(171d) Rational and Combinatorial Analysis of 3D Biomaterials for Optimized Responses of Neural and Non-Neural Cells for Neural Tissue Engineering
Peripheral nervous system (PNS) regeneration after an injury is limited by presence of scar tissue, inhibitory myelin, cell death and lack of growth promotive substrate. Rational design of biomaterials supporting neurons and non-neural cells (Schwann cells and endothelial cells) should lead to an ideal growth promotive nerve guidance channel. We hypothesize rational selection of scaffold, matrix proteins and growth factors and screening of cell responses to combinatorial library will lead to discovery of optimal tissue-specific biomaterial supporting all cell types. To address this goal, we have designed and developed a novel high-throughput screening platform for generation of composite biomaterials and evaluation of cell responses. Composite libraries are generated using a high-throughput printer which combines scaffold and matrix protein at relevant concentrations. Cell encapsulated 3D biomaterials are cultured in microarrays and cell responses are evaluated through high throughput imaging. A confocal flatbed scanner and confocal high content array scanner are used for image acquisition and cell proliferation, morphology and metabolic activity are assessed. We evaluated the cell responses of HUVEC, brain endothelial cells, schwann cells and neurons. Collagen type I was used as base scaffold and matrigel, laminin, fibronectin and collagen type IV were used as matrix proteins. Relevant growth factors to cell type VEGF and FGF (endothelial cells), NGF and BDNF (neurons and schwann cells) are screened. High throughput screening results indicate cell response is dependent upon both collagen and matrix protein concentration and each cell type exhibited unique behavior. The high-throughput screening system provides an efficient way to screen combinatorial biomaterials in a cost-effective, resourceful manner and identify biomaterial ‘leads’. The results cell-specific screens will provide useful information for designing optimized tissue-specific biomaterials.