Background Cartilage injuries and osteoarthritis remain major clinical challenges because of the limited intrinsic regenerative capacity of articular cartilage and the inability of conventional two-dimensional cultures and tissue-engineered constructs to fully reproduce its complex cellular, extracellular matrix, and biomechanical microenvironment. Cartilage organoids have therefore emerged as promising three-dimensional models that combine tissue-level self-organization with physiologically relevant cartilage phenotypes. Technology This review systematically summarizes recent advances in engineered cartilage organoids, with particular emphasis on their construction principles, functional characteristics, biomedical applications, and translational challenges. Major technological determinants are discussed, including cell sources such as mesenchymal stem cells, primary chondrocytes, and induced pluripotent stem cells; extracellular matrix and hydrogel systems; cytokines and differentiation inducers; and engineering strategies involving scaffold-free self-assembly, scaffold-assisted culture, 3D bioprinting, bioreactors, microfluidic platforms, gene editing, and computational approaches. Results Engineered cartilage organoids have demonstrated substantial potential for disease modeling, drug screening, genetic and microenvironmental regulation, and cartilage or osteochondral regeneration by reproducing pathological phenotypes and enabling more physiologically relevant therapeutic evaluation. However, major barriers remain, including phenotypic instability during long-term culture, incomplete control of cartilage–bone and vascular interactions, insufficient biomechanical integration with host tissue, batch-to-batch variability, and unresolved regulatory and ethical issues. Overall, engineered cartilage organoids provide a versatile bridge between mechanistic cartilage research, therapeutic screening, and regenerative medicine, although further standardization and validation are required before widespread clinical translation.
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Chen et al. (2026) studied this question.
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