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

Adherent biological cells generate traction forces on a substrate that play a central role for migration, mechanosensing, differentiation, and collective behavior. The established method for quantifying this cell–substrate interaction is traction force microscopy (TFM). In spite of recent advancements, inference of the traction forces from measurements remains very sensitive to noise. However, suppression of the noise reduces the measurement accuracy and the spatial resolution, which makes it crucial to select an optimal level of noise reduction. Here, we present a fully automated method for noise reduction and robust, standardized traction-force reconstruction. The method, termed Bayesian Fourier transform traction cytometry, combines the robustness of Bayesian L2 regularization with the computation speed of Fourier transform traction cytometry. We validate the performance of the method with synthetic and real data. The method is made freely available as a software package with a graphical user-interface for intuitive usage.贴壁生物细胞可在底物上产生牵引力，这类牵引力在细胞迁移、机械感知、分化及群体行为中发挥核心作用。用于量化此类细胞-底物相互作用的经典方法为牵引力显微镜（Traction Force Microscopy, TFM）。尽管近年来该领域取得了诸多进展，但从测量数据中推断牵引力的过程仍对噪声极为敏感。然而，噪声抑制会降低测量精度与空间分辨率，因此选择最优的噪声抑制水平至关重要。本研究提出一种可实现降噪与稳健、标准化牵引力重建的全自动化方法。该方法被称为贝叶斯傅里叶变换牵引力细胞术（Bayesian Fourier Transform Traction Cytometry），它结合了贝叶斯L2正则化的稳健性与傅里叶变换牵引力细胞术（Fourier Transform Traction Cytometry）的计算速度。本研究通过合成数据与真实实验数据验证了该方法的性能。该方法已作为带有图形用户界面的软件包免费公开，便于直观使用。