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法医毒理学中识别尿液和毛发掺假企图的新方法:基于液相色谱 - 质谱联用(LC-MS)蛋白质组学方法的概念验证研究

New Approaches To Identify Urine and Hair Adulteration Attempts in Forensic Toxicology: A Proof-of-Concept Study Using a Proteomics Approach Based on Liquid Chromatography-Mass Spectrometry (LC-MS).

作者信息

Schneider Tom D, Binz Tina M, Kraemer Thomas, Steuer Andrea E

机构信息

Department of Forensic Pharmacology & Toxicology, Zurich Institute of Forensic Medicine, University of Zurich, 8057 Zurich, Switzerland.

出版信息

Anal Chem. 2025 Aug 19;97(32):17761-17769. doi: 10.1021/acs.analchem.5c03096. Epub 2025 Aug 5.

DOI:10.1021/acs.analchem.5c03096
PMID:40763070
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC12368831/
Abstract

Urine and hair are among the primary biological matrices used for drug and abstinence testing in clinical, forensic, and antidoping settings. A persistent challenge in such analyses is the adulteration of samples: through dilution, substitution, or chemical modification, aimed at concealing the presence of xenobiotics such as drugs of abuse, ethanol, or doping agents. In this study, we investigated whether chemical adulteration, specifically oxidative treatment with hydrogen peroxide (HO), induces detectable and characteristic changes in the proteomes of urine and hair samples. Using a bottom-up proteomics approach involving LC-HR-MS/MS with data-dependent acquisition, we compared urine samples before and after treatment (10% HO) and untreated hair samples with those exposed to increasing concentrations of hydrogen peroxide (3%, 6%, 9%, and 12%). Data are available via ProteomeXchange with the identifier PXD064208. We identified distinct peptides, including oxidatively modified forms, that were exclusively present in either untreated or chemically treated groups. In hair samples, the appearance of some of those peptides was dependent on the peroxide concentration. Peptides detectable only after oxidative exposure were of particular interest, as they appeared to be nonphysiological and specific to the adulteration process. These species serve as candidate biomarkers for indirect detection of sample manipulation or for assessing the integrity of compromised samples. The extraction and characterization of these potential marker peptides constitute the primary outcomes of this study. These findings should lay the groundwork for further validation and the development of proteomic methods aimed at enhancing the reliability of drug testing and sample authenticity assessment in forensic and antidoping contexts.

摘要

尿液和毛发是临床、法医和反兴奋剂检测中用于药物和戒毒检测的主要生物基质。此类分析中一个持续存在的挑战是样本掺假:通过稀释、替换或化学修饰,旨在掩盖滥用药物、乙醇或兴奋剂等外源性物质的存在。在本研究中,我们调查了化学掺假,特别是用过氧化氢(HO)进行氧化处理,是否会在尿液和毛发样本的蛋白质组中引起可检测到的特征性变化。我们采用自下而上的蛋白质组学方法,结合液相色谱-高分辨质谱/质谱(LC-HR-MS/MS)和数据依赖采集,比较了处理前和处理后的尿液样本(10% HO),以及未处理的毛发样本与暴露于不同浓度过氧化氢(3%、6%、9%和12%)的毛发样本。数据可通过ProteomeXchange获取,标识符为PXD064208。我们鉴定出了不同的肽段,包括氧化修饰形式,这些肽段仅存在于未处理组或化学处理组中。在毛发样本中,其中一些肽段的出现取决于过氧化物浓度。仅在氧化暴露后可检测到的肽段特别令人感兴趣,因为它们似乎是非生理性的,且是掺假过程所特有的。这些物质可作为间接检测样本操纵或评估受损样本完整性的候选生物标志物。这些潜在标记肽段的提取和表征是本研究的主要成果。这些发现应为进一步验证以及开发蛋白质组学方法奠定基础,旨在提高法医和反兴奋剂背景下药物检测和样本真实性评估的可靠性。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0ad7/12368831/6354cb8e2b6b/ac5c03096_0004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0ad7/12368831/bd7d441e9a8a/ac5c03096_0001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0ad7/12368831/fbb6875d2dc8/ac5c03096_0002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0ad7/12368831/9d2560d95fbd/ac5c03096_0003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0ad7/12368831/6354cb8e2b6b/ac5c03096_0004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0ad7/12368831/bd7d441e9a8a/ac5c03096_0001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0ad7/12368831/fbb6875d2dc8/ac5c03096_0002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0ad7/12368831/9d2560d95fbd/ac5c03096_0003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0ad7/12368831/6354cb8e2b6b/ac5c03096_0004.jpg

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本文引用的文献

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Anal Sci. 2025 May;41(5):667-675. doi: 10.1007/s44211-025-00749-1. Epub 2025 Mar 26.
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Proteomic Analysis of Single Hairs.单根毛发的蛋白质组学分析
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Evaluation of biochemical assays and optimization of LC-MS-MS analysis for the detection of synthetic urine.合成尿检测的生化分析评估及 LC-MS-MS 分析优化。
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