蛋白质组学
计算机科学
蛋白质组
泌尿系统
数据挖掘
生物信息学
医学
化学
生物
内科学
生物化学
基因
作者
Xiang Liu,Haidan Sun,Xinhang Hou,Jian 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
标识
DOI:10.1038/s41467-025-56337-4
摘要
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.
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