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Comprehensive mapping of immune perturbations associated with severe COVID-19

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Science Immunology  15 Jul 2020:
Vol. 5, Issue 49, eabd7114
DOI: 10.1126/sciimmunol.abd7114
  • 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 HDs (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. Examples 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 an 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) peripheral blood mononuclear cells (PBMCs) (viable CD45+ cells excluding neutrophils and eosinophils) on an HD and a severe COVID-19+ individual. (E) NTR calculated using flow cytometry measurements within viable CD45+ cells. (F) NLR calculated using CBCs (fig. S1, I and 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, and *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 HDs (n = 12) and moderate (n = 7), severe (n = 27), and recovered (n = 7) COVID-19+ individuals. (A and B) Distribution and representative plots of B cell plasmablasts (defined as CD27+ CD38+ B cells) and nonplasmablast subsets defined by CD21 and CD27 expression in HDs (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 nonplasmablasts for CD21/CD27 subsets). (C) Frequencies of Ki-67 and CD11c in nonplasmablast B cell subsets defined in (A). Analyses of CD11c are shown for four of seven individuals with moderate COVID-19. Plots from a representative HD and a 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 HDs (n = 12) and moderate (n = 7), severe (n = 27), and recovered (n = 7) COVID-19+ individuals. Antibody measurements were performed by ELISA using plates coated with the RBD from the SARS-CoV-2 spike protein. Serum and plasma samples were heat-inactivated at 56°C for 1 hour before testing in ELISA to inactivate virus. Antibody levels were measured as IgG and IgM arbitrary units (A.U.) based on optical density 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, and *P < 0.05.

  • Fig. 3 Abundant antibody heavy chain sequences from severe COVID-19+ individuals have long, diverse CDR3 sequences and higher levels of SHM.

    (A) Clone size distribution by sequence copies. For each donor, the fraction of total sequence copies occupied by the top 10 clones (yellow), clones 11 to 100 (gray), 101 to 1000 (orange), and more than 1000 (blue) are shown. Total donor level clone counts are given in parentheses. (B) Percentage of sequence copies occupied by the top 20 ranked clones (D20) shown for HDs (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 an HD (H4), a moderate COVID-19+ individual (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 heatmapped 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 four or more sequencing libraries, three libraries, two libraries, and one 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). nt, nucleotide. (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, and *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 HDs (n = 12) and 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, and E) 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 an HD and a severe COVID-19+ individual were shown in (C) and (E). The numbers inside the plots indicate the percentage of CD16+ cells in the corresponding parent population. (D and 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, and *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 HDs (n = 12) and moderate (n = 7), severe (n = 27), and recovered (n = 7) COVID-19+ individuals were performed to assess the percentage of activated memory T cells. Frequencies of CD38+, HLA-DR+ CD38+, PD-1+, and Ki-67+ 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 COVID-19 (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 PBMCs from HDs (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, and *P < 0.05.

  • Fig. 6 Unbiased analyses of immunophenotyping reveal selective clustering of severe COVID-19+ individuals.

    (A) Heatmap of flow cytometric analyses of HDs (n = 12) and 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 to 6. H, HD; M, moderate COVID-19; S, severe COVID-19; R, recovered COVID-19. Asterisks above the symbols indicate donors who died during hospitalization. (B) PCA generated using all flow cytometric data from (A).

  • Table 1 Demographics and clinical characteristics.

    Data are shown as number and percentage, n (%). Age is reported in median years (minimum to maximum). Days since onset of symptoms are reported as median (minimum to maximum). Not all data were collected for HDs and recovered individuals. ARDS, acute respiratory distress syndrome; HFNC-NIV, high flow nasal cannula–noninvasive; ECMO, extracorporeal membrane oxygenation.

