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      Microanatomy of the Human Atherosclerotic Plaque by Single-Cell Transcriptomics

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          Abstract

          Supplemental Digital Content is available in the text.

          Abstract

          Rationale:

          Atherosclerotic lesions are known for their cellular heterogeneity, yet the molecular complexity within the cells of human plaques has not been fully assessed.

          Objective:

          Using single-cell transcriptomics and chromatin accessibility, we gained a better understanding of the pathophysiology underlying human atherosclerosis.

          Methods and Results:

          We performed single-cell RNA and single-cell ATAC sequencing on human carotid atherosclerotic plaques to define the cells at play and determine their transcriptomic and epigenomic characteristics. We identified 14 distinct cell populations including endothelial cells, smooth muscle cells, mast cells, B cells, myeloid cells, and T cells and identified multiple cellular activation states and suggested cellular interconversions. Within the endothelial cell population, we defined subsets with angiogenic capacity plus clear signs of endothelial to mesenchymal transition. CD4 + and CD8 + T cells showed activation-based subclasses, each with a gradual decline from a cytotoxic to a more quiescent phenotype. Myeloid cells included 2 populations of proinflammatory macrophages showing IL (interleukin) 1B or TNF (tumor necrosis factor) expression as well as a foam cell-like population expressing TREM2 (triggering receptor expressed on myeloid cells 2) and displaying a fibrosis-promoting phenotype. ATACseq data identified specific transcription factors associated with the myeloid subpopulation and T cell cytokine profiles underlying mutual activation between both cell types. Finally, cardiovascular disease susceptibility genes identified using public genome-wide association studies data were particularly enriched in lesional macrophages, endothelial, and smooth muscle cells.

          Conclusions:

          This study provides a transcriptome-based cellular landscape of human atherosclerotic plaques and highlights cellular plasticity and intercellular communication at the site of disease. This detailed definition of cell communities at play in atherosclerosis will facilitate cell-based mapping of novel interventional targets with direct functional relevance for the treatment of human disease.

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          Most cited references112

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          Comprehensive Integration of Single-Cell Data

          Single-cell transcriptomics has transformed our ability to characterize cell states, but deep biological understanding requires more than a taxonomic listing of clusters. As new methods arise to measure distinct cellular modalities, a key analytical challenge is to integrate these datasets to better understand cellular identity and function. Here, we develop a strategy to "anchor" diverse datasets together, enabling us to integrate single-cell measurements not only across scRNA-seq technologies, but also across different modalities. After demonstrating improvement over existing methods for integrating scRNA-seq data, we anchor scRNA-seq experiments with scATAC-seq to explore chromatin differences in closely related interneuron subsets and project protein expression measurements onto a bone marrow atlas to characterize lymphocyte populations. Lastly, we harmonize in situ gene expression and scRNA-seq datasets, allowing transcriptome-wide imputation of spatial gene expression patterns. Our work presents a strategy for the assembly of harmonized references and transfer of information across datasets.
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            Is Open Access

