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Super-resolution Fluorescence Microscopy

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Super-resolution fluorescence microscopy (SRFM) provides a better resolution than conventional fluorescence microscopy by reducing the point spread function (PSF). PSF is the light intensity distribution from a point that causes it to appear blurred. Due to PSF, each fluorescing point appears bigger than its actual size, and it is the PSF interference of nearby fluorophores that causes the blurred image. Various approaches to achieving higher resolution through SRFM have recently been...

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Confocal and Super-Resolution Imaging of Polarized Intracellular Trafficking and Secretion of Basement Membrane Proteins During Drosophila Oogenesis

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SpiDe-Sr: blind super-resolution network for precise cell segmentation and clustering in spatial proteomics imaging.

Rui Chen^1,2, Jiasu Xu^1,2, Boqian Wang^1,2

¹Department of Anesthesiology and Surgical Intensive Care Unit, Xinhua Hospital, School of Medicine and School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, China.

Nature Communications

|March 29, 2024

Summary

SpiDe-Sr enhances spatial proteomics imaging resolution by 4x, improving cell analysis accuracy. This deep learning tool also reveals bacteria

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Ground State Depletion Super-resolution Imaging in Mammalian Cells

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Area of Science:

Biomedical imaging
Proteomics
Computational biology

Background:

Spatial proteomics using imaging mass cytometry (IMC) offers deep cellular insights but is limited by imaging noise and resolution.
Accurate clinical analysis requires enhanced precision in spatial proteomic data.
Existing methods struggle to overcome the inherent limitations of IMC resolution and noise.

Purpose of the Study:

To develop a super-resolution network, SpiDe-Sr, for enhancing the spatial resolution of IMC data.
To improve the accuracy and reliability of spatial proteomics analysis for clinical applications.
To investigate the tumor microenvironment in breast cancer using enhanced spatial proteomic data.

Main Methods:

Developed SpiDe-Sr, a deep learning network with an integrated denoising module for spatial resolution enhancement.
Applied SpiDe-Sr to cellular, mouse, and human tissue samples to assess performance.
Validated SpiDe-Sr on a cohort of 20 breast cancer patients, analyzing 269,556 single cells.

Main Results:

SpiDe-Sr achieved a 4x improvement in spatial resolution and effectively reduced imaging noise.
Demonstrated significant increases in peak signal-to-noise ratio (18.95%-27.27%) and cell extraction accuracy (15.52%-31.63%) across different sample types.
Identified a correlation between Gram-negative bacteria invasion, carcinogenesis markers, and immunological markers in breast cancer.

Conclusions:

SpiDe-Sr is a robust tool for enhancing spatial proteomics data quality from IMC.
The method improves cellular analysis accuracy and enables deeper insights into complex biological systems like the tumor microenvironment.
SpiDe-Sr's compatibility with fluorescence microscopy suggests broader applicability in bioimaging super-resolution.