Identifying Changes in Peripheral Lymphocyte Subpopulations in Adult Onset Type 1 Diabetes

TCL1A in naïve B cells as a therapeutic target for type 1 diabetes

BACKGROUND

Type 1 diabetes (T1D) is an autoimmune disease characterised by the attack of pancreatic β cells by "self" immune cells. Although previous studies demonstrated that B cells contribute to T1D through antigen presentation and autoantibody production, the involvement of different populations of B cells, particularly in the early stages of T1D, has not been fully elucidated.

METHODS

In this study, we employed single-cell RNA sequencing (scRNA-seq) and flow cytometry to investigate immune cell populations in patients with newly diagnosed T1D, their relative controls and age-matched healthy controls. Phosphoprotein microarray analysis was employed to investigate changes in protein phosphorylation in B cells. Furthermore, we developed a siRNA-based nanomedicine and evaluated its therapeutic potential in the NOD mouse. The integration of scRNA-seq, flow cytometry, phosphoprotein microarrays, and functional assays established a robust framework for understanding and targeting B cell-mediated autoimmunity in T1D.

FINDINGS

Using single-cell RNA sequencing, we discovered that patients with T1D exhibited increased humoural immunity in the early stage of T1D. Specifically, the population of naïve B cells increased in patients with newly diagnosed T1D who expressed elevated levels of the AKT kinase coactivator TCL1A. Using a protein phosphorylation microarray, we confirmed that TCL1A knockdown specifically impaired AKT2 phosphorylation and affected B cell survival and proliferation. Notably, we discovered that the naïve B cell population increased and TCL1A expression was upregulated in NOD mice that developed T1D. Both the levels of naïve B cells and TCL1A were strongly associated with glucose intolerance in T1D mice. Importantly, treatment with a siRNA-based nanomedicine targeting Tcl1a mRNA effectively reduced the number of naïve B cells, prevented the loss of pancreatic β cells, and improved glucose intolerance in T1D mice.

INTERPRETATION

Using single-cell RNA-seq, we have not only uncovered a naïve B cell specific gene that may contribute to the pathogenesis of T1D but also highlighted the potential of siRNA-based nanomedicine for treating T1D. The clinical translation of these findings offers a new approach for the treatment of T1D.

FUNDING

See Acknowledgements.

Lymphocyte Subset Imbalance in Cardiometabolic Diseases: Are T Cells the Missing Link?

Cardiometabolic and cardiovascular diseases (CVDs) remain the leading cause of death worldwide, with well-established risk factors such as smoking, obesity, and diabetes contributing to plaque formation and chronic inflammation. However, emerging evidence suggests that the immune system plays a more significant role in the development and progression of CVD than previously thought. Specifically, the finely tuned regulation of lymphocyte subsets governs post-injury inflammation and tissue damage resolution and orchestrates the functions and activation of endothelial cells, cardiomyocytes, and fibroblasts in CVD-associated lesions (e.g., atherosclerotic plaques). A deeper understanding of the immune system's involvement in CVD development and progression will provide new insights into disease biology and uncover novel therapeutic targets aimed at re-establishing immune homeostasis. In this review, we summarize the current state of knowledge on the distribution and involvement of lymphocyte subsets in CVD, including atherosclerosis, diabetes, hypertension, myocardial infarction, and stroke.

How do immune cells shape type 1 diabetes? Insights from Mendelian randomization

Objective

The role of immune cells in type 1 diabetes (T1D) is unclear. The aim of this study was to assess the causal effect of different immune cells on T1D using Mendelian randomization (MR).

Methods

A dataset of immune cell phenotypes (numbered from GCST0001391 to GCST0002121) was obtained from the European Bioinformatics Institute, while a T1D dataset (numbered finngen_R10_T1D) was obtained from FinnGen. Single nucleotide polymorphisms meeting the conditions were screened stepwise according to the assumptions of association, independence, and exclusivity. Inverse variance weighted was used as the main method for the MR analysis. MR-Egger was used to assess the horizontal pleiotropy of the results. Cochran's Q and the leave-one-out method were respectively used for the heterogeneity analysis and the sensitivity analysis of the results.

