Tsygankov Denis, Chu Pei-Hsuan, Chen Hsin, Elston Timothy C, Hahn Klaus M
Department of Pharmacology, University of North Carolina, Chapel Hill, North Carolina, USA.
Department of Pharmacology and Cancer Biology, Duke University, Durham, North Carolina, USA.
Methods Cell Biol. 2014;123:409-27. doi: 10.1016/B978-0-12-420138-5.00022-7.
Understanding the heterogeneous dynamics of cellular processes requires not only tools to visualize molecular behavior but also versatile approaches to extract and analyze the information contained in live-cell movies of many cells. Automated identification and tracking of cellular features enable thorough and consistent comparative analyses in a high-throughput manner. Here, we present tools for two challenging problems in computational image analysis: (1) classification of motion for cells with complex shapes and dynamics and (2) segmentation of clustered cells and quantification of intracellular protein distributions based on a single fluorescence channel. We describe these methods and user-friendly software(1) (MATLAB applications with graphical user interfaces) so these tools can be readily applied without an extensive knowledge of computational techniques.
了解细胞过程的异质动力学不仅需要可视化分子行为的工具，还需要通用的方法来提取和分析许多细胞的活细胞电影中包含的信息。细胞特征的自动识别和跟踪能够以高通量的方式进行全面且一致的比较分析。在这里，我们展示了用于计算图像分析中两个具有挑战性问题的工具：（1）对具有复杂形状和动力学的细胞的运动进行分类，以及（2）基于单个荧光通道对聚集细胞进行分割并量化细胞内蛋白质分布。我们描述了这些方法和用户友好的软件（1）（具有图形用户界面的MATLAB应用程序），以便这些工具无需广泛的计算技术知识即可轻松应用。
