PMC:7402624 / 11996-18448 JSON TXT

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LitCovid-PD-FMA-UBERON

LitCovid-PD-UBERON

{"project":"LitCovid-PD-UBERON","denotations":[{"id":"T7","span":{"begin":2404,"end":2417},"obj":"Body_part"},{"id":"T8","span":{"begin":4777,"end":4782},"obj":"Body_part"}],"attributes":[{"id":"A7","pred":"uberon_id","subj":"T7","obj":"http://purl.obolibrary.org/obo/UBERON_0034922"},{"id":"A8","pred":"uberon_id","subj":"T8","obj":"http://purl.obolibrary.org/obo/UBERON_0000178"}],"text":"SARS-CoV2 infection is associated with CD8 T cell activation in a subset of patients\nWe 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).\nFig. 2 CD8 T cell subset skewing and activation patterns in COVID-19 patients and potential links to T cell driven cytokines.\n(A) Principle Component Analysis (PCA) of aggregated flow cytometry data. (B) Representative flow cytometry plots of the gating strategy for CD8 T cell subsets. (C) Frequencies of CD8 T cell subsets as indicated. (D) Frequencies of PD1+ and CD39+ cells. Frequencies of (E) KI67+ and (F) HLA-DR+CD38+ cells and representative flow cytometry plots; green line at upper decile of HD. (G) (Top) Global viSNE projection of non-naïve CD8 T cells for all subjects pooled, non-naïve CD8 T cells from healthy donor (HD), recovered donor (RD), and COVID-19 patients concatenated and overlaid. (Bottom) viSNE projections of indicated protein expression. (H) viSNE projection of non-naïve CD8 T cell clusters identified by FlowSOM clustering. (I) Mean fluorescence intensity (MFI) as indicated, column-scaled z-score. (J) Percentage of non-naive CD8 T cells from each cohort in each FlowSOM cluster. Boxes represent IQR. (C, D, E, F, J) Each dot represents an individual healthy donor (HD; green), recovered donor (RD; blue), or COVID-19 patient (red). Significance determined by unpaired Wilcoxon test with BH correction: *p \u003c 0.05, **p \u003c 0.01, ***p \u003c 0.001, and ****p \u003c 0.0001. Most acute viral infections induce proliferation and activation of CD8 T cells detectable by increases in KI67 or co-expression of CD38 and HLA-DR (34, 35). There was a significant increase in KI67+ and also HLA-DR+CD38+ non-naïve CD8 T cells in COVID-19 patients compared to HD or RD (Fig. 2, E and F). In COVID-19 patients, KI67+ CD8 T cells were increased compared to HD and RD across all subsets of non-naïve CD8 T cells, including CM and EM1 (fig. S2B). These data indicate broad T cell activation, potentially driven by bystander activation and/or homeostatic proliferation in addition to antigen-driven activation of virus-specific CD8 T cells. This activation phenotype was confirmed by HLA-DR and CD38 co-expression that was significantly increased for all non-naïve CD8 T cell subsets (Fig. 2F and fig. S2C). However, the magnitude of the KI67+ or CD38+HLA-DR+ CD8 T cells varied widely in this cohort. The frequency of KI67+ CD8 T cells correlated with the frequency of CD38+HLA-DR+ CD8 T cells (fig. S2D). However, the frequency of CD38+HLA-DR+, but not KI67+ CD8 T cells, was elevated in COVID-19 patients who had concomitant infection with another microbe but was not impacted by pre-existing immunosuppression or treatment with steroids (fig. S2E). Moreover, these changes in CD8 T cell subsets in COVID-19 patients did not show clear correlations with individual metrics of clinical disease such as hsCRP or D-dimer (fig. S2E), although the frequency of KI67+ CD8 T cells associated with IL-6 and ferritin levels. Although CD8 T cell activation was common, ~20% of patients had no increase in KI67+ or CD38+HLA-DR+ CD8 T cells above the level found in HD (Fig. 2, E and F). Thus, although robust CD8 T cell activation was a clear characteristic of many hospitalized COVID-19 patients, a substantial fraction of patients had little evidence of CD8 T cell activation in the blood compared to controls.\nTo gain more insights, we applied global high-dimensional mapping of the 27-parameter flow cytometry data. A tSNE representation of the data highlighted key regions of non-naïve CD8 T cells found preferentially in COVID-19 patients (Fig. 2G). A major region of this tSNE map present in COVID-19 patients, but not HD or RD, were CD8 T cells that enriched for expression of CD38, HLA-DR, KI67, CD39, and PD1 (Fig. 2G), highlighting the co-expression of these activation markers with other features including CD95 (i.e., FAS). Notably, although non-naïve CD8 T cells from RD were highly similar to those from HD, subtle differences existed, including in the lower region highlighted by T-bet and CX3CR1 (Fig. 2G). To further define and quantify these differences between COVID-19 patients and controls, we performed FlowSOM clustering (Fig. 2H) and compared expression of fourteen CD8 T cell markers to identify each cluster (Fig. 2I). This approach identified an increase in cells in several clusters including Clusters 1, 2, and 5 in COVID-19 patients, reflecting CD45RA+CD27−CCR7− TEMRA-like populations that expressed CX3CR1 and varying levels of T-bet (Fig. 2, I and J). Clusters 12 and 14 contained CD27+HLA-DR+CD38+KI67+PD-1+ activated, proliferating cells and were more prevalent in COVID-19 disease (Fig. 2, I and J, and fig. S2F). In contrast, the central Eomes+CD45RA−CD27+CCR7− EM1-like Cluster 6 and T-bethiCX3CR1+ Cluster 11 were decreased compared to HD (Fig. 2, I and J, and fig. S2F). Thus, CD8 T cell responses in COVID-19 patients were characterized by populations of activated, proliferating CD8 T cells in a subgroup of patients."}

