Abstract
Dynamics of cell fate decisions are commonly investigated by inferring temporal sequences of gene expression states by assembling snapshots of individual cells where each cell is measured once. Ordering cells according to minimal differences in expression patterns and assuming that differentiation occurs by a sequence of irreversible steps, yields unidirectional, eventually branching Markov chains with a single source node. In an alternative approach, we used multi-nucleate cells to follow gene expression taking true time series. Assembling state machines, each made from single-cell trajectories, gives a network of highly structured Markov chains of states with different source and sink nodes including cycles, revealing essential information on the dynamics of regulatory events. We argue that the obtained networks depict aspects of the Waddington landscape of cell differentiation and characterize them as reachability graphs that provide the basis for the reconstruction of the underlying gene regulatory network.
Highlights
Single-cell analyses revealed complex dynamics of gene regulation in differentiating cells (Spiller et al, 2010; Junker and van Oudenaarden, 2014; Paul et al, 2015; Marr et al, 2016; Plass et al, 2018)
There was no obvious difference between dark controls and far-red stimulated plasmodia which were measured at 6 h after the light pulse when genes were already differentially regulated (Supplementary Figure 1 and Supplementary Table 1), indicating that even during the period where the mRNA abundance changed in time, the homogeneity in gene expression levels is maintained
Control experiments have demonstrated that the gene expression patterns in samples simultaneously retrieved from different sites of a large plasmodial cell did not deviate within the range of the technical accuracy of the measurements
Summary
Single-cell analyses revealed complex dynamics of gene regulation in differentiating cells (Spiller et al, 2010; Junker and van Oudenaarden, 2014; Paul et al, 2015; Marr et al, 2016; Plass et al, 2018). Algorithms have been developed to infer the gene expression trajectory of a typical cell in pseudo-time from static snapshots of gene expression states in a cell population, resulting in Markov chains of states (Bendall et al, 2014; Cannoodt et al, 2016; Chen et al, 2019; Saelens et al, 2019; Setty et al, 2019). Even though regulatory mechanisms cannot be directly and rigorously inferred from snapshots (Weinreb et al, 2018), dynamic analyses may be of immediate importance to resolve competing views on basic mechanisms and the role of stochasticity in cell fate decisions (Moris et al, 2016)
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