ArticleiScience2021
Microscopy deep learning predicts virus infections and reveals mechanics of lytic-infected cells.
Article in iScience, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers.
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Who cites it
21 citing papers in PubMed, 37 citations in OpenAlex.
- Reactivation and Management of Endogenous Latent Herpesviruses in the Spaceflight Environment.Current microbiology · 2026Review
- A Benchmark for Virus Infection Reporter Virtual Staining in Fluorescence and Brightfield Microscopy.Scientific data · 2025Article
- Activated blood-derived human primary T cells support replication of HAdV C5 and virus transmission to polarized human primary epithelial cells.Journal of virology · 2025Article
- Endothelial Cells Stably Infected with Recombinant Kaposi's Sarcoma-Associated Herpesvirus Display Distinct Viscoelastic and Morphological Properties.Cellular and molecular bioengineering · 2025Article
- AI-based hardware and software tools in microscopy to boost research in immunology and virology.Frontiers in immunology · 2025Review
- Article
- Stepwise virus assembly in the cell nucleus revealed by spatiotemporal click chemistry of DNA replication.Science advances · 2024Article
- Significance of Artificial Intelligence in the Study of Virus-Host Cell Interactions.Biomolecules · 2024Review
- A versatile automated pipeline for quantifying virus infectivity by label-free light microscopy and artificial intelligence.Nature communications · 2024Article
- Organotypic culture of human brain explants as a preclinical model for AI-driven antiviral studies.EMBO molecular medicine · 2024Article
- Developments in Image Processing Using Deep Learning and Reinforcement Learning.Journal of imaging · 2023Review
- Machine learning for cross-scale microscopy of viruses.Cell reports methods · 2023Review
- Label-free microscopy for virus infections.Microscopy (Oxford, England) · 2023Article
- Review
- Detection of Cytopathic Effects Induced by Influenza, Parainfluenza, and Enterovirus Using Deep Convolution Neural Network.Biomedicines · 2021Article
- Labels in a haystack: Approaches beyond supervised learning in biomedical applications.Patterns (New York, N.Y.) · 2021Review
- Human Adenovirus Type 5 Infection Leads to Nuclear Envelope Destabilization and Membrane Permeability Independently of Adenovirus Death Protein.International journal of molecular sciences · 2021Article
- Virus Infection Variability by Single-Cell Profiling.Viruses · 2021Review
- Machine Learning and Artificial Intelligence for the Prediction of Host-Pathogen Interactions: A Viral Case.Infection and drug resistance · 2021Review
- Comparison of deep learning approaches to predict COVID-19 infection.Chaos, solitons, and fractals · 2020Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors at 2 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Imaging across scales reveals disease mechanisms in organisms, tissues, and cells. Yet, particular infection phenotypes, such as virus-induced cell lysis, have remained difficult to study. Here, we developed imaging modalities and deep learning procedures to identify herpesvirus and adenovirus (AdV) infected cells without virus-specific stainings. Fluorescence microscopy of vital DNA-dyes and live-cell imaging revealed learnable virus-specific nuclear patterns transferable to related viruses of the same family. Deep learning predicted two major AdV infection outcomes, non-lytic (nonspreading) and lytic (spreading) infections, up to about 20 hr prior to cell lysis. Using these predictive algorithms, lytic and non-lytic nuclei had the same levels of green fluorescent protein (GFP)-tagged virion proteins but lytic nuclei enriched the virion proteins faster, and collapsed more extensively upon laser-rupture than non-lytic nuclei, revealing impaired mechanical properties of lytic nuclei. Our algorithms may be used to infer infection phenotypes of emerging viruses, enhance single cell biology, and facilitate differential diagnosis of non-lytic and lytic infections.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.