Unsupervised clustering reveals a unique Treg profile in slow progressors to type 1 diabetes

Author:

Boldison JoanneORCID,Long Anna E,Aitken Rachel J,Wilson Isabel V,Megson Claire,Hanna Stephanie J,Wong F. SusanORCID,Gillespie Kathleen M

Abstract

AbstractObjectiveTo profile CD4+ regulatory T cells (Tregs) in a well-characterised cohort of slow progressors to type 1 diabetes, individuals positive for multiple islet autoantibodies who remain diabetes-free for at least 10 years.Research Design and MethodsPeripheral blood samples were obtained from extreme slow progressor individuals (n=8), with up to 32 years follow-up, and age and gender-matched to healthy donors. One participant in this study was identified with a raised HbA1c at the time of assessment, and was individually evaluated in the data analysis. PBMCs were isolated, from donors, and to assess frequency, phenotype and function of Tregs, multi-parameter flow cytometry and T cell suppression assays were performed. Unsupervised clustering analysis, FlowSOM and CITRUS, was used to evaluate Treg phenotypes.ResultsTreg mediated suppression of CD4+ effector T cells, from slow progressors was significantly impaired, compared to healthy donors (P<0.05). Effector CD4 T cells, from slow progressors, were more responsive to Treg suppression, compared to healthy donors, demonstrated by increased suppression of CD25 expression on effector CD4 T cells (P<0.05). Unsupervised clustering on memory CD4 T cells, from slow progressors, showed an increased frequency of activated-memory CD4 Tregs associated with increased expression of GITR, compared to healthy donors (P<0.05). The participant with a raised HbA1c had a different Treg profile, compared to slow progressors and the matched controls.ConclusionsCD4+ Tregs from slow progressor individuals have a unique Treg signature. This report highlights the need for further study of Treg heterogeneity in individuals at-risk of developing type 1 diabetes.

Publisher

Cold Spring Harbor Laboratory

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