Spatial Transcriptomic Characterization of COVID-19 Pneumonitis Identifies Immune Pathways Related to Tissue Injury

bioRxiv preprint doi: https://doi.org/10.1101/2021.06.21.449178; this version posted June 21, 2021. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY 4.0 International license.

Spatial transcriptomic characterization of COVID-19 pneumonitis identifies immune pathways related to tissue injury

Cross AR1, de Andrea CE2, Landecho Acha MF2,3, Cerundolo L1, Etherington R4, Denney L4, Ho LP4, Roberts I5, Klenerman P3, Hester J1, Melero I2, Sansom SN6*, Issa F1*

1. Nuffield Department of Surgical Sciences, University of Oxford, Oxford, UK 2. Department of Immunology and Immunotherapy, Clínica de la Universdad de Navarra, Pamplona, Spain 3. Department of Internal Medicine, Clinica de la Universdad de Navarra, Pamplona, Spain 4. Nuffield Department of Medicine, University of Oxford, Oxford, UK 5. Department of Cellular Pathology, Oxford University Hospitals NHS Foundation Trust, Oxford, UK 6. Kennedy Institute of Rheumatology, University of Oxford, UK

*Joint senior authors. Corresponding author: [email protected]

ABSTRACT

Severe lung damage in COVID-19 is known to involve complex interactions between diverse populations of immune and stromal cells. In this study, we applied a spatial transcriptomics approach to better delineate the cells, pathways and genes responsible for promoting and perpetuating severe tissue pathology in COVID-19 pneumonitis. Guided by tissue histology and immunohistochemistry we performed a targeted sampling of dozens of regions representing a spectrum of diffuse alveolar damage from the post-mortem lung of three COVID-19 patients. Application of a combination of differential gene expression, weighted gene correlation network, pathway and spatial deconvolution analysis stratified the sampled regions into five distinct groups according to degree of alveolar damage, levels of cytotoxic inflammation and innate activation, epithelial reorganization, and fibrosis. Integrative network analysis of the identified groups revealed the presence of proliferating CD8 T and NK cells in severely damaged areas along with signatures of cytotoxicity, interferon signalling and high expression of immune cell chemoattractants (including CXCL9/10/11 and CCL2). Areas of milder damage were marked by innate immune signalling (including TLR response, IL-1, IL-6) together with signatures of antigen presentation, and fibrosis. Based on these data we present a cellular model of tissue damage in terminal COVID-19 that confirms previous observations and highlights novel opportunities for therapeutic intervention.

INTRODUCTION

Mortality following severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection is largely related to the antiviral response and immune-mediated lung injury1. Histopathologically, coronavirus disease 2019 (COVID-19) pneumonitis is associated with diffuse alveolar damage (DAD), fibrosis, leukocytic infiltrates, and microvascular thromboses2- 4. Features of DAD include alveolar wall thickening, interstitial expansion, hyaline membrane deposition, and pneumocyte hyperplasia. Emerging studies have begun to describe the transcriptomic profiles of these histopathological features, although this has been with a specific emphasis on the analysis of foci of SARS-CoV-2 infection5-7. To the best of our knowledge, severe pathology is not consistently associated with active viral replication, and the absence or relatively low levels of SARS-CoV-2 RNA or antigen in lung tissue from prolonged cases of severe COVID-195 supports this model of inflammation-induced disease. The immune contributors and biological pathways associated with the severe alveolar injury therefore remain unclear. A greater understanding of the host response to COVID-19 within the lung and correlation to DAD would complement the increasing knowledge of both tissue and blood-based immune profiles8.

Advanced spatial profiling techniques provide new tools to identify the distribution of proteins and RNAs in situ within tissue, allowing a spatial dissection of biological processes in and around specific histological features of interest9,10. However, published studies have yet to leverage the technique to map immune spatial heterogeneity to tissue pathology through network analysis. Here, we employ an advanced multiplexed in situ hybridization tissue analysis platform to provide detailed transcriptomic descriptions across multiple spatially discrete areas in lung samples from three COVID-19 patients. Uniquely, these tissues were obtained via open sampling at the point of death, which ensured high quality RNA analyses and avoided the caveats associated with late autopsies. We provide a comprehensive visualisation of the processes active within each sampled area, mapping gene modules to degree of damage. To further the analysis, we develop a network analytical approach which may be used as a template for other spatial genomic studies, and which facilitates the unbiased investigation of spatially divergent cellular identities, interactions, and pathways.

RESULTS

Immune cell infiltration is associated with severe local tissue damage in COVID-19

The histological and immune cell landscape within COVID-19 lung tissue from three patients with fatal disease was assessed ensuring coverage of the intra-tissue range of disease (mean specimen area 1.78cm2; Supplementary Table 1 for patient information). Each sample featured a spectrum of DAD comprising type II pneumocyte hyperplasia, hyaline membrane formation, and interstitial expansion. There was a non-uniform distribution of immune infiltrates (Figure 1A-B and Supplementary Table 2). Histological features beyond DAD included vacuolated macrophages, oedema, vascular thrombi, and squamous metaplasia. Bulk detection for SARS-CoV-2 nucleocapsid RNA in patient lung samples found low or absent viral loads (Supplementary Table 1) and SARS-CoV-2 Spike RNA could not be detected with in situ hybridisation although weak and sparse foci of intracellular SARS-CoV-2 viral protein were observed within morphologically-defined alveolar macrophages (data not shown). Immunofluorescent staining for CD3, CD68 and pan-cytokeratin (panCK) enabled the identification of lymphocytes, myeloid cells, epithelial cells, and general tissue architecture (Figure 1B). Based on this staining we identified areas of interest (AOIs) that represented the bioRxiv preprint doi: https://doi.org/10.1101/2021.06.21.449178; this version posted June 21, 2021. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY 4.0 International license.

spectrum of DAD severity and immune composition (Figure 1B and 1C). These were annotated for histopathological DAD severity by two independent pathologists, as well as by cell identity using an automated nucleated cell count analysis for CD3 and CD68 (Figure 1C). To profile the pathological pathways active in each AOI, we applied a multiplexed in situ hybridisation approach to quantitate the expression of a curated panel of >1800 genes that was enriched in immune targets and augmented with a COVID-19-specific gene set (Supplementary File 1). Differential expression analysis comparing mild vs. severe DAD across all sampled AOIs (16 mild and 29 severe) identified n=59 genes with significantly higher expression in severe pathology (Benjamini Hochberg [BH] adjusted p<0.05, |fold change| >1.5) including those encoding chemokines (CXCL9, CXCL11, and CCL5), cytotoxic molecules (GZMA, GZMB, GZMK, PRF1, LYZ, NKG7), complement factors (C1QA, C3, C2, C1QB), and proteins involved in antigen processing and presentation (CD74, HLA genes, CTSS) (Figure 1D). Genes upregulated in the severe areas showed an a significant over-representation (BH adjusted p<0.05) gene ontology (GO) biological process terms related to T cell activation and differentiation, antigen presentation, cytokine production, cytotoxicity, and response to interferon gamma (Figure 1E). By contrast, genes that showed higher expressed in areas of mild damage (n=40, BH adjusted p<0.05, |fold change| >1.5) included those with known roles in immune-mediated repair (IL1RL1, AREG, CXCL5) together with a significant over-representation (BH adjusted p<0.05) of pathways associated with wound healing, epithelial re-organization and adhesion, as well as extracellular matrix deposition (Figure 1D and 1E).

Network analysis implicates CD8 T cells, mononuclear phagocytes and the TLR, interferon, and IL-1 signaling pathways in lung inflammation in COVID-19

In order to perform an unbiased exploration of the cellular and phenotypic variation present in the set of the profiled AOIs we employed the weighted gene correlation network analysis (WGCNA) method11. This analysis identified 17 distinct modules of co-expressed genes (n=27– 266 genes per module, median 88) (Figure 2A). We began a systematic characterization of these identified gene modules by identifying biological processes and pathways over- represented amongst the gene members of each module (Figure 2B). Next we assessed the association of the modules with specific cell types by correlating expression of the module eigengenes (the representative module expression patterns) with separate cell type abundance estimates that were determined for each AOI by automatic cell type deconvolution analysis12 (Figure 2C and Supplementary Figure 2). Finally, we investigated correlation of the module eigengenes with the expression of known cell type marker genes (Figure 2D) and with genes involved in immune signaling, the complement system, viral infection and fibrosis (Figure 2E). Based on these analyses we named each module according to its cell type associations and biological pathway enrichment.

As expected for lung tissue, we found a set of stromal modules representing (i) “Epithelial cells” (Blue, containing EPCAM), (ii) “Type 2 pneumocytes” (Red, containing the surfactant encoding genes SFTPB, SFTPC and SFTPD), (iii) fibroblasts (“Fibroblast phenotype”, Black, containing COL1A1, COL3A2, COL5A1 and THY1) and (iv) “Vasculature” (Yellow, containing CDH5, THBD, ENG), which all showed corresponding pathway and cell type associations + (Figures 2B-D). Immunofluorescent imaging established the presence of CD68 macrophages within sampled areas (Figure 1B and 1C) and we found three modules that showed a high correlation with CD68 expression (Figure 2D). These comprised (i) an “Alveolar macrophage” module (Cyan) that also displayed high expression correlation with the macrophage receptor MARCO and enrichment for the “phagocytosis, engulfment” GO biological process, (ii) a “Macrophage identity” module (Grey60) that also showed high expression correlation with bioRxiv preprint doi: https://doi.org/10.1101/2021.06.21.449178; this version posted June 21, 2021. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY 4.0 International license.

the mannose receptor MRC1 (a marker of alternative “M2” macrophages) and SIRPA, and (iii) an “Antigen presentation” module (Greenyellow) that showed a high correlation with predicted MARCO- macrophage abundance along with high expression correlation with SIGLEC1 and C1QA/B as might be expected for interstitial macrophages. We also noted a “IFITM2/HSP/ECM” module (Pink) that contained MERTK and PDGFRA and a module with pathway enrichments for “Apelin/mTOR signalling” (Purple).

Severe COVID-19 is associated with massive lung immune cell infiltration, and in line with this we discovered modules of genes with clear associations with lymphocytes and myeloid cells. The “Cytotoxicity and T cells” module (Turquoise) was associated with the GO “Interferon- gamma production” biological process, as well as the KEGG “T cell receptor signalling” and “Natural Killer mediated cytotoxicity” pathways (Figure 2B). The expression of this module was positively associated with predicted presence of CD8+ cytotoxic T cells, NK cells and activated dendritic cells (DCs) (Figure 2C). This module contained known T cell markers including CD3D/E, CD2 and CD8A as well as genes associated with cytotoxicity such as PRF1 and GNLY (Figure 2D). It also showed a high correlation with the expression of specific chemokines including CXCL9, CXCL10 and CXCL11 and had the highest correlation with the expression of IFNG (Figure 2E). The “TLR signaling and monocytes” module (Midnightblue) contained the gene encoding the classical monocyte marker CD14 along with CD163 (Figure 2D). In addition, we discovered several modules that contained genes associated with active inflammation including an “Interferon responses” module (Brown), a “TLR and IL-1 signaling” module (Salmon) and an “IL-1 response: IL-6/IL-8” module (Lightcyan) that were named according to their pathway enrichments (Figure 2B) and correlations with chemokine/cytokine expression (Figure 2E). We found three sets of genes associated with more general cellular processes including “Cell cycling” (Lightgreen), “Chromatin remodelling” (Lightyellow) and “Hypoxic response” (Green) which are indicative of local immune cell proliferation and oxygen stress.

