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

Xiang Liu, Haidan Sun, Xinhang Hou, Jiameng Sun, Min Tang, Yong-Biao Zhang, Yongqian Zhang, Wei Sun () and Chao Liu ()
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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

Nature Communications, 2025, vol. 16, issue 1, 1-18

Abstract: 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.

Date: 2025
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DOI: 10.1038/s41467-025-56337-4

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