How to design scRNA-seq experiments

The sequencing of the transcriptome of single cells, or single-cell RNA-sequencing, has now become the dominant technology for the identification of novel cell types in heterogeneous cell populations or for the study of stochastic gene expression. In recent years, various experimental methods and computational tools for analysing single-cell RNA-sequencing data have been proposed. However, most of them are tailored to different experimental designs or biological questions, and in many cases, their performance has not been benchmarked yet, thus increasing the difficulty for a researcher to choose the optimal single-cell transcriptome sequencing (scRNA-seq) experiment and analysis workflow.

In this review, University of Padova researchers aim to provide an exhaustive overview of the current available experimental and computational methods developed to handle single-cell RNA-sequencing data and, based on their peculiarities, they suggest possible analysis frameworks depending on specific experimental designs. Together the researchers propose an evaluation of challenges and open questions and future perspectives in the field.

In particular, the researchers go through scRNA-seq experimental protocols (cell isolation, mRNA capture, reverse transcription and amplification), use of quantitative standards (spike-ins and UMIs), current experimental challenges, preprocessing, alignment and quantification of scRNA-seq data, normalization issues, batch effect correction and methods to control confounding effects.

 Workflow diagram of possible experimental designs of a scRNA-seq experiment

rna-seq

Dashed lines represent optional choices. Figure inspired by Cannoodt et al. Ref – Cannoodt Robrecht, Saelens Wouter, Saeys Yvan (2016) Computational methods for trajectory inference from single-cell transcriptomics. European Journal of Immunology. [abstract]

Dal Molin A, Di Camillo B. (2018) How to design a single-cell RNA-sequencing experiment: pitfalls, challenges and perspectives. Briefings in Bioinformatics [Epub ahead of print]. [abstract]

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