Severe alveolar damage in COVID-19 is linked with myeloid cell antigen presentation, T cell cytotoxicity and expression of the CXCL9/10/11 interferon response genes

Within the AOIs from the each of three patients we found that severe DAD was consistently associated with the expression of the “Interferon responses”, “Cytotoxicity and T cells”, “Chromatin remodelling”, “Antigen presentation”, and TLR signaling (“TLR and IL-1 signalling” or “TLR signaling and monocytes”) modules. The “IL-1 response: IL-6/IL-8” module did not show an obvious correlation with DAD severity while higher expression of gene modules associated with “Epithelial cells” and “Vasculature” was associated with the lowest DAD scores (Figure 3A). We next sought to better understand the variation in cellular and immune phenotype that was present in the sampled AOIs. To do so we hierarchically clustered the AOIs by expression of the WGCNA module eigengenes. This analysis revealed 5 groups of AOIs with different transcriptional signatures and associations with severity (Figure 3B-C). We numbered these spatial groups of AOI according to their association with severe damage: spatial group 1 contained the lowest proportion of severe AOIs while spatial group 5 was comprised exclusively of AOIs from regions with severe DAD. The breakdown of the identified AOI groups by severity, mean presence of CD3+ cells and by patient of origin is shown in Figure 4A.

Spatial group 1 included AOIs from patients B and C that were marked with high expression of the stromal eigengenes (with expression of the vascular genes ENG, PECAM1, CDH5; the surfactant genes SFTPA1, STFPB, and SFTBC; and the epithelial genes KRT19, EPCAM and bioRxiv preprint doi: https://doi.org/10.1101/2021.06.21.449178; this version posted June 21, 2021. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY 4.0 International license.

CDH1), as well as the “IL-1 response: IL-6/IL-8” eigengene, and that were mostly of mild severity. The AOIs in spatial group 2 all came from patient A, showed expression of macrophage and “Hypoxic response” eigengenes in addition to that of many of the stromal signatures in spatial group 1, but also a number of macrophage and antigen-presenting genes (HLA-DR genes, MARCO, MRC1 and CD68), and were associated with both mild and severe DAD. The remaining three spatial groups (3, 4 and 5) lacked enrichment of the epithelial eigengenes, shared higher expression of the “Interferon responses” module and were predominantly composed of AOIs with severe DAD and higher fractions of CD3+ T cells. AOIs from spatial groups 3 and 5 showed higher expression of the “Cytotoxicity and T cells” and “cell cycling” eigengenes, while the AOIs from spatial group 4 (which were all from patient B) showed higher expression of the “TLR signalling and monocytes” eigengene. Spatial groups 3 and 5 shared expression of NK, CD8, and cytotoxicity genes (e.g. NKG7, CD8A, GNLY, GZMK, and PRF1), yet differed in their expression of interferon genes (spatial group 3) vs. macrophage genes (spatial group 5). Spatial group 4 however had a strong association with both interferon and TLR signalling genes (e.g. IFNA1, IRF1, TLR2, TLR4, TLR5, TLR6), but also had a weak overlap with both cytotoxicity and macrophage identity genes. Similar to spatial group 2, the AOIs in spatial group 5 also showed higher expression of the “Hypoxic response” eigengene and were also mostly from patient A. Importantly, despite variability in immune signatures across AOIs, there was a consistency in lymphocyte characteristics across all patients. AOIs with a lymphocytic infiltrate demonstrated a consistent phenotype enriched principally with the “Cytotoxicity and T cells” gene module, the expression of which showed little spatial heterogeneity across AOIs of similar cellular makeup.

Finally, we constructed correlation networks to investigate the associations between the expression of the WGCNA eigengenes, genes encoding immune signaling factors, and the predicted cell type abundances in each of the 5 spatial groups. Inspection of the networks from the three spatial groups associated with the highest levels of damage (spatial groups 3,4,5; Figure 4A) identified the following correlates: (i) expression of the WGCNA modules “Cytotoxicity and T cells”, “Antigen presentation”, “Interferon response”, “Cell cycling” and “Chromatin remodelling”, (ii) predicted abundances of “MARCO- macrophages”, “Activated DC”, “NK”, “CD8 T cells”, “Dividing T cells”, “Dividing macrophages” and (iii) expression of CCL2/3/4/5/13/18/19, CXCL9/10/11, FASLG, IFNA1, IFNA17, IL16/18/32, LTB, TNF, TNFSF10, TNFSF13B and XCL2 as core features of severe DAD in all three patients. The “TLR and IL-1 signalling” module eigengene was implicated in the spatial group 3 and 4 networks from patients B and C while the “IL-1 response: IL-6/IL-8” module was observed in the spatial group 1 and 2 networks from patients A, B and C suggesting that IL-1 signaling is centrally involved in the COVID-19-induced lung tissue damage. In agreement with the computational predictions, immunofluorescent confocal microscopy analysis confirmed the presence of a large number of T cells in the AOIs with the highest levels of tissue damage (see representative inset images, Figure 4B-F, Figure 3B, and Figure 4A). Unexpectedly, the expression of IL-6, IL-6 receptors, viral receptors/co-factors, genes involved in fibrosis, and complement regulatory genes were largely associated with alveolar epithelium, endothelium, and alveolar macrophages (Figure 4G). These areas, although associated with innate IL-1 signalling, correlated with the less inflamed spatial groups (1 and 2, Figure 4B-C).

In summary these analyses suggest that severe tissue damage in COVID-19 involves an ensemble of interacting and proliferating immune cells in which myeloid cells such as MARCO- macrophages and DCs are activated by TLR-mediated signaling, express IL-1 and interferon alpha, and present antigen to cytotoxic lymphocytes driving the production of IFNG and a specific set of chemokines and cytokines. This includes high expression of CXCL9, CXCL10 and CXCL11, factors which are known to act via CXCR3 to promote immune cell chemotaxis, bioRxiv preprint doi: https://doi.org/10.1101/2021.06.21.449178; this version posted June 21, 2021. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY 4.0 International license.

extravasation and activation13, as well as IL-32 which is known to stimulate TNFa and IL-6 secretion from macrophages14, and CCL19 which acts via CCR7 to promote DC and central memory T cell migration15.

DISCUSSION

Analytically, the correlation network-based analysis approach presented here provides a powerful means for the interpretation of spatial transcriptomic data in a novel and evolving field. Differential gene expression analysis between histological categories has been the most commonly employed approach in the analysis of spatial datasets but this method can reduce complex heterogeneity into binary categories and limit the potential for novel insight into spatial variation. In this study, we therefore integrated WGCNA and cell type deconvolution to perform an unbiased analysis of spatial variation in gene expression and cell type abundance. Together with pathological and immunohistological cell count analysis this approach permitted elucidation of the biological circuits active in the lung tissue of patients with critical COVID-19, demonstrating the potential of spatial genomics and correlation-based approaches to help discover the cellular basis of disease pathology.

The early innate response to COVID-19 is dominated by antiviral cytokine secretion, principally type I/III interferons, that have been reported to inhibit SARS-CoV-2 replication in vitro16-18, and which mediate downstream protective effects via IFN-stimulated gene (ISG) products19. While impaired peripheral blood IFN responses have been reported20, it is not clear if this also reflects local responses, particularly since IFN genes have been detected in bronchio-alveolar lavage (BAL) fluid21 and lung tissue5. More recent work has identified an upregulation of type I interferons in peripheral blood mononuclear cells from patients with severe COVID-19 which is not seen in other viral pneumonias such as severe influenza22. We found IFN responses in the lung to be largely associated with DAD severity, although there was variability in associated modules and genes even within the most severe spatial AOI groups. A reduced expression of IFN, TLR and IL-1 signalling modules was accompanied with a loss in chemokine expression. Specifically, there was spatial variability in the association with TLR signalling, specific chemokines (e.g. CXCL9), and T cell cytotoxicity, with the more moderate spatial groups associating with TLR/IL-1 signalling and the most severe groups displaying an IFN signalling signature without obvious TLR/IL-1 response. This may represent a switch from TLR dependent type I (IFNA1/IFNA17) IFN production by myeloid cells in the less severe AOIs to secondary T and NK cell driven type III (IFNG) responses in areas of severe tissue damage23.

Myeloid cell dysregulation is a key finding of severe or progressive COVID-19 infection24-27. A single cell RNA sequencing (scRNAseq) study of BAL fluid from severe COVID-19 cases identified an increase in the ratio of CD14+HLA-DRlo inflammatory monocytes to tissue- resident alveolar macrophages28. This myeloid-neutrophil crosstalk may be associated with neutrophil extracellular trap formation (NETosis) and ongoing inflammation29. However, we did not detect a strong neutrophil signature in our tissue samples, which is in keeping with reports of low neutrophil abundance in the lung in late stage disease6 but may also relate to our sampling strategy, probe panel or to technical problems associated with the detection of low abundance neutrophil mRNA or its degradation by neutrophil RNAse30. Our spatial analysis could separate alveolar from infiltrating monocytes, and while both populations expressed antigen presenting genes, only interstitial monocytes associated with lymphocytes and were correlated with severe damage. bioRxiv preprint doi: https://doi.org/10.1101/2021.06.21.449178; this version posted June 21, 2021. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY 4.0 International license.

IL-6 is a key inflammatory cytokine in viral infection and is produced by a wide range of cell types including macrophages, fibroblasts, and endothelial cells31. Its importance in COVID-19 stems from its association with disease severity32-34 and the success of tocilizumab (anti-IL-6R) therapy35. Unexpectedly, our data revealed IL-6 response genes to most prominently associate with the less damaged areas of spatial groups 3 and 4 that had the highest abundance of macrophages, fibroblasts, and vascular cells. It is possible that IL-6-targeting therapies act to protect areas which have not yet progressed to severe irreversible damage. This may in turn explain the divergent innate signatures seen in Patient A who received tocilizumab, where there was a weaker correlation between fibroblasts and macrophages with severity together with reduced TLR/IL-1 signalling.

Chemokine ligands were strongly represented in our dataset, with the key leukocyte- recruiting ligands CCL5, CCL18 and CCL19 clearly associating with severe pathology. In addition, we found a very strong association of CXCL9, CXCL10 and CXCL11 with severe histopathological damage suggesting a role for the CXCL9, CXCL10, CXCL11/CXCR3 axis in driving lung damage in COVID-19. These chemokines may be produced by macrophages, fibroblasts, or endothelial cells on stimulation with IFNg13,36 and are crucial in the recruitment of NK cells as well as cytotoxic T cells, therefore presenting a potential avenue for therapeutic intervention. In a scRNAseq study of BAL, CXCL9/10 as well as granzyme-expressing cytotoxic CD8 T cells have been detected, with differences between moderate and severe patient groups principally in degree of proliferation and TCR clonality28. A reduction in peripheral blood NK cells is a signature of COVID-19 severity, yet data from BAL scRNAseq show that NK transcripts are present at equivalent levels to normal lung28. It is likely therefore that the phenotype of NK cells and their activation profile is of relevance to disease severity37. We identified a cytotoxic gene module within areas of lymphocytic infiltrate with overlapping CD8 and NK cell signatures. These could not be independently resolved, suggesting either co- localisation or a converging phenotype. Despite contrasting findings in the peripheral blood, activated and SARS-CoV-2 reactive CD8+ T cells have been found to be associated with severe disease, indicating active T cell immunity38. CD4 transcripts correlated with myeloid rather than T cell modules in our dataset, which may reflect a relative lack of CD4+ T cells in areas of severe damage. Regulatory mechanisms were largely undetectable apart from IL-10R expression in severe areas (associating with T cell modules) and complement regulatory genes (CD46, CD55, CD59) in milder areas. Together with our data, these studies of lung immune pathology contrast with peripheral blood data where CD8 lymphopaenia and exhaustion is a feature of severe COVID-19 infection39 and may highlight dissimilarities in tissue and systemic responses8.

