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      Integrated Transcriptome and Network Analysis Reveals Spatiotemporal Dynamics of Calvarial Suturogenesis

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          SUMMARY

          Craniofacial abnormalities often involve sutures, the growth centers of the skull. To characterize the organization and processes governing their development, we profile the murine frontal suture, a model for sutural growth and fusion, at the tissue- and single-cell level on embryonic days (E)16.5 and E18.5. For the wild-type suture, bulk RNA sequencing (RNA-seq) analysis identifies mesenchyme-, osteogenic front-, and stage-enriched genes and biological processes, as well as alternative splicing events modifying the extracellular matrix. Single-cell RNA-seq analysis distinguishes multiple subpopulations, of which five define a mesenchymeosteoblast differentiation trajectory and show variation along the anteroposterior axis. Similar analyses of in vivo mouse models of impaired frontal suturogenesis in Saethre-Chotzen and Apert syndromes, Twist1 +/− and Fgfr2 +/S252W , demonstrate distinct transcriptional changes involving angiogenesis and ribogenesis, respectively. Co-expression network analysis reveals gene expression modules from which we validate key driver genes regulating osteoblast differentiation. Our study provides a global approach to gain insights into suturogenesis.

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          In Brief

          Calvarial suture dysgenesis is a significant human pathology. Holmeset al. transcriptionally profile the frontal suture in normal mice and mouse models of frontal suture dysgenesis at multiple embryonic ages using bulk and single-cell RNA-seq. This comprehensive dataset allows high-resolution exploration of suture development at the transcriptional and subpopulation levels.

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          Distinct fibroblast lineages determine dermal architecture in skin development and repair

          Fibroblasts are the major mesenchymal cell type in connective tissue and deposit the collagen and elastic fibers of the extracellular matrix (ECM) 1 . Even within a single tissue fibroblasts exhibit remarkable functional diversity, but it is not known whether this reflects the existence of a differentiation hierarchy or is a response to different environmental factors. Here we show, using transplantation assays and lineage tracing, that the fibroblasts of skin connective tissue arise from two distinct lineages. One forms the upper dermis, including the dermal papilla that regulates hair growth and the arrector pili muscle (APM), which controls piloerection. The other forms the lower dermis, including the reticular fibroblasts that synthesise the bulk of the fibrillar ECM, and the pre-adipocytes and adipocytes of the hypodermis. The upper lineage is required for hair follicle formation. In wounded adult skin, the initial wave of dermal repair is mediated by the lower lineage and upper dermal fibroblasts are recruited only during re-epithelialisation. Epidermal beta-catenin activation stimulates expansion of the upper dermal lineage, rendering wounds permissive for hair follicle formation. Our findings explain why wounding is linked to formation of ECM-rich scar tissue that lacks hair follicles 2-4 . They also form a platform for discovering fibroblast lineages in other tissues and for examining fibroblast changes in ageing and disease.
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            Single-cell sequencing reveals dissociation-induced gene expression in tissue subpopulations

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              Annotation-free quantification of RNA splicing using LeafCutter

              The excision of introns from pre-mRNA is an essential step in mRNA processing. We developed LeafCutter to study sample and population variation in intron splicing. LeafCutter identifies variable splicing events from short-read RNA-seq data and finds events of high complexity. Our approach obviates the need for transcript annotations and circumvents the challenges in estimating relative isoform or exon usage in complex splicing events. LeafCutter can be used both for detecting differential splicing between sample groups, and for mapping splicing quantitative trait loci (sQTLs). Compared to contemporary methods, we find 1.4–2.1 times more sQTLs, many of which help us ascribe molecular effects to disease-associated variants. Strikingly, transcriptome-wide associations between LeafCutter intron quantifications and 40 complex traits increased the number of associated disease genes at 5% FDR by an average of 2.1-fold as compared to using gene expression levels alone. LeafCutter is fast, scalable, easy to use, and available online.
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                Author and article information

                Journal
                101573691
                39703
                Cell Rep
                Cell Rep
                Cell reports
                2211-1247
                10 July 2020
                07 July 2020
                24 July 2020
                : 32
                : 1
                : 107871
                Affiliations
                [1 ]Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
                [2 ]Icahn Institute for Data Science and Genomic Technology, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
                [3 ]Department of Pathology, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
                [4 ]Division of Developmental Biology, Cincinnati Children’s Medical Center, Cincinnati, OH 45229, USA
                [5 ]Department of Psychiatry, and Seaver Autism Center for Research and Treatment, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
                [6 ]The Mindich Child Health and Development Institute, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
                [7 ]Department of Cell, Developmental and Regenerative Biology and Department of Pediatrics, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
                [8 ]Present address: Center for Personalized Cancer Therapy, University of Massachusetts, Boston, MA 02125, USA
                [9 ]These authors contributed equally
                [10 ]Senior author
                [11 ]Lead Contact
                Author notes

                AUTHOR CONTRIBUTIONS

                Conceptualization, G.H., H.v.B., and E.W.J.; Methodology, G.H., A.S.G.-R., R.S., M.J.D., S.S.P., D.P., B.Z., and H.v.B.; Validation, G.H., A.S.G.-R., and N.L.; Formal Analysis, G.H., A.S.G.-R., N.L., X.Z., D.K., H.v.B., and E.W.J.; Investigation, G.H., N.L., J.R., and A.A.W.; Data Curation, G.H., A.S.G.-R., D.K., and H.v.B.; Writing – Original Draft, G.H., A.S.G.-R., N.L., X.Z., and H.v.B.; Writing – Review & Editing, G.H., A.S.G.-R., N.L., X.Z., D.P., B.Z., H.v.B., and E.W.J.; Visualization, G.H., A.S.G.-R., N.L., and X.Z.; Supervision, G.H., B.Z., H.v.B., and E.W.J.; Project Administration, G.H., H.v.B., and E.W.J.; Funding Acquisition, G.H., D.P., B.Z., H.v.B., and E.W.J.

                Article
                NIHMS1610246
                10.1016/j.celrep.2020.107871
                7379176
                32640236
                8d27d762-899e-4386-be28-4ead91af053b

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

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                Cell biology
                Cell biology

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