Applications of Data Characteristic AI-Assisted Raman Spectroscopy in Pathological Classification.

Raman spectroscopy has been widely used for label-free biomolecular analysis of cells and tissues for pathological diagnosis in vitro and in vivo. AI technology facilitates disease diagnosis based on Raman spectroscopy, including machine learning (PCA and SVM), manifold learning (UMAP), and deep learning (ResNet and AlexNet). However, it is not clear how to optimize the appropriate AI classification model for different types of Raman spectral data.

VHL mutation drives human clear cell renal cell carcinoma progression through PI3K/AKT-dependent cholesteryl ester accumulation.

Background: Cholesteryl ester (CE) accumulation in intracellular lipid droplets (LDs) is an essential signature of clear cell renal cell carcinoma (ccRCC), but its molecular mechanism and pathological significance remain elusive.

Methods: Enabled by the label-free Raman spectromicroscopy, which integrated stimulated Raman scattering microscopy with confocal Raman spectroscopy on the same platform, we quantitatively analyzed LD distribution and composition at the single cell level in intact ccRCC cell and tissue specimens in situ without any processing or exogenous labeling. Since we found that commonly used ccRCC cell lines actually did not show the CE-rich signature, primary cancer cells were isolated from human tissues to retain the lipid signature of ccRCC with CE level as high as the original tissue, which offers a preferable cell model for the study of cholesterol metabolism in ccRCC.