Inferring population dynamics from single-cell RNA-sequencing time series data
Inferring population dynamics from single-cell RNA-sequencing time series data
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DOI：
10.1038/s41587-019-0088-0
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发表时间：
2019-04-01
影响因子：
46.9
通讯作者：
Theis, Fabian J.
中科院分区：
文献类型：
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作者：
Fischer, David S.;Fiedler, Anna K.;Theis, Fabian J.
最近的单细胞RNA测序研究表明，细胞在发育过程中以异步方式遵循连续的转录组轨迹。然而，观察细胞通量沿着轨迹与人口规模的影响，在快照实验，因此很难解释。特别是，增殖和死亡率的变化可能被误认为是细胞通量。在这里，我们提出了pseudodynamics，一个数学框架，调和人口动态的概念，从时间序列单细胞数据推断的发展轨迹。伪动力学模型人口分布的轨迹变化，以量化选择压力，人口扩张和发展潜力。将该模型应用于T细胞和胰腺β细胞成熟的时间分辨单细胞RNA测序，我们表征了增殖和凋亡率，并确定了关键的发育检查点，现有方法无法获得的数据。
Recent single-cell RNA-sequencing studies have suggested that cells follow continuous transcriptomic trajectories in an asynchronous fashion during development. However, observations of cell flux along trajectories are confounded with population size effects in snapshot experiments and are therefore hard to interpret. In particular, changes in proliferation and death rates can be mistaken for cell flux. Here we present pseudodynamics, a mathematical framework that reconciles population dynamics with the concepts underlying developmental trajectories inferred from time-series single-cell data. Pseudodynamics models population distribution shifts across trajectories to quantify selection pressure, population expansion, and developmental potentials. Applying this model to time-resolved single-cell RNA-sequencing of T-cell and pancreatic beta cell maturation, we characterize proliferation and apoptosis rates and identify key developmental checkpoints, data inaccessible to existing approaches.