Each experimental step in metabolomics based on mass spectrometry for microorganisms, such as cultivation, sampling, extraction of metabolites, analysis, and data processing includes different systematic errors. Even if the same protocol is used, it is difficult to compare the data from different cultivation days or different analysis days. To obtain reliable quantitative data, it is necessary to develop an analytical workflow that can reduce errors from different batch of cultivation and analysis days. We compared metabolomics methods for Saccharomyces cerevisiae in terms of reproducibility to optimize the analytical workflow, particularly quenching and data processing. Our data also showed that reproducible data could be obtained with high signal to noise ratio. Therefore, we optimized a time segmented selective ion monitoring (SIM) method for high sensitive analysis with low-risk of false positives. The optimized workflow was applied to metabolome analysis of single transcription factor deletion mutants. As a result, we obtained clusters that were independent of cultivation day and analysis day but were strain-dependent. This study can help to implement large-scale or long-term studies, in which samples are divided among several laboratories because of the high number of samples.
Keywords: Different batches; Gas chromatography mass spectrometry; Metabolomics; Saccharomyces cerevisiae; Time segmented selective ion monitoring.
Copyright © 2013. Published by Elsevier B.V.