For Publishers
DiscoveryMetadataPeer reviewHostingPublishing
For Researchers
JoinPublishReviewCollect
Register
Blog
About
Search
Advanced search
My ScienceOpen
Search
For Publishers
For Researchers
Blog
About
13
views
63
references
 Top references
cited by
46
    
0 reviews
 Review
0
comments
 Comment
0
recommends
+1 Recommend
0 collections
    0
    shares
      56
      similar
       All similar
      • Record: found
      • Abstract: found
      • Article: found
      Is Open Access

      Dissecting the microenvironment around biosynthetic scaffolds in murine skin wound healing

      research-article

      Read this article at

      ScienceOpenPublisherPMC
      Bookmark
          There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.

          Abstract

          Scaffolds with aligned fibers promoted a T cell-dominant adaptive immune response in murine skin wound healing.

          Abstract

          The structural properties of biomaterials play crucial roles in guiding cell behavior and influencing immune responses against the material. We fabricated electrospun membranes with three types of surface topography (random, aligned, and latticed), introduced them to dorsal skin excisional wounds in mice and rats, and evaluated their effects on wound healing and immunomodulatory properties. An overview of different immune cells in the microenvironment with the help of single-cell RNA sequencing revealed diverse cellular heterogeneity in vivo. The time course of immune response was advanced toward an adaptive immunity–dominant stage by the aligned scaffold. In mice without mature T lymphocytes, lack of wound-induced hair neogenesis indicated a regulatory role of T cells on hair follicle regeneration. The microenvironment around scaffolds involved an intricate interplay of immune and cutaneous cells.

          Related collections

          Most cited references63

          • Record: found
          • Abstract: found
          • Article: not found

          Comprehensive Integration of Single-Cell Data

          Tim Stuart, Andrew Butler, Paul Hoffman … (2019)
          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.
          Article 0 comments Cited 5659 times – based on 0 reviews     Review now
            Bookmark
            • Record: found
            • Abstract: found
            • Article: found
            Is Open Access

            Inference and analysis of cell-cell communication using CellChat

            Suoqin Jin, Christian Guerrero-Juarez, Lihua Zhang … (2021)
            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.
            Article 0 comments Cited 1736 times – based on 0 reviews     Review now
              Bookmark
              • Record: found
              • Abstract: found
              • Article: not found

              CXCL9, CXCL10, CXCL11/CXCR3 axis for immune activation - A target for novel cancer therapy.

              Ryuma Tokunaga, Wu Zhang, Madiha Naseem … (2018)
              Chemokines are proteins which induce chemotaxis, promote differentiation of immune cells, and cause tissue extravasation. Given these properties, their role in anti-tumor immune response in the cancer environment is of great interest. Although immunotherapy has shown clinical benefit for some cancer patients, other patients do not respond. One of the mechanisms of resistance to checkpoint inhibitors may be chemokine signaling. The CXCL9, -10, -11/CXCR3 axis regulates immune cell migration, differentiation, and activation, leading to tumor suppression (paracrine axis). However, there are some reports that show involvements of this axis in tumor growth and metastasis (autocrine axis). Thus, a better understanding of CXCL9, -10, -11/CXCR3 axis is necessary to develop effective cancer control. In this article, we summarize recent evidence regarding CXCL9, CXCL10, CXCL11/CXCR3 axis in the immune system and discuss their potential role in cancer treatment.
              Article 0 comments Cited 483 times – based on 0 reviews     Review now
                Bookmark
                All references

                Author and article information

                Journal
                Journal ID (nlm-ta): Sci Adv
                Journal ID (iso-abbrev): Sci Adv
                Journal ID (publisher-id): SciAdv
                Journal ID (hwp): advances
                Title: Science Advances
                Publisher: American Association for the Advancement of Science
                ISSN (Electronic): 2375-2548
                Publication date Collection: May 2021
                Publication date (Electronic): 26 May 2021
                Volume: 7
                Issue: 22
                Electronic Location Identifier: eabf0787
                Affiliations
                [1 ]Department of Oral Implantology and State Key Laboratory of Oral Diseases and National Clinical Research Center for Oral Diseases, West China Hospital of Stomatology, Sichuan University, Chengdu, Sichuan 610041, China.
                [2 ]State Key Laboratory of Biotherapy, West China Hospital, Sichuan University, Collaborative Innovation Center for Biotherapy, Chengdu, Sichuan 610041, China.
                [3 ]Research Center for Nano-Biomaterials, Analytical, and Testing Center, Sichuan University, Chengdu 610064, China.
                Author notes
                [* ]Corresponding author. Email: manyi780203@ 123456126.com
                [†]

                These authors contributed equally to this work.

                Author information
                http://orcid.org/0000-0001-6826-9637
                http://orcid.org/0000-0002-3938-334X
                http://orcid.org/0000-0001-6536-6765
                http://orcid.org/0000-0002-0483-3304
                http://orcid.org/0000-0002-7546-3486
                Article
                Publisher ID: abf0787
                DOI: 10.1126/sciadv.abf0787
                PMC ID: 8153724
                PubMed ID: 34039601
                SO-VID: 10f44af0-059e-4d1b-92f2-aea7827e7070
                Copyright © Copyright © 2021 The Authors, some rights reserved; exclusive licensee American Association for the Advancement of Science. No claim to original U.S. Government Works. Distributed under a Creative Commons Attribution License 4.0 (CC BY).
                License:

                This is an open-access article distributed under the terms of the Creative Commons Attribution license, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

                History
                Date received : 02 October 2020
                Date accepted : 02 April 2021
                Funding
                Funded by: doi http://dx.doi.org/10.13039/501100001809, National Natural Science Foundation of China;
                Award ID: 81671023
                Funded by: doi http://dx.doi.org/10.13039/501100013365, West China Hospital, Sichuan University;
                Award ID: LCYJ2019-19
                Funded by: doi http://dx.doi.org/10.13039/501100013365, West China Hospital, Sichuan University;
                Award ID: RD-03-202006
                Funded by: National Key Research and Development Program of China;
                Award ID: 2016YFA0201703/2016YFA0201700
                Categories
                Subject: Research Article
                Subject: Research Articles
                Subject: SciAdv r-articles
                Subject: Applied Sciences and Engineering
                Custom metadata
                Copyeditor Penchie Limbo

                Data availability:

                Comments

                Comment on this article

                scite_

                Similar content56

                See all similar

                Cited by45

                See all cited by