COVID-19 and RA share an SPP1 myeloid pathway that drives PD-L1<sup>+</sup> neutrophils and CD14<sup>+</sup> monocytes

Lucy MacDonald; Stefano Alivernini; Barbara Tolusso; Aziza Elmesmari; Domenico Somma; Simone Perniola; Annamaria Paglionico; Luca Petricca; Silvia L. Bosello; Angelo Carfì; Michela Sali; Egidio Stigliano; Antonella Cingolani; Rita Murri; Vincenzo Arena; Massimo Fantoni; Massimo Antonelli; Francesco Landi; Francesco Franceschi; Maurizio Sanguinetti; Iain B. McInnes; Charles McSharry; Antonio Gasbarrini; Thomas D. Otto; Mariola Kurowska-Stolarska; Elisa Gremese

doi:10.1172/jci.insight.147413

COVID-19 and RA share an SPP1 myeloid pathway that drives PD-L1⁺ neutrophils and CD14⁺ monocytes

Lucy MacDonald,¹ Stefano Alivernini,^1,2,3 Barbara Tolusso,^1,3 Aziza Elmesmari,¹ Domenico Somma,¹ Simone Perniola,² Annamaria Paglionico,² Luca Petricca,² Silvia L. Bosello,² Angelo Carfì,⁴ Michela Sali,^5,6 Egidio Stigliano,⁷ Antonella Cingolani,^8,9 Rita Murri,^8,9 Vincenzo Arena,⁷ Massimo Fantoni,^8,9 Massimo Antonelli,^10,11 Francesco Landi,⁴ Francesco Franceschi,^12,13 Maurizio Sanguinetti,^5,6 Iain B. McInnes,¹ Charles McSharry,¹⁴ Antonio Gasbarrini,^15,16 Thomas D. Otto,¹ Mariola Kurowska-Stolarska,¹ and Elisa Gremese^1,2,3

Authorship note: LM and SA are co–first authors and contributed equally to this work. MKS and EG are co–senior authors and contributed equally to this work.

Published June 18, 2021 - More info

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Abstract

We explored the potential link between chronic inflammatory arthritis and COVID-19 pathogenic and resolving macrophage pathways and their role in COVID-19 pathogenesis. We found that bronchoalveolar lavage fluid (BALF) macrophage clusters FCN1⁺ and FCN1⁺SPP1⁺ predominant in severe COVID-19 were transcriptionally related to synovial tissue macrophage (STM) clusters CD48^hiS100A12⁺ and CD48⁺SPP1⁺ that drive rheumatoid arthritis (RA) synovitis. BALF macrophage cluster FABP4⁺ predominant in healthy lung was transcriptionally related to STM cluster TREM2⁺ that governs resolution of synovitis in RA remission. Plasma concentrations of SPP1 and S100A12 (key products of macrophage clusters shared with active RA) were high in severe COVID-19 and predicted the need for Intensive Care Unit transfer, and they remained high in the post–COVID-19 stage. High plasma levels of SPP1 were unique to severe COVID-19 when compared with other causes of severe pneumonia, and IHC localized SPP1⁺ macrophages in the alveoli of COVID-19 lung. Investigation into SPP1 mechanisms of action revealed that it drives proinflammatory activation of CD14⁺ monocytes and development of PD-L1⁺ neutrophils, both hallmarks of severe COVID-19. In summary, COVID-19 pneumonitis appears driven by similar pathogenic myeloid cell pathways as those in RA, and their mediators such as SPP1 might be an upstream activator of the aberrant innate response in severe COVID-19 and predictive of disease trajectory including post–COVID-19 pathology.

Introduction

The severity of acute COVID-19 is attributable to genetics, immune dysregulation, abnormal blood clotting and tissue disruption, particularly implicating aberrant proinflammatory and antiviral innate immunity (1–14). Rheumatoid arthritis (RA) represents a proinflammatory cytokine–driven chronic articular condition often accompanied by interstitial lung disease and alveolitis (15). We (16) and others (17) have reported that SARS-CoV-2 infection is associated with emergence of polyarthritis, or flares of synovitis in RA patients in sustained disease remission, suggesting shared mechanisms of pathogenesis. RA immunopathogenesis and therapeutic targets (18) are well understood and might be informative for COVID-19 therapeutic strategies. In RA, articular inflammation and remission are driven by distinct synovial tissue macrophage clusters (19). Emerging data suggest that similar aberrant activation of myeloid cells in the blood (7, 20–23) and lung (24, 25) contribute to acute COVID-19 severity. However, there remains a knowledge gap on myeloid cell pathways that determine the severity and resolution of acute COVID-19 pneumonitis. In addition, the immune mechanisms of long-lasting clinical sequelae (26) described in convalescent COVID-19 patients are unresolved, hindering development of effective treatments and biomarkers of disease trajectory.

Single-cell profiling and fate-mapping indicate spatial and functional macrophage heterogeneity that maintains lung homeostasis (27–31). Healthy lung alveolar macrophages (AM) expressing Fatty Acid Binding Protein 4 (FABP4) localize to the alveolar epithelial surface and recycle surfactants with type-2 alveolar epithelial cells to maintain compliance and efficient gas exchange (32, 33). This function is compromised in severe COVID-19 (34). The single-cell RNA sequencing (scRNAseq) analysis (24) of bronchoalveolar lavage fluid (BALF) from severe COVID-19 patients found abnormally low numbers of resident AM and an increase in 2 macrophage clusters that share expression of ficolin-1 (FCN1) and are distinguished by their relative expression of osteopontin (SPP1): FCN1⁺ and FCN1⁺SPP1⁺ clusters. Their precise roles in the pathogenesis of severe COVID-19 have yet to be established. We recently identified similar macrophage diversity in synovial tissues (ST) of healthy donors and patients with active or remission RA (19). We delineated in RA chronic synovitis that the CD48^hiS100A12⁺ and CD48⁺SPP1⁺ macrophage clusters produced their distinctive pathogenic mediators (S100A12 and SPP1, respectively) in addition to hallmark proinflammatory cytokines IL-6, IL-8, IL-1β, and TNF-α, and activated ST stromal cells. Early evidence suggested that this serum IL-6, IL-8, IL-1β, and TNF-α cytokine signature also predicts the prognosis of patients with acute COVID-19 (6), and targeting the IL-6 pathway seems an effective therapeutic strategy in critically ill COVID-19 patients (35, 36). Resolution of synovitis in RA was associated with the functions of a distinct subpopulation of synovial macrophages (TREM2⁺ and FOLR2⁺ clusters) that produced abundant lipid resolvins instead of inflammatory mediators and induced a repair phenotype in ST stromal cells (19). Altogether, this raised the hypothesis that macrophage clusters in the lung, functionally equivalent to those in the synovium, may govern chronic inflammation or resolution of COVID-19 pneumonitis, and that the hallmark cytokine signature related to these clusters (e.g., SPP1) might be a useful biomarker of prognosis and a therapeutic target during the unpredictable trajectory of COVID-19.

Results

COVID-19 BALF FCN1⁺ and FCN1⁺SPP1⁺ macrophage clusters are transcriptionally similar to CD48^hiS100A12⁺ and CD48⁺SPP1⁺ clusters that drive RA synovitis. In COVID-19 BALF, Liao et al. (24) identified 4 major clusters of macrophages characterized by a combination of expression of SPP1, FCN1, and FABP4. Importantly, expansion of the FCN1⁺ and FCN1⁺SPP1⁺ clusters was indicative of COVID-19 severity (Supplemental Figure 1, A and B; supplemental material available online with this article; https://doi.org/10.1172/jci.insight.147413DS1). Among the 9 phenotypically distinct clusters of ST macrophages (STMs) that differed in distribution between healthy donors and patients with RA, the CD48^hiS100A12⁺ and CD48⁺SPP1⁺ clusters were expanded in RA patients with active disease (Supplemental Figure 1, C and D) (19). To test the relationship between COVID-19 BALF and RA STM clusters, we integrated the macrophage scRNAseq data sets from COVID-19 BALF (GSE145926; Supplemental Figure 1, A and B) (24) and our RA ST (E-MTAB-8322; Supplemental Figure 1, C and D) (19) using Seurat V3 anchor integration strategy (37). The dimensional reduction of integrated macrophage data sets illustrated by Uniform Manifold Approximation and Projection (UMAP) visualize overlapping and tissue-specific macrophage clusters (Figure 1, A and B). To investigate the relationship between these macrophage clusters from synovitis and pneumonitis, hierarchical clustering (Figure 1C) was performed on the matrix of the average expression of each ST and BALF cluster of all 8902 genes that were common to both data sets. The hierarchical clustering dendrogram illustrating the relationship between the clusters by branch point (split) and branch length (distance) revealed that the macrophage clusters were separated predominantly by their precursor origin and function — i.e., embryonic-origin (homeostasis) or monocyte-derived (inflammation) rather than source tissue. The first branch point separated 4 tissue-resident macrophage clusters (healthy lung FABP4⁺ and FABP4^loFCN^–SPP1⁺, and synovial lining TREM2^hi and TREM2^lo) from the other BALF and STM clusters that were likely of monocytic origin (19, 24, 25). The split-points and distances in the second branch of hierarchical clustering indicate that the proinflammatory clusters (i.e., FCN1⁺ and FCN1⁺SPP1⁺) that dominate in severe COVID-19 share transcriptomic profiles with proinflammatory clusters (i.e., CD48^hiS100A12⁺ and CD48⁺SPP1⁺, respectively) in active RA. Pearson correlation analysis between BALF FCN1⁺ and ST CD48^hiS100A12⁺, and between BALF FCN1⁺SPP1⁺ and ST CD48⁺SPP1⁺, confirmed resemblance (r = 0.56, P = 2.2 × 10^–16 and r = 0.65, P = 2.2 × 10^–16) of respective BALF/ST pairs. Independent differential gene expression analysis identified striking similarities in the pathogenic pathways of COVID-19 pneumonitis FCN1⁺ and active RA synovitis CD48^hiS100A12⁺ clusters. They share 238 top marker genes (Figure 1D) that include upregulation of inflammatory and prothrombotic pathways. These consisted of IFN pathway (e.g., IFITM2, IFITM3, and ISG15), inflammation-triggering alarmins (S100A8/9/12A), B cell activation factors (e.g., BAFF-TNFSF15B), promotors of IL-1β and TNF production (e.g., CARD16 and LITAF), prothrombotic factors (e.g., FGL2), and integrins mediating cell migration and adhesion (e.g., ITGB2 and ITGX). They also share receptor expression profiles — e.g., TNFR2 (TNFRSF1B), G-CSFR (CSF3R), and the complement receptor (C5aR; ref. 38) — that may render them increasingly susceptible to proinflammatory mediators to further escalate inflammation (Figure 1E). The hierarchical analysis and comparison of cluster markers also indicate a shared transcriptional profile of COVID-19 FCN1⁺SPP1⁺ and synovitis CD48⁺SPP1⁺ clusters consisting of 86 common marker genes including SPP1, which have broad proinflammatory and fibrotic functions (Figure 1, F and G) (39). These transcriptomic profiles suggest that BALF FCN⁺ and FCN⁺SPP1⁺ macrophage clusters predominant in severe COVID-19 shared pathogenic molecular pathways, including the expression of their signature mediators S100A12 and SPP1 (Figure 1H) with ST CD48^hiS100A12⁺ and CD48⁺SPP1⁺ clusters predominant in active RA. Analysis of SPP1 and S100A12 expression in other cell types present in lung tissue (Figure 1I and Supplemental Figure 2) demonstrated that BALF macrophage populations are the main cells expressing SPP1 and S100A12 in severe COVID-19, and due to their high numbers in BALF of severe COVID-19(Supplemental Figure 1), FCN⁺ and FCN⁺SPP1⁺ macrophage clusters are likely the main contributors to the S100A12 and SPP1 pool in the COVID lung.

