Department of Functional Genomics, Faculty of Science, Center for Neurogenomics and Cognitive Research, Vrije Universiteit, De Boelelaan 1085, 1081HV, Amsterdam, The Netherlands.
Department of Clinical Genetics, Center for Neurogenomics and Cognitive Research, VU Medical Center, De Boelelaan 1085, 1081HV, Amsterdam, The Netherlands.
Sci Rep. 2018 Oct 19;8(1):15523. doi: 10.1038/s41598-018-33847-4.
Recent advances in live Ca imaging with increasing spatial and temporal resolution offer unprecedented opportunities, but also generate an unmet need for data processing. Here we developed SICT, a MATLAB program that automatically identifies rapid Ca rises in time-lapse movies with low signal-to-noise ratios, using fluorescent indicators. A graphical user interface allows visual inspection of automatically detected events, reducing manual labour to less than 10% while maintaining quality control. The detection performance was tested using synthetic data with various signal-to-noise ratios. The event inspection phase was evaluated by four human observers. Reliability of the method was demonstrated in a direct comparison between manual and SICT-aided analysis. As a test case in cultured neurons, SICT detected an increase in frequency and duration of spontaneous Ca transients in the presence of caffeine. This new method speeds up the analysis of elementary Ca transients.
近年来，活细胞钙成像技术在空间和时间分辨率方面取得了进展，为研究提供了前所未有的机会，但也产生了对数据处理的迫切需求。在这里，我们开发了 SICT，这是一个使用荧光指示剂自动识别低信噪比时移电影中快速钙升高的 MATLAB 程序。图形用户界面允许对自动检测到的事件进行视觉检查，将手动工作减少到不到 10%，同时保持质量控制。使用具有不同信噪比的合成数据测试了检测性能。通过四位人类观察者评估了事件检查阶段。通过手动和 SICT 辅助分析的直接比较，证明了该方法的可靠性。作为培养神经元的测试案例，SICT 检测到在咖啡因存在下自发钙瞬变的频率和持续时间增加。这种新方法加快了基本钙瞬变的分析速度。
