IFN- γ and TNF- α drive a CXCL10 + CCL2 + macrophage phenotype expanded in severe COVID-19 and other diseases with tissue inflammation

Abstract

Immunosuppressive and anti-cytokine treatment may have a protective effect for patients with COVID-19. Understanding the immune cell states shared between COVID-19 and other inflammatory diseases with established therapies may help nominate immunomodulatory therapies. Using an integrative strategy, we built a reference by meta-analyzing > 300,000 immune cells from COVID-19 and 5 inflammatory diseases including rheumatoid arthritis (RA), Crohn's disease (CD), ulcerative colitis (UC), lupus, and interstitial lung disease. Our cross-disease analysis revealed that an FCN1 + inflammatory macrophage state is common to COVID-19 bronchoalveolar lavage samples, RA synovium, CD ileum, and UC colon. We also observed that a CXCL10 + CCL2 + inflammatory macrophage state is abundant in severe COVID-19, inflamed CD and RA, and expresses inflammatory genes such as GBP1, STAT1 , and IL1B . We found that the CXCL10 + CCL2 + macrophages are transcriptionally similar to blood-derived macrophages stimulated with TNF- α and IFN- γ ex vivo . Our findings suggest that IFN- γ , alongside TNF- α , might be a key driver of this abundant inflammatory macrophage phenotype in severe COVID-19 and other inflammatory diseases, which may be targeted by existing immunomodulatory therapies.

Figure 1.

Integrative transcriptomic analysis of >300,000 single-cell profiles from 6 inflammatory disease tissues and COVID-19 reveals shared immune cell populations. a. Overall study design and single-cell analysis, including the integrative pipeline, a single-cell reference dataset, and ex vivo stimulated macrophage dataset. Shared states, specifically macrophages, are identified across disease tissues, and then compared to the ex vivo cells to identify the stimuli driving their phenotype. b. Number of cells and donor samples from each healthy and disease tissue. SS lung denotes systemic sclerosis lung; HP lung denotes hypersensitivity pneumonitis lung. c. Integrative clustering of 307,084 cells reveals common immune cell types from different tissue sources. Cells from the same cell types are projected together in UMAP space. d. Immune cells from separate tissue sources in the same UMAP coordinates as in c. e. Expression of cell type lineage marker genes in the UMAP space. f. Percent of variance explained in the gene expression data by pre-defined broad cell types, donor samples, tissue sources, and technologies for the first and second principal component (PC1 and PC2) after batch effect correction. g. Proportions of identified immune cell types within each disease tissue or healthy control.

Figure 2.

Integrative analysis of macrophages reveals shared CXCL10+ CCL2+ and FCN1+ inflammatory macrophage states across inflammatory disease tissues and COVID-19. a. Integrative clustering of 74,373 macrophages and monocytes from 108 individuals from BALF, lung, kidney, colon, ileum, and synovium reveals four distinct macrophage states. Two inflammatory macrophage states are observed: CXCL10+ CCL2+ and FCN1+ inflammatory macrophages. b. Density plot of cells with non-zero expression of cluster marker genes in UMAP space. c. Previously defined inflammatory macrophages from different inflammatory disease tissues are clustered together with the majority of the macrophages from severe COVID-19 in the integrative embeddings. Inflammatory macrophages are separated into the CXCL10+ CCL2+ and FCN1+ inflammatory states. d. Proportion of expressing (non-zero) inflammatory cytokines and genes from inflammatory macrophages in inflamed RA, CD, and UC compared to those in severe COVID-19. Genes that are highly expressed in the CXCL10+ CCL2+ inflammatory macrophages are highlighted in orange. e. PCA analysis on the identified inflammatory macrophages. The first PC captures a gradient from the FCN1+ state to the CXCL10+ CCL2+ state. Two distributions are shown to represent the density of the macrophages mapping to PC1. Macrophages from inflamed tissues are mapped to PC1 coordinates. A shift on PC1 loadings between inflammatory macrophages from inflamed UC and severe COVID-19 (Wilcoxon rank-sum test P < 2.2e−16), inflamed RA and severe COVID-19 (P = 0.001), and inflamed CD and severe COVID-19 (P = 1.4e−07) are displayed, respectively. f. Heatmap of Z-score of the average expression of top marker genes for the CXCL10+ CCL2+ and FCN1+ inflammatory macrophage states. Rows include genes and columns show pseudo-bulk expression per condition within each state. Gene signatures were selected based on AUC > 0.6 and Bonferroni-adjusted P < 10⁻⁵ comparing cells from one cluster to the others using pseudo-bulk analysis.

