Stem Cell Differentiation as a Non-Markov Stochastic Process

Patrick S. Stumpf, Rosanna C.G. Smith, Michael Lenz, Andreas Schuppert, Franz-Josef Müller, Ann Babtie, Thalia E. Chan, Michael P.H. Stumpf, Colin P. Please, Sam D. Howison, Fumio Arai, Ben D. MacArthur
2017 Cell Systems  
Graphical Abstract Highlights d We profile individual stem cells as they differentiate along the neural lineage d Regulatory network changes and increased cell variability accompany differentiation d Analysis of dynamics with a hidden Markov model reveals unobserved molecular states d We propose a model of stem cell differentiation as a non-Markov stochastic process SUMMARY Pluripotent stem cells can self-renew in culture and differentiate along all somatic lineages in vivo. While much is known
more » ... about the molecular basis of pluripotency, the mechanisms of differentiation remain unclear. Here, we profile individual mouse embryonic stem cells as they progress along the neuronal lineage. We observe that cells pass from the pluripotent state to the neuronal state via an intermediate epiblast-like state. However, analysis of the rate at which cells enter and exit these observed cell states using a hidden Markov model indicates the presence of a chain of unobserved molecular states that each cell transits through stochastically in sequence. This chain of hidden states allows individual cells to record their position on the differentiation trajectory, thereby encoding a simple form of cellular memory. We suggest a statistical mechanics interpretation of these results that distinguishes between functionally distinct cellular "macrostates" and functionally similar molecular "microstates" and propose a model of stem cell differentiation as a non-Markov stochastic process.
doi:10.1016/j.cels.2017.08.009 pmid:28957659 pmcid:PMC5624514 fatcat:hh377pbol5azrj2yeew6e3eehi