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

Our take —

Immune responses to COVID-19 are not well understood, but evidence suggests that they may be crucial to patient outcomes. This analysis considered a very large number of immune responses to COVID-19 among hospitalized patients, noting considerable heterogeneity in responses, but identifying several broad patterns. Most patients showed a strong plasmablast response, and subgroups of patients exhibited B cell and T cell activation and proliferation that persisted for at least 7 days. Although the authors identified three “immunotypes” associated with disease severity, more studies are required to determine whether these immune response clusters are present in the broader population of COVID-19 patients.

Study design

Case-Control

Study population and setting

This study included blood samples from 125 hospitalized patients (median age 60 years, 51% male, 68% African-American) with laboratory-confirmed COVID-19 at the University of Pennsylvania (collected 1-3 days after admission and 7 days after admission for those who remained hospitalized), along with samples from 36 non-hospitalized patients who had recovered from COVID-19, and from 60 healthy donors. Flow cytometry was used to analyze peripheral blood mononuclear cells (PBMCs) with respect to a large number of immunological markers.

Summary of Main Findings

Among the 125 hospitalized patients, 83% had cardiovascular comorbidities, 30% required mechanical ventilation, and 14% died. Most hospitalized patients had elevated concentrations of inflammatory biomarkers such as CRP, d-dimer, and ferritin; troponin levels were also commonly elevated. Nearly half of hospitalized patients were clinically lymphopenic, but most had normal monocyte, eosinophil, and basophil counts. In principal components analysis of 193 immune parameters, the immune profile of hospitalized patients was clearly distinct from that in recovered and healthy donors, while recovered and healthy donors exhibited overlap. Approximately 80% of hospitalized patients exhibited CD8+ T cell activation above the levels seen in the control groups. There was a heterogeneous degree of CD4+ T cell response, with some distinct proliferating subpopulations of CD4+ T cells. In a subset of 8 SARS-CoV-2-negative blood samples, inflammatory cytokines and chemokines were elevated. Naive B cell counts were similar in hospitalized patients relative to controls, but there were significant changes in B cell subpopulation frequencies that did not appear to be related to systemic inflammation. Approximately two-thirds of hospitalized patients exhibited significantly elevated plasmablast (PB) frequency; declines in memory B cell frequencies and loss of CXCR5 expression were also commonly seen. In a subset of patients hospitalized for at least 7 days (n=48), changes in T cell and B cell responses between admission and day 7 were highly heterogeneous. More severe disease was associated with lower frequencies of CD4+ and CD8+ T cells, but there were no significant associations between temporal changes in B or T cell response and clinical severity. The authors used Uniform Manifold Approximation and Projection (UMAP) to identify clusters of immune response and correlate them with clinical outcomes; they defined three “immunotypes” having varying correlations with disease severity.

Study Strengths

The primary strengths of this study are the very large number of immune parameters considered, and the detailed manner in which variations in these parameters was assessed.

Limitations

Patients providing samples for analysis (n=125) were not large in number, and were limited to a single institution. Little information was provided about patient selection and eligibility criteria. Clusters of immune response observed, and their correlation with clinical severity, may not be generalizable to other populations. Although analyses attempted to adjust for age, sex, and race, they did not appear to adjust for comorbidities. In addition to comorbidity data, there may be other uncontrolled confounding variables associated with both immune parameters and clinical outcomes, such as (for example) cigarette smoking.

Value added

This study provided highly detailed measurements of a wide variety of immune responses to COVID-19, and outlined some broad patterns worthy of further research.

This review was posted on: 3 August 2020