Figure 3.

Human blood-derived macrophages stimulated by eight mixtures of inflammatory factors present heterogeneous macrophage phenotypes. a. Schematic representation of the single-cell cell hashing experiment on human blood-derived macrophages stimulated by eight mixtures of inflammatory factors from 4 donor samples. A diagram of the single-cell antibody-based hashing strategy used to multiplex samples from different stimulatory conditions in one sequencing run. Here fibro denotes fibroblasts. b. Condition labels of the stimulated 25,823 blood-derived macrophages from 4 donor samples are colored and labeled in UMAP space. c. Proportion of different stimulatory conditions for each donor sample are calculated. d. Log-normalized expressions of genes that are specific to different conditions are displayed in violin plots. Mean of normalized gene expression is marked by a line and each condition by individual coloring. CPM denotes counts per million. e. Fold changes in gene expression after TNF-α stimulation vs. TNF-α and IFN-γstimulation (left), and IFN-γ vs. TNF-α and IFN-γ stimulation (right) for each gene. Genes in red have fold change > 2, Bonferroni-adjusted P <10⁻⁷, and a ratio of TNF-α and IFN-γ fold change to TNF-α fold change greater than 1 (left) or a ratio of TNF-α and IFN-γ fold change to IFN-γ fold change greater than 1. Genes that are most responsive to either IFN-γ (left) or TNF-α (right) are labeled. f. Stimulation effect estimates of genes that are most responsive to conditions with IFN-γ or TNF-α with fibroblasts comparing each condition to untreated macrophages using linear modeling. Fold changes with 95% CI are shown.

Figure 4.

Identification of TNF-α and IFN-γ driven CXCL10+ CCL2+ inflammatory macrophages expanded in severe COVID-19 and other inflamed disease tissues. a. Integrative clustering of stimulated blood-derived macrophages with tissue-level macrophages from COVID-19 BALF, UC colon, CD ileum, and RA synovium. b. The previously identified tissue-level CXCL10+ CCL2+ state corresponds to cluster 1 (orange), and the FCN1+ inflammatory macrophage state corresponds to cluster 2 (yellow). Macrophages from each tissue source are displayed separately in the same UMAP coordinates as in a. c. Heatmap indicates the concordance between stimulatory conditions and cluster assignments. Z-score of the number of cells from one stimulatory condition to each of the clusters is shown. d. For the blood-derived stimulated macrophages, the proportion of CXCL10+ CCL2+ macrophages per stimulated donor sample of total macrophages are shown. e and f. For each tissue source, we show the proportion of CXCL10+ CCL2+ macrophages per sample of total macrophages from healthy BALF (n = 3), mild (n = 3) and severe (n = 6) COVID-19 BALF, non-inflamed CD (n = 10) and inflamed CD (n = 12), OA (n = 2) and RA (n = 15), and healthy colon (n = 12), non-inflamed (n = 18) and inflamed UC (n = 18). Medians of proportions for each group are shown. P is calculated by Wilcoxon rank-sum test within each tissue source. For each tissue source, the association of each cluster with severe/inflamed compared to healthy control was tested. 95% CI for the odds ratio (OR) is given for each cluster. MASC P is calculated based on one-sided F tests conducted on nested models with MASC. The clusters above the dashed red line (MASC P threshold after Bonferroni correction) are statistically significantly associated with inflammation/severity compared to non-inflammatory/healthy status. Clusters that have less than 30 cells are removed from association testing.