Human lung cancer harbors spatially organized stem-immunity hubs associated with response to immunotherapy.

The organization of immune cells in human tumors is not well understood. Immunogenic tumors harbor spatially localized multicellular 'immunity hubs' defined by expression of the T cell-attracting chemokines CXCL10/CXCL11 and abundant T cells. Here, we examined immunity hubs in human pre-immunotherapy lung cancer specimens and found an association with beneficial response to PD-1 blockade. Critically, we discovered the stem-immunity hub, a subtype of immunity hub strongly associated with favorable PD-1-blockade outcome. This hub is distinct from mature tertiary lymphoid structures and is enriched for stem-like TCF7+PD-1+CD8+ T cells, activated CCR7+LAMP3+ dendritic cells and CCL19+ fibroblasts as well as chemokines that organize these cells. Within the stem-immunity hub, we find preferential interactions between CXCL10+ macrophages and TCF7-CD8+ T cells as well as between mature regulatory dendritic cells and TCF7+CD4+ and regulatory T cells. These results provide a picture of the spatial organization of the human intratumoral immune response and its relevance to patient immunotherapy outcomes.

Notch signalling drives synovial fibroblast identity and arthritis pathology.

The synovium is a mesenchymal tissue composed mainly of fibroblasts, with a lining and sublining that surround the joints. In rheumatoid arthritis the synovial tissue undergoes marked hyperplasia, becomes inflamed and invasive, and destroys the joint1,2. It has recently been shown that a subset of fibroblasts in the sublining undergoes a major expansion in rheumatoid arthritis that is linked to disease activity3-5; however, the molecular mechanism by which these fibroblasts differentiate and expand is unknown. Here we identify a critical role for NOTCH3 signalling in the differentiation of perivascular and sublining fibroblasts that express CD90 (encoded by THY1). Using single-cell RNA sequencing and synovial tissue organoids, we found that NOTCH3 signalling drives both transcriptional and spatial gradients-emanating from vascular endothelial cells outwards-in fibroblasts. In active rheumatoid arthritis, NOTCH3 and Notch target genes are markedly upregulated in synovial fibroblasts. In mice, the genetic deletion of Notch3 or the blockade of NOTCH3 signalling attenuates inflammation and prevents joint damage in inflammatory arthritis. Our results indicate that synovial fibroblasts exhibit a positional identity that is regulated by endothelium-derived Notch signalling, and that this stromal crosstalk pathway underlies inflammation and pathology in inflammatory arthritis.

Distinct fibroblast subsets drive inflammation and damage in arthritis.

The identification of lymphocyte subsets with non-overlapping effector functions has been pivotal to the development of targeted therapies in immune-mediated inflammatory diseases (IMIDs)1,2. However, it remains unclear whether fibroblast subclasses with non-overlapping functions also exist and are responsible for the wide variety of tissue-driven processes observed in IMIDs, such as inflammation and damage3-5. Here we identify and describe the biology of distinct subsets of fibroblasts responsible for mediating either inflammation or tissue damage in arthritis. We show that deletion of fibroblast activation protein-α (FAPα)+ fibroblasts suppressed both inflammation and bone erosions in mouse models of resolving and persistent arthritis. Single-cell transcriptional analysis identified two distinct fibroblast subsets within the FAPα+ population: FAPα+THY1+ immune effector fibroblasts located in the synovial sub-lining, and FAPα+THY1- destructive fibroblasts restricted to the synovial lining layer. When adoptively transferred into the joint, FAPα+THY1- fibroblasts selectively mediate bone and cartilage damage with little effect on inflammation, whereas transfer of FAPα+ THY1+ fibroblasts resulted in a more severe and persistent inflammatory arthritis, with minimal effect on bone and cartilage. Our findings describing anatomically discrete, functionally distinct fibroblast subsets with non-overlapping functions have important implications for cell-based therapies aimed at modulating inflammation and tissue damage.

Fast, sensitive and accurate integration of single-cell data with Harmony.

