VirusAwareScopes: Machine Learning-Driven Adaptive Microscopy for Long-Term Viral Infection Studies
Agency: La Caixa Foundation
Type: Health Research
Principal Investigator: Ricardo Henriques
Start-date: November 2025
End-date: October 2028
Grant Code: HR25-00453
VirusAwareScopes addresses a fundamental challenge in viral research: high-resolution imaging inherently damages living cells, preventing long-term observation of infection dynamics. This project develops an intelligent microscopy platform that adapts imaging conditions in real-time using machine learning, enabling sustained nanoscale imaging while preserving cell viability. By combining multiple super-resolution modalities with AI-driven adaptive optics and PhotoFiTT technology to predict phototoxicity, the system achieves observation periods of 24+ hours while maintaining sub-120nm resolution. The platform will initially map complete HIV-1 infection cycles whilst remaining deployable to standard microscopy setups through integration with established frameworks.
Technology explored
Supported publications
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AI-Driven Cell-Fate Prediction in Microscopy Rita Carlota, Mario Del Rosario, Inês Cunha, Juliette Griffié, Guillaume Jacquemet, Ricardo Henriques Preprint published in preprints.org, July 2026 Technologies: mAIcrobe () and PhotoFiTT () Funded by: Chan Zuckerberg Initiative (CZI), The Kavli Foundation, and The Wellcome Trust, CZI, EMBO, ERC, H2022 and La Caixa Foundation DOI: 10.20944/preprints202607.1414.v1 |
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AI4Life Open Calls and Public Challenges: why, how, and what we have learned Vera Galinova, Mehdi Seifi, Beatriz Serrano Solano, Kristína Lidayová, Damian Dalle Nogare, Agustín Andrés Corbat, Joshua Talks, Edoardo Giacomello, Estibaliz Gómez-de-Mariscal, Mariana G. Ferreira, Caterina Fuster-Barceló, Juan Manuel Battagliotti, Carlos García-López-de-Haro, Benjamin Salmon, Melisande Croft, Si Young Yie, Guillermo Rey-Paniagua, Xiaotian Hu, Sungjun Cho, Aagam Sheth, Chhayansh Porwal, Xiaomeng Li, AI4Life Consortium, Ricardo Henriques, Xinyang Li, Alexander Krull, Anna Klemm, Arrate Muñoz Barrutia, Anna Kreshuk, Wei Ouyang, Florian Jug, Joran Deschamps Preprint published in bioRxiv, July 2026 Technologies: BioImage Model Zoo (), CARE (), DL4MicEverywhere (), mAIcrobe (), Nuclear-Pores as references and ZeroCostDL4Mic () Funded by: Chan Zuckerberg Initiative (CZI), The Kavli Foundation, and The Wellcome Trust, ERC, H2021, H2022 and La Caixa Foundation DOI: 10.64898/2026.07.21.739486 |
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VLab4Mic: prediction of structural resolvability in super-resolution microscopy Damián Martínez, Bruno M. Saraiva, Tayla Shakespeare, Mark Bates, Dylan M. Owen, Christophe Leterrier, Mario Del Rosario, Ricardo Henriques Preprint published in bioRxiv, June 2026 Technologies: EZInput (), mAIcrobe (), nano-org, NanoJ (), NanoJ-SQUIRREL (), Nuclear-Pores as references and ZeroCostDL4Mic () Funded by: Chan Zuckerberg Initiative (CZI), The Kavli Foundation, and The Wellcome Trust, CZI, EMBO, ERC, H2022 and La Caixa Foundation DOI: 10.64898/2026.06.02.729521 |
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Packaging Jupyter notebooks as installable desktop apps using LabConstrictor Iván Hidalgo-Cenalmor, Marcela Xiomara Rivera Pineda, Bruno M Saraiva, Ricardo Henriques, Guillaume Jacquemet Preprint published in arXiv, March 2026 Technologies: CARE (), CellTracksColab (), DL4MicEverywhere (), mAIcrobe () and ZeroCostDL4Mic () Funded by: Chan Zuckerberg Initiative (CZI), The Kavli Foundation, and The Wellcome Trust, EMBO, ERC, H2022 and La Caixa Foundation DOI: 10.48550/arXiv.2603.10704 |
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