Fibrosis is of particular interest given the emerging data linking its development with the long- term respiratory complications of COVID-1940,41. While DAD is a defining feature of COVID- 1942, post-mortem studies have highlighted fibrotic changes to be commonplace in patients with a longer duration of illness43, similar to the histopathological findings in our series. This fibrosis is widely reported and characterized by lung parenchymal remodelling, fibroblast proliferation, and airspace obliteration2,42-44. Interestingly, our data highlight the association of fibrotic genes and pathways (in particular TGF-B signalling) with epithelial and endothelial cells as well as MARCO+ macrophages in areas of lower DAD severity. These areas were distinct in their expression of prostaglandins such as PTGS2, PLA2G4F, and PTGER4, supporting a potential role for prostaglandin inhibition in COVID-19 independent of its anti- thrombotic effects45. While immune modulatory agents have received attention as therapies in the treatment of COVID-19, we suggest that further work should focus on understanding how and when to intervene on the pathways promoting fibrosis. bioRxiv preprint doi: https://doi.org/10.1101/2021.06.21.449178; this version posted June 21, 2021. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY 4.0 International license.

Of note, complement activating genes were expressed in areas that were over-represented in pathways involved in cytotoxicity, T cells, and antigen presentation and were largely associated with areas of severe DAD. By contrast, complement inhibitory genes (CD46, CD55, CD59) were associated with epithelial and endothelial cells in milder areas of damage. Several studies are now emerging implicating complement or its regulation in COVID-19. In one study, patients with severe COVID-19 were found to have increased levels of Factor B and Ba, a zymogen which downregulates complement inhibitory protein expression46. In another, C3 expression was found to correlate with viral load, with an increase in its expression in AT2 cells from lung biopsy and BAL scRNAseq datasets, and its processing into its active form in SARS-CoV-2 infected lung epithelial cell lines in vitro47. There is therefore good evidence to support exploration of complement inhibition in severe COVID-19.

Within the lung, COVID-19 manifests in a wide spectrum of DAD and fibroproliferation, making the dissection of pathways specific to areas of damage a challenge using bulk assessment techniques. However, the histopathological features are non-specific and there are no clear findings that differentiate SARS-CoV-2 from a number of other respiratory viral infections, particularly those developing following infection with other betacoronaviridae such as SARS-CoV and MERS-CoV48-53. Our data therefore provide a clearer picture of the immune landscape within lung tissue after severe COVID-19 infection through the assessment of dozens of histopathological regions and using a network-based analytical workflow. Overall, our data suggest a model (Figure 5) in which terminal COVID-19 lung damage is initiated by TLR-mediated myeloid cell activation which involves type I IFN signalling, antigen presentation, complement activation and IL-1 expression. This leads to T and NK cell activation, IFNG and IL-32 driven immune cell proliferation and immune cell recruitment including via the CXCL9, CXCL10, CXCL11/CXCR3 axis. In this model, tissue damage is linked to CD8 T and NK cell cytotoxicity as well as to complement activation. In parallel our data indicate that elevated levels of IL-1, IL-6 and TNF (the so-called “cytokine storm”) also drive abnormal stromal cell responses including fibrosis (and, as shown by others, endothelitis54). This model highlights a series of partially interlinked processes including innate immune activation, leukocyte activation, immune cell recruitment, cytokine storm, and stromal dysregulation as possible targets for therapeutic intervention in severe COVID-19 (red targets, Figure 5). Currently deployed therapeutics such as dexamethasone and tocilizumab tend to target cytokine storm and while effective are not curative in all cases. The partial independence of the processes identified, and multiple feedback loops present, suggest that combination approaches targeting different aspects of immune and tissue dysregulation may be needed. While therapeutics are available or in development to target the virus (such as remdesivir and recombinant soluble ACE255), innate immune activation (such as intranasal IFNB1a/b56) and the complement system (via complement inhibition57) less attention has been paid to immune cell recruitment. Our data suggest that manipulation of the CXCL9, CXCL10, CXCL11/CXCR3 axis may be a valuable strategy in severe COVID-19 as this pathway is implicated upstream of both tissue damage and cytokine storm (Figure 5). Finally, the limited quantities of SARS-CoV-2 detected in the lung tissue from our patients suggests that identified processes of immune activation and tissue dysregulation may become (at least partially) uncoupled from viral infection in terminal cases. This observation highlights a likely need for stage-specific therapeutic interventions and suggests that specific therapeutics may promote different outcomes in early and late disease.

MATERIALS AND METHODS

Ethics statement

This study was approved by the ethics committee of the University of Navarra, Spain (15/05/2020) and the Medical Sciences Interdivisional Research Ethics Committee of the University of Oxford (Approval R76045/RE001). Tissues were stored at the John Radcliffe Hospital according to Human Tissue Authority regulations (Licence 12433).

Patients and tissue processing

Medical records of the patients were retrospectively reviewed58 and three COVID-19 patients were selected for in-depth analysis based on: their clinical manifestation of ARDS (Supplementary Table 1), typical COVID-19 histology (Supplementary Table 2) with a 4-5 score on the Brescia-COVID Respiratory Severity Scale, and a lung-restricted presence of SARS- COV-2 (absence in heart, liver and kidney biopsies). Post-mortem lung tissues were obtained through open biopsy at the point of death and processed as described58. In brief, tissues were immediately fixed in neutral buffered formalin for <24 hours and then paraffin embedded. Sections (5μm each) were cut for H&E staining, ISH and DSP analysis. Six pathologists reviewed the histology in and agreed on the gross histological characteristics (Supplementary Table 2). RNA was extracted from 4-8 x 5μm sections for quantification of SARS-CoV-2 nucleocapsid (N) and envelope protein (E) transcripts (Supplementary Table 2).

NanoString GeoMx Digital Spatial Profiling

This technique was carried out according to manufacturer’s recommendations for GeoMx- NGS RNA BOND RX slide preparation (manual: MAN-10131-02). Deparaffinization, rehydration, heat-induced epitope retrieval (ER2 for 20 minutes at 100°C) and enzymatic digestion (1μg/ml proteinase K for 15 minutes at 37°C) were carried on the Leica BOND-RX. Tissues were incubated with 10% neutral buffered formalin for 5 minutes and 5 minutes with NBF Stop buffer. The tissue sections hybridized with the oligonucleotide probe mix (Cancer Transcriptome Atlas and COVID-19 spike-in panel) overnight, then blocked and incubated with PanCK-532 (AE1+AE3, Novus), CD3-647 (UMAB54, Origene), CD68-594 (KP1, Santa Cruz) and DNA dye (Syto13-488; Invitrogen) for 1 hour. Tissue sections were then loaded into the GeoMx platform and scanned for immunofluorescent signal.

At least 14 AOIs from each COVID-19 tissue were selected for analysis, spanning on average 0.2mm2 (range: 0.05 to 0.33mm2) with exclusion of empty space. Areas were selected to represent the spectrum of alveolar injury within each tissue covering regions of a) mild/moderate injury with some conservation of alveolar architecture and b) severe injury with a loss of alveolar structure and significant inflammation. Selected AOIs occupy alveolar and interstitial spaces, except from A_16 and A_17 containing bronchiolar epithelium. The severity grade of each AOI was confirmed post-hoc by 2 pathologists.

After selection of areas of interest (AOIs), UV light directed at each AOI released oligonucleotides that were collected and prepared for sequencing. Illumina i5 and i7 dual indexing primers were added during PCR (4 µL of collected oligonucleotide/AOI) to uniquely index each AOI. AMPure XP beads (Beckman Coulter) were used for PCR purification. Library concentration as measured using a Qubit fluorometer (Thermo Fisher Scientific) and quality was assessed using a Bioanalyzer (Agilent). Sequencing was performed on an Illumina NextSeq bioRxiv preprint doi: https://doi.org/10.1101/2021.06.21.449178; this version posted June 21, 2021. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY 4.0 International license.

2000 and FASTQ files were processed by the NanoString DND pipeline, resulting in count data for each target probe in each AOI.

Analysis of immunofluorescent images for cell counts

The number of nuclei, CD3+ and CD68+ cell counts were determined using CellProfiler software. A pipeline was designed to quantify circular objects within RBG files for each AOI59. Global manual intensity thresholds were set for object identification of nuclei and CD3+ cells, whilst CD68+ cells were identified by adapted intensity thresholds. The efficacy of object identification for each AOI was visually confirmed.

Quality control and pre-processing of GeoMx transcript expression data

Quality control and initial data exploration was conducted using the GeoMx DSP Analysis Suite. Sequencing quality per AOI was examined and an under-sequenced area (B_04) with zero deduplicated reads was excluded. Expression of each transcript was measured by 5 or more probes; outlier probes (geomean probe in all AOI/geomean of all probes for a transcript = <0.1; or probe failure in the Grubbs outlier test in over 20% AOIs) were excluded and the remaining probes combined to generate a single (post “biological probe QC”) expression value per gene target per AOI.

We evaluated the performance of two normalization strategies, upper quartile and quantile normalization, by investigating their ability to standardise the expression distributions of housekeeping genes, negative control probes as well as the full expression distribution between the AOIs. The expression values for negative control probes that were not reported as global outliers were appended to the gene expression matrix. Based on this assessment (and the results of subsequent PCA analyses) we proceeded with the quantile normalized expression values. We investigated the influence of known technical and biological factors on the variance structure of the dataset by performing PCA of the log2(n+1) transformed quantile-normalized expression values. This analysis revealed that the first component in the data was associated with the aligned read depth statistic. We therefore corrected the quantile normalized expression values for this technical factor using Limma “removeBatchEffect” function. Finally, to distinguish gene expression from background noise, we modelled the expression distribution of the set of negative probes, defining the median of the negative probe expression values + 2*median absolute deviations as a robust detection threshold. In total we retained 1631 genes that passed this detection threshold in at least 2 AOIs. The normalized expression distributions and sample PCA plots obtained following normalization, aligned read depth correction and expression level filtering are shown in Supplementary Figure 1.

Differential gene expression and over-representation analysis

Differential gene expression was calculated for each gene between areas of mild/moderate and severe alveolar damage using linear mixed models (fixed effect: severity, random variable: patient identity). Significance was estimated using Satterthwaite’s degrees of freedom method and p values adjusted using Benjamini-Hochberg methods (R libraries: lme4, lmerTest). Over-representation analysis for Gene Ontology Biological Processes (GO.BP) terms was performed on genes with >1.5 fold change and FDR > 0.05 (R library: clusterProfiler60) with the parameters: pvalueCutoff = 0.05 and qvalueCutoff = 0.10. Redundant and duplicated pathways were removed for Figure 1D; for full list of pathways see Supplementary data. bioRxiv preprint doi: https://doi.org/10.1101/2021.06.21.449178; this version posted June 21, 2021. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY 4.0 International license.

