Technology

PhotoFiTT

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GitHub:
- HenriquesLab/PhotoFiTT

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Publication: Overview of the 'PhotoFiTT' technology, its features, associated publications, funding and more.
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PhotoFiTT is a quantitative framework for assessing phototoxicity in live-cell microscopy experiments. It uses machine learning and cell cycle dynamics to measure mitotic timing, changes in cell size and cellular activity. PhotoFiTT enables researchers to evaluate the impact of light exposure on living cells during imaging, helping to optimize experimental conditions and ensure the integrity of biological observations.

Publications featuring PhotoFiTT

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

PhotoFiTT: a quantitative framework for assessing phototoxicity in live-cell microscopy experiments Mario Del Rosario, Estibaliz Gómez-de-Mariscal, Leonor Morgado, Raquel Portela, Guillaume Jacquemet, Pedro M. Pereira, Ricardo Henriques Paper published in Nature Communications, December 2025 Technologies: mAIcrobe (), PhotoFiTT () and ZeroCostDL4Mic () Funded by: Chan Zuckerberg Initiative (CZI), The Kavli Foundation, and The Wellcome Trust, CZI, EMBO, ERC, FCT, H2021 and H2022 DOI: 10.1038/s41467-025-66209-6

Funding contributing to PhotoFiTT

VirusAwareScopes: Machine Learning-Driven Adaptive Microscopy for Long-Term Viral Infection Studies Ricardo Henriques Alias: VirusAwareScopes Funded by: La Caixa Foundation - Health Research Duration: November 2025 - October 2028 Publications: 4