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

Item Type:Article
Title:Benchmarking single-cell RNA-sequencing protocols for cell atlas projects
Creators Name:Mereu, E. and Lafzi, A. and Moutinho, C. and Ziegenhain, C. and McCarthy, D.J. and Álvarez-Varela, A. and Batlle, E. and Sagar, and Grün, D. and Lau, J.K. and Boutet, S.C. and Sanada, C. and Ooi, A. and Jones, R.C. and Kaihara, K. and Brampton, C. and Talaga, Y. and Sasagawa, Y. and Tanaka, K. and Hayashi, T. and Braeuning, C. and Fischer, C. and Sauer, S. and Trefzer, T. and Conrad, C. and Adiconis, X. and Nguyen, L.T. and Regev, A. and Levin, J.Z. and Parekh, S. and Janjic, A. and Wange, L.E. and Bagnoli, J.W. and Enard, W. and Gut, M. and Sandberg, R. and Nikaido, I. and Gut, I. and Stegle, O. and Heyn, H.
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.
Keywords:Benchmarking, Cell Line, Genetic Databases, Genomics, RNA Sequence Analysis, Single-Cell Analysis, Animals, Mice
Source:Nature Biotechnology
ISSN:1087-0156
Publisher:Nature Publishing Group
Volume:38
Number:6
Page Range:747-755
Date:June 2020
Additional Information:Copyright © 2020, The Author(s), under exclusive licence to Springer Nature America, Inc.
Official Publication:https://doi.org/10.1038/s41587-020-0469-4
External Fulltext:View full text on external repository or document server
PubMed:View item in PubMed

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