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      Type I interferons drive the maturation of human DC3s with a distinct costimulatory profile characterized by high GITRL

      1 , 1 , 1 , 1
      Science Immunology
      American Association for the Advancement of Science (AAAS)

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          Abstract

          Human mononuclear phagocytes comprise specialized subsets of dendritic cells (DCs) and monocytes, but how these subsets individually regulate expression of the molecular signals involved in T cell costimulation is incompletely understood. Here, we used multiparameter flow cytometry and CITE-sequencing to investigate the cell type–specific responses of human peripheral blood DC and monocyte subsets to type I interferons (IFN-I), focusing on differential regulation of costimulatory molecules. We report that IFN-β drives the maturation of the recently identified human CD1c + CD5 DC3 subset into cells with higher GITRL and lower CD86 expression compared with other conventional DC subsets. Transcriptomic analysis confirmed that DC3s have an intermediate phenotype between that of CD1c + CD5 + DC2s and CD14 + monocytes, characterized by high expression of MHCII, Fc receptors, and components of the phagocyte NADPH oxidase. IFN-β induced a shared core response in human DC and monocyte subsets as well as subset-specific responses, including differential expression of costimulatory molecules. Gene regulatory network analysis suggests that upon IFN-β stimulation NFKB1 drives DC3s to acquire a maturation program shared with DC2s. Accordingly, inhibition of NF-κB activation prevented the acquisition of a mature phenotype by DC3s upon IFN-β exposure. Collectively, this study provides insight into the cell type–specific response of human DC and monocyte subsets to IFN-I and highlights the distinct costimulatory potential of DC3s.

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          Most cited references40

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          Is Open Access

          GSVA: gene set variation analysis for microarray and RNA-Seq data

          Background Gene set enrichment (GSE) analysis is a popular framework for condensing information from gene expression profiles into a pathway or signature summary. The strengths of this approach over single gene analysis include noise and dimension reduction, as well as greater biological interpretability. As molecular profiling experiments move beyond simple case-control studies, robust and flexible GSE methodologies are needed that can model pathway activity within highly heterogeneous data sets. Results To address this challenge, we introduce Gene Set Variation Analysis (GSVA), a GSE method that estimates variation of pathway activity over a sample population in an unsupervised manner. We demonstrate the robustness of GSVA in a comparison with current state of the art sample-wise enrichment methods. Further, we provide examples of its utility in differential pathway activity and survival analysis. Lastly, we show how GSVA works analogously with data from both microarray and RNA-seq experiments. Conclusions GSVA provides increased power to detect subtle pathway activity changes over a sample population in comparison to corresponding methods. While GSE methods are generally regarded as end points of a bioinformatic analysis, GSVA constitutes a starting point to build pathway-centric models of biology. Moreover, GSVA contributes to the current need of GSE methods for RNA-seq data. GSVA is an open source software package for R which forms part of the Bioconductor project and can be downloaded at http://www.bioconductor.org.
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            The Molecular Signatures Database (MSigDB) hallmark gene set collection.

            The Molecular Signatures Database (MSigDB) is one of the most widely used and comprehensive databases of gene sets for performing gene set enrichment analysis. Since its creation, MSigDB has grown beyond its roots in metabolic disease and cancer to include >10,000 gene sets. These better represent a wider range of biological processes and diseases, but the utility of the database is reduced by increased redundancy across, and heterogeneity within, gene sets. To address this challenge, here we use a combination of automated approaches and expert curation to develop a collection of "hallmark" gene sets as part of MSigDB. Each hallmark in this collection consists of a "refined" gene set, derived from multiple "founder" sets, that conveys a specific biological state or process and displays coherent expression. The hallmarks effectively summarize most of the relevant information of the original founder sets and, by reducing both variation and redundancy, provide more refined and concise inputs for gene set enrichment analysis.
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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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                Author and article information

                Contributors
                (View ORCID Profile)
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                Journal
                Science Immunology
                Sci. Immunol.
                American Association for the Advancement of Science (AAAS)
                2470-9468
                November 13 2020
                November 13 2020
                November 13 2020
                November 13 2020
                : 5
                : 53
                : eabe0347
                Affiliations
                [1 ]Department of Immunology, Faculty of Medicine, University of Toronto, Toronto, Ontario M5S 1A8, Canada.
                Article
                10.1126/sciimmunol.abe0347
                33188059
                e5d2f03a-0013-46ba-862d-a8974bd76b31
                © 2020

                https://www.sciencemag.org/about/science-licenses-journal-article-reuse

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