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. 2021 Dec 22;184(26):6243-6261.e27.
doi: 10.1016/j.cell.2021.11.033. Epub 2021 Nov 27.

SARS-CoV-2 infection triggers profibrotic macrophage responses and lung fibrosis

Daniel Wendisch  1 , Oliver Dietrich  2 , Tommaso Mari  3 , Saskia von Stillfried  4 , Ignacio L Ibarra  5 , Mirja Mittermaier  6 , Christin Mache  7 , Robert Lorenz Chua  8 , Rainer Knoll  9 , Sara Timm  10 , Sophia Brumhard  1 , Tobias Krammer  2 , Henrik Zauber  3 , Anna Luisa Hiller  1 , Anna Pascual-Reguant  11 , Ronja Mothes  12 , Roman David Bülow  4 , Jessica Schulze  7 , Alexander M Leipold  2 , Sonja Djudjaj  4 , Florian Erhard  13 , Robert Geffers  14 , Fabian Pott  15 , Julia Kazmierski  15 , Josefine Radke  16 , Panagiotis Pergantis  1 , Kevin Baßler  9 , Claudia Conrad  1 , Anna C Aschenbrenner  17 , Birgit Sawitzki  18 , Markus Landthaler  19 , Emanuel Wyler  19 , David Horst  20 ; Deutsche COVID-19 OMICS Initiative (DeCOI); Stefan Hippenstiel  21 , Andreas Hocke  21 , Frank L Heppner  22 , Alexander Uhrig  1 , Carmen Garcia  1 , Felix Machleidt  1 , Susanne Herold  23 , Sefer Elezkurtaj  20 , Charlotte Thibeault  1 , Martin Witzenrath  21 , Clément Cochain  24 , Norbert Suttorp  21 , Christian Drosten  25 , Christine Goffinet  15 , Florian Kurth  26 , Joachim L Schultze  27 , Helena Radbruch  28 , Matthias Ochs  29 , Roland Eils  8 , Holger Müller-Redetzky  1 , Anja E Hauser  11 , Malte D Luecken  5 , Fabian J Theis  30 , Christian Conrad  8 , Thorsten Wolff  7 , Peter Boor  4 , Matthias Selbach  31 , Antoine-Emmanuel Saliba  32 , Leif Erik Sander  33
Affiliations

SARS-CoV-2 infection triggers profibrotic macrophage responses and lung fibrosis

Daniel Wendisch et al. Cell. .

Abstract

COVID-19-induced "acute respiratory distress syndrome" (ARDS) is associated with prolonged respiratory failure and high mortality, but the mechanistic basis of lung injury remains incompletely understood. Here, we analyze pulmonary immune responses and lung pathology in two cohorts of patients with COVID-19 ARDS using functional single-cell genomics, immunohistology, and electron microscopy. We describe an accumulation of CD163-expressing monocyte-derived macrophages that acquired a profibrotic transcriptional phenotype during COVID-19 ARDS. Gene set enrichment and computational data integration revealed a significant similarity between COVID-19-associated macrophages and profibrotic macrophage populations identified in idiopathic pulmonary fibrosis. COVID-19 ARDS was associated with clinical, radiographic, histopathological, and ultrastructural hallmarks of pulmonary fibrosis. Exposure of human monocytes to SARS-CoV-2, but not influenza A virus or viral RNA analogs, was sufficient to induce a similar profibrotic phenotype in vitro. In conclusion, we demonstrate that SARS-CoV-2 triggers profibrotic macrophage responses and pronounced fibroproliferative ARDS.

Keywords: ARDS; COVID-19; IPF; SARS-CoV-2; fibrosis; lung; macrophages; monocytes; proteomics; pulmonary fibrosis; single-cell transcriptomics.

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Conflict of interest statement

Declaration of interests The authors declare no competing interests.

