CNAG-CRG, Centre for Genomic Regulation (CRG), Barcelona Institute of Science and Technology (BIST), Barcelona, Spain.
Research Institute for Neurodegenerative Diseases (DZNE), Bonn, Germany.
Nat Protoc. 2018 Dec;13(12):2742-2757. doi: 10.1038/s41596-018-0073-y.
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.
单细胞 RNA 测序是复杂样本高分辨率表型实验的前沿技术。尽管这种方法需要专门的设备和专业知识，但现在已广泛应用于研究中。然而，由于每个实验都需要用户就样本制备、RNA 测序和数据分析做出明智的决策，因此很难创建广泛适用的实验设计。为了促进这一决策过程，在本教程中，我们总结了当前的方法和分析选择，并讨论了它们在一系列研究场景中的适用性。具体来说，我们提供了有关分离单个细胞的最佳实践信息，并概述了当前在不同细胞分辨率和尺度下的单细胞捕获方法。RNA 测序文库的制备方法在应用中差异很大，我们讨论了对知情选择过程很重要的特征。错误或有偏差的分析可能导致对生物学重要信息的误解或掩盖。我们提供了主要数据处理步骤和有意义的数据解释选项的指南。这些指南将作为参考，支持用户构建单细胞实验框架——从样本制备到数据解释——以适应基础研究背景。
