RT Journal Article
SR Electronic
T1 CyberSco.Py: open-source software for event-based, conditional microscopy
JF bioRxiv
FD Cold Spring Harbor Laboratory
SP 2022.03.16.484589
DO 10.1101/2022.03.16.484589
A1 Chiron, Lionel
A1 LeBec, Matthias
A1 Cordier, Céline
A1 Pouzet, Sylvain
A1 Milunov, Dimitrije
A1 Banderas, Alvaro
A1 Meglio, Jean-Marc Di
A1 Sorre, Benoit
A1 Hersen, Pascal
YR 2022
UL http://biorxiv.org/content/early/2022/03/16/2022.03.16.484589.abstract
AB 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.Competing Interest StatementThe authors have declared no competing interest.