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      TCRαβ +NK1.1 -CD4 -CD8 - double-negative T cells inhibit central and peripheral inflammation and ameliorate ischemic stroke in mice

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          Abstract

          Background: Excessive immune activation leads to secondary injury and impedes injured brain recovery after ischemic stroke. However, few effective methods are currently used for equilibrating immune balance. CD3 +NK1.1 -TCRβ +CD4 -CD8 - double-negative T (DNT) cells which do not express NK cell surface markers are unique regulatory cells that maintain homeostasis in several immune-related diseases. However, the therapeutic potential and regulatory mechanism of DNT cells in ischemic stroke are still unknown.

          Methods: Mouse ischemic stroke is induced by occlusion of the distal branches of the middle cerebral artery (dMCAO). DNT cells were adoptively transferred intravenously into ischemic stroke mice. Neural recovery was evaluated by TTC staining and behavioral analysis. Using immunofluorescence, flow cytometry, and RNA sequencing, the immune regulatory function of DNT cells was investigated at different time points post ischemic stroke.

          Results: Adoptive transfer of DNT cells significantly reduces infarct volume and improves sensorimotor function after ischemic stroke. DNT cells suppress peripheral Trem1 + myeloid cell differentiation during the acute phase. Furthermore, they infiltrate the ischemic tissue via CCR5 and equilibrate the local immune balance during the subacute phase. During the chronic phase, DNT cells enhance Treg cell recruitment through CCL5, eventually developing an immune homeostatic milieu for neuronal recovery.

          Conclusions: DNT cell treatment renders the comprehensive anti-inflammatory roles in specific phases of ischemic stroke. Our study suggests that the adoptive transfer of regulatory DNT cells may be a potential cell-based therapy for ischemic stroke.

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          Most cited references43

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          Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2

          In comparative high-throughput sequencing assays, a fundamental task is the analysis of count data, such as read counts per gene in RNA-seq, for evidence of systematic changes across experimental conditions. Small replicate numbers, discreteness, large dynamic range and the presence of outliers require a suitable statistical approach. We present DESeq2, a method for differential analysis of count data, using shrinkage estimation for dispersions and fold changes to improve stability and interpretability of estimates. This enables a more quantitative analysis focused on the strength rather than the mere presence of differential expression. The DESeq2 package is available at http://www.bioconductor.org/packages/release/bioc/html/DESeq2.html. Electronic supplementary material The online version of this article (doi:10.1186/s13059-014-0550-8) contains supplementary material, which is available to authorized users.
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            Integrated analysis of multimodal single-cell data

            Summary The simultaneous measurement of multiple modalities represents an exciting frontier for single-cell genomics and necessitates computational methods that can define cellular states based on multimodal data. Here, we introduce “weighted-nearest neighbor” analysis, an unsupervised framework to learn the relative utility of each data type in each cell, enabling an integrative analysis of multiple modalities. We apply our procedure to a CITE-seq dataset of 211,000 human peripheral blood mononuclear cells (PBMCs) with panels extending to 228 antibodies to construct a multimodal reference atlas of the circulating immune system. Multimodal analysis substantially improves our ability to resolve cell states, allowing us to identify and validate previously unreported lymphoid subpopulations. Moreover, we demonstrate how to leverage this reference to rapidly map new datasets and to interpret immune responses to vaccination and coronavirus disease 2019 (COVID-19). Our approach represents a broadly applicable strategy to analyze single-cell multimodal datasets and to look beyond the transcriptome toward a unified and multimodal definition of cellular identity.
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              clusterProfiler 4.0: A universal enrichment tool for interpreting omics data

              Summary Functional enrichment analysis is pivotal for interpreting high-throughput omics data in life science. It is crucial for this type of tool to use the latest annotation databases for as many organisms as possible. To meet these requirements, we present here an updated version of our popular Bioconductor package, clusterProfiler 4.0. This package has been enhanced considerably compared with its original version published 9 years ago. The new version provides a universal interface for functional enrichment analysis in thousands of organisms based on internally supported ontologies and pathways as well as annotation data provided by users or derived from online databases. It also extends the dplyr and ggplot2 packages to offer tidy interfaces for data operation and visualization. Other new features include gene set enrichment analysis and comparison of enrichment results from multiple gene lists. We anticipate that clusterProfiler 4.0 will be applied to a wide range of scenarios across diverse organisms.
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                Author and article information

                Journal
                Theranostics
                Theranostics
                thno
                Theranostics
                Ivyspring International Publisher (Sydney )
                1838-7640
                2023
                10 January 2023
                : 13
                : 3
                : 896-909
                Affiliations
                [1 ]General Surgery Department, Beijing Friendship Hospital, Capital Medical University, Beijing, China.
                [2 ]Immunology Research Center for Oral and Systemic Health, Beijing Friendship Hospital, Capital Medical University, Beijing, China.
                [3 ]Beijing Key Laboratory of Tolerance Induction and Organ Protection in Transplantation, Beijing, China.
                [4 ]National Clinical Research Center for Digestive Diseases, Beijing, China.
                [5 ]Beijing Laboratory of Oral Health, Capital Medical University School of Stomatology, Beijing, China.
                [6 ]Beijing Clinical Research Institute, Beijing, China.
                [7 ]Department of Neurology, Beijing Friendship Hospital, Capital Medical University, Beijing, China.
                [8 ]Beijing Institute of Brain Disorders, Capital Medical University, Beijing, China.
                Author notes
                ✉ Corresponding authors: Dong Zhang, E-mail: zhangd@ 123456ccmu.edu.cn , No. 95 Yong-an Road, Xi-cheng District, Beijing 100050, P.R. China. E-mail: zhangd@ 123456ccmu.edu.cn ; Songlin Wang, No. 10 Youanmen Waixi 1st Alley, Feng-tai District, Beijing, 100069, P.R. China. E-mail: slwang@ 123456ccmu.edu.cn ; Yongbo Zhang, No. 95 Yong-an Road, Xi-cheng District, Beijing 100050, P.R. China. E-mail: yongbozhang@ 123456ccmu.edu.cn .

                *These authors contributed equally to this work.

                Competing Interests: D.T., Y.T., W.S., and D.Z. inventors of a Chinese patent for the ex vivo generation of DNT cells. The remaining authors declare no conflicts of interest.

                Article
                thnov13p0896
                10.7150/thno.80307
                9925325
                36793857
                c6473999-a041-437d-a1c4-21ea57caea42
                © 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
                : 29 October 2022
                : 24 December 2022
                Categories
                Research Paper

                Molecular medicine
                ischemic stroke,double-negative t cell,regulatory t cell,myeloid cell
                Molecular medicine
                ischemic stroke, double-negative t cell, regulatory t cell, myeloid cell

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