Graded electrospun scaffold from aligned fibers to honeycomb micropatterns: Application to bone-tendon tissue engineering
Source abstract
Abstract
Scaffolds' production for hard to soft tissues recently become of great interest, as bone-tendon insertion tissue engineering, where injuries mainly occur. Interfacial tissue engineering aims at developing grafts to mimic the gradients of those tissues as far as composition, mechanical properties and structures are concerned. Additive manufacturing can offer solutions to meet these requirements, but still requires to improve processes to achieve such gradients in a few steps. In this study, we developed a 3D-printed collector to combine gap-spinning and micropatterning. We were able to manufacture a scaffold (60 mm long, 5 mm wide) with a smooth gradient of 5 mm long from honeycomb structure to aligned fibers (promoting bone and tendon fate, respectively) in a single step. We estimated a gradient in Young modulus from 20 MPa to 30 MPa from the bone to the tendon side. Deformation tracking permitted to highlight significant difference of local strains between both areas, which could then impact cells' response. Murine stem cells C3H10T1/2 were then seeded at both scaffold parts and cultivated without any growth factors in stretching conditions. Alkaline phosphatase staining and tenomodulin immunostaining suggested the effect of stretching to cells' behavior between the bone and tendon area, compared to static condition. However, benefit of topographical and mechanical cues only cannot be fully established to foster cells to specific fate probably due to the limits of this cell line. This novel collector system however permitted to produce a relevant scaffold to study interfaces where a topographical gradient might be needed.
Source: openalex_inverted_index · Quality: baseline
Research context
OpenAlex topics: Tissue Engineering and Regenerative Medicine · Electrospun Nanofibers in Biomedical Applications · Bone Tissue Engineering Materials
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
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