Meakins-Christie Laboratories, Department of Medicine, McGill University Health Centre, 1001 Decarie Boulevard, Montreal QC H4A 3J1, Canada.
Computational Biology Department, School of Computer Science, Carnegie Mellon University, 5000 Forbes Avenue, Pittsburgh, PA 15213, USA.
Cell Rep Methods. 2021 Oct 4;1(6):100087. doi: 10.1016/j.crmeth.2021.100087. eCollection 2021 Oct 25.
Single-cell technologies are revolutionizing the ability of researchers to infer the causes and results of biological processes. Although several studies of pluripotent cell differentiation have recently utilized single-cell sequencing data, other aspects related to the optimization of differentiation protocols, their validation, robustness, and usage are still not taking full advantage of single-cell technologies. In this review, we focus on computational approaches for the analysis of single-cell omics and imaging data and discuss their use to address many of the major challenges involved in the development, validation, and use of cells obtained from pluripotent cell differentiation.
单细胞技术正在彻底改变研究人员推断生物过程的原因和结果的能力。尽管最近有几项多能干细胞分化的研究利用了单细胞测序数据,但与优化分化方案、验证其稳健性以及使用相关的其他方面仍未充分利用单细胞技术。在这篇综述中,我们专注于单细胞组学和成像数据分析的计算方法,并讨论它们如何用于解决多能干细胞分化获得的细胞的开发、验证和使用中涉及的许多主要挑战。