Wang Yu-Li, Lin Yun-Chu
Department of Biomedical Engineering, Carnegie Mellon University, Pittsburgh, Pennsylvania.
Department of Biomedical Engineering, Carnegie Mellon University, Pittsburgh, Pennsylvania.
Biophys J. 2021 Aug 3;120(15):3079-3090. doi: 10.1016/j.bpj.2021.06.011. Epub 2021 Jun 30.
Cells interact mechanically with their surroundings by exerting and sensing forces. Traction force microscopy (TFM), purported to map cell-generated forces or stresses, represents an important tool that has powered the rapid advances in mechanobiology. However, to solve the ill-posed mathematical problem, conventional TFM involved compromises in accuracy and/or resolution. Here, we applied neural network-based deep learning as an alternative approach for TFM. We modified a neural network designed for image processing to predict the vector field of stress from displacements. Furthermore, we adapted a mathematical model for cell migration to generate large sets of simulated stresses and displacements for training and testing the neural network. We found that deep learning-based TFM yielded results that resemble those using conventional TFM but at a higher accuracy than several conventional implementations tested. In addition, a trained neural network is appliable to a wide range of conditions, including cell size, shape, substrate stiffness, and traction output. The performance of deep learning-based TFM makes it an appealing alternative to conventional methods for characterizing mechanical interactions between adherent cells and the environment.
细胞通过施加和感知力与周围环境进行机械相互作用。牵引力量显微镜（TFM）旨在绘制细胞产生的力或应力，是推动力学生物学快速发展的一项重要工具。然而，为了解决不适定的数学问题，传统的TFM在准确性和/或分辨率方面做出了妥协。在此，我们应用基于神经网络的深度学习作为TFM的替代方法。我们修改了一个为图像处理设计的神经网络，以根据位移预测应力矢量场。此外，我们采用了一个细胞迁移的数学模型来生成大量模拟应力和位移，用于训练和测试神经网络。我们发现，基于深度学习的TFM产生的结果与使用传统TFM的结果相似，但准确性高于测试的几种传统方法。此外，经过训练的神经网络适用于多种条件，包括细胞大小、形状、底物硬度和牵引输出。基于深度学习的TFM的性能使其成为表征贴壁细胞与环境之间机械相互作用的传统方法的一个有吸引力的替代方案。
