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 different 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 differentiation in a well-mixed population. These models describe dynamics where differentiation 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 differentiation 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 differentiation. 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 differentiation, our analysis indicates a possible shift in dynamics, with these processes becoming more coupled over time.

通过测序、成像和表观遗传标记获得的大量单细胞数据导致对细胞状态的描述越来越详细。然而，确定细胞如何在不同状态之间转换仍然具有挑战性，部分原因是数据通常仅限于时间快照。从此类快照推断细胞状态转换的先决条件是区分转换是否与细胞分裂相关。为了解决这个问题，我们在混合良好的群体中提出了两种细胞分裂和分化的最小分支过程模型。这些模型描述了分化和分裂耦合或不耦合的动态。对于每个模型，我们为每个子群的均值和方差以及似然导出解析表达式，在完全观察到分化和分裂事件轨迹的理想情况下，允许精确的贝叶斯参数推断和模型选择。在快照的情况下，我们提出了一个样本路径算法，并使用它来预测实验设计的最佳测量时间间隔。然后我们将这种方法应用于体外数据集测定外胚层干细胞在促进自我更新或分化的培养条件下的克隆生长。这里，大量的细胞状态需要近似贝叶斯计算。对于这两种培养条件，我们的推论支持细胞状态转换与分裂耦合的模型。对于促进分化的文化条件，我们的分析表明动态可能发生转变，这些过程随着时间的推移变得更加耦合。