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Immunophenotyping of COVID-19 and influenza highlights the role of type I interferons in development of severe COVID-19

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Science Immunology  10 Jul 2020:
Vol. 5, Issue 49, eabd1554
DOI: 10.1126/sciimmunol.abd1554
  • Fig. 1 Single-cell transcriptomes of PBMCs from COVID-19 and influenza patients.

    (A) tSNE projections of 59,572 PBMCs from healthy donors (HDs) (four samples, 17,590 cells), patients with severe influenza (FLU) (five samples, 10,519 cells), patients with COVID-19 (asymptomatic: one sample, 4425 cells; mild COVID-19: four samples, 16,742 cells; severe COVID-19: six samples, 10,296 cells) colored by group information. (B) Normalized expression of known marker genes on a tSNE plot. (C) tSNE plot colored by annotated cell types. (D) Proportion of cell types in each group excluding Uncategorized 1, Uncategorized 2, RBC, and platelet. The colors indicate cell type information. (E) Boxplots showing the fold enrichment in cell type proportions from patients with mild COVID-19 (n = 4), severe COVID-19 (n = 6), and FLU (n = 5) compared with the HD group (mild COVID-19 versus HD: n = 16; severe COVID-19 versus HD: n = 24; FLU versus HD: n = 20). For the boxplots, the box represents the interquartile range (IQR), and the whiskers correspond to the highest and lowest points within 1.5 × IQR. Uncategorized 1 (relatively high UMIs per cell and the presence of multiple marker genes), Uncategorized 2 (B cell like and high expression of ribosomal protein genes), RBC, and platelet were excluded. Two-sided KS tests were conducted for each cell type between the disease and HD groups. *P < 0.05, **P < 0.01, and ***P < 0.001.

  • Fig. 2 Immune landscape of COVID-19.

    (A) Hierarchical clustering using the PCC of a normalized transcriptome between diseases in cell type resolution (n = 33). The color intensity of the heatmap indicates the PCC values. The color bars above the heatmap indicate the cell type and disease group. The black box indicates the cell types that highly correlate between the severe COVID-19 and FLU groups. (B) Illustration of the enrichment P values for the select GO biological pathways (n = 49) of DEGs in COVID-19 and FLU patients (left, six columns: DEGs for COVID-19 and FLU groups compared with HD; right, two columns: DEGs between COVID-19 and FLU groups). MHC, major histocompatibility complex. (C) tSNE plot of representative gene expression patterns for GBP1 (FLU specific), CREM (COVID-19 specific), and CCL3 (COVID-19/FLU common). (D) Top: Dendrogram from WGCNA analysis performed using relative normalized gene expression between the COVID-19 and FLU groups for the genes belonging to the select biological pathways in (B) (n = 316). Bottom: Heat map of relative normalized gene expression between the COVID-19 and FLU groups. The color bar (left) indicates cell type information clustered by hierarchical clustering based on the PCC for relative normalized gene expression. Modularized gene expression patterns by WGCNA are shown together (G1, n = 10; G2, n = 147; G3, n = 27; G4, n = 17; G5, n = 12; G6, n = 64; G7, n = 34; G8, n = 5).

  • Fig. 3 Subpopulation analysis of CD8+ T cells.

    (A) tSNE plot of the non–EM-like CD8+ T cell subpopulations in all groups (left, n = 6253), COVID-19 (top right, n = 2653), FLU (middle right, n = 1452), and HD (bottom right, n = 2148) colored by cluster information. (B and C) Boxplots showing the proportion of individual subclusters from the non–EM-like CD8+ T cell cluster within each group (COVID-19, n = 10; FLU, n = 5; HD, n = 4). The proportions follow normal distribution as tested by the Shapiro-Wilk normality test except the proportion of cluster 3 in the COVID-19 group (P = 0.04). Cluster 1 and cluster 3 were highly enriched in the FLU and COVID-19 group, respectively. Two-sided Welch’s t test P values were 4.4 × 10−3 between COVID-19 and FLU in cluster 1, 3.5 × 10−2 between FLU and HD donor in cluster 1, 8.6 × 10−3 between COVID-19 and FLU in cluster 3, and 5.8 × 10−3 between COVID-19 and HD in cluster 3. *P < 0.05 and **P < 0.01. (D) STRING analysis using the top 30 up-regulated genes in cluster 1 (left) and cluster 3 (right). (E) Bar plots showing enrichment P values of eight representative GO biological pathways for proinflammation and IFN in cluster 1– or cluster 3–specific up-regulated genes (cluster 1, n = 66; cluster 3, n = 183).

  • Fig. 4 Transcriptome of classical monocytes in patients with COVID-19.

    (A) Venn diagram of DEGs in COVID-19 and FLU compared with HD. The representative genes are shown together. (B) K-means clustering of DEGs between all pairs of FLU, mild COVID-19, and severe COVID-19 (n = 499). The color indicates the relative gene expression between the diseases and HD. The representative genes are shown together. (C) Bar plots showing the average −log10(P value) values in enrichment analysis using the perturbed genes of four different cell lines listed in L1000 LINCS for up-regulated genes in cluster 2 (C2, left) and cluster 3 (C3, right). Error bars indicate SD. (D) Combined enrichment scores were compared between C2 and C3 for the gene sets of the type I IFN response (left; GSE26104) and TNF response (right; GSE2638 and GSE2639). **P < 0.01. Each dot indicates an individual. (E) Bar plots showing the average −log10(P value) values in the enrichment analysis using the perturbed genes listed of four different cell lines in L1000 LINCS for up-regulated genes in cluster 4 (C4, left) and cluster 5 (C5, right). Error bars indicate SD (C and E).

