A Bioinformatic Toolkit for Single-Cell mRNA Analysis
The recent technological developments in the field of single-cell RNA-Seq enable us to assay the transcriptome of up to a million single cells in parallel. However, the analyses of such big datasets present a major challenge. During the last decade, a wid
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Introduction Recent technologies have enabled to assay the transcriptome of up to a million of single cells in parallel. However, the analyses of such big datasets become a challenge. Moreover, the vast number of different analytical tools that emerge almost every week are overwhelming for a researcher, especially when his or her focus is on wet-lab experiments and not on bioinformatics. Here we introduce
Valentina Proserpio (ed.), Single Cell Methods: Sequencing and Proteomics, Methods in Molecular Biology, vol. 1979, https://doi.org/10.1007/978-1-4939-9240-9_26, © Springer Science+Business Media, LLC, part of Springer Nature 2019
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the main steps of a typical bioinformatics pipeline for the analysis of single-cell mRNA-Seq data starting from quality assessment of reads and alignment toward cell-subtype discovery in low-dimensional space. Figure 1 presents a comprehensive overview of the main analytical steps covered in this chapter. Since each of these steps presents some downsides, we not only introduce algorithms, methods, and tools but also critically revise their applicability and limitations. Furthermore, we aim this section at biologists and bench scientists to help them understand the meaning of each analysis step and to get an overview about existing methods in the field.
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Experimental Planning One of the most important steps toward a successful application of single-cell mRNA-Seq to a biological question is a detailed planning of the experiment. In the first two sections of this chapter, we focus on aspects that are needed to be taken into account, when planning a proper single-cell mRNA-Seq experiment.
2.1 Choosing a Single-Cell mRNASeq Technology
Following the first description of single-cell mRNA sequencing in 2009 [1], a wide variety of single-cell mRNA-Seq methods has been proposed. All methods have certain advantages, which demand an experimenter to choose a technique that is best suited for the biological question in mind. Regarding the gene body coverage of single-cell mRNA-Seq data, two major protocol types have emerged. Full-length methods (e.g., SMART-Seq2 [2] and Strt-Seq [3]) provide read coverage across the complete transcript allowing the investigation of, for example, alternative RNA processing. However, most available single-cell protocols (e.g., Drop-Seq [4], Seq-Well [5] or sci-RNA-Seq [6]) sacrifice full-length coverage for the sake of early multiplexing, which minimizes the cost. Another important consideration during planning of a singlecell mRNA-Seq experiment is the procedure of isolating single cells from a cell mixture. Early isolation protocols focus on manual cell isolation techniques, such as micropipetting or laser capture microdissection. While these techniques gain spatial information about selected cells, their throughput is very low [7]. Fluorescenceactivated cell sorting (FACS) is a widely established technique that can be used for isolation of single cells. In addition, FACS records the protein expression of a cell, which allows to
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