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

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. LC-MS-based proteomics often relies on data-dependent acquisition (DDA) for quality control. Here, the authors demonstrate that data-independent acquisition (DIA) outperforms DDA in detecting subtle changes in LC-MS status in large-scale quantitative proteomics experiments. They further prioritized 15 QC metrics and developed an AI model, implemented in a free software called iDIA-QC, for detecting LC-MS faults.