ArticlePLoS computational biology2020
Practical fluorescence reconstruction microscopy for large samples and low-magnification imaging.
Article in PLoS computational biology, 2020. 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, 26 citations in OpenAlex.
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- Parameter identifiability and model selection for partial differential equation models of cell invasion.Journal of the Royal Society, Interface · 2024Article
- E-cadherin biomaterials reprogram collective cell migration and cell cycling by forcing homeostatic conditions.Cell reports · 2024Article
- MMV_Im2Im: an open-source microscopy machine vision toolbox for image-to-image transformation.GigaScience · 2024Article
- Virtual Fluorescence Translation for Biological Tissue by Conditional Generative Adversarial Network.Phenomics (Cham, Switzerland) · 2023Article
- When seeing is not believing: application-appropriate validation matters for quantitative bioimage analysis.Nature methods · 2023Article
- Self-assembly of tessellated tissue sheets by expansion and collision.Nature communications · 2022Article
- Label-free monitoring of spatiotemporal changes in the stem cell cytoskeletons in time-lapse phase-contrast microscopy.Biomedical optics express · 2022Article
- Learning the rules of collective cell migration using deep attention networks.PLoS computational biology · 2022Article
- Short-term bioelectric stimulation of collective cell migration in tissues reprograms long-term supracellular dynamics.PNAS nexus · 2022Article
- Interpretable deep learning uncovers cellular properties in label-free live cell images that are predictive of highly metastatic melanoma.Cell systems · 2021Article
- Democratising deep learning for microscopy with ZeroCostDL4Mic.Nature communications · 2021Article
- Size-dependent patterns of cell proliferation and migration in freely-expanding epithelia.eLife · 2020Article
Corrections and comments
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Authors and funding
2 authors at 1 institution in 1 country.
Funding
Abstract
Fluorescence reconstruction microscopy (FRM) describes a class of techniques where transmitted light images are passed into a convolutional neural network that then outputs predicted epifluorescence images. This approach enables many benefits including reduced phototoxicity, freeing up of fluorescence channels, simplified sample preparation, and the ability to re-process legacy data for new insights. However, FRM can be complex to implement, and current FRM benchmarks are abstractions that are difficult to relate to how valuable or trustworthy a reconstruction is. Here, we relate the conventional benchmarks and demonstrations to practical and familiar cell biology analyses to demonstrate that FRM should be judged in context. We further demonstrate that it performs remarkably well even with lower-magnification microscopy data, as are often collected in screening and high content imaging. Specifically, we present promising results for nuclei, cell-cell junctions, and fine feature reconstruction; provide data-driven experimental design guidelines; and provide researcher-friendly code, complete sample data, and a researcher manual to enable more widespread adoption of FRM.
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