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Adherent biological cells generate traction forces on a substrate that play acentral role for migration, mechanosensing, differentiation, and collectivebehavior. The established method for quantifying this cell-substrateinteraction is traction force microscopy (TFM). In spite of recentadvancements, inference of the traction forces from measurements remains verysensitive to noise. However, suppression of the noise reduces the measurementaccuracy and the spatial resolution, which makes it crucial to select anoptimal level of noise reduction. Here, we present a fully automated method fornoise reduction and robust, standardized traction-force reconstruction. Themethod, termed Bayesian Fourier transform traction cytometry, combines therobustness of Bayesian L2 regularization with the computation speed of Fouriertransform traction cytometry. We validate the performance of the method withsynthetic and real data. The method is made freely available as a softwarepackage with a graphical user-interface for intuitive usage.

A Bayesian traction force microscopy method with automated denoising in a user-friendly software package