Shanghai Key Laboratory of Metabolic Remodeling and Health, Institute of Metabolism and Integrative Biology, Fudan University, Shanghai, China.
Lewis-Sigler Institute for Integrative Genomics, Princeton University, Princeton, NJ, USA.
Nat Methods. 2021 Nov;18(11):1377-1385. doi: 10.1038/s41592-021-01303-3. Epub 2021 Oct 28.
Liquid chromatography-high-resolution mass spectrometry (LC-MS)-based metabolomics aims to identify and quantify all metabolites, but most LC-MS peaks remain unidentified. Here we present a global network optimization approach, NetID, to annotate untargeted LC-MS metabolomics data. The approach aims to generate, for all experimentally observed ion peaks, annotations that match the measured masses, retention times and (when available) tandem mass spectrometry fragmentation patterns. Peaks are connected based on mass differences reflecting adduction, fragmentation, isotopes, or feasible biochemical transformations. Global optimization generates a single network linking most observed ion peaks, enhances peak assignment accuracy, and produces chemically informative peak-peak relationships, including for peaks lacking tandem mass spectrometry spectra. Applying this approach to yeast and mouse data, we identified five previously unrecognized metabolites (thiamine derivatives and N-glucosyl-taurine). Isotope tracer studies indicate active flux through these metabolites. Thus, NetID applies existing metabolomic knowledge and global optimization to substantially improve annotation coverage and accuracy in untargeted metabolomics datasets, facilitating metabolite discovery.
基于液相色谱-高分辨质谱（LC-MS）的代谢组学旨在鉴定和定量所有代谢物，但大多数 LC-MS 峰仍然无法识别。在这里，我们提出了一种全局网络优化方法 NetID，用于注释非靶向 LC-MS 代谢组学数据。该方法旨在为所有实验观察到的离子峰生成与测量质量、保留时间和（如有可用）串联质谱碎裂模式匹配的注释。根据反映加合、碎裂、同位素或可行生化转化的质量差异连接峰。全局优化生成一个将大多数观察到的离子峰连接起来的单一网络，提高峰分配准确性，并生成具有化学信息的峰-峰关系，包括缺乏串联质谱图谱的峰。将该方法应用于酵母和小鼠数据，我们鉴定了五个以前未被识别的代谢物（硫胺素衍生物和 N-葡萄糖基牛磺酸）。同位素示踪研究表明这些代谢物的活性通量。因此，NetID 应用现有的代谢组学知识和全局优化来大大提高非靶向代谢组学数据集的注释覆盖率和准确性，从而促进代谢物的发现。
