Third Institute of Physics-Biophysics, Georg-August-University Göttingen, Göttingen, Germany.
Institute of Pharmacology and Toxicology, University Medical Center, Göttingen, Germany.
PLoS One. 2023 Feb 6;18(2):e0279336. doi: 10.1371/journal.pone.0279336. eCollection 2023.
Cytoskeletal pattern formation and structural dynamics are key to a variety of biological functions and a detailed and quantitative analysis yields insight into finely tuned and well-balanced homeostasis and potential pathological alterations. High content life cell imaging of fluorescently labeled cytoskeletal elements under physiological conditions is nowadays state-of-the-art and can record time lapse data for detailed experimental studies. However, systematic quantification of structures and in particular the dynamics (i.e. frame-to-frame tracking) are essential. Here, an unbiased, quantitative, and robust analysis workflow that can be highly automatized is needed. For this purpose we upgraded and expanded our fiber detection algorithm FilamentSensor (FS) to the FilamentSensor 2.0 (FS2.0) toolbox, allowing for automatic detection and segmentation of fibrous structures and the extraction of relevant data (center of mass, length, width, orientation, curvature) in real-time as well as tracking of these objects over time and cell event monitoring.
细胞骨架的形态形成和结构动力学是多种生物学功能的关键，详细和定量的分析可以深入了解精细调节和良好平衡的动态平衡以及潜在的病理改变。在生理条件下对荧光标记的细胞骨架元件进行高内涵活细胞成像如今是最先进的方法，可以记录延时数据以进行详细的实验研究。然而，对结构，特别是动态（即逐帧跟踪）的系统定量分析是必不可少的。为此，我们需要一个无偏、定量和稳健的可高度自动化的分析工作流程。为此，我们将纤维检测算法 FilamentSensor (FS) 升级和扩展到 FilamentSensor 2.0 (FS2.0) 工具箱，实现了自动检测和分割纤维结构，并实时提取相关数据（质心、长度、宽度、方向、曲率），以及随时间跟踪这些对象和细胞事件监测。