Figures

None
Graphical abstract
Figure 1
Figure 1
CD163+ macrophages accumulate in the lung in severe COVID-19 (A) Overview of study design and analyses. CT, computed tomography; BAL, bronchoalveolar lavage; scRNA-seq, single-cell RNA sequencing; snRNA-seq, single-nucleus RNA sequencing; IHC, immunohistochemistry; IF, immunofluorescence microscopy; MELC, multi-epitope ligand cartography; EM, electron microscopy; VCin, inspiratory vital capacity; PBMC, peripheral blood mononuclear cells; IAV, Influenza A virus. (B) Postmortem analysis of consecutive histological sections of non-COVID-19 (left) and COVID-19 autopsy lung samples (right) by hematoxylin and eosin (H&E; top) and CD68 IHC (bottom). Scale bar, 100 μm. (C) IF of CD68 (green) and CD163 (red) in lung tissue autopsy samples of COVID-19 patients and non-COVID-19 controls. Arrows indicate CD68+CD163– macrophages, and arrowheads indicate CD68+CD163+ macrophages. Scale bar, 20 μm. (D) Quantification of CD68+ macrophage density (left) and the proportion of CD163+ macrophages (right) in lung autopsy samples from fifteen donors (as in C). Mann-Whitney test; ∗p < 0.05. (E) Representative images of consecutive histological sections of lung autopsy samples. H&E (left), CD68 IHC (middle), and SARS-CoV-2 RNA-FISH (right). Arrowheads indicate SARS-CoV-2 RNA-positive macrophages. Scale bars, 50 μm, 25 μm. RNA-FISH, RNA-fluorescence in situ hybridization. (F) Lung autopsy samples of 9 COVID-19 patients were analyzed by MELC with a panel of 22 markers on 19 fields of view (FOVs). Two-dimensional embedding computed by UMAP on 9,684 computationally identified CD45 positive cells (T cells, CD3+; B cells, CD20+; NK cells, CD56+; neutrophils, MRP14+/CD66b+; monocytes, MRP14+/CCR2+; macrophages, MRP14+/HLA-DR+). (G) Relative proportion (of total CD45+ cells) of cell types in all 19 FOVs (left), and average cell numbers (summary, right).
Figure S1
Figure S1
Study cohorts and (immuno-)histological analysis of lung tissues, related to Figure 1 (A) Schematic overview of all patients enrolled in the two cohorts. Clinical characteristics, course of disease, treatments, analysis time points, and outcomes are indicated. (B) Consecutive histological sections of COVID-19 lung tissue showing H&E (left), CD68 (middle) and SARS-CoV-2 RNA (right; scale bar, 50 µm; insert scale bar, 25 µm). (C) MELC analysis of lung autopsy tissue showing collagen and immune cell staining. (D) UMAP embedding as shown in Figure 1F color-coded by donor. (E) UMAP embedding as in Figure 1F, color-coded arcsin-transformed mean fluorescence intensity across all epitopes measured by MELC. (F) CD163 fluorescence intensity across the different cellular populations identified by MELC as presented in Figure 1F, every dot represents one cell. The line indicates the threshold of CD163+ and CD163- cells.
Figure 2
Figure 2
Monocyte-derived macrophages adopt a damage response signature in severe COVID-19 (A) UMAP (Uniform Manifold Approximation and Projection) embedding of 46,060 single-cell transcriptomes in the BAL fluid of patients with severe COVID-19 ARDS. Cell-type annotation was based on expression of canonical marker genes. (B) UMAP embedding and slingshot trajectory of 7,816 transcriptomes of monocytes/macrophages identified in (A). Clusters were defined by comparing gene expression patterns of Leiden clusters (Mono, monocytes; Mono/Mφ, transitory monocyte-macrophages; AMφ, alveolar macrophages). (C) Marker gene expression and SARS-CoV-2 mRNA counts, color-coded and projected onto the UMAP embedding in (B). Statistical significance of differential expression for each gene per cluster shown in Table S2. (D) Dot plot of scaled, log-normalized expression of marker genes of the clusters in (B). Gene names color-coded by functional categories. Dot size indicates percentage of cells per cluster with any mRNAs detected, and color shows Z-scores of log-normalized mRNA counts. Statistics in Table S2. (E) Relative proportions of cell types across all BAL scRNA-seq samples derived from (B) and Figure S2M ordered by increasing days post symptom onset. (F) Heatmap displaying -log10 transformed adjusted p values (one-sided Fisher’s exact test) assessing the overlap between gene sets from COVID-19-associated monocyte/macrophage clusters identified in (B) (y axis) and published transcriptional signatures of COVID-19-associated monocytes/macrophages (cluster names and reference studies indicated; Table S3).
Figure S2
Figure S2
