Time-lapse RESOLFT nanoscopy empowered by deep learning
Abstract
Capturing the dynamic interplay of proteins and organelles within cells requires microscopy methods with high spatial and temporal resolution. Reversible Saturable OpticaL Fluorescent Transition (RESOLFT) demonstrated great potential for live cell nanoscopy by using patterned light and reversibly switching fluorescent probes. Despite the latest improvement in both switching probes and optical schemes, the duration and frame rate of the time-lapse are still limited by photobleaching and spatial sampling steps. Here, we present two distinct strategies based on deep learning to restore low signal-to-noise-ratio (SNR) or under-sampled images pushing the experimental boundaries of RESOLFT. We demonstrate the capability of Hierarchical DivNoising (HDN) to restore low-SNR RESOLFT data while maintaining full spatial resolution, both in supervised and unsupervised settings. This allows a 10-fold reduction of light dose per frame, and up to 5 times longer imaging. Then, we present Fast-MoNaLISA, a method that reduces the acquisition time of any point-scanning systems. This allows a 4-fold increase in frame rate, and a reduction of scanning artefacts. Overall, our methods open up possibilities for dynamic data acquisition at the sub-organelle level, such as vesicles budding in and out from intact mitochondria, or the actin meshwork formation and dissociation.
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BibTeX
@article{minet2026timelapse,
title = {{Time-lapse RESOLFT nanoscopy empowered by deep learning}},
author = {Guillaume Minet and Anirban Ray and Francesca Pennacchietti and Giovanna Coceano and Florian Jug and Ilaria Testa},
journal = {Nature Communications},
year = {2026},
month = oct,
doi = {10.1038/s41467-026-78580-z},
url = {https://www.nature.com/articles/s41467-026-78580-z},
note = {CC BY 4.0},
}
RIS
TY - JOUR
TI - Time-lapse RESOLFT nanoscopy empowered by deep learning
AU - Guillaume Minet
AU - Anirban Ray
AU - Francesca Pennacchietti
AU - Giovanna Coceano
AU - Florian Jug
AU - Ilaria Testa
PY - 2026
DA - 2026/10/11
JO - Nature Communications
DO - 10.1038/s41467-026-78580-z
UR - https://www.nature.com/articles/s41467-026-78580-z
AB - Capturing the dynamic interplay of proteins and organelles within cells requires microscopy methods with high spatial and temporal resolution. Reversible Saturable OpticaL Fluorescent Transition (RESOLFT) demonstrated great potential for live cell nanoscopy by using patterned light and reversibly switching fluorescent probes. Despite the latest improvement in both switching probes and optical schemes, the duration and frame rate of the time-lapse are still limited by photobleaching and spatial sampling steps. Here, we present two distinct strategies based on deep learning to restore low signal-to-noise-ratio (SNR) or under-sampled images pushing the experimental boundaries of RESOLFT. We demonstrate the capability of Hierarchical DivNoising (HDN) to restore low-SNR RESOLFT data while maintaining full spatial resolution, both in supervised and unsupervised settings. This allows a 10-fold reduction of light dose per frame, and up to 5 times longer imaging. Then, we present Fast-MoNaLISA, a method that reduces the acquisition time of any point-scanning systems. This allows a 4-fold increase in frame rate, and a reduction of scanning artefacts. Overall, our methods open up possibilities for dynamic data acquisition at the sub-organelle level, such as vesicles budding in and out from intact mitochondria, or the actin meshwork formation and dissociation.
SN - 2041-1723
ER -