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Coupled differentiation and division of embryonic stem cells inferred from clonal snapshots

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Original languageEnglish
JournalPhysical Biology
Early online date25 Jun 2020
DOIs
Publication statusE-pub ahead of print - 25 Jun 2020

Abstract

The deluge of single-cell data obtained by sequencing, imaging and
epigenetic markers has led to an increasingly detailed description of cell state. However, it remains challenging to identify how cells transition between dierent states, in part because data are typically limited to snapshots in time. A prerequisite for inferring cell state transitions from such snapshots is to distinguish whether transitions are coupled to cell divisions. To address this, we present two minimal branching process models of cell division and dierentiation in a well-mixed population. These models describe dynamics where dierentiation and division are coupled or uncoupled. For each model,
we derive analytic expressions for each subpopulation's mean and variance and for the likelihood, allowing exact Bayesian parameter inference and model selection in the idealised case of fully observed trajectories of dierentiation and division events. In the case of snapshots, we present a sample path algorithm and use this to predict optimal temporal spacing of measurements for experimental design. We then apply this methodology to an in vitro dataset assaying the clonal growth of epiblast stem cells in culture conditions promoting self-renewal or dierentiation. Here, the larger number of cell states necessitates approximate Bayesian computation. For both culture conditions, our inference supports the model where cell state transitions are coupled to division. For culture conditions promoting dierentiation, our analysis indicates a possible shift in dynamics, with these processes becoming more coupled over time.

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