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 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.

Imaging of cellular dynamicsin vitroandin situ: from a whole organism to sub-cellular imaging with self-driving, multi-scale microscopy

“…We implemented the software in Python, a popular choice for microscope control software solutions 19,20 . Precise timing of all hardware components was ensured with Python-based control of an NI Data Acquisition card (NI PCIe-6738) with sufficient digital and analog outputs (Supplementary Fig.…”

Harnessing artificial intelligence to reduce phototoxicity in live imaging

“…There are already a number of conceptualised and proven approaches towards such gentle smart microscopy. Of those, event-driven approaches automatically identify specific objects or incidents in images acquired in a less phototoxic setup, which triggers their acquisition in real-time ( Alvelid et al, 2022 ; André et al, 2023 ; Chiron et al, 2022 ; Fox et al, 2022 ; Mahecic et al, 2022 ) ( Fig. 3 ).…”