Deep learning massively accelerates super-resolution localization microscopy
Accelerating PALM/STORM microscopy with deep learning allows super-resolution imaging of >1,000 cells in a few hours. 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...
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| Published in | Nature biotechnology Vol. 36; no. 5; pp. 460 - 468 |
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| Main Authors | , , , , |
| Format | Journal Article |
| Language | English |
| Published |
New York
Nature Publishing Group US
01.06.2018
Nature Publishing Group |
| Subjects | |
| Online Access | Get full text |
| ISSN | 1087-0156 1546-1696 1546-1696 |
| DOI | 10.1038/nbt.4106 |
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| Abstract | Accelerating PALM/STORM microscopy with deep learning allows super-resolution imaging of >1,000 cells in a few hours.
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. |
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| AbstractList | 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.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. 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. 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 [greater than] 1,000 fields of view containing [greater than] 1,000 cells in [similar]3 h, yielding an image spanning spatial scales from [similar]20 nm to [similar]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. Accelerating PALM/STORM microscopy with deep learning allows super-resolution imaging of >1,000 cells in a few hours. 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. |
| Audience | Academic |
| Author | Zimmer, Christophe Ouyang, Wei Lelek, Mickaël Hao, Xian Aristov, Andrey |
| Author_xml | – sequence: 1 givenname: Wei surname: Ouyang fullname: Ouyang, Wei organization: Institut Pasteur, Unité Imagerie et Modélisation, UMR 3691, CNRS, C3BI, USR 3756, IP CNRS – sequence: 2 givenname: Andrey surname: Aristov fullname: Aristov, Andrey organization: Institut Pasteur, Unité Imagerie et Modélisation, UMR 3691, CNRS, C3BI, USR 3756, IP CNRS – sequence: 3 givenname: Mickaël surname: Lelek fullname: Lelek, Mickaël organization: Institut Pasteur, Unité Imagerie et Modélisation, UMR 3691, CNRS, C3BI, USR 3756, IP CNRS – sequence: 4 givenname: Xian surname: Hao fullname: Hao, Xian organization: Institut Pasteur, Unité Imagerie et Modélisation, UMR 3691, CNRS, C3BI, USR 3756, IP CNRS – sequence: 5 givenname: Christophe surname: Zimmer fullname: Zimmer, Christophe email: czimmer@pasteur.fr organization: Institut Pasteur, Unité Imagerie et Modélisation, UMR 3691, CNRS, C3BI, USR 3756, IP CNRS |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/29658943$$D View this record in MEDLINE/PubMed https://pasteur.hal.science/pasteur-02074397$$DView record in HAL |
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| Snippet | Accelerating PALM/STORM microscopy with deep learning allows super-resolution imaging of >1,000 cells in a few hours.
The speed of super-resolution microscopy... 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... |
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| Title | Deep learning massively accelerates super-resolution localization microscopy |
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