Guidelines for standardizing T‐cell cytometry assays to link biomarkers, mechanisms, and disease outcomes in type 1 diabetes

Yang, Jennie H.M.; Ward‐Hartstonge, Kirsten A.; Perry, Daniel J.; Blanchfield, J. Lori; Posgai, Amanda L.; Wiedeman, Alice; Diggins, Kirsten E.; Rahman, Adeeb; Tree, Timothy; Brusko, Todd M.; Levings, Megan K.; James, Eddie A.; Kent, Sally C.; Speake, Cate; Homann, Dirk; Long, S. Alice

doi:10.1002/eji.202049067

Cited by 12 publications

(11 citation statements)

References 105 publications

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“…From rested peripheral blood samples, we used six flow cytometry panels adapted from HIPC recommendations (13) as we previously published (18; 32), to generate detailed immunophenotyping data encompassing proportions of innate and adaptive immune cells as well as phenotypes of memory T cell, Treg, Teff, T follicular helper (Tfh), B cell, DC, monocyte, and NK cell subsets ( Figure 1A , with detailed gating schematics in Figures S1-S6 ). We first tested an initial cohort of 12 individuals in duplicate to evaluate assay reproducibility and observed that the biological coefficient of variance (CV) largely outweighed variance between technical duplicates (45.23 ± 25.66% vs. 8.87 ± 7.56%, Figure 1B ), in agreement with previously established guidelines for replicability in flow cytometric studies (33). These studies were then extended to characterize flow cytometric immunophenotypes with accompanying complete blood count (CBC) measurements, for a total of 192 total outcome measures, on a large cross-sectional cohort of n =826 persons with or at varying levels of risk for T1D ( Table 1 ).…”

Section: Resultssupporting

confidence: 80%

“…Samples were collected in ethylenediaminetetraacetic acid (EDTA)-coated vacutainer tubes for flow cytometry, complete blood count, HbA1c, TCRβ sequencing, and genotyping assays; serum separator vacutainer tubes for islet autoantibody and CMV IgG antibody measurement; and sodium fluoride/potassium oxalate-coated tubes (BD Biosciences) for rested blood glucose quantification, and stored in accordance with institutional review board (IRB)-approved protocols at each participating institution. Samples were shipped or rested overnight prior to flow cytometric staining in order to standardize duration between collection and evaluation at UF (33). Data were collected from all incoming blood samples to the UFDI from 2014-2018, hence the lack of age-matching between clinical subgroups based on clinical status.…”

Section: Methodsmentioning

confidence: 99%

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Human immune phenotyping reveals accelerated aging in type 1 diabetes

Shapiro

Dong

Perry

et al. 2023

Preprint

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The composition of immune cells in peripheral blood is dramatically remodeled throughout the human lifespan, as environmental exposures shape the proportion and phenotype of cellular subsets. These dynamic shifts complicate efforts to identify disease-associated immune signatures in type 1 diabetes (T1D), which is variable in age of onset and rate of beta-cell decline. Herein, we conducted standardized flow cytometric immune profiling on peripheral blood from a cross-sectional cohort of T1D participants (n=240), their first-degree relatives (REL, n=310), those at increased risk with two or more islet autoantibodies (RSK, n=24), and autoantibody negative healthy controls (CTR, n=252). We constructed an immune-age predictive model in healthy subjects and developed an interactive data visualization portal (ImmScape; https://ufdiabetes.shinyapps.io/ImmScape/). When applied to the T1D cohort, this model revealed accelerated immune aging (p<0.001) as well as phenotypic signatures of disease after age correction. Of 192 investigated flow cytometry and complete blood count readouts, 46 were significantly associated with age only, 25 with T1D only, and 23 with both age and T1D. Phenotypes associated with T1D after age-correction were predictive of T1D status (AUROC=82.3%). Phenotypes associated with accelerated aging in T1D included increased CXCR3+ and PD-1+ frequencies in naive and memory T cell subsets, despite reduced PD-1 expression levels (mean fluorescence intensity) on memory T cells. Additionally, quantitative trait locus analysis linked an increase in HLA-DR expression on monocytes with the T1D-associated HLA-DR4/DQ8 genotype, regardless of clinical group. Our findings demonstrate advanced immune aging in T1D and highlight disease-associated phenotypes for biomarker monitoring and therapeutic interventions.

