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

Author

Listed:
  • Xiang Liu

    (Beihang University)

  • Haidan Sun

    (School of Basic Medicine Peking Union Medical College)

  • Xinhang Hou

    (Beihang University)

  • Jiameng Sun

    (School of Basic Medicine Peking Union Medical College)

  • Min Tang

    (Beihang University)

  • Yong-Biao Zhang

    (Beihang University)

  • Yongqian Zhang

    (Beijing Institute of Technology)

  • Wei Sun

    (School of Basic Medicine Peking Union Medical College)

  • Chao Liu

    (Beihang University)

Abstract

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.

Suggested Citation

  • Xiang Liu & Haidan Sun & Xinhang Hou & Jiameng Sun & Min Tang & Yong-Biao Zhang & Yongqian Zhang & Wei Sun & Chao Liu, 2025. "Standard operating procedure combined with comprehensive quality control system for multiple LC-MS platforms urinary proteomics," Nature Communications, Nature, vol. 16(1), pages 1-18, December.
  • Handle: RePEc:nat:natcom:v:16:y:2025:i:1:d:10.1038_s41467-025-56337-4
    DOI: 10.1038/s41467-025-56337-4
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    References listed on IDEAS

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    1. Yulin Sun & Zhengguang Guo & Xiaoyan Liu & Lijun Yang & Zongpan Jing & Meng Cai & Zhaoxu Zheng & Chen Shao & Yefan Zhang & Haidan Sun & Li Wang & Minjie Wang & Jun Li & Lusong Tian & Yue Han & Shuangm, 2022. "Noninvasive urinary protein signatures associated with colorectal cancer diagnosis and metastasis," Nature Communications, Nature, vol. 13(1), pages 1-15, December.
    2. Yi Liu & Yun Yang & Wendong Chen & Feng Shen & Linhai Xie & Yingying Zhang & Yuanjun Zhai & Fuchu He & Yunping Zhu & Cheng Chang, 2023. "DeepRTAlign: toward accurate retention time alignment for large cohort mass spectrometry data analysis," Nature Communications, Nature, vol. 14(1), pages 1-12, December.
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