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

Aug 5, 2026

The study explores how time-resolved, label-free Raman microspectroscopy can elucidate the kinetics of cellular and subcellular glycolysis pathway dynamics. A549 human lung cells were cultured with glucose under three modulated conditions using oligomycin and 2-deoxyglucose to stimulate or inhibit glycolysis. Kinetic end-point assays guided development of a numerical model, while Raman spectra from biological and technical replicates were analyzed with multivariate statistics and machine learning to discriminate conditions and mine spectral fingerprints. The work shows Raman spectral fingerprints can track compensatory metabolic processes and supports applications in high-content drug screening, diagnostics, and real-time bioprocess monitoring.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages3

Reading Time8 min

AccessFree to download

Abstract

Methods and experimental design

Kinetic modeling and numerical simulation

Raman data acquisition and replication

Multivariate statistics and machine learning analysis

Technological University Dublin ARROW@TU Dublin | SAML-25 Workshop on Statistical and Machine Learning | Research Institutes/Centres/Groups | | --- | --- | | 2025-06-05
Shedding light on cellular glycolysis pathway kinetics using a Spectralomics approach, integrating multivariate statistical and machine learning analytical approaches
Nitin Patil
Technological University Dublin, d21[127295@mytudublin.ie](127295@mytudublin.ie)
Zohreh Mirveis
Technological University Dublin, D21[127294@mytudublin.ie](127294@mytudublin.ie)
Hugh Byrne
Technological University Dublin, [hugh.byrne@tudublin.ie](hugh.byrne@tudublin.ie)
Follow this and additional works at: [https://arrow.tudublin.ie/saml](https://arrow.tudublin.ie/saml)
Part of the Statistics and Probability Commons | | Recommended Citation Patil, Nitin; Mirveis, Zohreh; and Byrne, Hugh, "Shedding light on cellular glycolysis pathway kinetics using a Spectralomics approach, integrating multivariate statistical and machine learning analytical approaches" (2025) . SAML-25 Workshop on Statistical and Machine Learning. 9. [https://arrow.tudublin.ie/saml/9](https://arrow.tudublin.ie/saml/9) This Conference Paper is brought to you by the EUt+ Academic Press a free to read and publish press of the European University of Technology. This work is licensed under a Creative Commons Attribution-NonCommercial-Share Alike 4.0 International License. Shedding light on cellular glycolysis pathway kinetics using a Spectralomics approach, integrating multivariate statistical and machine learning analytical approaches Nitin Patil∗ [D21127295@mytudublin.ie](D21127295@mytudublin.ie)[ ](D21127295@mytudublin.ie)Physical to Life Sciences Research Hub, TU Dublin School of Physics, Optometric and Clinical Sciences, TU Dublin Dublin, Ireland Zohreh Mirveis Physical to Life Sciences Research Hub, TU Dublin School of Physics, Optometric and Clinical Sciences, TU Dublin Dublin, Ireland [D21127294@mytudublin.ie](D21127294@mytudublin.ie) Hugh J Byrne Physical to Life Sciences Research Hub, TU Dublin Dublin, Ireland ABSTRACT 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 ofthe 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 w

FAQs

How was glycolysis pathway kinetics stimulated and inhibited in the experiments?

A549 cells were cultured with glucose as the sole carbon source under three modulated conditions. Oligomycin stimulated the glycolysis pathway, while 2-deoxyglucose inhibited it.

What role did the numerical model play in the study?

A numerical model was developed to simulate kinetic end-point behavior and to augment glycolysis pathway insights beyond the assay’s sensitivity. It also served as a reference for datamining spectral fingerprints.

Which analytical approaches were used to extract metabolic information from Raman spectra?

The study used multivariate statistical and machine learning tools including PCA, PLSR, PLS-DA, and Multivariate Curve Resolution–Alternating Least Squares (MCR-ALS) to discriminate metabolic conditions and mine spectral fingerprints.

Aug 5, 2026

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