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Mass Cytometry Identifies Distinct Subsets of Regulatory T Cells and Natural Killer Cells Associated With High Risk for Type 1 Diabetes

Type 1 diabetes (T1D) is characterized by autoimmune destruction of insulin producing β-cells. The time from onset of islet autoimmunity to manifest clinical disease can vary widely in length, and it is fairly uncharacterized both clinically and immunologically. In the current study, peripheral bloo...

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Autores principales: Barcenilla, Hugo, Åkerman, Linda, Pihl, Mikael, Ludvigsson, Johnny, Casas, Rosaura
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2019
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6509181/
https://www.ncbi.nlm.nih.gov/pubmed/31130961
http://dx.doi.org/10.3389/fimmu.2019.00982
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author Barcenilla, Hugo
Åkerman, Linda
Pihl, Mikael
Ludvigsson, Johnny
Casas, Rosaura
author_facet Barcenilla, Hugo
Åkerman, Linda
Pihl, Mikael
Ludvigsson, Johnny
Casas, Rosaura
author_sort Barcenilla, Hugo
collection PubMed
description Type 1 diabetes (T1D) is characterized by autoimmune destruction of insulin producing β-cells. The time from onset of islet autoimmunity to manifest clinical disease can vary widely in length, and it is fairly uncharacterized both clinically and immunologically. In the current study, peripheral blood mononuclear cells from autoantibody-positive children with high risk for T1D, and from age-matched healthy individuals, were analyzed by mass cytometry using a panel of 32 antibodies. Surface markers were chosen to identify multiple cell types including T, B, NK, monocytes, and DC, and antibodies specific for identification of differentiation, activation and functional markers were also included in the panel. By applying dimensional reduction and computational unsupervised clustering approaches, we delineated in an unbiased fashion 132 phenotypically distinct subsets within the major immune cell populations. We were able to identify an effector memory Treg subset expressing HLA-DR, CCR4, CCR6, CXCR3, and GATA3 that was increased in the high-risk group. In addition, two subsets of NK cells defined by CD16(+) CD8(+) CXCR3(+) and CD16(+) CD8(+) CXCR3(+) CD11c(+) were also higher in the same subjects. High-risk individuals did not show impaired glucose tolerance at the time of sampling, suggesting that the changes observed were not the result of metabolic imbalance, and might be potential biomarkers predictive of T1D.
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spelling pubmed-65091812019-05-24 Mass Cytometry Identifies Distinct Subsets of Regulatory T Cells and Natural Killer Cells Associated With High Risk for Type 1 Diabetes Barcenilla, Hugo Åkerman, Linda Pihl, Mikael Ludvigsson, Johnny Casas, Rosaura Front Immunol Immunology Type 1 diabetes (T1D) is characterized by autoimmune destruction of insulin producing β-cells. The time from onset of islet autoimmunity to manifest clinical disease can vary widely in length, and it is fairly uncharacterized both clinically and immunologically. In the current study, peripheral blood mononuclear cells from autoantibody-positive children with high risk for T1D, and from age-matched healthy individuals, were analyzed by mass cytometry using a panel of 32 antibodies. Surface markers were chosen to identify multiple cell types including T, B, NK, monocytes, and DC, and antibodies specific for identification of differentiation, activation and functional markers were also included in the panel. By applying dimensional reduction and computational unsupervised clustering approaches, we delineated in an unbiased fashion 132 phenotypically distinct subsets within the major immune cell populations. We were able to identify an effector memory Treg subset expressing HLA-DR, CCR4, CCR6, CXCR3, and GATA3 that was increased in the high-risk group. In addition, two subsets of NK cells defined by CD16(+) CD8(+) CXCR3(+) and CD16(+) CD8(+) CXCR3(+) CD11c(+) were also higher in the same subjects. High-risk individuals did not show impaired glucose tolerance at the time of sampling, suggesting that the changes observed were not the result of metabolic imbalance, and might be potential biomarkers predictive of T1D. Frontiers Media S.A. 2019-05-03 /pmc/articles/PMC6509181/ /pubmed/31130961 http://dx.doi.org/10.3389/fimmu.2019.00982 Text en Copyright © 2019 Barcenilla, Åkerman, Pihl, Ludvigsson and Casas. http://creativecommons.org/licenses/by/4.0/ 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.
spellingShingle Immunology
Barcenilla, Hugo
Åkerman, Linda
Pihl, Mikael
Ludvigsson, Johnny
Casas, Rosaura
Mass Cytometry Identifies Distinct Subsets of Regulatory T Cells and Natural Killer Cells Associated With High Risk for Type 1 Diabetes
title Mass Cytometry Identifies Distinct Subsets of Regulatory T Cells and Natural Killer Cells Associated With High Risk for Type 1 Diabetes
title_full Mass Cytometry Identifies Distinct Subsets of Regulatory T Cells and Natural Killer Cells Associated With High Risk for Type 1 Diabetes
title_fullStr Mass Cytometry Identifies Distinct Subsets of Regulatory T Cells and Natural Killer Cells Associated With High Risk for Type 1 Diabetes
title_full_unstemmed Mass Cytometry Identifies Distinct Subsets of Regulatory T Cells and Natural Killer Cells Associated With High Risk for Type 1 Diabetes
title_short Mass Cytometry Identifies Distinct Subsets of Regulatory T Cells and Natural Killer Cells Associated With High Risk for Type 1 Diabetes
title_sort mass cytometry identifies distinct subsets of regulatory t cells and natural killer cells associated with high risk for type 1 diabetes
topic Immunology
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6509181/
https://www.ncbi.nlm.nih.gov/pubmed/31130961
http://dx.doi.org/10.3389/fimmu.2019.00982
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