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      Regional specialization and fate specification of bone stromal cells in skeletal development

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          Summary

          Bone stroma contributes to the regulation of osteogenesis and hematopoiesis but also to fracture healing and disease processes. Mesenchymal stromal cells from bone (BMSCs) represent a heterogenous mixture of different subpopulations with distinct molecular and functional properties. The lineage relationship between BMSC subsets and their regulation by intrinsic and extrinsic factors are not well understood. Here, we show with mouse genetics, ex vivo cell differentiation assays, and transcriptional profiling that BMSCs from metaphysis (mpMSCs) and diaphysis (dpMSCs) are fundamentally distinct. Fate-tracking experiments and single-cell RNA sequencing indicate that bone-forming osteoblast lineage cells and dpMSCs, including leptin receptor-positive (LepR +) reticular cells in bone marrow, emerge from mpMSCs in the postnatal metaphysis. Finally, we show that BMSC fate is controlled by platelet-derived growth factor receptor β (PDGFRβ) signaling and the transcription factor Jun-B. The sum of our findings improves our understanding of BMSC development, lineage relationships, and differentiation.

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          Highlights

          • Mesenchymal stromal cells from metaphysis and diaphysis have distinct properties

          • Metaphyseal mpMSCs include cells with multi-lineage differentiation potential

          • mpMSCs give rise to osteoprogenitors and reticular cells in marrow during development

          • Microenvironmental cues and cell-autonomous transcription factors control BMSC fate

          Abstract

          Sivaraj et al. characterize the heterogeneity of bone mesenchymal stromal cells (BMSCs) during skeletal development. A subpopulation of metaphyseal MSCs (mpMSCs) has self-renewing and multi-lineage differentiation potential to generate bone cells and LepR + marrow stromal cells. BMSCs fate determination is controlled extrinsically by PDGF-B and intrinsically by Jun-B.

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          Most cited references63

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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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            featureCounts: an efficient general purpose program for assigning sequence reads to genomic features.

            Next-generation sequencing technologies generate millions of short sequence reads, which are usually aligned to a reference genome. In many applications, the key information required for downstream analysis is the number of reads mapping to each genomic feature, for example to each exon or each gene. The process of counting reads is called read summarization. Read summarization is required for a great variety of genomic analyses but has so far received relatively little attention in the literature. We present featureCounts, a read summarization program suitable for counting reads generated from either RNA or genomic DNA sequencing experiments. featureCounts implements highly efficient chromosome hashing and feature blocking techniques. It is considerably faster than existing methods (by an order of magnitude for gene-level summarization) and requires far less computer memory. It works with either single or paired-end reads and provides a wide range of options appropriate for different sequencing applications. featureCounts is available under GNU General Public License as part of the Subread (http://subread.sourceforge.net) or Rsubread (http://www.bioconductor.org) software packages.
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              Integrating single-cell transcriptomic data across different conditions, technologies, and species

              Computational single-cell RNA-seq (scRNA-seq) methods have been successfully applied to experiments representing a single condition, technology, or species to discover and define cellular phenotypes. However, identifying subpopulations of cells that are present across multiple data sets remains challenging. Here, we introduce an analytical strategy for integrating scRNA-seq data sets based on common sources of variation, enabling the identification of shared populations across data sets and downstream comparative analysis. We apply this approach, implemented in our R toolkit Seurat (http://satijalab.org/seurat/), to align scRNA-seq data sets of peripheral blood mononuclear cells under resting and stimulated conditions, hematopoietic progenitors sequenced using two profiling technologies, and pancreatic cell 'atlases' generated from human and mouse islets. In each case, we learn distinct or transitional cell states jointly across data sets, while boosting statistical power through integrated analysis. Our approach facilitates general comparisons of scRNA-seq data sets, potentially deepening our understanding of how distinct cell states respond to perturbation, disease, and evolution.
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                Author and article information

                Contributors
                Journal
                Cell Rep
                Cell Rep
                Cell Reports
                Cell Press
                2211-1247
                13 July 2021
                13 July 2021
                13 July 2021
                : 36
                : 2
                : 109352
                Affiliations
                [1 ]Max Planck Institute for Molecular Biomedicine, Department of Tissue Morphogenesis, 48149 Münster, Germany
                [2 ]Electron Microscopy Unit, Max Planck Institute for Molecular Biomedicine, 48149 Münster, Germany
                [3 ]Max Planck Institute for Heart and Lung Research, Angiogenesis and Metabolism Laboratory, 61231 Bad Nauheim, Germany
                Author notes
                []Corresponding author ralf.adams@ 123456mpi-muenster.mpg.de
                [4]

                Lead contact

                Article
                S2211-1247(21)00728-2 109352
                10.1016/j.celrep.2021.109352
                8293626
                34260921
                60025410-5aa0-4af1-a708-f0d0b79e0510
                © 2021 The Authors

                This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).

                History
                : 15 May 2020
                : 30 September 2020
                : 16 June 2021
                Categories
                Article

                Cell biology
                bone mesenchymal stromal cells,metaphysis,diaphysis,osteoblast lineage cells,adipocytes,mouse genetics,single-cell rna sequencing

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