Abstract
Atomic force microscopy (AFM) is a powerful tool for studying the surface structure and mechanical properties of cells at the nanoscale. In this study, AFM was used to examine live oral cancer cells and normal oral keratinocytes under physiological conditions. We aimed to overcome limitations in AFM analysis, including inaccurate selection of surface measurement settings, data filtering methods, and mechanical models that estimate stiffness.
To improve surface analysis, we applied scale-sensitive fractal analysis, averaged power spectral density, and nesting index calculations using MountainsSPIP® 10 software. This allowed clear separation of surface roughness (fine features) from waviness (larger features). We systematically tested multiple mechanical models for calculating stiffness (Young’s modulus) and found that the Derjagin-Muller-Toropov and Johnson-Kendall-Roberts models were most accurate because they accounted for how cells adhere to the probe. We identified that adjusting the Poisson ratio between 0.3 and 0.5 resulted in over a 10% difference in stiffness measurements, highlighting the sensitivity of results to this parameter. To explore mechanical energy behaviour, we mapped distributions of adhesion, energy dissipation, total deformation, and elastic versus plastic energy.
Our findings revealed distinct mechanical signatures between cancer and normal cells. Specifically, cancer cells showed altered energy distribution patterns and stiffness values, suggesting structural and functional changes at the nanoscale. Finally, we developed an artificial intelligence-based classifier that successfully distinguished cancer cells from normal cells based on these mechanical and structural features. The study also demonstrated a novel link between nanoscale mechanical properties and thermodynamic behaviour, suggesting that changes in surface energy and thermal conductivity may serve as potential diagnostic markers.