基础医学与临床 ›› 2025, Vol. 45 ›› Issue (8): 992-998.doi: 10.16352/j.issn.1001-6325.2025.08.0992

• 特邀专题: 尿液组学 • 上一篇    下一篇

基于液质联用技术的尿液蛋白质组学的质量控制

刘响1,2, 魏静3*   

  1. 北京航空航天大学 1.医学科学与工程学院;2.生物与医学工程学院,北京 100191;
    3.国家儿童医学中心 首都医科大学附属北京儿童医院 临床研究中心,北京 100045
  • 收稿日期:2025-04-10 修回日期:2025-06-12 出版日期:2025-08-05 发布日期:2025-07-11
  • 通讯作者: *weijingbch@126.com
  • 基金资助:
    国家自然科学基金青年项目(82301540)

Quality control of urine proteome based on liquid chromatography-mass spectrometry

LIU Xiang1,2, WEI Jing3*   

  1. 1. School of Engineering Medicine; 2. School of Biological Science and Medical Engineering, Beihang University, Beijing 100191;
    3. Clinical Research Center, Beijing Children's Hospital, Capital Medical University, National Center for Children's Health, Beijing 100045, China
  • Received:2025-04-10 Revised:2025-06-12 Online:2025-08-05 Published:2025-07-11
  • Contact: *weijingbch@126.com

摘要: 随着自动化样本前处理技术和高通量质谱平台的迭代升级,基于大规模临床队列的蛋白质组学研究已逐步实现“千例样本-万例蛋白”级别的系统性解析。然而,多中心协作中仪器异质性导致可产生批次效应、长期实验的系统性偏移,以及低丰度生物标志物的检测可靠性问题,为此对全流程质量控制体系提出了更高要求。本文系统梳理了从样本制备、色谱分离、质谱采集到蛋白质鉴定的多维度质控参数,重点综述了适用于不同采集模式下单针实验和队列实验数据质量评估的主流软件及其主要功能。最后探讨了动态算法融合的质控模型和多组学数据质控策略在未来研究中的可行性。

关键词: 蛋白质组学, LC-MS/MS, 质量控制, 质量控制工具

Abstract: With the development of automated sample pretreatment technologies and high-throughput mass spectrometry platforms, large-scale clinical cohort-based proteomic studies have progressively achieved systematic analysis at the scale of thousands of samples and tens of thousands of proteins. However, the heterogeneity of instruments across multiple centers may introduce batch effects, systematic bias in long-term experiments, and reliability issues in detecting low-abundance biomarkers, thereby imposing higher demands on the entire workflow quality control system.This review systematically summarizes multidimensional QC parameters including sample preparation, chromatographic separation, mass spectrometric acquisition and protein identification. In particular, this review focuses on mainstream QC tools for intra-experiment and inter-experiment, emphasizing their primary functions under different acquisition modes. Finally, it discusses the feasibility of dynamically integrated algorithm-based QC models and multi-omics QC strategies in future research.

Key words: proteomics, LC-MS/MS, quality control, quality control tools

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