Department of Biotechnology, Graduate School of Engineering, Osaka University, 2-1 Yamadaoka, Suita, Osaka 565-0871, Japan.
Department of Biotechnology, Graduate School of Engineering, Osaka University, 2-1 Yamadaoka, Suita, Osaka 565-0871, Japan; RIKEN Center for Sustainable Resource Science, Tsurumi-ku, Yokohama 230-0045, Japan.
J Biosci Bioeng. 2014 Feb;117(2):248-255. doi: 10.1016/j.jbiosc.2013.07.008. Epub 2013 Aug 19.
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.
基于质谱的微生物代谢组学的每个实验步骤，如培养、采样、代谢物提取、分析和数据处理，都包含不同的系统误差。即使使用相同的方案，也很难比较来自不同培养日或不同分析日的数据。为了获得可靠的定量数据，有必要开发一种分析工作流程，可以减少不同批培养和分析日的误差。我们比较了酿酒酵母代谢组学方法的重现性，以优化分析工作流程，特别是淬灭和数据处理。我们的数据还表明，可以用高信噪比获得可重现的数据。因此，我们优化了一种时间分段选择离子监测（SIM）方法，用于高灵敏度分析，降低假阳性风险。优化后的工作流程应用于单个转录因子缺失突变体的代谢组学分析。结果，我们获得了与培养日和分析日无关但与菌株有关的聚类。这项研究有助于实施大规模或长期研究，由于样本数量众多，这些研究将样本分配到几个实验室。
