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      Heparin-binding epidermal growth factor and fibroblast growth factor 2 rescue Müller glia-derived progenitor cell formation in microglia- and macrophage-ablated chick retinas

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          ABSTRACT

          Recent studies have demonstrated the impact of pro-inflammatory signaling and reactive microglia/macrophages on the formation of Müller glial-derived progenitor cells (MGPCs) in the retina. In chick retina, ablation of microglia/macrophages prevents the formation of MGPCs. Analyses of single-cell RNA-sequencing chick retinal libraries revealed that quiescent and activated microglia/macrophages have a significant impact upon the transcriptomic profile of Müller glia (MG). In damaged monocyte-depleted retinas, MG fail to upregulate genes related to different cell signaling pathways, including those related to Wnt, heparin-binding epidermal growth factor (HBEGF), fibroblast growth factor (FGF) and retinoic acid receptors. Inhibition of GSK3β, to simulate Wnt signaling, failed to rescue the deficit in MGPC formation, whereas application of HBEGF or FGF2 completely rescued the formation of MGPCs in monocyte-depleted retinas. Inhibition of Smad3 or activation of retinoic acid receptors partially rescued the formation of MGPCs in monocyte-depleted retinas. We conclude that signals produced by reactive microglia/macrophages in damaged retinas stimulate MG to upregulate cell signaling through HBEGF, FGF and retinoic acid, and downregulate signaling through TGFβ/Smad3 to promote the reprogramming of MG into proliferating MGPCs.

          Abstract

          Summary: Signals provided by reactive microglia and macrophages promote the formation of Muller glia-derived progenitors by activated HBEGF and FGF pathways.

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          Most cited references70

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          Cytoscape: a software environment for integrated models of biomolecular interaction networks.

          Cytoscape is an open source software project for integrating biomolecular interaction networks with high-throughput expression data and other molecular states into a unified conceptual framework. Although applicable to any system of molecular components and interactions, Cytoscape is most powerful when used in conjunction with large databases of protein-protein, protein-DNA, and genetic interactions that are increasingly available for humans and model organisms. Cytoscape's software Core provides basic functionality to layout and query the network; to visually integrate the network with expression profiles, phenotypes, and other molecular states; and to link the network to databases of functional annotations. The Core is extensible through a straightforward plug-in architecture, allowing rapid development of additional computational analyses and features. Several case studies of Cytoscape plug-ins are surveyed, including a search for interaction pathways correlating with changes in gene expression, a study of protein complexes involved in cellular recovery to DNA damage, inference of a combined physical/functional interaction network for Halobacterium, and an interface to detailed stochastic/kinetic gene regulatory models.
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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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              Spatial reconstruction of single-cell gene expression

              Spatial localization is a key determinant of cellular fate and behavior, but spatial RNA assays traditionally rely on staining for a limited number of RNA species. In contrast, single-cell RNA-seq allows for deep profiling of cellular gene expression, but established methods separate cells from their native spatial context. Here we present Seurat, a computational strategy to infer cellular localization by integrating single-cell RNA-seq data with in situ RNA patterns. We applied Seurat to spatially map 851 single cells from dissociated zebrafish (Danio rerio) embryos, inferring a transcriptome-wide map of spatial patterning. We confirmed Seurat’s accuracy using several experimental approaches, and used it to identify a set of archetypal expression patterns and spatial markers. Additionally, Seurat correctly localizes rare subpopulations, accurately mapping both spatially restricted and scattered groups. Seurat will be applicable to mapping cellular localization within complex patterned tissues in diverse systems.
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                Author and article information

                Contributors
                Journal
                Development
                Development
                DEV
                Development (Cambridge, England)
                The Company of Biologists Ltd
                0950-1991
                1477-9129
                1 December 2023
                6 December 2023
                6 December 2023
                : 150
                : 23
                : dev202070
                Affiliations
                [ 1 ]Department of Neuroscience, College of Medicine, The Ohio State University , Columbus, OH 43221, USA
                [ 2 ]Solomon Snyder Department of Neuroscience, Johns Hopkins University , Baltimore, MD 21205, USA
                Author notes
                [*]

                These authors contributed equally to this work

                []Author for correspondence ( andrew.fischer@ 123456osumc.edu )

                Handling Editor: François Guillemot

                Competing interests

                The authors declare no competing or financial interests.

                Author information
                http://orcid.org/0000-0002-5847-4298
                http://orcid.org/0000-0003-4913-5945
                http://orcid.org/0000-0001-6123-7405
                Article
                DEV202070
                10.1242/dev.202070
                10730090
                37971210
                5bed0352-8d70-431f-98cf-8df6ba84219a
                © 2023. Published by The Company of Biologists Ltd

                This is an Open Access article distributed under the terms of the Creative Commons Attribution License ( https://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution and reproduction in any medium provided that the original work is properly attributed.

                History
                : 6 July 2023
                : 2 November 2023
                Funding
                Funded by: National Eye Institute, http://dx.doi.org/10.13039/100000053;
                Award ID: RO1 EY032141-02
                Award ID: RO1 EY022030-10
                Funded by: Ohio State University, http://dx.doi.org/10.13039/100006928;
                Categories
                Stem Cells and Regeneration

                Developmental biology
                müller glia,cell signaling,microglia,retinal regeneration,scrna-seq
                Developmental biology
                müller glia, cell signaling, microglia, retinal regeneration, scrna-seq

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