Weighted gene correlation network analysis (WGCNA)

WGCNA was applied to the log2(n+1) transformed, quantile normalized, aligned read corrected and filtered expression values. The parameter values were set as follows: minimum fraction of non-missing samples for a gene to be considered good=0.5, minimum number of non-missing samples for a gene to be considered good=4, minimum number of good genes=4, cut height for removing outlying samples=100 (no samples removed), minimum number of objects on branch to be considered a cluster=2, network type=signed-hybrid, soft power=4, adjacency correlation function=bicor, adjacency distance function=dist, TOM type=signed, minimum module size (number of genes)=10, dissimilarity threshold used for merging modules: 0.25. The analysis identified 17 distinct modules labelled with colours, plus a module of 3 unassigned genes (grey module). The WGCNA analysis was performed using pipeline_wgcna.py (https://github.com/sansomlab/cornet).

The expression patterns of the modules were summarized by calculation of module eigengenes using the WGCNA package (which defines a modules’ eigengene as the first principal component of the expression of the modules’ gene members). AOIs were hierarchically clustered by expression of the module eigengenes (Pearson correlation distance; optimized leaf ordering) to identify n=5 distinct groups (R “cutree” function). The over-representation of Gene Ontology (GO) categories, KEGG pathways and Reactome pathways in module gene members was tested using one-sided Fishers exact tests (https://github.com/sansomlab/gsfisher) using the union of gene members from all the modules as the background geneset. Representative genesets and pathways that showed significant (BH adjusted p < 0.05) over-representations are displayed in the figures (with full results are provided in supplementary table X). WGCNA modules were further characterized by assessing the correlation of the module eigengenes with a) histological severity (Pearson correlation) b) predicted cell abundances (as estimated using Spatial Decon; Spearman correlation) and c) that of selected genes (Spearman correlation, R library Hmisc).

Cell deconvolution of GeoMx transcript expression data

Deconvolution of cell types from the gene expression data was performed using the SpatialDecon R library12. For this analysis the full matrix of quantile normalized gene counts (without correction), mean negative probe counts and the cell profile matrix ‘Lung_plus_neut’ were applied as input. The cell profile matrix retrieved from the package was generated from the Human Cell Atlas lung scRNAseq dataset and appended with neutrophil profiles as described in Desai et al. (2020)5. For correlation analyses, the abundance of cell types is reported for cell types present with > 2 relative abundance in > 2 AOIs. Complete cell type output in Supplementary figure.

Construction of AOI group correlation networks

We constructed correlation networks to investigate the relationship between the WGCNA modules, the estimated cell abundances (from SpatialDecon) and the expression levels of immune signalling genes for each of the n=5 AOI spatial groups (R igraph library; layout = layout_with_dh). Nodes were scaled in size and/or colour according to cell abundance, normalized gene expression or WGCNA module eigengene expression. Drawn edges represent significant correlations (p < 0.05) and were weighted according to the value of the correlation coefficient (Spearman’s Rho). WGCNA modules were included in the network if they showed a bioRxiv preprint doi: https://doi.org/10.1101/2021.06.21.449178; this version posted June 21, 2021. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY 4.0 International license.

positive module eigengene expression in >70% of the AOI of the relevant group. Immune signalling genes from the KEGG ‘cytokine-cytokine receptor interaction’ pathway (hsa04060) (human) were included if they i) correlated with the positively expressed gene modules for a given spatial group and ii) showed a median expression above the expression detection threshold (as defined in the pre-processing section) in a given AOI group. Cell type abundance estimates were included if they i) correlated with the positively expressed gene modules for a given spatial group and ii) if the cell types were present with > 2 relative abundance in > 2 of the 46 AOIs.

FIGURE AND SUPPLEMENTARY DATA LEGENDS

Figure 1: A spectrum of diffuse alveolar damage and inflammation was observed within and across COVID-19 lung biopsies. Selection and annotation of areas of diffuse alveolar damage in COVID-19 lung tissues for transcriptomic analysis. (A) Haematoxylin and eosin-stained sections of the lung samples from patients A, B and C (left; scale bars: 5mm). Merged immunofluorescence (IF) images of the lung samples from patients A, B and C (right; scale bars: 5mm, pan cytokeratin-green, DNA-blue, CD3-red, CD68-yellow). IF sections are within 15μm of the H&E-stained sections. 47 areas of interest (AOI) selected for transcript profiling are highlighted, of which 1 AOI was excluded from further analysis for sequencing failure. (B) Representative IF images of AOIs demonstrating the morphology and immune infiltrate observed within areas of severe and mild/moderate diffuse alveolar damage (pan cytokeratin- green, DNA-blue, CD3-red, CD68-yellow). AOIs spanned on average 0.2mm2 (range: 0.05 to 0.33mm2) with exclusion of empty space. (C) The proportion of CD3+ and CD68+ cells of total nucleated cells were derived from the immunofluorescent imaging, plotted for each AOI and coloured by histological severity (mild/moderate-light blue, severe-pink) or inclusion of bronchiolar epithelium (grey). AOI are annotated with their origin from patient A (circle), patient B (triangle), and patient C (square). (D) Differential gene expression between areas of severe and mild/moderate damage. Coloured and annotated genes have a fold change expression > 1.5 and a Benjamini-Hochberg (BH) adjusted p value <0.05 in a linear mixed model. (E) Selected GO biological processes significantly over-represented in genes differentially expressed between mild and severe areas of damage (BH corrected p < 0.05, one-sided Fisher’s exact test); see also Supplementary Table 3.

Figure 2: Identification and characterization of gene modules with spatially heterogenous expression in COVID-19 lung tissue. Application of weighted gene correlation network analysis (WGCNA) to spatial transcriptomic data identified 17 modules of co-expressed genes (each designated a colour name). (A) The number of genes assigned to each module. (B) Selected GO Biological processes, KEGG and Reactome pathways that were significantly over- represented in the detected modules (BH adjusted p values < 0.05; one-side Fisher’s exact test; see also Supplementary Table 3). (C) Correlation between estimated cell type abundance (as determined by cell deconvolution) and WGCNA module eigengene expression (all AOIs; only positive Spearman correlation values are shown). (D) Association of cell type marker and immune function genes with the WGCNA module eigengenes (only positive Spearman correlation values are shown; asterisks indicate high module membership > 0.7, p value < 0.05). Simplified module aliases (right) describe each module based on their association with cell lineages, function-associated genes and defining biological pathways. (E) Genes were selected for impact on pathology: chemokines/receptors, cytokines/receptors, viral receptors and cofactors, IL-6 signalling, complement, fibrosis and prostaglandins. Genes were correlated with the eigengene modules (Spearman’s correlation; asterisks indicate high WGCNA membership > 0.7, p value < 0.05). bioRxiv preprint doi: https://doi.org/10.1101/2021.06.21.449178; this version posted June 21, 2021. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY 4.0 International license.

Figure 3: Combination of WGCNA modules reveals five groups of alveolar damage with distinct transcriptional profiles. (A) The severity of tissue damage was correlated to module eigengene expression separately for the AOIs of each patient (Pearson correlation). (B)WGCNA module eigengene expression is shown for each AOI. Sampled areas (AOI) are annotated with patient identity, the severity of damage and the percentage of CD3+ and CD68+ cells of total nucleated cells. Hierarchical clustering of the 46 areas (AOI) by expression of the WGCNA module eigengenes identified 5 “spatial groups” with distinct patterns of module expression. (C) The expression of selected genes that exemplify key differences between the 5 “spatial groups” of AOIs.

Figure 4: Transcriptomic subgroups of alveolar damage are associated different soluble factor profiles. (A) Key characteristics of the five spatial AOI groups: the average percentage of CD3+ lymphocytes of nucleated cells (x axis), the percentage of AOI with severe alveolar damage (y axis), and patient of origin (pie charts). (B-F) Correlation networks for each spatial group. Networks were constructed with selected WGCNA eigengene modules (grey nodes), cytokines and chemokines red gradient nodes) and cell abundance estimates from cell deconvolution (blue gradient nodes) (see methods for node inclusion criteria). Nodes are scaled in size and/or colour to the relative module eigengene expression (grey nodes), the normalized gene counts (red gradient nodes) and the cell abundance (blue gradient nodes). Edges represent positive correlations (Spearman; p<0.05) between a gene/cell type and eigengene modules across the whole dataset and are weighted to the Spearman Rho coefficient (+0.1 to +1). Three immunofluorescent images of representative AOI from each spatial group are shown to illustrate major morphological features and cell composition (pan cytokeratin-green, DNA-blue, CD3-red, CD68-yellow). (G) Positive correlations between spatial groups and the expression of genes selected for impact on pathology is shown: chemokines/receptors, cytokines/receptors, viral receptors and cofactors, IL-6 signalling, complement, fibrosis and prostaglandins (Spearman’s rank correlation, white 0 to red +1).

Figure 5: A cellular model of late-stage lung tissue damage in terminal COVID-19. Our data indicate that dysregulation of lung tissue homeostasis in late-stage terminal COVID-19 involves (1) SARS-CoV-2 mediated TLR activation of myeloid cells such as alveolar macrophages and dendritic cells that leads to (2) complement system activation, (3) Type I interferon (IFNA1/A17) expression and (4) antigen presentation. The type I interferon signaling and antigen presentation are hypothesized to lead to (5) activation of NK and T cells resulting in their expression IFNG and IL32 driving (6) proliferation and activation of immune cells in the tissue, including that of MARCO- macrophages. These activated immune cells are thought to (7) recruit peripheral monocytes, T and NK cells into the tissue via the CXCL9, CXCL10, CXCL11/CXCR3 axis. Tissue damage (8) is understood to be a consequence of NK and CD8 cell cytotoxicity, complement activation as well as the actions of infiltrating myeloid cells. Cytokine storm (9) involves IL-1 expression from activated myeloid cells as well as TNF and IL6 expression from activated leukocytes and infiltrating immune cells. High levels of these pro- inflammatory cytokines lead to detectable (10) IL-1/IL-6 responses in epithelial cells and is suggested to cause (11) fibrosis. In addition, (12) endothelitis has been reported in the literature54. Established and potential therapeutic targets are indicated with red target symbols. Only genes and cell types (with the exception of endothelitis) for which there is evidence of involvement from this study are shown in the figure.

Supplementary Figure 1: Normalization and variance of transcriptomic data across areas of interest (AOIs). (A) The distribution of all gene and (B) selected housekeeping gene expression levels after the application of quantile normalization across the AOIs. (C) Principal bioRxiv preprint doi: https://doi.org/10.1101/2021.06.21.449178; this version posted June 21, 2021. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY 4.0 International license.

component analysis of the variance between the sampled AOIs (PCA performed with the pre- processed quantile normalised, log2 transformed, aligned read corrected and expression-level filtered gene expression values). Here PCs 1-4 are shown, annotated by the severity of alveolar damage (severe: pink/square, mild/moderate: light blue/circle, respiratory epithelium: grey triangle), patient identity (A: blue/circle, B: green/triangle, C: pink/square), percentage of CD3+ (red gradient) and CD68+ cells (blue gradient) of total nucleated cells.

