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<p>Urinary proteomics is emerging as a potent tool for detecting sensitive and non-invasive biomarkers. At present, the comparability of urinary proteomics data across diverse liquid chromatography−mass spectrometry (LC-MS) platforms remains an area that requires investigation. In this study, we conduct a comprehensive evaluation of urinary proteome across multiple LC-MS platforms. To systematically analyze and assess the quality of large-scale urinary proteomics data, we develop a comprehensive quality control (QC) system named MSCohort, which extracted 81 metrics for individual experiment and the whole cohort quality evaluation. Additionally, we present a standard operating procedure (SOP) for high-throughput urinary proteome analysis based on MSCohort QC system. Our study involves 20 LC-MS platforms and reveals that, when combined with a comprehensive QC system and a unified SOP, the data generated by data-independent acquisition (DIA) workflow in urine QC samples exhibit high robustness, sensitivity, and reproducibility across multiple LC-MS platforms. Furthermore, we apply this SOP to hybrid benchmarking samples and clinical colorectal cancer (CRC) urinary proteome including 527 experiments. Across three different LC-MS platforms, the analyses report high quantitative reproducibility and consistent disease patterns. This work lays the groundwork for large-scale clinical urinary proteomics studies spanning multiple platforms, paving the way for precision medicine research.</p>

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Standard operating procedure combined with comprehensive quality control system for multiple LC-MS platforms urinary proteomics

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年1月26日

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  • Xiang Liu,
  • Haidan Sun,
  • Xinhang Hou,
  • Jiameng Sun,
  • Min Tang,
  • Yong-Biao Zhang,
  • Yongqian Zhang,
  • Wei Sun,
  • Chao Liu,
  • Youhe Gao,
  • Shuxuan Tang,
  • Ziyun Shen,
  • Kehui Liu,
  • Lulu Jia,
  • Jing Wei,
  • Jianqiang Wu,
  • Xiaoyue Tang,
  • Yanchang Li,
  • Guibin Wang,
  • Xinying Sui,
  • Lihua Zhang,
  • Huiming Yuan,
  • Xinxin Liu,
  • Dong Liu,
  • Qi Zhang,
  • Xindan Qiu,
  • Guanbo Wang,
  • Mo Hu,
  • Ye Tian,
  • Minjie Tan,
  • Peng Xue,
  • Liman Guo,
  • Qing Zhang,
  • Yongsheng Chen,
  • Jianguo Ji,
  • Weiyi Hu,
  • Wenyuan Zhu,
  • Min Huang,
  • Yingzi Qi,
  • Xianming Liu,
  • Xiaoxian Du,
  • Ji Luo,
  • Lingsheng Chen,
  • Yinghua Zhao

摘要

Urinary proteomics is emerging as a potent tool for detecting sensitive and non-invasive biomarkers. At present, the comparability of urinary proteomics data across diverse liquid chromatography−mass spectrometry (LC-MS) platforms remains an area that requires investigation. In this study, we conduct a comprehensive evaluation of urinary proteome across multiple LC-MS platforms. To systematically analyze and assess the quality of large-scale urinary proteomics data, we develop a comprehensive quality control (QC) system named MSCohort, which extracted 81 metrics for individual experiment and the whole cohort quality evaluation. Additionally, we present a standard operating procedure (SOP) for high-throughput urinary proteome analysis based on MSCohort QC system. Our study involves 20 LC-MS platforms and reveals that, when combined with a comprehensive QC system and a unified SOP, the data generated by data-independent acquisition (DIA) workflow in urine QC samples exhibit high robustness, sensitivity, and reproducibility across multiple LC-MS platforms. Furthermore, we apply this SOP to hybrid benchmarking samples and clinical colorectal cancer (CRC) urinary proteome including 527 experiments. Across three different LC-MS platforms, the analyses report high quantitative reproducibility and consistent disease patterns. This work lays the groundwork for large-scale clinical urinary proteomics studies spanning multiple platforms, paving the way for precision medicine research.