School of Pharmaceutical Sciences, University of Geneva, University of Lausanne, Geneva, Switzerland.
Swiss Centre for Applied Human Toxicology (SCAHT), Universities of Basel and Geneva, Basel, Switzerland.
Methods Mol Biol. 2025;2855:539-554. doi: 10.1007/978-1-0716-4116-3_29.
Assessing potential alterations of metabolic pathways using large-scale approaches plays today a central role in clinical research. Because several thousands of mass features can be measured for each sample with separation techniques hyphenated to mass spectrometry (MS) detection, adapted strategies have to be implemented to detect altered pathways and help to elucidate the mechanisms of pathologies. These procedures include peak detection, sample alignment, normalization, statistical analysis, and metabolite annotation. Interestingly, considerable advances have been made over the last years in terms of analytics, bioinformatics, and chemometrics to help massive and complex metabolomic data to be more adequately handled with automated processing and data analysis workflows. Recent developments and remaining challenges related to MS signal processing, metabolite annotation, and biomarker discovery based on statistical models are illustrated in this chapter in light of their application to clinical research.
利用大规模方法评估代谢途径的潜在变化在今天的临床研究中起着核心作用。由于与质谱 (MS) 检测联用的分离技术可以为每个样本测量数千个质量特征，因此必须实施适应的策略来检测改变的途径并帮助阐明病理学的机制。这些程序包括峰检测、样品对齐、归一化、统计分析和代谢物注释。有趣的是，近年来在分析、生物信息学和化学计量学方面取得了相当大的进展，以帮助更适当地处理大规模和复杂的代谢组学数据，并实现自动化处理和数据分析工作流程。本章根据其在临床研究中的应用，说明了与 MS 信号处理、代谢物注释和基于统计模型的生物标志物发现相关的最新进展和遗留挑战。