Results

MR analysis showed that effector memory (EM) double-negative (DN) (CD4-CD8-) %T cells [odds ratio (OR) = 1.157, 95% confidence interval (95% CI) = 1.016-1.318, p = 0.028, false discovery rate (FDR) = 0.899], EM CD8br %T cells (OR = 1.049, 95% CI = 1.003-1.098, p = 0.037, FDR = 0.902), CD28 on CD28+CD45RA+CD8br (OR = 1.334, 95% CI = 1.132-1.571, p = 0.001, FDR = 0.044), IgD+CD38dim %lymphocytes (OR = 1.045, 95% CI = 1.002-1.089, p = 0.039, FDR = 0.902), CD80 on monocytes (OR = 1.084, 95% CI = 1.013-1.161, p = 0.020, FDR = 0.834), SSC-A on plasmacytoid dendritic cells (pDCs) (OR = 1.174, 95% CI = 1.004-1.372, p = 0.044, FDR = 0.902), and FSC-A on pDCs (OR = 1.182, 95% CI = 1.011-1.382, p = 0.036, FDR = 0.902) were associated with an increased genetic susceptibility to T1D. Cochran's Q showed that there was heterogeneity for CD28 on the CD28+CD45RA+CD8br results (p = 0.043), whereas there was no heterogeneity for the other results (p ≥ 0.05). The sensitivity analysis showed that the MR analysis results were robust.

Conclusion

The MR analysis demonstrated that seven immune cell phenotypes were associated with an increased genetic susceptibility to T1D. These findings provide a new direction for the pathogenesis of and the drug development for T1D.

Temporal Alterations in CD8+ T Cells During the Progression From Stage 1 to Stage 3 Type 1 Diabetes

CD8+ T cells are perceived to play a major role in the pathogenesis of type 1 diabetes (T1D). In this study, we characterized the function and phenotype of circulating CD8+ memory T cells in samples from individuals at different stages of T1D progression using flow cytometry and single-cell multiomics. We observed two distinct CD8+ T-cell signatures during progression of T1D within the highly differentiated CD27-CD8+ memory T-cell subset. A proinflammatory signature, with an increased frequency of IFN-γ+TNF-α+ CD27-CD8+ memory T cells, was observed in children with newly diagnosed T1D (stage 3) and correlated with the level of dysglycemia at diagnosis. In contrast, a coinhibitory signature, with an increased frequency of KLRG1+TIGIT+ CD27-CD8+ memory T cells, was observed in islet autoantibody-positive children who later progressed to T1D (stage 1). No alterations within CD27-CD8+ memory T cells were observed in adults with established T1D or in children during the initial seroconversion to islet autoantibody positivity. Single-cell multiomics analyses suggested that CD27-CD8+ T cells expressing the IFNG+TNF+ proinflammatory signature may be distinct from those expressing the KLRG1+TIGIT+ coinhibitory signature at the single-cell level. Collectively, our findings suggest that distinct blood CD8+ T-cell signatures could be employed as potential biomarkers of T1D progression.

ARTICLE HIGHLIGHTS

Blood immune cell profiling in adults with longstanding type 1 diabetes is associated with macrovascular complications

Aims/hypothesis

There is increasing evidence for heterogeneity in type 1 diabetes mellitus (T1D): not only the age of onset and disease progression rate differ, but also the risk of complications varies markedly. Consequently, the presence of different disease endotypes has been suggested. Impaired T and B cell responses have been established in newly diagnosed diabetes patients. We hypothesized that deciphering the immune cell profile in peripheral blood of adults with longstanding T1D may help to understand disease heterogeneity.

Methods

Adult patients with longstanding T1D and healthy controls (HC) were recruited, and their blood immune cell profile was determined using multicolour flow cytometry followed by a machine-learning based elastic-net (EN) classification model. Hierarchical clustering was performed to identify patient-specific immune cell profiles. Results were compared to those obtained in matched healthy control subjects.

Results

Hierarchical clustering analysis of flow cytometry data revealed three immune cell composition-based distinct subgroups of individuals: HCs, T1D-group-A and T1D-group-B. In general, T1D patients, as compared to healthy controls, showed a more active immune profile as demonstrated by a higher percentage and absolute number of neutrophils, monocytes, total B cells and activated CD4+CD25+ T cells, while the abundance of regulatory T cells (Treg) was reduced. Patients belonging to T1D-group-A, as compared to T1D-group-B, revealed a more proinflammatory phenotype characterized by a lower percentage of FOXP3+ Treg, higher proportions of CCR4 expressing CD4 and CD8 T cell subsets, monocyte subsets, a lower Treg/conventional Tcell (Tconv) ratio, an increased proinflammatory cytokine (TNFα, IFNγ) and a decreased anti-inflammatory (IL-10) producing potential. Clinically, patients in T1D-group-A had more frequent diabetes-related macrovascular complications.

Conclusions

Machine-learning based classification of multiparameter flow cytometry data revealed two distinct immunological profiles in adults with longstanding type 1 diabetes; T1D-group-A and T1D-group-B. T1D-group-A is characterized by a stronger pro-inflammatory profile and is associated with a higher rate of diabetes-related (macro)vascular complications.

