Automatic Actin Filament Quantification and Cell Shape Modeling of Osteoblasts on Charged Ti Surfaces

Abstract

Surface charges at the cell–biomaterial interface are known to determine cellular functions. Previous findings on cell signaling indicate that osteoblastic cells favor certain moderately positive surface charges, whereas highly positive charges are not tolerated. In this study, we aimed to gain deeper insights into the influence exerted by surface charges on the actin cytoskeleton and the cell shape. We analyzed surfaces with a negative, moderately positive, and highly positive zeta (ζ) potential: titanium (Ti), Ti with plasma polymerized allylamine (PPAAm), and Ti with a polydiallyldimethylammonium chloride (PDADMA) multilayer, respectively. We used the software FilaQuant for automatic actin filament quantification of osteoblastic MG-63s, analyzed the cell edge height with scanning ion conductance microscopy (SICM), and described the cellular shape via a mathematical vertex model. A significant enhancement of actin filament formation was achieved on moderately positive (+7 mV) compared with negative ζ-potentials (−87 mV). A hampered cell spreading was reflected in a diminished actin filament number and length on highly positively charged surfaces (+50 mV). Mathematical simulations suggested that in these cells, cortical tension forces dominate the cell–substrate adhesion forces. Our findings present new insights into the impact of surface charges on the overall cell shape and even intracellular structures.

Response of Osteoblasts on Amine-Based Nanocoatings Correlates with the Amino Group Density

“…This suggests that amino-rich surfaces possess high cell adhesion capacity. The slow cell migration is also the effect of the moderately positive surface charges from the amine groups on the surfaces, and this is supported by the works of Gruening et al [57], who showed that moderately positive surface charges exhibit a higher number of long actin filaments that promote cell-material adhesion. The changes in the cells' circularity over time also correlate with the cell spreading and gene expression results.…”

Contact area and tissue growth dynamics shape synthetic juxtacrine signaling patterns

“…In signal receiving cells co-cultured with signal sending cells, Notch activation increases with the area of contact between the two cell types 40 . To directly test whether synNotch output in vivo correlates with the contact area between synNotch and ligand cells, we developed a vertex-based 41,42 mathematical model with mechanochemical coupling between cell-cell contact length and synNotch output (see Fig. 1(D) and Materials and Methods Sections 2-4 for details).…”

“…1(C i,ii)). To address this, we developed a novel vertex-based 36,37 mathematical model with mechanochemical coupling between vertex dynamics and intracellular synNotch output (see Fig. 1(D) and Materials and Methods Sections 2-4 for details).…”

“…To address this, we develop a vertex-based mathematical model of spatiotemporal synNotch activation in proliferating tissues. Cells are represented as two-dimensional polygons (33,34), with chemomechanical coupling between vertex and intracellular synNotch output, including cell growth, and division (Fig. 1(D) and see Materials and Methods Sections 2-4 for details of the model).…”

Cell-cell interactions and fluctuations in the direction of motility promote directed migration of osteoblasts in direct current electrotaxis

“…The latter, electrotaxis, also termed galvanotaxis, is being increasingly studied, in particular in keratinocytes and fibroblasts, since it may provide a promising strategy to foster skin wound healing ( Liang et al, 2020 ), ( Cho et al, 2018 ), ( Lin et al, 2017 ), ( Saltukoglu et al, 2015 ). Electric fields are an attractive approach for manipulating cell behavior because of their broad application across multiple cells and tissue types ( Dawson et al, 2020 ; Gruening et al, 2021 ). In most cells, the direction of migration seems to be cathodal (e.g., in fibroblasts and mesenchymal stem and corneal epithelial cells ( Lin et al, 2017 ) ( Zhao et al, 2002 ).…”