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      SoupX removes ambient RNA contamination from droplet-based single-cell RNA sequencing data

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      GigaScience
      Oxford University Press
      scRNA-seq, decontamination, pre-processing

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          Abstract

          Background

          Droplet-based single-cell RNA sequence analyses assume that all acquired RNAs are endogenous to cells. However, any cell-free RNAs contained within the input solution are also captured by these assays. This sequencing of cell-free RNA constitutes a background contamination that confounds the biological interpretation of single-cell transcriptomic data.

          Results

          We demonstrate that contamination from this "soup" of cell-free RNAs is ubiquitous, with experiment-specific variations in composition and magnitude. We present a method, SoupX, for quantifying the extent of the contamination and estimating "background-corrected" cell expression profiles that seamlessly integrate with existing downstream analysis tools. Applying this method to several datasets using multiple droplet sequencing technologies, we demonstrate that its application improves biological interpretation of otherwise misleading data, as well as improving quality control metrics.

          Conclusions

          We present SoupX, a tool for removing ambient RNA contamination from droplet-based single-cell RNA sequencing experiments. This tool has broad applicability, and its application can improve the biological utility of existing and future datasets.

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          Most cited references23

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          Integrating single-cell transcriptomic data across different conditions, technologies, and species

          Computational single-cell RNA-seq (scRNA-seq) methods have been successfully applied to experiments representing a single condition, technology, or species to discover and define cellular phenotypes. However, identifying subpopulations of cells that are present across multiple data sets remains challenging. Here, we introduce an analytical strategy for integrating scRNA-seq data sets based on common sources of variation, enabling the identification of shared populations across data sets and downstream comparative analysis. We apply this approach, implemented in our R toolkit Seurat (http://satijalab.org/seurat/), to align scRNA-seq data sets of peripheral blood mononuclear cells under resting and stimulated conditions, hematopoietic progenitors sequenced using two profiling technologies, and pancreatic cell 'atlases' generated from human and mouse islets. In each case, we learn distinct or transitional cell states jointly across data sets, while boosting statistical power through integrated analysis. Our approach facilitates general comparisons of scRNA-seq data sets, potentially deepening our understanding of how distinct cell states respond to perturbation, disease, and evolution.
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            • Record: found
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            Massively parallel digital transcriptional profiling of single cells

            Characterizing the transcriptome of individual cells is fundamental to understanding complex biological systems. We describe a droplet-based system that enables 3′ mRNA counting of tens of thousands of single cells per sample. Cell encapsulation, of up to 8 samples at a time, takes place in ∼6 min, with ∼50% cell capture efficiency. To demonstrate the system's technical performance, we collected transcriptome data from ∼250k single cells across 29 samples. We validated the sensitivity of the system and its ability to detect rare populations using cell lines and synthetic RNAs. We profiled 68k peripheral blood mononuclear cells to demonstrate the system's ability to characterize large immune populations. Finally, we used sequence variation in the transcriptome data to determine host and donor chimerism at single-cell resolution from bone marrow mononuclear cells isolated from transplant patients.
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              • Record: found
              • Abstract: found
              • Article: not found

              Highly Parallel Genome-wide Expression Profiling of Individual Cells Using Nanoliter Droplets.

              Cells, the basic units of biological structure and function, vary broadly in type and state. Single-cell genomics can characterize cell identity and function, but limitations of ease and scale have prevented its broad application. Here we describe Drop-seq, a strategy for quickly profiling thousands of individual cells by separating them into nanoliter-sized aqueous droplets, associating a different barcode with each cell's RNAs, and sequencing them all together. Drop-seq analyzes mRNA transcripts from thousands of individual cells simultaneously while remembering transcripts' cell of origin. We analyzed transcriptomes from 44,808 mouse retinal cells and identified 39 transcriptionally distinct cell populations, creating a molecular atlas of gene expression for known retinal cell classes and novel candidate cell subtypes. Drop-seq will accelerate biological discovery by enabling routine transcriptional profiling at single-cell resolution. VIDEO ABSTRACT.
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                Author and article information

                Contributors
                Journal
                Gigascience
                Gigascience
                gigascience
                GigaScience
                Oxford University Press
                2047-217X
                26 December 2020
                December 2020
                26 December 2020
                : 9
                : 12
                : giaa151
                Affiliations
                Wellcome Trust Sanger Institute, Cellular Genetics , Wellcome Genome Campus, Hinxton, CB10 1SA, UK
                Wellcome Trust Sanger Institute, Cellular Genetics , Wellcome Genome Campus, Hinxton, CB10 1SA, UK
                Cambridge University Hospitals NHS Foundation Trust , Hills Road, Cambridge, CB2 0QQ, UK
                University of Cambridge, Department of Paediatrics , Cambridge Biomedical Campus, Cambridge, CB2 0QQ, UK
                Author notes
                Correspondence address. Matthew D. Young, Wellcome Trust Sanger Institute, Cellular Genetics, Wellcome Genome Campus, Hinxton, CB10 1SA, UK. E-mail: my4@ 123456sanger.ac.uk
                Author information
                http://orcid.org/0000-0003-0937-5290
                http://orcid.org/0000-0002-6600-7665
                Article
                giaa151
                10.1093/gigascience/giaa151
                7763177
                33367645
                023db69a-624e-4a0a-95fa-1225b57f78e6
                © The Author(s) 2020. Published by Oxford University Press GigaScience.

                This is an Open Access article distributed under the terms of the Creative Commons Attribution License ( http://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited.

                History
                : 03 February 2020
                : 13 October 2020
                : 27 November 2020
                Page count
                Pages: 13
                Funding
                Funded by: Wellcome Trust, DOI 10.13039/100010269;
                Categories
                Technical Note
                AcademicSubjects/SCI00960
                AcademicSubjects/SCI02254

                scrna-seq,decontamination,pre-processing
                scrna-seq, decontamination, pre-processing

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