Viral infection and host immune response in diabetes

Diabetes, a chronic metabolic disorder disrupting blood sugar regulation, has emerged as a prominent silent pandemic. Uncontrolled diabetes predisposes an individual to develop fatal complications like cardiovascular disorders, kidney damage, and neuropathies and aggravates the severity of treatable infections. Escalating cases of Type 1 and Type 2 diabetes correlate with a global upswing in diabetes-linked mortality. As a growing global concern with limited preventive interventions, diabetes necessitates extensive research to mitigate its healthcare burden and assist ailing patients. An altered immune system exacerbated by chronic hyperinflammation heightens the susceptibility of diabetic individuals to microbial infections, including notable viruses like SARS-CoV-2, dengue, and influenza. Given such a scenario, we scrutinized the literature and compiled molecular pathways and signaling cascades related to immune compartments in diabetics that escalate the severity associated with the above-mentioned viral infections in them as compared to healthy individuals. The pathogenesis of these viral infections that trigger diabetes compromises both innate and adaptive immune functions and pre-existing diabetes also leads to heightened disease severity. Lastly, this review succinctly outlines available treatments for diabetics, which may hold promise as preventive or supportive measures to effectively combat these viral infections in the former.

Systematic immune cell dysregulation and molecular subtypes revealed by single-cell RNA-seq of subjects with type 1 diabetes

BACKGROUND

Type 1 diabetes mellitus (T1DM) is a prototypic endocrine autoimmune disease resulting from an immune-mediated destruction of pancreatic insulin-secreting β  cells. A comprehensive immune cell phenotype evaluation in T1DM has not been performed thus far at the single-cell level.

METHODS

In this cross-sectional analysis, we generated a single-cell transcriptomic dataset of peripheral blood mononuclear cells (PBMCs) from 46 manifest T1DM (stage 3) cases and 31 matched controls.

RESULTS

We surprisingly detected profound alterations in circulatory immune cells (1784 dysregulated genes in 13 immune cell types), far exceeding the count in the comparator systemic autoimmune disease SLE. Genes upregulated in T1DM were involved in WNT signaling, interferon signaling and migration of T/NK cells, antigen presentation by B cells, and monocyte activation. A significant fraction of these differentially expressed genes were also altered in T1DM pancreatic islets. We used the single-cell data to construct a T1DM metagene z-score (TMZ score) that distinguished cases and controls and classified patients into molecular subtypes. This score correlated with known prognostic immune markers of T1DM, as well as with drug response in clinical trials.

CONCLUSIONS

Our study reveals a surprisingly strong systemic dimension at the level of immune cell network in T1DM, defines disease-relevant molecular subtypes, and has the potential to guide non-invasive test development and patient stratification.

Prediction of type 1 diabetes with machine learning algorithms based on FTIR spectral data in peripheral blood mononuclear cells

The incidence of autoimmunity is increasing, to ensure timely and comprehensive treatment, there must be a diagnostic method or markers that would be available to the general public. Fourier-transform infrared spectroscopy (FTIR) is a relatively inexpensive and accurate method for determining metabolic fingerprint. The metabolism, molecular composition and function of blood cells vary according to individual physiological and pathological conditions. Thus, by obtaining autoimmune disease-specific metabolic fingerprint markers in peripheral blood mononuclear cells (PBMC) and subsequently using machine learning algorithms, it might be possible to create a tool that will allow the diagnosis of autoimmune diseases. In this preliminary study, it was found that the peak shift at 1545 cm-1 could be considered specific for autoimmune disease type 1 diabetes (T1D), while the shifts at 1070 and 1417 cm-1 could be more attributed to the autoimmune condition per se. The prediction of T1D, despite the small number of participants in the study, showed an inverse AUC = 0.33 ± 0.096, n = 15, indicating a stable trend in the prediction of T1D based on FTIR metabolic fingerprint data in the PBMC.

