Cell proliferation and migration explain pore bridging dynamics in 3D printed scaffolds of different pore size. (15th September 2020)

• Record Type: Journal Article
• Authors: Buenzli, Pascal R.; Lanaro, Matthew; Wong, Cynthia S.; McLaughlin, Maximilian P.; Allenby, Mark C.; Woodruff, Maria A.; Simpson, Matthew J.
• Is Part Of: Acta biomaterialia. Volume 114(2020)
• Page Start: 285
• Page End: 295
• Publication Date: 2020-09-15
• Subjects: Tissue engineering -- 3D Printing -- Biofabrication -- Parameter estimation -- Mathematical modelling; Biomedical materials -- Periodicals; 610.28
• DOI: 10.1016/j.actbio.2020.07.010
• Languages: English
• ISSNs: 1742-7061

Abstract

Graphical abstract: Abstract: Tissue growth in bioscaffolds is influenced significantly by pore geometry, but how this geometric dependence emerges from dynamic cellular processes such as cell proliferation and cell migration remains poorly understood. Here we investigate the influence of pore size on the time required to bridge pores in thin 3D-printed scaffolds. Experimentally, new tissue infills the pores continually from their perimeter under strong curvature control, which leads the tissue front to round off with time. Despite the varied shapes assumed by the tissue during this evolution, we find that time to bridge a pore simply increases linearly with the overall pore size. To disentangle the biological influence of cell behaviour and the mechanistic influence of geometry in this experimental observation, we propose a simple reaction–diffusion model of tissue growth based on Porous-Fisher invasion of cells into the pores. First, this model provides a good qualitative representation of the evolution of the tissue; new tissue in the model grows at an effective rate that depends on the local curvature of the tissue substrate. Second, the model suggests that a linear dependence of bridging time with pore size arises due to geometric reasons alone, not to differences in cell behaviours across pores of different sizes. Our analysis suggests that tissue growth dynamics in these experimental constructs is dominated by mechanistic crowding effects that influence collective cell proliferation and migration processes, and that can be predicted by simple reaction–diffusion models of cells that have robust, consistent behaviours.