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IDIA-QC: AI-empowered Data-Independent Acquisition Mass Spectrometry-based Quality Control

ABSTRACT: Quality control (QC) in mass spectrometry (MS)-based proteomics is mainly based on data-dependent acquisition (DDA) analysis of standard samples. Here, we collected 2638 files acquired by data independent acquisition (DIA) and paired DDA files from mouse liver digests using 21 mass spectrometers across nine laboratories over 31 months. Our data demonstrated that DIA-based LC-MS/MS-related consensus QC metric exhibit higher sensitivity compared to DDA-based QC metric in detecting changes in LC-MS status. We then optimized 15 metrics and invited 21 experts to manually assess the quality of 2638 DIA files based on those metrics. Based on the annotation results, we developed an AI model for DIA-based QC in the training set of 2110 DIA files. This model predicted the liquid chromatography (LC) performance with an AUC of 0.91 and the MS performance with an AUC of 0.97 in an independent validation dataset (n = 528). Finally, we developed an offline software called iDIA-QC for convenient adoption of this methodology for LC-MS QC.

INSTRUMENT(S): Q Exactive HF-X, TripleTOF 5600, TripleTOF 6600, Orbitrap Fusion, Q Exactive Plus, Orbitrap Exploris 480, Q Exactive HF, Q Exactive

ORGANISM(S): Mus Musculus (mouse)

TISSUE(S): Liver

SUBMITTER: Tiannan Guo

LAB HEAD: Tiannan Guo

PROVIDER: PXD052871 | Pride | 2024-11-08

REPOSITORIES: Pride

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			Action	DRS
	A20190813_newml_check_30min_500ng_DIA.raw	Raw
	A20190816_newml_check_30min_500ng_DIA.raw	Raw
	A20190820_newml_check_30min_500ng_DIA.raw	Raw
	A20190820_newml_check_30min_500ng_DIA_1.raw	Raw
	A20190820_newml_check_30min_500ng_DIA_2.raw	Raw

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