Monocyte/macrophage transcriptional profiles in BAL, related to Figure 2 (A–C) UMAP embedding (as in Figure 2A, dataset 1) of BAL scRNA-seq transcriptomes color-coded according to the patients of origin (A), by sampling time after symptom onset (B), and SARS-CoV-2 mRNA molecule counts (C). (D) Dot plot displaying the expression of canonical marker genes delineates the cell types identified in BAL (Figure 2A). Dot size shows the percentage of cells with any mRNA counts, color shows the z-scores of log-normalized expression. (E) Cellular composition of BAL fluid across patients by cell type according to scRNA-seq. Bar height shows proportion in percent, labels show the real cell numbers, color indicates the cell type. Summary shows average across patients. (F) Heatmap displaying differential expressed (DE, FDR < 10e-15) genes between macrophage populations (as in Figure 2B) and across the different patients analyzed. (G) UMAP as Figure 2B split by patient, color indicates macrophage clusters as in Figure 2B. (H) Heatmap showing the mean rank of ChEA3 transcription factor enrichment. Clusters (y axis) as in Figure 2B, transcription factors (x axis) ordered by cluster and mean rank. Input to ChEA3 were the DE genes shown in F, TFs were selected by mean rank < 30. (I–K) UMAP embedding of 26,554 single-cell transcriptomes in the BAL fluid of severe COVID-19 patients at late stage of disease (dataset 2), color-coded according to identified cell types using canonical markers (I), patients of origin (J) and sampling time after symptom onset (K). (L) Cellular composition of BAL fluid (dataset 2) across patients by cell type according to scRNA-seq. Bar height shows proportion in percent, labels show the total cell numbers, colors indicate the cell type. Summary shows average across patients. (M) UMAP embedding of 12,712 transcriptomes of monocytes/macrophages in (I). Cell subtype labels were defined by cluster specific expression of previously identified BAL monocyte/macrophage markers (Figure 2F) (Mono; Monocytes, Mono/Mφ; Monocyte-derived macrophages, AMφ; Alveolar macrophages). Low quality refers to a cluster of cells with very high mitochondrial marker gene expression. (N) UMAP from (M) split by patient, colors indicate macrophage clusters. (O) Dot plot showing the previously identified monocyte/macrophage markers as in Figure 2F for the cell subtype labels from (M). Dot size shows the percentage of cells per cluster, color shows average expression of log-normalized mRNA counts.
Figure 3
Figure 3
Gene set enrichment and data integration reveals a profibrotic phenotype of COVID-19-associated macrophages (A) Gene set module score of “IPF-expanded macrophages” (IPFe-Mφ) and alveolar FABP4+Mφ (Ayaub et al., 2021), calculated based on single transcriptomes. Projected onto the UMAP embedding (top) and plotted as violin plots (bottom) across the monocyte/macrophage clusters (annotated in Figure 2B). Dot color indicates signature module score. Violin colors show cluster identity, numbers indicate -log10 transformed adjusted p values (one-sided wilcoxon test compared to average), and lines in violins indicate median scores per cluster. (B) Heatmap representing -log10 transformed adjusted p values (one-sided Fisher’s exact test) assessing the overlap of gene sets from monocyte/macrophage clusters identified in Figure 2B (y axis) and published transcriptional signatures of monocyte/macrophage clusters derived from the indicated IPF datasets (cluster names and reference studies indicated on the x axis; Table S3). (C) Schematic depicting monocyte/macrophage data integration from present study and Bharat et al. (2020) with two human lung fibrosis reference datasets (Adams et al., 2020; Morse et al., 2019) via scVI. COVID-19 macrophages were mapped to IPF or control macrophages based on a kNN (k-nearest neighbor)-proximity mapping. (D) UMAP of 138,341 cells derived from all four datasets based on integrated scVI embedding. (E) UMAP as in (D) highlighting COVID-19-associated macrophage clusters annotated in Figure 2B. Cells from reference datasets shaded in gray. (F) Proximity analysis of macrophage clusters annotated in Figure 2B and macrophages identified in IPF and healthy controls, respectively. Circle size shows cell fraction, color codes indicate the -log10 transformed adjusted p values, and bold black circle indicates statistical significance (adjusted p < 0.0001, Fisher’s exact test, one-tailed with Benjamini-Hochberg correction).
Figure S3
Figure S3
