Liquid chromatography - mass spectrometry (LC-MS) based untargeted metabolomics allows to measure both known and unknown metabolites in the metabolome. However, unknown metabolite annotation is a major challenge in untargeted metabolomics. Here, we develop an approach, namely, knowledge-guided multi-layer network (KGMN), to enable global metabolite annotation from knowns to unknowns in untargeted metabolomics. The KGMN approach integrates three-layer networks, including knowledge-based metabolic reaction network, knowledge-guided MS/MS similarity network, and global peak correlation network. To demonstrate the principle, we apply KGMN in an in vitro enzymatic reaction system and different biological samples, with ~100–300 putative unknowns annotated in each data set. Among them, >80% unknown metabolites are corroborated with in silico MS/MS tools. Finally, we validate 5 metabolites that are absent in common MS/MS libraries through repository mining and synthesis of chemical standards. Together, the KGMN approach enables efficient unknown annotations, and substantially advances the discovery of recurrent unknown metabolites for common biological samples from model organisms, towards deciphering dark matter in untargeted metabolomics.

基于非靶向代谢组学的液相色谱-质谱 (LC-MS) 允许测量代谢组中的已知和未知代谢物。然而，未知代谢物注释是非靶向代谢组学的主要挑战。在这里，我们开发了一种方法，即知识引导多层网络 (KGMN)，以在非靶向代谢组学中启用从已知到未知的全局代谢物注释。KGMN 方法集成了三层网络，包括基于知识的代谢反应网络、知识引导的 MS/MS 相似性网络和全局峰相关网络。为了证明这一原理，我们将 KGMN 应用于体外酶促反应系统和不同的生物样本，每个数据集中注释了约 100-300 个假定的未知数。其中，> 80% 的未知代谢物已通过计算机 MS/MS 工具得到证实。最后，我们通过化学标准品的存储库挖掘和合成验证了常见 MS/MS 库中不存在的 5 种代谢物。总之，KGMN 方法可以实现有效的未知注释，并大大推进从模式生物中发现常见生物样本的反复出现的未知代谢物，从而破译非靶向代谢组学中的暗物质。