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

Authors

  • E. Mereu
  • A. Lafzi
  • C. Moutinho
  • C. Ziegenhain
  • D.J. McCarthy
  • A. Álvarez-Varela
  • E. Batlle
  • Sagar
  • D. Grün
  • J.K. Lau
  • S.C. Boutet
  • C. Sanada
  • A. Ooi
  • R.C. Jones
  • K. Kaihara
  • C. Brampton
  • Y. Talaga
  • Y. Sasagawa
  • K. Tanaka
  • T. Hayashi
  • C. Braeuning
  • C. Fischer
  • S. Sauer
  • T. Trefzer
  • C. Conrad
  • X. Adiconis
  • L.T. Nguyen
  • A. Regev
  • J.Z. Levin
  • S. Parekh
  • A. Janjic
  • L.E. Wange
  • J.W. Bagnoli
  • W. Enard
  • M. Gut
  • R. Sandberg
  • I. Nikaido
  • I. Gut
  • O. Stegle
  • H. Heyn

Journal

  • Nature Biotechnology

Citation

  • Nat Biotechnol 38 (6): 747-755

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.


DOI

doi:10.1038/s41587-020-0469-4