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Updated: Jul 26, 2025

Author Spotlight: Universal Molecular Retention with 11-Fold Expansion Microscopy
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Plasmon-Enhanced Expansion Microscopy.

Priya Rathi1, Prashant Gupta2, Avishek Debnath2

  • 1Department of Chemistry, Washington University in St. Louis, St. Louis, Missouri 63130, United States.

Nano Letters
|June 12, 2023
PubMed
Summary
This summary is machine-generated.

Plasmon-enhanced expansion microscopy (p-ExM) uses bright plasmonic-fluor nanolabels to overcome signal dilution in expansion microscopy (ExM). This novel technique significantly improves neural network tracing and morphological analysis.

Keywords:
expansion microscopyfluorescence microscopynanoneuro interactionplasmon-enhanced fluorescenceplasmonic-fluor

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Area of Science:

  • Biophysics
  • Microscopy
  • Nanotechnology

Background:

  • Expansion microscopy (ExM) enhances spatial resolution by isotropically expanding biological samples.
  • A key limitation of ExM is fluorescence signal dilution, hindering its widespread adoption.
  • Improved nanolabels are needed to enhance signal intensity and retention in ExM.

Purpose of the Study:

  • To introduce plasmon-enhanced expansion microscopy (p-ExM) for overcoming signal dilution in ExM.
  • To utilize ultrabright plasmonic-fluor (PF) nanoconstructs as nanolabels for p-ExM.
  • To demonstrate the efficacy of p-ExM in improving neural network analysis.

Main Methods:

  • Development and application of plasmonic-fluor (PF) nanolabels.
  • Implementation of the p-ExM protocol for biological sample expansion.
  • Imaging and quantification of neural network morphology using p-ExM.

Main Results:

  • PFs exhibit significantly higher fluorescence intensity (15000-fold) and retention (76%) compared to conventional labels (<16%).
  • p-ExM enables improved tracing and decryption of neural networks.
  • Quantification of morphological markers showed a 2.5-fold increase in neurite terminal points.

Conclusions:

  • p-ExM effectively addresses the signal dilution challenge in ExM.
  • PF nanolabels are highly efficient for super-resolution imaging in ExM.
  • p-ExM offers a valuable tool for studying structure-function relationships in biological systems.