Figure 1

COVID-19 BALF FCN1⁺ and FCN1⁺SPP1⁺ macrophage clusters are transcriptionally related to rheumatoid arthritis synovial CD48^hiS100A12⁺ and CD48⁺SPP1⁺ macrophage clusters. (A and B) UMAP of an equal number of synovial tissue and BALF macrophages (32,000 from each data set), colored according to their ST and BALF cluster identification. (C) Dendrogram of hierarchical clustering analysis of integrated pseudobulk gene expression (average expression in each cluster) of ST and BALF clusters. (D) Venn diagram illustrating the numbers of unique and shared marker genes of ST CD48^hiS100A12⁺ and BALF FCN1⁺ clusters. P < 0.05 after Bonferroni correction for multiple comparisons. (E) Heatmap illustrating scaled, pseudo-bulk gene expression of shared upregulated marker genes (highlighted in D) by BALF and ST clusters. (F) Venn diagram illustrating numbers of unique and shared marker genes of ST CD48⁺SPP1⁺ and BALF FCN1⁺SPP1⁺ clusters generated as in E. (G) Heatmap illustrating scaled, pseudobulk gene expression of shared upregulated marker genes (highlighted in F) by ST and BALF clusters. (H) Split UMAP plots comparing S100A12 and SPP1 expression in BALF and ST macrophage clusters across different conditions. Intensity of purple indicates expression level. HC, healthy control. (I) Dot plots illustrating normalized (mean ± SEM) expression values of S100A12 and SPP1 per cell across all immune and epithelial cell clusters in severe COVID-19 BALF (n = 6). Framed populations showed the highest expression of these markers.

Healthy lung alveolar FABP4⁺ macrophages share a homeostatic regulatory transcriptomic profile, including the TAM pathway, with ST lining layer TREM2^hi macrophages. The innate immune pathways that restrain inflammation in acute COVID-19 are yet to be characterized. Recently uncovered innate mechanisms reinstating ST homeostasis in RA (19) might shed new light on potential mechanisms of resolution for COVID-19. The ST of RA patients in sustained disease remission is dominated by macrophage clusters with protective and inflammation-resolving properties (19). Of these resolving clusters, ST TREM2^hi clustered tightly with the FABP4⁺ AM cluster in unsupervised hierarchical analysis (Figure 1C). Pearson correlation of 986 TREM2⁺ and FABP4⁺ unique and common cluster markers (r = 0.83 P = 2.2 × 10^–16) confirmed the similarities. Differential expression analysis of their profiles identified that BALF FABP4⁺ and ST TREM2^hi clusters share 170 markers indicating analogous homeostatic functions (Figure 2, A and B). These include the complement pathways (e.g., C1q) that facilitates uptake of apoptotic bodies, high expression of genes of retinoic acid production (e.g., ALDH1A1 and RBP4) that drives Treg differentiation (40), and the B7-related coinhibitory molecule VSIG4 that inhibits T effector cells (41); this suggests a primary role of this cluster in governing lung immunity (Figure 2B). While the functional contribution of FABP4⁺ macrophages to the resolution of SARS-CoV-2 infection is yet to be established, we have shown previously that their counterpart TREM2⁺ STM clusters produced inflammation-resolving lipid mediators and induced a repair phenotype in tissue stromal cells that maintain disease remission. These homeostatic responses were driven by MerTK, a member of the immunosuppressive tyrosine kinase receptor TAM family (TYRO, AXL, and MerTK; ref. 19). TAM receptors and their ligands GAS6 or the PROS1 gene product Protein S (PROS1) form a homeostatic brake on inflammation and autoimmunity (42–44). In addition, Protein S is an essential inhibitor of blood coagulation preventing thrombosis (45). Lung-resident FABP4 macrophages uniquely express AXL rather than MerTK. Their AXL is constitutively ligated to GAS6 (46, 47) and is key in preventing exacerbated inflammation — e.g., during influenza virus infection (46, 47). Analysis of the GSE145926 data set (24) identified profoundly altered macrophage expression of TAM receptors and their ligands in the lung (Figure 2, C and D) that might explain the inadequate regulation of the tissue hyperinflammatory and thrombotic responses in severe COVID-19. The TAM receptors and ligand pathway was mostly expressed by lung myeloid cells, with a contribution from ciliated epithelium to the lung Protein S pool (Supplemental Figures 2 and 3). AM from healthy lungs showed high expression levels of AXL and PROS1, and these were markedly reduced in patients with severe COVID-19 (Figure 2, C and D). GAS6 and MerTK were not expressed by resident AMs; instead, they were increasingly expressed by infiltrating FCN⁺ and FCN⁺SPP1⁺ macrophage clusters, suggesting an inflammation-triggered attempt to counterbalance pathogenic responses. However, the reduced PROS1, which is the preferred activating ligand for MerTK (44, 48), and the fewer homeostatic resident AMs in severe COVID-19 might enable unrestricted proinflammatory cytokine production by locally differentiated MerTK-expressing FCN⁺ and FCN⁺SPP1⁺ macrophages.

Figure 2

COVID-19 BALF FABP4⁺ and RA synovial TREM2⁺ macrophages share transcriptomic profiles and regulatory TAM receptor pathways. (A) Venn diagram illustrating numbers of unique and shared marker genes of ST TREM2^hi and BALF FABP4⁺ macrophage clusters as described in Figure 1. Marker genes were identified prior to integration of data sets (19, 24) and were calculated using MAST, setting a minimum percentage of cells in clusters expressing each marker to 40%. Genes considered differentially expressed at P < 0.05 after Bonferroni correction. (B) Heatmap illustrating scaled, pseudobulk expression of shared upregulated marker genes from ST and BALF clusters indicated in A. (C) Split UMAP plots comparing BALF macrophage clusters in health, and in mild and severe COVID-19, illustrating changes in expression of the TAM receptors AXL and MerTK, with their respective preferred ligands GAS6 and PROS1. Intensity of purple indicates expression level. (D) Heatmap illustrating scaled, pseudobulk expression of TAM receptors and associated ligands by each BALF cluster, across patient groups. TAM receptors and their ligands were significantly differentially expressed in severe COVID-19 versus healthy tissues (P ≤ 0.005), with Bonferroni correction for multiple comparison, as confirmed by MAST.

High plasma SPP1 is associated with a severe disease trajectory of COVID-19. To validate the computational scRNAseq findings of shared pathogenic macrophage clusters in COVID-19 and RA, we quantified plasma concentrations of their key shared functional mediators SPP1 (osteopontin) and S100A12 (calgranulin C) in a cross-sectional comparison of additional patient groups. These consisted of (a) hospitalized acute COVID-19 patients (n = 92), (b) COVID-19 patients in their post–COVID-19 phase (convalescence) at outpatient clinics (n = 41), (c) hospitalized patients with non–SARS-CoV-2 community–acquired severe acute pneumonia (n = 29), and (d) healthy donors (n = 10; Figure 3A). Demographic and clinical information, and laboratory biomarkers are presented in Supplemental Table 1. Blood samples for groups (a) and (c) were taken on hospital admission or shortly afterward and before antiinflammatory treatment (see Methods).

Figure 3

High levels of SPP1 and S100A12 are associated with a severe COVID-19 disease trajectory. (A) Patients and healthy donors, shown as the following: n = 121 patients with acute pneumonia (n = 29 community acquired SARS-CoV-2^– pneumonia, n = 29 mild/moderate COVID-19, n = 63 severe COVID-19), convalescent COVID-19 (n = 41), and healthy controls (n = 10). Representative images of lung CT scans. (B) Plasma levels of SPP1, S100A12, GAS6, and PROS1 in groups as in A. (C) Spearman’s rank correlations between SPP1, S100A12, GAS6, and PROS1 plasma levels in patients with acute COVID-19 pneumonia (n = 92) with demographic and clinical parameters. Each box displays the r value, and an asterisk indicates statistical significance of P < 0.05. (D) Plasma levels of SPP1, S100A12, GAS6, and PROS1 in patients with acute COVID-19 pneumonia (n = 92) stratified based on lung functions measured by PaO₂/FiO₂ at the time of hospital admission. Severe respiratory failure was defined by PaO₂/FiO₂ ≤ 200. (E) Percentage of acute COVID-19 pneumonia patients (n = 92) with PaO₂/FiO₂ ≤ 200 based on high plasma levels of SPP1 (≥108 ng/mL), S100A12 (≥59 ng/mL), GAS6 (≥24 ng/mL), and PROS1 (≥15 μg/mL). (F) COVID-19 patient plasma levels of SPP1, S100A12, GAS6, and PROS1 at the time of hospital admission (n = 92) stratified based on a patient’s subsequent need to be transferred to ICU. (G) Percentage of patients with acute COVID-19 pneumonia (n = 92) transferred to ICU during the hospitalization based on having high levels of SPP1 (≥108 ng/mL), S100A12 (≥59 ng/mL), GAS6 (≥24 ng/mL), and PROS1 (≥15 μg/mL) at the time of hospital admission. (B, D, and F) Data are presented as violin plots with median and interquartile range. Asterisk indicates 1-way ANOVA (Kruskal-Wallis test) with Dunn’s correction for multiple comparisons if more than 2 groups were compared (B), or 2-sided Mann-Whitney U was used when 2 groups were compared (B and D–G). (H) Kaplan-Meier analysis of the rate of transfer of COVID-19 patients to ICU based on their cut-off values for SPP1, S100A12, GAS6, and PROS1 at the time of hospital admission.

Patients with acute COVID-19 had significantly higher plasma levels of proinflammatory SPP1 and S100A12 than healthy donors and patients at the post–COVID-19 stage (Supplemental Figure 4A). Based on clinical criteria (see Methods) and before antiviral/antiinflammatory treatment administration, the acute COVID-19 patients were categorized into those who subsequently developed either mild/moderate or severe disease. We found that the S100A12 plasma levels were higher in COVID-19 patients categorized as severe compared with mild/moderate, and compared with those in convalescence, and with healthy donors, but comparable with the levels in patients with community acquired pneumonia (Figure 3B). Interestingly, levels of SPP1 were uniquely higher in those with severe COVID-19 compared with any other groups, including severe pneumonia induced by other pathogens (Figure 3B).