Human immune phenotyping reveals accelerated aging in type 1 diabetes

The proportions and phenotypes of immune cell subsets in peripheral blood undergo continual and dramatic remodeling throughout the human life span, which complicates efforts to identify disease-associated immune signatures in type 1 diabetes (T1D). We conducted cross-sectional flow cytometric immune profiling on peripheral blood from 826 individuals (stage 3 T1D, their first-degree relatives, those with ≥2 islet autoantibodies, and autoantibody-negative unaffected controls). We constructed an immune age predictive model in unaffected participants and observed accelerated immune aging in T1D. We used generalized additive models for location, shape, and scale to obtain age-corrected data for flow cytometry and complete blood count readouts, which can be visualized in our interactive portal (ImmScape); 46 parameters were significantly associated with age only, 25 with T1D only, and 23 with both age and T1D. Phenotypes associated with accelerated immunological aging in T1D included increased CXCR3+ and programmed cell death 1-positive (PD-1+) frequencies in naive and memory T cell subsets, despite reduced PD-1 expression levels on memory T cells. Phenotypes associated with T1D after age correction were predictive of T1D status. Our findings demonstrate advanced immune aging in T1D and highlight disease-associated phenotypes for biomarker monitoring and therapeutic interventions.

Investigating immune profile by CyTOF in individuals with long-standing type 1 diabetes

Type 1 diabetes (T1D) is an autoimmune disease caused by T-cell mediated destruction of pancreatic beta cells. Eosinophils are found in pancreatic tissue from individuals with T1D. Eosinophilic suppression of T cells is dependent of the protein galectin-10. Little is known when it comes to the role of eosinophil granulocytes in type 1 diabetes. Here we show that individuals with long-standing T1D had lower levels of galectin-10^hi eosinophils and a subgroup of galectin-10^hi eosinophils were entirely absent in all T1D patients. In addition, 7% immature eosinophils were present in the circulation of T1D patients whereas 0.8% in healthy individuals. Furthermore, higher levels of CD4+CD8+ T cells and Th17 cells were observed in patients with T1D. Blood samples from 12 adult individuals with long-standing T1D and 12 healthy individuals were compared using cytometry by time-of-flight. Lower levels of galectin-10^hi eosinophils, which are potent T cell suppressors, in individuals with T1D could indicate that activated T cells are enabled to unrestrictedly kill the insulin producing beta cells. This is the first study showing absence of galectin-10^hi eosinophilic subgroup in individuals with T1D compared with healthy controls. This study is a first important step toward unraveling the role of the eosinophils in patients with T1D.

Use of the NIH consensus criteria in cellular and soluble biomarker research in chronic graft-versus-host disease: A systematic review

Objectives

Chronic graft-versus-host disease (cGvHD) is the most frequent cause of late non-relapse mortality after allogeneic haematopoietic stem cell transplantation (alloHCT). Nevertheless, established biomarkers of cGvHD are still missing. The National Institutes of Health (NIH) Consensus Development Project on Criteria for Clinical Trials in cGvHD provided recommendations for biomarker research. We evaluated to which extent studies on cellular and soluble biomarkers in cGvHD published in the last 10 years complied with these recommendations. Also, we highlight the most promising biomarker candidates, verified in independent cohorts and/or repeatedly identified by separate studies.

Methods

We searched Medline and EMBASE for “cGvHD”, “biomarkers”, “soluble” and “cells” as MeSH terms or emtree subject headings, and their variations on July 28th, 2021, limited to human subjects, English language and last ten years. Reviews, case reports, conference abstracts and single nucleotide polymorphism studies were excluded. Criteria based on the set of recommendations from the NIH group for biomarker research in cGvHD were used for scoring and ranking the references.

Results

A total of 91 references encompassing 15,089 participants were included, 54 prospective, 17 retrospective, 18 cross-sectional, and 2 studies included both prospective and retrospective cohorts. Thirty-five papers included time-matched controls without cGvHD and 20 studies did not have any control subjects. Only 9 studies were randomized, and 8 were multicentric. Test and verification cohorts were included in 11 studies. Predominantly, diagnostic biomarkers were explored (n=54). Assigned scores ranged from 5-34. None of the studies fulfilled all 24 criteria (48 points). Nevertheless, the scores improved during the last years. Three cell subsets (CXCR3+CD56bright NK cells, CD19+CD21low and BAFF/CD19+ B cells) and several soluble factors (BAFF, IL-15, CD163, DKK3, CXCL10 and the panel of ST2, CXCL9, MMP3 and OPN) had the highest potential as diagnostic and/or prognostic biomarkers in cGvHD.

Conclusion

Despite several limitations of this review (limited applicability for paediatric population, definition of verification, missing data on comorbidities), we identified promising candidate biomarkers for further evaluation in multicentre collaborative studies. This review confirms the importance of the NIH consensus group criteria for improving the quality and reproducibility of cGvHD biomarker research.

