Picelli Simone
German Centre for Neurodegenerative Diseases (DZNE), Bonn, Germany.
Methods Mol Biol. 2019;1979:25-44. doi: 10.1007/978-1-4939-9240-9_3.
In the last few years single-cell RNA sequencing (scRNA-seq) has enabled the investigation of cellular heterogeneity at the transcriptional level, the characterization of rare cell types as well as the detailed analysis of the stochastic nature of gene expression. A large number of methods have been developed, varying in their throughput, sensitivity, and scalability. A major distinction is whether they profile only 5'- or 3'-terminal part of the transcripts or allow for the characterization of the entire length of the transcripts. Among the latter, Smart-seq2 is still considered the "gold standard" due to its sensitivity, precision, lower cost, scalability and for being easy to set up on automated platforms. In this chapter I describe how to efficiently generate sequencing-ready libraries, highlight common issues and pitfalls, and offer solutions for generating high-quality data.
在过去几年中，单细胞RNA测序（scRNA-seq）使得在转录水平上研究细胞异质性、鉴定稀有细胞类型以及详细分析基因表达的随机性成为可能。已经开发出了大量方法，这些方法在通量、灵敏度和可扩展性方面各不相同。一个主要区别在于它们是仅对转录本的5'端或3'端部分进行分析，还是能够对转录本的全长进行鉴定。在后者中，Smart-seq2因其灵敏度、精度、低成本、可扩展性以及易于在自动化平台上设置而仍被视为“金标准”。在本章中，我将描述如何高效地生成可用于测序的文库，突出常见问题和陷阱，并提供生成高质量数据的解决方案。
