TY - JOUR
T1 - Systematic comparison of single-cell and single-nucleus RNA-sequencing methods
AU - Ding, Jiarui
AU - Adiconis, Xian
AU - Simmons, Sean K.
AU - Kowalczyk, Monika S.
AU - Hession, Cynthia C.
AU - Marjanovic, Nemanja D.
AU - Hughes, Travis K.
AU - Wadsworth, Marc H.
AU - Burks, Tyler
AU - Nguyen, Lan T.
AU - Kwon, John Y.H.
AU - Barak, Boaz
AU - Ge, William
AU - Kedaigle, Amanda J.
AU - Carroll, Shaina
AU - Li, Shuqiang
AU - Hacohen, Nir
AU - Rozenblatt-Rosen, Orit
AU - Shalek, Alex K.
AU - Villani, Alexandra Chloé
AU - Regev, Aviv
AU - Levin, Joshua Z.
N1 - Publisher Copyright: © 2020, The Author(s), under exclusive licence to Springer Nature America, Inc.
PY - 2020/6/1
Y1 - 2020/6/1
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=85083174394&partnerID=8YFLogxK
U2 - https://doi.org/10.1038/s41587-020-0465-8
DO - https://doi.org/10.1038/s41587-020-0465-8
M3 - مقالة
C2 - 32341560
SN - 1087-0156
VL - 38
SP - 737
EP - 746
JO - Nature biotechnology
JF - Nature biotechnology
IS - 6
ER -