Single-Frame Deep-Learning Super-resolution Microscopy for Intracellular Dynamics Imaging

Authors

Rong Chen
Xiao Tang
Yuxuan Zhao
Zeyu Shen
Meng Zhang
Yusheng Shen
Tiantian Li
Casper Ho Yin Chung
Lijuan Zhang
Ji Wang
Binbin Cui
Peng Fei
Yusong Guo
Shengwang Du
Shuhuai Yao

Language

English

Publication Date

5-18-2023

Journal

Nature Communications

DOI

10.1038/s41467-023-38452-2

PMID

37202407

PMCID

PMC10195829

PubMedCentral® Posted Date

5-18-2023

PubMedCentral® Full Text Version

Post-print

Abstract

Single-molecule localization microscopy (SMLM) can be used to resolve subcellular structures and achieve a tenfold improvement in spatial resolution compared to that obtained by conventional fluorescence microscopy. However, the separation of single-molecule fluorescence events that requires thousands of frames dramatically increases the image acquisition time and phototoxicity, impeding the observation of instantaneous intracellular dynamics. Here we develop a deep-learning based single-frame super-resolution microscopy (SFSRM) method which utilizes a subpixel edge map and a multicomponent optimization strategy to guide the neural network to reconstruct a super-resolution image from a single frame of a diffraction-limited image. Under a tolerable signal density and an affordable signal-to-noise ratio, SFSRM enables high-fidelity live-cell imaging with spatiotemporal resolutions of 30 nm and 10 ms, allowing for prolonged monitoring of subcellular dynamics such as interplays between mitochondria and endoplasmic reticulum, the vesicle transport along microtubules, and the endosome fusion and fission. Moreover, its adaptability to different microscopes and spectra makes it a useful tool for various imaging systems.

Keywords

Deep Learning, Microscopy, Fluorescence, Single Molecule Imaging, Neural Networks, Computer, Super-resolution microscopy, Fluorescence imaging

Published Open-Access

yes

Recommended Citation

Chen, Rong; Tang, Xiao; Zhao, Yuxuan; et al., "Single-Frame Deep-Learning Super-resolution Microscopy for Intracellular Dynamics Imaging" (2023). Faculty, Staff and Student Publications. 5468.
https://digitalcommons.library.tmc.edu/uthgsbs_docs/5468