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      Cell Hashing with barcoded antibodies enables multiplexing and doublet detection for single cell genomics

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          Abstract

          Despite rapid developments in single cell sequencing, sample-specific batch effects, detection of cell multiplets, and experimental costs remain outstanding challenges. Here, we introduce Cell Hashing, where oligo-tagged antibodies against ubiquitously expressed surface proteins uniquely label cells from distinct samples, which can be subsequently pooled. By sequencing these tags alongside the cellular transcriptome, we can assign each cell to its original sample, robustly identify cross-sample multiplets, and “super-load” commercial droplet-based systems for significant cost reduction. We validate our approach using a complementary genetic approach and demonstrate how hashing can generalize the benefits of single cell multiplexing to diverse samples and experimental designs.

          Electronic supplementary material

          The online version of this article (10.1186/s13059-018-1603-1) contains supplementary material, which is available to authorized users.

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          Most cited references16

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          Fast unfolding of communities in large networks

          We propose a simple method to extract the community structure of large networks. Our method is a heuristic method that is based on modularity optimization. It is shown to outperform all other known community detection method in terms of computation time. Moreover, the quality of the communities detected is very good, as measured by the so-called modularity. This is shown first by identifying language communities in a Belgian mobile phone network of 2.6 million customers and by analyzing a web graph of 118 million nodes and more than one billion links. The accuracy of our algorithm is also verified on ad-hoc modular networks. .
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            OUP accepted manuscript

            Until recently, high-throughput gene expression technology, such as RNA-Sequencing (RNA-seq) required hundreds of thousands of cells to produce reliable measurements. Recent technical advances permit genome-wide gene expression measurement at the single-cell level. Single-cell RNA-Seq (scRNA-seq) is the most widely used and numerous publications are based on data produced with this technology. However, RNA-seq and scRNA-seq data are markedly different. In particular, unlike RNA-seq, the majority of reported expression levels in scRNA-seq are zeros, which could be either biologically-driven, genes not expressing RNA at the time of measurement, or technically-driven, genes expressing RNA, but not at a sufficient level to be detected by sequencing technology. Another difference is that the proportion of genes reporting the expression level to be zero varies substantially across single cells compared to RNA-seq samples. However, it remains unclear to what extent this cell-to-cell variation is being driven by technical rather than biological variation. Furthermore, while systematic errors, including batch effects, have been widely reported as a major challenge in high-throughput technologies, these issues have received minimal attention in published studies based on scRNA-seq technology. Here, we use an assessment experiment to examine data from published studies and demonstrate that systematic errors can explain a substantial percentage of observed cell-to-cell expression variability. Specifically, we present evidence that some of these reported zeros are driven by technical variation by demonstrating that scRNA-seq produces more zeros than expected and that this bias is greater for lower expressed genes. In addition, this missing data problem is exacerbated by the fact that this technical variation varies cell-to-cell. Then, we show how this technical cell-to-cell variability can be confused with novel biological results. Finally, we demonstrate and discuss how batch-effects and confounded experiments can intensify the problem.
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              Fluorescent cell barcoding in flow cytometry allows high-throughput drug screening and signaling profiling.

              Flow cytometry allows high-content, multiparameter analysis of single cells, making it a promising tool for drug discovery and profiling of intracellular signaling. To add high-throughput capacity to flow cytometry, we developed a cell-based multiplexing technique called fluorescent cell barcoding (FCB). In FCB, each sample is labeled with a different signature, or barcode, of fluorescence intensity and emission wavelengths, and mixed with other samples before antibody staining and analysis by flow cytometry. Using three FCB fluorophores, we were able to barcode and combine entire 96-well plates, reducing antibody consumption 100-fold and acquisition time to 5-15 min per plate. Using FCB and phospho-specific flow cytometry, we screened a small-molecule library for inhibitors of T cell-receptor and cytokine signaling, simultaneously determining compound efficacy and selectivity. We also analyzed IFN-gamma signaling in multiple cell types from primary mouse splenocytes, revealing differences in sensitivity and kinetics between B cells, CD4+ and CD4- T cells and CD11b-hi cells.
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                Author and article information

                Contributors
                psmibert@nygenome.org
                rsatija@nygenome.org
                Journal
                Genome Biol
                Genome Biol
                Genome Biology
                BioMed Central (London )
                1474-7596
                1474-760X
                19 December 2018
                19 December 2018
                2018
                : 19
                : 224
                Affiliations
                [1 ]GRID grid.429884.b, Technology Innovation Lab, , New York Genome Center, ; New York, NY USA
                [2 ]GRID grid.429884.b, NYU Center for Genomics and Systems Biology, , New York Genome Center, ; New York, NY USA
                [3 ]GRID grid.422444.0, BioLegend Inc., ; San Diego, CA USA
                Author information
                http://orcid.org/0000-0001-9448-8833
                Article
                1603
                10.1186/s13059-018-1603-1
                6300015
                30567574
                cc0dec8b-dce5-4ca9-b83d-fddffcc2de29
                © The Author(s). 2018

                Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License ( http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver ( http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.

                History
                : 5 June 2018
                : 4 December 2018
                Funding
                Funded by: FundRef http://dx.doi.org/10.13039/100000051, National Human Genome Research Institute;
                Award ID: DP2-HG-009623
                Award ID: NIHR21-HG-009748
                Award Recipient :
                Funded by: Chan Zuckerberg Initiative
                Award ID: HCA-A1704-01895
                Award ID: HCA2-A-1708-02755
                Award Recipient :
                Categories
                Method
                Custom metadata
                © The Author(s) 2018

                Genetics
                Genetics

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