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      Decoding myofibroblast origins in human kidney fibrosis

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          Abstract

          Kidney fibrosis is the hallmark of chronic kidney disease progression, however, currently no antifibrotic therapies exist. This is largely because the origin, functional heterogeneity and regulation of scar-forming cells during human kidney fibrosis remains poorly understood. Here, using single cell RNA-seq, we profiled the transcriptomes of proximal tubule and non-proximal tubule cells in healthy and fibrotic human kidneys to map the entire human kidney in an unbiased approach. This enabled mapping of all matrix-producing cells at high resolution, revealing distinct subpopulations of pericytes and fibroblasts as the major cellular sources of scar forming myofibroblasts during human kidney fibrosis. We used genetic fate-tracing, time-course single cell RNA-seq and ATAC-seq experiments in mice, and spatial transcriptomics in human kidney fibrosis to functionally interrogate these findings, shedding new light on the origin, heterogeneity and differentiation of human kidney myofibroblasts and their fibroblast and pericyte precursors at unprecedented resolution. Finally, we used this strategy to facilitate target discovery, identifying Nkd2 as a myofibroblast-specific target in human kidney fibrosis.

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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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            Gene Ontology: tool for the unification of biology

            Genomic sequencing has made it clear that a large fraction of the genes specifying the core biological functions are shared by all eukaryotes. Knowledge of the biological role of such shared proteins in one organism can often be transferred to other organisms. The goal of the Gene Ontology Consortium is to produce a dynamic, controlled vocabulary that can be applied to all eukaryotes even as knowledge of gene and protein roles in cells is accumulating and changing. To this end, three independent ontologies accessible on the World-Wide Web (http://www.geneontology.org) are being constructed: biological process, molecular function and cellular component.
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              Is Open Access

              The Sequence Alignment/Map format and SAMtools

              Summary: The Sequence Alignment/Map (SAM) format is a generic alignment format for storing read alignments against reference sequences, supporting short and long reads (up to 128 Mbp) produced by different sequencing platforms. It is flexible in style, compact in size, efficient in random access and is the format in which alignments from the 1000 Genomes Project are released. SAMtools implements various utilities for post-processing alignments in the SAM format, such as indexing, variant caller and alignment viewer, and thus provides universal tools for processing read alignments. Availability: http://samtools.sourceforge.net Contact: rd@sanger.ac.uk
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                Author and article information

                Journal
                0410462
                Nature
                Nature
                Nature
                0028-0836
                1476-4687
                23 August 2021
                01 January 2021
                11 November 2020
                06 September 2021
                : 589
                : 7841
                : 281-286
                Affiliations
                [1 ]Division of Nephrology and Clinical Immunology, RWTH Aachen University, Aachen, Germany
                [2 ]Institute of Experimental Medicine and Systems Biology, RWTH Aachen University, Germany
                [4 ]Department of Urology and Paediatric Urology, St. Antonius Hospital, Eschweiler, Germany
                [5 ]Department of Urology and Kidney Transplantation, Martin-Luther-University, Halle (Saale), Germany
                [6 ]Heidelberg University, Faculty of Medicine, and Heidelberg University Hospital, Institute for Computational Biomedicine, Bioquant, Heidelberg, Germany
                [7 ]Joint Research Center for Computational Biomedicine, RWTH Aachen University Hospital, 52074 Aachen, Germany
                [8 ]Department of Pathology, Radboud Institute for Molecular Life Sciences, Radboud University Medical Center, Nijmegen, The Netherlands
                [9 ]Department of Pediatric Nephrology, Radboud Institute for Molecular Life Sciences, Radboud University Medical Center, Amalia Children's Hospital, Nijmegen, The Netherlands
                [10 ]Institute for Biomedical Technologies, Department of Cell Biology, RWTH Aachen University, Aachen, Germany
                [11 ]Centre for Inflammation Research, The Queen’s Medical Research Institute, University of Edinburgh, Edinburgh, UK
                [12 ]III. Department of Medicine, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
                [13 ]Department of Anatomy and Developmental Biology, Monash University, Melbourne, Australia.Gman
                [14 ]Department of Pathology, RWTH Aachen University, Aachen, Germany
                [15 ]Department of Hematology, Erasmus MC Cancer Institute, Rotterdam, The Netherlands
                [16 ]Molecular Medicine Partnership Unit, European Molecular Biology Laboratory and Heidelberg University, Heidelberg, Germany
                [17 ]MRC Human Genetics Unit, Institute of Genetics and Molecular Medicine, University of Edinburgh, Crewe Road South, Edinburgh, UK
                [18 ]Department of Internal Medicine, Nephrology and Transplantation, Erasmus Medical Center, Rotterdam, The Netherlands
                Author notes
                Correspondence to: Rafael Kramann, MD, PhD, Department of Experimental Medicine and Systems Biology and Division of Nephrology and Clinical Immunology, Medical Faculty RWTH Aachen University, Pauwelsstrasse 30, 52074 Aachen, Germany, Phone.: 0049-241-80 37750, Fax.: +49-241-80-82446, rkramann@ 123456gmx.net
                [3]

                Present address: Bayer Pharma AG, Germany

                [*]

                Co-senior authors

                Article
                EMS114558
                10.1038/s41586-020-2941-1
                7611626
                33176333
                edb39770-dcad-4d87-9b95-ebd856e7e5c2

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