how the HES5 dynamics of clusters 1 and 2 are generated and how they may transition from aperiodic to periodic expression, we used a stochastic delay differential equation model of an auto-negative feedback network ( Fig.6a and Methods) 30,[44][45][46] . This model applies to progenitors in clusters 1 and 2 where HES5 fluctuates around a more or less stable mean.…”

“…An additional revelation of single-cell live imaging studies is that gene expression is characterised by varying degrees of noise due to the stochastic nature of transcription [27][28][29] . Current ideas for the role of such embedded stochasticity include cases where it would be an advantage 30,31 or conversely, an impediment for cell fate decisions 32,33 and mechanisms to suppress noise after a fate-decision 34 .…”