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      Defining cellular complexity in human autosomal dominant polycystic kidney disease by multimodal single cell analysis

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          Abstract

          Autosomal dominant polycystic kidney disease (ADPKD) is the leading genetic cause of end stage renal disease characterized by progressive expansion of kidney cysts. To better understand the cell types and states driving ADPKD progression, we analyze eight ADPKD and five healthy human kidney samples, generating single cell multiomic atlas consisting of ~100,000 single nucleus transcriptomes and ~50,000 single nucleus epigenomes. Activation of proinflammatory, profibrotic signaling pathways are driven by proximal tubular cells with a failed repair transcriptomic signature, proinflammatory fibroblasts and collecting duct cells. We identify GPRC5A as a marker for cyst-lining collecting duct cells that exhibits increased transcription factor binding motif availability for NF-κB, TEAD, CREB and retinoic acid receptors. We identify and validate a distal enhancer regulating GPRC5A expression containing these motifs. This single cell multiomic analysis of human ADPKD reveals previously unrecognized cellular heterogeneity and provides a foundation to develop better diagnostic and therapeutic approaches.

          Abstract

          Autosomal dominant polycystic kidney disease (ADPKD) is a complicated disease that involves numerous cell types. Here the authors used a multiomics approach consisting of single nucleus transcriptomes and epigenomes to redefine cell states in ADPKD and to dissect the cellular interactions and molecular mechanisms of ADPKD.

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          Most cited references57

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          Integrated analysis of multimodal single-cell data

          Summary The simultaneous measurement of multiple modalities represents an exciting frontier for single-cell genomics and necessitates computational methods that can define cellular states based on multimodal data. Here, we introduce “weighted-nearest neighbor” analysis, an unsupervised framework to learn the relative utility of each data type in each cell, enabling an integrative analysis of multiple modalities. We apply our procedure to a CITE-seq dataset of 211,000 human peripheral blood mononuclear cells (PBMCs) with panels extending to 228 antibodies to construct a multimodal reference atlas of the circulating immune system. Multimodal analysis substantially improves our ability to resolve cell states, allowing us to identify and validate previously unreported lymphoid subpopulations. Moreover, we demonstrate how to leverage this reference to rapidly map new datasets and to interpret immune responses to vaccination and coronavirus disease 2019 (COVID-19). Our approach represents a broadly applicable strategy to analyze single-cell multimodal datasets and to look beyond the transcriptome toward a unified and multimodal definition of cellular identity.
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            Fast, sensitive, and accurate integration of single cell data with Harmony

            The emerging diversity of single cell RNAseq datasets allows for the full transcriptional characterization of cell types across a wide variety of biological and clinical conditions. However, it is challenging to analyze them together, particularly when datasets are assayed with different technologies. Here, real biological differences are interspersed with technical differences. We present Harmony, an algorithm that projects cells into a shared embedding in which cells group by cell type rather than dataset-specific conditions. Harmony simultaneously accounts for multiple experimental and biological factors. In six analyses, we demonstrate the superior performance of Harmony to previously published algorithms. We show that Harmony requires dramatically fewer computational resources. It is the only currently available algorithm that makes the integration of ~106 cells feasible on a personal computer. We apply Harmony to PBMCs from datasets with large experimental differences, 5 studies of pancreatic islet cells, mouse embryogenesis datasets, and cross-modality spatial integration.
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              Inference and analysis of cell-cell communication using CellChat

              Understanding global communications among cells requires accurate representation of cell-cell signaling links and effective systems-level analyses of those links. We construct a database of interactions among ligands, receptors and their cofactors that accurately represent known heteromeric molecular complexes. We then develop CellChat, a tool that is able to quantitatively infer and analyze intercellular communication networks from single-cell RNA-sequencing (scRNA-seq) data. CellChat predicts major signaling inputs and outputs for cells and how those cells and signals coordinate for functions using network analysis and pattern recognition approaches. Through manifold learning and quantitative contrasts, CellChat classifies signaling pathways and delineates conserved and context-specific pathways across different datasets. Applying CellChat to mouse and human skin datasets shows its ability to extract complex signaling patterns. Our versatile and easy-to-use toolkit CellChat and a web-based Explorer (http://www.cellchat.org/) will help discover novel intercellular communications and build cell-cell communication atlases in diverse tissues.
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                Author and article information

                Contributors
                humphreysbd@wustl.edu
                Journal
                Nat Commun
                Nat Commun
                Nature Communications
                Nature Publishing Group UK (London )
                2041-1723
                30 October 2022
                30 October 2022
                2022
                : 13
                : 6497
                Affiliations
                [1 ]GRID grid.4367.6, ISNI 0000 0001 2355 7002, Division of Nephrology, Department of Medicine, , Washington University in St. Louis, ; St. Louis, MO USA
                [2 ]GRID grid.4367.6, ISNI 0000 0001 2355 7002, Department of Pathology and Immunology, , Washington University in St. Louis, ; St. Louis, MO USA
                [3 ]Chinook Therapeutics, Inc., Seattle, WA USA
                [4 ]Chinook Therapeutics, Inc., Vancouver, BC Canada
                [5 ]GRID grid.411024.2, ISNI 0000 0001 2175 4264, Department of Medicine, , University of Maryland School of Medicine, ; Baltimore, MD USA
                [6 ]GRID grid.411024.2, ISNI 0000 0001 2175 4264, Department of Physiology, , University of Maryland School of Medicine, ; Baltimore, MD USA
                [7 ]GRID grid.21107.35, ISNI 0000 0001 2171 9311, Johns Hopkins School of Medicine, ; Baltimore, MD USA
                [8 ]GRID grid.4367.6, ISNI 0000 0001 2355 7002, Department of Developmental Biology, , Washington University in St. Louis, ; St. Louis, MO USA
                Author information
                http://orcid.org/0000-0002-0358-9442
                http://orcid.org/0000-0003-4604-7907
                http://orcid.org/0000-0002-7866-2544
                http://orcid.org/0000-0002-9237-3634
                http://orcid.org/0000-0001-8647-9662
                http://orcid.org/0000-0001-7037-417X
                http://orcid.org/0000-0001-8741-2638
                http://orcid.org/0000-0002-1510-8714
                http://orcid.org/0000-0001-9514-2180
                http://orcid.org/0000-0002-6420-8703
                Article
                34255
                10.1038/s41467-022-34255-z
                9618568
                36310237
                212dba0b-a5fb-4870-8e25-67a5ac2d0a5e
                © The Author(s) 2022

                Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.

                History
                : 10 November 2021
                : 17 October 2022
                Funding
                Funded by: NIDDK P30DK090868
                Categories
                Article
                Custom metadata
                © The Author(s) 2022

                Uncategorized
                mechanisms of disease,polycystic kidney disease,epigenetics,rna sequencing
                Uncategorized
                mechanisms of disease, polycystic kidney disease, epigenetics, rna sequencing

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