We next applied high-dimensional flow cytometric analysis to further investigate lymphocyte activation and differentiation during COVID-19 disease. We first used principal component analysis (PCA) to examine the general distribution of immune profiles from COVID-19 patients (n = 118), RD (n = 60), and HD (n = 36) using 193 immune parameters identified by high-dimensional flow cytometry (tables S5 and S6). COVID-19 patients clearly segregated from RD and HD in PCA space, whereas RD and HD largely overlapped (Fig. 2A). We investigated the immune features driving this COVID-19 immune signature. Given their role in response to viral infection, we focused on CD8 T cells. Six major CD8 T cell populations were examined using the combination of CD45RA, CD27, CCR7, and CD95 cell surface markers to define naïve (CD45RA+CD27+CCR7+CD95−), central memory (CD45RA−CD27+CCR7+ [CM]), effector memory (CD45RA−CD27+CCR7− [EM1], CD45RA−CD27−CCR7+ [EM2], CD45RA−CD27−CCR7− [EM3]), and EMRA (CD45RA+CD27−CCR7−) (Fig. 2B) CD8 T cells. Among the CD8 T cell populations, there was an increase in the EM2 and EMRA populations and a decrease in EM1 (Fig. 2C). Furthermore, the frequency of CD39+ cells was increased in COVID-19 patients compared to HD (Fig. 2D). Although the frequency of PD-1+ cells was not different in the total CD8 population (Fig. 2D), it was increased for both CM and EM1 (fig. S2A). Finally, all major CD8 T cell naive/memory populations in RD were comparable to HD (Fig. 2, C and D, and fig. S2A).