LitCovid-PubTator

LitCovid-PD-MONDO

LitCovid-PD-CLO

LitCovid-PD-CHEBI

LitCovid-PD-GO-BP

{"project":"LitCovid-PD-GO-BP","denotations":[{"id":"T29","span":{"begin":43,"end":60},"obj":"http://purl.obolibrary.org/obo/GO_0042110"},{"id":"T30","span":{"begin":45,"end":60},"obj":"http://purl.obolibrary.org/obo/GO_0001775"},{"id":"T31","span":{"begin":166,"end":187},"obj":"http://purl.obolibrary.org/obo/GO_0046649"},{"id":"T32","span":{"begin":716,"end":731},"obj":"http://purl.obolibrary.org/obo/GO_0016032"},{"id":"T33","span":{"begin":932,"end":938},"obj":"http://purl.obolibrary.org/obo/GO_0007613"},{"id":"T34","span":{"begin":974,"end":980},"obj":"http://purl.obolibrary.org/obo/GO_0007613"},{"id":"T35","span":{"begin":1514,"end":1520},"obj":"http://purl.obolibrary.org/obo/GO_0007613"},{"id":"T36","span":{"begin":2900,"end":2916},"obj":"http://purl.obolibrary.org/obo/GO_0016032"},{"id":"T37","span":{"begin":3374,"end":3391},"obj":"http://purl.obolibrary.org/obo/GO_0042110"},{"id":"T38","span":{"begin":3376,"end":3391},"obj":"http://purl.obolibrary.org/obo/GO_0001775"},{"id":"T39","span":{"begin":4432,"end":4449},"obj":"http://purl.obolibrary.org/obo/GO_0042110"},{"id":"T40","span":{"begin":4434,"end":4449},"obj":"http://purl.obolibrary.org/obo/GO_0001775"},{"id":"T41","span":{"begin":4605,"end":4622},"obj":"http://purl.obolibrary.org/obo/GO_0042110"},{"id":"T42","span":{"begin":4607,"end":4622},"obj":"http://purl.obolibrary.org/obo/GO_0001775"},{"id":"T43","span":{"begin":4752,"end":4769},"obj":"http://purl.obolibrary.org/obo/GO_0042110"},{"id":"T44","span":{"begin":4754,"end":4769},"obj":"http://purl.obolibrary.org/obo/GO_0001775"}],"text":"SARS-CoV2 infection is associated with CD8 T cell activation in a subset of patients\nWe 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).\nFig. 2 CD8 T cell subset skewing and activation patterns in COVID-19 patients and potential links to T cell driven cytokines.\n(A) Principle Component Analysis (PCA) of aggregated flow cytometry data. (B) Representative flow cytometry plots of the gating strategy for CD8 T cell subsets. (C) Frequencies of CD8 T cell subsets as indicated. (D) Frequencies of PD1+ and CD39+ cells. Frequencies of (E) KI67+ and (F) HLA-DR+CD38+ cells and representative flow cytometry plots; green line at upper decile of HD. (G) (Top) Global viSNE projection of non-naïve CD8 T cells for all subjects pooled, non-naïve CD8 T cells from healthy donor (HD), recovered donor (RD), and COVID-19 patients concatenated and overlaid. (Bottom) viSNE projections of indicated protein expression. (H) viSNE projection of non-naïve CD8 T cell clusters identified by FlowSOM clustering. (I) Mean fluorescence intensity (MFI) as indicated, column-scaled z-score. (J) Percentage of non-naive CD8 T cells from each cohort in each FlowSOM cluster. Boxes represent IQR. (C, D, E, F, J) Each dot represents an individual healthy donor (HD; green), recovered donor (RD; blue), or COVID-19 patient (red). Significance determined by unpaired Wilcoxon test with BH correction: *p \u003c 0.05, **p \u003c 0.01, ***p \u003c 0.001, and ****p \u003c 0.0001. Most acute viral infections induce proliferation and activation of CD8 T cells detectable by increases in KI67 or co-expression of CD38 and HLA-DR (34, 35). There was a significant increase in KI67+ and also HLA-DR+CD38+ non-naïve CD8 T cells in COVID-19 patients compared to HD or RD (Fig. 2, E and F). In COVID-19 patients, KI67+ CD8 T cells were increased compared to HD and RD across all subsets of non-naïve CD8 T cells, including CM and EM1 (fig. S2B). These data indicate broad T cell activation, potentially driven by bystander activation and/or homeostatic proliferation in addition to antigen-driven activation of virus-specific CD8 T cells. This activation phenotype was confirmed by HLA-DR and CD38 co-expression that was significantly increased for all non-naïve CD8 T cell subsets (Fig. 2F and fig. S2C). However, the magnitude of the KI67+ or CD38+HLA-DR+ CD8 T cells varied widely in this cohort. The frequency of KI67+ CD8 T cells correlated with the frequency of CD38+HLA-DR+ CD8 T cells (fig. S2D). However, the frequency of CD38+HLA-DR+, but not KI67+ CD8 T cells, was elevated in COVID-19 patients who had concomitant infection with another microbe but was not impacted by pre-existing immunosuppression or treatment with steroids (fig. S2E). Moreover, these changes in CD8 T cell subsets in COVID-19 patients did not show clear correlations with individual metrics of clinical disease such as hsCRP or D-dimer (fig. S2E), although the frequency of KI67+ CD8 T cells associated with IL-6 and ferritin levels. Although CD8 T cell activation was common, ~20% of patients had no increase in KI67+ or CD38+HLA-DR+ CD8 T cells above the level found in HD (Fig. 2, E and F). Thus, although robust CD8 T cell activation was a clear characteristic of many hospitalized COVID-19 patients, a substantial fraction of patients had little evidence of CD8 T cell activation in the blood compared to controls.\nTo gain more insights, we applied global high-dimensional mapping of the 27-parameter flow cytometry data. A tSNE representation of the data highlighted key regions of non-naïve CD8 T cells found preferentially in COVID-19 patients (Fig. 2G). A major region of this tSNE map present in COVID-19 patients, but not HD or RD, were CD8 T cells that enriched for expression of CD38, HLA-DR, KI67, CD39, and PD1 (Fig. 2G), highlighting the co-expression of these activation markers with other features including CD95 (i.e., FAS). Notably, although non-naïve CD8 T cells from RD were highly similar to those from HD, subtle differences existed, including in the lower region highlighted by T-bet and CX3CR1 (Fig. 2G). To further define and quantify these differences between COVID-19 patients and controls, we performed FlowSOM clustering (Fig. 2H) and compared expression of fourteen CD8 T cell markers to identify each cluster (Fig. 2I). This approach identified an increase in cells in several clusters including Clusters 1, 2, and 5 in COVID-19 patients, reflecting CD45RA+CD27−CCR7− TEMRA-like populations that expressed CX3CR1 and varying levels of T-bet (Fig. 2, I and J). Clusters 12 and 14 contained CD27+HLA-DR+CD38+KI67+PD-1+ activated, proliferating cells and were more prevalent in COVID-19 disease (Fig. 2, I and J, and fig. S2F). In contrast, the central Eomes+CD45RA−CD27+CCR7− EM1-like Cluster 6 and T-bethiCX3CR1+ Cluster 11 were decreased compared to HD (Fig. 2, I and J, and fig. S2F). Thus, CD8 T cell responses in COVID-19 patients were characterized by populations of activated, proliferating CD8 T cells in a subgroup of patients."}

