Yuan Guo-Cheng, Cai Long, Elowitz Michael, Enver Tariq, Fan Guoping, Guo Guoji, Irizarry Rafael, Kharchenko Peter, Kim Junhyong, Orkin Stuart, Quackenbush John, Saadatpour Assieh, Schroeder Timm, Shivdasani Ramesh, Tirosh Itay
Department of Biostatistics and Computational Biology, Dana-Farber Cancer Institute, Boston, MA, USA.
Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, USA.
Genome Biol. 2017 May 8;18(1):84. doi: 10.1186/s13059-017-1218-y.
Single-cell analysis is a rapidly evolving approach to characterize genome-scale molecular information at the individual cell level. Development of single-cell technologies and computational methods has enabled systematic investigation of cellular heterogeneity in a wide range of tissues and cell populations, yielding fresh insights into the composition, dynamics, and regulatory mechanisms of cell states in development and disease. Despite substantial advances, significant challenges remain in the analysis, integration, and interpretation of single-cell omics data. Here, we discuss the state of the field and recent advances and look to future opportunities.
单细胞分析是一种快速发展的方法，用于在单个细胞水平上表征基因组规模的分子信息。单细胞技术和计算方法的发展使得对广泛组织和细胞群体中的细胞异质性进行系统研究成为可能，从而为发育和疾病中细胞状态的组成、动态和调控机制提供了新的见解。尽管取得了重大进展，但在单细胞组学数据的分析、整合和解释方面仍存在重大挑战。在这里，我们讨论该领域的现状和最新进展，并展望未来的机遇。
