Longitudinal immune profiling of mild and severe COVID-19 reveals innate and adaptive immune dysfunction and provides an early prediction tool for clinical progression

Abstract

With a rising incidence of COVID-19-associated morbidity and mortality worldwide, it is critical to elucidate the innate and adaptive immune responses that drive disease severity. We performed longitudinal immune profiling of peripheral blood mononuclear cells from 45 patients and healthy donors. We observed a dynamic immune landscape of innate and adaptive immune cells in disease progression and absolute changes of lymphocyte and myeloid cells in severe versus mild cases or healthy controls. Intubation and death were coupled with selected natural killer cell KIR receptor usage and IgM+ B cells and associated with profound CD4 and CD8 T cell exhaustion. Pseudo-temporal reconstruction of the hierarchy of disease progression revealed dynamic time changes in the global population recapitulating individual patients and the development of an eight-marker classifier of disease severity. Estimating the effect of clinical progression on the immune response and early assessment of disease progression risks may allow implementation of tailored therapies.

Figure 1:. Immuno-profiling of COVID-19 patients reveals a disarrayed immune system.

a) Composition of the study cohort. b) Description of immune panels and their target epitopes. c) Composition of major immune compartments as a percentage of all live CD45⁺ cells. d) Abundance of major lymphphoid compartments as a percentage of all lymphocytes. For (c) and (d), the upper panels divide patients by general disease status and three lower panels further divide the study subjects by clinical intervention or outcome. Significance was assessed using Mann-Whitney U tests and corrected for multiple testing with the Benjamini-Hochberg false discovery rate (FDR). **, FDR-adjusted p-value <0.01; *, FDR-adjusted p-value of 0.01–0.05.

Figure 2:. T cells from COVID-19 patients have high levels of CD25, FAS, and exhaustion markers.

a) The ratio of CD4 to CD8 cells is dependent on disease state and clinical intervention. b) The abundance of CD45RA/RO cells in either CD4⁺ or CD8⁺ compartments is dependent on disease state or clinical intervention. c) Abundance of immune populations changes significantly between disease states. d) Uniform Manifold Approximation and Projection (UMAP) projection of all cells colored by either surface receptor expression, cluster assignment, or disease severity. e) Immune phenotype of each cluster (top) and its composition in disease severity (bottom). f) Expression levels of CD25 and FAS receptors in the UMAP projection. g) FAS expression across all clusters depending on disease severity (left) and the proportion of cells not expressing FAS for each sample (right). h) Scatter plot of CD25 and FAS expression for each cell according to disease severity. i) Abundance of CD4+ CXCR5+ PD-1+ TFH by disease severity. j) Immune populations with significantly different amounts of cells expressing immune checkpoint receptors by disease severity.Significance was assessed by Mann-Whitney U tests and corrected for multiple testing with the Benjamini-Hochberg false discovery rate (FDR). **, FDR-adjusted p-value <0.01; *, FDR-adjusted p-value 0.01–0.05.

Figure 3:. Emergence of granulocytic MDSCs and preferential expression of specific NK cell receptors in the innate immune system of COVID-19 patients.

a) Abundance of MDSCs as a percentage of all immune cells according to disease severity. b) Uniform Manifold Approximation and Projection (UMAP) projection of all cells from all patients colored by the expression levels of surface receptors, derived clusters, or disease severity among all patients. c) Immune profile of each cluster from (b) based on the expression of surface markers (top) and composition in disease severity (bottom). d) Expression levels of CD15 dependent on disease severity (left) and quantification of cells expressing it (right) according to CD16, CD3, and CD33 expression. e) Abundance of cells expressing various KIR receptors as a percentage of NK cells according to disease severity. f) UMAP projection of all cells from all patients colored by the expression of surface receptors, derived clusters, or disease severity. g) Immune profile of each cluster from (f) based on the expression of surface markers (top) and composition in disease severity (bottom). h) Expression levels of all four measured KIR receptors in each disease state. Significance was assessed using Mann-Whitney U tests and corrected for multiple testing with the Benjamini-Hochberg false discovery rate (FDR). **, FDR-adjusted p-value <0.01; *, FDR-adjusted p-value 0.01–0.05.

Figure 4:. B cells of COVID-19 patients are marked by a shift toward a plasmocytic IgM phenotype.

a-b) The abundance of total B cells, plasma, and IgG+ and IgG+ cells between disease states. c) Uniform Manifold Approximation and Projection (UMAP) projection of all cells colored by surface receptor expression, cluster assignment, or disease severity. d) Immunophenotype of each cluster (top) and its composition by disease severity (bottom). e) Identification and quantification of five populations of B cells dependent on CD20 and CD19 expression. f) Comparison of the abundance of the populations identified in e) between disease states. Significance was assessed using Mann-Whitney U tests and corrected for multiple testing with Benjamini-Hochberg FDR. **, FDR-adjusted p-value <0.01; *, FDR-adjusted p-value 0.01–0.05.

Figure 5:. Pseudo-temporal reconstitution of disease progression reveals a hierarchy of immune changes in COVID-19 disease.

a) Hierarchical clustering of the abundance of immune populations for all samples reveals an organized structure by disease states and clinical factors. b) Projection of immune profiles into a two-dimensional latent space that reconstructs the hierarchy of disease progression. The x-axis represents disease progression in the pseudo-temporal space. c) Distribution of samples grouped by disease state along the pseudo-temporal axis derived in (b). d) Immune populations associated with the pseudo-temporal axis represented by either the absolute change in percentage in their extremes (x-axis) or strength of linear association (y-axis). e) Heatmap of immunotypes and immune populations sorted by their order or relative abundance in the pseudo-temporal axis, respectively. f) Clusters of immune populations based on their abundance along the pseudo-temporal axis. g) Examples of immune populations from each cluster in (f).

Figure 6:. Factors conditioning the immune response during COVID-19 and predicting disease severity

a) Directed graph of clinical factors (green) and immune populations (pink). Edges represent the association between factors and immune populations and are colored by the direction and strength of association (blue, negative; red, positive). b) Abundance of select immune populations with significantly different responses between sexes dependent on outcome. c) Estimated coefficients of change for severe vs. mild disease (left) o tocilizumab treatment (right) for immune populations that change discordantly. d–e) Abundance of select immune populations with significantly different responses between sexes dependent on tocilizumab treatment (d) or intubation (e). f) Graphical depiction of the machine learning framework for predicting disease severity using the earliest available samples per patient and cross-validation. g–h) Performance of classifiers trained with real or randomly shuffled labels and either all immune populations (g) or with selection for the top most predictive eight populations (h). i) Predicted severity scores over time since symptoms started for immune profiles from patients with at least three longitudinal sampling points. j) Relative expression of CD25, CD45RA and CD45RO over time in four patients from (g). **, FDR-adjusted p-value <0.01; *, FDR-adjusted p-value 0.01–0.05.