Huan Tao, Wu Yiman, Tang Chenqu, Lin Guohui, Li Liang
Departments of Chemistry and ‡Computing Science, University of Alberta , Edmonton, Alberta T6G2G2, Canada.
Anal Chem. 2015 Oct 6;87(19):9838-45. doi: 10.1021/acs.analchem.5b02282. Epub 2015 Sep 10.
High-performance chemical isotope labeling (CIL) liquid chromatography-mass spectrometry (LC-MS) is an enabling technology based on rational design of labeling reagents to target a class of metabolites sharing the same functional group (e.g., all the amine-containing metabolites or the amine submetabolome) to provide concomitant improvements in metabolite separation, detection, and quantification. However, identification of labeled metabolites remains to be an analytical challenge. In this work, we describe a library of labeled standards and a search method for metabolite identification in CIL LC-MS. The current library consists of 273 unique metabolites, mainly amines and phenols that are individually labeled by dansylation (Dns). Some of them produced more than one Dns-derivative (isomers or multiple labeled products), resulting in a total of 315 dansyl compounds in the library. These metabolites cover 42 metabolic pathways, allowing the possibility of probing their changes in metabolomics studies. Each labeled metabolite contains three searchable parameters: molecular ion mass, MS/MS spectrum, and retention time (RT). To overcome RT variations caused by experimental conditions used, we have developed a calibration method to normalize RTs of labeled metabolites using a mixture of RT calibrants. A search program, DnsID, has been developed in www.MyCompoundID.org for automated identification of dansyl labeled metabolites in a sample based on matching one or more of the three parameters with those of the library standards. Using human urine as an example, we illustrate the workflow and analytical performance of this method for metabolite identification. This freely accessible resource is expandable by adding more amine and phenol standards in the future. In addition, the same strategy should be applicable for developing other labeled standards libraries to cover different classes of metabolites for comprehensive metabolomics using CIL LC-MS.
高效化学同位素标记（CIL）液相色谱-质谱联用（LC-MS）是一项基于合理设计标记试剂的技术，旨在针对一类具有相同官能团的代谢物（例如，所有含胺代谢物或胺亚代谢组），从而在代谢物分离、检测和定量方面实现同步改进。然而，标记代谢物的鉴定仍然是一项分析挑战。在这项工作中，我们描述了一个标记标准品库以及一种用于CIL LC-MS中代谢物鉴定的搜索方法。当前的库包含273种独特的代谢物，主要是胺类和酚类，它们分别通过丹磺酰化（Dns）进行标记。其中一些产生了不止一种丹磺酰衍生物（异构体或多重标记产物），使得库中共有315种丹磺酰化合物。这些代谢物涵盖42条代谢途径，为在代谢组学研究中探究它们的变化提供了可能性。每种标记代谢物包含三个可搜索参数：分子离子质量、MS/MS谱图和保留时间（RT）。为了克服实验条件导致的保留时间变化，我们开发了一种校准方法，使用保留时间校准物混合物对标记代谢物的保留时间进行归一化。一个名为DnsID的搜索程序已在www.MyCompoundID.org上开发出来，用于基于样品中一种或多种三个参数与库标准品的匹配，自动鉴定丹磺酰标记的代谢物。以人尿液为例，我们说明了该方法用于代谢物鉴定的工作流程和分析性能。这个可免费获取的资源未来可通过添加更多胺类和酚类标准品进行扩展。此外，相同的策略应适用于开发其他标记标准品库，以涵盖不同类别的代谢物，用于使用CIL LC-MS进行全面的代谢组学研究。
