Open AccessSystematic comparison of single-cell and single-nucleus RNA-sequencing methods
Author(s) -
Jiarui Ding,
Xian Adiconis,
Sean Simmons,
Monika S. Kowalczyk,
Cynthia C. Hession,
Nemanja D. Marjanovic,
Travis Hughes,
Marc H. Wadsworth,
Tyler Burks,
Lan Nguyễn,
John Kwon,
Boaz Barak,
William Ge,
Amanda J. Kedaigle,
Shaina L. Carroll,
Shuqiang Li,
Nir Hacohen,
Orit Rozenblatt-Rosen,
Alex K. Shalek,
Alexandra–Chloé Villani,
Aviv Regev,
Joshua Z. Levin
Publication year - 2020
Publication title -
nature biotechnology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 15.358
H-Index - 445
eISSN - 1546-1696
pISSN - 1087-0156
DOI - 10.1038/s41587-020-0465-8
Subject(s) - computer science , computational biology , pipeline (software) , rna , single cell analysis , profiling (computer programming) , cell , throughput , biology , gene , genetics , programming language , operating system , telecommunications , wireless
The scale and capabilities of single-cell RNA-sequencing methods have expanded rapidly in recent years, enabling major discoveries and large-scale cell mapping efforts. However, these methods have not been systematically and comprehensively benchmarked. Here, we directly compare seven methods for single-cell and/or single-nucleus profiling-selecting representative methods based on their usage and our expertise and resources to prepare libraries-including two low-throughput and five high-throughput methods. We tested the methods on three types of samples: cell lines, peripheral blood mononuclear cells and brain tissue, generating 36 libraries in six separate experiments in a single center. To directly compare the methods and avoid processing differences introduced by the existing pipelines, we developed scumi, a flexible computational pipeline that can be used with any single-cell RNA-sequencing method. We evaluated the methods for both basic performance, such as the structure and alignment of reads, sensitivity and extent of multiplets, and for their ability to recover known biological information in the samples.