Supplementary Figure 2: Deconvolution of cell lineages across sampled areas in COVID-19 lung tissue. (A) The abundance of cell lineages was estimated from the RNA expression data obtained from each sampled area (AOI) across the post-mortem COVID-19 lung tissues (R package SpatialDecon using cell profile matrix derived of healthy lung scRNAseq appended with neutrophil populations ‘Lung_plus_neut’). The abundance is hierarchically clustered and annotated with patient identity, the percentage of CD3 or CD68 cells of total nuclei, and the alveolar damage severity. (B) The estimated cell abundance is plotted with the percentage of CD3+ (top panel) or CD68+ (bottom panel) cells of total nuclei, calculated from analysis of the immunofluorescent images (linear model; confidence interval = 0.95). (C) The correlation coefficients were calculated between deconvoluted cell types (> 2 abundance in > 2 AOI) and genes selected for potential impact on pathology: chemokines/receptors, cytokines/receptors, viral receptors and cofactors, IL-6 signalling, complement, fibrosis and prostaglandins (Spearman’s rank correlation, white 0 to red +1).

DATA AVAILABILITY

Phase contrast images of H&E-stained slides and RBG composite images taken of immunofluorescent stained slides will be deposited in GEO together with NanoString GeoMx DSP data and metadata. Annotated scripts for the computational analysis are available from https://github.com/sansomlab/covidlung.

AUTHOR CONTRIBUTIONS

C.E.DA., M.F.LA. and I.M. obtained consent, clinical data and the patient samples. I.R. and C.E.DA. reported on tissue histology. A.R.C, L.C. and F.I. designed and performed the experiments. A.R.C., F.I. and S.S. analysed data and generated the figures. A.R.C, S.S. and F.I. acquired funding and oversaw the project. A.R.C., J.H., P.K., I.M. and F.I. conceived the work and all authors contributed to writing the manuscript.

ACKNOWLEDGEMENTS

Thanks to David Scoville and Andy Nam at NanoString for their assistance with GeoMx and to Bradley Spencer-Dene at ACDBio for RNAScope assistance.

FUNDING STATEMENT

A.R.C. is funded by the Oxford–Bristol Myers Squibb Fellowship. J.H. is a KRUK Senior Fellow. F.I. is a Wellcome Trust CRCD Fellow. The study was partly funded by the University of Oxford COVID-19 Research Response Fund.

KEY WORDS

COVID-19; ARDS; spatial genomics; diffuse alveolar damage.

REFERENCES

1 Speranza, E. et al. Single-cell RNA sequencing reveals SARS-CoV-2 infection dynamics in lungs of African green monkeys. Sci Transl Med 13, doi:10.1126/scitranslmed.abe8146 (2021). 2 Wichmann, D. et al. Autopsy Findings and Venous Thromboembolism in Patients With COVID- 19: A Prospective Cohort Study. Ann Intern Med 173, 268-277, doi:10.7326/M20-2003 (2020). 3 Grasselli, G. et al. Pathophysiology of COVID-19-associated acute respiratory distress syndrome: a multicentre prospective observational study. Lancet Respir Med 8, 1201-1208, doi:10.1016/S2213-2600(20)30370-2 (2020). 4 Short, K. R., Kroeze, E., Fouchier, R. A. M. & Kuiken, T. Pathogenesis of influenza-induced acute respiratory distress syndrome. Lancet Infect Dis 14, 57-69, doi:10.1016/S1473-3099(13)70286- X (2014). 5 Desai, N. et al. Temporal and spatial heterogeneity of host response to SARS-CoV-2 pulmonary infection. Nat Commun 11, 6319, doi:10.1038/s41467-020-20139-7 (2020). 6 Rendeiro, A. F. et al. The spatial landscape of lung pathology during COVID-19 progression. Nature, doi:10.1038/s41586-021-03475-6 (2021). 7 Delorey, T. M. et al. COVID-19 tissue atlases reveal SARS-CoV-2 pathology and cellular targets. Nature, doi:10.1038/s41586-021-03570-8 (2021). 8 Ahern, D. J. et al. A blood atlas of COVID-19 defines hallmarks of disease severity and specificity. medRxiv, 2021.2005.2011.21256877, doi:10.1101/2021.05.11.21256877 (2021). 9 Marx, V. Method of the Year: spatially resolved transcriptomics. Nat Methods 18, 9-14, doi:10.1038/s41592-020-01033-y (2021). 10 Merritt, C. R. et al. Multiplex digital spatial profiling of proteins and RNA in fixed tissue. Nat Biotechnol 38, 586-599, doi:10.1038/s41587-020-0472-9 (2020). 11 Langfelder, P. & Horvath, S. WGCNA: an R package for weighted correlation network analysis. BMC Bioinformatics 9, 559, doi:10.1186/1471-2105-9-559 (2008). 12 Danaher, P. et al. Advances in mixed cell deconvolution enable quantification of cell types in spatially-resolved gene expression data. bioRxiv, doi:https://doi.org/10.1101/2020.08.04.235168 (2020). 13 Tokunaga, R. et al. CXCL9, CXCL10, CXCL11/CXCR3 axis for immune activation - A target for novel cancer therapy. Cancer Treat Rev 63, 40-47, doi:10.1016/j.ctrv.2017.11.007 (2018). 14 Ribeiro-Dias, F., Saar Gomes, R., de Lima Silva, L. L., Dos Santos, J. C. & Joosten, L. A. Interleukin 32: a novel player in the control of infectious diseases. J Leukoc Biol 101, 39-52, doi:10.1189/jlb.4RU0416-175RR (2017). 15 Yan, Y. et al. CCL19 and CCR7 Expression, Signaling Pathways, and Adjuvant Functions in Viral Infection and Prevention. Front Cell Dev Biol 7, 212, doi:10.3389/fcell.2019.00212 (2019). 16 Blanco-Melo, D. et al. Imbalanced Host Response to SARS-CoV-2 Drives Development of COVID-19. Cell 181, 1036-1045 e1039, doi:10.1016/j.cell.2020.04.026 (2020). 17 Lokugamage, K. G. et al. Type I Interferon Susceptibility Distinguishes SARS-CoV-2 from SARS- CoV. J Virol 94, doi:10.1128/JVI.01410-20 (2020). 18 Stanifer, M. L. et al. Critical Role of Type III Interferon in Controlling SARS-CoV-2 Infection in Human Intestinal Epithelial Cells. Cell Rep 32, 107863, doi:10.1016/j.celrep.2020.107863 (2020). 19 Pfaender, S. et al. LY6E impairs coronavirus fusion and confers immune control of viral disease. Nat Microbiol 5, 1330-1339, doi:10.1038/s41564-020-0769-y (2020). 20 Hadjadj, J. et al. Impaired type I interferon activity and inflammatory responses in severe COVID-19 patients. Science 369, 718-724, doi:10.1126/science.abc6027 (2020). 21 Zhou, Z. et al. Heightened Innate Immune Responses in the Respiratory Tract of COVID-19 Patients. Cell Host Microbe 27, 883-890 e882, doi:10.1016/j.chom.2020.04.017 (2020). 22 Lee, J. S. et al. Immunophenotyping of COVID-19 and influenza highlights the role of type I interferons in development of severe COVID-19. Sci Immunol 5, doi:10.1126/sciimmunol.abd1554 (2020). 23 Perkins, D. J. & Vogel, S. N. Space and time: New considerations about the relationship between Toll-like receptors (TLRs) and type I interferons (IFNs). Cytokine 74, 171-174, doi:10.1016/j.cyto.2015.03.001 (2015). bioRxiv preprint doi: https://doi.org/10.1101/2021.06.21.449178; this version posted June 21, 2021. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY 4.0 International license.

24 Mathew, D. et al. Deep immune profiling of COVID-19 patients reveals patient heterogeneity and distinct immunotypes with implications for therapeutic interventions. bioRxiv, doi:10.1101/2020.05.20.106401 (2020). 25 Vabret, N. et al. Immunology of COVID-19: Current State of the Science. Immunity 52, 910-941, doi:10.1016/j.immuni.2020.05.002 (2020). 26 Giamarellos-Bourboulis, E. J. et al. Complex Immune Dysregulation in COVID-19 Patients with Severe Respiratory Failure. Cell Host Microbe 27, 992-1000 e1003, doi:10.1016/j.chom.2020.04.009 (2020). 27 Mann, E. R. et al. Longitudinal immune profiling reveals key myeloid signatures associated with COVID-19. Sci Immunol 5, doi:10.1126/sciimmunol.abd6197 (2020). 28 Liao, M. et al. Single-cell landscape of bronchoalveolar immune cells in patients with COVID- 19. Nat Med 26, 842-844, doi:10.1038/s41591-020-0901-9 (2020). 29 Barnes, B. J. et al. Targeting potential drivers of COVID-19: Neutrophil extracellular traps. J Exp Med 217, doi:10.1084/jem.20200652 (2020). 30 He, J. et al. Single-cell analysis reveals bronchoalveolar epithelial dysfunction in COVID-19 patients. Protein Cell 11, 680-687, doi:10.1007/s13238-020-00752-4 (2020). 31 Mauer, J., Denson, J. L. & Bruning, J. C. Versatile functions for IL-6 in metabolism and cancer. Trends Immunol 36, 92-101, doi:10.1016/j.it.2014.12.008 (2015). 32 Mehta, P. et al. COVID-19: consider cytokine storm syndromes and immunosuppression. Lancet 395, 1033-1034, doi:10.1016/S0140-6736(20)30628-0 (2020). 33 Thwaites, R. S. et al. Inflammatory profiles across the spectrum of disease reveal a distinct role for GM-CSF in severe COVID-19. Sci Immunol 6, doi:10.1126/sciimmunol.abg9873 (2021). 34 Mudd, P. A. et al. Distinct inflammatory profiles distinguish COVID-19 from influenza with limited contributions from cytokine storm. Sci Adv 6, doi:10.1126/sciadv.abe3024 (2020). 35 Group, R. C. Tocilizumab in patients admitted to hospital with COVID-19 (RECOVERY): a randomised, controlled, open-label, platform trial. Lancet 397, 1637-1645, doi:10.1016/S0140- 6736(21)00676-0 (2021). 36 House, I. G. et al. Macrophage-Derived CXCL9 and CXCL10 Are Required for Antitumor Immune Responses Following Immune Checkpoint Blockade. Clin Cancer Res 26, 487-504, doi:10.1158/1078-0432.CCR-19-1868 (2020). 37 Maucourant, C. et al. Natural killer cell immunotypes related to COVID-19 disease severity. Sci Immunol 5, doi:10.1126/sciimmunol.abd6832 (2020). 38 Saini, S. K. et al. SARS-CoV-2 genome-wide T cell epitope mapping reveals immunodominance and substantial CD8(+) T cell activation in COVID-19 patients. Sci Immunol 6, doi:10.1126/sciimmunol.abf7550 (2021). 39 Kusnadi, A. et al. Severely ill COVID-19 patients display impaired exhaustion features in SARS- CoV-2-reactive CD8(+) T cells. Sci Immunol 6, doi:10.1126/sciimmunol.abe4782 (2021). 40 Fraser, E. Long term respiratory complications of covid-19. Bmj 370, m3001, doi:10.1136/bmj.m3001 (2020). 41 Spagnolo, P. et al. Pulmonary fibrosis secondary to COVID-19: a call to arms? Lancet Respir Med 8, 750-752, doi:10.1016/S2213-2600(20)30222-8 (2020). 42 Carsana, L. et al. Pulmonary post-mortem findings in a series of COVID-19 cases from northern Italy: a two-centre descriptive study. Lancet Infect Dis 20, 1135-1140, doi:10.1016/S1473- 3099(20)30434-5 (2020). 43 Grillo, F., Barisione, E., Ball, L., Mastracci, L. & Fiocca, R. Lung fibrosis: an undervalued finding in COVID-19 pathological series. Lancet Infect Dis 21, e72, doi:10.1016/S1473-3099(20)30582-X (2021). 44 Fox, S. E. et al. Pulmonary and cardiac pathology in African American patients with COVID-19: an autopsy series from New Orleans. Lancet Respir Med 8, 681-686, doi:10.1016/S2213- 2600(20)30243-5 (2020). 45 Chow, J. H. et al. Aspirin Use Is Associated With Decreased Mechanical Ventilation, Intensive Care Unit Admission, and In-Hospital Mortality in Hospitalized Patients With Coronavirus Disease 2019. Anesthesia and analgesia 132, 930-941, doi:10.1213/ANE.0000000000005292 (2021). 46 Yan, B. et al. SARS-CoV-2 drives JAK1/2-dependent local complement hyperactivation. Sci Immunol 6, doi:10.1126/sciimmunol.abg0833 (2021). bioRxiv preprint doi: https://doi.org/10.1101/2021.06.21.449178; this version posted June 21, 2021. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY 4.0 International license.

