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      An injury-induced tissue niche shaped by mesenchymal plasticity coordinates the regenerative and disease response in the lung

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          Abstract

          Severe lung injury causes basal stem cells to migrate and outcompete alveolar stem cells resulting in dysplastic repair and a loss of gas exchange function. This “stem cell collision” is part of a multistep process that is now revealed to generate an injury-induced tissue ni che (iTCH) containing Keratin 5+ epithelial cells and plastic Pdgfra+ mesenchymal cells. Temporal and spatial single cell analysis reveals that iTCHs are governed by mesenchymal proliferation and Notch signaling, which suppresses Wnt and Fgf signaling in iTCHs. Conversely, loss of Notch in iTCHs rewires alveolar signaling patterns to promote euplastic regeneration and gas exchange. The signaling patterns of iTCHs can differentially phenotype fibrotic from degenerative human lung diseases, through apposing flows of FGF and WNT signaling. These data reveal the emergence of an injury and disease associated iTCH in the lung and the ability of using iTCH specific signaling patterns to discriminate human lung disease phenotypes.

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          STAR: ultrafast universal RNA-seq aligner.

          Accurate alignment of high-throughput RNA-seq data is a challenging and yet unsolved problem because of the non-contiguous transcript structure, relatively short read lengths and constantly increasing throughput of the sequencing technologies. Currently available RNA-seq aligners suffer from high mapping error rates, low mapping speed, read length limitation and mapping biases. To align our large (>80 billon reads) ENCODE Transcriptome RNA-seq dataset, we developed the Spliced Transcripts Alignment to a Reference (STAR) software based on a previously undescribed RNA-seq alignment algorithm that uses sequential maximum mappable seed search in uncompressed suffix arrays followed by seed clustering and stitching procedure. STAR outperforms other aligners by a factor of >50 in mapping speed, aligning to the human genome 550 million 2 × 76 bp paired-end reads per hour on a modest 12-core server, while at the same time improving alignment sensitivity and precision. In addition to unbiased de novo detection of canonical junctions, STAR can discover non-canonical splices and chimeric (fusion) transcripts, and is also capable of mapping full-length RNA sequences. Using Roche 454 sequencing of reverse transcription polymerase chain reaction amplicons, we experimentally validated 1960 novel intergenic splice junctions with an 80-90% success rate, corroborating the high precision of the STAR mapping strategy. STAR is implemented as a standalone C++ code. STAR is free open source software distributed under GPLv3 license and can be downloaded from http://code.google.com/p/rna-star/.
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            clusterProfiler: an R package for comparing biological themes among gene clusters.

            Increasing quantitative data generated from transcriptomics and proteomics require integrative strategies for analysis. Here, we present an R package, clusterProfiler that automates the process of biological-term classification and the enrichment analysis of gene clusters. The analysis module and visualization module were combined into a reusable workflow. Currently, clusterProfiler supports three species, including humans, mice, and yeast. Methods provided in this package can be easily extended to other species and ontologies. The clusterProfiler package is released under Artistic-2.0 License within Bioconductor project. The source code and vignette are freely available at http://bioconductor.org/packages/release/bioc/html/clusterProfiler.html.
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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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                Author and article information

                Journal
                bioRxiv
                BIORXIV
                bioRxiv
                Cold Spring Harbor Laboratory
                29 February 2024
                : 2024.02.26.582147
                Affiliations
                [1 - ]Department of Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.
                [2 - ]Penn-CHOP Lung Biology Institute, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.
                [3 - ]Penn Cardiovascular Institute, University of Pennsylvania, Philadelphia, PA, USA.
                [4 - ]Department of Biomedical Sciences, School of Veterinary Medicine, University of Pennsylvania, Philadelphia, PA, USA
                [5 - ]Department of Cell and Developmental Biology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
                Author notes
                [* ]Corresponding Author Edward E. Morrisey, University of Pennsylvania, Smilow Translational Research Center, Room 11-124, Philadelphia, PA 19104-5129, emorrise@ 123456pennmedicine.upenn.edu
                Author information
                http://orcid.org/0000-0001-5740-643X
                Article
                10.1101/2024.02.26.582147
                10962740
                38529490
                403321e5-f910-43e0-826f-092848714fd5

                This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which allows reusers to copy and distribute the material in any medium or format in unadapted form only, for noncommercial purposes only, and only so long as attribution is given to the creator.

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