{"title":"iDIA-QC：基于人工智能的数据独立采集质谱的质量控制","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","doi":"10.1038/s41467-024-54871-1","DOIUrl":null,"url":null,"abstract":"<p>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 (<i>n</i> = 528), and 0.78 (LC) and 0.94 (MS) in an independent validation dataset (<i>n</i> = 116). Finally, we develop an offline software called iDIA-QC for convenient adoption of this methodology.</p>","PeriodicalId":19066,"journal":{"name":"Nature Communications","volume":"37 1","pages":""},"PeriodicalIF":15.7000,"publicationDate":"2025-01-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"iDIA-QC: AI-empowered data-independent acquisition mass spectrometry-based quality control\",\"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\",\"doi\":\"10.1038/s41467-024-54871-1\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p>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 (<i>n</i> = 528), and 0.78 (LC) and 0.94 (MS) in an independent validation dataset (<i>n</i> = 116). Finally, we develop an offline software called iDIA-QC for convenient adoption of this methodology.</p>\",\"PeriodicalId\":19066,\"journal\":{\"name\":\"Nature Communications\",\"volume\":\"37 1\",\"pages\":\"\"},\"PeriodicalIF\":15.7000,\"publicationDate\":\"2025-01-21\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Nature Communications\",\"FirstCategoryId\":\"103\",\"ListUrlMain\":\"https://doi.org/10.1038/s41467-024-54871-1\",\"RegionNum\":1,\"RegionCategory\":\"综合性期刊\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"MULTIDISCIPLINARY SCIENCES\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Nature Communications","FirstCategoryId":"103","ListUrlMain":"https://doi.org/10.1038/s41467-024-54871-1","RegionNum":1,"RegionCategory":"综合性期刊","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"MULTIDISCIPLINARY SCIENCES","Score":null,"Total":0}
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.
期刊介绍：
Nature Communications, an open-access journal, publishes high-quality research spanning all areas of the natural sciences. Papers featured in the journal showcase significant advances relevant to specialists in each respective field. With a 2-year impact factor of 16.6 (2022) and a median time of 8 days from submission to the first editorial decision, Nature Communications is committed to rapid dissemination of research findings. As a multidisciplinary journal, it welcomes contributions from biological, health, physical, chemical, Earth, social, mathematical, applied, and engineering sciences, aiming to highlight important breakthroughs within each domain.
求助内容：
应助结果提醒方式：
京公网安备 11010802042870号