LitCovid-PD-GlycoEpitope

{"project":"LitCovid-PD-GlycoEpitope","denotations":[{"id":"T1","span":{"begin":1001,"end":1004},"obj":"GlycoEpitope"},{"id":"T2","span":{"begin":1026,"end":1029},"obj":"GlycoEpitope"},{"id":"T3","span":{"begin":1173,"end":1176},"obj":"GlycoEpitope"},{"id":"T4","span":{"begin":1216,"end":1219},"obj":"GlycoEpitope"},{"id":"T5","span":{"begin":1462,"end":1465},"obj":"GlycoEpitope"},{"id":"T6","span":{"begin":3332,"end":3335},"obj":"GlycoEpitope"},{"id":"T7","span":{"begin":6192,"end":6195},"obj":"GlycoEpitope"}],"attributes":[{"id":"A1","pred":"glyco_epitope_db_id","subj":"T1","obj":"http://www.glycoepitope.jp/epitopes/AN0701"},{"id":"A2","pred":"glyco_epitope_db_id","subj":"T2","obj":"http://www.glycoepitope.jp/epitopes/AN0703"},{"id":"A3","pred":"glyco_epitope_db_id","subj":"T3","obj":"http://www.glycoepitope.jp/epitopes/AN0703"},{"id":"A4","pred":"glyco_epitope_db_id","subj":"T4","obj":"http://www.glycoepitope.jp/epitopes/AN0701"},{"id":"A5","pred":"glyco_epitope_db_id","subj":"T5","obj":"http://www.glycoepitope.jp/epitopes/AN0701"},{"id":"A6","pred":"glyco_epitope_db_id","subj":"T6","obj":"http://www.glycoepitope.jp/epitopes/AN0701"},{"id":"A7","pred":"glyco_epitope_db_id","subj":"T7","obj":"http://www.glycoepitope.jp/epitopes/AN0701"}],"text":"SARS-CoV2 infection is associated with CD8 T cell activation in a subset of patients\nWe 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).\nFig. 2 CD8 T cell subset skewing and activation patterns in COVID-19 patients and potential links to T cell driven cytokines.\n(A) Principle Component Analysis (PCA) of aggregated flow cytometry data. (B) Representative flow cytometry plots of the gating strategy for CD8 T cell subsets. (C) Frequencies of CD8 T cell subsets as indicated. (D) Frequencies of PD1+ and CD39+ cells. Frequencies of (E) KI67+ and (F) HLA-DR+CD38+ cells and representative flow cytometry plots; green line at upper decile of HD. (G) (Top) Global viSNE projection of non-naïve CD8 T cells for all subjects pooled, non-naïve CD8 T cells from healthy donor (HD), recovered donor (RD), and COVID-19 patients concatenated and overlaid. (Bottom) viSNE projections of indicated protein expression. (H) viSNE projection of non-naïve CD8 T cell clusters identified by FlowSOM clustering. (I) Mean fluorescence intensity (MFI) as indicated, column-scaled z-score. (J) Percentage of non-naive CD8 T cells from each cohort in each FlowSOM cluster. Boxes represent IQR. (C, D, E, F, J) Each dot represents an individual healthy donor (HD; green), recovered donor (RD; blue), or COVID-19 patient (red). Significance determined by unpaired Wilcoxon test with BH correction: *p \u003c 0.05, **p \u003c 0.01, ***p \u003c 0.001, and ****p \u003c 0.0001. Most acute viral infections induce proliferation and activation of CD8 T cells detectable by increases in KI67 or co-expression of CD38 and HLA-DR (34, 35). There was a significant increase in KI67+ and also HLA-DR+CD38+ non-naïve CD8 T cells in COVID-19 patients compared to HD or RD (Fig. 2, E and F). In COVID-19 patients, KI67+ CD8 T cells were increased compared to HD and RD across all subsets of