Surface design of photon-upconversion nanoparticles for high-contrast immunocytochemistry

Zdeněk Farka1, Matthias J Mickert, Zuzana Mikušová

  • 1Institute of Analytical Chemistry, Chemo- and Biosensors, University of Regensburg, 93053 Regensburg, Germany. farka@mail.muni.cz hans-heiner.gorris@ur.de.

Nanoscale
|April 3, 2020
PubMed

Insights

Photon-upconversion nanoparticles (UCNP) offer enhanced contrast for cancer cell detection. Optimized UCNP-based nanoconjugates significantly improve signal-to-background ratios, advancing digital pathology diagnostics.

Area of Science:

  • Biomedical Engineering
  • Nanotechnology
  • Cancer Diagnostics

Background:

  • Immunohistochemistry (IHC) and immunocytochemistry (ICC) are standard methods for cancer cell identification.
  • Conventional staining techniques suffer from low contrast, limiting diagnostic accuracy.
  • Photon-upconversion nanoparticles (UCNP) offer a solution to optical background interference.

Purpose of the Study:

  • To develop highly specific UCNP-based nanoconjugates for detecting the cancer biomarker HER2.
  • To improve signal-to-background ratios in immunolabeling for breast cancer cell lines.
  • To reduce non-specific binding of labels in histological samples.

Main Methods:

  • Design and characterization of several UCNP-based nanoconjugates.
  • Utilizing streptavidin-PEG-neridronate-UCNP for HER2 detection in breast cancer cell lines.
  • Comparison of UCNP labeling with conventional fluorescent labeling techniques.

Main Results:

  • An optimized streptavidin-PEG-neridronate-UCNP conjugate achieved a signal-to-background ratio of 319.
  • This represents a 50-fold improvement over conventional fluorescent labeling.
  • Demonstrated elimination of optical interference and non-specific binding.

Conclusions:

  • UCNP-based nanoconjugates provide superior specificity and contrast for cancer biomarker detection.
  • The developed method significantly enhances signal-to-background ratios, outperforming traditional methods.
  • This approach enables reliable computer-based data evaluation for digital pathology.