ArticleNature communications2026
Time-lapse RESOLFT nanoscopy empowered by deep learning.
Article in Nature communications, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
1 citing paper in PubMed.
- Time-lapse RESOLFT nanoscopy empowered by deep learning.Nature communications · 2026Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
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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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.