Introducing Undergraduate Students to Metabolomics Using Liquid Chromatography-High Resolution Mass Spectrometry Analysis of Horse Blood
Introducing Undergraduate Students to Metabolomics Using Liquid Chromatography-High Resolution Mass Spectrometry Analysis of Horse Blood
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DOI：
10.1021/acs.jchemed.8b00625
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发表时间：
2019-04-01
影响因子：
3
通讯作者：
Reinke, Stacey N.
中科院分区：
文献类型：
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作者：
Boyce, Mary C.;Lawler, Nathan G.;Reinke, Stacey N.
代谢组学是一门以数据为导向的小分子科学。使用LC-HRMS的非靶向代谢物组学与使用标称质量LC-MS仪器的靶向代谢物分析有很大不同，因为它产生了数千种没有量化的代谢物特征，并在特定情况下进行了鉴定。因此，大量时间专门用于数据处理工作流。尽管非靶向代谢组学和LC-HRMS在当代研究中盛行，但这一领域的本科教育几乎不存在。为了让高年级的分析化学本科生接触非靶向代谢组学，开发了一个典型的生物医学研究的真实实验室实验。在临床上，溶血可能是由于样本处理不当造成的。在这项实验中，学生们人工诱导马血中的溶血，并评估了由此产生的代谢组学差异。使用XCMS Online，一个开源的在线平台和一个有指导的工作表，学生们浏览他们处理的数据，了解非靶向代谢组学与他们之前进行的靶向分析有何不同。这项实验指导了他们对关键概念的理解，如检测到的代谢物特征的数量、质量评估、代谢物鉴定和数据可视化。
Metabolomics is the data-driven science of small molecules. Untargeted metabolomics, using liquid chromatography-high resolution mass spectrometry (LC-HRMS), differs greatly from targeted metabolite assays, using nominal mass LC-MS instruments, as it generates thousands of metabolite features which are not quantified and are identified post hoc. Thus, a substantial amount of time is dedicated to the data processing workflow. Despite the prevalence of untargeted metabolomics and LC-HRMS in contemporary research, undergraduate education in this area is almost nonexistent. To expose upper-division undergraduate analytical chemistry students to untargeted metabolomics, a realistic laboratory experiment, typical of biomedical research, was developed. In the clinic, hemolysis can result from poor sample handling. In this experiment, students artificially induced hemolysis in horse blood and assessed the resulting metabolomic differences. Using XCMS Online, an open-source online platform, and a guided worksheet, students navigated their processed data, learning how untargeted metabolomics differs from the targeted assays they previously performed. This experiment guided their understanding of key concepts such as the number of metabolite features detected, quality assessment, metabolite identification, and data visualization.