Quantitative single-cell transcriptomics

Single-cell RNA sequencing (scRNA-seq) is currently transforming our understanding of biology, as it is a powerful tool to resolve cellular heterogeneity and molecular networks. Over 50 protocols have been developed in recent years and also data processing and analyzes tools are evolving fast.

Here, researchers at Ludwig-Maximilians University review the basic principles underlying the different experimental protocols and how to benchmark them. They also review and compare the essential methods to process scRNA-seq data from mapping, filtering, normalization and batch corrections to basic differential expression analysis.

 Overview of commonly used scRNA-seq libraries

rna-seq

Shown are the length and position of barcodes that distinguish cells [Barcode (BC)], UMIs, sequencing primers, Illumina indices (i5, i7) and adapter sequences needed for PCR, tagmentation and sequencing. Note that except for Smart-seq1/2, all methods contain BCs and UMIs and preserve the strand information (star). As a consequence, only Smart-seq1/2 among the shown libraries provides full-length information.

Ziegenhain C, Vieth B, Parekh S, Hellmann I, Enard W. (2018) Quantitative single-cell transcriptomics. Brief Funct Genomics [Epub ahead of print]. [article]

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