Caldas Victor E A, Punter Christiaan M, Ghodke Harshad, Robinson Andrew, van Oijen Antoine M
Zernike Institute for Advanced Materials, Centre for Synthetic Biology, University of Groningen, The Netherlands.
Mol Biosyst. 2015 Oct;11(10):2699-708. doi: 10.1039/c5mb00321k.
Recent technical advances have made it possible to visualize single molecules inside live cells. Microscopes with single-molecule sensitivity enable the imaging of low-abundance proteins, allowing for a quantitative characterization of molecular properties. Such data sets contain information on a wide spectrum of important molecular properties, with different aspects highlighted in different imaging strategies. The time-lapsed acquisition of images provides information on protein dynamics over long time scales, giving insight into expression dynamics and localization properties. Rapid burst imaging reveals properties of individual molecules in real-time, informing on their diffusion characteristics, binding dynamics and stoichiometries within complexes. This richness of information, however, adds significant complexity to analysis protocols. In general, large datasets of images must be collected and processed in order to produce statistically robust results and identify rare events. More importantly, as live-cell single-molecule measurements remain on the cutting edge of imaging, few protocols for analysis have been established and thus analysis strategies often need to be explored for each individual scenario. Existing analysis packages are geared towards either single-cell imaging data or in vitro single-molecule data and typically operate with highly specific algorithms developed for particular situations. Our tool, iSBatch, instead allows users to exploit the inherent flexibility of the popular open-source package ImageJ, providing a hierarchical framework in which existing plugins or custom macros may be executed over entire datasets or portions thereof. This strategy affords users freedom to explore new analysis protocols within large imaging datasets, while maintaining hierarchical relationships between experiments, samples, fields of view, cells, and individual molecules.
最近的技术进步使得在活细胞内可视化单个分子成为可能。具有单分子灵敏度的显微镜能够对低丰度蛋白质进行成像,从而对分子特性进行定量表征。此类数据集包含有关广泛重要分子特性的信息,不同的成像策略突出了不同的方面。图像的延时采集提供了长时间尺度上蛋白质动态的信息,有助于深入了解表达动态和定位特性。快速爆发成像实时揭示单个分子的特性,提供有关其扩散特征、结合动态以及复合物内化学计量的信息。然而,这种丰富的信息给分析协议增加了显著的复杂性。一般来说,必须收集和处理大量的图像数据集,以产生具有统计稳健性的结果并识别罕见事件。更重要的是,由于活细胞单分子测量仍处于成像前沿,几乎没有建立分析协议,因此通常需要针对每个具体情况探索分析策略。现有的分析软件包要么面向单细胞成像数据,要么面向体外单分子数据,并且通常使用为特定情况开发的高度特定的算法运行。我们的工具iSBatch则允许用户利用流行的开源软件包ImageJ的固有灵活性,提供一个分层框架,在其中可以对整个数据集或其部分执行现有的插件或自定义宏。这种策略为用户提供了在大型成像数据集中探索新分析协议的自由,同时保持实验、样本、视野、细胞和单个分子之间的层次关系。