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, 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.
Author: | Kaarjel K. NarayanasamyORCiDGND, Johanna Viola RahmORCiDGND, Siddharth Tourani, Mike HeilemannORCiDGND |
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URN: | urn:nbn:de:hebis:30:3-742279 |
DOI: | https://doi.org/10.1038/s41467-022-32626-0 |
ISSN: | 2041-1723 |
Parent Title (German): | Nature Communications |
Publisher: | Nature Publishing Group UK |
Place of publication: | London |
Document Type: | Article |
Language: | English |
Date of Publication (online): | 2022/08/27 |
Date of first Publication: | 2022/08/27 |
Publishing Institution: | Universitätsbibliothek Johann Christian Senckenberg |
Release Date: | 2023/08/03 |
Volume: | 13 |
Issue: | 5047 |
Page Number: | 11 |
HeBIS-PPN: | 511237049 |
Institutes: | Biochemie, Chemie und Pharmazie |
Dewey Decimal Classification: | 5 Naturwissenschaften und Mathematik / 57 Biowissenschaften; Biologie / 570 Biowissenschaften; Biologie |
Sammlungen: | Universitätspublikationen |
Licence (German): | Creative Commons - Namensnennung 4.0 |