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 showed that DIA-based LC-MS/MS related consensus QC metric is more sensitive than DDA-based QC in detecting MS status changes. We then optimized 15 DIA-QC metrics, and invited to manually assess the quality of 2638 DIA files generated by 21 mass spectrometers based on each metric. Based on the annotation results, we developed an AI model for DIA-based QC in the training set of 2059 DIA files, and 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 = 523). Finally, we developed an offline software called iDIA-QC for convenient adoption of this methodology for LC-MS QC

INSTRUMENT(S): timsTOF Pro, TripleTOF 5600, Q Exactive HF, Q Exactive

ORGANISM(S): Mus Musculus (mouse)

TISSUE(S): Liver

SUBMITTER: Tiannan Guo  

LAB HEAD: Tiannan Guo

PROVIDER: PXD051878 | Pride | 2025-05-06

REPOSITORIES: Pride