Osteochondral Tissue Chip Derived From iPSCs: Modeling OA Pathologies and Testing Drugs

Osteoarthritis (OA) is a chronic disease mainly characterized by degenerative changes in cartilage, but other joint elements such as bone are also affected. To date, there are no disease-modifying OA drugs (DMOADs), owing in part to a deficiency of current models in simulating OA pathologies and etiologies in humans. In this study, we aimed to develop microphysiological osteochondral (OC) tissue chips derived from human induced pluripotent stem cells (iPSCs) to model the pathologies of OA. We first induced iPSCs into mesenchymal progenitor cells (iMPCs) and optimized the chondro- and osteo-inductive conditions for iMPCs. Then iMPCs were encapsulated into photocrosslinked gelatin scaffolds and cultured within a dual-flow bioreactor, in which the top stream was chondrogenic medium and the bottom stream was osteogenic medium. After 28 days of differentiation, OC tissue chips were successfully generated and phenotypes were confirmed by real time RT-PCR and histology. To create an OA model, interleukin-1 beta (IL-1 beta) was used to challenge the cartilage component for 7 days. While under control conditions, the bone tissue promoted chondrogenesis and suppressed chondrocyte terminal differentiation of the overlying chondral tissue. Under conditions modeling OA, the bone tissue accelerated the degradation of chondral tissue which is likely via the production of catabolic and inflammatory cytokines. These findings suggest active functional crosstalk between the bone and cartilage tissue components in the OC tissue chip under both normal and pathologic conditions. Finally, a selective COX-2 inhibitor commonly prescribed drug for OA, Celecoxib, was shown to downregulate the expression of catabolic and proinflammatory cytokines in the OA model, demonstrating the utility of the OC tissue chip model for drug screening. In summary, the iPSC-derived OC tissue chip developed in this study represents a high-throughput platform applicable for modeling OA and for the screening and testing of candidate DMOADs.