Technology
PhotoFiTT
GitHub:
- HenriquesLab/PhotoFiTT
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. This approach leverages machine learning and cell cycle dynamics to analyze mitotic timing, cell size changes, and overall 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
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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 Article published in Nature Communications, December 2025 Technologies: DL4MicEverywhere (), 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 |
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Funding contributing to PhotoFiTT
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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 |