Olsen Thale Kristin, Baryawno Ninib
Childhood Cancer Research Unit, Department of Women's and Children's Health, Karolinska Institutet, Stockholm, Sweden.
Curr Protoc Mol Biol. 2018 Apr;122(1):e57. doi: 10.1002/cpmb.57.
During the last decade, high-throughput sequencing methods have revolutionized the entire field of biology. The opportunity to study entire transcriptomes in great detail using RNA sequencing (RNA-seq) has fueled many important discoveries and is now a routine method in biomedical research. However, RNA-seq is typically performed in "bulk," and the data represent an average of gene expression patterns across thousands to millions of cells; this might obscure biologically relevant differences between cells. Single-cell RNA-seq (scRNA-seq) represents an approach to overcome this problem. By isolating single cells, capturing their transcripts, and generating sequencing libraries in which the transcripts are mapped to individual cells, scRNA-seq allows assessment of fundamental biological properties of cell populations and biological systems at unprecedented resolution. Here, we present the most common scRNA-seq protocols in use today and the basics of data analysis and discuss factors that are important to consider before planning and designing an scRNA-seq project. © 2018 by John Wiley & Sons, Inc.
在过去十年中,高通量测序方法彻底改变了整个生物学领域。利用RNA测序(RNA-seq)详细研究整个转录组的机会推动了许多重要发现,如今它已成为生物医学研究中的常规方法。然而,RNA-seq通常是在“批量”条件下进行的,数据代表了数千到数百万个细胞的基因表达模式的平均值;这可能会掩盖细胞之间生物学上相关的差异。单细胞RNA测序(scRNA-seq)是一种克服这一问题的方法。通过分离单个细胞、捕获其转录本并生成将转录本映射到单个细胞的测序文库,scRNA-seq能够以前所未有的分辨率评估细胞群体和生物系统的基本生物学特性。在此,我们介绍当今使用的最常见的scRNA-seq方案以及数据分析的基础知识,并讨论在规划和设计scRNA-seq项目之前需要考虑的重要因素。© 2018约翰威立父子公司版权所有