We next investigated the relationship between plasma levels of SPP1 and S100A12, with systemic inflammation measured by blood cytometry and biochemistry, and lung function measured by PaO₂/FiO₂ tested around the time of blood sampling and before administration of antiinflammatory treatment (Supplemental Table 1). Plasma concentrations of SPP1 and S100A12 correlated negatively with PaO₂/FiO₂ and positively with inflammatory biomarkers — e.g., IL-6, CRP, and LDH (Figure 3C and Supplemental Table 1). Moreover, SPP1 and S100A12 each strongly correlated with the neutrophil/lymphocyte ratio, which itself is a prognostic biomarker for COVID-19 severity (Figure 3C) (49). Stratification of patients according to levels of PaO₂/FiO₂ reflecting those with or without severe respiratory distress (PaO₂/FiO₂ ≤ 200 and PaO₂/FiO₂ > 200, respectively) clearly showed that the former had significantly higher levels of SPP1 and S100A12 (Figure 3D). A refinement of this observation suggested that COVID-19 patients with plasma levels of SPP1 ≥ 108 ng/mL and S100A12 ≥ 59 ng/mL (cut-off values based on their medians in all COVID-19 patients) were predictive of those more likely to have severe respiratory distress (PaO₂/FiO₂ ≤ 200) compared with those with lower SPP1 and S100A12 levels (Figure 3E and Supplemental Figure 4B). Of prognostic importance for COVID-19 patients, higher levels of SPP1 and S100A12 at the time of hospital admission and before antiinflammatory treatment were predictive of urgency for subsequent Intensive Care Unit (ICU) transfer (Figure 3, F and G). The time to ICU transfer was significantly more rapid for patients with plasma SPP1 ≥ 108 ng/mL and S100A12 ≥ 59 ng/mL (Figure 3H). These data suggest that a plasma biomarker signature associated with pathogenic macrophage clusters in the lung and shared with RA synovitis (i.e., SPP1 and S100A12) might be useful for predicting the trajectory of disease severity and indicative of mechanism of pathogenesis of severe COVID-19. Analysis of the SPP1 (CD44/integrins) and S100A12 (TLR4/CD36) receptor distribution in the lungs of COVID-19 patients demonstrated that many cell types expressed receptors for SPP1, including macrophages, neutrophils, T cells, and epithelial cells, whereas predominantly macrophages expressed receptors for S100A12 (Supplemental Figure 5). This suggested that the lungs acted as a receptive environment for the actions of proinflammatory SPP1 and S100A12.

We next investigated the plasma concentrations of COVID-19/RA shared regulatory TAM receptor pathway ligands GAS6 and PROS1 in the patient groups described above. They showed a distinct plasma signature compared with proinflammatory SPP1 and S100A12. At the time of acute pneumonia, plasma levels of GAS6, the preferable ligand for the AXL receptor, did not differ between acute severe COVID-19 and healthy donors. However, GAS6 levels were lower in patients with milder acute COVID-19 and in post–COVID-19 subjects in whom acute inflammation had resolved, compared with healthy donors and patients with severe COVID-19 (Figure 3B). Consistent with lower levels of GAS6 in patients with mild/moderate disease, GAS6 levels in COVID-19 patients correlated negatively with PaO₂/FiO₂ ratio (Figure 3C). COVID-19 patients with PaO₂/FiO₂ > 200 had lower plasma GAS6 levels than those with PaO₂/FiO₂ ≤ 200 (Figure 3D), and optimum cut-off plasma levels of GAS6 < 24 ng/mL detected those more likely to maintain good lung function (PaO₂/FiO₂ > 200) (Figure 3E). Prognostically, low levels of GAS6 at the time of hospital admission and/or before antiinflammatory treatment were predictive of low risk for ICU transfer (Figure 3F); for example, 6.5% of COVID-19 patients with GAS6 < 24 ng/mL required transfer to ICU compared with 44.4% of those with GAS6 ≥ 24 ng/mL (Figure 3, G and H). These data suggest that lower GAS6 levels were predictive of better disease outcomes. GAS6 can be produced by many immune cells (45, 50), including lung-infiltrating inflammatory FCN1⁺ and FCN⁺SPP1⁺ macrophages and DCs (Figure 2C and Supplemental Figure 2). Thus, low levels of plasma GAS6 in patients with milder disease may reflect a lower degree of lung infiltration by inflammatory macrophages.

Plasma level of PROS1, a marker of healthy lung FABP4⁺ macrophages and the preferable ligand for MerTK, did not differ between any of the COVID-19 patient categories or healthy donors (Figure 3B). However, the levels of PROS1 in COVID-19 patients with mild/moderate disease showed a wider spread of the values as illustrated by interquartile range compared with other groups, suggesting potential association between PROS1 and individual composites of the classification criteria. In contrast to SPP1, A100A12, and GAS6, PROS1 levels correlated positively with PaO₂/FiO₂ and negatively with IL-6 (Figure 3C). COVID-19 patients with PaO₂/FiO₂ > 200 had higher plasma levels of PROS1 than those with PaO₂/FiO₂ ≤ 200 (Figure 3D). An optimum plasma PROS1 cut-off value was calculated at 15 μg/mL, above which 34.1% of COVID-19 patients had severe respiratory distress as compared with 59% of those with PROS1 < 15 μg/mL (Figure 3E). This observation is consistent with the single-cell transcriptomic data (Figure 2C) showing that the expression of inflammation resolving, antithrombotic PROS1 and antithrombotic PROS1 in resident alveolar FABP4⁺ macrophages decreases with increasing disease severity.

Next, we investigated whether concomitant pharmacological drug treatment, demographic factors (age and sex), and COVID-19 comorbidities (6) contributed to the plasma levels of SPP1, S100A12, GAS6, and PROS1. None of the pharmacological treatments affected the plasma levels of SPP1, S100A12, GAS6, and PROS1 in moderate and severe COVID-19 pneumonia (Supplemental Figure 6). S100A12 and PROS1 were unrelated to age in COVID-19 and in SARS-CoV-2^– pneumonia (Supplemental Figure 4, C–E). SPP1 was unaffected by age in SARS-CoV-2^– pneumonia but was higher in COVID-19 patients > 70 years old (Supplemental Figure 4, C–E), suggesting that SPP1 levels are related more to severity of pneumonia than age (Figure 3). GAS6 was higher in COVID-19 and non–SARS-CoV-2 pneumonia patients > 70 years old and higher in COVID-19 patients with arterial hypertension, diabetes mellitus, and ischemic cardiopathy (Supplemental Figure 7); thus, age and comorbidities may confound interpretation of increased GAS6 in COVID-19.

Increased SPP1 levels persist after COVID-19. We investigated the persistence of increased plasma SPP1, S100A12, GAS6, and PROS1 into the SARS-CoV-2^– post–COVID-19 phase, often characterized by complex pathologies (26). We compared plasma concentrations in 41 post–COVID-19 patients attending outpatient clinic at (mean ± SEM) 68.60 ± 4.36 days after discharge (Figure 4A), 26 of whom had plasma samples that were available from the peak of acute COVID-19. Longitudinal comparison showed that plasma levels of SPP1, S100A12, and GAS6 were significantly lower in convalescence compared with peak disease, but PROS1 was unchanged (Figure 4B). However, although reduced, the levels of SPP1 and S100A12 remained significantly higher than in healthy control donors, irrespective of whether the prior disease trajectory of COVID-19 was mild/moderate or severe (Figure 4C). This was in contrast to the levels of sensitive markers of inflammation, including IL-6 and LDH, which normalized in post–COVID-19 to levels of those in healthy donors, consistent with resolved acute inflammation (Figure 4D and Supplemental Table 1). GAS6, lower than normal control levels in mild/moderate COVID-19 on hospital admission (Figure 3B), remained low after COVID-19 (Figure 4C).

Figure 4

Increased SPP1 and S100A12 levels persist in post–COVID-19 phase. (A) Representative images of lung CT scans (transversal and sagittal view) of a COVID-19 patient taken during acute pneumonia and during convalescence (68.60 ± 4.36 days after hospital discharge). (B) Plasma levels of SPP1, S100A12, GAS6, and PROS1 in paired plasma samples from COVID-19 patients at the time of acute pneumonia and at the convalescent phase (n = 26). (C) Plasma levels of SPP1, S100A12, GAS6, and PROS1 in convalescent COVID-19 patients (n = 41) stratified based on the severity of prior acute pneumonia and compared with the levels of healthy donors (n = 10). (D) Plasma levels of IL-6 in acute pneumonias and post–COVID-19. (E) SPP1, S100A12, GAS6, and PROS1 in convalescent COVID-19 patients (n = 41) stratified based on suffering (n = 36) or not (n = 5) at least 1 of the symptoms (fatigue, musculoskeletal, or respiratory symptoms). (B) Data are presented as before-and-after plot. Wilcoxon test on paired samples was used, and exact P values are provided on the graphs. (C–E) Data are presented as violin plots with median and interquartile range. Asterisks indicate 1-way ANOVA with correction for multiple comparisons if more than 2 groups were compared, or 2-sided Mann-Whitney U test was used when 2 groups were compared (C–E). Exact P values are provided on the graphs.

Most (36 of 41) convalescent patients reported persistence of at least 1 post–COVID-19 symptom (fatigue, musculoskeletal, or respiratory). Increased levels of SPP1 and S100A12 persisted in all patients, although extremely high concentrations SPP1 were restricted to symptomatic patients (Figure 4E). Stratification of patients by symptoms showed that SPP1 and S100A12 levels remained increased and GAS6 decreased in convalescent COVID-19 patients, irrespective of symptom category (Supplemental Figure 8).

Together, these data suggest that the SPP1 and S100A12 myeloid cell inflammatory signature persists after resolution of SARS-CoV-2 infection and may contribute to the pathogenesis of long–COVID-19 syndrome (26).

SPP1 protein is expressed by COVID-19 BALF macrophages, drives proinflammatory activation of classical monocytes, and the differentiation of neutrophils toward a proinflammatory CD274⁺ (PD-L1⁺) phenotype. High plasma SPP1 selective for severe COVID-19 (Figure 3B) suggested a role for SPP1 in pathogenesis. SPP1 and CD68 staining of postmortem lung tissue confirmed abundant clusters of SPP1⁺ macrophages in alveoli of COVID-19 patients (n = 2), while it was rare in normal lung (n = 3; Figure 5, A and B and Supplemental Table 2) and sparse in alveoli of bacterial (n = 3) and H1N1 (n = 3) pneumonia (Supplemental Figure 9, A and B). To investigate the biological effects of SPP1, we stimulated healthy whole blood cells with SPP1 at concentrations equivalent to those in severe and post–COVID-19 (200 and 50 ng/mL, respectively) (Figure 3B). SPP1 receptors (e.g., CD44/integrins) are expressed by many immune cell types in lung and blood (Supplemental Figure 5 and Supplemental Figure 10); therefore, to capture the effects of SPP1 on all cell types, we used scRNAseq of the whole blood culture (n = 3 healthy donors) using the immune gene panel (399 genes) with the BD_Rhapsody system after 16-hour stimulation with SPP1. We sequenced 13,580 cells and identified 14 distinct immune clusters (Figure 6, A and B), including neutrophil, eosinophil, monocyte, DC, NK cell, and lymphocyte cell clusters. The relative proportions of cell clusters revealed that SPP1 stimulation at 200 ng/mL, equivalent to plasma concentration of severe COVID-19, was associated with a significantly increased neutrophil proportion (Figure 6, B and C) with a shift from the normally dominant CD10⁺ (MME⁺) neutrophil phenotype to a CD274⁺ (PD-L1⁺) neutrophil phenotype. The top 20 marker genes of the CD10⁺ and SPP1-differentiated CD274⁺ (PD-L1⁺) neutrophil clusters (Figure 6D and Supplemental Data) revealed that the CD274⁺ (PD-L1⁺) neutrophils represent an aberrantly activated phenotype characterized by high expression of PD-L1 (CD274), which mediates suppression of adaptive immunity, high levels of proinflammatory cytokines/chemokines (e.g., IL1B, TNF, CCL3, and CCL4), c-type lectins (CLEC4E/D) that facilitate neutrophils migration, and the SPP1 receptor CD44. Similar aberrant neutrophil activation is a key pathogenic characteristic of severe COVID-19 (51, 52). To investigate if the in vitro SPP1-driven neutrophil cluster transcriptionally replicated any of the neutrophil phenotypes observed in severe COVID-19, we performed unsupervised mapping of the SPP1-driven neutrophil gene module (37 genes; Supplemental Data) into a whole-blood scRNAseq transcriptomic signature of healthy, mild, and severe COVID-19 (22). We identified that enrichment of transcriptional signature of SPP1-driven CD274⁺ (PD-L1⁺) neutrophils was significantly higher in Neutrophil_2 phenotype than any other cell population (Figure 6, E and F) in COVID-19 patients and that this phenotype was uniquely increased in severe disease, suggesting that SPP1 might be responsible for the pathogenic activation of neutrophils in severe COVID-19. This link between SPP1 and neutrophils is supported by the correlation between plasma SPP1 and the blood neutrophils/lymphocytes ratio in our COVID-19 cohort (Figure 3C).

