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      Single-cell analyses highlight the proinflammatory contribution of C1q-high monocytes to Behçet’s disease

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          Significance

          Behçet’s disease (BD) is a systemic vasculitis, and its pathogenesis is elusive. Limited understanding of the immune disturbances in BD hindered the identification of therapeutic targets. Here, we performed single-cell and bulk RNA sequencing to reveal the comprehensive landscape of cellular and molecular changes in BD blood. We observed the mobilization of monocytes, especially a monocyte subset (C1q-high monocytes) in BD. Further assays revealed the proinflammatory features and clinical relevance of this subset. Activated interferon-γ (IFN-γ) signaling in BD patients induced C1q-high monocyte expansion, which was recovered by tofacitinib treatment. Our findings provide comprehensive understanding of BD immunopathogenesis, highlighting the proinflammatory contribution and therapeutic potential of C1q-high monocytes.

          Abstract

          Behçet’s disease (BD) is a chronic vasculitis characterized by systemic immune aberrations. However, a comprehensive understanding of immune disturbances in BD and how they contribute to BD pathogenesis is lacking. Here, we performed single-cell and bulk RNA sequencing to profile peripheral blood mononuclear cells (PBMCs) and isolated monocytes from BD patients and healthy donors. We observed prominent expansion and transcriptional changes in monocytes in PBMCs from BD patients. Deciphering the monocyte heterogeneity revealed the accumulation of C1q-high (C1q hi) monocytes in BD. Pseudotime inference indicated that BD monocytes markedly shifted their differentiation toward inflammation-accompanied and C1q hi monocyte–ended trajectory. Further experiments showed that C1q hi monocytes enhanced phagocytosis and proinflammatory cytokine secretion, and multiplatform analyses revealed the significant clinical relevance of this subtype. Mechanistically, C1q hi monocytes were induced by activated interferon-γ (IFN-γ) signaling in BD patients and were decreased by tofacitinib treatment. Our study illustrates the BD immune landscape and the unrecognized contribution of C1q hi monocytes to BD hyperinflammation, showing their potential as therapeutic targets and clinical assessment indexes.

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          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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            We introduce CIBERSORT, a method for characterizing cell composition of complex tissues from their gene expression profiles. When applied to enumeration of hematopoietic subsets in RNA mixtures from fresh, frozen, and fixed tissues, including solid tumors, CIBERSORT outperformed other methods with respect to noise, unknown mixture content, and closely related cell types. CIBERSORT should enable large-scale analysis of RNA mixtures for cellular biomarkers and therapeutic targets (http://cibersort.stanford.edu).
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              SCENIC: Single-cell regulatory network inference and clustering

              Although single-cell RNA-seq is revolutionizing biology, data interpretation remains a challenge. We present SCENIC for the simultaneous reconstruction of gene regulatory networks and identification of cell states. We apply SCENIC to a compendium of single-cell data from tumors and brain, and demonstrate that the genomic regulatory code can be exploited to guide the identification of transcription factors and cell states. SCENIC provides critical biological insights into the mechanisms driving cellular heterogeneity.
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                Author and article information

                Journal
                Proc Natl Acad Sci U S A
                Proc Natl Acad Sci U S A
                pnas
                PNAS
                Proceedings of the National Academy of Sciences of the United States of America
                National Academy of Sciences
                0027-8424
                1091-6490
                21 June 2022
                28 June 2022
                21 December 2022
                : 119
                : 26
                : e2204289119
                Affiliations
                [1] aDepartment of Rheumatology and Clinical Immunology, Peking Union Medical College Hospital (PUMCH), Chinese Academy of Medical Sciences and Peking Union Medical College (CAMS&PUMC) , 100730 Beijing, China;
                [2] bNational Clinical Research Center for Dermatologic and Immunologic Diseases (NCRC-DID), Ministry of Science and Technology , 100730 Beijing, China;
                [3] cState Key Laboratory of Complex Severe and Rare Diseases, PUMCH, Chinese Academy of Medical Sciences and Peking Union Medical College , 100730 Beijing, China;
                [4] dKey Laboratory of Rheumatology and Clinical Immunology, Ministry of Education , 100730 Beijing, China;
                [5] eDepartment of Biochemistry and Molecular Biology, State Key Laboratory of Medical Molecular Biology, Institute of Basic Medical Sciences, Chinese Academy of Medical Sciences and Peking Union Medical College , 100005 Beijing, China;
                [6] fSchool of Nursing, Chinese Academy of Medical Sciences and Peking Union Medical College , 100144 Beijing, China;
                [7] gState Key Laboratory of Toxicology and Medical Countermeasures, Beijing Institute of Pharmacology and Toxicology , 100850 Beijing, China;
                [8] hBeijing Key Laboratory of Therapeutic Gene Engineering Antibody , 100850 Beijing, China;
                [9] iDepartment of Rheumatology, Peking University Shougang Hospital , 100144 Beijing, China;
                [10] jTranslational Medical Center for Stem Cell Therapy, Tongji Hospital, Frontier Science Center for Stem Cells, School of Life Science and Technology, Tongji University , 200092 Shanghai, China
                Author notes