            A global reference for human genetic variation

            The 1000 Genomes Project set out to provide a comprehensive description of common human genetic variation by applying whole-genome sequencing to a diverse set of individuals from multiple populations. Here we report completion of the project, having reconstructed the genomes of 2,504 individuals from 26 populations using a combination of low-coverage whole-genome sequencing, deep exome sequencing, and dense microarray genotyping. We characterized a broad spectrum of genetic variation, in total over 88 million variants (84.7 million single nucleotide polymorphisms (SNPs), 3.6 million short insertions/deletions (indels), and 60,000 structural variants), all phased onto high-quality haplotypes. This resource includes >99% of SNP variants with a frequency of >1% for a variety of ancestries. We describe the distribution of genetic variation across the global sample, and discuss the implications for common disease studies.
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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
                Journal
                Circ Res
                Circ Res
                RES
                Circulation Research
                Lippincott Williams & Wilkins (Hagerstown, MD )
                0009-7330
                1524-4571
                28 September 2020
                6 November 2020
                : 127
                : 11
                : 1437-1455
                Affiliations
                [1 ]Leiden Academic Centre for Drug Research, Division of Biotherapeutics, Leiden University, Einsteinweg 55, Leiden, the Netherlands (M.A.C.D., I.B., B.S., J.K.).
                [2 ]Amsterdam University Medical Centers–Location AMC, University of Amsterdam, Experimental Vascular Biology, Department of Medical Biochemistry, Amsterdam Cardiovascular Sciences, Amsterdam Infection and Immunity, Meibergdreef 9, the Netherlands (K.H.M.P., M.P.J.d.W.).
                [3 ]Laboratory of Clinical Chemistry and Haematology, University Medical Center, Heidelberglaan 100, Utrecht, the Netherlands (L.S., A.B., F.W.A., S.W.v.d.L., M.M., G.P.).
                [4 ]A.I.Virtanen Institute for Molecular Sciences, University of Eastern Finland, Kuopio, Finland (T.O., E.A., M.U.K., S.Y.-H.).
                [5 ]Laboratory for Experimental Cardiology (D.E., S.C.A.d.J), University Medical Center Utrecht, Heidelberglaan 100, the Netherlands.
                [6 ]Vascular Surgery (G.J.d.B.), University Medical Center Utrecht, Heidelberglaan 100, the Netherlands.
                [7 ]Cardiology (H.M.d.R., M.M.), University Medical Center Utrecht, Heidelberglaan 100, the Netherlands.
                [8 ]Turku Bioscience Centre, University of Turku and Åbo Akademi University, Finland (T.L.).
                [9 ]Institute for Cardiovascular Prevention (IPEK), Munich, Germany (E.L., M.P.J.d.W.).
                [10 ]German Center for Cardiovascular Research (DZHK), partner site Munich Heart Alliance, Munich, Germany (E.L., M.P.J.d.W.).
                [11 ]Cell and Molecular Medicine (C.K.G.), University of California San Diego, CA.
                [12 ]School of Medicine (C.K.G.), University of California San Diego, CA.
                Author notes
                Corresponding authors: Johan Kuiper Leiden Academic Centre for Drug Research, Division of Biotherapeutics, Leiden University, Einsteinweg 55, 2333 CC Leiden, The Netherlands. Email: j.kuiper@ 123456lacdr.leidenuniv.nl
                Menno P.J. de Winther: Amsterdam University Medical Centers–location AMC, University of Amsterdam, Experimental Vascular Biology, Department of Medical Biochemistry, Amsterdam Cardiovascular Sciences, Amsterdam Infection and Immunity, Meibergdreef 9, Amsterdam, The Netherlands. Email: m.dewinther@ 123456amsterdamumc.nl
                Michal Mokry: Laboratory of Clinical Chemistry and Haematology, University Medical Center, Heidelberglaan 100, Utrecht, The Netherlands Email: m.mokry@ 123456umcutrecht.nl
                Gerard Pasterkamp: Laboratory of Clinical Chemistry and Haematology, University Medical Center, Heidelberglaan 100, Utrecht, The Netherlands. Email: g.pasterkamp@ 123456umcutrecht.nl .
                Article
                00013
                10.1161/CIRCRESAHA.120.316770
                7641189
                32981416
                de8d0f6a-90ef-489f-a9f3-640f623c7702
                © 2020 The Authors.

                Circulation Research is published on behalf of the American Heart Association, Inc., by Wolters Kluwer Health, Inc. This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution, and reproduction in any medium, provided that the original work is properly cited.

                History
                : 3 March 2020
                : 23 September 2020
                : 25 February 2020
                Categories
                10014
                10058
                10084
                10188
                Original Research
                Custom metadata
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                T

                atherosclerosis,cardiovascular disease,genome-wide association study,single-cell analysis

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