Autoreactive CD8+ T cell exhaustion distinguishes subjects with slow type 1 diabetes progression

Abstract

Although most patients with type 1 diabetes (T1D) retain some functional insulin-producing islet β cells at the time of diagnosis, the rate of further β cell loss varies across individuals. It is not clear what drives this differential progression rate. CD8+ T cells have been implicated in the autoimmune destruction of β cells. Here, we addressed whether the phenotype and function of autoreactive CD8+ T cells influence disease progression. We identified islet-specific CD8+ T cells using high-content, single-cell mass cytometry in combination with peptide-loaded MHC tetramer staining. We applied a new analytical method, DISCOV-R, to characterize these rare subsets. Autoreactive T cells were phenotypically heterogeneous, and their phenotype differed by rate of disease progression. Activated islet-specific CD8+ memory T cells were prevalent in subjects with T1D who experienced rapid loss of C-peptide; in contrast, slow disease progression was associated with an exhaustion-like profile, with expression of multiple inhibitory receptors, limited cytokine production, and reduced proliferative capacity. This relationship between properties of autoreactive CD8+ T cells and the rate of T1D disease progression after onset make these phenotypes attractive putative biomarkers of disease trajectory and treatment response and reveal potential targets for therapeutic intervention.

Keywords: Autoimmune diseases; Autoimmunity; Diabetes; Immunology; T cells.

Figure 1. Islet-specific CD8⁺ T cells are dominated by three CXCR3⁺ memory phenotypes across subjects with T1D.

The DISCOV-R analysis method was applied to total CD8⁺ and islet-specific T cells from subjects with T1D (n = 46); the T cells had been assayed with the Tmr-CyTOF panel. (A) Schematic of the DISCOV-R method (see Methods and Supplemental Figure 3 for details) for 1 individual. (B and C) Distribution of islet-specific cells across the 12 aligned clusters for subjects with at least 5 Tmr⁺ cells (n = 39). (B) Data are displayed as a stacked bar graph for each subject, colored by cluster. The 3 clusters that are most dominant among islet-specific cells across subjects (clusters 1, 11, and 12) have heavy outlining and are stacked at the bottom. (C) Clusters containing more than 20% islet-specific cells for an individual are indicated in black. Arrows indicate clusters predominant in at least 25% of the samples; the detached bottom row indicates the mean frequency of cells within a cluster for all individuals on a scale from 0% (white) to 20% or higher (black). (D) Heatmap of Z scores using arcsinh-transformed expression of 22 consistent markers (rows) for all individual clusters (columns) from all T1D subjects (n = 46), grouped into 12 aligned clusters (annotated with numbers and colors). Negative Z scores (aqua) represent underexpression, and positive Z scores (yellow) represent overexpression of markers in an individual cluster compared with the mean of expression intensity on total CD8⁺ T cells within a subject. Frequency of islet-specific (Tmr⁺) cells within an individual cluster is annotated above (white = 0%, black = 20%+). (E) Heatmap of the mean absolute arcsinh-transformed expression of 24 markers for the 3 islet-specific clusters and total CD8⁺ T cells. Expression intensity ranges from 0 (dark purple) to 4+ (yellow).

Figure 2. Islet-specific CD8⁺ T cell frequency and phenotype do not differ between HCs and individuals with T1D.

HCs (n = 20) were assayed with our Tmr-CyTOF panel and included in the DISCOV-R analysis as in Figure 1. (A) Frequency of islet-specific (Tmr⁺) cells within total CD8⁺ T cells was assessed and compared for HCs (n = 20) and T1D subjects (n = 46) using a Mann-Whitney U test. (B) Frequency of islet-specific CD8⁺ T cells among the 3 common islet-specific clusters was assessed for HCs (n = 13) and individuals with T1D (n = 39) with 5 or more Tmr⁺ events using 2-way ANOVA with Sidak’s test for multiple comparisons. Data represent the mean ± SD. TM, transitional memory.

Figure 3. Phenotype, not frequency, of islet-specific CD8⁺ T cells is associated with the rate of disease progression in T1D.

The frequency of (A) islet-specific (Tmr⁺) cells within total CD8⁺ T cells was assessed for rapid (n = 14) and slow (n = 23) T1D progressors using a Mann-Whitney U test. The frequency of (B) islet Tmr⁺ or (D) total CD8⁺ T cells among the 3 common islet-specific clusters was assessed for rapid (n = 11, red solid triangles) and slow (n = 20, blue open squares) T1D progressors with 5 or more Tmr⁺ events using 2-way ANOVA with Sidak’s test for multiple comparisons. Data represent the mean ± SD. *P < 0.05 and ***P < 0.001. (C) Distribution of islet Tmr⁺ cells in clusters for individual samples; rapid progressors (n = 11) and slow progressors (n = 20) were organized by decreasing frequency of cluster 11 and increasing frequency of clusters 1 and 12 within each group.

Figure 4. Age and disease duration do not determine islet-specific CD8⁺ T cell exhaustion.

The frequency of islet-specific phenotypes among islet-specific CD8⁺ T cells was assessed for subjects with 5 or more Tmr⁺ events. (A) Frequencies in HCs (n = 13) as a function of age based on DISCOV-R results from Figure 2. Statistical significance was determined by Spearman’s correlation. (B) Frequencies in T1D subjects who were not classified as rapid or slow progressors, grouped by disease duration (<5 years, n = 3, solid orange circles; ≥5 years, n = 5, open purple diamonds) on the basis of DISCOV-R results from Figure 1. A 2-way ANOVA with Sidak’s test for multiple comparisons revealed no statistically significant differences between the groups. Data represent the mean ± SD. (C) Frequencies in T1D subjects (n = 4) with samples drawn at 2 time points following disease onset, shown as paired, stacked bar graphs. The time points of the first draw were 3.2, 3.8, 4.8, and 5.5 years after disease onset, respectively.

Figure 5. Islet-specific CD8⁺ T cells with an abundant cluster 1 (exhausted) phenotype are hypoproliferative and produce limited levels of the cytokines IL-2 and IFN-γ.

PBMCs from individuals with T1D (n = 11) with varying frequencies of cluster 1 among their islet-specific cells were stimulated with anti-CD3 plus anti-CD28. Cells were assayed by flow cytometry to identify islet-specific (Tmr⁺) CD8⁺ T cells (Supplemental Figure 13). Examples of gating for proliferation and cytokine production are shown for a rapid progressor (T1D-02) and a slow progressor (T1D-34) with low (4%) and high (60%) frequencies of cluster 1, respectively. (A) Representative examples of the frequency of proliferated cells on day 5 among stimulated (black line) islet Tmr⁺ cells as measured by CellTrace dye dilution, using unstimulated (solid gray) cells as a gating control. (B) Frequency of proliferated cells among islet Tmr⁺ cells after 5 days of stimulation, plotted against the frequency of cluster 1 determined by mass cytometry for each individual (n = 11). (C) Representative examples of IL-2 and IFN-γ production assessed at 6 hours among islet Tmr⁺ (black) or Tmr^– CD8⁺ T cells (gray). (D) Frequency of IL-2⁺ and IFN-γ⁺ cells among islet Tmr⁺ cells after 6 hours of stimulation, plotted against the frequency of cluster 1 determined by mass cytometry for each individual (n = 10); no substantial cytokine production (<1%) was observed in the absence of stimulation. Statistical significance was determined by Spearman’s correlation. The difference in proliferation between islet-specific cells of rapid progressors (triangles, n = 3) and slow progressors (squares, n = 4) was not significant (P = 0.057), nor was cytokine production (P > 0.05) by Mann-Whitney U test.