Relative expansion of CD8 T cells in CVID comprises mainly increased percentages of EM2 and EM3 subpopulations
30 adult patients with complex form of CVID (CVIDc) (17 female, 13 male), 10 adult patients with infection only form of CVID (CVIDio) (3 female, 7 male) and 17 healthy adults with no history of immune disease were included in this study. Detailed description of the cohort, including genetic findings, Freiburg and EUROclass classifications, can be found in Table 1. The most common clinical complications were recurrent non-infectious diarrhea (n = 21), interstitial lung disease (ILD, n = 17) and lymphadenopathy (LAD, n = 16), followed by autoimmune cytopenia (AIC, n = 14). Splenomegaly was present in 30 patients, but when found in isolation with no other clinical complications did not qualify the patient as CVIDc. A graph showing co-incidence of various clinical features in the studied cohort can be found in Fig. 1A, and the proportion of patients in the different EUROclass phenotypes can be found in Fig. 1B.
We next performed a detailed analysis of the differentiation and exhaustion of CD8 T cells in CVID patients. Patients with CVID had higher proportion of CD8 T cells than healthy controls (HD) in the peripheral blood (Fig. 1C) (Welch’s ANOVA p = 0.008, unpaired t-tests with Welch’s correction) (CD4:8 ratio in Figure S2), which were skewed away from naïve (CD45RA+CCR7+) and into effector memory (EM, CD45RA−CCR7−) and terminal effector memory re-expressing CD45RA (TEMRA, CD45RA+CCR7−) stages (Fig. 1D). In particular, EM2 (CD27+28−) and EM3 (CD27−28−) were significantly expanded in both CVIDio and CVIDc compared to HD (Fig. 1E) (CVIDio EM2 unpaired t-test with Welch’s correction p < 0.001, EM3 p = 0.007, CVIDc EM2 p = 0.002, EM3 p = 0.01).
To assess if the analysis of these pre-defined canonical T cell subsets correlated with data-driven approaches, we performed trajectory-inference analysis using Wanderlust  based on 29 phenotypic markers (see Methods). The resulting pseudotime variable corresponded well with these canonical differentiation stages from naïve into central, effector memory and ultimately TEMRA populations (Fig. 1F, G), documenting expected changes with gradual decrease and final re-expression of CD45RA, loss of CCR7, preserved in CM, and loss of CD27 and CD28.
For detailed exhaustion profiling, where phenotyping of T cells based on differentiation markers is insufficient to precisely identify exhausted T-cell populations, we combined a highly multiplexed phenotyping of exhaustion-biased immune markers with a functional analysis of the expression patterns of (n = 10) cytokines important for exhausted T-cell biology. This advanced approach allows for the identification of relevant exhausted T-cell phenotypes and evaluation of an exhaustion-typical dysfunction pattern (i.e. reduced IL-2, TNF production, but possible monoproduction of IFN-g and XCL1 chemokine production) that can be assessed on a single-cell level and using a functional exhaustion score, as published earlier . For this reason, we stimulated PBMCs from 4 healthy donors, 4 CVIDio and 4 CVIDc patients with PMA + ionomycin and then measured them using a mass cytometry panel of 44 extracellular markers, transcription factors, cytokines and chemokines (for a complete list of markers see Table S2).
When stimulated with PMA and ionomycin, the EM2 and EM3 CD8 T cells had a unique phenotype which on average had decreased production of IL-2, but instead showed high production of cytotoxic molecules perforin, granulysin, and the lymphotactin XCL-1 (Fig. 1H, dotplots in Figure S3). EM3 in particular also displayed high expression of the senescence marker CD57, increased in chronic immune activation , exhaustion markers Tim3, the ectonucleotidase CD39, and the TNF-α receptor CD120b. Together, this data revealed significant heterogeneity of exhaustion markers and functional T cell profiles in canonical T cell differentiation subsets.
CD8 T cell landscape is grossly disturbed in CVID, with enriched activated and exhausted cellular populations and cells with immunoregulatory potential, but lacking progenitor capability
To understand the exhaustion programs of CD8 T cells in CVID in more detail, we analyzed the high-dimensional dataset in a data-driven, unbiased manner. Dimensionality reduction on 29 phenotypic markers was performed by UMAP, revealing major differences between HD, CVIDio and CVIDc cohorts (Fig. 2A). Clustering of high-dimensional data by a self-Organizing Maps algorithm (FlowSOM) revealed 20 distinct cell clusters (optimal number of clusters selected using the elbow method) (Fig. 2B), the proportion of which was significantly different between the three cohorts (Fig. 2C, Figure S4). Cluster 8 was significantly expanded in CVIDc compared to HD (unpaired t-test with Welch’s correction p = 0.0002), cluster 16 was significantly expanded in CVIDio compared to HD (p = 0.0002, CVIDc × HD p = 0.1), whereas cluster 18 was significantly diminished in both CVIDio and CVIDc compared to HD (CVIDio × HD p = 0.01, CVIDc × HD p = 0.009). Several other clusters (cluster 3, 6, 10, 12) were insignificantly expanded in both CVID cohorts. The clusters provided higher granularity compared to the canonical differentiation stages shown in Fig. 1 and showed different localization across the canonical developmental pseudotime (Fig. 2E).
