Decellularized extracellular matrix bioinks for 3D bioprinting of the tendon–bone interface: From biomimetic gradients to regenerative healing
The tendon–bone interface anchors compliant tendon to rigid bone through continuous gradients in collagen orientation, proteoglycan content, mineralization, and local modulus. Following tendon–bone injury in adults, this spatiotemporal organization is difficult to restore, and repair typically produces a fibrous connection rather than a functional enthesis. The main barrier is not the absence of a single pro-reparative factor but a spatiotemporal mismatch among immune resolution, regional cell identity, matrix maturation, controlled mineralization, and load transfer. Tissue-derived decellularized extracellular matrix (dECM) can retain source-associated matrix cues, while 3D bioprinting enables their controlled spatial placement. However, none of printability, increased lineage-marker expression, or a higher ultimate failure load alone is sufficient to establish functional enthesis regeneration. This review examines how dECM identity is retained during decellularization, solubilization, and printing; how regionalized constructs are designed; and how these strategies may influence cell identity, immune regulation, matrix remodeling, perfusion, and mechanical maturation. Current evidence supports the feasibility of dECM bioprinting for regionalized tissue formation and interface integration, but remains insufficient to establish tissue-specific potency, long-term gradient stability, or neotissue-mediated load transfer after scaffold degradation. We therefore propose a progressive evidence framework comprising material identity, manufacturing fidelity, regional tissue formation, host integration, and load takeover to distinguish proof of concept, enthesis-like tissue formation, and functional regeneration. Future progress will require reproducible, attributable, and translatable evidence generated using physicochemically matched controls, spatiotemporal quantification, and long-term loading models.
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