Systematic comparison of single-cell and single-nucleus RNA-sequencing methods

• Published in: Nature biotechnology Vol. 38; no. 6; pp. 737 - 746
• Main Authors: Ding, Jiarui, Adiconis, Xian, Simmons, Sean K., Kowalczyk, Monika S., Hession, Cynthia C., Marjanovic, Nemanja D., Hughes, Travis K., Wadsworth, Marc H., Burks, Tyler, Nguyen, Lan T., Kwon, John Y. H., Barak, Boaz, Ge, William, Kedaigle, Amanda J., Carroll, Shaina, Li, Shuqiang, Hacohen, Nir, Rozenblatt-Rosen, Orit, Shalek, Alex K., Villani, Alexandra-Chloé, Regev, Aviv, Levin, Joshua Z.
• Format: Journal Article
• Language: English
• Published: New York Nature Publishing Group US 01.06.2020; Nature Publishing Group
• Subjects: 631/1647/2217/2018; 631/1647/2217/2218; 631/1647/48; 631/1647/514/1949; 631/337/2019; Agriculture; Analysis; Animals; Bioinformatics; Biomedical and Life Sciences; Biomedical Engineering/Biotechnology; Biomedicine; Biotechnology; Brain - cytology; Brain research; Cell lines; Cells, Cultured; Comparative analysis; Computational neuroscience; Experiments; Gene sequencing; Genes; Humans; Leukocytes (mononuclear); Leukocytes, Mononuclear - cytology; Libraries; Life Sciences; Mapping; Medical research; Methods; Mice; Nuclei (cytology); Peripheral blood mononuclear cells; Ribonucleic acid; RNA; RNA sequencing; Sequence Analysis, RNA - methods; Single-Cell Analysis - methods; Software; United States
• ISSN: 1087-0156; 1546-1696; 1546-1696
• DOI: 10.1038/s41587-020-0465-8

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. Seven methods for single-cell RNA sequencing are benchmarked on cell lines, primary cells and mouse cortex.