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      Molecular determinants of nephron vascular specialization in the kidney

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          Abstract

          Although kidney parenchymal tissue can be generated in vitro, reconstructing the complex vasculature of the kidney remains a daunting task. The molecular pathways that specify and sustain functional, phenotypic and structural heterogeneity of the kidney vasculature are unknown. Here, we employ high-throughput bulk and single-cell RNA sequencing of the non-lymphatic endothelial cells (ECs) of the kidney to identify the molecular pathways that dictate vascular zonation from embryos to adulthood. We show that the kidney manifests vascular-specific signatures expressing defined transcription factors, ion channels, solute transporters, and angiocrine factors choreographing kidney functions. Notably, the ontology of the glomerulus coincides with induction of unique transcription factors, including Tbx3, Gata5, Prdm1, and Pbx1. Deletion of Tbx3 in ECs results in glomerular hypoplasia, microaneurysms and regressed fenestrations leading to fibrosis in subsets of glomeruli. Deciphering the molecular determinants of kidney vascular signatures lays the foundation for rebuilding nephrons and uncovering the pathogenesis of kidney disorders.

          Abstract

          The kidney is vascularized with highly specialized and zonated endothelial cells that are essential for its filtration function. Here, Barry et al. provide a single-cell RNA sequencing analysis of the kidney vasculature that highlights its transcriptional heterogeneity and uncovers pathways important for its development and function.

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          Most cited references56

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          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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            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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              limma powers differential expression analyses for RNA-sequencing and microarray studies

              limma is an R/Bioconductor software package that provides an integrated solution for analysing data from gene expression experiments. It contains rich features for handling complex experimental designs and for information borrowing to overcome the problem of small sample sizes. Over the past decade, limma has been a popular choice for gene discovery through differential expression analyses of microarray and high-throughput PCR data. The package contains particularly strong facilities for reading, normalizing and exploring such data. Recently, the capabilities of limma have been significantly expanded in two important directions. First, the package can now perform both differential expression and differential splicing analyses of RNA sequencing (RNA-seq) data. All the downstream analysis tools previously restricted to microarray data are now available for RNA-seq as well. These capabilities allow users to analyse both RNA-seq and microarray data with very similar pipelines. Second, the package is now able to go past the traditional gene-wise expression analyses in a variety of ways, analysing expression profiles in terms of co-regulated sets of genes or in terms of higher-order expression signatures. This provides enhanced possibilities for biological interpretation of gene expression differences. This article reviews the philosophy and design of the limma package, summarizing both new and historical features, with an emphasis on recent enhancements and features that have not been previously described.
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                Author and article information

                Contributors
                dbarry553@gmail.com
                srafii@med.cornell.edu
                Journal
                Nat Commun
                Nat Commun
                Nature Communications
                Nature Publishing Group UK (London )
                2041-1723
                13 December 2019
                13 December 2019
                2019
                : 10
                : 5705
                Affiliations
                [1 ]ISNI 000000041936877X, GRID grid.5386.8, Division of Regenerative Medicine, Ansary Stem Cell Institute, Weill Cornell Medicine, ; New York, NY 10065 USA
                [2 ]ISNI 000000041936877X, GRID grid.5386.8, Genomics Resources Core Facility, Weill Cornell Medicine, ; New York, NY 10065 USA
                [3 ]ISNI 0000 0000 9482 7121, GRID grid.267313.2, Department of Molecular Biology, , University of Texas Southwestern Medical Center, ; Dallas, TX 75235 USA
                [4 ]ISNI 000000041936877X, GRID grid.5386.8, Division of Nephrology and Hypertension, Weill Cornell Medicine, ; New York, NY 10065 USA
                [5 ]ISNI 000000041936877X, GRID grid.5386.8, Pathology and Laboratory Medicine, Weill Cornell Medicine, ; New York, NY 10065 USA
                [6 ]ISNI 0000 0001 2284 9943, GRID grid.257060.6, Bioengineering Program, DeMatteis School of Engineering and Applied Science, , Hofstra University, ; Hempstead, NY 11549 USA
                Author information
                http://orcid.org/0000-0001-5396-918X
                http://orcid.org/0000-0003-3208-9655
                Article
                12872
                10.1038/s41467-019-12872-5
                6910926
                31836710
                eb6f5cd1-6335-496c-8e88-fd533382b69b
                © The Author(s) 2019

                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
                : 14 November 2018
                : 22 September 2019
                Categories
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                Custom metadata
                © The Author(s) 2019

                Uncategorized
                angiogenesis,transcriptomics,kidney,nephrons,glomerulus
                Uncategorized
                angiogenesis, transcriptomics, kidney, nephrons, glomerulus

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