Rogge H, Artelt N, Endlich N, Endlich K
Department of Anatomy and Cell Biology, University Medicine Greifswald, Greifswald, Germany.
J Microsc. 2017 Nov;268(2):129-140. doi: 10.1111/jmi.12593. Epub 2017 Aug 14.
The actin cytoskeleton is a main component of cells and it is crucially involved in many physiological processes, e.g. cell motility. Changes in the actin organization can be effected by diseases or vice versa. Due to the nonuniform pattern, it is difficult to quantify reasonable features of the actin cytoskeleton for a significantly high cell number. Here, we present an approach capable to fully segment and analyse the actin cytoskeleton of 2D fluorescence microscopic images with a special focus on stress fibres. The extracted feature data include length, width, orientation and intensity distributions of all traced stress fibres. Our approach combines morphological image processing techniques and a trace algorithm in an iterative manner, classifying the segmentation result with respect to the width of the stress fibres and in nonfibre-like actin. This approach enables us to capture experimentally induced processes like the condensation or the collapse of the actin cytoskeleton. We successfully applied the algorithm to F-actin images of cells that were treated with the actin polymerization inhibitor latrunculin A. Furthermore, we verified the robustness of our algorithm by a sensitivity analysis of the parameters, and we benchmarked our algorithm against established methods. In summary, we present a new approach to segment actin stress fibres over time to monitor condensation or collapse processes.
肌动蛋白细胞骨架是细胞的主要组成部分，它在许多生理过程中起着关键作用，例如细胞运动。肌动蛋白组织的变化可能由疾病引起，反之亦然。由于模式不均匀，对于大量细胞来说，很难合理量化肌动蛋白细胞骨架的特征。在此，我们提出一种方法，能够对二维荧光显微镜图像中的肌动蛋白细胞骨架进行完全分割和分析，特别关注应力纤维。提取的特征数据包括所有追踪到的应力纤维的长度、宽度、方向和强度分布。我们的方法以迭代方式结合形态图像处理技术和追踪算法，根据应力纤维的宽度以及非纤维状肌动蛋白对分割结果进行分类。这种方法使我们能够捕捉实验诱导的过程，如肌动蛋白细胞骨架的凝聚或塌陷。我们成功地将该算法应用于用肌动蛋白聚合抑制剂Latrunculin A处理的细胞的F - 肌动蛋白图像。此外，我们通过参数敏感性分析验证了算法的稳健性，并将我们的算法与已有的方法进行了基准测试。总之，我们提出了一种随时间分割肌动蛋白应力纤维以监测凝聚或塌陷过程的新方法。
