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基于液质联用的代谢组学在临床化学和毒理学中的应用:综述。

Applications of liquid chromatography coupled to mass spectrometry-based metabolomics in clinical chemistry and toxicology: A review.

机构信息

Service de Pharmacologie et d'Immunoanalyse, DSV/iBiTec-S, Gif-sur-Yvette, France.

出版信息

Clin Biochem. 2011 Jan;44(1):119-35. doi: 10.1016/j.clinbiochem.2010.08.016. Epub 2010 Aug 26.

DOI:10.1016/j.clinbiochem.2010.08.016
PMID:20800591
Abstract

The metabolome is the set of small molecular mass organic compounds found in a given biological media. It includes all organic substances naturally occurring from the metabolism of the studied living organism, except biological polymers, but also xenobiotics and their biotransformation products. The metabolic fingerprints of biofluids obtained by mass spectrometry (MS) or nuclear magnetic resonance (NMR)-based methods contain a few hundreds to thousands of signals related to both genetic and environmental contributions. Metabolomics, which refers to the untargeted quantitative or semi-quantitative analysis of the metabolome, is a promising tool for biomarker discovery. Although proof-of-concept studies by metabolomics-based approaches in the field of toxicology and clinical chemistry have initially been performed using NMR, the use of liquid chromatography hyphenated to mass spectrometry (LC/MS) has increased over the recent years, providing complementary results to those obtained with other approaches. This paper reviews and comments the input of LC/MS in this field. We describe here the overall process of analysis, review some seminal papers in the field and discuss the perspectives of metabolomics for the biomonitoring of exposure and diagnosis of diseases.

摘要

代谢组学是指在特定生物介质中发现的小分子质量有机化合物的集合。它包括除生物聚合物外,还包括外源性物质及其生物转化产物,由研究生物体内代谢产生的所有天然存在的有机物质。通过质谱(MS)或基于核磁共振(NMR)的方法获得的生物流体的代谢指纹图谱包含几百到几千个与遗传和环境因素都有关的信号。代谢组学是指对代谢组进行非靶向定量或半定量分析,是发现生物标志物的一种很有前途的工具。虽然基于代谢组学的方法在毒理学和临床化学领域的概念验证研究最初是使用 NMR 进行的,但近年来,液相色谱-质谱联用(LC/MS)的使用有所增加,为其他方法获得的结果提供了补充。本文综述和评论了 LC/MS 在该领域的应用。我们在这里描述了分析的整个过程,回顾了该领域的一些开创性论文,并讨论了代谢组学在暴露生物监测和疾病诊断中的应用前景。

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