Figure 5

SPP1 protein is expressed in COVID-19 lung by macrophages but not healthy alveolar macrophages. (A) Representative immunofluorescence staining of normal lung (n = 3) showing SPP1^– alveolar macrophage (CD68⁺). (B) Representative immunofluorescence staining of COVID-19 lung (n = 2) showing SPP1⁺ macrophages (CD68⁺SPP1⁺) in alveoli. Solid white arrows indicate macrophages double-positive for SPP1 and CD68; hollow arrows indicate CD68⁺ and SPP1^– macrophages.

Figure 6

SPP1 stimulation drives proinflammatory CD14⁺ monocyte and CD274⁺ (PD-L1⁺) neutrophil phenotypes. Whole blood cells from healthy donors (n = 3) were stimulated with SPP1 (50 or 200 ng/mL) or were unstimulated, for 16 hours. (A) UMAP of 13,580 integrated control and SPP1-stimulated blood cells colored by cluster identity. (B) Stacked bar plot illustrating cluster proportion of total whole blood cells per condition/dose of SPP1. * P < 0.05, 1-way ANOVA with correction for multiple comparisons. (-), unstimulated control. (C) UMAPs illustrating change in expression of MME (CD10) and CD274 (PD-L1) by neutrophil clusters following SPP1 stimulation. (D) Heatmap showing expression of the top 20 marker genes of each neutrophil phenotype, illustrated as average expression of each gene per sample per condition. Adjusted P < 0.05, MAST with Bonferroni correction. (E) Average of SPP1-driven CD274⁺ neutrophil gene module score by neutrophil clusters of COVID-19 whole blood cell data set (22). (F) UMAP expression of the SPP1-driven CD274⁺ neutrophil gene module in COVID-19 whole blood data set. (G) Heatmap showing expression of genes significantly differentially expressed in CD14⁺ monocytes comparing SPP1 (200 ng/mL) stimulation with unstimulated control, illustrated as average expression of gene per condition. Adjusted P < 0.05, MAST with Bonferroni correction. Red and orange arrows in D and G represent genes regulated by 200 ng/mL only, or by 200 and 50 ng/mL SPP1, respectively.

Our analysis also revealed that SPP1 induced the activation of CD14⁺ classical monocytes. At 200 ng/mL, SPP1 strongly increased the expression of alarmins (S100A12, S100A9), IL-1β, and chemokines (CXCL8, CCL2-4) with a commensurate reduction of MHCII and antiinflammatory ENTPD1 (encoding CD39; Figure 6G), which resembles the proinflammatory changes in the blood CD14⁺ monocyte subset in severe COVID-19 (22, 23). SPP1 at post–COVID-19 convalescent plasma concentration (50 ng/mL) did not affect neutrophil activation. However, it increased expression levels of alarmins S100A12 and S100A9, and it decreased the expression of MHCII and ENTPD1 of CD14⁺ monocytes (Figure 6G), suggesting that persistent levels of SPP1 may contribute to long–COVID-19 pathologies by skewing monocytes toward a proinflammatory phenotype. The effect of SPP1 on other cell types was minimal (Supplemental Data). In summary, SPP1 produced by proinflammatory lung-infiltrating macrophages might be an upstream activator of the aberrant innate response in COVID-19 by driving proinflammatory activity of CD14⁺ monocytes and differentiation of CD274⁺ (PD-L1⁺) neutrophils that suppress adaptive immunity and support inflammation.

Discussion

Our comparative single-cell transcriptomic analysis integrating myeloid cell clusters from COVID-19 pneumonitis and RA synovitis suggested that COVID-19 and RA pathogenesis and resolution might be driven by similar myeloid cell clusters and their signature functional pathways (Supplemental Figure 11). The shared key pathogenic macrophages were SPP1⁺ and S100A12⁺ clusters, and their signature SPP1 and S100A12 mediators were confirmed in COVID-19 cross-sectional and longitudinal patient plasma samples. COVID-19 patients with severe respiratory distress were characterized by an aberrant raised SPP1 and S100A12 cytokine signature that could predict the urgency for ICU transfer and persisted into the post–COVID-19 phase after hospital discharge. An abundance of SPP1⁺ macrophages in the alveolar spaces and high SPP1 plasma levels were unique to severe COVID-19 pneumonia as compared with pneumonias induced by other pathogens. Investigation into SPP1 mechanisms of action revealed that it drives proinflammatory activation of CD14⁺ monocytes and development of PD-L1⁺ pathogenic neutrophils, both hallmarks of severe COVID-19 (1–14). Thus, COVID-19 pneumonitis appears driven by similar pathogenic myeloid cell pathways as those in RA, and their mediators such as SPP1 may be an upstream activator of the aberrant innate response in severe COVID-19 and predictive of disease trajectory including post–COVID-19 monitoring.

In contrast, COVID-19 patients with milder respiratory distress were characterized by increased plasma levels of the inflammation-resolving, antithrombotic cytokine PROS1, which characterized the resident AM FABP4⁺ cluster. This cluster shared transcriptomic similarities with the inflammation-resolving STM TREM2⁺ cluster, suggesting a potential role in controlling lung inflammation.

The BALF FCN1⁺ and FCN1⁺SPP1⁺ macrophage clusters from severe COVID-19 patients shared transcriptomic profiles with STM CD48^hiS100A12⁺ and CD48⁺SPP1⁺ clusters from active RA, respectively. The functional biology of the STM counterparts (i.e., CD48^hiS100A12⁺ and CD48⁺SPP1⁺) showed that they were the main producers of pathogenic TNF, IL-6, IL-1β, and chemokines in the synovium of RA, and this resembles the lung hypercytokine environment characterizing severe COVID-19 respiratory distress syndrome (53, 54), suggesting that SPP1⁺ and S100A12⁺ BALF macrophage clusters might be the key source of these mediators in the COVID-19 lung. These shared macrophage clusters of RA/COVID-19 are also characterized by their cluster-unique mediators, SPP1 (CD48⁺SPP1⁺ cluster) and S100A12 (CD48^hiS100A12⁺cluster; ref. 19). These mediators were upregulated in severe compared with mild/moderate COVID-19, and their high plasma concentrations correlated with respiratory insufficiency and was predictive for urgency of ICU transfer. The increased number of SPP1⁺ macrophages and upregulation of SPP1 plasma levels were selective for severe COVID-19. Many biological functions for SPP1, mostly in tissue remodeling, have been described (39, 55). However, its contribution to COVID-19 pathologies is unclear. We revealed here that SPP1 at the level detected in patients with severe COVID-19 is a potent driver of pathogenic PD-L1⁺ neutrophils that are characteristic of severe COVID-19 (22). Thus, SPP1 might be an upstream regulator of neutrophil-mediated tissue damage and immune-thrombosis that are key pathogenic features of severe COVID-19 (51, 52). In addition, SPP1 induced strong proinflammatory activation of CD14⁺ monocytes, including the expression of S100A alarmins, suggesting that it may contribute to the development of S100A12⁺ macrophage clusters in the lung.

There is an increasing recognition of a syndrome of persistent debilitating symptoms after the COVID-19 patients become negative for SARS-CoV-2 (post–COVID-19). The symptom patterns are complex, but dyspnoea and fatigue are prominent (26). The pathogenic mechanisms of this syndrome are unknown. Emerging transcriptomic evidence shows that changes in blood myeloid cells elicited during acute infection can normalize within 14 days of becoming negative for SARS2-CoV-2 (56). However, some changes can persist for at least 12 weeks, including high levels of surface molecules that regulate monocyte migration into tissue — e.g., CXCR6 and VLA-4 (57). We found that — in contrast, for example, to IL-6 — plasma SPP1 and S100A12 remained significantly higher than normal for at least 10 weeks after infection clearance in the post–COVID-19 convalescent phase. Our study revealed that SPP1, at the concentration detected in the post–COVID-19 stage, induced some of the features of proinflammatory CD14⁺ monocytes — for example, an increase in alarmins (S100A12 and S100A9) and a decrease in MHCII molecules. This suggests that a sustained raised level of SPP1 may contribute to post–COVID-19 pathologies and may influence future responses against pathogens by skewing monocytes toward proinflammatory phenotype. At this stage of understanding, it remains unclear whether the plasma SPP1 and S100A12 exclusively originate from the pathogenic BALF clusters persisting in the lung of these patients. However, regardless of SPP1 and S100A12 source, their wide range of proinflammatory and fibrotic functions may be responsible for the respiratory and musculoskeletal symptoms that persist in convalescence.

One of the aims of this study was to apply our recent discovery of inflammation-resolving macrophage pathways in RA to investigate potentially similar inflammation-resolving pathways for COVID-19. We found that tissue-resident lung alveolar FABP4⁺ macrophages and homeostatic synovial lining layer TREM2⁺ macrophages share transcriptomic profiles. This may reflect their homeostatic functions in their respective tissues. In the synovium, the TREM2⁺ macrophages form a lining-layer producing and recycling lubricant synovial fluid, which facilitates joint movement (58). While in lung alveoli, the FABP4⁺ macrophages contribute to alveolar integrity, the recycling of surfactants to maintain patency, and the facilitation of gas exchange (33). One of the pathways they shared was inflammation-resolving TAM (TYRO, AXL, MerTK) receptor pathways. TAM pathways are widely expressed by resident macrophages in many tissues (59, 60), and their deregulation has proinflammatory/autoimmune consequences in preclinical animal models (61, 62) and in patients with RA (19). The scRNAseq data show that, in healthy human BALF, the FABP4⁺ macrophages express the TAM receptor AXL and the ligand for the TAM receptor MerTK (PROS1) and that these were profoundly repressed in severe COVID-19. Consistent with scRNAseq data, plasma Protein S levels were higher in COVID-19 patients who maintained lung functions (i.e., sustained PaO₂/FiO₂ > 200), suggesting a potential role in counterbalancing the severity of inflammation of SARS-CoV-2 infection.

Limitations of the study included the younger age and fewer numbers of subjects within the healthy control group used as a comparator of some of the COVID-19 analysis. These limitations were mitigated by findings that SPP1, S100A12, and PROS1 were unaffected by age. In addition, the control group for the key mediator SPP1 in severe COVID-19 was the non–SARS-CoV-2 pneumonia group that matched COVID-19 for age, and it showed lower levels of SPP1 compared with COVID-19, supporting the link between SPP1 and COVID-19 pathogenesis.