We then compared the expression of all phenotypic and functional markers across all 20 clusters (Fig. 2D). Further, we calculated a functional exhaustion score (FES) as described previously to identify clusters with a polyfunctionality pattern typical for exhausted T cells , based on TNF-α, IFN-γ, IL-2 and XCL-1 production patterns. FES was mediocre or high in clusters expanded in CVIDc, with a similar trend in CVIDio, and low in clusters more prevalent in healthy donors, and it was significantly different between clusters (Welch’s ANOVA p < 0.0001, Figure S5). These findings link clusters with an exhaustion phenotype and FES to CVID, in particular in CVIDc patients.
Clusters 3 and 16, which were upregulated in CVID, displayed strong expression of the activation markers CD38, HLA-DR, the proliferation-associated nuclear protein Ki67, and had preserved production of several cytokines (IL-10, IL-17A, to a lesser extent also effector cytokine IFN-γ, but low or absent TNF-α production as well as no perforin and granulysin). Their expression patterns then diverged, with cluster 3 having low expression of most exhaustion-associated markers, whereas cluster 16 displayed an elevated proportion of cells staining positive for CXCR5, higher expression levels of transcription factors TOX, FOXO1, Helios and Eomes, and inhibitory molecules such as PD-1, TIGIT, LAG3, Tim3 and CTLA4, and had a particularly high FES. The phenotype of cells in cluster 16 is reminiscent of the elusive regulatory CD8 T cell population, thanks to their production of IL-10, PD1 and, in comparison to other CD8 T cells, higher average expression of the transcription factor FoxP3 (Figure S6) [33, 34].
Cluster 6 bore the hallmarks of senescent cells expressing CD57 and effector capabilities.
Cluster 8, also expanded in CVIDc, displayed an above average expression of exhaustion-associated phenotypic markers Eomes, PD-1, TIGIT, LAG-3, 2B4 and CD160, with low Tbet and TCF1, suggesting terminal differentiation. These cells retained their capacity to produce TNF-α, IFN-γ and XCL-1, but did not produce large amounts of cytotoxic molecules such as granulysin or perforin and unlike cluster 16 were not activated or proliferating. These cells together with cluster 12, which had very high expression of PD-1, fulfil most canonical signs of being exhausted and have a correspondingly high FES.
Cluster 18, severely diminished in CVID, had the hallmarks of naïve CD8 T cells, including expression of CD45RA, CCR7, CD27, CD28, CD73, CD127 and CD7.
Cluster 20, a small population but seemingly lower in CVID compared to HD, clustered together with the naïve CD8 T cells of cluster 18, but was notable for its high expression of TCF1 and FOXO1, transcription factors associated with progenitor capabilities of effector cells, in conjunction with CCR7. Similarly, cluster 19 also expressed TCF1, but also high levels of CXCR5 intermediate levels of PD-1 and no TOX, which bears similarity to progenitor population of exhausted T cells [35, 36]. Cluster 19 was comparably prevalent in both CVID patients and HD.
Thus, the CyTOF analysis revealed the reduction of naïve CD8 T cells and CCR7+TCF1+FOXO1+ stem-cell-like progenitors, but not exhaustion-specific CXCR5+PD1intTCF1+ progenitors. In contrary, two distinct groups of cells were more prevalent in CVID, a group of highly differentiated classical exhausted cells with high functional exhaustion scores lacking signs of ongoing activation and production of cytokines, and a group of highly activated and proliferating cells with immunoregulatory features such as the production of IL-10 and expression of CXCR5 and higher average expression of FoxP3.
Bulk CVID CD8 T cells show features of exhaustion, activation and cytotoxicity
Following the markers of interest discovered by unbiased clustering approach shown above, we verified the expression pattern of exhaustion-associated markers PD1, TIGIT, CD127 and 2B4, activation markers CD38 and HLA-DR, nuclear protein Ki67 and the cytotoxicity markers perforin and granzyme B across several flow cytometric panels in the larger cohort of 40 patients and 17 healthy donors.