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Section: Resultssupporting

confidence: 80%

Section: Methodsmentioning

confidence: 99%

Human immune phenotyping reveals accelerated aging in type 1 diabetes

Shapiro

Dong

Perry

et al. 2023

Preprint

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“…This information will also guide subsequent approaches to therapeutic interventions. There are excellent recent reviews on factors impeding progress toward the development of effective T-cell biomarkers in T1D ( 52 , 98 , 99 ).…”

Section: Biomarkersmentioning

confidence: 99%

Monitoring immunomodulation strategies in type 1 diabetes

et al. 2023

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Type 1 diabetes (T1D) is a T-cell mediated autoimmune disease. Short-term treatment with agents targeting T cells, B cells and inflammatory cytokines to modify the disease course resulted in a short-term pause in disease activity. Lessons learnt from these trials will be discussed in this review. It is expected that effective disease-modifying agents will become available for use in earlier stages of T1D. Progress has been made to analyze antigen-specific T cells with standardization of T cell assay and discovery of antigen epitopes but there are many challenges. High-dimensional profiling of gene, protein and TCR expression at single cell level with innovative computational tools should lead to novel biomarker discovery. With this, assays to detect, quantify and characterize the phenotype and function of antigen-specific T cells will continuously evolve. An improved understanding of T cell responses will help researchers and clinicians to better predict disease onset, and progression, and the therapeutic efficacy of interventions to prevent or arrest T1D.

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“…We anticipate that PBMCs from this study will be useful for flow cytometry or mass cytometry by time-of-flight studies for phenotypic and functional bulk characterization of circulating immune cells. [52][53][54] Importantly, PBMCs enable the measurement of antigen-specific T-cells specific for either endocrine or exocrine tissue, helping to define whether antigen specificity differs in the setting of the AP inflammatory insult compared with the well-described range of antigens detectable in typical T1D (recently reviewed in Refs. 55 and 56).…”

Section: Biobanking For the Future: Peripheral Blood Mononuclear Cellsmentioning

confidence: 99%

Evaluating the Immunopathogenesis of Diabetes After Acute Pancreatitis in the Diabetes RElated to Acute Pancreatitis and Its Mechanisms Study

Casu¹,

Grippo²,

Wasserfall³

et al. 2022

Pancreas

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The association between acute pancreatitis (AP) and diabetes mellitus (DM) has long been established, with the initial descriptions of AP patients presenting with DM after a bout of AP published in the 1940s and 50s. However, the potential mechanisms involved, particularly those components related to the immune system, have not been well defined. The Diabetes RElated to Acute pancreatitis and its Mechanisms (DREAM) study is a multicenter clinical study designed to understand the frequency and phenotype of DM developing after AP. This article describes one objective of the DREAM study: to determine the immunologic mechanisms of DM after AP, including the contribution of β-cell autoimmunity. This component of the study will assess the presence of islet autoimmunity, as well as the magnitude and kinetics of the innate and adaptive immune response at enrollment and during longitudinal follow-up after 1 or more episodes of AP. Finally, DREAM will evaluate the relationship between immune features, DM development, and pancreatitis etiology and severity.

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Guidelines for standardizing T‐cell cytometry assays to link biomarkers, mechanisms, and disease outcomes in type 1 diabetes

Abstract: Cytometric immunophenotyping is a powerful tool to discover and implement T-cell biomarkers of type 1 diabetes (T1D) progression and response to clinical therapy. Although

Cited by 12 publications

References 105 publications

Human immune phenotyping reveals accelerated aging in type 1 diabetes

Human immune phenotyping reveals accelerated aging in type 1 diabetes

Monitoring immunomodulation strategies in type 1 diabetes

Evaluating the Immunopathogenesis of Diabetes After Acute Pancreatitis in the Diabetes RElated to Acute Pancreatitis and Its Mechanisms Study

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