Cellular immunity in subacute thyroiditis: a new perspective through neopterin

Subacute thyroiditis (SAT) is an inflammatory disorder of the thyroid gland. Although its etiology is not fully understood, it is believed to occur shortly after viral infections and is mostly associated with human leukocyte antigen (HLA)-B*35. Cellular immunity is prominent in SAT. Neopterin is produced by activated monocytes/macrophages and is a marker of cellular immunity. Its production is stimulated by interferon gamma (IFN-γ), provided mainly by activated helper T lymphocytes type 1 (Th1) in the adaptive immune system. Therefore, with these cells' activation, an increase in serum neopterin levels is expected. We aimed to evaluate neopterin levels in demonstrating cellular immunity in SAT and compared 15 SAT patients with 16 healthy controls. Since all SAT patients were in the active thyrotoxic phase, we found a significant difference in thyroid functions. Classical inflammatory markers, erythrocyte sedimentation rate, and C-reactive protein were markedly elevated in the patient group. Although we expected to find an increase considering that cellular immunity is at the forefront in the pathogenesis of SAT, we found serum neopterin levels significantly lower in the patient group than in the control group. There is an increase in CD8+ T cells in the thyroid tissue in SAT. The possible relationship with HLA-B*35- major histocompatibility complex class I in SAT, and the antigen presentation to CD8+ T cells may be the reason why we observed low serum neopterin levels in patients due to the cytokine imbalance. Neopterin provides unique and independent data from classical acute phase response indicators.

Enhanced T Cell Glucose Uptake Is Associated With Progression of Beta-Cell Function in Type 1 Diabetes

Background

Abnormal intracellular glucose/fatty acid metabolism of T cells has tremendous effects on their immuno-modulatory function, which is related to the pathogenesis of autoimmune diseases. However, the association between the status of intracellular metabolism of T cells and type 1 diabetes is unclear. This study aimed to investigate the uptake of glucose and fatty acids in T cells and its relationship with disease progression in type 1 diabetes.

Methods

A total of 86 individuals with type 1 diabetes were recruited to detect the uptake of glucose and fatty acids in T cells. 2-NBDG uptake and expression of glucose transporter 1 (GLUT1); or BODIPY uptake and expression of carnitine palmitoyltransferase 1A(CPT1A) were used to assess the status of glucose or fatty acid uptake in T cells. Patients with type 1 diabetes were followed up every 3-6 months for 36 months, the progression of beta-cell function was assessed using generalized estimating equations, and survival analysis was performed to determine the status of beta-cell function preservation (defined as 2-hour postprandial C-peptide >200 pmol/L).

Results

Patients with type 1 diabetes demonstrated enhanced intracellular glucose uptake of T cells as indicated by higher 2NBDG uptake and GLUT1 expression, while no significant differences in fatty acid uptake were observed. The increased T cells glucose uptake is associated with lower C-peptide and higher hemoglobin A1c levels. Notably, patients with low T cell glucose uptake at onset maintained high levels of C-peptide within 36 months of the disease course [fasting C-petite and 2-hour postprandial C-peptide are 60.6 (95%CI: 21.1-99.8) pmol/L and 146.3 (95%CI: 14.1-278.5) pmol/L higher respectively], And they also have a higher proportion of beta-cell function preservation during this follow-up period (P<0.001).

Conclusions

Intracellular glucose uptake of T cells is abnormally enhanced in type 1 diabetes and is associated with beta-cell function and its progression.

Single-Cell RNA Sequencing Identifies Intra-Graft Population Heterogeneity in Acute Heart Allograft Rejection in Mouse

Transplant rejection remains a major barrier to graft survival and involves a diversity of cell types. However, the heterogeneity of each cell type in the allograft remains poorly defined. In the present study, we used single-cell RNA sequencing technology to analyze graft-infiltrating cells to describe cell types and states associated with acute rejection in a mouse heart transplant model. Unsupervised clustering analysis revealed 21 distinct cell populations. Macrophages formed five cell clusters: two resident macrophage groups, two infiltrating macrophage groups and one dendritic cell-like monocyte group. Infiltrating macrophages were predominantly from allogeneic grafts. Nevertheless, only one infiltrating macrophage cluster was in an active state with the upregulation of CD40, Fam26f and Pira2, while the other was metabolically silent. Re-clustering of endothelial cells identified five subclusters. Interestingly, one of the endothelial cell populations was almost exclusively from allogeneic grafts. Further analysis of this population showed activation of antigen processing and presentation pathway and upregulation of MHC class II molecules. In addition, Ubiquitin D was specifically expressed in such endothelial cell population. The upregulation of Ubiquitin D in rejection was validated by staining of mouse heart grafts and human kidney biopsy specimens. Our findings present a comprehensive analysis of intra-graft cell heterogeneity, describe specific macrophage and endothelial cell populations which mediate rejection, and provide a potential predictive biomarker for rejection in the clinic.