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

Author

Listed:
  • Huanhuan Gao

    (Westlake University
    Westlake Laboratory of Life Sciences and Biomedicine
    Westlake University)

  • Yi Zhu

    (Westlake University
    Westlake Laboratory of Life Sciences and Biomedicine
    Westlake University)

  • Dongxue Wang

    (Beijing Institute of Lifeomics
    International Academy of Phronesis Medicine)

  • Zongxiang Nie

    (Ltd.)

  • He Wang

    (Westlake University
    Westlake Laboratory of Life Sciences and Biomedicine
    Westlake University)

  • Guibin Wang

    (Beijing Institute of Lifeomics)

  • Shuang Liang

    (Zhejiang Academy of Agricultural Sciences)

  • Yuting Xie

    (Westlake University
    Westlake Laboratory of Life Sciences and Biomedicine
    Westlake University)

  • Yingying Sun

    (Westlake University
    Westlake Laboratory of Life Sciences and Biomedicine
    Westlake University)

  • Wenhao Jiang

    (Westlake University
    Westlake Laboratory of Life Sciences and Biomedicine
    Westlake University)

  • Zhen Dong

    (Westlake University
    Westlake Laboratory of Life Sciences and Biomedicine
    Westlake University)

  • Liqin Qian

    (Westlake University
    Westlake Laboratory of Life Sciences and Biomedicine
    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
    Huazhong Agricultural University)

  • Shan Li

    (Hubei University of Medicine
    Huazhong Agricultural University)

  • 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
    International Academy of Phronesis Medicine)

  • Tiannan Guo

    (Westlake University
    Westlake Laboratory of Life Sciences and Biomedicine
    Westlake University)

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.

Suggested Citation

  • 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 &, 2025. "iDIA-QC: AI-empowered data-independent acquisition mass spectrometry-based quality control," Nature Communications, Nature, vol. 16(1), pages 1-13, December.
    • Handle: RePEc:nat:natcom:v:16:y:2025:i:1:d:10.1038_s41467-024-54871-1
    DOI: 10.1038/s41467-024-54871-1
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    References listed on IDEAS

    as
    1. Vadim Demichev & Lukasz Szyrwiel & Fengchao Yu & Guo Ci Teo & George Rosenberger & Agathe Niewienda & Daniela Ludwig & Jens Decker & Stephanie Kaspar-Schoenefeld & Kathryn S. Lilley & Michael Mülleder, 2022. "dia-PASEF data analysis using FragPipe and DIA-NN for deep proteomics of low sample amounts," Nature Communications, Nature, vol. 13(1), pages 1-8, December.
    2. Ruedi Aebersold & Matthias Mann, 2016. "Mass-spectrometric exploration of proteome structure and function," Nature, Nature, vol. 537(7620), pages 347-355, September.
    3. Yue Xuan & Nicholas W. Bateman & Sebastien Gallien & Sandra Goetze & Yue Zhou & Pedro Navarro & Mo Hu & Niyati Parikh & Brian L. Hood & Kelly A. Conrads & Christina Loosse & Reta Birhanu Kitata & Sand, 2020. "Standardization and harmonization of distributed multi-center proteotype analysis supporting precision medicine studies," Nature Communications, Nature, vol. 11(1), pages 1-12, December.
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