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Prioritization of disease mechanisms, biomarkers, and drug targets in immune-mediated inflammatory dis-eases (IMIDs) is complicated by altered interactions between thousands of genes. Our multi-organ single -cell RNA sequencing of a mouse IMID model, namely collagen-induced arthritis, shows highly complex and heterogeneous expression changes in all analyzed organs, even though only joints showed signs of inflammation. We organized those into a multi-organ multicellular disease model, which shows predicted mo-lecular interactions within and between organs. That model supports that inflammation is switched on or off by altered balance between pro-and anti-inflammatory upstream regulators (URs) and downstream path-ways. Meta-analyses of human IMIDs show a similar, but graded, on/off switch system. This system has the potential to prioritize, diagnose, and treat optimal combinations of URs on the levels of IMIDs, subgroups, and individual patients. That potential is supported by UR analyses in more than 600 sera from patients with systemic lupus erythematosus., Funding Agencies|Luminex data analysis; National Institutes of Health [HG007690, HL155107]; American Heart Association [957729, D007382]; EU Horizon Health [2021, 101057619]; Ruggeri Network Medicine Research Fund; Swedish Cancer Society [CAN 2017/411]; Research Council of Norway, under the frame of Nord-forsk [90825]; Basic Science Research Program through the Na-tional Research Foundation of Korea (NRF) - Korea govern-ment (Ministry of Science and information communication technology (ICT)) [NRF-2021R1A2C1010082]; Yonsei Univer-sity Wonju College of Medicine [2022-52-0054]; Swedish Research Council VR grant [2019-01165, 82171791]; Swedish Rheumatism Association [R-939149]; Region Ostergtland (ALF grant RO?) [-960604]; Ulla and Roland Gustafsson Foundation; King Gustaf Vs 80-Year Anniversary Foundation [FAI-2020-0663]

Personalized medicine requires the integration and processing of vast amounts of data. Here, we propose a solution to this challenge that is based on constructing Digital Twins. These are high-resolution models of individual patients that are computationally treated with thousands of drugs to find the drug that is optimal for the patient., Funding Agencies|Swedish Research CouncilSwedish Research Council; Swedish Cancer Foundation; Nordic Council; European CommissionEuropean Commission Joint Research Centre

Background: Genomic medicine has paved the way for identifying biomarkers and therapeutically actionable targets for complex diseases, but is complicated by the involvement of thousands of variably expressed genes across multiple cell types. Single-cell RNA-sequencing study (scRNA-seq) allows the characterization of such complex changes in whole organs. Methods: The study is based on applying network tools to organize and analyze scRNA-seq data from a mouse model of arthritis and human rheumatoid arthritis, in order to find diagnostic biomarkers and therapeutic targets. Diagnostic validation studies were performed using expression profiling data and potential protein biomarkers from prospective clinical studies of 13 diseases. A candidate drug was examined by a treatment study of a mouse model of arthritis, using phenotypic, immunohistochemical, and cellular analyses as read-outs. Results: We performed the first systematic analysis of pathways, potential biomarkers, and drug targets in scRNA-seq data from a complex disease, starting with inflamed joints and lymph nodes from a mouse model of arthritis. We found the involvement of hundreds of pathways, biomarkers, and drug targets that differed greatly between cell types. Analyses of scRNA-seq and GWAS data from human rheumatoid arthritis (RA) supported a similar dispersion of pathogenic mechanisms in different cell types. Thus, systems-level approaches to prioritize biomarkers and drugs are needed. Here, we present a prioritization strategy that is based on constructing network models of disease-associated cell types and interactions using scRNA-seq data from our mouse model of arthritis, as well as human RA, which we term multicellular disease models (MCDMs). We find that the network centrality of MCDM cell types correlates with the enrichment of genes harboring genetic variants associated with RA and thus could potentially be used to prioritize cell types and genes for diagnostics and therapeutics. We validated thi

