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

Study Summary

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 holotomography (HT), 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 (RI) using correlative imaging comprising HT 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.
Overall procedures for label-free monitoring of macrophages or foam cells are shown in Figure 1

[Fig. 1] HT for label-free 3-D imaging using RI mapping in macrophages and foam cells. (A) Schematic diagram of the optical setup based on Mach−Zehnder interferometric microscopy. (B) Imaging process of reconstruction with the 3D RI distribution. (C, D) 2-D cross-sectional isosurface images by RI distribution of a (C) macrophage and (D) foam cell induced by LDL and LPS.

[Fig. 2] HT in label-free foam cell for drug efficacy

Until now, no studies are available regarding RIs and the quantitative change of LDs in foam cells in relation to atherogenesis. Here, we utilized a 3-D HT imaging technique to identify the RI of LDs in foam cells and demonstrated the differentiation of macrophages into foam cells in living cells in real time without staining, which could not be achieved using conventional fluorescence microscopy. Furthermore, we evaluated the therapeutic efficacy of a nanodrug by real-time monitoring of biophysical parameters of LDs in living foam cells along with machine learning assistance.

[Fig. 3] Quantitative analysis of time-lapse monitoring for volume of LDs/volume of cells every 15 min for 24 h in a living single foam cell.

We believe that the label-free and rapid 3-D HT technique, combined with emerging machine learning techniques, may provide readily interpretable and meaningful quantification of drug efficacy to discover new drugs for the treatment of these degenerative diseases.

[Fig. 4] Main analysis instruments, HT-2 (left, red arrow) and supplementary journal cover in ACS nano (right)