ArticleCell reports methods2023
Data-driven microscopy allows for automated context-specific acquisition of high-fidelity image data.
Article in Cell reports methods, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
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Who cites it
15 citing papers in PubMed.
- DySTrack - a modular smart microscopy tool for live tracking of dynamic samples on modern commercial microscopes.Journal of cell science · 2026Article
- Characterization of white adipocyte heterogeneity using data-driven microscopy.International journal of obesity (2005) · 2026Article
- Smart event-triggered MINFLUX microscopy to catch and follow rare events.Nature communications · 2026Article
- Review
- Self-Driving Microscopes: AI Meets Super-Resolution Microscopy.Small methods · 2026Review
- On the importance of simultaneous label-free multimodal nonlinear optical imaging for biomedical applications.APL photonics · 2025Article
- Live-cell painting: Image-based profiling in live cells using acridine orange.Molecular biology of the cell · 2025Article
- Data stewardship and curation practices in AI-based genomics and automated microscopy image analysis for high-throughput screening studies: promoting robust and ethical AI applications.Human genomics · 2025Review
- Synthetic gene circuit evolution: Insights and opportunities at the mid-scale.Cell chemical biology · 2024Review
- The rise of data-driven microscopy powered by machine learning.Journal of microscopy · 2024Article
- Smart lattice light-sheet microscopy for imaging rare and complex cellular events.Nature methods · 2024Article
- Harnessing artificial intelligence to reduce phototoxicity in live imaging.Journal of cell science · 2024Article
- Photophysical image analysis: Unsupervised probabilistic thresholding for images from electron-multiplying charge-coupled devices.PloS one · 2024Article
- Live-cell imaging in the deep learning era.Current opinion in cell biology · 2023Review
- Imagining the future of optical microscopy: everything, everywhere, all at once.Communications biology · 2023Review
Corrections and comments
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Authors and funding
5 authors.
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Abstract
Light microscopy is a powerful single-cell technique that allows for quantitative spatial information at subcellular resolution. However, unlike flow cytometry and single-cell sequencing techniques, microscopy has issues achieving high-quality population-wide sample characterization while maintaining high resolution. Here, we present a general framework, data-driven microscopy (DDM) that uses real-time population-wide object characterization to enable data-driven high-fidelity imaging of relevant phenotypes based on the population context. DDM combines data-independent and data-dependent steps to synergistically enhance data acquired using different imaging modalities. As a proof of concept, we develop and apply DDM with plugins for improved high-content screening and live adaptive microscopy for cell migration and infection studies that capture events of interest, rare or common, with high precision and resolution. We propose that DDM can reduce human bias, increase reproducibility, and place single-cell characteristics in the context of the sample population when interpreting microscopy data, leading to an increase in overall data fidelity.
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