Systematic comparison of single-cell and single-nucleus RNA-sequencing methods
By Jiarui Ding, Xian Adiconis, Sean K. Simmons, Monika S. Kowalczyk, Cynthia C. Hession, Nemanja Marjanović, T. Hughes, M. Wadsworth, Tyler Burks, Lan Nguyen and 12 more. Nature Biotechnology, 2020
723 citations recorded by the source corpus.
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.
Subfields: Immunology, Molecular Biology, Neurology
View Paper: https://www.semanticscholar.org/paper/9801c18d3ed0085d9cc3bd8007086682bcafc31e
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Citations in the corpus
- TISCH: a comprehensive web resource enabling interactive single-cell transcriptome visualization of tumor microenvironment (2020, background): “Large scale repositories, such as Single Cell Portal from the Broad Institute (14) and Single Cell Expression Atlas from European Bioinformatics Institute (EMBL-EBL) (15), provide greater numbers of datasets, but they are not cancer-focused, and have limited and often inconsistent cell-type annotati”
- Comparison and evaluation of statistical error models for scRNA-seq (2021, methodology): “Other PBMC datasets are available from GSE132044 [69].”
- Integrated analysis of multimodal single-cell data (2020, background): “Despite tremendous functional diversity, distinct populations of T cells such as effector, regulatory, gd, and mucosal associated invariant T (MAIT), often cannot be effectively separated by scRNA-seq alone, even when using the most sensitive and cutting-edge technologies (Ding et al., 2020; Mereu e”