Dataset Information

Systematic Study of Immune Cell Diversity in ischemic postconditioning Using High-Dimensional Single-Cell Analysis with Mass Cytometry.

ABSTRACT: Ischemic postconditioning (IPostC) is a concept of ischemic stroke treatment, in which several cycles of brief reocclusion after reperfusion are repeated. It is essential to have an accurate understanding of the immune response in IPostC. By using high parametric single-cell mass cytometry, immune cell subsets and characterize their unique functions from ischemic brain and peripheral blood were identified after IPostC. This study enabled us to better understand the immune cell phenotypical and functional characteristics in ischemic brain and peripheral blood at the single-cell and protein levels. Since some cell surface markers can serve as functional markers, reflecting the degree of inflammation, the cell surface marker intensity among different groups was analyzed. The results showed that downregulation of 4E-BP1 and p38 of Microglia and MoDM in the ischemic brain was involved in IPostC-induced protection. In the peripheral blood, downregulation of P38 of CD4 T cell and Treg has also participated in IPostC-induced protection.

SUBMITTER: Yao Y

PROVIDER: S-EPMC8139206 | biostudies-literature | 2021 Jun

REPOSITORIES: biostudies-literature

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Systematic Study of Immune Cell Diversity in ischemic postconditioning Using High-Dimensional Single-Cell Analysis with Mass Cytometry.

Yao Yang Y Li Yaning Y Ni Weihua W Li Zhijun Z Feng Liangshu L Wang Yan Y Meng Jihong J Zhao Heng H

Aging and disease 20210601 3

Ischemic postconditioning (IPostC) is a concept of ischemic stroke treatment, in which several cycles of brief reocclusion after reperfusion are repeated. It is essential to have an accurate understanding of the immune response in IPostC. By using high parametric single-cell mass cytometry, immune cell subsets and characterize their unique functions from ischemic brain and peripheral blood were identified after IPostC. This study enabled us to better understand the immune cell phenotypical and f ...[more]

PMID: 34094644

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Project description:IntroductionDiabetic ocular complications include sight-threatening consequences and decreased corneal sensitivity, characterized by decreased tear production, corneal sensitivity and delayed corneal epithelial wound healing. The pathogenesis of diabetic corneal disorders remains largely unknown. Growing evidence implies the participation of immune cells in the development of diabetic corneal diseases. Nonetheless, the immunological changes that result in diabetic corneal problems are largely unknown.MethodsMass cytometry by time of flight (CyTOF) was used to investigate immune cell cluster alterations associated with diabetic corneal disorders. CyTOF test was performed on corneal cells at a single level from 21-week-old diabetic (db/db) and non-diabetic (db/m) mice. A panel of 41 immune-related markers monitored different immune cell types in diabetic corneas. To investigate the proportion of each immune cell subpopulation, an unsupervised clustering method was employed, and T-distributed stochastic neighbor embedding was used to visualize the distinctions between different immune cell subsets.ResultsThrough CyTOF test, we identified 10 immune cell subsets in the corneal tissues. In a novel way, we discovered significant immune alterations in diabetic corneas, including pronounced alterations in T cells and myeloid cell subgroups in diabetic corneas linked to potential biomarkers, including CD103, CCR2, SiglecF, Ly6G, and CD172a. Comprehensive immunological profiling indicated remarkable changes in the immune microenvironment in diabetic corneas, characterized by a notable decrease in CD103+CD8+ tissue-resident memory T (TRM) cells and Tregs, as well as a dramatic increase of γδT cells and subsets of CD11b+Ly6G+ myeloid-derived suppressor cells (MDSCs).ConclusionCyTOF analysis revealed significant alterations in the immune microenvironment during the development of diabetic corneal complications. This study mapped the immune microenvironment landscape of type 2 diabetic corneas, providing a fundamental understanding of immune-driven diabetic corneal disorders.

Project description:Mass cytometry by time-of-flight (CyTOF) is a valuable technology for high-dimensional analysis at the single cell level. Identification of different cell populations is an important task during the data analysis. Many clustering tools can perform this task, which is essential to identify "new" cell populations in explorative experiments. However, relying on clustering is laborious since it often involves manual annotation, which significantly limits the reproducibility of identifying cell-populations across different samples. The latter is particularly important in studies comparing different conditions, for example in cohort studies. Learning cell populations from an annotated set of cells solves these problems. However, currently available methods for automatic cell population identification are either complex, dependent on prior biological knowledge about the populations during the learning process, or can only identify canonical cell populations. We propose to use a linear discriminant analysis (LDA) classifier to automatically identify cell populations in CyTOF data. LDA outperforms two state-of-the-art algorithms on four benchmark datasets. Compared to more complex classifiers, LDA has substantial advantages with respect to the interpretable performance, reproducibility, and scalability to larger datasets with deeper annotations. We apply LDA to a dataset of ~3.5 million cells representing 57 cell populations in the Human Mucosal Immune System. LDA has high performance on abundant cell populations as well as the majority of rare cell populations, and provides accurate estimates of cell population frequencies. Further incorporating a rejection option, based on the estimated posterior probabilities, allows LDA to identify previously unknown (new) cell populations that were not encountered during training. Altogether, reproducible prediction of cell population compositions using LDA opens up possibilities to analyze large cohort studies based on CyTOF data. © 2019 The Authors. Cytometry Part A published by Wiley Periodicals, Inc. on behalf of International Society for Advancement of Cytometry.

Dataset Information

Systematic Study of Immune Cell Diversity in ischemic postconditioning Using High-Dimensional Single-Cell Analysis with Mass Cytometry.

Publications

Systematic Study of Immune Cell Diversity in ischemic postconditioning Using High-Dimensional Single-Cell Analysis with Mass Cytometry.

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