PulseExploreJournal ClubDebatesTrendingResearchersJournals
Instagram
HomeExploreJournal ClubTrending
Synapse
⌘+K
Synapse
July 27, 2024Advanced Functional Materials8 citationsOpen Access

3D‐Printed and Recombinant Spider Silk Particle Reinforced Collagen Composite Scaffolds for Soft Tissue Engineering

View Full Paper
KKKim Sarah KoeckVTVanessa T. TrossmannTSThomas Scheibel

Key Points

Key points are not available for this paper at this time.

Abstract

Abstract Collagen is one main component of the extracellular matrix (ECM) in natural tissues and is, therefore, well suited as a biomaterial for tissue engineering. In this study, a method is presented to 3D‐bioprint collagen into a precipitation bath comprising recombinantly produced spider silk protein eADF4(C16) yielding a composite with excellent mechanical properties. The spider silk precipitation bath induced assembly of the collagen into fibrils, and subsequent addition of potassium phosphate buffer lead to the formation of silk particles and stabilization of the collagen fibrils. The produced collagen‐silk composite scaffolds show an internal structure of homogeneously distributed and interacting collagen fibrils and spider silk particles with significantly better mechanical properties compared to plain collagen scaffolds. Further, enzymatic degradation assays of the scaffolds over a 7‐day period show higher stability of the collagen‐silk scaffolds compared to plain collagen scaffolds in the presence of wound proteases. Using the spider silk variant eADF4(C16‐RGD) further increases compressive stress and elastic modulus compared to that of the unmodified variant. Finally, it is shown that the unique collagen‐spider silk composite scaffolds comprising the cell‐binding domains of collagen and the RGD sequence in the spider silk variant represent a promising material for soft tissue regeneration.

Ask AI
Helpful
Bookmark
Share
View Full Paper

Cite This Study

Koeck et al. (2024) studied this question.

synapsesocial.com/papers/68e5ec4fb6db6435875817b2https://doi.org/10.1002/adfm.202407760
Ask AI
Helpful
Bookmark
Share
View Full Paper