47 Ma, L. et al. Increased complement activation is a distinctive feature of severe SARS-CoV-2 infection. bioRxiv, doi:10.1101/2021.02.22.432177 (2021). 48 Xu, Z. et al. Pathological findings of COVID-19 associated with acute respiratory distress syndrome. Lancet Respir Med 8, 420-422, doi:10.1016/S2213-2600(20)30076-X (2020). 49 Barton, L. M., Duval, E. J., Stroberg, E., Ghosh, S. & Mukhopadhyay, S. COVID-19 Autopsies, Oklahoma, USA. Am J Clin Pathol 153, 725-733, doi:10.1093/ajcp/aqaa062 (2020). 50 Menter, T. et al. Postmortem examination of COVID-19 patients reveals diffuse alveolar damage with severe capillary congestion and variegated findings in lungs and other organs suggesting vascular dysfunction. Histopathology 77, 198-209, doi:10.1111/his.14134 (2020). 51 Franks, T. J. et al. Lung pathology of severe acute respiratory syndrome (SARS): a study of 8 autopsy cases from Singapore. Hum Pathol 34, 743-748, doi:10.1016/s0046-8177(03)00367-8 (2003). 52 Martines, R. B. et al. Pathology and Pathogenesis of SARS-CoV-2 Associated with Fatal Coronavirus Disease, United States. Emerg Infect Dis 26, 2005-2015, doi:10.3201/eid2609.202095 (2020). 53 Ng, D. L. et al. Clinicopathologic, Immunohistochemical, and Ultrastructural Findings of a Fatal Case of Middle East Respiratory Syndrome Coronavirus Infection in the United Arab Emirates, April 2014. Am J Pathol 186, 652-658, doi:10.1016/j.ajpath.2015.10.024 (2016). 54 Varga, Z. et al. Endothelial cell infection and endotheliitis in COVID-19. Lancet 395, 1417-1418, doi:10.1016/S0140-6736(20)30937-5 (2020). 55 Zoufaly, A. et al. Human recombinant soluble ACE2 in severe COVID-19. Lancet Respir Med 8, 1154-1158, doi:10.1016/S2213-2600(20)30418-5 (2020). 56 Sallard, E., Lescure, F. X., Yazdanpanah, Y., Mentre, F. & Peiffer-Smadja, N. Type 1 interferons as a potential treatment against COVID-19. Antiviral Res 178, 104791, doi:10.1016/j.antiviral.2020.104791 (2020). 57 Zelek, W. M. et al. Complement Inhibition with the C5 Blocker LFG316 in Severe COVID-19. Am J Respir Crit Care Med 202, 1304-1308, doi:10.1164/rccm.202007-2778LE (2020). 58 Recalde-Zamacona, B. et al. Histopathological findings in fatal COVID-19 severe acute respiratory syndrome: preliminary experience from a series of 10 Spanish patients. Thorax 75, 1116-1118, doi:10.1136/thoraxjnl-2020-215577 (2020). 59 Lamprecht, M. R., Sabatini, D. M. & Carpenter, A. E. CellProfiler: free, versatile software for automated biological image analysis. Biotechniques 42 , 71-75, doi:10.2144/000112257 (2007). 60 Yu, G., Wang, L. G., Han, Y. & He, Q. Y. clusterProfiler: an R package for comparing biological themes among gene clusters. OMICS 16, 284-287, doi:10.1089/omi.2011.0118 (2012).

(which wasnotcertifiedbypeerreview)istheauthor/funder,whohasgrantedbioRxivalicensetodisplaypreprintinperpetuity.Itmade bioRxiv preprint

SUPPLEMENTARY TABLES doi: Supplementary Table 1: Patient characteristics https://doi.org/10.1101/2021.06.21.449178 Lymphocytes Neutrophils CRP Ferritin D-dimer Platelets Troponin T Pro-BNP 9 9 9 Age/ (10 /L) (10 /L) (mg/dl) (ng/ml) (ng/ml) (10 /L) (ng/L) (pg/ml) DCC Patient Medical history CT findings Treatment Sex NR:15- NR:150- NR: 150- (ICU) NR:1.2-4 NR: 1.5-6.5 NR: ≤0.5 NR: ≤14 NR: ≤222 150 500 450

A 64/F HTN, heart GGO, 0.91 2.19 9.17 n/a 1880 12 n/a n/a HCQ, AZM, 46 failure, atrial consolidation CCS, (19) available undera fibrillation, Tocilizumab lymphoma Remdesivir

B 80/M HTN, COPD GGO, RETp 0.24 10.99 0.43 768 2120 133 27.3 2854 HCQ, AZM, 17 CC-BY 4.0Internationallicense ;

CRO, CCS, (0) this versionpostedJune21,2021. (Gen), pleural LPV/r effusion

C 60/F HTN, type II GGO, 0.85 3.58 18.94 998 7010 310 495.2 14858 HCQ, AZM, 29 diabetes, consolidation CRO, CCS (24) obesity, LPV/r dyslipidaemia, . heart failure, ischaemic cardiomyopathy The copyrightholderforthispreprint

Clinical, CT and laboratory findings. Hypertension (HTN); Ground-glass opacities (GGO); C-Reactive protein (CRP); Hydroxychloroquine (HCQ); Azithromycin (AZM); Lopinavir/ritonavir (LPV/r); Corticosteroids (CCS); Ceftriaxone (CRO); Intensive Unit Care (ICU). Duration of clinical course (DCC).

(which wasnotcertifiedbypeerreview)istheauthor/funder,whohasgrantedbioRxivalicensetodisplaypreprintinperpetuity.Itmade bioRxiv preprint

Supplementary Table 2: Histological characteristics doi:

Lung tissue https://doi.org/10.1101/2021.06.21.449178 Patient Histopathological Description Cellular findings SARS-CoV-2 qPCR

A This displays DAD consistent with a later organising or proliferative phase of Type II pneumocyte hyperplasia and hypertrophy with reactive Gen N: 34.2 the pathology, with fewer hyaline membranes, continued type II atypia; Vacuolated macrophages; Hyaline membranes; Normal Gen E: 35.8 pneumocyte hyperplasia, and organized pneumonia. The tissue is split into vascular growth; Medium size-vessel partial thrombi; Interstitial two segments: one segment features severe DAD, interstitial inflammation available undera pneumonia with mild inflammation; Squamous metaplasia. and haemorrhage and one segment features mild/moderate DAD with conserved alveolar structure (airspace). The immune infiltrate was diffusely and patchily distributed across the section. This tissue exhibits endarteritis and medium sized-vessel thrombi.

B This tissue features widespread diffuse alveolar damage (DAD) with evident Type II pneumocyte hyperplasia and hypertrophy with reactive Gen N: 27.3 CC-BY 4.0Internationallicense

hyaline membranes and type II pneumocyte hyperplasia. These features ;

atypia; Vacuolated macrophages; Hyaline membranes; Vascular this versionpostedJune21,2021. are present across the tissue, yet the severity ranges from mild/moderate wall hyperplasia; Medium size-vessel partial thrombi; Squamous Gen E: 25.5 to severe. The interstitium is expanded in many areas, demonstrating a metaplasia. fibroproliferative response. There is evidence of diffuse and patchy interstitial inflammation across the tissue with infiltrate morphology consistent with lymphocytes, macrophages and plasma cells. C This features DAD consistent with an organising or proliferative phase of Type II pneumocyte hyperplasia and hypertrophy with reactive Gen N: Neg the pathology, with fewer hyaline membranes, continued type II atypia; Vacuolated macrophages; Hyaline membranes; pneumocyte hyperplasia and organising pneumonia. Interstitial Moderate inflammation; Vascular wall hyperplasia; Medium Gen E: Neg inflammation is present and diffusely distributed across the section. The size-vessel partial thrombi; Focal edema. . vasculature exhibits arterial thrombi and endarteritis with intimal

expansion. The copyrightholderforthispreprint

Figure 1

A B A_11 B_13 C_05 bioRxiv preprint doi: https://doi.org/10.1101/2021.06.21.449178; this version posted June 21, 2021. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY 4.0 International license. Severe Patient A Mild/Moderate A_10 B_08 C_02

Patient ABC Severity Bronchiolar epithelium Mild/Moderate Severe Patient B C B_01

B_02

B_15

30 A_04 B_05 B_11 A_13 A_12 A_07 A_15

A_02 A_09 20 A_03 A_14 A_08 B_12 B_03 B_13 B_06 A_10 A_05 B_10 Patient C C_11 C_05 A_11 B_16 10 C_09 C_01 A_16 % CD68+ of total nucleated cells % CD68+ of total nucleated C_08 C_07 C_12 A_17 C_14 C_10 B_09 C_13 B_14 C_03 C_06 A_01 B_08 C_02 C_04 0 B_07 A_06 0 20 40 60 % CD3+ of total nucleated cells

GO Biological Processes enriched in areas of severe diffuse alveolar damage

T cell activation antigen processing and presentation D E of exogenous peptide antigen Count regulation of immune effector process Differentially expressed genes between mild and severe diffuse alveolar damage 5.0 leukocyte cell−cell adhesion 7.5

CD9 TNFRSF12A T cell receptor signaling pathway 10.0 CAPN2 ITGA3 response to interferon−gamma 12.5 5 regulation of mononuclear cell proliferation 15.0

