Journal
NATURE BIOTECHNOLOGY
Volume 38, Issue 6, Pages 747-+Publisher
NATURE PORTFOLIO
DOI: 10.1038/s41587-020-0469-4
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Funding
- Chan Zuckerberg Initiative DAF, an advised fund of the Silicon Valley Community Foundation [2018-182827]
- Spanish Institute of Health Carlos III (ISCIII) [CP14/00229]
- AECC postdoctoral fellowship
- European Union [H2020-MSCA-ITN-2015-675752]
- Ministerio de Ciencia, Innovacion y Universidades (AEI/FEDER, UE) [SAF2017-89109-P]
- German Research Foundation's (DFG's) Behrens-Weise-Foundation [GR4980]
- Max Planck Society
- European Molecular Biology Organization [ALTF 673-2017]
- National Institute of Allergy and Infectious Diseases [U24AI118672]
- Manton Foundation
- Klarman Cell Observatory
- JST CREST, Japan [JPMJCR16G3]
- Projects for Technological Development, Research Center Network for Realization of Regenerative Medicine by Japan
- Japan Agency for Medical Research and Development
- DFG [EN 1093/2-1, SFB1243 TP A14]
- Eukaryotic Single Cell Genomics Facility at Scilifelab (Stockholm, Sweden)
- European Research Council under the European Union [810287]
- ISCIII
- Generalitat de Catalunya
- European Research Council (ERC) [810287] Funding Source: European Research Council (ERC)
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A multicenter study compares 13 commonly used single-cell RNA-seq protocols. 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.
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