Kyburz, D. | Search bioRxiv

bioRxiv2026

Metabolic-secretory decoupling defines a disease-intrinsic state in rheumatoid arthritis monocytes

ObjectivesCirculating monocytes from rheumatoid arthritis (RA) patients are pre-primed for inflammatory activation, but their disease-intrinsic features have not been systematically characterized. Given the important role of metabolism in shaping immune cell function, we aimed to determine how this pre-primed state is underpinned metabolically and whether these changes persist across different activation states, using an unbiased multi-omics approach. MethodsPeripheral blood CD14 monocytes from RA patients and matched healthy donors were analyzed in an undifferentiated state (M0) and after differentiation into classically activated M(IFN{gamma}+LPS) and alternatively activated M(IL-4) macrophages, followed by acute lipopolysaccharide (LPS) stimulation. Metabolomic (untargeted LC-MS/MS), transcriptomic (RNA-seq), and proteomic (label-free mass LC-MS/MS) profiling were performed. Data was comprehensively analyzed by weighted gene correlation network analysis, differential analysis, gene set enrichment analysis, multi-omics factor analysis and metabolic flux modeling. ResultsRA monocytes exhibited a stable disease-driven signature across activation states. Integration of metabolomic, transcriptomic and proteomic data revealed an unexpected convergence on metabolic-secretory coupling, with depletion of nucleotide and redox metabolites, downregulation of mitochondrial and translational pathways, and remodeling of the secretory apparatus, including loss of cis-Golgi components. Consistently, metabolic modeling predicted reduced glycosylation fluxes, connecting metabolic changes to altered secretory capacity. ConclusionsRA monocytes adopt a stable, disease-intrinsic state that persists across activation conditions. Multi-omics data identify a linked metabolic and secretory defect, with reduced glycosylation capacity as a potential functional consequence. This metabolic-secretory coupling represents a defining feature of RA monocyte dysfunction and a potential therapeutic target.

immunology↗

bioRxiv2025

Identification and proteomic profiling of CD90+ small EVs using a refined immunocapture separation approach targeting stromal-derived EV subpopulations in synovial fluid of arthritis patients

Extracellular vesicles (EVs) have emerged as essential drivers in disease progression and promising biomarkers across various disease conditions, including autoimmune diseases such as rheumatoid arthritis (RA). RA is characterized by chronic inflammation driven by patient-specific cellular sub-phenotypes within the synovium. Since EVs reflect their cellular origin and are detectable in biofluids like synovial fluid, they offer a promising alternative for novel biomarker discovery or diagnostics in arthritis, potentially reducing the need for invasive tissue biopsies. However, the inherent complexity, viscosity and extracellular matrix (ECM) composition of synovial fluid pose significant challenges for efficient EV isolation. Moreover, most previous studies have focused on heterogenous bulk EV populations, neglecting the diverse EV subsets present in patient biofluids. To address these challenges, we established a novel immunocapture-based separation method, that selectively targets and isolates cell type-specific EV subpopulation from the heterogenous EV pool present in synovial fluid of arthritis patients. As a proof-of-concept approach, we targeted a stromal-derived EV population by leveraging the fibroblast-associated surface marker CD90/THY1, representing a subset of synovial fibroblasts implicated as important drivers in synovitis and chronic inflammation in RA. Western Blot and data independent acquisition (DIA) liquid chromatography coupled to tandem mass spectrometry (LC-MS/MS) proteomic analysis of immunocaptured CD90+ EVs isolated from cultured primary synovial fibroblasts and synovial fluid of arthritis patients, confirmed the successful separation of stromal vs a myeloid/lymphoid EV populations. The in-depth proteomic profiling of synovial fluid EVs offers unprecedented insights into EV heterogeneity in arthritis. This analysis enables the classification of cell-type associated EV subsets and the identification of novel EV markers for future immunocapture-based EV separation strategies. Our refined fit-for-purpose EV separation approach, coupled to in depth proteomic profiling, provides a powerful tool to identify disease-relevant EV subpopulations and novel biomarker candidates in synovial fluid, facilitating advances in arthritis diagnostics and personalized medicine

immunology↗