The emerging diversity of single-cell RNA-seq datasets allows for the full transcriptional characterization of cell types across a wide variety of biological and clinical conditions. However, it is challenging to analyze them together, particularly when datasets are assayed with different technologies, because biological and technical differences are interspersed. We present Harmony (https://github.com/immunogenomics/harmony), an algorithm that projects cells into a shared embedding in which cells group by cell type rather than dataset-specific conditions. Harmony simultaneously accounts for multiple experimental and biological factors. In six analyses, we demonstrate the superior performance of Harmony to previously published algorithms while requiring fewer computational resources. Harmony enables the integration of ~106 cells on a personal computer. We apply Harmony to peripheral blood mononuclear cells from datasets with large experimental differences, five studies of pancreatic islet cells, mouse embryogenesis datasets and the integration of scRNA-seq with spatial transcriptomics data.

Maternal oxytocin administration modulates gene expression in the brains of perinatal mice.

OBJECTIVES: Oxytocin (OXT) is widely used to facilitate labor. However, little is known about the effects of perinatal OXT exposure on the developing brain. We investigated the effects of maternal OXT administration on gene expression in perinatal mouse brains. METHODS: Pregnant C57BL/6 mice were treated with saline or OXT at term (n=6-7/group). Dams and pups were euthanized on gestational day (GD) 18.5 after delivery by C-section. Another set of dams was treated with saline or OXT (n=6-7/group) and allowed to deliver naturally; pups were euthanized on postnatal day 9 (PND9). Perinatal/neonatal brain gene expression was determined using Illumina BeadChip Arrays and real time quantitative PCR. Differential gene expression analyses were performed. In addition, the effect of OXT on neurite outgrowth was assessed using PC12 cells. RESULTS: Distinct and sex-specific gene expression patterns were identified in offspring brains following maternal OXT administration at term. The microarray data showed that female GD18.5 brains exhibited more differential changes in gene expression compared to male GD18.5 brains. Specifically, Cnot4 and Frmd4a were significantly reduced by OXT exposure in male and female GD18.5 brains, whereas Mtap1b, Srsf11, and Syn2 were significantly reduced only in female GD18.5 brains. No significant microarray differences were observed in PND9 brains. By quantitative PCR, OXT exposure reduced Oxtr expression in female and male brains on GD18.5 and PND9, respectively. PC12 cell differentiation assays revealed that OXT induced neurite outgrowth. CONCLUSIONS: Prenatal OXT exposure induces sex-specific differential regulation of several nervous system-related genes and pathways with important neural functions in perinatal brains.

BioNetGen 2.2: advances in rule-based modeling.

: BioNetGen is an open-source software package for rule-based modeling of complex biochemical systems. Version 2.2 of the software introduces numerous new features for both model specification and simulation. Here, we report on these additions, discussing how they facilitate the construction, simulation and analysis of larger and more complex models than previously possible. AVAILABILITY AND IMPLEMENTATION: Stable BioNetGen releases (Linux, Mac OS/X and Windows), with documentation, are available at http://bionetgen.org Source code is available at http://github.com/RuleWorld/bionetgen CONTACT: bionetgen.help@gmail.comSupplementary information: Supplementary data are available at Bioinformatics online.

CAPRI: efficient inference of cancer progression models from cross-sectional data.

UNLABELLED: We devise a novel inference algorithm to effectively solve the cancer progression model reconstruction problem. Our empirical analysis of the accuracy and convergence rate of our algorithm, CAncer PRogression Inference (CAPRI), shows that it outperforms the state-of-the-art algorithms addressing similar problems. MOTIVATION: Several cancer-related genomic data have become available (e.g. The Cancer Genome Atlas, TCGA) typically involving hundreds of patients. At present, most of these data are aggregated in a cross-sectional fashion providing all measurements at the time of diagnosis. Our goal is to infer cancer 'progression' models from such data. These models are represented as directed acyclic graphs (DAGs) of collections of 'selectivity' relations, where a mutation in a gene A 'selects' for a later mutation in a gene B. Gaining insight into the structure of such progressions has the potential to improve both the stratification of patients and personalized therapy choices. RESULTS: The CAPRI algorithm relies on a scoring method based on a probabilistic theory developed by Suppes, coupled with bootstrap and maximum likelihood inference. The resulting algorithm is efficient, achieves high accuracy and has good complexity, also, in terms of convergence properties. CAPRI performs especially well in the presence of noise in the data, and with limited sample sizes. Moreover CAPRI, in contrast to other approaches, robustly reconstructs different types of confluent trajectories despite irregularities in the data. We also report on an ongoing investigation using CAPRI to study atypical Chronic Myeloid Leukemia, in which we uncovered non trivial selectivity relations and exclusivity patterns among key genomic events. AVAILABILITY AND IMPLEMENTATION: CAPRI is part of the TRanslational ONCOlogy R package and is freely available on the web at: http://bimib.disco.unimib.it/index.php/Tronco CONTACT: daniele.ramazzotti@disco.unimib.it SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Adipocytes regulate fibroblast function, and their loss contributes to fibroblast dysfunction in inflammatory diseases.