Gene set enrichment analysis and data integration analysis with reference datasets, related to Figure 3 (A) Signature module scores of monocyte-macrophage clusters associated to idiopathic pulmonary fibrosis identified in two publically available datasets (Adams et al., 2020; Morse et al., 2019; Reyfman et al., 2019) projected on the UMAP embedding (top), and plotted as violin plots (bottom) across the clusters of monocyte-macrophage clusters of BAL scRNA-seq (annotation in Figure 2B). Violin plots are filled with color displaying cluster identity as in Figure 2B. Boxes above the violins show negative log10 transformed adjusted p values (one-sided Wilcoxon test compared to average). The lines in the violin plots represent the median of the respective scores per cluster. (B) UMAP with kernel density overlay showing the density of cells from each condition (Control, IPF, and COPD) in the embedding (related to Figure 3D). Darker red indicates higher relative fractions of those cells in that UMAP region. (C) Cell population density of macrophage clusters identified in this study (top) and in Bharat et al. (2020) (bottom). Kernel density overlay on UMAP embedding as in Figure 3D, color intensity shows relative fraction of cells. (D) Marker gene expression projected on the UMAP of COVID-19/lung diseases integration analysis as presented in Figure 3D. Color shows normalized gene counts in ln(CPM+1). CPM: counts per million. (E) Proximity analysis shows similarity of macrophage populations in COVID-19 (Bharat et al., 2020) to those in IPF and healthy patients (control). Circle size shows cell fraction, color codes the -log10 transformed adjusted p values, and bold black circle indicates statistical significance (adjusted p < 0.0001) (Fisher exact test, one-tailed with Benjamini-Hochberg correction).
Figure 4
Figure 4
Macrophage-fibroblast interactions in COVID-19 lungs (A) UMAP embedding of 48,656 snRNA-seq transcriptomes of lung tissue of six patients with fatal COVID-19 and three non-COVID-19 controls. Cell-type annotation based on expression of canonical marker genes. (B) UMAP embedding of 7,504 macrophages identified in (A). (C) UMAP embedding of 7,492 fibroblasts, smooth muscle cells (SMCs), and pericytes identified in (A). (D) Circle plot showing cell-cell interaction strength between macrophage, fibroblast, SMC, and pericyte clusters predicted by CellChat. Each circle represents one cell type, edges between circles represent intracellular signaling between cell types, and edge thickness reflects interaction strength, while the colored edges show differential interaction strength, where red represents increased interaction strength in late (n = 3) versus early (n = 3) samples. (E) Signaling pathways ranked by differential overall information flow of inferred interactions in early (red) and late (blue) samples. (F) IF of lung tissue stained for macrophages (CD68, red) and myofibroblasts (SM22, green), nuclei (DAPI, blue), and autofluorescence visible as faint gray. Macrophages are indicated by arrows, expanded SM22 foci are indicated by arrowheads, and asterisks denote erythrocyte filled capillaries in alveolar septa (scale bar, 50 μm; insert scale bar, 20 μm). (G) Two representative MELC FOVs showing CD163+ macrophages (yellow), collagen (cyan), and nuclei (DAPI, magenta). Scale bar, 100 μm.
Figure S4
Figure S4
Macrophages-fibroblast interactions in COVID-19 lungs, related to Figure 4 (A) Marker gene expression delineates the macrophage embedding in Figure 4A. Color shows the normalized mRNA counts. (B) Dot plot showing marker genes used to annotate the fibroblast, SMC and pericyte subclusters. Related to Figure 4A (right). (C) Dot plot depicting scaled average expression of profibrotic factors in fibroblasts and myofibroblasts split according to control and disease duration. Scaled expression levels are color coded and the percentage of cells expressing the gene is size coded. Significant differences between early (d < 30) and late (d > 30) patients are highlighted by a black circle. Genes highlighted in early/late patients indicate th e condition where the gene is upregulated. (D) Autopsy lung tissue reveals close association between macrophages (CD68, red) and fibroblasts (SM22, green) in COVID-19 compared to control (left). Cell nuclei are stained with DAPI (blue), autofluorescence is visible in faint gray (Scale bar, 50 μm; Insert scale bar, 20 μm). (E) Analysis of MELC-imaging displayed in Figure 4G. (Left) Center coordinates of CD163+ (blue) and CD163- (red) macrophage localizations in respect to collagen IV staining. (Right) Segregation of macrophages into localization areas named ‘in collagen IV’, ‘adjacent to collagen IV’ or ‘outside of collagen IV’. (F) Proportions of CD163+ and CD163- macrophages per field of view of analyzed autopsy tissue localized ‘in’, ‘adjacent’ or ‘outside’ of collagen IV structures (∗∗ = Bonferroni corrected p value < 0.01, paired two-sided Wilcoxon signed rank test).
Figure S5
Figure S5