Background: Identification of clinically useful biomarkers for Nasal Polyposis in chronic rhinosinusitis (CRSwNP) has proven difficult. We analyzed gene expression profiling data to find explanations for this. Methods:We analyzed mRNA expression profiling data, GSE36830, of six uncinate tissues from healthy controls and six NP from CRSwNP patients. We performed Ingenuity Pathway Analysis (IPA) of differentially expressed genes to identify pathways and predicted upstream regulators. Results: We identified 1,608 differentially expressed genes and 177 significant pathways, of which Th1 and Th2 activation pathway and leukocyte extravasation signaling were most significant. We identified 75 upstream regulators whose activity was predicted to be upregulated.These included regulators of known pathogenic and therapeutic relevance, like IL-4. However, only seven of the 75 regulators were actually differentially expressed in NP, namely CSF1, TYROBP, CCL2, CCL11, SELP, ADORA3, ICAM1. Interestingly, these did not include IL-4, and four of the seven were receptors. This suggested a potential explanation for the discrepancy between the predicted and observed expression levels of the regulators, namely that the receptors, and not their ligands, were upregulated. Indeed, we found that 10 receptors of key predicted upstream regulators were upregulated, including IL4R. Conclusion: Our findings indicate that the difficulties in finding specific biomarkers for CRSwNP depend on the complex underlying mechanisms, which include multiple pathways and regulators, each of which may be subdivided into multiple components such as ligands, soluble and membrane-bound receptors. This suggests that combinations of biomarkers may be needed for CRSwNP diagnostics., Funding Agencies|Swedish Cancer Foundation [17 0542, 15 0532]; European Commission grantEuropean Commission Joint Research Centre [305033]; Swedish Research CouncilSwedish Research Council [2015-02575, 2015-03495, 2015-03807]; Clinical Cancer Research, Jonkoping Sweden; Torsten Soderberg Foundation; East Gothia Regional Funding

n/a, Funding Agencies|Nordforsk; Swedish Cancer Foundation [17 0542, 15 0532]; European Commission grantEuropean Commission Joint Research Centre [305033]; Swedish Research CouncilSwedish Research Council [2015-02575, 2015-03495, 2015-03807]; Clinical Cancer Research, Jonkoping Sweden; Torsten Soderberg Foundation

Background: Unbiased studies using different genome-wide methods have identified several novel biomarkers for diagnosis and treatment response in Rheumatoid Arthritis (RA). However, clinical translation has proven difficult. Here, we hypothesized that one reason could be that inflammatory responses in peripheral blood are different from those in the arthritic joint. Methods: We performed meta-analysis of gene expression microarray data from synovium, whole blood cells (WBC), peripheral blood mononuclear cells (PBMC), and CD4+ T cells from patients with RA and healthy controls in order to identify overlapping pathways, upstream regulators and potential biomarkers. We also analyzed single cell RNA-sequencing (scRNA-seq) data from peripheral blood and whole joints from a mouse model of antigen-induced arthritis. Results: Analyses of two profiling data sets from synovium from RA patients and healthy controls all showed significant activation of pathways with known pathogenic relevance, such as the Th1 pathway, the role of NFAT in regulation of the immune response, dendritic cell maturation, iCOS-iCOSL signaling in T helper cells, Fc gamma receptor-mediated phagocytosis, interferon signaling, Cdc42 signaling, and cytotoxic T lymphocyte-mediated apoptosis. The most activated upstream regulators included TNF, an important drug target, as well as IFN-gamma and CD40LG, all of which are known to play important pathogenic roles in RA. The differentially expressed genes from synovium included several potential biomarkers, such as CCL5, CCL13, CCL18, CX3CL1, CXCL6, CXCL9, CXCL10, CXCL13, ILLS, IL32, IL1RN, SPP1, and TNFSF11. By contrast, microarray studies of WBC, PBMC and CD4+ T cells showed variable pathways and limited pathway overlap with synovium. Similarly, scRNA-seq data from a mouse model of arthritis did not support that inflammatory responses in peripheral blood reflect those in the arthritic joints. These data showed pathway overlap between mouse joint cells and synov, Funding Agencies|Swedish Cancer Foundation [17 0542, 15 0532]; European CommissionEuropean Commission Joint Research Centre [305033]; Swedish Research CouncilSwedish Research Council [2015-02575, 2015-03495, 2015-03807]; Clinical Cancer Research, Jonkoping Sweden, Torsten Soderberg Foundation