  • Fig. 5 Trajectory analysis of classical monocytes.

    (A) Volcano plot showing DEGs between mild and severe COVID-19 groups. Each dot indicates individual gene, colored by red when a gene is significant DEG. (B) Bar plot showing the average −log10(P value) values in enrichment analysis using the perturbed genes of four different cell lines listed in L1000 LINCS for up-regulated genes in the severe COVID-19 group. Error bars indicate SD. (C) Trajectory analysis of classical monocytes from specimens obtained at two different time points in a single COVID-19 patient (mild: C7-2, 1,197 cells; severe: C7-1, 631 cells). The color indicates cluster information (left) or the severity of COVID-19 (right). (D) Relative expression patterns of representative genes in the trajectory analysis are plotted along the pseudotime. The color indicates the relative gene expression calculated by Monocle 2. (E) Bar plots showing the average −log10(P value) values in the enrichment analysis using the perturbed genes of four different cell lines in L1000 LINCS for up-regulated genes in cluster 3 (left) and cluster 1 (right). Error bars indicate SD. (F) Comparison of combined enrichment scores between cluster 3 and cluster 1 for the gene sets from systemic lupus erythematosus (SLE) (n = 16) and rheumatoid arthritis (RA) (n = 5). ***P < 0.001; ns, not significant. (G) GSEA of up-regulated genes in cluster 3 (left) and cluster 1 (right) to the class 1 gene module of monocyte-derived macrophages by Park et al. (24). NES, normalized enrichment score.

  • Fig. 6 Validation of the combined IFN-I and inflammatory responses in the transcriptome of postmortem lung tissues from lethal COVID-19.

    (A) UCSC Genome Browser snapshots of representative genes. (B) Bar plot showing the average −log10(P value) values from the enrichment analysis using the perturbed genes of four different cell lines in L1000 LINCS for up-regulated genes (n = 386) in postmortem lung tissues compared with biopsied healthy lung tissue. Error bars indicate SD. (C) GSEA of significantly up- and down-regulated genes in postmortem lung tissues for gene sets originated from up-regulated genes in C2 (n = 96), C3 (n = 143), C4 (n = 218), and C5 (n = 30) of Fig. 4B. (D and E) GSEA of significantly up- and down-regulated genes in postmortem lung tissues for gene sets originated from the top 200 up-regulated genes in cluster 3 (left) and cluster 1 (right) from the trajectory analysis in Fig. 5C (D) and from gene sets originated from the top 200 up-regulated genes in classical monocytes of mild (left) and severe (right) COVID-19 (E).

Supplementary Materials

  • immunology.sciencemag.org/cgi/content/full/5/49/eabd1554/DC1

    Fig. S1. Clinical characteristics and assessment of the quality of scRNA-seq results.

    Fig. S2. Transcriptome features of highly variable genes.

    Fig. S3. Characterization of disease-specific CD8+ T cell subpopulations.

    Fig. S4. Subpopulation analysis of classical monocytes.

    Fig. S5. Serum cytokine concentration of COVID-19 patients (A), and STRING analysis of up-regulated genes in cluster 1 obtained from the trajectory analysis of classical monocytes (B).

    Table S1. Experimental batches of scRNA-seq.

    Table S2. Clinical characteristics of patients with severe influenza.

    Table S3. Clinical characteristics of patients with COVID-19.

    Table S4. The scRNA-seq results.

    Table S5. A list of marker genes for each cluster.

    Table S6. A list of DEGs and associated biological pathways in Fig. 2B.

    Table S7. Cell types in which the GBP1, CREM, and CCL3 were up-regulated in Fig. 2C.

    Table S8. A list of genes in each module obtained from WGCNA in Fig. 2D.

    Table S9. A list of up-regulated genes in non–EM-like CD8+ T cell subpopulations.

    Table S10. A list of genes included in each cluster defined by K-mean clustering of classical monocytes.

    Table S11. A list of genes up-regulated in early and late pseudotime.

  • Supplementary Materials

    This PDF file includes:

    • Fig. S1. Clinical characteristics and assessment of the quality of scRNA-seq results.
    • Fig. S2. Transcriptome features of highly variable genes.
    • Fig. S3. Characterization of disease-specific CD8+ T-cell subpopulations.
    • Fig. S4. Subpopulation analysis of classical monocytes.
    • Fig. S5. STRING analysis of up-regulated genes in cluster 1 obtained from the trajectory analysis of classical monocytes.

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    Other Supplementary Material for this manuscript includes the following:

    • Table S1. Experimental batches of scRNA-seq.
    • Table S2. Clinical characteristics of severe influenza patients.
    • Table S3. Clinical characteristics of COVID-19 patients.
    • Table S4. The scRNA-seq results.
    • Table S5. A list of marker genes for each cluster.
    • Table S6. A list of DEGs and associated biological pathways in Fig. 2B.
    • Table S7. Cell types in which the GBP1, CREM, and CCL3 were upregulated in Fig. 2C.
    • Table S8. A list of genes in each module obtained from WGCNA in Fig. 2D.
    • Table S9. A list of up-regulated genes in non-EM-like CD8+ T-cell subpopulations.
    • Table S10. A list of genes included in each cluster defined by K-mean clustering of classical monocytes.
    • Table S11. A list of genes up-regulated in early and late Pseudotime.

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