A Compartmentalized Joint‐on‐chip (JoC) Model to Unravel the Contribution of Cartilage and Synovium to Osteoarthritis Pathogenesis

A compartmentalized joint‐on‐chip (JoC) platform is here developed, modelling the interactions between cartilage and synovium in osteoarthritis (OA). Using independent culture and mechanical/biochemical stimulation, the JoC revealed how inflammation and mechanical damage drive mutual tissue changes. This tool offers new insights into OA onset and potential therapeutic targets by replicating complex joint dynamics in a controlled environment. Abstract Osteoarthritis (OA) is a joint disorder causing pain and disability, yet effective treatments are limited due to incomplete understanding of pathogenic mechanisms involving complex tissue interactions. Articular cartilage degradation is a hallmark, resulting from an imbalance in extracellular matrix turnover, influenced by mechanical and biochemical signals. The synovium also plays a central role in joint inflammation, with macrophages and fibroblasts releasing pro‐inflammatory cytokines and degradative enzymes. However, understanding cartilage‐synovium interactions in OA pathogenesis remains challenging. Here, a compartmentalized joint‐on‐chip (JoC) model that enables independent culture of 3D human cartilage and synovium constructs, allowing spatio‐temporal control over their communication, is presented. The JoC platform supports induction of OA characteristics in both tissues, by applying hyper‐physiological compression to cartilage constructs to mimic mechanical damage and by treating synovium constructs with TNFα and IFNγ to simulate inflammation. Moreover, the platform enables exploration of paracrine signaling between these tissues under pathophysiological conditions, showing that inflamed synovium constructs induce early cartilage degradation, while mechanically damaged cartilage promotes macrophage activation and inflammatory responses in the synovium. These findings support a bidirectional relationship in OA onset and underscore the JoC model as a tool for studying joint tissue interactions. A compartmentalized joint-on-chip (JoC) platform is here developed, modelling the interactions between cartilage and synovium in osteoarthritis (OA). Using independent culture and mechanical/biochemical stimulation, the JoC revealed how inflammation and mechanical damage drive mutual tissue changes. This tool offers new insights into OA onset and potential therapeutic targets by replicating complex joint dynamics in a controlled environment. Abstract Osteoarthritis (OA) is a joint disorder causing pain and disability, yet effective treatments are limited due to incomplete understanding of pathogenic mechanisms involving complex tissue interactions. Articular cartilage degradation is a hallmark, resulting from an imbalance in extracellular matrix turnover, influenced by mechanical and biochemical signals. The synovium also plays a central role in joint inflammation, with macrophages and fibroblasts releasing pro-inflammatory cytokines and degradative enzymes. However, understanding cartilage-synovium interactions in OA pathogenesis remains challenging. Here, a compartmentalized joint-on-chip (JoC) model that enables independent culture of 3D human cartilage and synovium constructs, allowing spatio-temporal control over their communication, is presented. The JoC platform supports induction of OA characteristics in both tissues, by applying hyper-physiological compression to cartilage constructs to mimic mechanical damage and by treating synovium constructs with TNFα and IFNγ to simulate inflammation. Moreover, the platform enables exploration of paracrine signaling between these tissues under pathophysiological conditions, showing that inflamed synovium constructs induce early cartilage degradation, while mechanically damaged cartilage promotes macrophage activation and inflammatory responses in the synovium. These findings support a bidirectional relationship in OA onset and underscore the JoC model as a tool for studying joint tissue interactions. Advanced Science, Volume 12, Issue 42, November 13, 2025.