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

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. This project has been made possible in part by grant no. 2018-182827 from the Chan Zuckerberg Initiative DAF, an advised fund of the Silicon Valley Community Foundation. H.H. is a Miguel Servet (CP14/00229) researcher funded by the Spanish Institute of Health Carlos III (ISCIII). C.M. is supported by an AECC postdoctoral fellowship. This work has received funding from the European Union’s Horizon 2020 research and innovation program under Marie Skłodowska-Curie grant agreement no. H2020-MSCA-ITN-2015-675752 (Singek), and the Ministerio de Ciencia, Innovación y Universidades (SAF2017-89109-P; AEI/FEDER, UE). S. was supported by the German Research Foundation’s (DFG’s) (GR4980) Behrens-Weise-Foundation. C.Z. was supported by the European Molecular Biology Organization through the long-term fellowship ALTF 673-2017. The snRNA-seq data were generated with support from the National Institute of Allergy and Infectious Diseases (grant no. U24AI118672), I.N. was supported by JST CREST (grant no. JPMJCR16G3) , Japan. A.J., L.E.W., J.W.B. and W.E. were supported by funding from the DFG (EN 1093/2-1 and SFB1243 TP A14). This publication is part of a project (BCLLATLAS) that received funding from the European Research Council under the European Union’s Horizon 2020 research and innovation program (grant agreement no. 810287). Core funding was from the ISCIII and the Generalitat de Catalunya