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

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.