In summary, this study suggests that the pathogenesis of acute severe COVID-19 pneumonitis and RA synovitis might be driven by similar pathogenic myeloid cell clusters/pathways, producing SPP1 that persists into post-COVID-19 syndrome. Further functional studies on identification of common tissue factors driving the differentiation of these pathogenetic macrophage clusters and better understanding the functional interaction between them and the lung environment could clarify mechanisms of pneumonitis and provide evidence for potential repurposing of current antiinflammatory/antifibrotic treatments for COVID-19 (63, 64). Promising data from current COVID-19 clinical trials of drugs already used for the treatment of RA (e.g., dexamethasone, tocilizumab, and baricitinib; refs. 35, 65–69) further support the concept of common pathogenic and resolution mechanisms that could be capitalized upon for COVID-19 therapeutic exploitation.

Methods

Data acquisition and analysis

Comparison and integration of BALF and ST myeloid cell scRNAseq data. BALF data were acquired as CellRanger output (GEO, GSE145926) and ST data were acquired from EMBL-EBI:E-MTAB-8322. COVID-19 blood and PBMC data (22) were from https://beta.fastgenomics.org/p/schulte-schrepping_covid19

Quality checks, filtering, clustering of 10X Genomics data sets

Seurat (3.1.2) in R created an object (CreateSeuratObject). Cell-filtering involved removal of cells with < 500 expressed genes (subset, subset = nFeatures_RNA > 500), with set-thresholds for gene-expression level, including mitochondrial genes (percent.mt) to exclude doublets and dying cells. Data normalized using Seurat’s NormalizeData function. Myeloid cells were filtered for expression of CD14, MARCO, and LYZ with the subset function. The top 2000 variable genes were identified using the FindVariableFeatures function. Sample integration of each data set followed the Seurat vignette, integrating all genes common between samples of each condition, using FindIntegrationAnchors and IntegrateData functions (features.to.integrate to normalize all common genes).

Individual clustering and dimensional reduction. UMAP based on PCA cell embeddings were generated by Seurat for each data set using RNA assay. To visualize ST data, the first 12 principal components were used (RunUMAP). These principal components determined the k-nearest neighbors for each cell during shared nearest neighbor (SNN) graph construction before clustering at a resolution of 0.5 (FindNeighbors, FindClusters) and identifying the populations as described (19). In comparison, the first 50 principal components (0.8 resolution) were used for visualization and clustering of BALF data (24), and populations were identified by merging clusters based on expression of FCN1, SPP1, and FABP4. The relative proportion of clusters between conditions was analyzed using Kruskal-Wallis test with Dunn’s correction (Prism v8.4.2).

Differential expression analysis. The Seurat FindAllMarkers function identified cluster markers in individual data sets. The “test.use” function determined genes differentially expressed between clusters within each data set using MAST. As recommended, for DE comparison the nonbatch normalized counts were used. Cluster markers must be expressed by ≥ 40% cells in the cluster (“min.pct” parameter 0.4). Default values were used for all other parameters. Genes are considered significantly DE if P < 0.05 (Bonferroni correction, multiplied by number of tests/clusters).

Ligand-receptor expression analysis. CellTalkDB (70) identified putative receptors of mediators of interest (S100A12, SPP1, GAS6, and PROS1). An object containing all sequenced BALF cell types was prepared for visualization of expression of mediators and identified receptors by distinct BALF immune and epithelial clusters. The same quality control (QC) and integrative clustering procedure were followed as described above for analysis of myeloid cells, without filtering cells for expression of CD14, MARCO, and LYZ. The first 30 principal components (0.3 resolution) were used for classification of BALF immune and epithelial cell types (24). Clusters were annotated by marker gene expression, and expression of genes of interest were visualized (FeaturePlot, RNA assay). Pseudobulk expression values of genes of interest were generated per sample, per cell type, and were exported for analysis (GraphPad Prism 9.1.0). A similar strategy was used to analyze SPP1 receptor expression in COVID-19 blood (22), and data were obtained as a Seurat object with clustering and annotation as described above (individual clustering and dimensional reduction).

Data set comparison

Data set integration was performed as described above for 8993 common genes between ST and BALF. These “integrated” batch-corrected values were set as the default assay, and gene expression values are scaled before principal component analysis (PCA).

Clustering and dimensional reduction. To prevent bias in clustering, we matched BALF and ST cell numbers at 32,139 random cells. Data were rescaled (ScaleData), and the first 30 principal components were visualized by UMAP. Cells colored by original identity illustrated how clusters of each data set overlay.

Comparative analyses. A count matrix with the average expression of all common genes by each cluster was generated (AverageExpression) and hierarchical clustering (dist, hclust) performed. Data were visualized by a dendrogram. PCA was performed on this pseudobulk expression matrix (prcomp). These analyses identified similar clusters between BALF and ST. Shared marker genes between similar clusters were identified by Venny (2.1). To visualize pseudobulk expression of overlapping marker genes, the pheatmap (1.0.12) package visualized shared genes as a heatmap using a custom script. Between–data set comparison of pseudobulk of expression marker genes (unique and common to all clusters) was performed by Pearson correlation (ggscatter, add = “reg.line”, conf.int = TRUE, cor.coef = TRUE, cor.method = “pearson”) performed on the 916, the 721, and the 986 unique and common genes of BALF FCN1⁺/ST CD48^hiS100A12⁺, BALF FCN1⁺SPP1⁺/ST CD48⁺SPP1⁺, and BALF FABP4⁺/ST TREM2^hi clusters, respectively.

Patients and clinical assessment

COVID-19 pneumonia patients. Reverse transcription PCR (RT-PCR) positive for SARS-CoV-2 by nasopharyngeal swab (NPS) were enrolled (COVID-Hospital, Fondazione Policlinico Universitario Agostino Gemelli, Rome, Italy). Each enrolled patient provided peripheral blood samples and COVID-19 severity was classified on hospital admission and/or before antiinflammatory treatment. Mild/moderate COVID-19 pneumonia was defined based on (mild) normal O₂ saturation (>94%), or (moderate) abnormal O₂ saturation (<94%); pneumonia based on imaging (chest x-ray or CT scan); and arterial oxygen partial pressure (PaO₂ in mmHg) to fractional inspired oxygen (FiO₂) PaO₂/FiO₂ (>200). Severe COVID-19 pneumonia was defined based on abnormal O₂ saturation (<94%), presence of pneumonia on imaging (x-ray or CT scan), and PaO₂/FiO₂ (≤200), or the use of high-flow nasal cannula (HFNC), nonrebreather mask (NRB), bilevel positive airway pressure (BIPAP) or mechanical ventilation and vasopressor drugs use, creatinine clearance greater than 30, and on alanine aminotransferase (ALT) less than 5× the upper limit of normal. Concomitant comorbidities including arterial hypertension, diabetes mellitus, ischemic cardiopathy, COPD, dyslipidaemia, and cancer were recorded. The subsequent need and date of ICU admission, intubation status, and O₂ therapy for each patient was recorded. Patients were treated following the internal protocol defined by the Gemelli Against COVID-19 task force as described (35).

Convalescent post–COVID-19 patients. Follow-up outpatient attendance (Fondazione Policlinico Universitario Agostino Gemelli) was offered to all discharged COVID-19 patients. Those recruited attended 68.6 ± 4.4 days (mean ± SEM) after discharge and were RT-PCR–negative for SARS-CoV-2. They were assessed by a multidisciplinary team who collected demographic and clinical data, collected blood samples, and performed physical examinations. Persistence of symptoms was recorded by a questionnaire.

Patients with community-acquired pneumonia. Patients with pneumonia, RT-PCR–negative for SARS-CoV-2 by NPS, were enrolled at the Emergency Room (Fondazione Policlinico Universitario Agostino Gemelli). Demographic and clinical parameters were recorded, and blood samples were collected. These formed a comparison pathological control group for COVID-19 pneumonia. All patients and healthy control demographics and clinical characteristics are summarized in Supplemental Table 1.

Lung tissue IHC for SPP1⁺ macrophages. Postmortem lung tissue was obtained from COVID-19 pneumonia (n = 2), bacterial pneumoniae (n = 3), H1N1 pneumonia (n = 3), and normal donors (n = 3). Demographic and clinical data are summarized in Supplemental Table 2. Autopsies were performed at the Department of Woman and Child Health and Public Health, Institute of Pathology of The Fondazione Policlinico Universitario Agostino Gemelli, in accordance with the guidelines of the Royal College of Pathologists (www.rcpath.org). Tissue collection was performed in accordance with appropriate protocols (71). Lung tissue was fixed in 10% neutral-buffered formalin, embedded in paraffin, sectioned at 4 mm, and stained as described (19). Briefly, after blocking (2 hours, 10% human serum, 10% of goat serum, 1% BSA in TBS; Thermo Fisher Scientific) at room temperature, sections were incubated overnight at 4°C with the primary rabbit anti–human osteopontin (SPP1) antibody (Abcam, ab8448, 1:100) and mouse anti–human CD68 (Dako, clone PG-M1, 1:40) in blocking buffer above. Sections were washed twice (5 minutes, TBS plus 0.025% Triton X-100). Secondary antibody goat anti-rabbit IgG Alexa Fluor 488 (1:100), and goat anti–mouse IgG Alexa Fluor 660 (1:100) (both from Invitrogen) were incubated in dilution buffer (TBS with 1% BSA) and incubated for 1 hour at room temperature. Sections were washed 3 times for 5 minutes in TBS and counterstained with DAPI (H-1800-2/VECTASHIELD Vibrance Antifade Mounting Medium with DAPI), visualized with confocal microscopy with Zeiss Zen Black software. Negative controls were performed throughout using isotype matched antibodies.

SPP1, S100A12, GAS6, PROS1, IL-6, IL-8, IL-1β, and TNF-α ELISA. Blood samples were centrifuged (600g/15 minutes) and plasma aliquots were stored at –80°C until analysis by ELISA for SPP1 (BMS2066; Thermo Fisher Scientific), S100A12 (DY1052; R&D Systems), GAS6 (DY885B; R&D Systems), and PROS1 (NBP2-60585; NOVUS Biological). IL-6, IL-8, TNF-α and IL-1β plasma levels were quantified by single-plex ELISA (Multi-cytokine test for ELLA, Bio-Techne).

SPP1 stimulation of whole blood for scRNAseq (BDRhapsody) analysis. Leukocytes were collected by centrifugation from heparinized blood samples (30 mL from 3 healthy donors) after RBC lysis (ACK Lysing Buffer, A1049201, Thermo Fischer Scientific). Leukocytes were plated at 5 × 10⁶/well in a 24-well cell-culture plate in 0.5 mL of RPMI 1640 compete medium containing SPP1 (PeproTech, 120-035) at concentrations of 0, 50, and 200 ng/mL. After 16 hours, cells were deattached (Accutase solution; A6964, Merck), transferred to U-bottom 96-well plates, and harvested by centrifugation at 200g for 4 minutes at 4°C. Cells from each donor/condition were labeled with unique Tags (below) using a Single-Cell Multiplexing Kit (BD Biosciences, 633781) for 20 minutes at room temperature according to manufacturer protocol.

