在用户友好的软件包中自动降噪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
摘要：
粘附的生物细胞在基底上产生牵引力，其对于迁移、机械感测、分化和集体行为起核心作用。用于定量这种细胞-基质相互作用的既定方法是牵引力显微镜（TFM）。尽管最近的进步，从测量的牵引力的推断仍然非常敏感的噪声。然而，噪声的抑制降低了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，我们提出了一个全自动的方法，降噪和强大的，标准化的牵引力重建。该方法将贝叶斯L2正则化的鲁棒性与傅里叶变换牵引细胞术的计算速度相结合，称为贝叶斯傅里叶变换牵引细胞术。我们用合成数据和实际数据验证了该方法的性能。该方法作为一个软件包免费提供，带有图形用户界面，便于直观使用。
英文摘要：
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.
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