Institut Curie, Université PSL, Sorbonne Université, CNRS UMR168, Laboratoire Physico Chimie Curie, 75005, Paris, France.
Laboratoire Matière et Systèmes Complexes, UMR 7057 CNRS, Université Paris Diderot, 10 rue Alice Domon et Léonie Duquet, 75013, Paris, France.
Sci Rep. 2022 Jul 8;12(1):11579. doi: 10.1038/s41598-022-15207-5.
Timelapse fluorescence microscopy imaging is routinely used in quantitative cell biology. However, microscopes could become much more powerful investigation systems if they were endowed with simple unsupervised decision-making algorithms to transform them into fully responsive and automated measurement devices. Here, we report CyberSco.Py, Python software for advanced automated timelapse experiments. We provide proof-of-principle of a user-friendly framework that increases the tunability and flexibility when setting up and running fluorescence timelapse microscopy experiments. Importantly, CyberSco.Py combines real-time image analysis with automation capability, which allows users to create conditional, event-based experiments in which the imaging acquisition parameters and the status of various devices can be changed automatically based on the image analysis. We exemplify the relevance of CyberSco.Py to cell biology using several use case experiments with budding yeast. We anticipate that CyberSco.Py could be used to address the growing need for smart microscopy systems to implement more informative quantitative cell biology experiments.
延时荧光显微镜成像在定量细胞生物学中被常规使用。然而，如果显微镜能够配备简单的无监督决策算法，将其转变为完全响应和自动化的测量设备，那么它们将成为更强大的研究系统。在这里，我们报告了 CyberSco.Py，这是一个用于高级自动化延时实验的 Python 软件。我们提供了一个用户友好框架的原理证明，该框架在设置和运行荧光延时显微镜实验时增加了可调性和灵活性。重要的是，CyberSco.Py 将实时图像分析与自动化功能相结合，使用户能够创建基于条件和事件的实验，其中可以根据图像分析自动更改成像采集参数和各种设备的状态。我们使用芽殖酵母的几个用例实验来说明 CyberSco.Py 与细胞生物学的相关性。我们预计 CyberSco.Py 可以用于满足对智能显微镜系统的日益增长的需求，以实现更具信息量的定量细胞生物学实验。
