Dynamic properties of noise and Her6 levels are optimized by miR‐9, allowing the decoding of the Her6 oscillator

Abstract

Noise is prevalent in biology and has been widely quantified using snapshot measurements. This static view obscures our understanding of dynamic noise properties and how these affect gene expression and cell state transitions. Using a CRISPR/Cas9 Zebrafish her6::Venus reporter combined with mathematical and in vivo experimentation, we explore how noise affects the protein dynamics of Her6, a basic helix‐loop‐helix transcriptional repressor. During neurogenesis, Her6 expression transitions from fluctuating to oscillatory at single‐cell level. We identify that absence of miR‐9 input generates high‐frequency noise in Her6 traces, inhibits the transition to oscillatory protein expression and prevents the downregulation of Her6. Together, these impair the upregulation of downstream targets and cells accumulate in a normally transitory state where progenitor and early differentiation markers are co‐expressed. Computational modelling and double smFISH of her6 and the early neurogenesis marker, elavl3, suggest that the change in Her6 dynamics precedes the downregulation in Her6 levels. This sheds light onto the order of events at the moment of cell state transition and how this is influenced by the dynamic properties of noise. Our results suggest that Her/Hes oscillations, facilitated by dynamic noise optimization by miR‐9, endow progenitor cells with the ability to make a cell state transition.

HES1 protein oscillations are necessary for neural stem cells to exit from quiescence

“…We therefore provide evidence that the HES1 dynamics are required for NSCs to exit quiescence in vitro , suggesting that oscillations are needed for cells to transition from one state to another rather than just maintaining a progenitor state. This revised view on the functional significance of HES1 is consistent with previous findings showing that when Her6 (orthologue of mouse HES1 in zebrafish) oscillations are diminished due to the increase of noise, cells are unable to transition from progenitor state to differentiation in zebrafish (Soto et al, 2020) whereas the quality and occurrence of HES5 oscillations in mouse spinal cord increases as cells approach the transition to differentiation (Manning et al, 2019). The significance of HES1 oscillations in controlling the state of quiescence may also have implications in tissue regeneration and in cancer where quiescent cancer stem cells are considered to drive cancer progression and metastasis (Chen et al, 2016; Lee et al, 2020; Phan and Croucher, 2020).…”

A dynamic, spatially periodic, micro-pattern of HES5 underlies neurogenesis in the mouse spinal cord

“…Random pattern (no coupling between cells) has similar rates of differentiation as weak coupling, however, weak coupling strength is advantageous because it allows local in phase synchronisation, which by analogy to global synchronisation ( oscillate as well as the peak-to-trough amplitude increases as cells enter the differentiation pathway (Manning et al, 2019). We have also shown that when the transition from noisy dynamic expression to oscillatory expression does not take place, progenitor cells are unable to downregulate HES levels and differentiate (Soto et al, 2020). We speculate that microclusters may act to select cells for differentiation by a transient increase in oscillation amplitude and the spatial periodicity of microclusters may space out differentiating cells to maintain tissue organisation.…”

“…Recent live imaging studies of cell fate decisions during neurogenesis have added a new dimension to this knowledge (Das & Storey, 2012, 2014Manning et al, 2019;Nelson et al, 2020;Soto et al, 2020;Vilas-Boas et al, 2011). They have shown the importance of understanding transcription factor (TF) expression dynamics in real time, including the key transcriptional basic helix-loop-helix repressors Hairy and enhancer of split (HES)1 and 5 (Bansod et al, 2017;Imayoshi & Kageyama, 2014;Ohtsuka et al, 1999), in regulating state transitions.…”

The adhesion GPCR Adgrg6 (Gpr126): Insights from the zebrafish model

“…Determining when and where different isoforms are expressed, and finding any changes in expression in disease situations, is challenging. Use of single molecule fluorescent in situ hybridization (smFISH) techniques, which are being employed successfully in zebrafish (Soto et al, 2020; Stapel et al, 2016) will enable a detailed analysis of different splice forms and provide tissue‐specific cellular localization of the transcripts.…”