ArticleeLife2018
Structured illumination microscopy combined with machine learning enables the high throughput analysis and classification of virus structure.
Article in eLife, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers.
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Who cites it
20 citing papers in PubMed.
- Implications of morphological variation in influenza viruses.Microbiology and molecular biology reviews : MMBR · 2025Review
- The nanoscale organization of the Nipah virus fusion protein informs new membrane fusion mechanisms.eLife · 2025Article
- Resolving viral structural complexity by super-resolution microscopy.Archives of virology · 2024Review
- Resolving the differential distribution of structural proteins in baculovirus using single-molecule localization microscopy.The Journal of general virology · 2024Article
- Heterogeneous virus classification using a functional deep learning model based on transmission electron microscopy images.Scientific reports · 2024Article
- AI analysis of super-resolution microscopy: Biological discovery in the absence of ground truth.The Journal of cell biology · 2024Review
- Machine learning for cross-scale microscopy of viruses.Cell reports methods · 2023Review
- High-throughput super-resolution analysis of influenza virus pleomorphism reveals insights into viral spatial organization.PLoS pathogens · 2023Article
- Review
- On The Potential of Image Moments for Medical Diagnosis.Journal of imaging · 2023Article
- Virus Detection and Identification in Minutes Using Single-Particle Imaging and Deep Learning.ACS nano · 2023Article
- Fluorescent Nanoparticles for Super-Resolution Imaging.Chemical reviews · 2022Review
- DeepBacs for multi-task bacterial image analysis using open-source deep learning approaches.Communications biology · 2022Article
- Virus morphology: Insights from super-resolution fluorescence microscopy.Biochimica et biophysica acta. Molecular basis of disease · 2022Review
- Self-contained and modular structured illumination microscope.Biomedical optics express · 2021Article
- Challenges facing quantitative large-scale optical super-resolution, and some simple solutions.iScience · 2021Review
- Review
- Towards a Quantitative Single Particle Characterization by Super Resolution Microscopy: From Virus Structures to Antivirals Design.Frontiers in bioengineering and biotechnology · 2021Review
- Automatic classification and segmentation of single-molecule fluorescence time traces with deep learning.Nature communications · 2020Article
- NanoJ: a high-performance open-source super-resolution microscopy toolbox.Journal of physics D: Applied physics · 2019Review
Corrections and comments
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Authors and funding
8 authors.
Funding
Abstract
Optical super-resolution microscopy techniques enable high molecular specificity with high spatial resolution and constitute a set of powerful tools in the investigation of the structure of supramolecular assemblies such as viruses. Here, we report on a new methodology which combines Structured Illumination Microscopy (SIM) with machine learning algorithms to image and classify the structure of large populations of biopharmaceutical viruses with high resolution. The method offers information on virus morphology that can ultimately be linked with functional performance. We demonstrate the approach on viruses produced for oncolytic viriotherapy (Newcastle Disease Virus) and vaccine development (Influenza). This unique tool enables the rapid assessment of the quality of viral production with high throughput obviating the need for traditional batch testing methods which are complex and time consuming. We show that our method also works on non-purified samples from pooled harvest fluids directly from the production line.
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Registered trials
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