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用于暴露组分析的液相色谱和气相色谱-质谱联用方法。

Liquid and gas-chromatography-mass spectrometry methods for exposome analysis.

作者信息

Castro-Alves Victor, Nguyen Anh Hoang, Barbosa João Marcos G, Orešič Matej, Hyötyläinen Tuulia

机构信息

School of Science and Technology, Örebro University, 702 81 Örebro, Sweden.

School of Medical Sciences, Örebro University, 702 81 Örebro, Sweden.

出版信息

J Chromatogr A. 2025 Mar 15;1744:465728. doi: 10.1016/j.chroma.2025.465728. Epub 2025 Jan 25.

Abstract

Mass spectrometry-based methods have become fundamental to exposome research, providing the capability to explore a broad spectrum of chemical exposures. Liquid and gas chromatography coupled with low/high-resolution mass spectrometry (MS) are among the most frequently employed platforms due to their sensitivity and accuracy. However, these approaches present challenges, such as the inherent complexity of MS data and the expertise of biologists, chemists, clinicians, and data analysts to integrate and interpret MS data with other datasets effectively. The "omics" era advances rapidly, driven by developments of AI-based algorithms and an increase in accessible data; nevertheless, further efforts are necessary to ensure that exposomics outputs are comparable and reproducible, thus enhancing research findings. This review outlines the principles of MS-based methods for the exposome analytical pipeline, from sample collection to data analysis. We summarize and review both standard and cutting-edge strategies in exposome research, covering sample preparation, focusing on MS-based platforms, data acquisition strategies, and data annotation. The ultimate goal of this review is to highlight applications that enable the simultaneous analysis of endogenous metabolites and xenobiotics, which can help enhance our understanding of the impact of human exposure on health and disease and support personalized healthcare.

摘要

基于质谱的方法已成为暴露组研究的基础,具备探索广泛化学暴露的能力。液相色谱和气相色谱与低/高分辨率质谱(MS)联用,因其灵敏度和准确性,是最常用的平台之一。然而,这些方法存在挑战,如MS数据固有的复杂性,以及生物学家、化学家、临床医生和数据分析师有效整合和解释MS数据与其他数据集的专业知识。在基于人工智能的算法发展和可获取数据增加的推动下,“组学”时代迅速发展;尽管如此,仍需进一步努力以确保暴露组学产出具有可比性和可重复性,从而增强研究结果。本综述概述了从样本采集到数据分析的暴露组分析流程中基于质谱方法的原理。我们总结并回顾了暴露组研究中的标准和前沿策略,涵盖样本制备,重点是基于质谱的平台、数据采集策略和数据注释。本综述的最终目标是突出能够同时分析内源性代谢物和外源性物质的应用,这有助于增进我们对人类暴露对健康和疾病影响的理解,并支持个性化医疗。

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