non-naïve CD8 T cells, including CM and EM1 (fig. S2B). These data indicate broad T cell activation, potentially driven by bystander activation and/or homeostatic proliferation in addition to antigen-driven activation of virus-specific CD8 T cells. This activation phenotype was confirmed by HLA-DR and CD38 co-expression that was significantly increased for all non-naïve CD8 T cell subsets (Fig. 2F and fig. S2C). However, the magnitude of the KI67+ or CD38+HLA-DR+ CD8 T cells varied widely in this cohort. The frequency of KI67+ CD8 T cells correlated with the frequency of CD38+HLA-DR+ CD8 T cells (fig. S2D). However, the frequency of CD38+HLA-DR+, but not KI67+ CD8 T cells, was elevated in COVID-19 patients who had concomitant infection with another microbe but was not impacted by pre-existing immunosuppression or treatment with steroids (fig. S2E). Moreover, these changes in CD8 T cell subsets in COVID-19 patients did not show clear correlations with individual metrics of clinical disease such as hsCRP or D-dimer (fig. S2E), although the frequency of KI67+ CD8 T cells associated with IL-6 and ferritin levels. Although CD8 T cell activation was common, ~20% of patients had no increase in KI67+ or CD38+HLA-DR+ CD8 T cells above the level found in HD (Fig. 2, E and F). Thus, although robust CD8 T cell activation was a clear characteristic of many hospitalized COVID-19 patients, a substantial fraction of patients had little evidence of CD8 T cell activation in the blood compared to controls.\nTo gain more insights, we applied global high-dimensional mapping of the 27-parameter flow cytometry data. A tSNE representation of the data highlighted key regions of non-naïve CD8 T cells found preferentially in COVID-19 patients (Fig. 2G). A major region of this tSNE map present in COVID-19 patients, but not HD or RD, were CD8 T cells that enriched for expression of CD38, HLA-DR, KI67, CD39, and PD1 (Fig. 2G), highlighting the co-expression of these activation markers with other features including CD95 (i.e., FAS). Notably, although non-naïve CD8 T cells from RD were highly similar to those from HD, subtle differences existed, including in the lower region highlighted by T-bet and CX3CR1 (Fig. 2G). To further define and quantify these differences between COVID-19 patients and controls, we performed FlowSOM clustering (Fig. 2H) and compared expression of fourteen CD8 T cell markers to identify each cluster (Fig. 2I). This approach identified an increase in cells in several clusters including Clusters 1, 2, and 5 in COVID-19 patients, reflecting CD45RA+CD27−CCR7− TEMRA-like populations that expressed CX3CR1 and varying levels of T-bet (Fig. 2, I and J). Clusters 12 and 14 contained CD27+HLA-DR+CD38+KI67+PD-1+ activated, proliferating cells and were more prevalent in COVID-19 disease (Fig. 2, I and J, and fig. S2F). In contrast, the central Eomes+CD45RA−CD27+CCR7− EM1-like Cluster 6 and T-bethiCX3CR1+ Cluster 11 were decreased compared to HD (Fig. 2, I and J, and fig. S2F). Thus, CD8 T cell responses in COVID-19 patients were characterized by populations of activated, proliferating CD8 T cells in a subgroup of patients."}