Fibroblasts play critical roles in tissue homeostasis, but in pathologic states can drive fibrosis, inflammation, and tissue destruction. In the joint synovium, fibroblasts provide homeostatic maintenance and lubrication. Little is known about what regulates the homeostatic functions of fibroblasts in healthy conditions. We performed RNA sequencing of healthy human synovial tissue and identified a fibroblast gene expression program characterized by enhanced fatty acid metabolism and lipid transport. We found that fat-conditioned media reproduces key aspects of the lipid-related gene signature in cultured fibroblasts. Fractionation and mass spectrometry identified cortisol in driving the healthy fibroblast phenotype, confirmed using glucocorticoid receptor gene ( NR3C1 ) deleted cells. Depletion of synovial adipocytes in mice resulted in loss of the healthy fibroblast phenotype and revealed adipocytes as a major contributor to active cortisol generation via Hsd11 ß 1 expression. Cortisol signaling in fibroblasts mitigated matrix remodeling induced by TNFα- and TGFß, while stimulation with these cytokines repressed cortisol signaling and adipogenesis. Together, these findings demonstrate the importance of adipocytes and cortisol signaling in driving the healthy synovial fibroblast state that is lost in disease.

Making inroads to precision medicine for the treatment of autoimmune diseases: Harnessing genomic studies to better diagnose and treat complex disorders.

Precision Medicine is an emerging approach for disease treatment and prevention that takes into account individual variability in genes, environment, and lifestyle. Autoimmune diseases are those in which the body's natural defense system loses discriminating power between its own cells and foreign cells, causing the body to mistakenly attack healthy tissues. These conditions are very heterogeneous in their presentation and therefore difficult to diagnose and treat. Achieving precision medicine in autoimmune diseases has been challenging due to the complex etiologies of these conditions, involving an interplay between genetic, epigenetic, and environmental factors. However, recent technological and computational advances in molecular profiling have helped identify patient subtypes and molecular pathways which can be used to improve diagnostics and therapeutics. This review discusses the current understanding of the disease mechanisms, heterogeneity, and pathogenic autoantigens in autoimmune diseases gained from genomic and transcriptomic studies and highlights how these findings can be applied to better understand disease heterogeneity in the context of disease diagnostics and therapeutics.

Spatial analysis of human lung cancer reveals organized immune hubs enriched for stem-like CD8 T cells and associated with immunotherapy response.

The organization of immune cells in human tumors is not well understood. Immunogenic tumors harbor spatially-localized multicellular 'immunity hubs' defined by expression of the T cell-attracting chemokines CXCL10/CXCL11 and abundant T cells. Here, we examined immunity hubs in human pre-immunotherapy lung cancer specimens, and found that they were associated with beneficial responses to PD-1-blockade. Immunity hubs were enriched for many interferon-stimulated genes, T cells in multiple differentiation states, and CXCL9/10/11 + macrophages that preferentially interact with CD8 T cells. Critically, we discovered the stem-immunity hub, a subtype of immunity hub strongly associated with favorable PD-1-blockade outcomes, distinct from mature tertiary lymphoid structures, and enriched for stem-like TCF7+PD-1+ CD8 T cells and activated CCR7 + LAMP3 + dendritic cells, as well as chemokines that organize these cells. These results elucidate the spatial organization of the human intratumoral immune response and its relevance to patient immunotherapy outcomes.

TREC/KREC Levels and T and B Lymphocyte Subpopulations in COVID-19 Patients at Different Stages of the Disease.

