Abstract：
The employment of liquid chromatography-mass spectrometry (LC-MS) untargeted and targeted metabolomics has led to the discovery of novel biomarkers and improved the understanding of various disease mechanisms. Numerous strategies have been reported to expand the metabolite coverage in LC-MS-untargeted and targeted metabolomics. To improve the sensitivity of low-abundance or poor-ionized metabolites for reducing the amount of clinical sample, chemical derivatization methods are used to target different functional groups. Proper sample preparation is beneficial for reducing the matrix effect, maintaining the stability of the LC-MS system, and increasing the metabolite coverage. Machine learning has recently been integrated into the workflow of LC-MS metabolomics to accelerate metabolite identification and data-processing automation, and increase the accuracy of disease classification and clinical outcome prediction. Due to the rapidly growing utility of LC-MS metabolomics in discovering disease markers, this review will address the recent advances in the field and offer perspectives on various strategies for expanding metabolite coverage, chemical derivatization, sample preparation, clinical disease markers, and machining learning for disease modeling.
摘要：
液相色谱-质谱（LC-MS）非靶向和靶向代谢组学的使用导致了新生物标志物的发现，并提高了对各种疾病机制的理解。已经报道了许多策略来扩大LC-MS非靶向和靶向代谢组学中的代谢物覆盖率。为了提高低丰度或低电离代谢物对减少临床样品量的敏感性，化学衍生方法用于靶向不同的官能团。适当的样品制备有利于降低基体效应，保持LC-MS系统的稳定性，增加代谢物的覆盖率。机器学习最近已被集成到LC-MS代谢组学的工作流程中，以加速代谢物识别和数据处理自动化。提高疾病分类和临床结局预测的准确性。由于LC-MS代谢组学在发现疾病标志物方面的迅速增长的效用，这篇综述将讨论该领域的最新进展，并就扩大代谢物覆盖范围的各种策略提供观点，化学衍生化,样品制备,临床疾病标志物，和用于疾病建模的加工学习。
