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

Abstract

During embryogenesis cells make fate decisions within complex tissue environments. The levels and dynamics of transcription factor expression regulate these decisions.Here we use single cell live imaging of an endogenous HES5 reporter and absolute protein quantification to gain a dynamic view of neurogenesis in the embryonic mammalian spinal cord. We report that dividing neural progenitors show both aperiodic and periodic HES5 protein fluctuations. Mathematical modelling suggests that in progenitor cells the HES5 oscillator operates close to its bifurcation boundary where stochastic conversions between dynamics are possible. HES5 expression becomes more frequently periodic as cells transition to differentiation which, coupled with an overall decline in HES5 expression, creates a transient period of oscillations with higher fold expression change. This increases the decoding capacity of HES5 oscillations and correlates with interneuron versus motor neuron cell fate. Thus, HES5 undergoes complex changes in gene expression dynamics as cells differentiate.

Decoding the Notch signal

“…Oscillatory behavior of Notch target genes has also been observed in neural progenitors(Manning et al, 2019), neural and muscle stem cells(Lahmann et al, 2019;Sueda, Imayoshi, Harima, & Kageyama, 2019) and during somitogenesis in mouse, chick and zebrafish embryos(Dequéant et al, 2006;Holley, Jülich, Rauch, Geisler, & Nüsslein-Volhard, 2002;Oates & Ho, 2002;Palmeirim, Henrique, Ish-Horowicz, & Pourquié, 1997). Oscillatory behavior of Notch target genes has also been observed in neural progenitors(Manning et al, 2019), neural and muscle stem cells(Lahmann et al, 2019;Sueda, Imayoshi, Harima, & Kageyama, 2019) and during somitogenesis in mouse, chick and zebrafish embryos(Dequéant et al, 2006;Holley, Jülich, Rauch, Geisler, & Nüsslein-Volhard, 2002;Oates & Ho, 2002;Palmeirim, Henrique, Ish-Horowicz, & Pourquié, 1997).…”

Inferring kinetic parameters of oscillatory gene regulation from single cell time series data

“…Having validated the method on one-dimensional posterior distributions, we further test the performance of the method by simultaneously inferring multiple model parameters from a single in We choose a data set that shares characteristics with typically collected time course data from single cells. Specifically, our in silico data set is of similar length and observation intervals as previously analysed by Manning et al (2019). In this paper, the degradation rates of protein and mRNA have been measured, so we assume these measurements as known values, leaving five unknown parameter values to infer.…”

“…There is an increasing amount of literature uncovering the relationship between gene expression dynamics, i.e. dynamic changes in protein copy numbers from a single gene, and cell state transitions [1][2][3][4][5][6][7] . For example, Imayoshi et al (2013) [1] used optogenetics to show that oscillatory expression of the transcription factor ASCL1 promotes cell proliferation of mouse neural progenitor cells, whereas sustained expression promotes differentiation.…”

“…Even simple mathematical models, such as the auto-negative feedback motif (Figure 1C), employ multiple model parameters that Colour bar shows Venus::HES5 intensity. Stills taken at time points 1.75h (1), 4.5h (2), 6h (3), 7h (4), 9.5h (5), 10.75h, (6), 15.5h (7), 17.25h (8). Scale bar 5µm.…”

A functionally divergent intrinsically disordered region underlying the conservation of stochastic signaling

“…We used a Gaussian Process regression model (19,45,72) with the squared exponential kernel to summarize each single-cell trajectory. The kernel can be expressed as…”