BACKGROUND: T and B cell-mediated immunity can be assessed using T cell receptor excision circle (TREC) and Kappa-deleting recombination excision circle (KREC) analysis, respectively, and successful implementation of this method requires evaluation of the correlation between the TREC frequencies and T cell subsets as well as KREC levels and B lymphocyte subsets. The aim of the present study was to evaluate the correlation between the TREC/KREC concentrations and T/B lymphocyte subsets at different stages of COVID-19. METHODS: We examined 33 patients in the acute stage of COVID-19 (including 8 patients with poor outcomes) and 33 COVID-19 survivors. TREC/KREC concentrations were measured using quantitative real-time PCR. T/B lymphocyte subsets were determined using flow cytometry. RESULTS: Blood TREC and KREC levels were found to be significantly lower in the acute stage of COVID-19 compared to control values. Moreover, a zero blood TREC level was a predictor of a poor disease outcome. Reductions in CD3+CD4+CD45RO-CD62L- and CD3+CD8+CD45RO-CD62L- T cell counts (as well as in the main fractions of B1 and B2 B cells) indicated a favorable outcome in COVID-19 patients in the acute stage of the disease. Decreased CD3+CD4+CD45RO-CD62L+ and CD3+CD8+CD45RO-CD62L+ T cell frequencies and increased CD3+CD8+CD45RO-CD62L- cell counts were found to indicate a poor outcome in patients with acute COVID-19. These patients were also found to have increased B1 cell counts while demonstrating no changes in B2 cell counts. The levels of effector T cell subsets an naïve B cells were normal in COVID-19 survivors. The most pronounced correlations between TREC/KREC levels and T/B cell subsets counts were observed in COVID-19 survivors: there were positive correlations with naïve T and B lymphocytes and negative correlations with central and effector memory T cell subsets. CONCLUSIONS: The assessment of correlations between TREC and T cell subsets as well as KREC levels and B cell subset counts in patients with acute COVID-19 and COVID-19 survivors has shown that blood concentrations of TREC and KREC are sensitive indicators of the stage of antigen-independent differentiation of adaptive immunity cells. The results of the TREC and KREC analysis correlated with the stages of COVID-19 and differed depending on the outcome of COVID-19.

Co-varying neighborhood analysis identifies cell populations associated with phenotypes of interest from single-cell transcriptomics.

As single-cell datasets grow in sample size, there is a critical need to characterize cell states that vary across samples and associate with sample attributes, such as clinical phenotypes. Current statistical approaches typically map cells to clusters and then assess differences in cluster abundance. Here we present co-varying neighborhood analysis (CNA), an unbiased method to identify associated cell populations with greater flexibility than cluster-based approaches. CNA characterizes dominant axes of variation across samples by identifying groups of small regions in transcriptional space-termed neighborhoods-that co-vary in abundance across samples, suggesting shared function or regulation. CNA performs statistical testing for associations between any sample-level attribute and the abundances of these co-varying neighborhood groups. Simulations show that CNA enables more sensitive and accurate identification of disease-associated cell states than a cluster-based approach. When applied to published datasets, CNA captures a Notch activation signature in rheumatoid arthritis, identifies monocyte populations expanded in sepsis and identifies a novel T cell population associated with progression to active tuberculosis.

A framework for employing longitudinally collected multicenter electronic health records to stratify heterogeneous patient populations on disease history.

OBJECTIVE: To facilitate patient disease subset and risk factor identification by constructing a pipeline which is generalizable, provides easily interpretable results, and allows replication by overcoming electronic health records (EHRs) batch effects. MATERIAL AND METHODS: We used 1872 billing codes in EHRs of 102 880 patients from 12 healthcare systems. Using tools borrowed from single-cell omics, we mitigated center-specific batch effects and performed clustering to identify patients with highly similar medical history patterns across the various centers. Our visualization method (PheSpec) depicts the phenotypic profile of clusters, applies a novel filtering of noninformative codes (Ranked Scope Pervasion), and indicates the most distinguishing features. RESULTS: We observed 114 clinically meaningful profiles, for example, linking prostate hyperplasia with cancer and diabetes with cardiovascular problems and grouping pediatric developmental disorders. Our framework identified disease subsets, exemplified by 6 "other headache" clusters, where phenotypic profiles suggested different underlying mechanisms: migraine, convulsion, injury, eye problems, joint pain, and pituitary gland disorders. Phenotypic patterns replicated well, with high correlations of ≥0.75 to an average of 6 (2-8) of the 12 different cohorts, demonstrating the consistency with which our method discovers disease history profiles. DISCUSSION: Costly clinical research ventures should be based on solid hypotheses. We repurpose methods from single-cell omics to build these hypotheses from observational EHR data, distilling useful information from complex data. CONCLUSION: We establish a generalizable pipeline for the identification and replication of clinically meaningful (sub)phenotypes from widely available high-dimensional billing codes. This approach overcomes datatype problems and produces comprehensive visualizations of validation-ready phenotypes.

