State Key Laboratory of Quality Research in Chinese Medicine, Institute of Chinese Medical Sciences, University of Macau, Taipa, Macao, 999078, China.
Cardiac Rehabilitation Department, Guangdong Cardiovascular Institute, Guangdong General Hospital, Guangdong Academy of Medical Sciences, Guangzhou, China.
J Pharm Biomed Anal. 2018 Aug 5;157:171-179. doi: 10.1016/j.jpba.2018.05.020. Epub 2018 May 17.
The narrow linear range and the limited scan time of the given ion make the quantification of the features challenging in liquid chromatography-mass spectrometry (LC-MS)-based untargeted metabolomics with the full-scan mode. And metabolite identification is another bottleneck of untargeted analysis owing to the difficulty of acquiring MS/MS information of most metabolites detected. In this study, an integrated workflow was proposed using the newly established multiple ion monitoring mode with time-staggered ion lists (tsMIM) and target-directed data-dependent acquisition with time-staggered ion lists (tsDDA) to improve data acquisition and metabolite identification in UHPLC/Q-TOF MS-based untargeted metabolomics. Compared to the conventional untargeted metabolomics, the proprosed workflow exhibited the better repeatability before and after data normalization. After selecting features with the significant change by statistical analysis, MS/MS information of all these features can be obtained by tsDDA analysis to facilitate metabolite identification. Using time-staggered ion lists, the workflow is more sensitive in data acquisition, especially for the low-abundant features. Moreover, the metabolites with low abundance tend to be wrongly integrated and triggered by full scan-based untargeted analysis with MS acquisition mode, which can be greatly improved by the proposed workflow. The integrated workflow was also successfully applied to discover serum biosignatures for the genetic modification of fat-1 in mice, which indicated its practicability and great potential in future metabolomics studies.
由于给定离子的线性范围较窄且扫描时间有限，因此在基于全扫描模式的液相色谱-质谱（LC-MS）非靶向代谢组学中，对特征进行定量具有一定的挑战性。由于大多数检测到的代谢物的 MS/MS 信息难以获取，因此代谢物鉴定也是非靶向分析的另一个瓶颈。在这项研究中，提出了一种集成工作流程，该流程使用新建立的多离子监测模式与时间交错离子列表（tsMIM）以及具有时间交错离子列表的目标定向数据依赖性采集（tsDDA）来改善基于 UHPLC/Q-TOF MS 的非靶向代谢组学中的数据采集和代谢物鉴定。与传统的非靶向代谢组学相比，该提议的工作流程在数据归一化前后显示出更好的可重复性。通过统计分析选择具有显著变化的特征后，可以通过 tsDDA 分析获得所有这些特征的 MS/MS 信息，从而有助于代谢物鉴定。通过使用时间交错离子列表，该工作流程在数据采集方面更灵敏，尤其是对于低丰度特征。此外，低丰度的代谢物在基于全扫描的非靶向分析中容易被错误地整合和触发，MS 采集模式可以大大改善这种情况，而这可以通过所提出的工作流程得到极大改善。该集成工作流程还成功应用于发现脂肪-1 基因修饰小鼠的血清生物标志物，表明其在未来代谢组学研究中具有实用性和巨大潜力。
