期刊
NATURE BIOTECHNOLOGY
卷 36, 期 5, 页码 460-+出版社
NATURE PORTFOLIO
DOI: 10.1038/nbt.4106
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资金
- Institut Pasteur, Agence Nationale de la Recherche [ANR 14 CE10 0018 02]
- Fondation pour la Recherche Medicale (Equipe FRM) [DEQ 20150331762]
- Region Ile de France (DIM Malinf)
- Investissement d'Avenir [ANR-16-CONV-0005]
- Institut Pasteur
- Agence Nationale de la Recherche (ANR) [ANR-16-CONV-0005, ANR-14-CE10-0018] Funding Source: Agence Nationale de la Recherche (ANR)
The speed of super-resolution microscopy methods based on single-molecule localization, for example, PALM and STORM, is limited by the need to record many thousands of frames with a small number of observed molecules in each. Here, we present ANNA-PALM, a computational strategy that uses artificial neural networks to reconstruct super-resolution views from sparse, rapidly acquired localization images and/or widefield images. Simulations and experimental imaging of microtubules, nuclear pores, and mitochondria show that high-quality, super-resolution images can be reconstructed from up to two orders of magnitude fewer frames than usually needed, without compromising spatial resolution. Super-resolution reconstructions are even possible from widefield images alone, though adding localization data improves image quality. We demonstrate super-resolution imaging of >1,000 fields of view containing >1,000 cells in similar to 3 h, yielding an image spanning spatial scales from similar to 20 nm to similar to 2 mm. The drastic reduction in acquisition time and sample irradiation afforded by ANNA-PALM enables faster and gentler high-throughput and live-cell super-resolution imaging.
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