Combination of Injection Volume Calibration by Creatinine and MS Signals鈥?Normalization to Overcome Urine Variability in LC-MS-Based Metabolomics Studies
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- 作者：Yanhua Chen ; Guoqing Shen ; Ruiping Zhang ; Jiuming He ; Yi Zhang ; Jing Xu ; Wei Yang ; Xiaoguang Chen ; Yongmei Song ; Zeper Abliz
- 刊名：Analytical Chemistry
- 出版年：2013
- 出版时间：August 20, 2013
- 年：2013
- 卷：85
- 期：16
- 页码：7659-7665
- 全文大小：438K
- 年卷期：v.85,no.16(August 20, 2013)
- ISSN：1520-6882
文摘
It is essential to choose one preprocessing method for liquid chromatography鈥搈ass spectrometry (LC-MS)-based metabolomics studies of urine samples in order to overcome their variability. However, the commonly used normalization methods do not substantially reduce the high variabilities arising from differences in urine concentration, especially for signal saturation (abundant metabolites exceed the dynamic range of the instrumentation) or missing values. Herein, a simple preacquisition strategy based on differential injection volumes calibrated by creatinine (to reduce the concentration differences between the samples), combined with normalization to 鈥渢otal useful MS signals鈥?or 鈥渁ll MS signals鈥? is proposed to overcome urine variabilities. This strategy was first systematically compared with other popular normalization methods by application to serially diluted urine samples. Then, the method has been verified using rat urine samples of pre- and postinoculation of Walker 256 carcinoma cells. The results showed that the calibration of injection volumes based on creatinine values could effectively eliminate intragroup differences caused by variations in the concentrations of urinary metabolites, thus giving better parallelism and clustering effects. In addition, peak area normalization could further eliminate intraclass differences. Therefore, the strategy of combining peak area normalization with calibration of injection volumes of urine samples based on their creatinine values is effective for solving problems associated with urinary metabolomics.