ArticleScientific reports2022
CyberSco.Py an open-source software for event-based, conditional microscopy.
Article in Scientific reports, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- Review
- A flexible framework for automated STED super-resolution microscopy.Scientific reports · 2026Article
- Smart hybrid microscopy for cell-friendly detection of rare events.Nature communications · 2026Article
- Self-Driving Microscopes: AI Meets Super-Resolution Microscopy.Small methods · 2026Review
- Imaging of cellular dynamics from a whole organism to subcellular scale with self-driving, multiscale microscopy.Nature methods · 2025Article
- Live-cell imaging powered by computation.Nature reviews. Molecular cell biology · 2024Review
- Harnessing artificial intelligence to reduce phototoxicity in live imaging.Journal of cell science · 2024Article
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Authors and funding
9 authors.
Funding
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.
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Registered trials
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