Polarization-Sensitive Holotomography for Multidimensional Label-Free Imaging and Characterization of Lipid Droplets...

Area of Science:

• Cell Biology
• Biophysics
• Cancer Research

Background:

• Lipid droplets (LDs) are crucial cellular components involved in metabolism, with alterations frequently observed in cancer.
• Conventional holotomography provides 3D imaging of LDs but requires complex thresholding and lacks biochemical specificity.
• Label-free imaging techniques are needed for precise LD analysis and cancer diagnostics.

Purpose of the Study:

• To introduce and validate polarization-sensitive holotomography (PS-HT) as a novel method for label-free imaging and analysis of lipid droplets.
• To assess the capability of PS-HT in quantifying LDs' biochemical and organizational properties.
• To evaluate PS-HT's potential for differentiating between healthy and cancerous cells based on lipid metabolism.

Main Methods:

• Development and application of polarization-sensitive holotomography (PS-HT) for imaging lipid droplets.
• Utilizing the intrinsic birefringence of lipid droplets for high-contrast imaging with a fixed near-zero threshold.
• Comparative analysis with fluorescence microscopy and standard holotomography using prostate cell models (PNT2 and PC3).
• Employing principal component analysis for cell classification.

Main Results:

• PS-HT enables accurate quantification of LD birefringence, volume, dry mass, molecular organization, and spatial distribution.
• Significantly higher LD birefringence was observed in cancer cells (PC3) after glucose treatment, indicating enhanced lipid accumulation.
• PS-HT combined with principal component analysis achieved near-perfect classification of cancer versus healthy cells.

Conclusions:

• PS-HT is a robust, label-free tool for studying cellular lipid metabolism and the role of LDs in cancer.
• The technique offers high-contrast, selective identification of LDs based on their intrinsic birefringence.
• PS-HT shows significant promise for cancer diagnostics and understanding metabolic alterations in disease.