Soft Matter for Biomedical Applications
Chapter 25: Decellularized Matrix Hydrogels for In Vitro Disease Modeling
Published:07 Jun 2021
L. P. Ferreira, M. V. Monteiro, V. M. Gaspar, and J. F. Mano, in Soft Matter for Biomedical Applications, ed. H. S. Azevedo, J. F. Mano, and J. Borges, The Royal Society of Chemistry, 2021, ch. 25, pp. 626-659.
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The extracellular matrix (ECM) operates as a complex network of cell-supporting macromolecules in tissue homeostasis and disease scenarios. Given ECM structural and bio-signaling roles, understanding and modeling matrix components and their dysfunction in disease is crucial for the development of novel therapeutic approaches for numerous pathologies including pulmonary, renal and intestinal fibrosis, osteoarthritis or cancer. The discovery and preclinical in vitro screening of candidate therapeutics for tackling such conditions remains challenging owing to the lack of in vitro models capable of recapitulating ECM biochemical/biophysical cues and its complex tri-dimensional bioarchitecture in a laboratory setting. Advances in the decellularization, processing and modification of naturally available ECM into cell-free extracellular matrices (dECM) obtained from human or animal tissues, and its processing into designer hydrogels with tunable mechanical/structural features, open opportunities for bioengineering a new generation of more organotypic 3D testing platforms. Herein, we provide an overview of state-of-the-art methodologies employed for the development of dECM-hydrogels showcasing their key applications for generating tumor and fibrotic disease models. Standard and advanced processing technologies for dECM hydrogels such as 3D bioprinting and organ-on-a-chip platforms are also presented and discussed in light of future opportunities and improvements. By taking advantage of the capacity of dECM-hydrogels to closely recapitulate key matrix components, it is foreseeable that in vitro generating organotypic 3D microtissues will better capture key aspects of human diseases and contribute with predictive data on candidate therapeutics bioperformance prior to clinical trials.