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 trained 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-color super-resolution imaging of structure-conserved semi-thin neuronal tissue and imaging of large samples. This improvement can be integrated into any single-molecule microscope and enables 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:30:3-729741 |
DOI: | https://doi.org/10.1101/2021.11.20.469366 |
Parent Title (English): | bioRxiv |
Document Type: | Preprint |
Language: | English |
Date of Publication (online): | 2021/11/20 |
Date of first Publication: | 2021/11/20 |
Publishing Institution: | Universitätsbibliothek Johann Christian Senckenberg |
Release Date: | 2023/07/22 |
Issue: | 2021.11.20.469366 |
Page Number: | 19 |
HeBIS-PPN: | 510563791 |
Institutes: | Biochemie, Chemie und Pharmazie / Biochemie und Chemie |
Dewey Decimal Classification: | 0 Informatik, Informationswissenschaft, allgemeine Werke / 00 Informatik, Wissen, Systeme / 004 Datenverarbeitung; Informatik |
5 Naturwissenschaften und Mathematik / 57 Biowissenschaften; Biologie / 570 Biowissenschaften; Biologie | |
Sammlungen: | Universitätspublikationen |
Licence (German): | Creative Commons - CC BY-NC - Namensnennung - Nicht kommerziell 4.0 International |