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Lipid metabolic profiling via quantitative stimulated Raman scattering imaging opens up new avenues for precision medicine

Lipid droplet (LD) is a dynamic organelle closely associated with cellular functions and energy homeostasis. Dysregulated LD biology underlies an increasing number of human diseases. Commonly used lipid staining and analytical tools have difficulty providing the information regarding LD distribution and composition at the same time. To address this problem, stimulated Raman scattering (SRS) microscopy uses the intrinsic chemical contrast of biomolecules to achieve both direct visualization of LD dynamics and quantitative analysis of LD composition with high molecular selectivity at the subcellular level. Recent developments of Raman tags have further enhanced sensitivity and specificity of SRS imaging without perturbing molecular activity. With these advantages, SRS microscopy has offered great promise for deciphering LD metabolism in single live cells. This talk will further showcase that lipid metabolic profiling via quantitative SRS imaging opens up new avenues for precision medicine. For instance, based on newly developed quantitative SRS microscopy, we revealed remarkably dysregulated lipid homeostasis in late-stage compared to early-stage liver fibrosis, i.e. increased unsaturated triglycerides with decreased lipid unsaturation degree. Inspiringly, injured hepatocytes could be rescued by lipid homeostasis remodeling via either supply of unsaturated fatty acids or enhancement of membrane fluidity. Collectively, this work offers new opportunities for treatment of liver fibrosis. As another example, we established an SRS-based intelligent molecular cytology (SRMC). In ascites from 80 gastric cancer (GC) patients, we revealed 12 single cell features that are significantly different between peritoneal metastasis (PM) positive and negative specimens, particularly LD amount. Assisted by AI phenotyping algorithm, the SRMC method reached the AUC of 0.85 within 20 minutes per patient. Together, our method shows great potential for accurate and rapid detection of PM from GC.

19 2023-12-07

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