Benchmarking single-cell RNA-sequencing protocols for cell atlas projects

Nature Biotechnology
Elisabetta MereuHolger Heyn

Abstract

Single-cell RNA sequencing (scRNA-seq) is the leading technique for characterizing the transcriptomes of individual cells in a sample. The latest protocols are scalable to thousands of cells and are being used to compile cell atlases of tissues, organs and organisms. However, the protocols differ substantially with respect to their RNA capture efficiency, bias, scale and costs, and their relative advantages for different applications are unclear. In the present study, we generated benchmark datasets to systematically evaluate protocols in terms of their power to comprehensively describe cell types and states. We performed a multicenter study comparing 13 commonly used scRNA-seq and single-nucleus RNA-seq protocols applied to a heterogeneous reference sample resource. Comparative analysis revealed marked differences in protocol performance. The protocols differed in library complexity and their ability to detect cell-type markers, impacting their predictive value and suitability for integration into reference cell atlases. These results provide guidance both for individual researchers and for consortium projects such as the Human Cell Atlas.

Associated Datasets

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Methods Mentioned

BETA
scRNA-seq
single-cell sequencing
FACS
snRNA-seq
Smart-seq2
inDrop
in vitro transcription
PCR
in
CEL-seq2

Software Mentioned

harmony
Smart
Seq2
ddSEQ
UMAP
Quartz
corplot
SCDE R package
nnet R package
Seurat

Related Concepts

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CZI Human Cell Atlas Seed Network

The aim of the Human Cell Atlas (HCA) is to build reference maps of all human cells in order to enhance our understanding of health and disease. The Seed Networks for the HCA project aims to bring together collaborators with different areas of expertise in order to facilitate the development of the HCA. Find the latest research from members of the HCA Seed Networks here.

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