%0 Journal Article %T Deep learning massively accelerates super-resolution localization microscopy %+ Imagerie et Modélisation - Imaging and Modeling %+ Centre de Bioinformatique, Biostatistique et Biologie Intégrative (C3BI) %A Ouyang, Wei %A Aristov, Andrey %A Lelek, Mickaël %A Hao, Xian %A Zimmer, Christophe %Z This work was funded by Institut Pasteur, Agence Nationale de la Recherche grant (ANR 14 CE10 0018 02), Fondation pour la Recherche Médicale (Equipe FRM, DEQ 20150331762), and the Région Ile de France (DIM Malinf). We also acknowledge Investissement d'Avenir grant ANR-16-CONV-0005 for funding a GPU farm used in this work. A.A. and X.H. are recipients of Pasteur-Roux fellowships from Institut Pasteur. W.O. is a scholar in the Pasteur–Paris University (PPU) International PhD program %< avec comité de lecture %@ 1087-0156 %J Nature Biotechnology %I Nature Publishing Group %V 36 %N 5 %P 460-468 %8 2018-04-16 %D 2018 %R 10.1038/nbt.4106 %M 29658943 %Z Life Sciences [q-bio]/Santé publique et épidémiologieJournal articles %X 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 ∼3 h, yielding an image spanning spatial scales from ∼20 nm 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. %G English %Z We thank the following colleagues for useful discussions and suggestions and/or critical reading of the manuscript: C. Leduc, S. Etienne-Manneville, S. Lévêque-Fort, N. Bourg, A. Echard, J.-B. Masson, T. Rose, P. Hersen, F. Mueller, M. Cohen, Z. Zhang, and P. Kanchanawong. We also thank the four anonymous reviewers for their constructive criticism, which led to significant improvements of ANNA-PALM. We further thank O. Faklaris, J. Sellés and M. Penrad (Institut Jacques Monod), and F. Montel (Ecole Normale Supérieure de Lyon) for providing Xenopus nuclear pore data, J. Bai (Institut Pasteur) for TOM22 antibodies, and C. Leterrier for fixation protocols. We thank E. Rensen and C. Weber for help with experiments and suggestions, B. Lelandais for help with PALM image processing, J.-B. Arbona for polymer simulations and J. Parmar for suggestions that led to the name ANNA-PALM. We thank the IT service of Institut Pasteur, including J.-B. Denis, N. Joly, and S. Fournier, for access to the HPC cluster and relevant assistance, and T. Huynh for help with GPU computing. %2 https://pasteur.hal.science/pasteur-02074397/document %2 https://pasteur.hal.science/pasteur-02074397/file/51139_2_merged_1518367653.pdf %L pasteur-02074397 %U https://pasteur.hal.science/pasteur-02074397 %~ PASTEUR %~ CNRS %~ SANTE_PUB_INSERM %~ ANR %~ FRM %~ IMAGING-MODELING %~ UMR3691