Cross-tissue, single-cell stromal atlas identifies shared pathological fibroblast phenotypes in four chronic inflammatory diseases.

BACKGROUND: Pro-inflammatory fibroblasts are critical for pathogenesis in rheumatoid arthritis, inflammatory bowel disease, interstitial lung disease, and Sjögren's syndrome and represent a novel therapeutic target for chronic inflammatory disease. However, the heterogeneity of fibroblast phenotypes, exacerbated by the lack of a common cross-tissue taxonomy, has limited our understanding of which pathways are shared by multiple diseases. METHODS: We profiled fibroblasts derived from inflamed and non-inflamed synovium, intestine, lungs, and salivary glands from affected individuals with single-cell RNA sequencing. We integrated all fibroblasts into a multi-tissue atlas to characterize shared and tissue-specific phenotypes. FINDINGS: Two shared clusters, CXCL10+CCL19+ immune-interacting and SPARC+COL3A1+ vascular-interacting fibroblasts, were expanded in all inflamed tissues and mapped to dermal analogs in a public atopic dermatitis atlas. We confirmed these human pro-inflammatory fibroblasts in animal models of lung, joint, and intestinal inflammation. CONCLUSIONS: This work represents a thorough investigation into fibroblasts across organ systems, individual donors, and disease states that reveals shared pathogenic activation states across four chronic inflammatory diseases. FUNDING: Grant from F. Hoffmann-La Roche (Roche) AG.

Multimodal platform for assessing drug distribution and response in clinical trials.

BACKGROUND: Response to targeted therapy varies between patients for largely unknown reasons. Here, we developed and applied an integrative platform using mass spectrometry imaging (MSI), phosphoproteomics, and multiplexed tissue imaging for mapping drug distribution, target engagement, and adaptive response to gain insights into heterogeneous response to therapy. METHODS: Patient-derived xenograft (PDX) lines of glioblastoma were treated with adavosertib, a Wee1 inhibitor, and tissue drug distribution was measured with MALDI-MSI. Phosphoproteomics was measured in the same tumors to identify biomarkers of drug target engagement and cellular adaptive response. Multiplexed tissue imaging was performed on sister sections to evaluate spatial co-localization of drug and cellular response. The integrated platform was then applied on clinical specimens from glioblastoma patients enrolled in the phase 1 clinical trial. RESULTS: PDX tumors exposed to different doses of adavosertib revealed intra- and inter-tumoral heterogeneity of drug distribution and integration of the heterogeneous drug distribution with phosphoproteomics and multiplexed tissue imaging revealed new markers of molecular response to adavosertib. Analysis of paired clinical specimens from patients enrolled in the phase 1 clinical trial informed the translational potential of the identified biomarkers in studying patient's response to adavosertib. CONCLUSIONS: The multimodal platform identified a signature of drug efficacy and patient-specific adaptive responses applicable to preclinical and clinical drug development. The information generated by the approach may inform mechanisms of success and failure in future early phase clinical trials, providing information for optimizing clinical trial design and guiding future application into clinical practice.

Efficient and precise single-cell reference atlas mapping with Symphony.

Recent advances in single-cell technologies and integration algorithms make it possible to construct comprehensive reference atlases encompassing many donors, studies, disease states, and sequencing platforms. Much like mapping sequencing reads to a reference genome, it is essential to be able to map query cells onto complex, multimillion-cell reference atlases to rapidly identify relevant cell states and phenotypes. We present Symphony ( https://github.com/immunogenomics/symphony ), an algorithm for building large-scale, integrated reference atlases in a convenient, portable format that enables efficient query mapping within seconds. Symphony localizes query cells within a stable low-dimensional reference embedding, facilitating reproducible downstream transfer of reference-defined annotations to the query. We demonstrate the power of Symphony in multiple real-world datasets, including (1) mapping a multi-donor, multi-species query to predict pancreatic cell types, (2) localizing query cells along a developmental trajectory of fetal liver hematopoiesis, and (3) inferring surface protein expression with a multimodal CITE-seq atlas of memory T cells.

