Shedding light on cellular glycolysis pathway kinetics using a Spectralomics approach, integrating multivariate statistical and machine learning analytical approaches

The potential of time resolved label-free Raman microspectroscopy to elucidate the kinetics of cellular and subcellular glycolysis pathway was explored in this study. A549, human lung cells were cultured in an unbuffered minimal medium with glucose as a sole carbon source under three different modulated conditions. Modulator drugs oligomycin and 2-deoxyglucose were used to stimulate and inhibit the glycolysis pathway. Initially the kinetic glycolysis assay was used to monitor the glycolysis end-point kinetics followed by development of a numerical model capable of simulating the end-point kinetics. For Raman spectroscopy, samples at different timepoints from the experiments with similar conditions as of the assay were acquired and Raman spectra were acquired in biological and technical replicates. Multivariate statistical and machine learning analytical tools were used to elucidate the sensitivity of Raman spectroscopy in the biologically relevant metabolite concentration range, to holistically discriminate among the different metabolic conditions, and to datamine the spectral fingerprints of the biological processes form the kinetic spectroscopic data. The numerical model developed for the kinetic assay augmented the glycolysis pathway kinetic insights beyond the assay’s sensitivity and aided as a reference for datamining spectral fingerprints. The extracellular Raman spectroscopy data highlighted the extracellular metabolic complexity which was overlooked in the targeted assay approach. The cellular spectroscopic data provided high-content insights into the cellular metabolic process and was able to capture the spectral fingerprints of the glycolysis pathway along with the compensatory cellular metabolic processes upon its inhibition. This study showcases the potential of label-free, kinetic subcellular Raman microspectroscopy coupled with Multivariate statistical and machine learning analytical tools for acquiring spectral fingerprints of kinetic process. This study finds applications in high-content drug screening in the drug discovery process, tracking disease progression for diagnostics and monitoring cellular bioprocesses in real-time for enhancing the process efficiency.