Injectable extracellular matrix derived hydrogel provides a platform for enhanced retention and delivery of a heparin-binding growth factor
Source abstract
Abstract
Injectable hydrogels derived from the extracellular matrix (ECM) of decellularized tissues have recently emerged as scaffolds for tissue-engineering applications. Here, we introduce the potential for using a decellularized ECM-derived hydrogel for the improved delivery of heparin-binding growth factors. Immobilization of growth factors on a scaffold has been shown to increase their stability and activity. This can be done via chemical crosslinking, covalent bonding, or by incorporating natural or synthetic growth factor-binding domains similar to those found in vivo in sulfated glycosaminoglycans (GAGs). Many decellularized ECM-derived hydrogels retain native sulfated GAGs, and these materials may therefore provide an excellent delivery platform for heparin-binding growth factors. In this study, the sulfated GAG content of an ECM hydrogel derived from decellularized pericardial ECM was confirmed by Fourier transform infrared spectroscopy and its ability to bind basic fibroblast growth factor (bFGF) was established. Delivery in the pericardial matrix hydrogel increased retention of bFGF both in vitro and in vivo in ischemic myocardium compared to delivery in collagen. In a rodent infarct model, intramyocardial injection of bFGF in pericardial matrix enhanced neovascularization by approximately 112% compared to delivery in collagen. Importantly, the newly formed vasculature was anastomosed with existing vasculature. Thus, the sulfated GAG content of the decellularized ECM hydrogel provides a platform for incorporation of heparin-binding growth factors for prolonged retention and delivery.
Source: europepmc · Quality: candidate unverified · Source record ↗
Research context
OpenAlex topics: Tissue Engineering and Regenerative Medicine · Electrospun Nanofibers in Biomedical Applications · Tribology and Lubrication Engineering
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
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