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

2020; Nature Portfolio; Volume: 38; Issue: 6 Linguagem: Inglês

10.1038/s41587-020-0469-4

1546-1696

Elisabetta Mereu, Atefeh Lafzi, Cátia Moutinho, Christoph Ziegenhain, Davis J. McCarthy, Adrián Álvarez-Varela, Eduard Batlle, Sagar Sagar, Dominic Grün, Julia K. Lau, Stéphane C. Boutet, Chad Sanada, Aik T. Ooi, Robert C. Jones, Kelly A. Kaihara, Chris Brampton, Yasha Talaga, Yohei Sasagawa, Kaori Tanaka, Tetsutaro Hayashi, Caroline Braeuning, Cornelius Fischer, Sascha Sauer, Timo B. Trefzer, Christian Conrad, Xian Adiconis, Lan Nguyễn, Aviv Regev, Joshua Z. Levin, Swati Parekh, Aleksandar Janjic, Lucas E. Wange, Johannes Bagnoli, Wolfgang Enard, Marta Gut, Rickard Sandberg, Itoshi Nikaido, Marta Gut, Oliver Stegle, Holger Heyn,

Extracellular vesicles in disease

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. A multicenter study compares 13 commonly used single-cell RNA-seq protocols.