LitCovid-sentences

2_test

{"project":"2_test","denotations":[{"id":"32669297-27031961-135105198","span":{"begin":3037,"end":3039},"obj":"27031961"},{"id":"32669297-26362266-135105199","span":{"begin":3041,"end":3043},"obj":"26362266"}],"text":"SARS-CoV2 infection is associated with CD8 T cell activation in a subset of patients\nWe 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).\nFig. 2 CD8 T cell subset skewing and activation patterns in COVID-19 patients and potential links to T cell driven cytokines.\n(A) Principle Component Analysis (PCA) of aggregated flow cytometry data. (B) Representative flow cytometry plots of the gating strategy for CD8 T cell subsets. (C) Frequencies of CD8 T cell subsets as indicated. (D) Frequencies of PD1+ and CD39+ cells. Frequencies of (E) KI67+ and (F) HLA-DR+CD38+ cells and representative flow cytometry plots; green line at upper decile of HD. (G) (Top) Global viSNE projection of non-naïve CD8 T cells for all subjects pooled, non-naïve CD8 T cells from healthy donor (HD), recovered donor (RD), and COVID-19 patients concatenated and overlaid. (Bottom) viSNE projections of indicated protein expression. (H) viSNE projection of non-naïve CD8 T cell clusters identified by FlowSOM clustering. (I) Mean fluorescence intensity (MFI) as indicated, column-scaled z-score. (J) Percentage of non-naive CD8 T cells from each cohort in each FlowSOM cluster. Boxes represent IQR. (C, D, E, F, J) Each dot represents an individual healthy donor (HD; green), recovered donor (RD; blue), or COVID-19 patient (red). Significance determined by unpaired Wilcoxon test with BH correction: *p \u003c 0.05, **p \u003c 0.01, ***p \u003c 0.001, and ****p \u003c 0.0001. Most acute viral infections induce proliferation and activation of CD8 T cells detectable by increases in KI67 or co-expression of CD38 and HLA-DR (34, 35). There was a significant increase in KI67+ and also HLA-DR+CD38+ non-naïve CD8 T cells in COVID-19 patients compared to HD or RD (Fig. 2, E and F). In COVID-19 patients, KI67+ CD8 T cells were increased compared to HD and RD across all subsets of non-naïve CD8 T cells, including CM and EM1 (fig. S2B). These data indicate broad T cell activation, potentially driven by bystander activation and/or homeostatic proliferation in addition to antigen-driven activation of virus-specific CD8 T cells. This activation phenotype was confirmed by HLA-DR and CD38 co-expression that was significantly increased for all non-naïve CD8 T cell subsets (Fig. 2F and fig. S2C). However, the magnitude of the KI67+ or CD38+HLA-DR+ CD8 T cells varied widely in this cohort. The frequency of KI67+ CD8 T cells correlated with the frequency of CD38+HLA-DR+ CD8 T cells (fig. S2D). However, the frequency of CD38+HLA-DR+, but not KI67+ CD8 T cells, was elevated in COVID-19 patients who had concomitant infection with another microbe but was not impacted by pre-existing immunosuppression or treatment with steroids (fig. S2E). Moreover, these changes in CD8 T cell subsets in COVID-19 patients did not show clear correlations with individual metrics of clinical disease such as hsCRP or D-dimer (fig. S2E), although the frequency of KI67+ CD8 T cells associated with IL-6 and ferritin levels. Although CD8 T cell activation was common, ~20% of patients had no increase in KI67+ or CD38+HLA-DR+ CD8 T cells above the level found in HD (Fig. 2, E and F). Thus, although robust CD8 T cell activation was a clear characteristic of many hospitalized COVID-19 patients, a substantial fraction of patients had little evidence of CD8 T cell activation in the blood compared to controls.\nTo gain more insights, we applied global high-dimensional mapping of the 27-parameter flow cytometry data. A tSNE representation of the data highlighted key regions of non-naïve CD8 T cells found preferentially in COVID-19 patients (Fig. 2G). A major region of this tSNE map present in COVID-19 patients, but not HD or RD, were CD8 T cells that enriched for expression of CD38, HLA-DR, KI67, CD39, and PD1 (Fig. 2G), highlighting the co-expression of these activation markers with other features including CD95 (i.e., FAS). Notably, although non-naïve CD8 T cells from RD were highly similar to those from HD, subtle differences existed, including in the lower region highlighted by T-bet and CX3CR1 (Fig. 2G). To further define and quantify these differences between COVID-19 patients and controls, we performed FlowSOM clustering (Fig. 2H) and compared expression of fourteen CD8 T cell markers to identify each cluster (Fig. 2I). This approach identified an increase in cells in several clusters including Clusters 1, 2, and 5 in COVID-19 patients, reflecting CD45RA+CD27−CCR7− TEMRA-like populations that expressed CX3CR1 and varying levels of T-bet (Fig. 2, I and J). Clusters 12 and 14 contained CD27+HLA-DR+CD38+KI67+PD-1+ activated, proliferating cells and were more prevalent in COVID-19 disease (Fig. 2, I and J, and fig. S2F). In contrast, the central Eomes+CD45RA−CD27+CCR7− EM1-like Cluster 6 and T-bethiCX3CR1+ Cluster 11 were decreased compared to HD (Fig. 2, I and J, and fig. S2F). Thus, CD8 T cell responses in COVID-19 patients were characterized by populations of activated, proliferating CD8 T cells in a subgroup of patients."}

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