CT imaging and histopathology analysis of severe COVID-19-associated ARDS, related to Figure 5 (A) P/F ratio (horowitz index) before and after vvECMO. ARDS severity is indicated by dashed lines. Statistical significance determined by paired t test (∗p < 0.05; ∗∗p < 0.01). (B) Arterial CO2 partial pressure before and after initiation of vvECMO therapy. Upper limit of normal pCO2 range is depicted by a dashed line. Statistical significance determined by Mann Whitney Test (∗p < 0.05; ∗∗p < 0.01). (C) Representative computed tomography (CT) images of the apical (top row) and basal (bottom row) lung from 13 additional COVID-19 patients (cohort 1). Columns indicate the first, intermediate and last available images. (D) Low power images of consecutive histological sections of autopsy lung tissue of fatal COVID-19 compared to control stained with H&E and chromogenic immunohistochemistry against collagen I. Scale bars represent 200 μm. (E) High power images of consecutive histological sections (same field of view of Figure 5C) of autopsy lung tissue of fatal COVID-19 compared to control stained with chromogenic immunohistochemistry against collagen III and IV. Scale bars represent 50 μm. (F) Quantification of collagen III and IV stained area in histological sections. Dots represent autopsy cases, significance of population shift of COVID-19 compared to control assessed by Mann Whitney Test (∗p < 0.05; ∗∗p < 0.01; ∗∗∗p < 0.001).
Figure 5
Figure 5
Severe COVID-19 induces pronounced fibroproliferative ARDS (A) Inspiratory vital capacity (VCin) in early phase, and acute vvECMO phase (paired t test ∗p < 0.05; ∗∗p < 0.01). (B) (Left) Schematic representation indicating imaging planes of CT. (Middle) Healthy lung and denomination of anatomical structures. RLL, right lower lobe; RUL, right upper lobe; LUL, left upper lobe; LLL, left lower lobe; A, aorta; T, trachea; C, vena cava; RV, right ventricle; LV, left ventricle. (Right) Representative images from a case of severe COVID-19 ARDS, representing the first available (left column), one intermediate (middle column), and the last available (right column) CT scan. (C) Histopathology of autopsy lung tissue of fatal COVID-19. High-power images of consecutive histological sections stained with H&E (top) and chromogenic IHC for collagen I (middle) and CD68 (bottom). Scale bar, 50 μm. (D) Quantification of pulmonary fibrosis (Ashcroft score) and collagen-I-stained area. Dots represent individual autopsies (line at mean with SEM), and significance of population shift of COVID-19 compared to control assessed by Mann Whitney Test (∗∗p < 0.01; ∗∗∗p < 0.001). (E) Transmission EM of healthy (1–2) and COVID-19 (3–6) autopsy lungs. 1: Alveolar septum between two alveolar lumina (Alv) with capillary (Cap), interstitium, and alveolar epithelium (Alvepi). The interstitium with interstitial cells (ICs) and a connective tissue network of collagen fibrils (col) and elastic fibers (el). 2: Alveolar macrophage with lysosomal vesicles. 3: Alveolar septum containing Cap and interstitium. The alveolar epithelium is only partly present, leaving the alveolar epithelial basal lamina denuded toward the alveolar lumen at sites of detachment. The septum is thickened due to swelling of the interstitium, containing cells, collagen fibrils, elastic fibers, and homogeneous matrix. ICs contain high numbers of vesicles. 4: Infolding of denuded alveolar epithelial basal lamina (bl) with collapsed alveolar lumen and partly “glued” opposing basal lamina (red arrowheads), features of collapse induration. 5: Foamy alveolar macrophages containing vesicles of varying size and content. Fibrin accumulations (fib) in close proximity. 6: Thickened alveolar septum containing capillaries with swollen endothelium. The alveolar epithelium is desquamated toward the alveolar lumen containing fibrin. Note vesicle-filled ICs with foamy appearance.
Figure 6
Figure 6
SARS-CoV-2 induces profibrotic programs in classical monocytes in vitro (A) Schematic depiction of the experimental layout. (B) UMAP embedding of 1,123 quality-filtered transcriptomes of human monocytes stimulated as outlined in (A). (C) Dot plot displaying differentially expressed (DE) genes in the indicated stimulation conditions. Label color indicates gene categories. Adjusted p values are available in Table S5. (D) Signature module score of IPF-expanded macrophages (IPFe-Mφ) and alveolar FABP4+Mφ (Ayaub et al., 2021) projected onto the UMAP embedding (top) and plotted as violin plots (bottom) across the clusters of stimulated monocytes. Numbers above violins show -log10 transformed adjusted p values (one-sided Wilcoxon test compared to average). Lines indicate median scores per cluster. (E) Heatmap displaying -log10 transformed adjusted p values (one-sided Fisher’s Exact Test) comparing overlap between gene sets from stimulated monocytes with published transcriptional signatures of IPF-associated monocytes/macrophages. Cluster names and reference studies are indicated on the x axis; Table S3.
