Temporal changes in immune cell populations occur during COVID-19 disease
A key question for hospitalized COVID-19 patients is how immune responses change over time. Thus, we used the global tSNE projections of overall CD8 T cell, CD4 T cell, and B cell differentiation states to interrogate temporal changes in these populations between D0 and D7 of hospitalization (Fig. 5A). Combining data for all patients revealed considerable stability of the tSNE distributions between D0 and D7 in CD8 T cell, CD4 T cell, and B cell populations, particularly for key regions of interest discussed above. For example, for CD8 T cells, the region of the tSNE map containing KI67+ and CD38+HLA-DR+ CD8 T cell populations that was enriched in COVID-19 patients at D0 (Fig. 2) was preserved at D7 (Fig. 5A). A similar temporal stability of CD4 T cell and B cell activation was also observed (Fig. 5A).
Fig. 5  Temporal relationships between immune responses and disease manifestation.
(A) Global viSNE projection of non-naïve CD8 T cells, non-naïve CD4 T cells, and B cells for all subjects pooled, with cells from COVID-19 patients at D0 and D7 concatenated and overlaid. Frequencies of (B) KI67+ and HLA-DR+CD38+ CD4 T cells, (C) KI67+ and HLA-DR+CD38+ CD8 T cells, or (D) PBs as indicated for healthy donor (HD; green), recovered donor (RD; blue), or COVID-19 patients (red) with paired samples at D0 and D7 indicated by the connecting line. Significance determined by paired Wilcoxon test: *p < 0.05, **p < 0.01, ***p < 0.001, and ****p < 0.0001. Longitudinal patterns (see Methods) of (E) HLA-DR+CD38+ CD4 T cells or (F) PBs in COVID-19 patients shown as frequency and representative flow cytometry plots. (G) Spearman correlations of clinical parameters with longitudinal fold changes in immune populations. Given this apparent stability between D0 and D7, we next investigated temporal changes in lymphocyte subpopulations of interest. Although there were no obvious temporal changes in major phenotypically defined CD4 and CD8 T cell or B cell subsets, including plasmablasts (Fig. 5D), the frequencies of HLA-DR+CD38+ and KI67+ non-naïve CD4 (Fig. 5B) and KI67+ non-naïve CD8 T cells were statistically increased at D7 compared to D0 (Fig. 5C).
However, in all cases, these temporal patterns were complex, with frequencies of subpopulations in individual patients appearing to increase, decrease, or stay the same over time. To quantify these inter-patient changes, we used a previously described data set (46) to define the stability of populations of interest in healthy individuals over time. We then used the range of this variation over time to identify COVID-19 patients with changes in immune cell subpopulations beyond that expected in healthy subjects (see methods). Using this approach, ~50% of patients had an increase in HLA-DR+CD38+ non-naïve CD4 T cells over time, whereas in ~30% of patients, these cells were stable and, in ~20%, they decreased (Fig. 5E). For KI67+ non-naïve CD8 T cells, there were no individuals in whom the response decreased. Instead, this proliferative CD8 T cell response stayed stable (~70%) or increased (~30%; fig. S6A). Notably, for patients in the stable category, the median frequency of KI67+ non-naïve CD8 T cells was ~10%, almost 5-fold higher than the ~1% detected for HD and RD subjects (Figs. 5C and 2E), suggesting a sustained CD8 T cell proliferative response to infection. A similar pattern was observed for HLA-DR+CD38+ non-naïve CD8 (fig. S6B), where only ~10% of patients had a decrease in this population, whereas ~65% were stable and ~25% increased over time. The high and even increasing activated or proliferating CD8 and CD4 T cell responses over ~1 week during acute viral infection contrasted with the sharp peak of KI67 in CD8 and CD4 T cells during acute viral infections, including smallpox vaccination with live vaccinia virus (47), live attenuated yellow fever vaccine YFV-17D (48), acute influenza virus infection (49), and acute HIV infection (35). Approximately 42% of patients had sustained PB responses, at high levels (>10% of B cells) in many cases (Fig. 5F). Thus, some patients displayed dynamic changes in T cell or B cell activation over 1 week in the hospital, but there were also other patients who remained stable. In the latter case, some patients remained stable without clear activation of key immune populations whereas others had stable T and or B cell activation or numerical perturbation (fig. S6C).
We next asked whether these T and B cell dynamics related to clinical measures of COVID-19 disease, by correlating changes in immune features from D0 to D7 with clinical information (Fig. 5G). These analyses revealed distinct correlations. Decreases in all populations of responding CD4 and CD8 T cells (HLA-DR+CD38+, KI67+, or activated cTfh) between D0 and D7 were positively correlated with PMN and WBC counts, suggesting a relationship between T cell activation and lymphopenia. Furthermore, decreases in CD4 and CD8 HLA-DR+CD38+ T cells positively correlated with APACHE III score. However, stable HLA-DR+CD38+ CD4 T cell responses correlated with coagulation complications and ferritin. Whereas decreasing activated cTfh over time was related to co-infection, the opposite pattern was observed for PB. Increases in proliferating KI67+ CD4 and CD8 T cells over time were positively correlated to increasing anti-SARS-CoV2 antibody from day 0 to day 7, suggesting that some individuals might have been hospitalized during the expansion phase of the antiviral immune response (Fig. 5G). Finally, neither Remdesivir nor HCQ treatment correlated with any of these immune features in Fig. 5G). Examining categorical rather than continuous clinical data, 80% of patients with decreasing PB over time had hyperlipidemia, whereas only 20% of patients with increasing PB over time had this comorbidity (fig. S6D). All patients who had decreasing CD38+HLA-DR+ CD8 T cells from day 0 to day 7 were treated with early vasoactive medication or inhaled nitric oxide whereas these treatments were less common for patients with stable or increasing CD38+HLA-DR+ CD8 T cells (fig. S6E). In contrast, vasoactive medication, inhaled nitric oxide, and early steroid treatment were equally common in patients with increasing or decreasing PB (fig. S6D). Similar patterns were apparent for other T cell populations and these categorical clinical data (fig. S6F). Thus, the trajectory of change in the T and B cell response in COVID-19 patients was strongly connected to clinical metrics of disease.