    CharacteristicHDRecoveredModerateSevere*
    n127728
    Age36 (24–61)30 (20–49)59 (29–64)68 (38–81)
    Male6 (46.1)5 (71.4)2 (28.6)19 (67.9)
    Race
    Black or African American5 (71.4)16 (57.1)
    Asian or Asian American02 (7.1)
    White or Caucasian2 (14.3)11 (39.2)
    Past smoking history2 (14.3)13 (46.4)
    Comorbidity
    Obesity3 (42.9)8 (28.6)
    Hypertension5 (71.4)21 (75)
    Diabetes1 (14.3)7 (25)
    Thromboembolic
    complications
    1 (14.3)7 (25)
    Coronary artery disease/
    myocardial infarction
    03 (10.7)
    Underlying lung disease†4 (57.1)7 (25)
    Renal insufficiency/chronic
    kidney disease
    2 (14.2)20 (71.4)
    Hyperlipidemia2 (71.4)14 (50)
    Treatment
    Hydroxychloroquine4 (57.1)25 (89.3)
    Remdesivir‡1 (14.2)12 (42.9)
    Days since onset of
    symptoms§
    27 (17–32)9 (1–16)9 (1–25)
    Oxygen therapy/ARDS
    Nasal cannula (oxygen < 6
    liters)
    3 (42.9)0
    HFNC/NIV4 (4.3)
    Ventilator non-ARDS2 (67.1)
    Mild ARDS3 (10.7)
    Moderate ARDS9 (32.1)
    Severe ARDS10 (35.7)
    ECMO1 (3.6)
    Mortality0007 (25)

    *Two severe COVID-19+ individuals were excluded from immunophenotyping and antibody quantification as they displayed clear outlier phenotype due to Rituxan treatment for lymphoma and acute lymphocytic leukemia, respectively.

    †Underlying lung disease includes asthma, chronic obstructive pulmonary disease, and interstitial lung disease.

    ‡Donors enrolled in a clinical trial to test remdesivir versus placebo. Remdesivir was administered after blood collection.

    §Days since onset of symptoms accounted from the time of blood collection.

    Supplementary Materials

    • immunology.sciencemag.org/cgi/content/full/5/49/eabd7114/DC1

      Materials and Methods

      Fig. S1. Demographic and clinical information.

      Fig. S2. Gating strategy used for flow cytometric analyses of immune cell subsets.

      Fig. S3. Extended innate immune subset characterization and phenotype during COVID-19 infection.

      Fig. S4. Extended T cell phenotype and activation during COVID-19 infection.

      Fig. S5. Extended B cell phenotype and total IgG measurements in COVID-19.

      Fig. S6. Abundance of the top 20 clones in each donor.

      Fig. S7. Heavy chain variable (VH) gene and CDR3 usage.

      Fig. S8. Extracellular and intracellular expression of CD16 on NK cells during COVID-19.

      Fig. S9. Expression of activation markers in CD4+ and CD8+ T cells subsets.

      Table S1. Detailed clinical characteristics of individuals with moderate and severe COVID-19.

      Table S2. Antibody heavy chain gene rearrangement metadata.

      Table S3. Rotation table extracted from PCA.

      References (92100)

    • Supplementary Materials

      This PDF file includes:

      • Materials and Methods
      • Fig. S1. Demographic and clinical information.
      • Fig. S2. Gating strategy used for flow cytometric analyses of immune cell subsets.
      • Fig. S3. Extended innate immune subset characterization and phenotype during COVID-19 infection.
      • Fig. S4. Extended T cell phenotype and activation during COVID-19 infection.
      • Fig. S5. Extended B cell phenotype and total IgG measurements in COVID-19.
      • Fig. S6. Abundance of the top 20 clones in each donor.
      • Fig. S7. Heavy chain variable (VH) gene and CDR3 usage.
      • Fig. S8. Extracellular and intracellular expression of CD16 on NK cells during COVID-19.
      • Fig. S9. Expression of activation markers in CD4+ and CD8+ T cell memory subsets.
      • Table S1. Detailed clinical characteristics of individuals with moderate and severe COVID-19.
      • Table S2. Antibody heavy chain gene rearrangement metadata.
      • Table S3. Rotation table extracted from PCA.

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