LAMA5 FSTL3 T cell differentiation CEACAM6 phagocytosis p.adjust KRT19 LAMB3 KRT7 NOTCH3 CD74 cell killing CD55 MCAM 7.5e−05 KRT18 SFN humoral immune response 4 CCND1 KLF4 LCP1 HOPX 5.0e−05 CD59 CD53 positive regulation of cytokine production GDF15 CTSS 2.5e−05 EPCAM TNFSF10 HLA−DRA leukocyte mediated cytotoxicity THBD TACSTD2 HLA−DOA neutrophil chemotaxis IL1RL1 IL10RA antigen processing and presentation NR4A1 C2 CD3E of endogenous antigen ITGA2 GIMAP4 HLA−DMB 3 0.10 0.15 0.20 0.25 0.30 CXCL5 CCR5 B2M PSMB9 CD48 GeneRatio AREG FCGR3A IL2RG PTPRC IFITM1 C1QA GO Biological Processes enriched in areas of mild diffuse alveolar damage RHOB CYBB TRAC TRBC1/2 TNC HLA−DMA WIPF1 CD3D SLAMF7 STAT1 cell−substrate adhesion THBS1 SLC11A1 JAK3 RAC2 CD2 GZMA p.adjust − log10(FDR) extracellular matrix organization SPP1 DUOX1 ITGAL C3 HLA−DQA1 MS4A6A LTBP1 HLA−B 2 NAPSA MUC1 ZAP70 CCL5 regulation of response to wounding 0.009 SFTPB TAP1 HLA−A CD8A GBP1 WIF1 negative regulation of cell adhesion PGC TYMP GZMH GBP4 LYZ CXCL9 0.006 NKG7 GZMB epithelial cell migration ITGB2 H3C10 0.003 PRF1 SIGLEC1 tissue migration CTSW GZMK IDO1 FDR = 0.05 CXCL11 regulation of epithelial cell proliferation 1 regulation of cell−cell adhesion Count

response to ketone 3

regeneration 4

regulation of transmembrane receptor protein 5 serine/threonine kinase signaling pathway negative regulation of G1/S transition 6 0 of mitotic cell cycle MildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMildMil response to hydrogen peroxide

−1 0 1 2 3 lung development 8 Log2 Fold Change negative regulation of blood coagulation

0.08 0.12 0.16 0.20 GeneRatio Figure 2

factor receptors and second messengers peptide antigen via MHC class II AB GO.BP:GO.BP: gluconeogenesisReactome: phagocytosis,GO.BP: surfactant engulfmentReactome: respiratory metabolismKEGG: cellgaseous GO.BP:Apelinjunction exchangeReactome: signaling bloodorganisation vesselReactome: bypathway disease respiratory endothelialReactome: of extracellular signal GO.BP:system cell transductioninterleukin migrationGO.BP: cellularmatrixGO.BP:− 6 organizationregulationresponseby family growth antigenGO.BP: signaling toof interleukin angiogenesisprocessingGO.BP: Fc receptorGO.BP: −toll 1and− likemediated presentationGO.BP: phagocytosisreceptor stimulatoryGO.BP: pattern signaling of exogenousGO.BP: neutrophilrecognition signaling pathwayGO.BP: collagen activation receptorpathwayGO.BP: regulation fibril involvedsignaling organizationGO.BP: connective of canonical in Reactome: pathwayATP immune tissue metabolicGO.BP: Wnt response developmenttranscriptional signaling GO.BP:processresponse pathwayGO.BP: nucleosome toregulation oxygenReactome: DNA levelsorganizationpackagingbyGO.BP: TP53 toll− GO.BP:antigenlike receptor processingGO.BP: type Icascades interferonGO.BP: interferon and presentationGO.BP: response signaling−gammaKEGG: regulation topathwayof− mediated virusendogenous KEGG:T cell of receptoradaptive signalingGO.BP:Natural antigen signalingimmuneGO.BP: killerlymphocyte pathway cellKEGG: interferonresponse pathwaymediated differentiation Chemokine− gammacytotoxicity productionsignaling pathway Cyan Red Blue Odds ratio Lightcyan 64 Yellow

Purple 16 Grey60 Greenyellow 4 Black Green

Eigengene modules Lightyellow # Genes

Lightgreen 0 Midnightblue 20 Salmon 40 Pink Brown Turquoise 200 100 0 Number of member genes C D

Cyan ** Alveolar macrophages

Red ***** ** Type 2 pneumocytes

Blue ** ***** Epithelial cells

Lightcyan * IL-1 response: IL-6/IL-8

Yellow ** *** Vasculature

Purple Apelin/mTOR signalling

Grey60 **** *Macrophage identity

Greenyellow ********* Antigen presentation

Black **** Fibroblast phenotype

Green Hypoxic response

Lightyellow Chromatin remodeling

Lightgreen Cell cycling

Midnightblue ** TLR signalling and monocytes

Salmon TLR and IL-1 signalling

Pink ** IFITM2/HSP/ECM

Brown * Interferon responses

Turquoise ******** Cytotoxicity and T cells

AT1AT2 SFN CD9 CD4 IRF8 LYZ CD68 THY1 TFRC CD55 CD14 FLT3 CTSS PRF1CD8ACD3D NKG7GNLYTRAC MRC1 SIRPA MSR1 ITGB4 ITGAXCDH5 MCAM KRT18 CD163 THBDITGA5IL3RAICAM1 C1QB C1QA ITGAM ZAP70 KLRD1 NK cellsated DC MARCO CEBPA SFTPDSFTPC SFTPBEPCAMMERTK COL5A1COL1A1COL3A1 LGALS3NKX2−1 PECAM1 PDGFRA FCGR3B SIGLEC1 FCGR3A CLEC7A FCGR1A Mast cells RASGRF1 HLA−DRA MonocytesFibroblasts CACNA2D2 ActivCD8+ T cells Muscle cells Dividing T cells Endothelial cells

Dividing macrophages MARCO− macrophages MARCO+ macrophages Viral receptors/cofactors E Complement Prostaglandins Fibrosis IL−6 signalling Chemokines/receptors Cytokines/receptors Interferon responses * ** *** Cytotoxicity and T cells ******** ** Antigen presentation ** * ** Epithelial cells *** * ** Type 2 pneumocytes * IL−1 response: IL−6/IL−8 * * Vasculature ** * Apelin/mTOR signalling * TLR and IL−1 signalling * TLR signalling and monocytes * IFITM2/HSP/ECM * * * Alveolar macrophages * Hypoxic response Macrophage identity * Fibroblast phenotype Chromatin remodeling * Cell cycling

C5 C2 C3 IL6 IL6R IL32 IFNGTNFIL1BIL1A CD55CD59CD46 C1QA CTSL NRP1DPP4ITGB6 FGF2 STAT3IL6ST CCL2 CCR2CCR1CCR5CCL5 CCR7CCR4 BST2 IL2RGIL2RB IL2RA IL1R1 PTGS2 FURIN PDGFCPDGFA TGFB2THBS1PDGFBTGFB3 FGFR2FGFR4FGFR1FGFR3PDGFD TGFB1NFKB1 CXCR2CXCL2CCL15CCL13CCL18 CXCL9 CCL19CXCR6 CXCR3 IL10RA IFNA17 IL1RAP PTGER4 TGFBR1 TGFBR2 CXCL16 CXCL10CXCL11 IFNGR2 IFNGR1IFNAR1 PLA2G4FPLA2G4APLA2G4C PDGFRAPDGFRB TNFSF10 TMPRSS2 TNFRSF1BTNFSF13B TNFRSF1A TNFRSF10ATNFRSF10B TNFRSF13C

Spearman correlation with module * WGCNA module membership > 0.7, P value < 0.05 0 0.2 0.4 0.6 0.8 1 Figure 3

3 4 1 2 5 Spatial Groups

Patient Severity bioRxiv preprint doi: https://doi.org/10.1101/2021.06.21.449178; this version posted June 21, 2021. The copyright% cells holder CD3+ for this preprint A (which was not certified byB peer review) is the author/funder, who has granted bioRxiv a license to display the preprint% cells CD68+in perpetuity. It is made Patient Interferon responses Interferon responses available under aCC-BY 4.0 International license. A B Cytotoxicity and T cells TLR and IL−1 signalling C

Chromatin remodeling TLR signalling and monocytes Severity Mild TLR and IL−1 signalling Antigen presentation Severe

Cell cycling IFITM2/HSP/ECM % cells CD3+ 80 Antigen presentation Apelin/mTOR signalling 60 40 Hypoxic response Alveolar macrophages 20 0 TLR signalling and monocytes Macrophage identity % cells CD68+ Macrophage identity Fibroblast phenotype 50 40 Fibroblast phenotype Hypoxic response 30 20 Alveolar macrophages Chromatin remodeling 10 0 IL−1 response: IL−6/IL− 8 Cell cycling Eigengene Type 2 pneumocytes Cytotoxicity and T cells expression 0.4 Apelin/mTOR signalling Epithelial cells 0.2 0 IFITM2/HSP/ECM Type 2 pneumocytes −0.2 −0.4 Vasculature IL−1 response: IL−6/IL−8

Epithelial cells Vasculature ABC B_03 B_16 B_10 B_14 B_01 B_13 B_02 B_15 B_11 B_06 B_12 B_05 B_07 B_08 B_09 A_02 A_04 A_03 A_10 A_15 A_07 A_14 A_16 A_17 A_08 A_09 A_12 A_13 A_11 A_05 A_06 A_01 Patient C_13 C_06 C_08 C_14 C_09 C_02 C_10 C_04 C_03 C_07 C_01 C_11 C_12 C_05 3 4 1 2 5 Spatial Groups 1 Scaled expression 0.5 Correlation ENG with 0 PECAM1 4 histological CDH5 −0.5 C severity TFRC 2 −1 NKX2−1 0 SFTPB SFTPC −2 SFTPA1 −4 KRT19 EPCAM CDH1 IFNA1 HLA−B IRF1 TLR2 HLA−A STAT1 OAS1 OAS2 CD14 CD163 TLR4 TLR6 TLR5 KLRK1 GNLY NKG7 GZMK PRF1 CD8A CD3E IRF2 ITGAX HLA−DRA HLA−DQA1 HLA−DPA1 TLR8 MARCO MRC1 CD68 COL1A2 COL5A2 THY1 B_03 B_16 B_10 B_14 B_01 B_13 B_02 B_15 B_11 B_06 B_12 B_05 B_07 B_08 B_09 A_02 A_04 A_03 A_10 A_15 A_07 A_14 A_16 A_17 A_08 A_09 A_12 A_13 A_11 A_05 A_06 A_01 C_13 C_06 C_08 C_14 C_09 C_02 C_10 C_04 C_03 C_07 C_01 C_11 C_12 C_05 Figure 4

Spatial Group 1 A 100 B Group 5 bioRxiv preprint doi: https://doi.org/10.1101/2021.06.21.449178CXCL10 ; this version posted June 21, 2021. The copyright holder Networkfor this preprint legend (B - E) TGFB1

(which was not certified by peer review) is the author/funder, who has grantedCCL19 bioRxiv a license to display the preprint in perpetuity. It is made Weighted edges IL32 Dividing T cells Group 4 available under aCC-BY 4.0 InternationalTNFSF14 license. C_08 (Spearman correlation coefficient) CCL18 +0.1 75 CXCL1 TNFSF13B CCL13 TLR and IL−1 signalling +0.5

CCL3 +1 IFNA17 NK cells

IFITM2/HSP/ECM TNFRSF14 CXCL9 Module eigengene expression CCL4 Immunofluorescent markers Group 3 GDF6 CXCL11 0.05

0.1 CD68 50 CCL2 Endothelial cells Group 2 AMH CD3 CCL21 B_09 DNA Apelin/mTOR signalling 0.25 PanCK TGFB3 CX3CL1 Vasculature Monocytes Muscle cells AT1 25 TGFB2 IL1RN BMP5 Normalised gene counts Cell abundance CSF1 % of AOI classified as 'Severe damage' as 'Severe classified % of AOI Patient LIF Mast cells 10 1