                Edited by Yuta Kochi, Rikagaku Kenkyujo; received March 24, 2022; accepted April 15, 2022 by Editorial Board Member Tadatsugu Taniguchi

                Author Contributions: W.Z., X.W., and H.-Z.C. conceptualized and designed the project; X.W. conducted the bioinformatics analysis with help from H.W., J.Y., and X.P.; J.L. and X.Y. performed scRNA-seq and immunological experiments; L.L., C.L., Z.W., and M.Z. participated in the sample collection; X.W. and X.Y. drafted the manuscript with help from W.Z., H.C., and H.-Z.C.; X.Z., F.Z., and C.W. reviewed the manuscript and provided valuable suggestions.

                1W.Z., X.W., J.L., and X.Y. contributed equally to this work.

                Author information
                https://orcid.org/0000-0002-3165-8185
                https://orcid.org/0000-0002-7833-6108
                https://orcid.org/0000-0003-2150-7540
                https://orcid.org/0000-0002-0641-2837
                https://orcid.org/0000-0001-6805-3182
                Article
                202204289
                10.1073/pnas.2204289119
                9245671
                35727985
                273a0e04-adc5-4f66-8dd7-17d6a2d638bf
                Copyright © 2022 the Author(s). Published by PNAS.

                This article is distributed under Creative Commons Attribution-NonCommercial-NoDerivatives License 4.0 (CC BY-NC-ND).

                History
                : 15 April 2022
                Page count
                Pages: 12
                Funding
                Funded by: National Natural Science Foundation of China (NSFC) 501100001809
                Award ID: 82171800
                Award Recipient : Wenjie Zheng Award Recipient : Xiaoman Wang Award Recipient : Jinjing Liu Award Recipient : Xin Yu Award Recipient : Heping Wang Award Recipient : Xiaoya Pei Award Recipient : Houzao Chen
                Funded by: National Natural Science Foundation of China (NSFC) 501100001809
                Award ID: 81871299
                Award Recipient : Wenjie Zheng Award Recipient : Xiaoman Wang Award Recipient : Jinjing Liu Award Recipient : Xin Yu Award Recipient : Heping Wang Award Recipient : Xiaoya Pei Award Recipient : Houzao Chen
                Funded by: National Natural Science Foundation of China (NSFC) 501100001809
                Award ID: 82030017
                Award Recipient : Wenjie Zheng Award Recipient : Xiaoman Wang Award Recipient : Jinjing Liu Award Recipient : Xin Yu Award Recipient : Heping Wang Award Recipient : Xiaoya Pei Award Recipient : Houzao Chen
                Funded by: National Key Research and Development Project of China
                Award ID: 2019YFA0801500
                Award Recipient : Xiaoman Wang Award Recipient : Houzao Chen
                Funded by: National Key Research and Development Project of China
                Award ID: 2020YFC2008003
                Award Recipient : Xiaoman Wang Award Recipient : Houzao Chen
                Funded by: Chinese Academy of Medical Sciences Innovation Fund for Medical Sciences
                Award ID: 2021-1-I2M-050
                Award Recipient : Xiaoman Wang Award Recipient : Houzao Chen
                Funded by: Chinese Academy of Medical Sciences Innovation Fund for Medical Sciences
                Award ID: 2019-RC-HL-006
                Award Recipient : Xiaoman Wang Award Recipient : Houzao Chen
                Categories
                420
                Biological Sciences
                Immunology and Inflammation

                behçet’s disease,single-cell analysis,monocytes,interferon

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