iDIA-QC: AI-empowered data-independent acquisition mass spectrometry-based quality control

Huanhuan Gao, Yi Zhu (), Dongxue Wang, Zongxiang Nie, He Wang, Guibin Wang, Shuang Liang, Yuting Xie, Yingying Sun, Wenhao Jiang, Zhen Dong, Liqin Qian, Xufei Wang, Mengdi Liang, Min Chen, Houqi Fang, Qiufang Zeng, Jiao Tian, Zeyu Sun, Juan Xue, Shan Li, Chen Chen, Xiang Liu, Xiaolei Lyu, Zhenchang Guo, Yingzi Qi, Ruoyu Wu, Xiaoxian Du, Tingde Tong, Fengchun Kong, Liming Han, Minghui Wang, Yang Zhao, Xinhua Dai, Fuchu He () and Tiannan Guo ()
Additional contact information
Huanhuan Gao: Westlake University
Yi Zhu: Westlake University
Dongxue Wang: Beijing Institute of Lifeomics
Zongxiang Nie: Ltd.
He Wang: Westlake University
Guibin Wang: Beijing Institute of Lifeomics
Shuang Liang: Zhejiang Academy of Agricultural Sciences
Yuting Xie: Westlake University
Yingying Sun: Westlake University
Wenhao Jiang: Westlake University
Zhen Dong: Westlake University
Liqin Qian: Westlake University
Xufei Wang: Guangzhou Medical University
Mengdi Liang: Guangzhou Medical University
Min Chen: Ltd
Houqi Fang: Ltd
Qiufang Zeng: Ltd
Jiao Tian: Ltd
Zeyu Sun: Zhejiang University
Juan Xue: Hubei University of Medicine
Shan Li: Hubei University of Medicine
Chen Chen: SCIEX
Xiang Liu: SCIEX
Xiaolei Lyu: SCIEX
Zhenchang Guo: Thermo Fisher Scientific
Yingzi Qi: Thermo Fisher Scientific
Ruoyu Wu: Bruker Daltonics
Xiaoxian Du: Bruker Daltonics
Tingde Tong: Thermo Fisher Scientific
Fengchun Kong: SCIEX
Liming Han: Bruker Daltonics
Minghui Wang: Bruker Daltonics
Yang Zhao: National Institute of Metrology
Xinhua Dai: National Institute of Metrology
Fuchu He: Beijing Institute of Lifeomics
Tiannan Guo: Westlake University

Nature Communications, 2025, vol. 16, issue 1, 1-13

Abstract: Abstract 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.

Date: 2025
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DOI: 10.1038/s41467-024-54871-1

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