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      Mechanosensitive protein polycystin-1 promotes periosteal stem/progenitor cells osteochondral differentiation in fracture healing

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          Abstract

          Background: Mechanical forces are indispensable for bone healing, disruption of which is recognized as a contributing cause to nonunion or delayed union. However, the underlying mechanism of mechanical regulation of fracture healing is elusive.

          Methods: We used the lineage-tracing mouse model, conditional knockout depletion mouse model, hindlimb unloading model and single-cell RNA sequencing to analyze the crucial roles of mechanosensitive protein polycystin-1 (PC1, Pkd1) promotes periosteal stem/progenitor cells (PSPCs) osteochondral differentiation in fracture healing.

          Results: Our results showed that cathepsin ( Ctsk)-positive PSPCs are fracture-responsive and mechanosensitive and can differentiate into osteoblasts and chondrocytes during fracture repair. We found that polycystin-1 declines markedly in PSPCs with mechanical unloading while increasing in response to mechanical stimulus. Mice with conditional depletion of Pkd1 in Ctsk + PSPCs show impaired osteochondrogenesis, reduced cortical bone formation, delayed fracture healing, and diminished responsiveness to mechanical unloading. Mechanistically, PC1 facilitates nuclear translocation of transcriptional coactivator TAZ via PC1 C-terminal tail cleavage, enhancing osteochondral differentiation potential of PSPCs. Pharmacological intervention of the PC1-TAZ axis and promotion of TAZ nuclear translocation using Zinc01442821 enhances fracture healing and alleviates delayed union or nonunion induced by mechanical unloading.

          Conclusion: Our study reveals that Ctsk + PSPCs within the callus can sense mechanical forces through the PC1-TAZ axis, targeting which represents great therapeutic potential for delayed fracture union or nonunion.

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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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            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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              Fast, sensitive, and accurate integration of single cell data with Harmony

              The emerging diversity of single cell RNAseq datasets allows for the full transcriptional characterization of cell types across a wide variety of biological and clinical conditions. However, it is challenging to analyze them together, particularly when datasets are assayed with different technologies. Here, real biological differences are interspersed with technical differences. We present Harmony, an algorithm that projects cells into a shared embedding in which cells group by cell type rather than dataset-specific conditions. Harmony simultaneously accounts for multiple experimental and biological factors. In six analyses, we demonstrate the superior performance of Harmony to previously published algorithms. We show that Harmony requires dramatically fewer computational resources. It is the only currently available algorithm that makes the integration of ~106 cells feasible on a personal computer. We apply Harmony to PBMCs from datasets with large experimental differences, 5 studies of pancreatic islet cells, mouse embryogenesis datasets, and cross-modality spatial integration.
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                Author and article information

                Journal
                Theranostics
                Theranostics
                thno
                Theranostics
                Ivyspring International Publisher (Sydney )
                1838-7640
                2024
                8 April 2024
                : 14
                : 6
                : 2544-2559
                Affiliations
                [1 ]Department of Endocrinology, Endocrinology Research Center, Xiangya Hospital of Central South University, Changsha, Hunan, 410008, China.
                [2 ]Department of Medicine, University of Tennessee Health Science Center, Memphis, TN, 38163, USA.
                [3 ]Department of Orthopaedics, The Second Affiliated Hospital of Fuyang Normal University, Fuyang, Anhui, 236000, China.
                [4 ]Department of Orthopaedics, The First Affiliated Hospital of Shihezi University, Shihezi 832061, China.
                [5 ]Key Laboratory of Aging-related Bone and Joint Diseases Prevention and Treatment, Ministry of Education, Xiangya Hospital, Central South University, Changsha, Hunan, 410008, China.
                [6 ]National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha, Hunan, 410008, China.
                [7 ]Laboratory Animal Center, Xiangya Hospital, Central South University, Changsha, Hunan, 410008, China.
                Author notes
                ✉ Corresponding author: Prof. Chang-Jun Li (Email: lichangjun@ 123456csu.edu.cn ), lead contact. Department of Endocrinology, Endocrinology Research Center, Xiangya Hospital of Central South University, 87# Xiangya Road, Changsha, Hunan 410008, PR China; Tel: +86-18670776745.

                # All authors contributed equally

                Competing Interests: The authors have declared that no competing interest exists.

                Article
                thnov14p2544
                10.7150/thno.93269
                11024844
                0f632787-bd13-4489-a479-08236963ba94
                © The author(s)

                This is an open access article distributed under the terms of the Creative Commons Attribution License ( https://creativecommons.org/licenses/by/4.0/). See http://ivyspring.com/terms for full terms and conditions.

                History
                : 15 December 2023
                : 28 March 2024
                Categories
                Research Paper

                Molecular medicine
                polycystin-1,periosteal stem/progenitor cells,fracture healing,mechanical stress

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