pyTFM: A tool for traction force and monolayer stress microscopy.
pyTFM: A tool for traction force and monolayer stress microscopy.
复制标题
牵引力和单层应力显微镜的工具。
DOI：
10.1371/journal.pcbi.1008364
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发表时间：
2021-06
影响因子：
4.3
通讯作者：
Fabry B
中科院分区：
文献类型：
--
作者：
Bauer A;Prechová M;Fischer L;Thievessen I;Gregor M;Fabry B
细胞力的产生和传递是许多生物过程的基础，可以用牵引力显微镜（TFM）和单层应力显微镜进行研究。牵引力显微镜和单层应力显微镜解决了从测量的具有已知弹性的粘附基底的细胞力引起的变形中重建细胞-基质牵引力和细胞间和细胞内应力的逆问题。虽然有几个实验室已经开发了用于牵引力显微镜和单层应力显微镜计算的软件，但目前还没有软件包允许非专业用户对此类实验进行全面评估。在这里，我们提出pyTFM，一个工具，执行牵引力显微镜和单层应力显微镜对细胞斑块和细胞层生长在二维环境。pyTFM为易用性进行了优化；它是开源的，并且文档齐全（托管于https://pytfm.readthedocs.io/），包括使用示例和理论背景的解释。pyTFM可以作为独立的Python包使用，也可以作为图像注释工具ClickPoints的附加组件使用。结合ClickPoints环境，pyTFM允许用户设置所有必要的分析参数，选择感兴趣的区域，检查输入数据和中间结果，并计算描述力、应力及其分布的广泛参数。在这项工作中，我们还利用上皮细胞贴片的合成和实验数据，深入分析了pyTFM的牵引力显微镜和单层应力显微镜算法的准确性和性能。细胞力的产生和传递分析是生物学研究领域一个日益重要的方面。然而，大多数研究细胞力产生或传递的方法需要复杂的计算，并且尚未在全面，易于使用的软件中实现。这是阻碍该领域更广泛应用的主要障碍。在这里，我们介绍了pyTFM，一个带有图形用户界面的开源Python包，可用于评估细胞和细胞菌落中的细胞力产生以及弹性基板表面生长的小细胞斑块和大细胞层中的力传递。结合图像注释和ClickPoints工具，pyTFM允许用户设置所有必要的分析参数，选择感兴趣的区域，检查输入数据和中间结果，并计算描述细胞力、应力及其分布的广泛参数。此外，pyTFM可以作为独立的python库使用。pyTFM附带了一个广泛的文档（托管于https://pytfm.readthedocs.io/），包括使用示例和理论背景的解释。
Cellular force generation and force transmission are of fundamental importance for numerous biological processes and can be studied with the methods of Traction Force Microscopy (TFM) and Monolayer Stress Microscopy. Traction Force Microscopy and Monolayer Stress Microscopy solve the inverse problem of reconstructing cell-matrix tractions and inter- and intra-cellular stresses from the measured cell force-induced deformations of an adhesive substrate with known elasticity. Although several laboratories have developed software for Traction Force Microscopy and Monolayer Stress Microscopy computations, there is currently no software package available that allows non-expert users to perform a full evaluation of such experiments. Here we present pyTFM, a tool to perform Traction Force Microscopy and Monolayer Stress Microscopy on cell patches and cell layers grown in a 2-dimensional environment. pyTFM was optimized for ease-of-use; it is open-source and well documented (hosted at https://pytfm.readthedocs.io/) including usage examples and explanations of the theoretical background. pyTFM can be used as a standalone Python package or as an add-on to the image annotation tool ClickPoints. In combination with the ClickPoints environment, pyTFM allows the user to set all necessary analysis parameters, select regions of interest, examine the input data and intermediary results, and calculate a wide range of parameters describing forces, stresses, and their distribution. In this work, we also thoroughly analyze the accuracy and performance of the Traction Force Microscopy and Monolayer Stress Microscopy algorithms of pyTFM using synthetic and experimental data from epithelial cell patches. The analysis of cellular force generation and transmission is an increasingly important aspect in the field of biological research. However, most methods for studying cellular force generation or transmission require complex calculations and have not yet been implemented in comprehensive, easy-to-use software. This is a major hurdle preventing a wider application in the field. Here we present pyTFM, an open-source Python package with a graphical user interface that can be used to evaluate cellular force generation in cells and cell colonies and force transfer within small cell patches and larger cell layers grown on the surface of an elastic substrate. In combination with the image annotation and tool ClickPoints, pyTFM allows the user to set all necessary analysis parameters, select regions of interest, examine the input data and intermediary results, and calculate a wide range of parameters describing cellular forces, stresses, and their distribution. Additionally, pyTFM can be used as standalone python library. pyTFM comes with an extensive documentation (hosted at https://pytfm.readthedocs.io/) including usage examples and explanations of the theoretical background.
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影响因子：
64.8
作者：
Harris CR;Millman KJ;van der Walt SJ;Gommers R;Virtanen P;Cournapeau D;Wieser E;Taylor J;Berg S;Smith NJ;Kern R;Picus M;Hoyer S;van Kerkwijk MH;Brett M;Haldane A;Del Río JF;Wiebe M;Peterson P;Gérard-Marchant P;Sheppard K;Reddy T;Weckesser W;Abbasi H;Gohlke C;Oliphant TE
通讯作者：
Oliphant TE
影响因子：
4.6
作者：
Huang, Yunfei;Schell, Christoph;Sabass, Benedikt
通讯作者：
Sabass, Benedikt
影响因子：
3.4
作者：
Sabass, Benedikt;Gardel, Margaret L.;Schwarz, Ulrich S.
通讯作者：
Schwarz, Ulrich S.
影响因子：
1.1
作者：
Boudou T;Ohayon J;Picart C;Pettigrew RI;Tracqui P
通讯作者：
Tracqui P
影响因子：
48
作者：
Steinwachs, Julian;Metzner, Claus;Fabry, Ben
通讯作者：
Fabry, Ben