CyberSco.Py an open-source software for event-based, conditional microscopy.

ABSTRACT: 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 v

SUBMITTER: Chiron L 

PROVIDER: S-EPMC9270370 | biostudies-literature | 2022 Jul

REPOSITORIES: biostudies-literature