Data-driven analysis for the evaluation of cortical mechanics of non-adherent cells.

ABSTRACT: Atomic Force Microscopy (AFM) analysis of single cells, especially nonadherent, is inherently slow and analysis-heavy. To address the inherent difficulty of measuring individual cells, and to scale up toward a large number of cells, we take a two-fold approach; first, we introduce an easy-to-fabricate reusable poly(dimethylsiloxane)-based array that consists of micron-sized traps for single-cell trapping, second, we apply a deep-learning method directly on the extracted curves to facilitate and automate the analysis. Our approach is validated using suspended cells, and by applying a small compression with a tipless cantilever AFM probe, we investigate the effect of various cytoskeletal drugs on their deformability. We then apply deep learning models to extract the elasticity of the cell di

SUBMITTER: Hallfors N 

PROVIDER: S-EPMC11926262 | biostudies-literature | 2025 Mar

REPOSITORIES: biostudies-literature