Deep immune profiling of COVID-19 patients reveals distinct immunotypes with therapeutic implications

D Mathew, JR Giles, AE Baxter, DA Oldridge… - Science, 2020 - science.org
Science, 2020science.org
INTRODUCTION Many patients with coronavirus disease 2019 (COVID-19), caused by
severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection, present with
severe respiratory disease requiring hospitalization and mechanical ventilation. Although
most patients recover, disease is complex and case fatality can be as high as 10%. How
human immune responses control or exacerbate COVID-19 is currently poorly understood,
and defining the nature of immune responses during acute COVID-19 could help identify …
INTRODUCTION
Many patients with coronavirus disease 2019 (COVID-19), caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection, present with severe respiratory disease requiring hospitalization and mechanical ventilation. Although most patients recover, disease is complex and case fatality can be as high as 10%. How human immune responses control or exacerbate COVID-19 is currently poorly understood, and defining the nature of immune responses during acute COVID-19 could help identify therapeutics and effective vaccines.
RATIONALE
Immune dysregulation during SARS-CoV-2 infection has been implicated in pathogenesis, but currently available data remain limited. We used high-dimensional cytometry to analyze COVID-19 patients and compare them with recovered and healthy individuals and performed integrated analysis of ~200 immune features. These data were combined with ~50 clinical features to understand how the immunology of SARS-CoV-2 infection may be related to clinical patterns, disease severity, and progression.
RESULTS
Analysis of 125 hospitalized COVID-19 patients revealed that although CD4 and CD8 T cells were activated in some patients, T cell responses were limited in others. In many patients, CD4 and CD8 T cell proliferation (measured by KI67 increase) and activation (detected by CD38 and HLA-DR coexpression) were consistent with antiviral responses observed in other infections. Plasmablast (PB) responses were present in many patients, reaching >30% of total B cells, and most patients made SARS-CoV-2–specific antibodies. However, ~20% of patients had little T cell activation or PB response compared with controls. In some patients, responses declined over time, resembling typical kinetics of antiviral responses; in others, however, robust T cell and PB responses remained stable or increased over time. These temporal patterns were associated with specific clinical features. With an unbiased uniform manifold approximation and projection (UMAP) approach, we distilled ~200 immune parameters into two major immune response components and a third pattern lacking robust adaptive immune responses, thus revealing immunotypes of COVID-19: (i) Immunotype 1 was associated with disease severity and showed robust activated CD4 T cells, a paucity of circulating follicular helper cells, activated CD8 “EMRAs,” hyperactivated or exhausted CD8 T cells, and PBs. (ii) Immunotype 2 was characterized by less CD4 T cell activation, Tbet+ effector CD4 and CD8 T cells, and proliferating memory B cells and was not associated with disease severity. (iii) Immunotype 3, which negatively correlated with disease severity and lacked obvious activated T and B cell responses, was also identified. Mortality occurred for patients with all three immunotypes, illustrating a complex relationship between immune response and COVID-19.
CONCLUSION
Three immunotypes revealing different patterns of lymphocyte responses were identified in hospitalized COVID-19 patients. These three major patterns may each represent a different suboptimal response associated with hospitalization and disease. Our findings may have implications for treatments focused on activating versus inhibiting the immune response.
High-dimensional immune response analysis of COVID-19 patients identifies three immunotypes.
Peripheral blood mononuclear cell immune profiling and clinical data were collected from 60 healthy donors (HDs), 36 recovered donors (RDs), and 125 hospitalized COVID-19 patients. High-dimensional flow cytometry and longitudinal analysis highlighted stability and fluctuations in …
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