论文标题
用用户友好的软件包中的自动denoing的贝叶斯牵引力显微镜方法
A Bayesian traction force microscopy method with automated denoising in a user-friendly software package
论文作者
论文摘要
粘附的生物细胞在底物上产生牵引力，该基材在迁移，机械感应，分化和集体行为中起着核心作用。量化这种细胞基底相互作用的已建立方法是牵引力显微镜（TFM）。尽管最近取得了进步，但测量的推论仍然对噪声非常敏感。但是，抑制噪声会降低测量精度和空间分辨率，这使得选择最佳降噪水平至关重要。在这里，我们提出了一种完全自动化的方法，用于降低降噪和稳健，标准化的牵引力重建。该方法称为贝叶斯傅立叶变换牵引细胞术，将贝叶斯L2正则化的鲁棒性与傅立叶变换牵引细胞仪的计算速度结合在一起。我们通过合成和真实数据验证该方法的性能。该方法可以作为软件包免费提供，该软件包具有图形用户界面，可直观使用。
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.
