Particle retracking algorithm capable of quantifying large, local matrix deformation for traction force microscopy.
Particle retracking algorithm capable of quantifying large, local matrix deformation for traction force microscopy.
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DOI：
10.1371/journal.pone.0268614
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发表时间：
2022
期刊：
影响因子：
3.7
通讯作者：
中科院分区：
文献类型：
--
作者：
变形测量是牵引力显微镜（TFM）中的一个关键环节。传统上，粒子图像测速（PIV）或基于相关性的粒子跟踪测速（cPTV）已被用于这样的目的。使用模拟珠图像，我们表明，这些方法无法捕获大的位移矢量，这是由于一个穷人的互相关。在这里，赎回潜在的大矢量，我们提出了一个两步的变形跟踪算法，结合cPTV，它表现得更好的小位移比PIV方法，和新设计的重跟踪算法，利用统计上有信心的向量从初始cPTV来指导相关峰的选择，不一定是全局最大值。因此，这种名为“cPTV-Retracking”或cPTVR的新方法能够跟踪超过92%的大型载体，而传统方法只能跟踪其中的43-77%。相应地，从cPTVR重建的牵引力显示出比旧方法更好的大牵引恢复。应用于实验珠图像的cPTVR显示出比传统方法更好的牵引力与不同大小的细胞-基质粘附的分辨率。总而言之，cPTVR方法在软基底存在大变形的情况下提高了TFM的准确性。我们通过TFMPackage软件分享这一进展。
Deformation measurement is a key process in traction force microscopy (TFM). Conventionally, particle image velocimetry (PIV) or correlation-based particle tracking velocimetry (cPTV) have been used for such a purpose. Using simulated bead images, we show that those methods fail to capture large displacement vectors and that it is due to a poor cross-correlation. Here, to redeem the potential large vectors, we propose a two-step deformation tracking algorithm that combines cPTV, which performs better for small displacements than PIV methods, and newly-designed retracking algorithm that exploits statistically confident vectors from the initial cPTV to guide the selection of correlation peak which are not necessarily the global maximum. As a result, the new method, named ‘cPTV-Retracking’, or cPTVR, was able to track more than 92% of large vectors whereas conventional methods could track 43–77% of those. Correspondingly, traction force reconstructed from cPTVR showed better recovery of large traction than the old methods. cPTVR applied on the experimental bead images has shown a better resolving power of the traction with different-sized cell-matrix adhesions than conventional methods. Altogether, cPTVR method enhances the accuracy of TFM in the case of large deformations present in soft substrates. We share this advance via our TFMPackage software.
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影响因子：
7.7
作者：
Han SJ;Azarova EV;Whitewood AJ;Bachir A;Guttierrez E;Groisman A;Horwitz AR;Goult BT;Dean KM;Danuser G
通讯作者：
Danuser G
DOI：
10.1002/cpz1.233
发表时间：
2021-09
期刊：
Current protocols
影响因子：
--
作者：
Mittal N;Han SJ
通讯作者：
Han SJ
影响因子：
4.6
作者：
Holenstein CN;Silvan U;Snedeker JG
通讯作者：
Snedeker JG
影响因子：
7.8
作者：
Gardel, Margaret L.;Sabass, Benedikt;Ji, Lin;Danuser, Gaudenz;Schwarz, Ulrich S.;Waterman, Clare M.
通讯作者：
Waterman, Clare M.
影响因子：
2.4
作者：
Schrijer, F. F. J.;Scarano, F.
通讯作者：
Scarano, F.