Fast DNA-PAINT imaging using a deep neural network
DNA points accumulation for imaging in nanoscale topography (DNA-PAINT) is a super-resolution technique with relatively easy-to-implement multi-target imaging. However, image acquisition is slow as sufficient statistical data has to be generated from spatio-temporally isolated single emitters. Here,...
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| Main Authors: | , , , |
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| Format: | Article (Journal) |
| Language: | English |
| Published: |
27 August 2022
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| In: |
Nature Communications
Year: 2022, Volume: 13, Pages: 1-11 |
| ISSN: | 2041-1723 |
| DOI: | 10.1038/s41467-022-32626-0 |
| Online Access: | Verlag, lizenzpflichtig, Volltext: https://doi.org/10.1038/s41467-022-32626-0 Verlag, lizenzpflichtig, Volltext: https://www.nature.com/articles/s41467-022-32626-0 
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| Author Notes: | Kaarjel K. Narayanasamy, Johanna V. Rahm, Siddharth Tourani & Mike Heilemann |
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| 520 | |a DNA points accumulation for imaging in nanoscale topography (DNA-PAINT) is a super-resolution technique with relatively easy-to-implement multi-target imaging. However, image acquisition is slow as sufficient statistical data has to be generated from spatio-temporally isolated single emitters. Here, we train the neural network (NN) DeepSTORM to predict fluorophore positions from high emitter density DNA-PAINT data. This achieves image acquisition in one minute. We demonstrate multi-colour super-resolution imaging of structure-conserved semi-thin neuronal tissue and imaging of large samples. This improvement can be integrated into any single-molecule imaging modality to enable fast single-molecule super-resolution microscopy. | ||
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