Lipid Droplets as Cellular Sensors of Lipid Metabolic Reprogramming in Colon Cancer: Insights from Essential Amino Acid Supplementation Using Raman Spectroscopy and Imaging.

Herein, we present a comprehensive single-cell investigation of the biochemical and metabolic responses of normal human colon fibroblasts (CCD-18Co) and colorectal adenocarcinoma cells (Caco-2) to supplementation with the amino acids leucine, threonine, and arginine, employing State-of-the-Art Raman spectroscopy and Raman imaging. This fully label-free and noninvasive methodology enabled high-spatial-resolution mapping of intracellular components, providing unprecedented insight into subcellular biochemical organization and metabolic remodeling associated with colorectal carcinogenesis. By synergistically integrating Raman spectroscopic data with advanced chemometric methods, we demonstrate robust, reproducible discrimination between normal and malignant colon cells, both in their native state and after amino acid treatment, based solely on their intrinsic vibrational fingerprints. Partial Least Squares Discriminant Analysis (PLS-DA) and one-way ANOVA revealed that perturbations in lipid metabolism and protein composition constitute key molecular determinants underlying the observed phenotypic divergence between control and amino acid-supplemented cells. Notably, detailed analysis of diagnostic Raman band intensity ratios (2845/3015, 2845/2930, 3015/2888, and 1444/1256) uncovered pronounced amino acid-driven alterations in metabolic pathways at the single-cell level. Raman imaging further enabled spatially resolved visualization of these biochemical shifts and changes in Raman band intensities, highlighting distinct lipid- and protein-rich subcellular domains that respond differentially to amino acid exposure in normal versus cancerous cells. Collectively, our findings establish Raman spectroscopy combined with chemometric analysis as a powerful and sensitive platform for decoding amino acid-induced metabolic reprogramming in colorectal cells. This approach deepens the mechanistic understanding of nutrient-cancer cell interactions and opens new avenues for the development of Raman-based strategies in cancer diagnostics and therapeutic response assessment.