A shift in lung macrophage composition is associated with COVID-19 severity and recovery

Abstract

Although it has been more than 2 years since the start of the coronavirus disease 2019 (COVID-19) pandemic, COVID-19 continues to be a worldwide health crisis. Despite the development of preventive vaccines, therapies to treat COVID-19 and other inflammatory diseases remain a major unmet need in medicine. Our study sought to identify drivers of disease severity and mortality to develop tailored immunotherapy strategies to halt disease progression. We assembled the Mount Sinai COVID-19 Biobank, which was composed of almost 600 hospitalized patients followed longitudinally through the peak of the pandemic in 2020. Moderate disease and survival were associated with a stronger antigen presentation and effector T cell signature. In contrast, severe disease and death were associated with an altered antigen presentation signature, increased numbers of inflammatory immature myeloid cells, and extrafollicular activated B cells that have been previously associated with autoantibody formation. In severely ill patients with COVID-19, lung tissue-resident alveolar macrophages not only were drastically depleted but also had an altered antigen presentation signature, which coincided with an influx of inflammatory monocytes and monocyte-derived macrophages. In addition, we found that the size of the alveolar macrophage pool correlated with patient outcome and that alveolar macrophage numbers and functionality were restored to homeostasis in patients who recovered from COVID-19. These data suggest that local and systemic myeloid cell dysregulation are drivers of COVID-19 severity and modulation of alveolar macrophage numbers and activity in the lung may be a viable therapeutic strategy for the treatment of critical inflammatory lung diseases.

Fig. 1.. Proteomic characterization of COVID-19 serum reveals distinct immune patterns associated with disease severity and clinical outcome.

(A) Mount Sinai COVID-19 Biobank serum collection scheme. (B) Histogram of patient samples across Olink clusters and Olink group is shown, denoted by clinical severity classification (n = 2001). (C) Histogram of first available COVID⁺ patient samples across Olink clusters and Olink group is shown, denoted by final clinical outcome (n = 583). (D) An averaged z score heatmap is shown of Olink inflammation panel analytes across Olink clusters. Olink clusters were grouped on the basis of clinical severity, projected outcome, and comorbidity distribution (n = 2001). (E) The boxplots showing Olink module score comparisons of all Olink samples by Olink group (n = 2001). (F) The boxplots show Olink module score comparisons of all Olink samples by the final clinical outcome (n = 2001). For box plots, each dot represents a patient sample; the center line indicates the median; box limits indicate the 25th and 75th percentile; whiskers indicate 1.5× inter-quartile range. The scheme in (A) was created with BioRender.com. COVID⁻ samples were obtained from healthy volunteers (B and D to F). Statistical significance in (E) and (F) is determined by two-way ANOVA with Tukey’s multiple comparisons correction. ns, not significant; *adj. P < 0.05; **adj. P < 0.01; ***adj. P < 0.001; ****adj. P < 0.0001.

Fig. 2.. Immature inflammatory myeloid cells are associated with increased COVID-19 severity.

(A) The frequency of neutrophils (% cells) and classical monocytes (% non-granulocytes) in whole blood were measured by CyTOF and separated by Olink group (n = 206). (B) DC population frequencies (% non-granulocytes) in whole blood were measured by CyTOF and separated by Olink group (n = 206). Conventional DC (cDC), conventional type 1 DC (DC1), conventional type 2 DC (DC2), and plasmacytoid DC (pDC) are shown. (C) Neutrophils (% cells) and classical monocyte frequencies (% non-granulocytes) in whole blood are shown on the basis of the final clinical outcome and were measured by CyTOF (n = 214). (D) DC population frequencies (% non-granulocytes) in whole blood are shown on the basis of the final clinical outcome and were measured by CyTOF (n = 214). (E) The heatmap shows unique molecular identifier (UMI) counts of selected genes from myeloid cell scRNAseq clusters from PBMCs. (F) scRNAseq cluster cell frequencies in indicated Olink groups are shown as percent of cells by Olink group (G) Frequencies of ISG-enriched classical monocytes are shown clustered by Olink group (n = 75). (H) ISG-enriched classical monocyte cell frequencies and days PSO are shown separated by clusters 6 and 7 versus clusters 8 and 9 (n = 10). (I) The matrix heatmap shows Spearman correlation coefficients between identified scRNAseq PBMC cell clusters (n = 81). Monos, monocytes. (*P < 0.05; **P < 0.01; ***P < 0.001). For bar graphs (A to D and F to H), each dot represents a patient sample. COVID⁻ samples were obtained from healthy volunteers (A to G and I). Statistical significance (A to D and F and G) was determined by Kruskal-Wallis followed by the multiple comparisons test with false discovery rate correction. ns, not significant; *q < 0.05; **q < 0.01; ***q < 0.001; ****q < 0.0001. Statistical significance in (H) was determined by the Mann-Whitney test; *P < 0.05; **P < 0.01.

Fig. 3.. Integrated analysis of scRNAseq cluster frequencies and Olink analyte abundance in serum reveals distinct immune responses to COVID-19.

Shown is a matrix heatmap of Spearman correlation coefficients between identified scRNAseq PBMC cell clusters (y axis) and Olink analyte–normalized concentrations (x axis) in serum. Axes are ordered by hierarchical clustering. Monocytes, monos.

Fig. 4.. AM loss and phenotypic changes are associated with COVID-19.

(A) The heatmap shows UMI counts of selected genes from myeloid cell scRNAseq clusters from BAL. (B) scRNAseq cluster cell frequencies are shown as percent of mononuclear phagocytes (MNP) in COVID⁻, COVID⁺, or convalescent patient BAL (n = 18). COVID⁻ and convalescent samples obtained from Mount Sinai Hospital patients (C to E). Differential gene expression is shown between alveolar macrophages (AMs) from COVID⁺ and COVID⁻ patients (C), AMs from patients that survived versus deceased (D), and AMs from convalescent and COVID⁺ patients (E). (F) Overlaid, pseudo-colored MICSSS images of COVID⁺ and COVID⁻ lungs are shown. Samples were stained for S100A12, CD68, CD14, FABP4, and hematoxylin (n = 5). (G) Quantification of myeloid cells in MICSSS images is shown as percent of cells. AMs were defined as FABP4⁺CD68⁺ cells; monocytes were defined as CD14⁺ cells; MoMΦ were defined as CD14⁺CD68⁺ cells; and granulocyte cells were defined as CD66b⁺ cells or by hematoxylin staining and morphology. (H) Quantification of S100A12⁺ cells in MICSSS images is shown as percent of cells in the COVID⁻ patient (n = 1), nonventilated COVID⁺ patients (n = 2), or ventilated COVID⁺ patients (n = 2). (I) Distribution of S100A12⁺ cells by cell type in COVID⁻, nonventilated, or ventilated COVID⁺ lungs is shown. For bar graphs, each dot represents a patient sample (B) or quantification of single MICSSS region of interest (G and H). Statistical significance in (B) was determined by the Kruskal-Wallis test followed by the multiple comparisons test with false discovery rate correction. *q < 0.05; **q < 0.01.