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      Single-cell immunophenotyping of the skin lesion erythema migrans identifies IgM memory B cells

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          Abstract

          The skin lesion erythema migrans (EM) is an initial sign of the Ixodes tick–transmitted Borreliella spirochetal infection known as Lyme disease. T cells and innate immune cells have previously been shown to predominate the EM lesion and promote the reaction. Despite the established importance of B cells and antibodies in preventing infection, the role of B cells in the skin immune response to Borreliella is unknown. Here, we used single-cell RNA-Seq in conjunction with B cell receptor (BCR) sequencing to immunophenotype EM lesions and their associated B cells and BCR repertoires. We found that B cells were more abundant in EM in comparison with autologous uninvolved skin; many were clonally expanded and had circulating relatives. EM-associated B cells upregulated the expression of MHC class II genes and exhibited preferential IgM isotype usage. A subset also exhibited low levels of somatic hypermutation despite a gene expression profile consistent with memory B cells. Our study demonstrates that single-cell gene expression with paired BCR sequencing can be used to interrogate the sparse B cell populations in human skin and reveals that B cells in the skin infection site in early Lyme disease expressed a phenotype consistent with local antigen presentation and antibody production.

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          SciPy 1.0: fundamental algorithms for scientific computing in Python

          SciPy is an open-source scientific computing library for the Python programming language. Since its initial release in 2001, SciPy has become a de facto standard for leveraging scientific algorithms in Python, with over 600 unique code contributors, thousands of dependent packages, over 100,000 dependent repositories and millions of downloads per year. In this work, we provide an overview of the capabilities and development practices of SciPy 1.0 and highlight some recent technical developments.
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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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              Enrichr: a comprehensive gene set enrichment analysis web server 2016 update

              Enrichment analysis is a popular method for analyzing gene sets generated by genome-wide experiments. Here we present a significant update to one of the tools in this domain called Enrichr. Enrichr currently contains a large collection of diverse gene set libraries available for analysis and download. In total, Enrichr currently contains 180 184 annotated gene sets from 102 gene set libraries. New features have been added to Enrichr including the ability to submit fuzzy sets, upload BED files, improved application programming interface and visualization of the results as clustergrams. Overall, Enrichr is a comprehensive resource for curated gene sets and a search engine that accumulates biological knowledge for further biological discoveries. Enrichr is freely available at: http://amp.pharm.mssm.edu/Enrichr.
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                Author and article information

                Contributors
                Journal
                JCI Insight
                JCI Insight
                JCI Insight
                JCI Insight
                American Society for Clinical Investigation
                2379-3708
                22 June 2021
                22 June 2021
                22 June 2021
                : 6
                : 12
                : e148035
                Affiliations
                [1 ]Department of Immunobiology,
                [2 ]Department of Pathology, and
                [3 ]Department of Neurology, Yale School of Medicine, New Haven, Connecticut, USA.
                [4 ]Broad Institute of MIT and Harvard University, Cambridge, Massachusetts, USA.
                [5 ]Mansfield Family Practice, Storrs, Connecticut, USA.
                [6 ]Department of Internal Medicine, Yale School of Medicine, New Haven, Connecticut, USA.
                [7 ]The Ragon Institute of MGH, MIT and Harvard, Cambridge, Massachusetts, USA.
                [8 ]Institute for Medical Engineering & Science, Department of Chemistry, and Koch Institute for Integrative Cancer Research, MIT, Cambridge, Massachusetts, USA.
                [9 ]Interdepartmental Program in Computational Biology and Bioinformatics, Yale University, New Haven, Connecticut, USA.
                Author notes
                Address correspondence to: Linda K. Bockenstedt, Department of Internal Medicine, Yale School of Medicine, 300 Cedar St., New Haven, Connecticut 06520, USA. Email: linda.bockenstedt@ 123456yale.edu .

                Authorship note: SHK and LKB are co–senior authors.

                Author information
                http://orcid.org/0000-0002-8494-082X
                http://orcid.org/0000-0002-2383-9744
                http://orcid.org/0000-0002-4691-6933
                http://orcid.org/0000-0003-0411-4307
                http://orcid.org/0000-0002-1315-6106
                http://orcid.org/0000-0002-8661-4454
                http://orcid.org/0000-0001-5670-8778
                http://orcid.org/0000-0003-4957-1544
                Article
                148035
                10.1172/jci.insight.148035
                8262471
                34061047
                338c35c6-7da7-43d2-833d-774bdfdec54c
                © 2021 Jiang et al.

                This work is licensed under the Creative Commons Attribution 4.0 International License. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.

                History
                : 18 February 2021
                : 19 May 2021
                Funding
                Funded by: NIH
                Award ID: U19-AI089992
                Funded by: NIH
                Award ID: R01-AI104739
                Funded by: NIH
                Award ID: U24 AI1867
                Funded by: Yale University
                Award ID: Harold W. Jockers Professorship
                Unrestricted endowed professorship awarded to Linda Bockenstedt
                Categories
                Research Article

                immunology,infectious disease,adaptive immunity,bacterial infections

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