Department of Pharmacy, National Yang Ming Chiao Tung University, Taipei, 11221, Taiwan.
Graduate Institute of Clinical Medicine, College of Medicine, National Taiwan University, Taipei 10617, Taiwan; Department of Neurology, National Taiwan University Hospital, Hsin-Chu Branch, Hsin-Chu, Taiwan, College of Medicine, National Taiwan University, Taipei 10617, Taiwan.
J Pharm Biomed Anal. 2022 Sep 20;219:114930. doi: 10.1016/j.jpba.2022.114930. Epub 2022 Jul 6.
Metabolomics is an omics strategy to study the metabolite alteration in the biological system. Unbiased observation of the metabolite level is essential for targeted metabolite quantification and untargeted metabolic profiling. State-of-the-art instruments and versatile tools have been developed for accurate observation of metabolic alterations in various studies. Several analytical pitfalls, such as sample overloading and signal-saturation-induced bias, have been revealed and addressed. In this study, we proposed incomplete-metabolite-extraction-caused bias is also an important issue that results in biased observation when performing metabolomics. In the demonstration example, numerous metabolites exhibited no significant difference between extracted plasma samples with different plasma contents, which is attributed to incomplete-metabolite-extraction-caused bias and matrix effect. Matrix effect is a well-known factor that result in biased observation, it can be reduced by sample dilution and compensated by using stable isotope labelled internal standards. The detection of metabolite signals in the following consecutive extractions provided further evidence of incomplete metabolite extraction. The completeness of metabolite extraction is crucial for unbiased observation of metabolic profile changes. To address this issue, we optimized the extraction time and methanol volume to reduce the incomplete-metabolite-extraction-caused bias and evaluated the metabolite signals in consecutive extractions. Methanol extraction performed with a plasma-to-methanol ratio of 1:14 resulted in metabolite responses of less than 18.1 % in the second extractions observed by metabolomic profiling. Finally, the optimized sample preparation procedure and untargeted profiling platform were applied to detect metabolite alterations associated with patients with cerebrovascular diseases and several features with significant difference were successfully identified. This study revealed and evaluated the bias caused by incomplete metabolite extraction and matrix effect in the commonly used methanol extraction method for human plasma sample preparation for metabolomics. We anticipate the proposed metabolite extraction evaluation method could benefit more clinical and biological metabolomics studies.
代谢组学是一种研究生物系统中代谢物变化的组学策略。对代谢物水平进行无偏观察对于靶向代谢物定量和非靶向代谢物分析至关重要。为了在各种研究中准确观察代谢变化,已经开发了最先进的仪器和多功能工具。已经揭示并解决了几个分析陷阱,例如样品过载和信号饱和引起的偏差。在这项研究中,我们提出不完全代谢物提取引起的偏差也是一个重要问题,当进行代谢组学分析时,会导致观察结果出现偏差。在示范示例中,许多代谢物在具有不同血浆含量的提取血浆样品之间没有表现出显著差异,这归因于不完全代谢物提取引起的偏差和基质效应。基质效应是导致观察结果出现偏差的一个众所周知的因素,可以通过样品稀释和使用稳定同位素标记的内标物进行补偿来降低。在随后的连续提取中检测到代谢物信号提供了不完全代谢物提取的进一步证据。代谢物提取的完全性对于无偏观察代谢谱变化至关重要。为了解决这个问题,我们优化了提取时间和甲醇体积,以减少不完全代谢物提取引起的偏差,并评估了连续提取中的代谢物信号。通过代谢组学分析观察到,在第二次提取中,血浆与甲醇的比例为 1:14 时,甲醇提取的代谢物响应值小于 18.1%。最后,优化后的样品制备程序和非靶向分析平台被用于检测与脑血管疾病患者相关的代谢物变化,并且成功地鉴定了几个具有显著差异的特征。本研究揭示并评估了在用于人类血浆样品制备的甲醇提取方法中常见的不完全代谢物提取和基质效应引起的偏差。我们预计所提出的代谢物提取评估方法将有益于更多的临床和生物学代谢组学研究。