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Related Experiment Video

Updated: Jul 30, 2025

Super-Resolution Live Cell Imaging of Subcellular Structures

Super-Resolution Live Cell Imaging of Subcellular Structures

Published on: January 13, 2021

Single-frame deep-learning super-resolution microscopy for intracellular dynamics imaging.

Rong Chen¹, Xiao Tang², Yuxuan Zhao³

¹Department of Chemical and Biological Engineering, The Hong Kong University of Science and Technology, Hong Kong, China.

Nature Communications

|May 18, 2023

Summary

This summary is machine-generated.

This study introduces a deep-learning method for single-frame super-resolution microscopy (SFSRM). SFSRM achieves high-resolution live-cell imaging in milliseconds, overcoming limitations of traditional methods.

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Published on: September 29, 2014

Area of Science:

Biophysics
Microscopy
Cell Biology

Background:

Single-molecule localization microscopy (SMLM) offers high spatial resolution but suffers from long acquisition times and phototoxicity.
These limitations hinder the observation of rapid intracellular dynamics.

Purpose of the Study:

To develop a deep-learning based single-frame super-resolution microscopy (SFSRM) method.
To enable high-fidelity live-cell imaging with improved spatiotemporal resolution.

Main Methods:

Utilized a deep neural network guided by a subpixel edge map and multicomponent optimization.
Reconstructed super-resolution images from single diffraction-limited frames.

Main Results:

Achieved spatiotemporal resolutions of 30 nm and 10 ms under tolerable signal conditions.
Enabled prolonged monitoring of subcellular dynamics like mitochondrial-ER interactions, vesicle transport, and endosome dynamics.
Demonstrated adaptability across different microscopes and spectral ranges.

Conclusions:

SFSRM significantly enhances live-cell imaging capabilities by overcoming SMLM's temporal and phototoxicity limitations.
This method provides a versatile tool for observing fast cellular processes with unprecedented detail.