Sol-gel derived materials as substrates for neuronal differentiation: effects of surface features and protein conformation
By S. Jedlicka, J. McKenzie, S. Leavesley, Kenneth M. Little, Thomas J. Webster, J. Robinson, D. Nivens, Jenna L. Rickus. Journal of Materials Chemistry A, 2006
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This work demonstrates the ability of sol-gel derived materials to support the differentiation of neuronal cells, and investigates the physiochemical interactions between the surface and extracellular matrix proteins as a mediator of the effects of surface features on differentiation. We have applied fluorescence resonance energy transfer (FRET) spectroscopy to study the conformational changes of human serum fibronectin, a critical extracellular cell adhesion protein, after adsorption onto native and poly-L-lysine doped sol-gel derived silica thin films and bulk materials. The global conformation of fibronectin varied dramatically between native and organically modified materials and most interestingly between thin films and bulk materials of the same chemistry. A comparison of the surface topography of thin films and bulk materials by atomic force microscopy reveals that films of native silica have surface features less than the AFM tip size (<25 nm) while bulk materials of the same precursor chemistry have features ranging from 50–100 nm in size. Fibronectin assumed an inactive, globular, solution-like state on the larger feature size bulk gels and an active, fully extended fibrillar-like state on the smaller feature size films. Neither native nor PLL-doped bulk materials could support cell growth or neuronal differentiation of PC12 cells, in stark contrast to the thin films, which supported a robust neuronal phenotype. Morphological analysis and expression levels of the neuronal proteins β-tubulin and neurofilament, in addition to the FRET data, indicate that the effects of surface chemistry on fibronectin conformation, cellular adhesion, and differentiation are dependent upon the surface topography.
Subfields: Biomaterials, Ocean Engineering
View Paper: https://www.semanticscholar.org/paper/4ab8e430ba4961226d238a9b5d015734ea5b2541
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