Background: Genomic medicine has paved the way for identifying biomarkers and therapeutically actionable targets for complex diseases, but is complicated by the involvement of thousands of variably expressed genes across multiple cell types. Single-cell RNA-sequencing study (scRNA-seq) allows the characterization of such complex changes in whole organs. Methods: The study is based on applying network tools to organize and analyze scRNA-seq data from a mouse model of arthritis and human rheumatoid arthritis, in order to find diagnostic biomarkers and therapeutic targets. Diagnostic validation studies were performed using expression profiling data and potential protein biomarkers from prospective clinical studies of 13 diseases. A candidate drug was examined by a treatment study of a mouse model of arthritis, using phenotypic, immunohistochemical, and cellular analyses as read-outs. Results: We performed the first systematic analysis of pathways, potential biomarkers, and drug targets in scRNA-seq data from a complex disease, starting with inflamed joints and lymph nodes from a mouse model of arthritis. We found the involvement of hundreds of pathways, biomarkers, and drug targets that differed greatly between cell types. Analyses of scRNA-seq and GWAS data from human rheumatoid arthritis (RA) supported a similar dispersion of pathogenic mechanisms in different cell types. Thus, systems-level approaches to prioritize biomarkers and drugs are needed. Here, we present a prioritization strategy that is based on constructing network models of disease-associated cell types and interactions using scRNA-seq data from our mouse model of arthritis, as well as human RA, which we term multicellular disease models (MCDMs). We find that the network centrality of MCDM cell types correlates with the enrichment of genes harboring genetic variants associated with RA and thus could potentially be used to prioritize cell types and genes for diagnostics and therapeutics. We validated thi

Background Genomic medicine has paved the way for identifying biomarkers and therapeutically actionable targets for complex diseases, but is complicated by the involvement of thousands of variably expressed genes across multiple cell types. Single-cell RNA-sequencing study (scRNA-seq) allows the characterization of such complex changes in whole organs. Methods The study is based on applying network tools to organize and analyze scRNA-seq data from a mouse model of arthritis and human rheumatoid arthritis, in order to find diagnostic biomarkers and therapeutic targets. Diagnostic validation studies were performed using expression profiling data and potential protein biomarkers from prospective clinical studies of 13 diseases. A candidate drug was examined by a treatment study of a mouse model of arthritis, using phenotypic, immunohistochemical, and cellular analyses as read-outs. Results We performed the first systematic analysis of pathways, potential biomarkers, and drug targets in scRNA-seq data from a complex disease, starting with inflamed joints and lymph nodes from a mouse model of arthritis. We found the involvement of hundreds of pathways, biomarkers, and drug targets that differed greatly between cell types. Analyses of scRNA-seq and GWAS data from human rheumatoid arthritis (RA) supported a similar dispersion of pathogenic mechanisms in different cell types. Thus, systems-level approaches to prioritize biomarkers and drugs are needed. Here, we present a prioritization strategy that is based on constructing network models of disease-associated cell types and interactions using scRNA-seq data from our mouse model of arthritis, as well as human RA, which we term multicellular disease models (MCDMs). We find that the network centrality of MCDM cell types correlates with the enrichment of genes harboring genetic variants associated with RA and thus could potentially be used to prioritize cell types and genes for diagnostics and therapeutics. We validated this h, Funding Agencies|Swedish Cancer Foundation [17 0542, 15 0532]; European Commission [305033]; Swedish Research Council [2015-02575, 2015-03495, 2015-03807]; Clinical Cancer Research, Jonkoping, Sweden; Torsten Soderberg Foundation; East Gothia Regional Funding