Wang Lingfei, Zhang Qian, Qin Qian, Trasanidis Nikolaos, Vinyard Michael, Chen Huidong, Pinello Luca
Molecular Pathology Unit and Center for Cancer Research, Massachusetts General Hospital Research Institute, Charlestown, USA.
Department of Pathology, Harvard Medical School, Boston, USA.
Curr Opin Syst Biol. 2021 Jun;26:1-11. doi: 10.1016/j.coisb.2021.03.006. Epub 2021 Mar 26.
Rapid technological advances in transcriptomics and lineage tracing technologies provide new opportunities to understand organismal development at the single-cell level. Building on these advances, various computational methods have been proposed to infer developmental trajectories and to predict cell fate. These methods have unveiled previously uncharacterized transitional cell types and differentiation processes. Importantly, the ability to recover cell states and trajectories has been evolving hand-in-hand with new technologies and diverse experimental designs; more recent methods can capture complex trajectory topologies and infer short- and long-term cell fate dynamics. Here, we summarize and categorize the most recent and popular computational approaches for trajectory inference based on the information they leverage and describe future challenges and opportunities for the development of new methods for reconstructing differentiation trajectories and inferring cell fates.
转录组学和谱系追踪技术的快速技术进步为在单细胞水平上理解生物体发育提供了新机会。基于这些进展,人们提出了各种计算方法来推断发育轨迹并预测细胞命运。这些方法揭示了以前未被表征的过渡细胞类型和分化过程。重要的是,恢复细胞状态和轨迹的能力一直与新技术和多样的实验设计携手发展;最新的方法可以捕捉复杂的轨迹拓扑结构并推断短期和长期的细胞命运动态。在这里,我们根据它们所利用的信息对最新和最流行的轨迹推断计算方法进行总结和分类,并描述重建分化轨迹和推断细胞命运的新方法开发的未来挑战和机遇。