What question did this study set out to answer?

To establish and evaluate an artificial intelligence-driven quality control framework for mass spectrometry-based proteomics using data-independent acquisition.

January 21, 2025Nature CommunicationsOpen Access

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

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Authors

HGHuanhuan GaoShandong University of Technology YZYi ZhuFirst Affiliated Hospital Zhejiang University DWDongxue WangBeijing Proteome Research Center

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Implication

Multicenter validation study demonstrates that AI-driven data-independent acquisition improves quality control in mass spectrometry, indicating more sensitive instrument fault detection.

Key Points

To establish and evaluate an artificial intelligence-driven quality control framework for mass spectrometry-based proteomics using data-independent acquisition.
Analyzed 2,754 data-independent acquisition (DIA) files and 2,638 paired data-dependent acquisition (DDA) files of mouse liver digests collected across 21 mass spectrometers in nine laboratories over 31 months.
Curated 15 prioritized quality control metrics assessed manually across 2,754 DIA files by 21 domain experts.
Trained an AI model on 2,110 files and evaluated performance on two validation datasets (n = 528 and n = 116), integrating the workflow into a standalone offline tool called iDIA-QC.
Consensus quality control metrics derived from DIA demonstrated greater sensitivity in detecting liquid chromatography and mass spectrometry status variations than conventional DDA metrics.
The AI model identified faults with an AUC of 0.91 for liquid chromatography and 0.97 for mass spectrometry in the first validation set (n = 528), and an AUC of 0.78 for LC and 0.94 for MS in an independent validation set (n = 116).

Cite This Study

Gao et al. (2025) studied this question.

synapsesocial.com/papers/6a906a3ced78fcc1e60d37f1 https://doi.org/10.1038/s41467-024-54871-1

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Implication

Multicenter validation study demonstrates that AI-driven data-independent acquisition improves quality control in mass spectrometry, indicating more sensitive instrument fault detection.

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

Discussion

Member takes

Implication

Key Points

Cite This Study

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

Discussion

Member takes

Implication

Key Points

Cite This Study

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