Tag 1, ATTCAAGGGCAGCCGCGTCACGATTGGATACGACTGTTGGACCGG; Tag 2, TGGATGGGATAAGTGCGTGATGGACCGAAGGGACCTCGTGGCCGG; Tag 3, CGGCTCGTGCTGCGTCGTCTCAAGTCCAGAAACTCCGTGTATCCT; Tag 4, ATTGGGAGGCTTTCGTACCGCTGCCGCCACCAGGTGATACCCGCT; Tag 5, CTCCCTGGTGTTCAATACCCGATGTGGTGGGCAGAATGTGGCTGG; Tag 6, TTACCCGCAGGAAGACGTATACCCCTCGTGCCAGGCGACCAATGC; Tag 7, TGTCTACGTCGGACCGCAAGAAGTGAGTCAGAGGCTGCACGCTGT; Tag 8, CCCCACCAGGTTGCTTTGTCGGACGAGCCCGCACAGCGCTAGGAT; Tag 9, GTGATCCGCGCAGGCACACATACCGACTCAGATGGGTTGTCCAGG.

Cells were washed ×3 with PBS, with centrifugation (200g for 4 minutes at 4°C), after which the tagged culture variants were pooled and loaded onto the scRNAseq BD Rhapsody Cartridge using the BD Rhapsody Cartridge Reagent Kit (catalog 633731) according to the manufacturer’s protocol. Single-cell cDNA was prepared using the BD Rhapsody cDNA Kit (catalog 633773). This was followed by single-cell mRNA and tag-library preparation using BD Rhapsody Targeted mRNA and the Tag Amplification Kit (catalog 633774) and primers for the BD Rhapsody Immune Response Panel (399 genes; catalog 633750). Libraries were sequenced at a depth of 1,083,775 ± 236,302 (mean ± SEM) reads per tag using Illumina NextSeq 500 (Glasgow Polyomics). Then, 1535 ± 383 cells (mean ± SEM) per tag were successfully sequenced. For analysis, the sequencing reads were processed with BD Genomics Rhapsody Analysis Pipeline CWL v.1.0 using the Seven Bridges platform. The Seurat package (3.1.5) in R was used to create an object from the RSEC_MolsPerCell.csv output file for each sample tag (CreateSeuratObject). Following the standard analysis protocol (19), normalization and data scaling were performed, followed by PCA of the top 2000 variable genes (RunPCA). A UMAP plot (RunUMAP) was generated from the first 30 principal components. The same principal components were used to determine k-nearest neighbors for each cell during SNN graph construction, before clustering at a chosen resolution of 1.2 (FindNeighbors, FindClusters). Differential expression was performed (FindAllMarkers, test.use = MAST) to identify cluster markers and variable genes between SPP1 doses. Genes are considered significantly DE if P < 0.05 with Bonferroni correction (Supplemental Data). To visualize heatmaps, the pheatmap (1.0.12) package was adapted. The normalized expression values were used to perform pseudobulk expression analysis of each sample (AverageExpression). Raw data are accessible at EMBL-EBI (E-MTAB-10430). Using Seurat (AddModuleScore), we scored the clusters of BDRhapsody COVID-19 whole-blood data set (22) based on average expression of 37 marker genes that characterized the SPP1-driven CD274⁺ neutrophil phenotype. A positive score suggests that this module of genes is expressed more highly in a particular cell than expected across the general population, described in more detail in ref. 72. The module score was used to create a new assay for visualization and was illustrated using UMAP expression and pseudobulk sample expression heatmap.

Statistics

The scRNAseq data comparison is described in data comparison and pathway analysis. Statistical analysis of patient clinical, demographic, and plasma cytokine levels were performed using SPSS V.20.0 (SPSS) and GraphPad Prism software version 9.0.0. Categorical and quantitative variables were described as frequencies, percentage, median with the interquartile range, or mean ± SEM. Data on demographic and clinical parameters were compared between patients by Mann-Whitney U test or χ² test, as appropriate. Cytokine concentrations between multiple patient groups and healthy donors were compared using 1-way ANOVA (or Kruskal-Wallis) with Dunn’s or Tukey’s correction for multiple comparisons, or 2-sided Mann-Whitney U test for 2 groups. Cytokine categories at cut-off level 108 ng/mL, 59 ng/mL, 24 ng/mL, and 15 μg/mL for SPP1, S100A12, GAS6, and PROS1, respectively, were selected based on the median levels in all COVID-19 patients. Linear correlations between cytokines and continuous parameters were performed using the Spearman’s rank test. Kaplan-Meyer analysis estimated the probability of “no need to be transferred to ICU during hospitalization” for COVID-19 patients with acute pneumonia based on the previously determined cut-off plasma values of SPP1, S100A12, GAS6, and PROS1 at the time of hospital admission. P < 0.05 was considered as statistically significant. Exact P values are provided on graphs.

Study approval

The study was approved by the Committee of the Fondazione Policlinico Universitario Agostino Gemelli IRCCS — Università Cattolica del Sacro Cuore (nos. 12401/20 and 0024184/20) and University of Glasgow, MVLS College Ethical Committee (no. 2012073).

Author contributions

MKS oversaw the project and, with SA, interpreted clinical results and, with LM, interpreted computational and experimental results. MKS, LM, SA, and CM wrote the manuscript with feedback from all authors. LM analyzed and integrated 10X Genomics scRNAseq data and interpreted all computational data. SA and BT evaluated cytokine levels in patients’ samples and performed statistical analysis with clinical data integration. LM and MKS performed SPP1 stimulation of whole blood and subsequent scRNAseq and data analysis. DS performed BD_Rhapsody sequencing library preparation and pathway analysis on scRNAseq data. LM analyzed BD_Rhapsody SPP1 data, and with DS mapped to COVID-19 data sets. TDO and MKS supervised all computational analysis in the study. BT, M. Sali, and M. Sanguinetti organized COVID-19 samples collection and handling and performed ELISA with ELLA technology. SP, AP, LP, A. Cingolani, RM, MF, MA, FL, FF, AG, A. Carfì, and EG provided clinical management of patient cohorts during acute phase hospitalization and at the postacute outpatient clinic and provided longitudinal clinical data. ES and VA provided lung tissues from cadavers and provided clinical data. AE assisted with ELISAs and preparation of tissue samples. IBM and EG assisted with running the project. EG conceived the COVID-19 translational research activity for the “Immunology Core facility” of the Fondazione Policlinico Universitario Agostino Gemelli IRCCS. Our study is a collaboration between 2 centers. The first 2 and last 2 authors contributed equally. With agreement between all authors, and reflection on each author’s contribution, we alternated the first and last authors as listed to accurately reflect the collaborative effort of the 2 centers.

Acknowledgments

We thank all the healthcare workers who are fighting SARS-CoV-2 on the front line, in particular the Gemelli Against-COVID-19 team and the COVID-19 Post-Acute Care team of the Fondazione Policlinico Universitario A. Gemelli IRCCS, and all the patients who took part to the study. This work was supported by UKRI-MRC (grant no. MR/V036998/1 to SA, MKS, EG, TDO, CM, and IBM); the Research into Inflammatory Arthritis Centre Versus Arthritis UK (grant no. 22072) to LM, TDO, IBM, and MKS; Versus Arthritis UK project grant (no.22253) to MKS and AE; Linea D1 (Università Cattolica del Sacro Cuore, no. R4124500654) to SA; Ricerca Finalizzata Ministero della Salute (no. GR-2018-12366992) to SA; Versus Arthritis UK Program grant (no. 21802) to MKS; Versus Arthritis UK project grant (no. 22272) to MKS; Wellcome Trust (no. 204820/Z/16/Z) to TDO and MKS; and BPF-Medical Research Trust to CM.

Address correspondence to: Mariola Kurowska-Stolarska or Thomas D. Otto, Research into Inflammatory Arthritis Centre Versus Arthritis (RACE), University of Glasgow, United Kingdom; 120 University Place, G12 8TA, Glasgow, United Kingdom. Phone: 44.141.330.6085; Email: mariola.kurowska-stolarska@glasgow.ac.uk (MKS); Phone: 44.141.330.4698; Email: ThomasDan.Otto@glasgow.ac.uk (TDO). Or to: Elisa Gremese, Institute of Internal Medicine and Geriatrics, Fondazione Policlinico Universitario Agostino Gemelli IRCCS, via Moscati 31, 00-168 Rome, Italy; Phone: 39.063.015.9876; Email: elisa.gremese@unicatt.it.

References

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Lucas C, et al. Longitudinal analyses reveal immunological misfiring in severe COVID-19. Nature. 2020;584(7821):463–469.
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Merrill JT, et al. Emerging evidence of a COVID-19 thrombotic syndrome has treatment implications. Nat Rev Rheumatol. 2020;16(10):581–589.
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Mathew D, et al. Deep immune profiling of COVID-19 patients reveals distinct immunotypes with therapeutic implications. Science. 2020;369(6508):eabc8511.
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Schulte-Schrepping J, et al. Severe COVID-19 is marked by a dysregulated myeloid cell compartment. Cell. 2020;182(6):1419–1440.
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Guo C, et al. Single-cell analysis of two severe COVID-19 patients reveals a monocyte-associated and tocilizumab-responding cytokine storm. Nat Commun. 2020;11(1):3924.
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Liao M, et al. Single-cell landscape of bronchoalveolar immune cells in patients with COVID-19. Nat Med. 2020;26(6):842–844.
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Xu G, et al. The differential immune responses to COVID-19 in peripheral and lung revealed by single-cell RNA sequencing. Cell Discov. 2020;6:73.
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Carfi A, et al. Persistent symptoms in patients after acute COVID-19. JAMA. 2020;324(6):603–605.
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Misharin AV, et al. Monocyte-derived alveolar macrophages drive lung fibrosis and persist in the lung over the life span. J Exp Med. 2017;214(8):2387–2404.
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Chakarov S, et al. Two distinct interstitial macrophage populations coexist across tissues in specific subtissular niches. Science. 2019;363(6432):eaau0964.
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Gibbings SL, et al. Three unique interstitial macrophages in the murine lung at steady state. Am J Respir Cell Mol Biol. 2017;57(1):66–76.
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Aran D, et al. Reference-based analysis of lung single-cell sequencing reveals a transitional profibrotic macrophage. Nat Immunol. 2019;20(2):163–172.
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Ural BB, et al. Identification of a nerve-associated, lung-resident interstitial macrophage subset with distinct localization and immunoregulatory properties. Sci Immunol. 2020;5(45):eaax8756.
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Reyfman PA, et al. Single-cell transcriptomic analysis of human lung provides insights into the pathobiology of pulmonary fibrosis. Am J Respir Crit Care Med. 2019;199(12):1517–1536.
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Morales-Nebreda L, et al. The heterogeneity of lung macrophages in the susceptibility to disease. Eur Respir Rev. 2015;24(137):505–509.
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Mirastschijski U, et al. Lung surfactant for pulmonary barrier restoration in patients with COVID-19 pneumonia. Front Med (Lausanne). 2020;7:254.
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Gremese E, et al. Sarilumab use in severe SARS-CoV-2 pneumonia. EClinicalMedicine. 2020;27:100553.
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Wise J. Covid-19: Critically ill patients treated with arthritis drug tocilizumab show improved outcomes, researchers report. BMJ. 2020;371:m4530.
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Stuart T, et al. Comprehensive integration of single-cell data. Cell. 2019;177(7):1888–1902.
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Campbell CM. The opening salvo of anti-complement therapy against COVID-19. Lancet Rheumatol. 2020;2(12):e729–e730.
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Kahles F, et al. Osteopontin: a novel regulator at the cross roads of inflammation, obesity and diabetes. Mol Metab. 2014;3(4):384–393.
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Nolting J, et al. Retinoic acid can enhance conversion of naive into regulatory T cells independently of secreted cytokines. J Exp Med. 2009;206(10):2131–2139.
View this article via: PubMed CrossRef Google Scholar

