Department of Molecular and Cell Biology, University of California, 265 LSA, #3200, Berkeley, CA 94720, USA.
Berkeley Institute for Data Science, University of California, Berkeley, CA 94720, USA.
Bioessays. 2018 Aug;40(8):e1800056. doi: 10.1002/bies.201800056. Epub 2018 Jun 26.
Mapping the paths that stem and progenitor cells take en route to differentiate and elucidating the underlying molecular controls are key goals in developmental and stem cell biology. However, with population level analyses it is difficult - if not impossible - to define the transition states and lineage trajectory branch points within complex developmental lineages. Single-cell RNA-sequencing analysis can discriminate heterogeneity in a population of cells and even identify rare or transient intermediates. In this review, we propose that using these data, one can infer the lineage trajectories of individual stem cells and identify putative branch points. Clonal lineage tracing of stem cells allows one to define the outcome of differentiation. Integrating these single cell-based approaches provides a robust strategy for establishing and testing models of how an individual stem cell changes through time to differentiate and self-renew.
绘制干细胞和祖细胞在分化过程中所走的路径,并阐明潜在的分子调控机制,是发育和干细胞生物学的主要目标。然而,在群体水平分析中,很难(如果不是不可能的话)定义复杂发育谱系中的过渡状态和谱系轨迹分支点。单细胞 RNA 测序分析可以区分细胞群体中的异质性,甚至可以识别罕见或短暂的中间产物。在这篇综述中,我们提出可以利用这些数据推断单个干细胞的谱系轨迹,并确定潜在的分支点。干细胞的克隆谱系追踪可以定义分化的结果。整合这些基于单细胞的方法为建立和测试个体干细胞如何随时间变化以分化和自我更新的模型提供了一种强大的策略。