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Unsupervised Variational-Autoencoder-Based Analysis of Morphological Representations in Magnetic-Nanoparticle-Treated

Su-Yeon Hwang1, Tae-Il Kang2, Hyeon-Seo Kim1

  • 1Graduate School of Data Science, Chonnam National University, Gwangju 61186, Republic of Korea.

Bioengineering (Basel, Switzerland)
|January 28, 2026
PubMed
Summary

Magnetic nanoparticles (MNPs) cause significant changes in macrophage cell shape. Unsupervised machine learning effectively quantifies these subtle morphological alterations, revealing cellular responses to nanoparticle exposure.

Keywords:
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Area of Science:

  • Biomedical Engineering
  • Cell Biology
  • Artificial Intelligence in Medicine

Background:

  • Magnetic nanoparticles (MNPs) are crucial in biomedicine for applications like drug delivery and bioimaging.
  • Macrophages, key immune cells, interact significantly with MNPs through phagocytosis, leading to cellular changes.
  • Previous research on MNP-macrophage interactions primarily focused on uptake and toxicity, neglecting detailed morphological assessments.

Purpose of the Study:

  • To systematically quantify macrophage morphological alterations induced by MNP treatment using advanced computational methods.
  • To evaluate the efficacy of unsupervised variational autoencoder (VAE)-based frameworks in detecting subtle cellular changes.

Main Methods:

  • Phase-contrast microscopy images of macrophages pre- and post-MNP treatment were analyzed.
  • Unsupervised VAE frameworks (β-VAE, β-total correlation VAE, multi-encoder VAE) were utilized to extract latent representations of cell morphology.
  • Quantitative analyses including effect size, kernel density estimation, latent traversal, and difference mapping were performed.

Main Results:

  • MNP-treated macrophages displayed significant morphological changes, including membrane expansion, altered central density, and shape distortions.
  • VAE frameworks successfully extracted and visualized these subtle structural alterations.
  • Quantitative evaluations confirmed the pronounced nature of the observed morphological changes.

Conclusions:

  • Unsupervised VAE-based learning offers a powerful and robust method for detecting subtle morphological responses in macrophages exposed to nanoparticles.
  • This approach has broad applicability for analyzing cellular morphology across different cell types, treatments, and imaging modalities.