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

Overview

Journal Nat Commun

Date 2025 Jan 21

PMID 39837863

Authors

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

Houqi Fang

Qiufang Zeng

Jiao Tian

Zeyu Sun

Juan Xue

Shan Li

Chen Chen

Xiang Liu

Xiaolei Lyu

Zhenchang Guo

Yingzi Qi

Ruoyu Wu

Xiaoxian Du

Tingde Tong

Fengchun Kong

Liming Han

Minghui Wang

Yang Zhao

Xinhua Dai

Fuchu He

Tiannan Guo

Affiliations

Soon will be listed here.

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.

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