Vogt L, et al. VSIG4, a B7 family-related protein, is a negative regulator of T cell activation. J Clin Invest. 2006;116(10):2817–2826.
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Rothlin CV, et al. TAM receptors are pleiotropic inhibitors of the innate immune response. Cell. 2007;131(6):1124–1136.
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Nagata K, et al. Identification of the product of growth arrest-specific gene 6 as a common ligand for Axl, Sky, and Mer receptor tyrosine kinases. J Biol Chem. 1996;271(47):30022–30027.
View this article via: PubMed CrossRef Google Scholar

Lew ED, et al. Differential TAM receptor-ligand-phospholipid interactions delimit differential TAM bioactivities. Elife. 2014;3:e03385.
View this article via: PubMed Google Scholar

Lemke G, Silverman GJ. Blood clots and TAM receptor signalling in COVID-19 pathogenesis. Nat Rev Immunol. 2020;20(7):395–396.
View this article via: PubMed CrossRef Google Scholar

Schmid ET, et al. AXL receptor tyrosine kinase is required for T cell priming and antiviral immunity. Elife. 2016;5:e12414.
View this article via: PubMed Google Scholar

Fujimori T, et al. The Axl receptor tyrosine kinase is a discriminator of macrophage function in the inflamed lung. Mucosal Immunol. 2015;8(5):1021–1030.
View this article via: PubMed CrossRef Google Scholar

Lemke G. Biology of the TAM receptors. Cold Spring Harb Perspect Biol. 2013;5(11):a009076.
View this article via: PubMed CrossRef Google Scholar

Liu J, et al. Neutrophil-to-lymphocyte ratio predicts critical illness patients with 2019 coronavirus disease in the early stage. J Transl Med. 2020;18(1):206.
View this article via: PubMed CrossRef Google Scholar

van der Meer JH, et al. TAM receptors, Gas6, and protein S: roles in inflammation and hemostasis. Blood. 2014;123(16):2460–2469.
View this article via: PubMed CrossRef Google Scholar

Skendros P, et al. Complement and tissue factor-enriched neutrophil extracellular traps are key drivers in COVID-19 immunothrombosis. J Clin Invest. 2020;130(11):6151–6157.
View this article via: JCI PubMed CrossRef Google Scholar

Radermecker C, et al. Neutrophil extracellular traps infiltrate the lung airway, interstitial, and vascular compartments in severe COVID-19. J Exp Med. 2020;217(12):e20201012.
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Pandolfi L, et al. Broncho-alveolar inflammation in COVID-19 patients: a correlation with clinical outcome. BMC Pulm Med. 2020;20(1):301.
View this article via: PubMed CrossRef Google Scholar

Zeng HL, et al. Proteomic characteristics of bronchoalveolar lavage fluid in critical COVID-19 patients. [published online October 24, 2020]. FEBS J. https://doi.org/10.1111/febs.15609.
View this article via: PubMed Google Scholar

Morse C, et al. Proliferating SPP1/MERTK-expressing macrophages in idiopathic pulmonary fibrosis. Eur Respir J. 2019;54(2):1802441.
View this article via: PubMed CrossRef Google Scholar

Wen W, et al. Immune cell profiling of COVID-19 patients in the recovery stage by single-cell sequencing. Cell Discov. 2020;6(1):31.
View this article via: CrossRef Google Scholar

Scott NA, et al. Recovery of monocyte exhaustion is associated with resolution of lung injury in COVID-19 convalescence [preprint]. https://doi.org/10.1101/2020.10.10.20207449 Posted on medRxiv October 16, 2020.

Kurowska-Stolarska M, Alivernini S. Synovial tissue macrophages: friend or foe? RMD Open. 2017;3(2):e000527.
View this article via: PubMed CrossRef Google Scholar

Zagorska A, et al. Diversification of TAM receptor tyrosine kinase function. Nat Immunol. 2014;15(10):920–928.
View this article via: PubMed CrossRef Google Scholar

Gautier EL, et al. Gene-expression profiles and transcriptional regulatory pathways that underlie the identity and diversity of mouse tissue macrophages. Nat Immunol. 2012;13(11):1118–1128.
View this article via: PubMed CrossRef Google Scholar

Waterborg CEJ, et al. Protective role of the MER tyrosine kinase via efferocytosis in rheumatoid arthritis models. Front Immunol. 2018;9:742.
View this article via: PubMed Google Scholar

Schwartzberg PL. Immunology. Tampering with the immune system. Science. 2001;293(5528):228–229.
View this article via: PubMed CrossRef Google Scholar

McGonagle D, et al. Mechanistic immunological based classification of rheumatoid arthritis. Autoimmun Rev. 2018;17(11):1115–1123.
View this article via: PubMed CrossRef Google Scholar

Schett G, et al. COVID-19: risk for cytokine targeting in chronic inflammatory diseases? Nat Rev Immunol. 2020;20(5):271–272.
View this article via: PubMed CrossRef Google Scholar

Lang FM, et al. GM-CSF-based treatments in COVID-19: reconciling opposing therapeutic approaches. Nat Rev Immunol. 2020;20(8):507–514.
View this article via: PubMed CrossRef Google Scholar

Xu X, et al. Effective treatment of severe COVID-19 patients with tocilizumab. Proc Natl Acad Sci U S A. 2020;117(20):10970–10975.
View this article via: PubMed CrossRef Google Scholar

De Luca G, et al. GM-CSF blockade with mavrilimumab in severe COVID-19 pneumonia and systemic hyperinflammation: a single-centre, prospective cohort study. Lancet Rheumatol. 2020;2(8):465–473.
View this article via: PubMed Google Scholar

Group RC, et al. Dexamethasone in hospitalized patients with Covid-19 — preliminary report. N Engl J Med. 2020;384(8):693–704.
View this article via: PubMed Google Scholar

Bronte V, et al. Baricitinib restrains the immune dysregulation in patients with severe COVID-19. J Clin Invest. 2020;130(12):6409–6416.
View this article via: JCI PubMed CrossRef Google Scholar

Shao X, et al. CellTalkDB: a manually curated database of ligand-receptor interactions in humans and mice. Brief Bioinform. 2020;:bbaa269.
View this article via: PubMed Google Scholar

Aquila I, et al. Severe acute respiratory syndrome Coronavirus 2 pandemic. Arch Pathol Lab Med. 2020;144(9):1048–1056.
View this article via: PubMed CrossRef Google Scholar

Tirosh I, et al. Dissecting the multicellular ecosystem of metastatic melanoma by single-cell RNA-seq. Science. 2016;352(6282):189–196.
View this article via: PubMed CrossRef Google Scholar

[1] Matacic C. Blood vessel injury may spur disease’s fatal second phase. Science. 2020;368(6495):1039–1040.
View this article via: PubMed CrossRef Google Scholar

[2] Tay MZ, et al. The trinity of COVID-19: immunity, inflammation and intervention. Nat Rev Immunol. 2020;20(6):363–374.
View this article via: PubMed CrossRef Google Scholar

[3] Chua RL, et al. COVID-19 severity correlates with airway epithelium-immune cell interactions identified by single-cell analysis. Nat Biotechnol. 2020;38(8):970–979.
View this article via: PubMed CrossRef Google Scholar

[4] Zhang Q, et al. Inborn errors of type I IFN immunity in patients with life-threatening COVID-19. Science. 2020;370(6515):eabd4570.
View this article via: PubMed CrossRef Google Scholar

[5] Bastard P, et al. Autoantibodies against type I IFNs in patients with life-threatening COVID-19. Science. 2020;370(6515):eabd4585.
View this article via: PubMed CrossRef Google Scholar

[6] Del Valle DM, et al. An inflammatory cytokine signature predicts COVID-19 severity and survival. Nat Med. 2020;26(10):1636–1643.
View this article via: PubMed CrossRef Google Scholar

[7] Silvin A, et al. Elevated calprotectin and abnormal myeloid cell subsets discriminate severe from mild COVID-19. Cell. 2020;182(6):1401–1418.
View this article via: PubMed CrossRef Google Scholar

[8] Chen L, et al. Elevated serum levels of S100A8/A9 and HMGB1 at hospital admission are correlated with inferior clinical outcomes in COVID-19 patients. Cell Mol Immunol. 2020;17(9):992–994.
View this article via: PubMed CrossRef Google Scholar

[9] Carvelli J, et al. Association of COVID-19 inflammation with activation of the C5a-C5aR1 axis. Nature. 2020;588(7836):146–150.
View this article via: PubMed CrossRef Google Scholar

[10] Lucas C, et al. Longitudinal analyses reveal immunological misfiring in severe COVID-19. Nature. 2020;584(7821):463–469.
View this article via: PubMed CrossRef Google Scholar

[11] Merrill JT, et al. Emerging evidence of a COVID-19 thrombotic syndrome has treatment implications. Nat Rev Rheumatol. 2020;16(10):581–589.
View this article via: PubMed CrossRef Google Scholar

[12] Laing AG, et al. A dynamic COVID-19 immune signature includes associations with poor prognosis. Nat Med. 2020;26(10):1623–1635.
View this article via: PubMed CrossRef Google Scholar

[13] Mathew D, et al. Deep immune profiling of COVID-19 patients reveals distinct immunotypes with therapeutic implications. Science. 2020;369(6508):eabc8511.
View this article via: PubMed CrossRef Google Scholar

[14] Pairo-Castineira E, et al. Genetic mechanisms of critical illness in Covid-19. Nature. 2021;591(7848):92–98.
View this article via: PubMed CrossRef Google Scholar

[15] Yunt ZX, Solomon JJ. Lung disease in rheumatoid arthritis. Rheum Dis Clin North Am. 2015;41(2):225–236.
View this article via: PubMed CrossRef Google Scholar

[16] Alivernini S, et al. Comparative analysis of synovial inflammation after SARS-CoV-2 infection. [published online July 6, 2020]. Ann Rheum Dis. https://doi.org/10.1136/annrheumdis-2020-218368.
View this article via: PubMed Google Scholar

[17] Yokogawa N, et al. Case of acute arthritis following SARS-CoV-2 infection. [published online June 26, 2020]. Ann Rheum Dis. https://doi.org/10.1136/annrheumdis-2020-218281.
View this article via: PubMed Google Scholar

[18] Firestein GS, McInnes IB. Immunopathogenesis of rheumatoid arthritis. Immunity. 2017;46(2):183–196.
View this article via: PubMed CrossRef Google Scholar

[19] Alivernini S, et al. Distinct synovial tissue macrophage subsets regulate inflammation and remission in rheumatoid arthritis. Nat Med. 2020;26(8):1295–1306.
View this article via: PubMed CrossRef Google Scholar

[20] Zhu L, et al. Single-cell sequencing of peripheral mononuclear cells reveals distinct immune response landscapes of COVID-19 and influenza patients. Immunity. 2020;53(3):685–696.
View this article via: PubMed CrossRef Google Scholar

[21] Zhang JY, et al. Single-cell landscape of immunological responses in patients with COVID-19. Nat Immunol. 2020;21(9):1107–1118.
View this article via: PubMed CrossRef Google Scholar

