利用时间信息的牵引力显微镜：基于 Kalman 滤波重建细胞牵引力场
Traction force microscopy using temporal information
一种利用时间信息的牵引力显微镜新方法被提出，用 Kalman 滤波这一时间序列分析中的数据同化算法从基底形变中重建细胞牵引力场。牵引力显微镜需从测得的基底形变反解力场，该反问题病态且条件数大， inversion 过程中必须抑制测量噪声放大，此前研究多用正则化或滤波等利用空间信息区分噪声与真实信号。在数值生成数据上，该方法比包括 L2 正则化在内的标准算法更准确，并能有效滤除时间噪声；在运动细胞的实验数据上，该方法可可靠推断随时间变化的细胞牵引力。作者还展示了两种扩展：纳入额外知识与直接推断低维力特征。
来源：PLOS Computational Biology · journals.plos.org
by Kazuko Hamaoka, Kasumi Kusakari, Hirokazu Tanimoto
Traction force microscopy is a simple, non-invasive method that can quantify cellular contact forces at the cell-substrate interface. It solves an inverse problem of determining the force field from measured substrate deformation. Because this problem is ill-posed with a large condition number, it is necessary to suppress the amplification of measurement noise during inversion. Previous studies have employed algorithms such as regularization or filtering that utilize spatial information to discriminate noise and true signals. Here, we propose an alternative approach that utilizes temporal information of the system. This method employs Kalman filter, a simple data assimilation algorithm in time series analysis, to recover the force field. Using numerically generated data, we show that the method is more accurate than standard algorithms including L2-regularization and is effective at filtering out temporal noise. Using experimental data on locomoting cells, we demonstrate that the method can reliably infer time-varying cellular traction forces. We also demonstrate two extensions of the proposed method: incorporation of additional knowledge and direct inference of low-dimensional force characteristics. These practical advantages will motivate the use of proposed framework for analyzing dynamic cell mechanics.