Gao Huanhuan, Zhu Yi, Wang Dongxue, Nie Zongxiang, Wang He, Wang Guibin, Liang Shuang, Xie Yuting, Sun Yingying, Jiang Wenhao, Dong Zhen, Qian Liqin, Wang Xufei, Liang Mengdi, Chen Min, Fang Houqi, Zeng Qiufang, Tian Jiao, Sun Zeyu, Xue Juan, Li Shan, Chen Chen, Liu Xiang, Lyu Xiaolei, Guo Zhenchang, Qi Yingzi, Wu Ruoyu, Du Xiaoxian, Tong Tingde, Kong Fengchun, Han Liming, Wang Minghui, Zhao Yang, Dai Xinhua, He Fuchu, Guo Tiannan
Affiliated Hangzhou First People's Hospital, State Key Laboratory of Medical Proteomics, School of Medicine, Westlake University, Hangzhou, Zhejiang Province, China.
Westlake Center for Intelligent Proteomics, Westlake Laboratory of Life Sciences and Biomedicine, Hangzhou, Zhejiang province, China.
Nat Commun. 2025 Jan 21;16(1):892. doi: 10.1038/s41467-024-54871-1.
Quality control (QC) in mass spectrometry (MS)-based proteomics is mainly based on data-dependent acquisition (DDA) analysis of standard samples. Here, we collect 2754 files acquired by data independent acquisition (DIA) and paired 2638 DDA files from mouse liver digests using 21 mass spectrometers across nine laboratories over 31 months. Our data demonstrate that DIA-based LC-MS/MS-related consensus QC metrics exhibit higher sensitivity compared to DDA-based QC metrics in detecting changes in LC-MS status. We then prioritize 15 metrics and invite 21 experts to manually assess the quality of 2754 DIA files based on those metrics. We develop an AI model for DIA-based QC using 2110 training files. It achieves AUCs of 0.91 (LC) and 0.97 (MS) in the first validation dataset (n = 528), and 0.78 (LC) and 0.94 (MS) in an independent validation dataset (n = 116). Finally, we develop an offline software called iDIA-QC for convenient adoption of this methodology.
基于质谱（MS）的蛋白质组学中的质量控制（QC）主要基于对标准样品的数据依赖型采集（DDA）分析。在此，我们收集了通过数据独立型采集（DIA）获得的2754个文件，并在31个月内使用九个实验室的21台质谱仪，将来自小鼠肝脏消化物的2638个DDA文件进行配对。我们的数据表明，在检测液相色谱-质谱（LC-MS）状态变化方面，基于DIA的液相色谱-串联质谱（LC-MS/MS）相关一致性质量控制指标比基于DDA的质量控制指标具有更高的灵敏度。然后，我们对15个指标进行优先排序，并邀请21位专家根据这些指标手动评估2754个DIA文件的质量。我们使用2110个训练文件开发了一种基于DIA的质量控制人工智能模型。在第一个验证数据集（n = 528）中，它在液相色谱（LC）和质谱（MS）方面的曲线下面积（AUC）分别达到0.91和0.97，在独立验证数据集（n = 116）中，液相色谱和质谱方面的曲线下面积分别为0.78和0.94。最后，我们开发了一款名为iDIA-QC的离线软件，以便于采用这种方法。
