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

Abstract

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.

Mass Cytometry Studies of Patients With Autoimmune Endocrine Diseases Reveal Distinct Disease-Specific Alterations in Immune Cell Subsets

“…It has been recently shown that children with newly diagnosed T1D had reduced frequencies of CD16 + CD56 dim and CD16 − CD56 dim NK cell subsets than HC (32). We have previously reported that multiple autoantibody-positive children, who later progressed to T1D, had higher frequencies of two CD8 + CD16 + CD56 dim NK cell subsets than autoantibody-negative individuals (18). By using the same mass cytometry panel, we found an expansion of activated mature/effector CD8 − CD11c + CD16 + CD56 dim CD69 + HLA-DR − NK cells that expressed IFN-γ in adults with N-T1D.…”

“…The antibody panel, stimulation conditions, and intracellular staining used for mass cytometry in this study were optimized and validated as described elsewhere (18). Monoclonal antibodies and reagents are described in detail in Supplementary Table 1.…”

“…Barcoded sample batches were processed simultaneously to reduce data collection variability. Sample barcoding and data acquisition were carried out as previously described (18). Briefly, cells were washed twice in Maxpar R Barcode perm buffer (Fluidigm; US) and each sample was stained with a different barcode combination using Cell-ID 20-plex Pd-barcoding kit (Fluidigm; US) for 30 min at room temperature.…”

“…Mass cytometry is a high-dimensional single-cell technology that enables simultaneous characterization of the immune system, which has yielded novel information about T1D (18)(19)(20), systemic lupus erythematous (21) and rheumatoid arthritis (22). A limited number of studies have used this approach to compare different diseases in parallel (23,24), but none have focused on immunological features in patients with autoimmune endocrine diseases.…”

Artificial-Cell-Type Aware Cell Type Classification in CITE-seq

“…Surface markers are the most reliable biomarkers for cell type classification (Cui, et al, 2019;Spitzer and Nolan, 2016). Numerous studies in recent decades have used cell surface markers to isolate and characterize cellular populations with distinct biological functions and have discovered many novel cell types in the process (Ahmed, et al, 2019;Barcenilla, et al, 2019). Recent advancement in single cell RNA sequencing (scRNA-seq) technologies enabled direct cell identity assessment through dissecting single cell gene expression profiles.…”