In the past decade, advances in liquid chromatography‐mass spectrometry (LC‐MS) have revolutionized untargeted metabolomics analyses. By mining metabolomes more deeply, researchers are now primed to uncover key metabolites and their associations with diseases. The employment of untargeted metabolomics has led to new biomarker discoveries and a better mechanistic understanding of diseases with applications in precision medicine. However, many major pertinent challenges remain. First, compound identification has been poor, and left an overwhelming number of unidentified peaks. Second, partial, incomplete metabolomes persist due to factors such as limitations in mass spectrometry data acquisition speeds, wide‐range of metabolites concentrations, and cellular/tissue/temporal‐specific expression changes that confound our understanding of metabolite perturbations. Third, to contextualize metabolites in pathways and biology is difficult because many metabolites partake in multiple pathways, have yet to be described species specificity, or possess unannotated or more‐complex functions that are not easily characterized through metabolomics analyses. From a translational perspective, information related to novel metabolite biomarkers, metabolic pathways, and drug targets might be sparser than they should be. Thankfully, significant progress has been made and novel solutions are emerging, achieved through sustained academic and industrial community efforts in terms of hardware, computational, and experimental approaches. Given the rapidly growing utility of metabolomics, this review will offer new perspectives, increase awareness of the major challenges in LC‐MS metabolomics that will significantly benefit the metabolomics community and also the broader the biomedical community metabolomics aspire to serve.

中文翻译：

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基于LC-MS / MS的疾病非靶向代谢组学的挑战和紧急解决方案

在过去的十年中，液相色谱-质谱（LC-MS）的进步彻底改变了非靶向代谢组学分析的方向。通过更深入地挖掘代谢组，研究人员现在已经准备好发现关键的代谢产物及其与疾病的关系。非靶向代谢组学的应用导致了新的生物标志物发现，并在精密医学中得到了更好的对疾病的机械理解。但是，仍然存在许多主要的相关挑战。首先，化合物的鉴定很差，并且留下了大量的未鉴定峰。其次，由于诸如质谱数据采集速度的局限性，代谢物浓度范围大，细胞/组织/时间特异性表达变化混淆了我们对代谢物扰动的理解。第三，很难确定代谢途径和生物学中的代谢物，因为许多代谢产物都参与多种途径，尚未描述物种特异性，或具有未注释的或更复杂的功能，而代谢组学分析不容易表征这些代谢物。从翻译的角度来看，与新型代谢物生物标记，代谢途径和药物靶标有关的信息可能比应有的稀疏。值得庆幸的是，通过在硬件，计算和实验方法方面的持续学术和工业界的努力，已经取得了重大进展，并且出现了新颖的解决方案。鉴于代谢组学的应用迅速增长，