Figure S6
Figure S6
Monocyte gene expression after stimulation with SARS-CoV-2, 3p-hpRNA, and R848 (A) SARS-CoV-2 mRNA counts projected onto the UMAP embedding (Figure 6B). (B) Transcriptomes derived from two donors (indicated in blue and red) and two technical replicates (circles and triangles) are indicated in the UMAP embedding corresponding to Figure 6B. (C) Heatmap displaying z-scores of log-normalized mRNA counts across all stimulation conditions. Differential expression (DE) cutoff was set at FDR of 1e-15. (D) Marker gene expression projected onto the UMAP embedding as in Figure 6B. (E) Heatmap showing the mean rank of ChEA3 transcription factor enrichment. Clusters (y axis) as in Figure 6B, transcription factors (x axis) ordered by cluster and mean rank. Input to ChEA3 were the DE genes shown in Figure S6C, TFs were selected by mean rank < 35. (F) Signature module scores of IPF-associated monocyte/macrophage clusters derived from two published datasets (Morse et al., 2019; Reyfman et al., 2019) projected onto the UMAP embedding (top), and plotted as violin plots (bottom) across the clusters of stimulated monocytes (annotation in Figure 6B). Negative log10-transformed adjusted p values (one-sided wilcoxon test compared to average) are displayed above violins. Lines indicate median scores per cluster.
Figure 7
Figure 7
Proteomic analyses of SARS-CoV-2-induced profibrotic phenotype in classical monocytes (A) Schematic depiction of the experimental layout. (B) Protein log-2-fold-changes over time for IAV (left, blue) and SARS-CoV-2 (right, red) and host proteins (gray). (C) Heatmap of DE host proteins (ANOVA test, filtered by Benjamini-Hochberg adjusted p value < 5%). Protein clusters obtained by fuzzy-c-means clustering of Z-scored protein intensities are indicated in the figure, and corresponding profiles are reported below the heatmap. (D) GSEA of protein intensity ratios of SARS-CoV-2 over IAV infection, calculated for the host proteome dataset. ∗FDR < 10%; ∗∗FDR < 5%; ∗∗∗FDR < 1%. (E) Schematic presentation of selected proteins regulated by SARS-CoV-2 stimulation in monocytes, color-coded by log2-fold changes (infection versus control, 18 hpi). (F) Heatmap representation of p-values (one sided Wilcoxon signed-rank test) for the enrichment of the indicated reference gene sets calculated by eCDF.
Figure S7
Figure S7
Quantitative shotgun proteomics and phosphoproteomics of SARS-CoV-2- and IAV-infected monocytes, related to Figure 7 (A) Principal component analysis of proteome and phosphoproteome for SARS-CoV-2, IAV and mock infection. (B) Annotated MS2 spectrum of one peptide identified from SARS-CoV-2 M protein (left) and heatmap representing the TMT reporter ion relative intensities for the specified peptide (right). (C) Schematic presentation of selected proteins involved in the inflammatory response pathways in monocytes, color-coded by log2-fold changes (IAV-infection versus control, 18h time point). (D) Heatmap for all CEBPB identified peptides (top panel) and schematic representation of peptide location within the CEBPB sequence (bottom panel). (E) Annotated MS2 spectrum of the phosphopeptide identified from IRF7 (left) and heatmap representing the TMT reporter ion relative intensities for the specified peptide (right). (F) Annotated MS2 spectrum of the phosphopeptide identified from CEBPB (left) and heatmap representing the TMT reporter ion relative intensities for the specified peptide (right). (G) Secretion of selected proteins quantified by ELISA. Bars represent the mean across all corresponding measurements. Error bars represent the standard deviation. Symbols depict donor-specific measurements. All experiments were tested against mock for significance (one-sided t test, on log transformed data). Differences between SARS-CoV-2 and IAV-stimulated cells were tested for significance using a two-sided t test. Significance reported in the figure corresponds to Benjamini-Hochberg adjusted p values of: ∗; p < 10%, ∗∗; p < 5%, ∗∗∗; p < 1%. (H) Empirical cumulative distributions of gene sets depicted in Figure 7F. Log2-fold-change distributions of the gene sets were tested against all other proteins by one-sided Wilcoxon signed-rank tests. p values are depicted next to each distribution.
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