Yao Yao, Smal Ihor, Grigoriev Ilya, Martin Maud, Akhmanova Anna, Meijering Erik
Departments of Medical Informatics and Radiology, Biomedical Imaging Group Rotterdam, Erasmus University Medical Center, 2040, 3000 CA, Rotterdam, The Netherlands.
Department of Cell Biology, Faculty of Science, Utrecht University, Padualaan 8, 3584 CH, Utrecht, The Netherlands.
Methods Mol Biol. 2017;1563:209-228. doi: 10.1007/978-1-4939-6810-7_14.
The study of intracellular dynamic processes is of fundamental importance for understanding a wide variety of diseases and developing effective drugs and therapies. Advanced fluorescence microscopy imaging systems nowadays allow the recording of virtually any type of process in space and time with super-resolved detail and with high sensitivity and specificity. The large volume and high information content of the resulting image data, and the desire to obtain objective, quantitative descriptions and biophysical models of the processes of interest, require a high level of automation in data analysis. Two key tasks in extracting biologically meaningful information about intracellular dynamics from image data are particle tracking and particle trajectory analysis. Here we present state-of-the-art software tools for these tasks and describe how to use them.
细胞内动态过程的研究对于理解多种疾病以及开发有效的药物和治疗方法至关重要。如今先进的荧光显微镜成像系统能够以超分辨细节、高灵敏度和特异性记录几乎任何类型的时空过程。所得图像数据的量大且信息含量高，以及获得感兴趣过程的客观、定量描述和生物物理模型的需求，都要求在数据分析中实现高度自动化。从图像数据中提取有关细胞内动力学的生物学有意义信息的两个关键任务是粒子跟踪和粒子轨迹分析。在此，我们介绍用于这些任务的最新软件工具，并描述如何使用它们。
