去细胞化
材料科学
组织工程
细胞外基质
聚合物
流变学
高分子
3D生物打印
基质(化学分析)
纳米技术
生物医学工程
脚手架
化学工程
生物加工
自愈水凝胶
仿生材料
仿生学
生物相容性
再生医学
智能聚合物
复合材料
3D打印
作者
Yasir Qasim Almajidi,Ho Soonmin,Mirza R. Baig,Rucha N. Acharya,Shahbaz Juneja,Ibrokhim Sapaev,Ozodbek Nematov,Rahul Saxena,Mohammad Ebrahim Astaneh,Narges Fereydouni
标识
DOI:10.1080/09205063.2026.2734517
摘要
Decellularized extracellular matrix (dECM) bioinks are widely regarded as one of the more compositionally faithful hydrogel platforms for three-dimensional (3D) bioprinting in tissue engineering. However, the field still lacks a unified polymer engineering framework that links tissue-specific macromolecular architecture to rheological behavior and printing performance. This review synthesizes recent experimental studies on dECM bioinks derived from six tissue sources: cardiac, cartilage, liver, adipose, dermal, and neural tissues. These systems are analyzed through polymer network design, focusing on matrix composition, crosslinking chemistry, rheological properties, and printability. Tissue origin defines the polymeric composition of dECM and therefore influences the crosslinking strategies required to produce printable constructs with relevant mechanical properties. Across several composite systems, dECM incorporation creates a rheological paradox: storage modulus and viscosity may decrease compared with single-component matrices, likely because bioactive ECM macromolecules interfere with pre-formed polymer networks. Methacrylation partially resolves this limitation by separating mechanical tunability from native compositional constraints, enabling concentration-dependent stiffness modulation across approximately two orders of magnitude. Decellularization methodology also emerges as a critical, yet often underestimated, determinant of bioink performance. A polymer engineering perspective provides a more mechanistically useful basis for dECM bioink design than biological fidelity alone. Matching tissue-specific matrix composition, crosslinking architecture, rheological behavior, and printing parameters is essential for advancing dECM-based constructs toward clinical translation.
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