Comprehensive mapping of immune perturbations associated with severe COVID-19

Abstract

Although critical illness has been associated with SARS-CoV-2-induced hyperinflammation, the immune correlates of severe COVID-19 remain unclear. Here, we comprehensively analyzed peripheral blood immune perturbations in 42 SARS-CoV-2 infected and recovered individuals. We identified extensive induction and activation of multiple immune lineages, including T cell activation, oligoclonal plasmablast expansion, and Fc and trafficking receptor modulation on innate lymphocytes and granulocytes, that distinguished severe COVID-19 cases from healthy donors or SARS-CoV-2-recovered or moderate severity patients. We found the neutrophil to lymphocyte ratio to be a prognostic biomarker of disease severity and organ failure. Our findings demonstrate broad innate and adaptive leukocyte perturbations that distinguish dysregulated host responses in severe SARS-CoV-2 infection and warrant therapeutic investigation.

Fig. 1. Atlas of immune perturbation in severe COVID-19.

Multiparametric flow cytometry analyses on fresh whole blood after red blood cell lysis characterizing immune cells subsets in healthy donors (HD, n= 12), and moderate (n=7), severe (n=27), and recovered (n=7) COVID-19+ individuals. A) Subset frequencies were calculated within the total viable leukocyte CD45+ population. B) Dot plots for each immune cell subset in a representative HD and severe COVID-19+ individual. Gates within each plot indicate cell subset and corresponding frequency within viable CD45+ cells. Example of parent gates are shown; frequencies were calculated using the specific gating strategies shown in Fig. S2. C) Representative examples of the peripheral blood immunologic atlas of a HD and dysregulation within a severe COVID-19+ individual. T-distributed stochastic neighbor embedding (t-SNE) analysis of cell subsets gated on total viable CD45+ cells or D) PBMC (viable CD45+ cells excluding neutrophils and eosinophils) on a HD and a severe COVID-19+ individual. E) NTR calculated using flow cytometry measurements within viable CD45+ cells. F) NLR calculated using CBC counts (Fig. S1I-J). G) Spearman correlations of APACHE III score and NTR or NLR in moderate and severe COVID-19+ donors. Differences between groups were calculated using Kruskal-Wallis test with Dunn’s multiple comparison post-test. **** p<0.0001, ***p<0.001, **p<0.01, *p<0.05.

Fig. 2. Elevated frequency of plasmablasts, changes in B cell subsets and SARS-CoV-2-specific antibody production in COVID-19+ individuals.

Multiparametric flow cytometry analyses on fresh whole blood after red blood cell lysis characterizing plasmablast and B cell subset frequencies from HD (n= 12), and moderate (n=7), severe (n=27), and recovered (n=7) COVID-19+ individuals. A), B) Distribution and representative plots of B cell plasmablasts (defined as CD27+ CD38+ B cells) and non-plasmablast subsets defined by CD21 and CD27 expression in HD (n= 12), and moderate (n=7), severe (n=27), and recovered (n=7) COVID-19+ individuals. Numbers inside the plots indicate the subset proportion of the corresponding parent population (within total B cells for plasmablasts, within non-plasmablasts for CD21/CD27 subsets). C) Frequencies of K_i-67 and CD11c in non-plasmablast B cell subsets defined in a). Analyses of CD11c are shown for 4/7 individuals with moderate COVID-19. Plots from a representative HD and severe COVID-19+ individual shown. Numbers in each plot indicate the frequency within the parent gate. D) Levels of SARS-CoV-2 spike RBD-specific IgM and IgG antibodies in serum or plasma of HD (n=12), moderate (n=7), severe (n=27), and recovered (n=7) COVID-19+ individuals. Antibody measurements were performed by ELISA using plates coated with the receptor binding domain (RBD) from the SARS-CoV-2 spike protein. Serum and plasma samples were heat-inactivated at 56°C for 1 hour prior to testing in ELISA to inactivate virus. Antibody levels were measured as IgG and IgM arbitrary units (A.U.) based on O.D. values relative to the CR3022 monoclonal antibody (recombinant human anti-SARS-CoV-2, specifically binds to spike protein RBD). E) Spearman correlations of plasma/serum levels of SARS-CoV-2 RBD-specific IgM (top) and IgG (bottom) and days since onset of symptoms on moderate and severe COVID-19+ individuals. Differences between groups were calculated using Kruskal-Wallis test with Dunn’s multiple comparison post-test. **** p<0.0001, ***p<0.001, **p<0.01, *p<0.05.

