Tracking the kinetics cellular glycolysis and glutaminolysis pathways using vibrational spectroscopy, combined with multivariate statistical and machine learning approaches for data mining

Understanding dynamic metabolic processes within living cells is crucial for gaining insights into cellular function and disease mechanisms. The kinetics of glycolysis and glutaminolysis pathways play significant roles, as alterations in their activity have been linked to various disorders, including cancer and mental health conditions such as bipolar disorder. These pathways therefore hold potential as biomarkers for disease diagnosis and therapy. However, real-time monitoring of their kinetics remains challenging due to the lack of suitable non-invasive techniques. Current gold-standard fluxomics approaches, such as mass spectrometry, are destructive to cells and thus unsuitable for time-resolved studies. In this study, we evaluate the potential of label-free vibrational spectroscopy techniques, including ATR-FTIR spectroscopy and Raman spectroscopy, as non-destructive tools for monitoring pathway kinetics and providing cellular insights into metabolic dynamics over time. Given the complexity and volume of spectral data, we also explore the application of several multivariate statistical and machine learning approaches for data mining. These include Principal Component Analysis (PCA), Linear Discriminant Analysis (LDA), and Multivariate Curve Resolution–Alternating Least Squares (MCR-ALS). Our findings highlight the potential of combining vibrational spectroscopy with advanced data analysis to enable real-time, label-free investigation of cellular metabolic pathways.