Liu Xiang, Sun Haidan, Hou Xinhang, Sun Jiameng, Tang Min, Zhang Yong-Biao, Zhang Yongqian, Sun Wei, Liu Chao
School of Biological Science and Medical Engineering & School of Engineering Medicine, Beihang University, Beijing, China.
Proteomics Center, Core Facility of Instrument, Institute of Basic Medical Sciences Chinese Academy of Medical Sciences, School of Basic Medicine Peking Union Medical College, Beijing, China.
Nat Commun. 2025 Jan 26;16(1):1051. doi: 10.1038/s41467-025-56337-4.
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.
尿液蛋白质组学正成为一种检测敏感且非侵入性生物标志物的有力工具。目前，不同液相色谱 - 质谱（LC - MS）平台间尿液蛋白质组学数据的可比性仍是一个需要研究的领域。在本研究中，我们对多个LC - MS平台上的尿液蛋白质组进行了全面评估。为了系统地分析和评估大规模尿液蛋白质组学数据的质量，我们开发了一个名为MSCohort的综合质量控制（QC）系统，该系统提取了81个指标用于个体实验和整个队列的质量评估。此外，我们基于MSCohort QC系统提出了一种高通量尿液蛋白质组分析的标准操作规程（SOP）。我们的研究涉及20个LC - MS平台，结果显示，当结合综合QC系统和统一的SOP时，尿液QC样本中数据非依赖采集（DIA）工作流程所产生的数据在多个LC - MS平台上表现出高稳健性、敏感性和可重复性。此外，我们将此SOP应用于混合基准样本和包括527个实验的临床结直肠癌（CRC）尿液蛋白质组。在三个不同的LC - MS平台上，分析报告了高定量可重复性和一致的疾病模式。这项工作为跨多个平台的大规模临床尿液蛋白质组学研究奠定了基础，为精准医学研究铺平了道路。