[22] Schulte-Schrepping J, et al. Severe COVID-19 is marked by a dysregulated myeloid cell compartment. Cell. 2020;182(6):1419–1440.
View this article via: PubMed CrossRef Google Scholar

[23] Guo C, et al. Single-cell analysis of two severe COVID-19 patients reveals a monocyte-associated and tocilizumab-responding cytokine storm. Nat Commun. 2020;11(1):3924.
View this article via: PubMed CrossRef Google Scholar

[24] Liao M, et al. Single-cell landscape of bronchoalveolar immune cells in patients with COVID-19. Nat Med. 2020;26(6):842–844.
View this article via: PubMed CrossRef Google Scholar

[25] Xu G, et al. The differential immune responses to COVID-19 in peripheral and lung revealed by single-cell RNA sequencing. Cell Discov. 2020;6:73.
View this article via: PubMed Google Scholar

[26] Carfi A, et al. Persistent symptoms in patients after acute COVID-19. JAMA. 2020;324(6):603–605.
View this article via: PubMed CrossRef Google Scholar

[27] Misharin AV, et al. Monocyte-derived alveolar macrophages drive lung fibrosis and persist in the lung over the life span. J Exp Med. 2017;214(8):2387–2404.
View this article via: PubMed CrossRef Google Scholar

[28] Chakarov S, et al. Two distinct interstitial macrophage populations coexist across tissues in specific subtissular niches. Science. 2019;363(6432):eaau0964.
View this article via: PubMed CrossRef Google Scholar

[29] Gibbings SL, et al. Three unique interstitial macrophages in the murine lung at steady state. Am J Respir Cell Mol Biol. 2017;57(1):66–76.
View this article via: PubMed CrossRef Google Scholar

[30] Aran D, et al. Reference-based analysis of lung single-cell sequencing reveals a transitional profibrotic macrophage. Nat Immunol. 2019;20(2):163–172.
View this article via: PubMed CrossRef Google Scholar

[31] Ural BB, et al. Identification of a nerve-associated, lung-resident interstitial macrophage subset with distinct localization and immunoregulatory properties. Sci Immunol. 2020;5(45):eaax8756.
View this article via: PubMed CrossRef Google Scholar

[32] Reyfman PA, et al. Single-cell transcriptomic analysis of human lung provides insights into the pathobiology of pulmonary fibrosis. Am J Respir Crit Care Med. 2019;199(12):1517–1536.
View this article via: PubMed CrossRef Google Scholar

[33] Morales-Nebreda L, et al. The heterogeneity of lung macrophages in the susceptibility to disease. Eur Respir Rev. 2015;24(137):505–509.
View this article via: PubMed CrossRef Google Scholar

[34] Mirastschijski U, et al. Lung surfactant for pulmonary barrier restoration in patients with COVID-19 pneumonia. Front Med (Lausanne). 2020;7:254.
View this article via: PubMed Google Scholar

[35] Gremese E, et al. Sarilumab use in severe SARS-CoV-2 pneumonia. EClinicalMedicine. 2020;27:100553.
View this article via: PubMed Google Scholar

[36] Wise J. Covid-19: Critically ill patients treated with arthritis drug tocilizumab show improved outcomes, researchers report. BMJ. 2020;371:m4530.
View this article via: PubMed Google Scholar

[37] Stuart T, et al. Comprehensive integration of single-cell data. Cell. 2019;177(7):1888–1902.
View this article via: PubMed CrossRef Google Scholar

[38] Campbell CM. The opening salvo of anti-complement therapy against COVID-19. Lancet Rheumatol. 2020;2(12):e729–e730.
View this article via: PubMed CrossRef Google Scholar

[39] Kahles F, et al. Osteopontin: a novel regulator at the cross roads of inflammation, obesity and diabetes. Mol Metab. 2014;3(4):384–393.
View this article via: PubMed CrossRef Google Scholar

[40] Nolting J, et al. Retinoic acid can enhance conversion of naive into regulatory T cells independently of secreted cytokines. J Exp Med. 2009;206(10):2131–2139.
View this article via: PubMed CrossRef Google Scholar

[41] Vogt L, et al. VSIG4, a B7 family-related protein, is a negative regulator of T cell activation. J Clin Invest. 2006;116(10):2817–2826.
View this article via: JCI PubMed CrossRef Google Scholar

[42] Rothlin CV, et al. TAM receptors are pleiotropic inhibitors of the innate immune response. Cell. 2007;131(6):1124–1136.
View this article via: PubMed CrossRef Google Scholar

[43] Nagata K, et al. Identification of the product of growth arrest-specific gene 6 as a common ligand for Axl, Sky, and Mer receptor tyrosine kinases. J Biol Chem. 1996;271(47):30022–30027.
View this article via: PubMed CrossRef Google Scholar

[44] Lew ED, et al. Differential TAM receptor-ligand-phospholipid interactions delimit differential TAM bioactivities. Elife. 2014;3:e03385.
View this article via: PubMed Google Scholar

[45] Lemke G, Silverman GJ. Blood clots and TAM receptor signalling in COVID-19 pathogenesis. Nat Rev Immunol. 2020;20(7):395–396.
View this article via: PubMed CrossRef Google Scholar

[46] Schmid ET, et al. AXL receptor tyrosine kinase is required for T cell priming and antiviral immunity. Elife. 2016;5:e12414.
View this article via: PubMed Google Scholar

[47] Fujimori T, et al. The Axl receptor tyrosine kinase is a discriminator of macrophage function in the inflamed lung. Mucosal Immunol. 2015;8(5):1021–1030.
View this article via: PubMed CrossRef Google Scholar

[48] Lemke G. Biology of the TAM receptors. Cold Spring Harb Perspect Biol. 2013;5(11):a009076.
View this article via: PubMed CrossRef Google Scholar

[49] Liu J, et al. Neutrophil-to-lymphocyte ratio predicts critical illness patients with 2019 coronavirus disease in the early stage. J Transl Med. 2020;18(1):206.
View this article via: PubMed CrossRef Google Scholar

[50] van der Meer JH, et al. TAM receptors, Gas6, and protein S: roles in inflammation and hemostasis. Blood. 2014;123(16):2460–2469.
View this article via: PubMed CrossRef Google Scholar

[51] Skendros P, et al. Complement and tissue factor-enriched neutrophil extracellular traps are key drivers in COVID-19 immunothrombosis. J Clin Invest. 2020;130(11):6151–6157.
View this article via: JCI PubMed CrossRef Google Scholar

[52] Radermecker C, et al. Neutrophil extracellular traps infiltrate the lung airway, interstitial, and vascular compartments in severe COVID-19. J Exp Med. 2020;217(12):e20201012.
View this article via: PubMed CrossRef Google Scholar

[53] Pandolfi L, et al. Broncho-alveolar inflammation in COVID-19 patients: a correlation with clinical outcome. BMC Pulm Med. 2020;20(1):301.
View this article via: PubMed CrossRef Google Scholar

[54] Zeng HL, et al. Proteomic characteristics of bronchoalveolar lavage fluid in critical COVID-19 patients. [published online October 24, 2020]. FEBS J. https://doi.org/10.1111/febs.15609.
View this article via: PubMed Google Scholar

[55] Morse C, et al. Proliferating SPP1/MERTK-expressing macrophages in idiopathic pulmonary fibrosis. Eur Respir J. 2019;54(2):1802441.
View this article via: PubMed CrossRef Google Scholar

[56] Wen W, et al. Immune cell profiling of COVID-19 patients in the recovery stage by single-cell sequencing. Cell Discov. 2020;6(1):31.
View this article via: CrossRef Google Scholar

[57] Scott NA, et al. Recovery of monocyte exhaustion is associated with resolution of lung injury in COVID-19 convalescence [preprint]. https://doi.org/10.1101/2020.10.10.20207449 Posted on medRxiv October 16, 2020.

[58] Kurowska-Stolarska M, Alivernini S. Synovial tissue macrophages: friend or foe? RMD Open. 2017;3(2):e000527.
View this article via: PubMed CrossRef Google Scholar

[59] Zagorska A, et al. Diversification of TAM receptor tyrosine kinase function. Nat Immunol. 2014;15(10):920–928.
View this article via: PubMed CrossRef Google Scholar

[60] Gautier EL, et al. Gene-expression profiles and transcriptional regulatory pathways that underlie the identity and diversity of mouse tissue macrophages. Nat Immunol. 2012;13(11):1118–1128.
View this article via: PubMed CrossRef Google Scholar

[61] Waterborg CEJ, et al. Protective role of the MER tyrosine kinase via efferocytosis in rheumatoid arthritis models. Front Immunol. 2018;9:742.
View this article via: PubMed Google Scholar

[62] Schwartzberg PL. Immunology. Tampering with the immune system. Science. 2001;293(5528):228–229.
View this article via: PubMed CrossRef Google Scholar

[63] McGonagle D, et al. Mechanistic immunological based classification of rheumatoid arthritis. Autoimmun Rev. 2018;17(11):1115–1123.
View this article via: PubMed CrossRef Google Scholar

[64] Schett G, et al. COVID-19: risk for cytokine targeting in chronic inflammatory diseases? Nat Rev Immunol. 2020;20(5):271–272.
View this article via: PubMed CrossRef Google Scholar

[65] Lang FM, et al. GM-CSF-based treatments in COVID-19: reconciling opposing therapeutic approaches. Nat Rev Immunol. 2020;20(8):507–514.
View this article via: PubMed CrossRef Google Scholar

[66] Xu X, et al. Effective treatment of severe COVID-19 patients with tocilizumab. Proc Natl Acad Sci U S A. 2020;117(20):10970–10975.
View this article via: PubMed CrossRef Google Scholar

[67] De Luca G, et al. GM-CSF blockade with mavrilimumab in severe COVID-19 pneumonia and systemic hyperinflammation: a single-centre, prospective cohort study. Lancet Rheumatol. 2020;2(8):465–473.
View this article via: PubMed Google Scholar

[68] Group RC, et al. Dexamethasone in hospitalized patients with Covid-19 — preliminary report. N Engl J Med. 2020;384(8):693–704.
View this article via: PubMed Google Scholar

[69] Bronte V, et al. Baricitinib restrains the immune dysregulation in patients with severe COVID-19. J Clin Invest. 2020;130(12):6409–6416.
View this article via: JCI PubMed CrossRef Google Scholar

[70] Shao X, et al. CellTalkDB: a manually curated database of ligand-receptor interactions in humans and mice. Brief Bioinform. 2020;:bbaa269.
View this article via: PubMed Google Scholar

[71] Aquila I, et al. Severe acute respiratory syndrome Coronavirus 2 pandemic. Arch Pathol Lab Med. 2020;144(9):1048–1056.
View this article via: PubMed CrossRef Google Scholar

[72] Tirosh I, et al. Dissecting the multicellular ecosystem of metastatic melanoma by single-cell RNA-seq. Science. 2016;352(6282):189–196.
View this article via: PubMed CrossRef Google Scholar

COVID-19 and RA share an SPP1 myeloid pathway that drives PD-L1⁺ neutrophils and CD14⁺ monocytes

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COVID-19 and RA share an SPP1 myeloid pathway that drives PD-L1+ neutrophils and CD14+ monocytes

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COVID-19 and RA share an SPP1 myeloid pathway that drives PD-L1⁺ neutrophils and CD14⁺ monocytes