Metabolism is the set of life‐sustaining reactions in organisms. These biochemical reactions are organized in metabolic pathways, in which one metabolite is converted through a series of steps catalyzed by enzymes in another chemical compound. Metabolic reactions are categorized as catabolic, the breaking down of metabolites to produce energy, and/or anabolic, the synthesis of compounds that consume energy. The balance between catabolism of the preferential fuel substrate and anabolism defines the overall metabolism of a cell or tissue. Metabolomics is a powerful tool to gain new insights contributing to the identification of complex molecular mechanisms in the field of biomedical research, both basic and translational. The enormous potential of this kind of analyses consists of two key aspects: (i) the possibility of performing so‐called targeted and untargeted experiments through which it is feasible to verify or formulate a hypothesis, respectively, and (ii) the opportunity to run either steady‐state analyses to have snapshots of the metabolome at a given time under different experimental conditions or dynamic analyses through the use of labeled tracers. In this review, we will highlight the most important practical (e.g., different sample extraction approaches) and conceptual steps to consider for metabolomic analysis, describing also the main application contexts in which it is used. In addition, we will provide some insights into the most innovative approaches and progress in the field of data analysis and processing, highlighting how this part is essential for the proper extrapolation and interpretation of data.

中文翻译：

---

通过质谱法研究代谢：从稳态到动态视图

代谢是生物体内维持生命的一系列反应。这些生化反应按代谢途径进行组织，其中一种代谢物通过由另一种化合物中的酶催化的一系列步骤进行转化。代谢反应被归类为分解代谢，代谢物分解以产生能量，和/或合成代谢，消耗能量的化合物的合成。优先燃料底物的分解代谢和合成代谢之间的平衡决定了细胞或组织的整体代谢。代谢组学是获得新见解的强大工具，有助于识别基础和转化生物医学研究领域的复杂分子机制。这种分析的巨大潜力包括两个关键方面：(i) 进行所谓的靶向和非靶向实验的可能性，通过它们分别验证或制定假设是可行的，以及 (ii) 有机会运行任一稳态分析以获得给定代谢组的快照不同实验条件下的时间或通过使用标记示踪剂进行动态分析。在这篇综述中，我们将重点介绍代谢组学分析中最重要的实践（例如，不同的样本提取方法）和概念步骤，同时描述使用它的主要应用环境。此外，我们将对数据分析和处理领域最具创新性的方法和进展提供一些见解，强调这部分对于正确推断和解释数据至关重要。