TY - JOUR
T1 - Different-batch metabolome analysis of Saccharomyces cerevisiae based on gas chromatography/mass spectrometry
AU - Kawase, Naoki
AU - Tsugawa, Hiroshi
AU - Bamba, Takeshi
AU - Fukusaki, Eiichiro
N1 - Funding Information:
This study was partially supported by JST , Strategic International Collaborative Research Program, SICORP for JP-US Metabolomics. HT was also supported by Grant-in-Aid for Young Scientists B 25871136 .
PY - 2014/2
Y1 - 2014/2
N2 - 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.
AB - 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.
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U2 - 10.1016/j.jbiosc.2013.07.008
DO - 10.1016/j.jbiosc.2013.07.008
M3 - Article
C2 - 23968869
AN - SCOPUS:84892366874
SN - 1389-1723
VL - 117
SP - 248
EP - 255
JO - Journal of Bioscience and Bioengineering
JF - Journal of Bioscience and Bioengineering
IS - 2
ER -