CAS Key Laboratory of Separation Science for Analytical Chemistry , Dalian Institute of Chemical Physics, Chinese Academy of Science , Dalian 116023 , China.
Dalian ChemDataSolution Information Technology Co. Ltd , Dalian 116023 , China.
Anal Chem. 2018 Jun 19;90(12):7635-7643. doi: 10.1021/acs.analchem.8b01482. Epub 2018 Jun 8.
Identification of the metabolites is an essential step in metabolomics study to interpret the regulatory mechanism of pathological and physiological processes. However, it is still difficult in LC-MS -based studies because of the complexity of mass spectrometry, chemical diversity of metabolites, and deficiency of standards database. In this work, a comprehensive strategy is developed for accurate and batch metabolite identification in nontargeted metabolomics studies. First, a well-defined procedure was applied to generate reliable and standard LC-MS data, including t, MS, and MS information at a standard operational procedure. An in-house database including about 2000 metabolites was constructed and used to identify the metabolites in nontargeted metabolic profiling by retention time calibration using internal standards, precursor ion alignment and ion fusion, auto-MS information extraction and selection, and database batch searching and scoring. As an application example, a pooled serum sample was analyzed to deliver the strategy, and 202 metabolites were identified in the positive ion mode. It shows our strategy is useful for LC-MS -based nontargeted metabolomics study.
代谢组学研究中鉴定代谢物是解释病理和生理过程调控机制的必要步骤。然而，由于质谱的复杂性、代谢物的化学多样性以及标准数据库的不足，基于 LC-MS 的研究仍然具有一定的难度。在这项工作中，我们开发了一种全面的策略，用于非靶向代谢组学研究中准确和批量鉴定代谢物。首先，应用一种明确的程序来生成可靠和标准的 LC-MS 数据，包括在标准操作程序下的 t、MS 和 MS 信息。构建了一个内部数据库，其中包含约 2000 种代谢物，并使用内部标准物进行保留时间校准、前体离子对齐和离子融合、自动 MS 信息提取和选择以及数据库批量搜索和评分来鉴定非靶向代谢物图谱中的代谢物。作为应用实例，分析了混合血清样本以展示该策略，在正离子模式下鉴定了 202 种代谢物。结果表明，我们的策略对于基于 LC-MS 的非靶向代谢组学研究是有用的。