Fig. 3

Abundant antibody heavy chain sequences from severe COVID-19+ individuals have long, diverse CDR3 sequences and higher levels of somatic hypermutation. A) Clone size distribution by sequence copies. For each donor, the fraction of total sequence copies occupied by the top ten clones (yellow), clones 11-100 (grey), 101-1000 (orange) and over 1000 (blue) are shown. Total donor level clone counts are given in parentheses. B) Percentage of sequence copies occupied by the top twenty ranked clones (D20) shown for HD (n=3) and COVID-19+ individuals with moderate (n=3) and severe disease (n=7). C) Spearman correlation between the D20 value and the percentage of plasmablasts within the total B cell population. D) Examples of the overlap of top 100 copy rearrangements that overlap in at least two sequencing libraries in HD (H4), a moderate COVID-19+ (M7) and a severe COVID-19+ individual (S21). Each horizontal string is a rearrangement and each column is an independently amplified sequencing library (see Materials and Methods). Lines are heat mapped by the copy number fraction for a given replicate library. E) Clone size estimation based on sampling (presence/absence in sequence libraries). Shown are the fractions of the top 100 clones that are found in 4 or more sequencing libraries, 3 libraries, 2 libraries and 1 library. All donors had six sequencing libraries, except for M5 (four libraries). F) Fractional identity to the nearest germline VH gene sequence (1.0 = unmutated) in the top 10 copy number clones of each donor. Each symbol is a clone. G) CDR3 length distributions of the top 50 productive rearrangements in each donor. H) CDR3 lengths of the top 10 copy number clones (symbols), stratified by condition. I) CDR3 length distribution of top 50 clones in COVID-19+ donors based on whether they are found in the Adaptive database (public) or not (private). J) Distribution of CDR3 amino acid (AA) edit distances of the top 50 copy clones (productive) per donor. Clone pair counts for each edit distance are averaged across all the donors in each disease category. Differences between groups were calculated using Mann-Whitney rank-sum test. **** p<0.0001, ***p<0.001, *p<0.05.

Fig. 4. Innate immune dysregulation in severe COVID-19.

Multiparametric flow cytometry analyses of fresh whole blood after red blood cell lysis characterizing the expression of CD16 and HLA-DR on innate immune cells from HD (n= 12), moderate (n=7), severe (n=27), and recovered (n=7) COVID-19+ individuals. A) Proportion of CD16+ cells in monocyte, NK cell and immature granulocyte subsets. B), C), E) Median fluorescence intensity (MFI) of CD16 on neutrophil, monocyte, NK cell and immature granulocyte subsets. MFI was calculated within CD16+ cells. Representative dot plots showing CD16 expression in NK cells and immature granulocytes of a HD and a severe COVID-19 individual shown in C) and E). The numbers inside the plots indicate the percentage of CD16+ cells in the corresponding parent population. D), F) t-SNE analyses of CD16 expression (MFI) in viable CD45+ cells or immature granulocytes, respectively, on a representative HD and a severe COVID-19+ individual. G) MFI of HLA-DR on monocytes; dot plots of a representative HD and a severe COVID-19+ individual shown, with monocyte gate outlined. H) t-SNE analyses of monocyte HLA-DR expression (MFI) on a representative HD and a severe COVID-19+ individual. Differences between groups were calculated using Kruskal-Wallis test with Dunn’s multiple comparison post-test. ***p<0.001, **p<0.01, *p<0.05.

Fig. 5. Heterogeneous T cell activation in severe COVID-19.

Multiparametric flow cytometry analyses on fresh whole blood after red blood cell lysis characterizing immune cells subsets in HD (n= 12), moderate (n=7), severe (n=27), and recovered (n=7) COVID-19 individuals was performed to assess the percentage of activated memory T cells. Frequencies of CD38+, HLA-DR+CD38+, PD-1+ and Ki67+ in A) CD4+, and B) CD8+ memory T cells (excluding naïve CCR7+ CD45RA+, detailed gating strategy shown in Fig. S2). C) Spearman correlations between the frequencies of HLA-DR+CD38+ CD4+ or CD8+ memory T cells and plasmablasts in donors with moderate (orange triangles) or severe COVID-19 (dark red circles). D) Frequencies of HLA-DR+CD38+ CD8+ MAIT cells. E) Frequency of cytotoxic memory CD8+ T cells. Multiparametric flow cytometry analyses were performed on freshly isolated PBMC from HD (n=5) and severe (n=16) COVID-19+ individuals to quantify the frequency and phenotype of cytotoxic (as defined by perforin and granzyme B expression). F) CD8+ T cells, and proportion of cytotoxic CD8+ T cells expressing PD-1 and CD38. Plots for a representative HD and a severe COVID-19+ individual are shown. Numbers inside the plots indicate the frequency within the corresponding parent population. Differences between groups were calculated using Kruskal-Wallis test with Dunn’s multiple comparison post-test and Mann-Whitney rank-sum test. **** p<0.0001, ***p<0.001, **p<0.01, *p<0.05.

Fig. 6

Unbiased analyses of immunophenotyping reveals selective clustering of severe COVID-19+ individuals. A) Heatmap of flow cytometric analyses of HD (n= 12), moderate (n=7), severe (n=27), and recovered (n=7) COVID-19+ individuals. Data are shown in z-score scaled values. Shape and color coding correspond to data shown in Figs. 1-6. H, HD; M, moderate COVID-19; S, severe COVID-19; R, recovered COVID-19. Stars above the symbols indicate donors who died during hospitalization. B) Principal component analysis generated using all flow cytometric data from A).