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      Molecular signatures distinguish senescent cells from inflammatory cells in aged mouse callus stromal cells

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          Abstract

          Cellular senescence plays important roles in age-related diseases, including musculoskeletal disorders. Senescent cells (SCs) exert a senescence-associated secretory phenotype (SASP) by producing SASP factors, some of which overlap with factors produced by inflammatory cells (Inf-Cs). However, the differences between SCs and Inf-Cs and how they interact with each other during fracture repair have not been well studied. Here, we analyzed single cell RNA sequencing data of aged mouse fracture callus stromal cells. We defined Inf-Cs as cells that express NF-κB Rela/Relb, SCs as cells that express the senescence genes, Cdkn1a, Cdkn2a or Cdkn2c, and inflammatory SCs (Inf-SCs) as cells that express both NF-κB and senescence genes. Differentially expressed genes and pathway analyses revealed that Inf-SCs and SCs had a similar gene expression profile and upregulated pathways that are related to DNA damage/oxidation-reduction and cellular senescence, while Inf-Cs expressed different gene signatures and pathways from SCs and Inf-SCs, mainly related to inflammation. Cellchat software analysis indicated that SCs and Inf-SCs are potential ligand-producing cells that affect Inf-Cs as target cells. Cell culture experiments demonstrated that SC conditioned medium promoted inflammatory gene expression by callus-derived mesenchymal progenitor cells, and Inf-Cs had reduced osteoblast differentiation capacity. In summary, we have identified three cell subclusters associated with inflammation and senescence in callus stromal cells, predicted potential effects of Inf-SCs and SCs on Inf-Cs by production of active ligands, and demonstrated that when mesenchymal progenitors acquire inflammatory phenotypes their osteogenic potential is reduced.

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          Most cited references50

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          Comprehensive Integration of Single-Cell Data

          Single-cell transcriptomics has transformed our ability to characterize cell states, but deep biological understanding requires more than a taxonomic listing of clusters. As new methods arise to measure distinct cellular modalities, a key analytical challenge is to integrate these datasets to better understand cellular identity and function. Here, we develop a strategy to "anchor" diverse datasets together, enabling us to integrate single-cell measurements not only across scRNA-seq technologies, but also across different modalities. After demonstrating improvement over existing methods for integrating scRNA-seq data, we anchor scRNA-seq experiments with scATAC-seq to explore chromatin differences in closely related interneuron subsets and project protein expression measurements onto a bone marrow atlas to characterize lymphocyte populations. Lastly, we harmonize in situ gene expression and scRNA-seq datasets, allowing transcriptome-wide imputation of spatial gene expression patterns. Our work presents a strategy for the assembly of harmonized references and transfer of information across datasets.
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            NF-κB signaling in inflammation

            The transcription factor NF-κB regulates multiple aspects of innate and adaptive immune functions and serves as a pivotal mediator of inflammatory responses. NF-κB induces the expression of various pro-inflammatory genes, including those encoding cytokines and chemokines, and also participates in inflammasome regulation. In addition, NF-κB plays a critical role in regulating the survival, activation and differentiation of innate immune cells and inflammatory T cells. Consequently, deregulated NF-κB activation contributes to the pathogenic processes of various inflammatory diseases. In this review, we will discuss the activation and function of NF-κB in association with inflammatory diseases and highlight the development of therapeutic strategies based on NF-κB inhibition.
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              Inference and analysis of cell-cell communication using CellChat

              Understanding global communications among cells requires accurate representation of cell-cell signaling links and effective systems-level analyses of those links. We construct a database of interactions among ligands, receptors and their cofactors that accurately represent known heteromeric molecular complexes. We then develop CellChat, a tool that is able to quantitatively infer and analyze intercellular communication networks from single-cell RNA-sequencing (scRNA-seq) data. CellChat predicts major signaling inputs and outputs for cells and how those cells and signals coordinate for functions using network analysis and pattern recognition approaches. Through manifold learning and quantitative contrasts, CellChat classifies signaling pathways and delineates conserved and context-specific pathways across different datasets. Applying CellChat to mouse and human skin datasets shows its ability to extract complex signaling patterns. Our versatile and easy-to-use toolkit CellChat and a web-based Explorer (http://www.cellchat.org/) will help discover novel intercellular communications and build cell-cell communication atlases in diverse tissues.
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                Author and article information

                Contributors
                Journal
                Front Endocrinol (Lausanne)
                Front Endocrinol (Lausanne)
                Front. Endocrinol.
                Frontiers in Endocrinology
                Frontiers Media S.A.
                1664-2392
                16 February 2023
                2023
                : 14
                : 1090049
                Affiliations
                [1] 1 Department of Pathology and Laboratory Medicine, University of Rochester Medical Center , Rochester, NY, United States
                [2] 2 Biostatistics and Computational Biology, University of Rochester Medical Center , Rochester, NY, United States
                [3] 3 Orthopaedics and Rehabilitation, Center for Musculoskeletal Research, University of Rochester Medical Center , Rochester, NY, United States
                Author notes

                Edited by: Angela Verma, Icahn School of Medicine at Mount Sinai, United States

                Reviewed by: Xiaobo Zhu, The Chinese University of Hong Kong, China; Dengshun Miao, Nanjing Medical University, China; David George Monroe, Mayo Clinic, United States

                *Correspondence: Lianping Xing, lianping_xing@ 123456urmc.rochester.edu

                This article was submitted to Cellular Endocrinology, a section of the journal Frontiers in Endocrinology

                Article
                10.3389/fendo.2023.1090049
                9981154
                36875448
                3289cae3-8591-4545-88c1-a440d92d9a31
                Copyright © 2023 Liu, Lin, McDavid, Yang, Zhang, Boyce and Xing

                This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

                History
                : 04 November 2022
                : 02 February 2023
                Page count
                Figures: 7, Tables: 0, Equations: 0, References: 50, Pages: 12, Words: 5910
                Funding
                Funded by: National Institutes of Health , doi 10.13039/100000002;
                This work was supported by NIH R01 grant AG059775 (to LX), R01 grant AR43510 (to BB), and P30 grant AR69655 pilot (to XL); and Schwartz discover grant from University of Rochester (to YY).
                Categories
                Endocrinology
                Original Research

                Endocrinology & Diabetes
                inflammation,scrna-seq,aging,fracture,senescence
                Endocrinology & Diabetes
                inflammation, scrna-seq, aging, fracture, senescence

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