Functional Profiling of Rheumatoid Arthritis Reveals Disease-Specific Immunomodulatory Responses and Novel Therapeutic Targets 2256858

Abstract

Abstract Introduction Immune-mediated inflammatory diseases (IMIDs) represent a high unmet clinical need. Despite numerous single cell studies describing IMIDs immune dysregulation, actionable data for drug discovery-relevant novel targets and biology prediction is still limited, which is at least partially due to their static nature. Here, we present a system level approach to reveal IMID-driving biology through functional perturbation modeling of patient-derived immune cells, combined with multi-omics integration. By systematically mapping perturbation responses to underlying molecular networks in realistic disease contexts, our lab-in-the-loop approach enables rapid prediction and experimental validation to uncover mechanism-of-action insights and therapeutically tractable targets in rheumatoid arthritis (RA). Methods As an ex vivo model for the RA synovial niche, we exposed RA and healthy human PBMCs to synovial fluid (SF) from patients with RA, osteoarthritis (OA), or healthy controls. We applied multiplexed high-content imaging to quantify cell-type morphologies, spatial interactions, and activation markers in response to SF exposure alone or under perturbations. By combining functional data with known drug-target relationships and mapping them to protein-protein interactions, we iteratively selected optimal perturbations for validation screens, sequentially uncovering novel disease specific biological insights. Results SF exposure induced RA specific immune cell activation and spatial interactions effects, which were reverted by standard of care. Molecular networks driving cellular responses to RA vs OA SF were integrated using proteomics and single-cell sequencing. Iterative prediction / validation cycles enriched for SF-induced immune inflammation modulators, revealing how functional perturbation mapping can comprehensively profile disease-driving immune networks and identify novel targets. Conclusion We present a novel, generalizable systems-level framework for therapeutic discovery across IMIDs. Funding Source Österreichische Forschungsförderungsgesellschaft (FFG), Austria Wirtschaftsservice (AWS) Topic Categories Computational and Systems Immunology (COMP)