UC Santa Barbara

Traction Force Microscopy (TFM) has become a well-established technique to assay the biophysical force produced by cells cultured on soft substrates of controlled stiffness. However, experimental conditions as well as computational implementations can have a large impact on the analysis results accuracy and reproducibility. While this can be alleviated using appropriate controls and a rigorous analytical approach, the comparison of results across studies remains difficult and there is a need for validation and benchmarking tools. To validate the accuracy of and compare various computational TFM analysis algorithms, we developed a virtual in silico model of a cell contracting on a soft substrate of controlled stiffness. The model utilizes user-defined parameters for the cell dimensions as well as for the strength and spatial distribution of a contraction dipole to calculate the deformation that would result on a soft substrate due to the cell contraction. The deformation is computed using the forward analytical stress-strain tensor calculation in the Fourier space. The resulting displacement field is used to apply, using image processing, a deformation on a real or simulated image of fluorescent microspheres embedded into a soft hydrogel, which is normally obtain experimentally by TFM imaging. The deformation field and resulting image then serve as input in the PIV and TFM analysis. The model also enables to create movies of a dynamic cell contraction, such as that of a cardiomyocyte, to validate the time accuracy of the TFM analysis after application of image processing algorithm, such as denoising. Our tool therefore addresses the need for validation and standardization of TFM analytical algorithms and its experimental implementations.