On average, bulk CVIDc derived CD8 T cells were activated (Fig. 3A), proliferating (Fig. 3B) and functionally active by producing cytotoxic molecules (Fig. 3C), corresponding to the expanded clusters 3, 8, 12 and 16. These features were generally more pronounced in CVIDc derived CD8 T cells compared to CVIDio, although the difference only reached significance for the percentage of activated HLA-DR+ cells.
CVID CD8 T cells on average had higher expression of the exhaustion-associated marker TIGIT and lost the expression of the IL-7 receptor CD127, with a significantly expanded population of CD127−TIGIT+PD1+ co-expressing both PD1 and TIGIT in CVIDc (Fig. 3D, E), corresponding to the expanded exhausted clusters 8 and 12.
The expanded exhausted CD127−TIGIT+PD1+ population was able to produce perforin, IFN-γ and TNF-α upon stimulation, but lost the capacity for production of IL-2, IL-17A and CXCL10 (Fig. 3F) and compared to bulk non-naïve CD127− cells, had fewer perforinhigh cells (Fig. 3G) (two-way ANOVA p < 0.001). These changes did not merely reflect changes in the distribution of the different memory stages shown in Fig. 1, but were also present within each stage (Figure E6).
Interestingly, CD127−TIGIT+PD1hi cells, characteristic with their high expression of Ki67, IL-17A, IL-10, corresponding to the immunoregulatory cluster 16, were also somewhat elevated in CVIDc compared to HD in the larger cohort assessed by flow cytometry (Fig. 3H) (p = 0.059).
The expansion of TIGIT+ CD8 T cells was also strongly associated with the EURO classification based on B cell phenotyping (see Methods and ) (Fig. 3I) (one-way ANOVA p = 0.002), as were the strongly exhausted CD127−TIGIT+PD1+ cells (one-way ANOVA p = 0.047) and interestingly also the proportion of activated CD38+ effector memory CD8 T cells (one-way ANOVA p = 0.007). The regulatory CD127−TIGIT+PD1hi did not correlate with the EURO classification scheme (one-way ANOVA 0.57).
Clinical complications of CVID such as interstitial lung disease and autoimmune cytopenia strongly associate with more pronounced features of exhaustion, whereas diarrhea associates with T cell activation
As the clinical phenotype of CVIDc is varied and patients can exhibit many different complications, we tested the association between CD8 T cell phenotype and individual clinical features seen in patients with complex disease. Figure 4A shows a table with unpaired student’s t-test with Welch’s correction p-values calculated between all CVID patients with and without each symptom for select easy-to-measure parameters. Whereas the expression of inhibitory receptors and loss of CD127 were significantly associated with AIC, lymphadenopathy and ILD, recurrent non-infectious diarrhea was most strongly associated with CD8 T-cell activation. Reduction of naïve CD8 T cells and expansion of highly differentiated TEMRA cells (CD27+28− pE2 and CD27−28− E) was significantly associated with peripheral lymphadenopathy, but less so with other complications.
Plots mapping the difference between healthy donors and CVID patients with and without ILD and AIC (Fig. 4B) show that while the trends for these markers are present even between HD and CVID patients without the specific complications, these changes are significantly more pronounced in patients with ILD and AIC. Interestingly the observed increased activation seems to be associated more exclusively with non-infectious diarrhea.
Calculating receiver operating curves (ROC) shows that the proportion of TIGIT+ cells is a significant predictor for AIC (p-value = 0.014, area 0.736) and ILD (p-value = 0.0123, area 0.734). For AIC, the TIGIT/CD127 ratio was even more helpful (p-value = 0.009, area 0.751). Cut-off of > 68% TIGIT+ cells had 64% sensitivity and 81% specificity for AIC. Cut-off of > 66% TIGIT+ cells had 64% sensitivity and 74% specificity for ILD. For diarrhea both CD38 (p-value = 0.002, area 0.784) and HLA-DR (p-value 0.006, area 0.753) were good predictors. Cut-off of > 41% CD38+ cells had 71% sensitivity and 84% specificity for diarrhea. Cut-off of > 6% HLA-DR+ cells had 71% sensitivity and 63% specificity for diarrhea.
The CD127−TIGIT+PD1hi population was also significantly associated with the presence or absence of ILD (one-way ANOVA p = 0.02) (Fig. 4E) and was successful in showing significant sensitivity and specificity in an ROC curve (p-value = 0.048, area 0.708), but did not outperform the percentage of TIGIT+ cells (Fig. 5D).