Evidence map›Paper›PMID 37202407›Full record

ArticleNature communications2023

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

Rong Chen, Xiao Tang, Yuxuan Zhao, Zeyu Shen, Meng Zhang, Yusheng Shen, Tiantian Li, Casper Ho Yin Chung, Lijuan Zhang, Ji Wang and 5 more

Open access · goldAbstract read
In one paragraph

Article in Nature communications, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 54 papers.

0numbers the graph read from it
0cells of the map it votes in
54citing papers in PubMed
35.0field-weighted citation impact, top 1% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

54 citing papers in PubMed, 98 citations in OpenAlex.

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  15. Stress transmission towards the nucleus of the cell.Frontiers in cell and developmental biology · 2026
    Review
  16. Article
  17. Review
  18. Artificial Neural Networks for Bioimage Analysis.Methods in molecular biology (Clifton, N.J.) · 2026
    Article
  19. Review
  20. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

15 authors at 4 institutions in 3 countries.

Rong ChenDepartment of Chemical and Biological Engineering, The Hong Kong University of Science and Technology, Hong Kong, China.
Xiao TangDivision of Life Science, The Hong Kong University of Science and Technology, Hong Kong, China.
Yuxuan ZhaoSchool of Optical and Electronic Information, Huazhong University of Science and Technology, 430074, Wuhan, China.
Zeyu ShenDivision of Life Science, The Hong Kong University of Science and Technology, Hong Kong, China.
Meng ZhangSchool of Optical and Electronic Information, Huazhong University of Science and Technology, 430074, Wuhan, China.ORCID 0000-0003-1677-3393
Yusheng ShenDivision of Life Science, The Hong Kong University of Science and Technology, Hong Kong, China.
Tiantian LiDivision of Life Science, The Hong Kong University of Science and Technology, Hong Kong, China.
Casper Ho Yin ChungDepartment of Mechanical and Aerospace Engineering, The Hong Kong University of Science and Technology, Hong Kong, China.ORCID 0000-0001-5868-7712
Lijuan ZhangSchool of Pharmaceutical Sciences, Guizhou University, 550025, Guizhou, China.
Ji WangDepartment of Mechanical and Aerospace Engineering, The Hong Kong University of Science and Technology, Hong Kong, China.
Binbin CuiDepartment of Chemical and Biological Engineering, The Hong Kong University of Science and Technology, Hong Kong, China.
Peng FeiSchool of Optical and Electronic Information, Huazhong University of Science and Technology, 430074, Wuhan, China.ORCID 0000-0003-3764-817X
Yusong GuoDivision of Life Science, The Hong Kong University of Science and Technology, Hong Kong, China. guoyusong@ust.hk.ORCID 0000-0002-5539-599X
Shengwang DuDepartment of Chemical and Biological Engineering, The Hong Kong University of Science and Technology, Hong Kong, China. dusw@utdallas.edu.ORCID 0000-0002-7174-4571
Shuhuai YaoDepartment of Chemical and Biological Engineering, The Hong Kong University of Science and Technology, Hong Kong, China. meshyao@ust.hk.ORCID 0000-0001-7059-4092
Hong Kong University of Science and Technology · HKHuazhong University of Science and Technology · CNGuizhou University · CNThe University of Texas at Dallas · US

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

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.

Indexed as

Deep LearningMicroscopy, FluorescenceNeural Networks, ComputerSingle Molecule Imaging

Identifiers

PMID37202407
PMCPMC10195829
OpenAlexW4377046888

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.