Label-Free Tomographic Imaging of Lipid Droplets in Foam Cells for Machine-Learning-Assisted Therapeutic Evaluation of Targeted Nanodrugs

Abstract

Lipid droplet (LD) accumulation, a key feature of foam cells, constitutes an attractive target for therapeutic intervention in atherosclerosis. However, despite advances in cellular imaging techniques, current noninvasive and quantitative methods have limited application in living foam cells. Here, using optical diffraction tomography (ODT), we performed quantitative morphological and biophysical analysis of living foam cells in a label-free manner. We identified LDs in foam cells by verifying the specific refractive index using correlative imaging comprising ODT integrated with three-dimensional fluorescence imaging. Through time-lapse monitoring of three-dimensional dynamics of label-free living foam cells, we precisely and quantitatively evaluated the therapeutic effects of a nanodrug (mannose-polyethylene glycol-glycol chitosan-fluorescein isothiocyanate-lobeglitazone; MMR-Lobe) designed to affect the targeted delivery of lobeglitazone to foam cells based on high mannose receptor specificity. Furthermore, by exploiting machine-learning-based image analysis, we further demonstrated therapeutic evaluation at the single-cell level. These findings suggest that refractive index measurement is a promising tool to explore new drugs against LD-related metabolic diseases.