计算机科学
计算生物学
核糖核酸
生物
遗传学
基因
作者
Atefeh Lafzi,Cátia Moutinho,Simone Picelli,Holger Heyn
出处
期刊:Nature Protocols
[Springer Nature]
日期:2018-11-15
卷期号:13 (12): 2742-2757
被引量:163
标识
DOI:10.1038/s41596-018-0073-y
摘要
Single-cell RNA sequencing is at the forefront of high-resolution phenotyping experiments for complex samples. Although this methodology requires specialized equipment and expertise, it is now widely applied in research. However, it is challenging to create broadly applicable experimental designs because each experiment requires the user to make informed decisions about sample preparation, RNA sequencing and data analysis. To facilitate this decision-making process, in this tutorial we summarize current methodological and analytical options, and discuss their suitability for a range of research scenarios. Specifically, we provide information about best practices for the separation of individual cells and provide an overview of current single-cell capture methods at different cellular resolutions and scales. Methods for the preparation of RNA sequencing libraries vary profoundly across applications, and we discuss features important for an informed selection process. An erroneous or biased analysis can lead to misinterpretations or obscure biologically important information. We provide a guide to the major data processing steps and options for meaningful data interpretation. These guidelines will serve as a reference to support users in building a single-cell experimental framework—from sample preparation to data interpretation—that is tailored to the underlying research context. In this tutorial, the authors provide a comprehensive description of the considerations for designing single-cell transcriptomics studies, from sample preparation and single-cell RNA sequencing methodologies through data processing and analysis.
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