Metabolomics refers to the large-scale detection, quantification, and analysis of small molecules (metabolites) in biological media. Although metabolomics, alone or combined with other omics data, has already demonstrated its relevance for patient stratification in the frame of research projects and clinical studies, much remains to be done to move this approach to the clinical practice. This is especially true in the perspective of being applied to personalized/precision medicine, which aims at stratifying patients according to their risk of developing diseases, and tailoring medical treatments of patients according to individual characteristics in order to improve their efficacy and limit their toxicity. In this review article, we discuss the main challenges linked to analytical chemistry that need to be addressed to foster the implementation of metabolomics in the clinics and the use of the data produced by this approach in personalized medicine. First of all, there are already well-known issues related to untargeted metabolomics workflows at the levels of data production (lack of standardization), metabolite identification (small proportion of annotated features and identified metabolites), and data processing (from automatic detection of features to multi-omic data integration) that hamper the inter-operability and reusability of metabolomics data. Furthermore, the outputs of metabolomics workflows are complex molecular signatures of few tens of metabolites, often with small abundance variations, and obtained with expensive laboratory equipment. It is thus necessary to simplify these molecular signatures so that they can be produced and used in the field. This last point, which is still poorly addressed by the metabolomics community, may be crucial in a near future with the increased availability of molecular signatures of medical relevance and the increased societal demand for participatory medicine.

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个性化医疗的代谢组学：分析化学从生物标志物发现到即时检测的输入

代谢组学是指对生物介质中的小分子（代谢物）进行大规模检测、量化和分析。尽管代谢组学，单独或与其他组学数据结合，已经证明了其在研究项目和临床研究框架中与患者分层的相关性，但要将这种方法应用于临床实践仍有许多工作要做。从应用于个体化/精准医疗的角度来看尤其如此，其旨在根据患者患疾病的风险对患者进行分层，并根据个体特征对患者进行定制治疗，以提高疗效并限制其毒性。在这篇评论文章中，我们讨论了与分析化学相关的主要挑战，这些挑战需要解决以促进代谢组学在临床中的实施以及在个性化医疗中使用这种方法产生的数据。首先，在数据生产（缺乏标准化）、代谢物识别（注释特征和识别的代谢物的比例很小）和数据处理（从特征的自动检测）层面，已经存在与非靶向代谢组学工作流程相关的众所周知的问题到多组学数据集成），这会妨碍代谢组学数据的互操作性和可重用性。此外，代谢组学工作流程的输出是几十种代谢物的复杂分子特征，通常丰度变化很小，并且使用昂贵的实验室设备获得。因此有必要简化这些分子特征，以便它们可以在现场生产和使用。最后一点，代谢组学界仍然很难解决，在不久的将来，随着医学相关分子特征的可用性增加和社会对参与式医学的需求增加，这可能是至关重要的。

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