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Updated: Jul 19, 2026

Quantitative Metabolomics of Saccharomyces Cerevisiae Using Liquid Chromatography Coupled with Tandem Mass Spectrometry
Published on: January 5, 2021
A comparison of direct infusion MS and GC-MS for metabolic footprinting of yeast mutants
Sandrine Mas1, Silas Granato Villas-Bôas, Michael Edberg Hansen
1Center for Microbial Biotechnology, BioCentrum-DTU, Technical University of Denmark, Building 223, DK-2800 Kgs. Lyngby, Denmark.
Abstract:
Recent technical advances in mass spectrometry (MS) have propelled this technology to the forefront of methods employed in metabolome analysis. Here, we compare two distinct analytical approaches based on MS for their potential in revealing specific metabolic footprints of yeast single-deletion mutants. Filtered fermentation broth samples were analyzed by GC-MS and direct infusion ESI-MS. The potential of both methods in producing specific and, therefore, discriminant metabolite profiles was evaluated using samples from several yeast deletion mutants grown in batch-culture conditions with glucose as the carbon source. The mutants evaluated were cat8, gln3, ino2, opi1, and nil1, all with deletion of genes involved in nutrient sensing and regulation. From the analysis, we found that both methods can be used to classify mutants, but the classification depends on which metabolites are measured. Thus, the GC-MS method is good for classification of mutants with altered nitrogen regulation as it primarily measures amino acids, whereas this method cannot classify mutants involved in regulation of phospholipids metabolism as well as the direct infusion MS (DI-MS) method. From the analysis, we find that it is possible to discriminate the mutants in both the exponential and stationary growth phase, but the data from the exponential growth phase provide more physiological relevant information. Based on the data, we identified metabolites that are primarily involved in discrimination of the different mutants, and hereby providing a link between high-throughput metabolome analysis, strain classification, and physiology.
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