小柯机器人

美国普林斯顿大学Joshua D. Rabinowitz团队通过全局注释非靶向代谢组学数据发现代谢物。2021年10月28日，《自然—方法学》在线发表了这项成果。

研究人员提出了一个全局网络优化方法，即NetID，用于注释非目标的基于液相色谱-高分辨质谱（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的质量、保留时间和（如果有的话）串联质谱碎片模式相匹配的注释。根据反映加合、碎片、同位素或生物化学转化的质量差异，将峰连接起来。全局优化产生了一个连接大多数观察到的离子峰单一网络，提高了峰分配的准确性，并产生了化学信息的峰-峰关系，包括缺乏串联质谱谱图的峰。

将这种方法应用于酵母和小鼠的数据，研究人员发现了五个以前未被识别的代谢物（硫胺素衍生物和N-葡糖基-牛磺酸）。同位素示踪研究表明这些代谢物有活跃的通量。因此，NetID应用现有的代谢组学知识和全局优化，大幅提高非目标代谢组学数据集的注释覆盖率和准确性，并促进了代谢物的发现。

据悉，LC-MS的代谢组学旨在识别和量化所有的代谢物，但大多数LC-MS峰仍然没有被识别。

附：英文原文

Title: Metabolite discovery through global annotation of untargeted metabolomics data

Author: Chen, Li, Lu, Wenyun, Wang, Lin, Xing, Xi, Chen, Ziyang, Teng, Xin, Zeng, Xianfeng, Muscarella, Antonio D., Shen, Yihui, Cowan, Alexis, McReynolds, Melanie R., Kennedy, Brandon J., Lato, Ashley M., Campagna, Shawn R., Singh, Mona, Rabinowitz, Joshua D.

Issue&Volume: 2021-10-28

Abstract: 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.

DOI: 10.1038/s41592-021-01303-3

Source: https://www.nature.com/articles/s41592-021-01303-3

Nature Methods：《自然—方法学》，创刊于2004年。隶属于施普林格·自然出版集团，最新IF：47.99
官方网址：https://www.nature.com/nmeth/
投稿链接：https://mts-nmeth.nature.com/cgi-bin/main.plex