Dimensionality Reduction and Clustering Analysis
After excluding death cells and doublets, the resulting flow cytometry data files were gated on the CD3+ population using FlowJo 10.7.1. Prior to further analysis, all groups were down-sampled to equal cell numbers and concatenated in a single file. We then used a tSNE algorithm to reduce dimensionality of the data followed by Self Organized Maps (FlowSOM) [11] for unsupervised clustering of the data into subpopulations. The resulting subpopulations were further analyzed for specific marker expression and frequency in each group. We also compared populations that have been reported in the literature and that were manually gated independently of the findings of the unsupervised clustering. These populations were: CD4/CD8 PD-1+, CD4/CD8 T-bet+, CD4/CD8 2B4, CD4/CD8 Tim-3, CD4/CD8 EOMES, CD4/CD8 CD57, CD4/CD8 CD38+HLA-DR+ (activated T cells) and CD4/CD8 CD38+HLA-DR+PD-1+ (exhausted T cells).