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      IL-1 β–dependent extravasation of preexisting lung-restricted autoantibodies during lung transplantation activates complement and mediates primary graft dysfunction

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          Abstract

          Preexisting lung-restricted autoantibodies (LRAs) are associated with a higher incidence of primary graft dysfunction (PGD), although it remains unclear whether LRAs can drive its pathogenesis. In syngeneic murine left lung transplant recipients, preexisting LRAs worsened graft dysfunction, which was evident by impaired gas exchange, increased pulmonary edema, and activation of damage-associated pathways in lung epithelial cells. LRA-mediated injury was distinct from ischemia-reperfusion injury since deletion of donor nonclassical monocytes and host neutrophils could not prevent graft dysfunction in LRA-pretreated recipients. Whole LRA IgG molecules were necessary for lung injury, which was mediated by the classical and alternative complement pathways and reversed by complement inhibition. However, deletion of Fc receptors in donor macrophages or mannose-binding lectin in recipient mice failed to rescue lung function. LRA-mediated injury was localized to the transplanted lung and dependent on IL-1β–mediated permeabilization of pulmonary vascular endothelium, which allowed extravasation of antibodies. Genetic deletion or pharmacological inhibition of IL-1R in the donor lungs prevented LRA-induced graft injury. In humans, preexisting LRAs were an independent risk factor for severe PGD and could be treated with plasmapheresis and complement blockade. We conclude that preexisting LRAs can compound ischemia-reperfusion injury to worsen PGD for which complement inhibition may be effective.

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          Most cited references51

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          Spatial reconstruction of single-cell gene expression

          Spatial localization is a key determinant of cellular fate and behavior, but spatial RNA assays traditionally rely on staining for a limited number of RNA species. In contrast, single-cell RNA-seq allows for deep profiling of cellular gene expression, but established methods separate cells from their native spatial context. Here we present Seurat, a computational strategy to infer cellular localization by integrating single-cell RNA-seq data with in situ RNA patterns. We applied Seurat to spatially map 851 single cells from dissociated zebrafish (Danio rerio) embryos, inferring a transcriptome-wide map of spatial patterning. We confirmed Seurat’s accuracy using several experimental approaches, and used it to identify a set of archetypal expression patterns and spatial markers. Additionally, Seurat correctly localizes rare subpopulations, accurately mapping both spatially restricted and scattered groups. Seurat will be applicable to mapping cellular localization within complex patterned tissues in diverse systems.
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            Reference-based analysis of lung single-cell sequencing reveals a transitional profibrotic macrophage

            Tissue fibrosis is a major cause of mortality that results from the deposition of matrix proteins by an activated mesenchyme. Macrophages accumulate in fibrosis, but the role of specific subgroups in supporting fibrogenesis has not been investigated in vivo. Here we used single-cell RNA sequencing (scRNA-seq) to characterize the heterogeneity of macrophages in bleomycin-induced lung fibrosis in mice. A novel computational framework for the annotation of scRNA-seq by reference to bulk transcriptomes (SingleR) enabled the subclustering of macrophages and revealed a disease-associated subgroup with a transitional gene expression profile intermediate between monocyte-derived and alveolar macrophages. These CX3CR1+SiglecF+ transitional macrophages localized to the fibrotic niche and had a profibrotic effect in vivo. Human orthologues of genes expressed by the transitional macrophages were upregulated in samples from patients with idiopathic pulmonary fibrosis. Thus, we have identified a pathological subgroup of transitional macrophages that are required for the fibrotic response to injury.
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              Scrublet: Computational Identification of Cell Doublets in Single-Cell Transcriptomic Data

              Single-cell RNA-sequencing has become a widely used, powerful approach for studying cell populations. However, these methods often generate multiplet artifacts, where two or more cells receive the same barcode, resulting in a hybrid transcriptome. In most experiments, multiplets account for several percent of transcriptomes and can confound downstream data analysis. Here, we present Single-Cell Remover of Doublets (Scrublet), a framework for predicting the impact of multiplets in a given analysis and identifying problematic multiplets. Scrublet avoids the need for expert knowledge or cell clustering by simulating multiplets from the data and building a nearest neighbor classifier. To demonstrate the utility of this approach, we test Scrublet on several datasets that include independent knowledge of cell multiplets. Scrublet is freely available for download at github.com/AllonKleinLab/scrublet.
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                Author and article information

                Contributors
                Journal
                J Clin Invest
                J Clin Invest
                J Clin Invest
                The Journal of Clinical Investigation
                American Society for Clinical Investigation
                0021-9738
                1558-8238
                17 October 2022
                17 October 2022
                17 October 2022
                : 132
                : 20
                : e157975
                Affiliations
                [1 ]Division of Thoracic Surgery,
                [2 ]Department of Pathology, and
                [3 ]Pulmonary and Critical Care Medicine, Feinberg School of Medicine, Northwestern University, Chicago, Illinois, USA.
                [4 ]Departments of Surgery, Pathology & Immunology, Washington University, St. Louis, Missouri, USA.
                [5 ]Norton Thoracic Institute, St. Joseph’s Hospital and Medical Center, Phoenix, Arizona, USA.
                Author notes
                Address correspondence to: Ankit Bharat, 676 St. Clair Ave. Suite 650, Chicago, Illinois 60611, USA. Phone: 312.926.7552; Email: abharat@ 123456nm.org .

                Authorship note: EL and AB are co–senior authors.

                Author information
                http://orcid.org/0000-0002-0095-2592
                http://orcid.org/0000-0002-0094-7726
                http://orcid.org/0000-0001-9873-4018
                http://orcid.org/0000-0003-4387-1386
                Article
                157975
                10.1172/JCI157975
                9566897
                36250462
                34484a8e-4289-4e7a-945b-fc56275d3a53
                © 2022 Yang 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
                : 4 January 2022
                : 25 August 2022
                Funding
                Funded by: National Heart, Lung, and Blood Institute, https://doi.org/10.13039/100000050;
                Award ID: HL145478
                Funded by: National Heart, Lung, and Blood Institute, https://doi.org/10.13039/100000050;
                Award ID: HL147575
                Funded by: National Heart, Lung, and Blood Institute, https://doi.org/10.13039/100000050;
                Award ID: HL147290
                Categories
                Research Article

                transplantation,innate immunity,organ transplantation

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