Mimicking exogenous amino acids: Raman-based and machine learning tracking of Colon cancer cells metabolism

Abstract

Amino acids are fundamental biomolecules that constitute the building blocks of proteins. Monitoring their dysregulation and fluctuations serves as an important indicator of human health. The aim of this study is to explore the role of exogenous amino acids metabolism in colon cancer development. Raman spectroscopy and Raman imaging were employed to investigate biochemical changes in normal human colon cells (CCD-18Co) and cancerous colon cells (Caco-2), both untreated and supplemented with leucine, threonine, and arginine. Spectroscopic data were analyzed using chemometric methods, specifically Partial Least Squares Discriminant Analysis (PLS-DA). Raman spectroscopy, combined with chemometric approach, identified unique Raman biomarkers – 1088/1262, 1444/1660, 1580/1004, and 1630/1444 - that correspond to changes in nucleic acids, lipids/lipids+Amide I, proteins, and lipids, respectively, in both normal and cancer cells supplemented with amino acids. These findings underscore the significant potential of Raman spectroscopy and Raman imaging as powerful tools for investigating the role of exogenous amino acids in colon cancer progression. We have also shown that the S₁-state lifetime of tetra-sulphonated aluminum phthalocyanine remains unaffected by amino acid supplementation, as confirmed by femtosecond transient absorption spectroscopy, supporting its stability as a photosensitizer for photodynamic therapy.