Machine Learning Assisted Large Area Cell Traction Stress Measurements of Continuous Cell Sheets

Detecting cell traction forces using conventional traction force microscopy (TFM) and fluorescence coated micro-pillars requires imaging using high magnification (60×) lenses for precise measurements. The fields-of-view are typically small. We present a novel polydimethylsiloxane (PDMS) platform with embedded gold nanoparticles that enables concurrent and sub-cellular traction stress measurements of continuous cell sheets over a large area (0.15mm
²
) using low magnification (20×) optics. A novel Fourier transform assisted machine learning approach is combined with finite element simulations, to extract the traction force distributions of Neonatal Rat Ventricular Myocyte (NRVM) tissue with sub-cellular resolution from the coupled gold nanoparticle displacements.