Engineering RGD nanopatterned hydrogels to control preosteoblast behavior: a combined computational and experimental approach.

The adhesion ligand arginine-glycine-aspartic acid (RGD) has been coupled to various materials to be used as tissue culture matrices or cell transplantation vehicles, and recent studies indicate that nanopatterning RGD into high-density islands alters key cell behaviors. Previous studies have failed, however, to conclusively decouple the effects of RGD bulk density and individual pattern parameters (i.e. RGDs/island and island distribution) on these altered cell responses. Using a nanopatterned RGD-coupled alginate hydrogel matrix, this work combines computational, statistical and experimental approaches to elucidate the effects of RGD patterns on four key cell responses. This study shows that in MC3T3 preosteoblasts focal adhesion kinase (FAK) Y397 phosphorylation, cell spreading, and osteogenic differentiation can be controlled by RGD nanopatterning, with the distribution of islands throughout the hydrogel (i.e. how closely spaced the islands are) being the most significant pattern parameter. More closely spaced islands favor FAK Y397 phosphorylation and cell spreading, while more widely spaced islands favor differentiation. Proliferation, in contrast, is primarily a function of RGD bulk density. Nanopatterning of cell adhesion ligands has tremendous potential as a simple tool to gain significant control over multiple cell behaviors in engineered extracellular matrix (ECM).

Multi-scale modeling to predict ligand presentation within RGD nanopatterned hydrogels.

The adhesion ligand RGD has been coupled to various materials to be used as tissue culture matrices or cell transplantation vehicles, and recent studies indicate that nanopatterning RGD into high-density islands alters cell adhesion, proliferation, and differentiation. However, elucidating the impact of nanopattern parameters on cellular responses has been stymied by a lack of understanding of the actual ligand presentation within these systems. We have developed a multi-scale predictive modeling approach to characterize the adhesion ligand nanopatterns within an alginate hydrogel matrix. The models predict the distribution of ligand islands, the spacing between ligands within an island and the fraction of ligands accessible for cell binding. These model predictions can be used to select pattern parameter ranges for experiments on the effects of individual parameters on cellular responses. Additionally, our technique could also be applied to other polymer systems presenting peptides or other signaling molecules.