Group 1 AOIsdamage mild of Epithelial cells MARCO+ macrophages A CXCL5 CXCL8 200 5 B GDF15 IL−1 response: IL−6/IL−8 C_07 AT2 CXCL3 C 10000 20 INHBA OSM 0 CXCL16

CXCL2 0 10 20 30 40 50 IL6 Average % CD3+ cells of total cells

C Spatial Group 2 D Spatial Group 3

CCL15 MARCO− macrophages Dividing macrophages

BMP5 Dividing macrophages A_15 C_06 Fibroblasts Cell cycling TNFSF13B IL18 Fibroblast phenotype IL18 Macrophage identity CXCL12 CCL19 Hypoxic response Dividing T cells BMP4 TNFSF12 TGFB3 LTB CXCL12 Chromatin remodeling MARCO+ macrophages TNFSF13

IFNA1 CXCL16 Alveolar macrophages CXCL10 INHBA CD8+ T cells Epithelial cells AT2 Type 2 pneumocytes IFNA17

XCL2 IL16 TNFSF10 TGFB2 A_02 CCL24 CCL18 CCL17 MARCO− macrophages Interferon responses B_14 AT1 CCL5 TNF CXCL1 CCL2 Activated DC TNFSF13B CCL3 AMH GDF15 LTA CXCL9 Muscle cells CXCL2 Cytotoxicity and T cells INHBB CXCL11 CXCL3 IL6 IL32 TNFSF14

CCL4 TLR and IL−1 signalling TGFB1

IL−1 response: IL−6/IL−8 CCL13 FASLG NK cells IFITM2/HSP/ECM CSF1 TNFRSF14 C_13 OSM A_08

CD70 Endothelial cells

LIF

Mast cells Monocytes CCL8

E Spatial Group 4 F Spatial Group 5

CCL2 IL32

B_12 CCL4 A_12

IFNA17 AOIs of severe damage severe of AOIs

TNFSF14 IL32

Endothelial cells NK cells TGFB1 CD70 CCL2 IFNA1 TNF CXCL10 TLR signalling and monocytes Activated DC CXCL1 CXCL10 CCL3L1 CCL13 IFITM2/HSP/ECM CXCL1 TNFRSF14 CXCL11 Antigen presentation CCL13 AMH TNFSF13B NK cells FASLG Cytotoxicity and T cells TNFRSF14 Interferon responses CXCL9 CCL3 CCL5 IFNA17 AMH TNFSF14 IL16 TLR and IL−1 signalling GDF6 TNFSF10 TGFB1 TNFSF12 TNFSF12 CCL18 CCL8 B_13 CCL19 C_05 CXCL9 FASLG CCL19 CD8+ T cells CCL18 MARCO− macrophages CXCL13 MARCO+ macrophages Dividing macrophages

XCL2 Dividing T cells

Dividing T cells LTB XCL2 CXCL11 Macrophage identity Cell cycling GDF15 AT2 MARCO− macrophages Interferon responses CCL22 TNFSF13B CCL17 CXCL16 TNF EBI3 CCL4 TNFSF13 Hypoxic response Chromatin remodeling CXCL12 Antigen presentation Activated DC INHBA

CCL3 IL16 IFNA1 BMP5 IL18 BMP4 LTB B_02 TGFB3 A_01 TNFSF10 CD8+ T cells Fibroblast phenotype

Dividing macrophages

Fibroblasts CCL5 CCL15

G Complement Prostaglandins Viral receptors/cofactors Fibrosis IL−6 signalling Chemokines/receptors Cytokines/receptors Spatialgroup 1 Spatialgroup 2 Spatialgroup 3

Spatialgroup 4 Spatialgroup 5

C5 C2 C3 IL6 IL6R IL32 IFNGTNFIL1BIL1A CD55CD59CD46 C1QA CTSL NRP1DPP4ITGB6 FGF2 STAT3IL6ST CCL2 CCR2CCR1CCR5CCL5 CCR7CCR4 BST2 IL2RGIL2RB IL2RA IL1R1 PTGS2 FURIN PDGFA TGFB2THBS1 PDGFBTGFB3 FGFR2FGFR4FGFR1FGFR3 TGFB1NFKB1 CXCR2CXCL2CCL15CCL13CCL18 CXCL9 CCL19CXCR6 CXCR3 IL10RA IFNA17 IL1RAP PTGER4 PDGFCTGFBR1 TGFBR2 PDGFD CXCL16 CXCL10CXCL11 IFNGR2 IFNGR1 IFNAR1 PLA2G4FPLA2G4APLA2G4C PDGFRAPDGFRB TNFSF10 TMPRSS2 TNFRSF1BTNFSF13B TNFRSF1A Spearman's correlation coefficient TNFRSF10ATNFRSF10B TNFRSF13C

0 0.2 0.4 0.6 0.8 1 Figure 5

Mild tissue alveoli bioRxiv preprint doi: https://doi.org/10.1101/2021.06.21.449178; this version posted June 21, 2021. The copyright holder for this preprint pathology(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint11. inFibrosis perpetuity. It is made 10. Stromalavailable under cell aCC-BY 4.0 International license. IL-1/IL-6 responses aveolar epithelium endothelial 12. Endothelitis cells SARS-CoV-2

6. Proliferation 9. Cytokine storm and activation TNF IL1 IL6 4. Antigen CXCL9/10/11 presentation CCL2

IFNG

IFNA1/17 IL32 1. TLR activation 3. IFN GNLY signaling 5. NK and CD8 7. Immune cell T cell activation recruitment and PRF1 extravasiation 2. Complement 8. Tissue damage activation C1QA/2/3/5 and apoptosis Severe alveolar damage

Legend

epithelial dendritic cell CD8 T cell Virus TCR cell interstitial therapeutic AT2 cells macrophage NK cell TLR targets

MARCO- broblast macrophage monocyte IL1-R Supplementary figure 1

Filtered genes Example housekeeping genes A B 10000

10000 bioRxiv preprint doi: https://doi.org/10.1101/2021.06.21.449178; this version posted June 21, 2021. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY 4.0 International license. Genes

ACTB 1000 EIF2B4 Patient 1000 HPRT1 A POLR1B B PUM1 C 100 TBP TUBB Quantile normalised counts Quantile normalised counts 100 UBB

10 A_01 A_02 A_03 A_04 A_05 A_06 A_07 A_08 A_09 A_10 A_11 A_12 A_13 A_14 A_15 A_16 A_17 B_01 B_02 B_03 B_05 B_06 B_07 B_08 B_09 B_10 B_11 B_12 B_13 B_14 B_15 B_16 A_01 A_02 A_03 A_04 A_05 A_06 A_07 A_08 A_09 A_10 A_11 A_12 A_13 A_14 A_15 A_16 A_17 B_01 B_02 B_03 B_05 B_06 B_07 B_08 B_09 B_10 B_11 B_12 B_13 B_14 B_15 B_16 C_01 C_02 C_03 C_04 C_05 C_06 C_07 C_08 C_09 C_10 C_11 C_12 C_13 C_14 C_01 C_02 C_03 C_04 C_05 C_06 C_07 C_08 C_09 C_10 C_11 C_12 C_13 C_14 Areas of interest Areas of interest

C Severity Patient % CD3+ cells % CD68+ cells

5 5 5 5 Severity Mild Resp. Epithelium 0 0 0 0 Severe

Patient

PC2 (31.2%) −5 PC2 (31.2%) −5 PC2 (31.2%) −5 PC2 (31.2%) −5 A B C −10 −10 −10 −10

−10 0 10 −10 0 10 −10 0 10 −10 0 10 % CD3+ PC1 (37.5%) PC1 (37.5%) PC1 (37.5%) PC1 (37.5%) 60

40 5 5 5 5 20

0 0 0 0 % CD68+

40 −5 −5 −5 −5

PC4 (5.1%) PC4 (5.1%) PC4 (5.1%) PC4 (5.1%) 30

10 −10 −10 −10 −10

−10 −5 0 5 10 −10 −5 0 5 10 −10 −5 0 5 10 −10 −5 0 5 10 PC3 (9.6%) PC3 (9.6%) PC3 (9.6%) PC3 (9.6%) Supplementary figure 2

A Patient B bioRxiv preprint doi: https://doi.org/10.1101/2021.06.21.449178; this version posted% CD3+ cells June 21, 2021. The copyright30 R = 0.93,holderp < 2.2e for−16 this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv% CD68+ cells a license to display the preprint in perpetuity. It is made available under aCC-BY 4.0 InternationalSeverity license. fibroblast 20 MARCOneg.macrophage Patient alveolar.epithelial.cell.type.1 A blood.vessel.cell B alveolar.epithelial.cell.type.2 C 10 CD8..cytotoxic.T.cell % CD3+ cells dividing.macrophage 80 MARCOpos.macrophage 60 0

muscle.cell 40 Estimated SpatialDecon T cell abundance 20 monocyte 0 20 40 60 0 dividing.T.cell Immunofluorescence CD3 percentage natural.killer.cell % CD68+ cells 60 activated.dendritic.cell 40 40 regulatory.T.cell R = 0.76, p = 6.1e−10 dividing.natural.killer.cell 20 lymph.vessel.cell 0 30 plasma.cell Severity mast.cell Mild plasmacytoid.dendritic.cell Severe neutrophil Abundance 20 dendritic.cell.dividing.monocyte 30 dendritic.cell.type.1 20 dendritic.cell.type.2 10 10 B.cell

CD4..T.cell 0 Estimated SpatialDecon macrophage abundance ciliated.cell 0 10 20 30 40 Immunofluorescence CD68 percentage B_08 A_10 A_14 A_15 A_08 A_09 B_14 B_03 B_10 B_16 A_01 A_06 A_05 A_11 B_13 B_12 B_06 A_02 B_05 A_13 A_12 B_09 B_07 A_03 A_07 A_04 B_02 B_15 B_01 B_11 A_16 A_17 C_10 C_04 C_02 C_06 C_09 C_14 C_08 C_12 C_13 C_01 C_11 C_03 C_07 C_05

Complement Prostaglandins Viral receptors/cofactors Fibrosis IL−6 signalling Chemokines/receptors Cytokines/receptors C NK cells Dividing T cells Activated DC CD8+ T cells MARCO− macrophages Dividing macrophages Fibroblasts AT2 AT1 Muscle cells Monocytes Mast cells MARCO+ macrophages Endothelial cells

C5 C2 C3 IL6 IL6R IL32 TNFIL1BIL1A CD55CD59CD46 C1QA CTSL NRP1DPP4ITGB6 FGF2 STAT3IL6ST CCL2 CCR2CCR1CCR5CCL5 CCR7CCR4 BST2 IL2RGIL2RB IL2RA IL1R1 IFNG PTGS2 FURIN PDGFCPDGFA TGFB2THBS1 PDGFBTGFB3 FGFR2FGFR4FGFR1FGFR3 TGFB1NFKB1 CXCR2CXCL2CCL15CCL13CCL18 CXCL9 CCL19CXCR6 CXCR3 IL10RA IFNA17 IL1RAP PTGER4 TGFBR1 TGFBR2 PDGFD CXCL16 CXCL10CXCL11 IFNGR2 IFNGR1IFNAR1 PLA2G4FPLA2G4APLA2G4C PDGFRAPDGFRB TNFSF10 TMPRSS2 Spearman's correlation coefficient TNFRSF1BTNFSF13B TNFRSF1A TNFRSF10ATNFRSF10B TNFRSF13C

0 0.2 0.4 0.6 0.8 1