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Related Experiment Video

Updated: Jun 30, 2025

Imaging Molecular Adhesion in Cell Rolling by Adhesion Footprint Assay

Imaging Molecular Adhesion in Cell Rolling by Adhesion Footprint Assay

Published on: September 27, 2021

Deep learning-enabled fast DNA-PAINT imaging in cells.

Min Zhu¹, Luhao Zhang^1,2, Luhong Jin¹

¹Department of Biomedical Engineering, Key Laboratory of Biomedical Engineering of Ministry of Education, State Key Laboratory of Extreme Photonics and Instrumentation, Zhejiang Provincial Key Laboratory of Cardio-Cerebral Vascular Detection Technology and Medicinal Effectiveness Appraisal, Zhejiang Provincial Key Laboratory of Traditional Chinese Medicine for Clinical Evaluation and Translational Research, Zhejiang University, Hangzhou 310027, China.

Biophysics Reports

|March 22, 2024

Summary

This summary is machine-generated.

This study introduces a faster DNA-PAINT method using deep learning, significantly reducing image data requirements for super-resolution microscopy. This innovation makes advanced nanoscale imaging more accessible for biological research.

Keywords:

DNA-PAINT Deep learning SMLM Super-resolution imaging U-Net

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Last Updated: Jun 30, 2025

Imaging Molecular Adhesion in Cell Rolling by Adhesion Footprint Assay

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Published on: September 27, 2021

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Simultaneous Multicolor Imaging of Biological Structures with Fluorescence Photoactivation Localization Microscopy

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Published on: December 9, 2013

Area of Science:

Biophysics
Microscopy
Molecular Biology

Background:

DNA-PAINT is a powerful super-resolution microscopy technique.
Traditional DNA-PAINT requires extensive image data, limiting its accessibility.
Current methods are time-consuming for subcellular structure imaging.

Purpose of the Study:

To develop a simplified DNA-PAINT labeling method.
To create a deep learning-enabled fast DNA-PAINT imaging strategy.
To accelerate super-resolution imaging of subcellular structures.

Main Methods:

Developed a simplified DNA-PAINT labeling protocol.
Implemented a deep learning approach for image reconstruction.
Utilized reduced raw data for super-resolution imaging.

Main Results:

Achieved super-resolution reconstruction with 10% of traditional data.
Enabled fast imaging of subcellular structures like microtubules.
Maintained high resolution (nanometer scale).

Conclusions:

The new method significantly accelerates DNA-PAINT imaging.
Deep learning enhances efficiency and accessibility of SMLM.
This approach broadens the application of DNA-PAINT in biological research.