Hydrogels with stiffness-degradation spatial patterns control anisotropic 3D cell response

Abstract

AbstractIn nature, tissues are patterned, but most biomaterials used in human applications are not. Patterned biomaterials offer the opportunity to mimic spatially segregating biophysical and biochemical properties found in nature. Engineering such properties allows to study cell-matrix interactions in anisotropic matrices in great detail. Here, we developed alginate-based hydrogels with patterns in stiffness and degradation, composed of distinct areas of soft non-degradable (Soft-NoDeg) and stiff degradable (Stiff-Deg) material properties. The hydrogels exhibit emerging patterns in stiffness and degradability over time, taking advantage of dual Diels-Alder covalent crosslinking and UV-mediated peptide crosslinking. The materials were mechanically characterized using rheology for single-phase and surface micro-indentation for patterned materials. 3D encapsulated mouse embryonic fibroblasts (MEFs) allowed to characterize the anisotropic cell-matrix interaction in terms of cell morphology by employing a novel image-based quantification tool. Live/dead staining showed no differences in cell viability but distinct patterns in proliferation, with higher cell number in Stiff-Deg materials at day 14. Patterns of projected cell area became visible already at day 1, with larger values in Soft-NoDeg materials. This was inverted at day 14, when larger projected cell areas were identified in Stiff-Deg. This shift was accompanied by a significant decrease in cell circularity in Stiff-Deg. The control of anisotropic cell morphology by the material patterns was also confirmed by a significant increase in filopodia number and length in Stiff-Deg materials. The novel image-based quantification tool was useful to spatially visualize and quantify the anisotropic cell response in 3D hydrogels with stiffness-degradation spatial patterns. Our results show that patterning of stiffness and degradability allows to control cell anisotropic response in 3D and can be quantified by image-based strategies. This allows a deeper understanding of cell-matrix interactions in a multicomponent material.