Maximizing statistical power to detect differentially abundant cell states with scPOST.

To estimate a study design's power to detect differential abundance, we require a framework that simulates many multi-sample single-cell datasets. However, current simulation methods are challenging for large-scale power analyses because they are computationally resource intensive and do not support easy simulation of multi-sample datasets. Current methods also lack modeling of important inter-sample variation, such as the variation in the frequency of cell states between samples that is observed in single-cell data. Thus, we developed single-cell POwer Simulation Tool (scPOST) to address these limitations and help investigators quickly simulate multi-sample single-cell datasets. Users may explore a range of effect sizes and study design choices (such as increasing the number of samples or cells per sample) to determine their effect on power, and thus choose the optimal study design for their planned experiments.

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

BACKGROUND: Immunosuppressive and anti-cytokine treatment may have a protective effect for patients with COVID-19. Understanding the immune cell states shared between COVID-19 and other inflammatory diseases with established therapies may help nominate immunomodulatory therapies. METHODS: To identify cellular phenotypes that may be shared across tissues affected by disparate inflammatory diseases, we developed a meta-analysis and integration pipeline that models and removes the effects of technology, tissue of origin, and donor that confound cell-type identification. Using this approach, we integrated > 300,000 single-cell transcriptomic profiles from COVID-19-affected lungs and tissues from healthy subjects and patients with five inflammatory diseases: rheumatoid arthritis (RA), Crohn's disease (CD), ulcerative colitis (UC), systemic lupus erythematosus (SLE), and interstitial lung disease. We tested the association of shared immune states with severe/inflamed status compared to healthy control using mixed-effects modeling. To define environmental factors within these tissues that shape shared macrophage phenotypes, we stimulated human blood-derived macrophages with defined combinations of inflammatory factors, emphasizing in particular antiviral interferons IFN-beta (IFN-ß) and IFN-gamma (IFN-γ), and pro-inflammatory cytokines such as TNF. RESULTS: We built an immune cell reference consisting of > 300,000 single-cell profiles from 125 healthy or disease-affected donors from COVID-19 and five inflammatory diseases. We observed a CXCL10+ CCL2+ inflammatory macrophage state that is shared and strikingly abundant in severe COVID-19 bronchoalveolar lavage samples, inflamed RA synovium, inflamed CD ileum, and UC colon. These cells exhibited a distinct arrangement of pro-inflammatory and interferon response genes, including elevated levels of CXCL10, CXCL9, CCL2, CCL3, GBP1, STAT1, and IL1B. Further, we found this macrophage phenotype is induced upon co-stimulation by IFN-γ and TNF-α. CONCLUSIONS: Our integrative analysis identified immune cell states shared across inflamed tissues affected by inflammatory diseases and COVID-19. Our study supports a key role for IFN-γ together with TNF-α in driving an abundant inflammatory macrophage phenotype in severe COVID-19-affected lungs, as well as inflamed RA synovium, CD ileum, and UC colon, which may be targeted by existing immunomodulatory therapies.

IL-1-driven stromal-neutrophil interactions define a subset of patients with inflammatory bowel disease that does not respond to therapies.

Current inflammatory bowel disease (IBD) therapies are ineffective in a high proportion of patients. Combining bulk and single-cell transcriptomics, quantitative histopathology and in situ localization across three cohorts of patients with IBD (total n = 376), we identify coexpressed gene modules within the heterogeneous tissular inflammatory response in IBD that map to distinct histopathological and cellular features (pathotypes). One of these pathotypes is defined by high neutrophil infiltration, activation of fibroblasts and vascular remodeling at sites of deep ulceration. Activated fibroblasts in the ulcer bed display neutrophil-chemoattractant properties that are IL-1R, but not TNF, dependent. Pathotype-associated neutrophil and fibroblast signatures are increased in nonresponders to several therapies across four independent cohorts (total n = 343). The identification of distinct, localized, tissular pathotypes will aid precision targeting of current therapeutics and provides a biological rationale for IL-1 signaling blockade in ulcerating disease.

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

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