ArticleScientific reports2020
Super resolution microscopy and deep learning identify Zika virus reorganization of the endoplasmic reticulum.
Article in Scientific reports, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers, 1 of them a synthesis that pooled it.
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The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
20 citing papers in PubMed, 1 synthesis or guideline pooled it, 33 citations in OpenAlex.
- Mapping the Cerebral Organoid Landscape: A Systematic Review of Preclinical 3D Models in Neuroscience.Advanced healthcare materials · 2026Pooled it
- A reversible broad-spectrum antiviral targets the human V-ATPase VResearch square · 2026Article
- Emerging approaches for characterizing spatial and temporal dynamics of pathogen-induced organelle remodeling.Cell systems · 2026Review
- nERdy: network analysis of endoplasmic reticulum dynamics.Communications biology · 2025Article
- Article
- AI-based hardware and software tools in microscopy to boost research in immunology and virology.Frontiers in immunology · 2025Review
- AI analysis of super-resolution microscopy: Biological discovery in the absence of ground truth.The Journal of cell biology · 2024Review
- Single-cell analysis reveals an antiviral network that controls Zika virus infection in human dendritic cells.Journal of virology · 2024Article
- Viral regulation of organelle membrane contact sites.PLoS biology · 2024Article
- Membrane contact site detection (MCS-DETECT) reveals dual control of rough mitochondria-ER contacts.The Journal of cell biology · 2024Article
- Machine learning for cross-scale microscopy of viruses.Cell reports methods · 2023Review
- Quantitative Single-Molecule Localization Microscopy.Annual review of biophysics · 2023Review
- Generating post-hoc explanation from deep neural networks for multi-modal medical image analysis tasks.MethodsX · 2023Article
- Dynamics and Patterning of 5-Hydroxytryptamine 2 Subtype Receptors in JC Polyomavirus Entry.Viruses · 2022Article
- Article
- Dengue virus NS4B protein as a target for developing antivirals.Frontiers in cellular and infection microbiology · 2022Review
- Methodologies for Generating Brain Organoids to Model Viral Pathogenesis in the CNS.Pathogens (Basel, Switzerland) · 2021Review
- Are the Organoid Models an Invaluable Contribution to ZIKA Virus Research?Pathogens (Basel, Switzerland) · 2021Review
- Challenges facing quantitative large-scale optical super-resolution, and some simple solutions.iScience · 2021Review
- Towards a Quantitative Single Particle Characterization by Super Resolution Microscopy: From Virus Structures to Antivirals Design.Frontiers in bioengineering and biotechnology · 2021Review
Corrections and comments
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Authors and funding
8 authors at 2 institutions in 1 country.
Funding
Abstract
The endoplasmic reticulum (ER) is a complex subcellular organelle composed of diverse structures such as tubules, sheets and tubular matrices. Flaviviruses such as Zika virus (ZIKV) induce reorganization of ER membranes to facilitate viral replication. Here, using 3D super resolution microscopy, ZIKV infection is shown to induce the formation of dense tubular matrices associated with viral replication in the central ER. Viral non-structural proteins NS4B and NS2B associate with replication complexes within the ZIKV-induced tubular matrix and exhibit distinct ER distributions outside this central ER region. Deep neural networks trained to distinguish ZIKV-infected versus mock-infected cells successfully identified ZIKV-induced central ER tubular matrices as a determinant of viral infection. Super resolution microscopy and deep learning are therefore able to identify and localize morphological features of the ER and allow for better understanding of how ER morphology changes due to viral infection.
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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.