Figure 2
Figure 2. Identification of Treg subset by automated clustering. (A) After initial gating for CD3+, CD4+, and CD8− T cells, the gated cells were clustered using viSNE (Cytobank). Treg populations were identified based on high expression of CD25 and FOXP3 and low expression of CD127. (B) Median expression of the 7 most discriminative parameters between total CD4+ cells and Tregs as identified by the automated clustering algorithm FLOCK on a subset of 700 000 cells proportionally selected from all samples. Tregs were defined as clusters whose median expression was simultaneously higher than the 90% quantile of FOXP3 expression, higher than the 90% quantile of CD25 expression, and lower than the 50% quantile of CD127 expression across all CD3+CD8− cells. Heat map plot is based on 19 AA samples (pre- and post-IST) and 5 HD samples. (C) Median expression of the 8 most discriminative parameters between the 2 Treg subpopulations identified by the automated clustering algorithm FLOCK. Expression values were transformed using the asinh function in a cofactor of 5. Heat map plot is based on 19 AA samples (pre- and post-IST) and 5 HD samples. (D) The density plot of viSNE plots revealed 2 subpopulations within Tregs, designated as Treg A and B (arrows). The frequencies of Treg A and B were different between HD and AA patients. Patients who did not respond to IST (IST NR) had a higher number of Treg A at the time of diagnosis compared with responder patients (IST R) and HDs. The viSNE plots (right) are an overlay of Tregs’ contour plots colored by density and CD4+ T cells uncolored contour plots. (E) At the time of diagnosis and before treatment, Treg A frequency was higher in responder as well as nonresponder patients compared with HDs (38.8% ± 5% and 63.5% ± 4.5% vs 20.3% ± 6.6%, P < .05, P < .0001), whereas the frequency of Treg B was lower in both responder and nonresponder AA patients at the time of diagnosis compared with HDs (48.8% ± 6.1% and 28.9% ± 2.7% vs 72.2% ± 6.7%, P = .005, P < .0001). The nonresponder patients, however, had significantly higher Treg A and lower Treg B compared with responder patients (63.5% ± 4.5% vs 38.8% ± 5.0, P < .005 for Treg A; 28.9% ± 2.7% vs 48.8% ± 6.1%, P < .05 for Treg B). Error bars are standard error of mean. Kruskal-Wallis 1-way analysis of variance test was used for statistical analysis. ****P < .0001, ***P < .001, **P < .01, *P < .05. (F) The overlap between the Treg subpopulations identified using viSNE and manually gated Treg populations based on CD45RA and FOXP3 expression. Although subpopulations A and B mainly overlap with subpopulations I (CD45RAhi, FOXP3lo) and II (CD45RAlo, FOXP3hi), respectively, subpopulation III (CD45RAlo, FOXP3lo) was spread over population B as well as outside the Treg area. Figures are overlays of manually gated Treg populations on viSNE plots of total CD4+ T cells from an IST responder AA patient.

Identification of Treg subset by automated clustering. (A) After initial gating for CD3+, CD4+, and CD8 T cells, the gated cells were clustered using viSNE (Cytobank). Treg populations were identified based on high expression of CD25 and FOXP3 and low expression of CD127. (B) Median expression of the 7 most discriminative parameters between total CD4+ cells and Tregs as identified by the automated clustering algorithm FLOCK on a subset of 700 000 cells proportionally selected from all samples. Tregs were defined as clusters whose median expression was simultaneously higher than the 90% quantile of FOXP3 expression, higher than the 90% quantile of CD25 expression, and lower than the 50% quantile of CD127 expression across all CD3+CD8 cells. Heat map plot is based on 19 AA samples (pre- and post-IST) and 5 HD samples. (C) Median expression of the 8 most discriminative parameters between the 2 Treg subpopulations identified by the automated clustering algorithm FLOCK. Expression values were transformed using the asinh function in a cofactor of 5. Heat map plot is based on 19 AA samples (pre- and post-IST) and 5 HD samples. (D) The density plot of viSNE plots revealed 2 subpopulations within Tregs, designated as Treg A and B (arrows). The frequencies of Treg A and B were different between HD and AA patients. Patients who did not respond to IST (IST NR) had a higher number of Treg A at the time of diagnosis compared with responder patients (IST R) and HDs. The viSNE plots (right) are an overlay of Tregs’ contour plots colored by density and CD4+ T cells uncolored contour plots. (E) At the time of diagnosis and before treatment, Treg A frequency was higher in responder as well as nonresponder patients compared with HDs (38.8% ± 5% and 63.5% ± 4.5% vs 20.3% ± 6.6%, P < .05, P < .0001), whereas the frequency of Treg B was lower in both responder and nonresponder AA patients at the time of diagnosis compared with HDs (48.8% ± 6.1% and 28.9% ± 2.7% vs 72.2% ± 6.7%, P = .005, P < .0001). The nonresponder patients, however, had significantly higher Treg A and lower Treg B compared with responder patients (63.5% ± 4.5% vs 38.8% ± 5.0, P < .005 for Treg A; 28.9% ± 2.7% vs 48.8% ± 6.1%, P < .05 for Treg B). Error bars are standard error of mean. Kruskal-Wallis 1-way analysis of variance test was used for statistical analysis. ****P < .0001, ***P < .001, **P < .01, *P < .05. (F) The overlap between the Treg subpopulations identified using viSNE and manually gated Treg populations based on CD45RA and FOXP3 expression. Although subpopulations A and B mainly overlap with subpopulations I (CD45RAhi, FOXP3lo) and II (CD45RAlo, FOXP3hi), respectively, subpopulation III (CD45RAlo, FOXP3lo) was spread over population B as well as outside the Treg area. Figures are overlays of manually gated Treg populations on viSNE plots of total CD4+ T cells from an IST responder AA patient.

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