独立于全窗口数据的采集方法，用于更深入的自上而下蛋白质组学研究

IF 6.7 1区化学 Q1 CHEMISTRY, ANALYTICAL

Analytical Chemistry Pub Date : 2025-03-22 DOI:10.1021/acs.analchem.4c06471

Chen Sun, Wenjing Zhang, Mowei Zhou, Martin Myu, Wei Xu

{"title":"独立于全窗口数据的采集方法，用于更深入的自上而下蛋白质组学研究","authors":"Chen Sun, Wenjing Zhang, Mowei Zhou, Martin Myu, Wei Xu","doi":"10.1021/acs.analchem.4c06471","DOIUrl":null,"url":null,"abstract":"Top-down proteomics (TDP) is emerging as a vital tool for the comprehensive characterization of proteoforms. However, as its core technology, top-down mass spectrometry (TDMS) still faces significant analytical challenges. While data-independent acquisition (DIA) has revolutionized bottom-up proteomics and metabolomics, they are rarely employed in TDP. The unique feature of protein ions in an electrospray mass spectrum as well as the data complexity require the development of new DIA strategies. This study introduces a machine learning-assisted Full Window DIA (FW-DIA) method that eliminates precursor ion isolation, making it compatible with a wide range of commercial mass spectrometers. Moreover, FW-DIA leverages all precursor protein ions to generate high-quality tandem mass spectra, enhancing signal intensities by ∼50-fold and protein sequence coverage by 3-fold in a modular protein analysis. The method was successfully applied to the analysis of a five-protein mixture under native conditions and <i>Escherichia coli</i> ribosomal proteoform characterization.","PeriodicalId":27,"journal":{"name":"Analytical Chemistry","volume":"24 1","pages":""},"PeriodicalIF":6.7000,"publicationDate":"2025-03-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Full Window Data-Independent Acquisition Method for Deeper Top-Down Proteomics\",\"authors\":\"Chen Sun, Wenjing Zhang, Mowei Zhou, Martin Myu, Wei Xu\",\"doi\":\"10.1021/acs.analchem.4c06471\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Top-down proteomics (TDP) is emerging as a vital tool for the comprehensive characterization of proteoforms. However, as its core technology, top-down mass spectrometry (TDMS) still faces significant analytical challenges. While data-independent acquisition (DIA) has revolutionized bottom-up proteomics and metabolomics, they are rarely employed in TDP. The unique feature of protein ions in an electrospray mass spectrum as well as the data complexity require the development of new DIA strategies. This study introduces a machine learning-assisted Full Window DIA (FW-DIA) method that eliminates precursor ion isolation, making it compatible with a wide range of commercial mass spectrometers. Moreover, FW-DIA leverages all precursor protein ions to generate high-quality tandem mass spectra, enhancing signal intensities by ∼50-fold and protein sequence coverage by 3-fold in a modular protein analysis. The method was successfully applied to the analysis of a five-protein mixture under native conditions and <i>Escherichia coli</i> ribosomal proteoform characterization.\",\"PeriodicalId\":27,\"journal\":{\"name\":\"Analytical Chemistry\",\"volume\":\"24 1\",\"pages\":\"\"},\"PeriodicalIF\":6.7000,\"publicationDate\":\"2025-03-22\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Analytical Chemistry\",\"FirstCategoryId\":\"92\",\"ListUrlMain\":\"https://doi.org/10.1021/acs.analchem.4c06471\",\"RegionNum\":1,\"RegionCategory\":\"化学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"CHEMISTRY, ANALYTICAL\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Analytical Chemistry","FirstCategoryId":"92","ListUrlMain":"https://doi.org/10.1021/acs.analchem.4c06471","RegionNum":1,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"CHEMISTRY, ANALYTICAL","Score":null,"Total":0}

引用次数: 0

摘要

本文章由计算机程序翻译，如有差异，请以英文原文为准。

Full Window Data-Independent Acquisition Method for Deeper Top-Down Proteomics

查看原文本刊更多论文

Full Window Data-Independent Acquisition Method for Deeper Top-Down Proteomics

Top-down proteomics (TDP) is emerging as a vital tool for the comprehensive characterization of proteoforms. However, as its core technology, top-down mass spectrometry (TDMS) still faces significant analytical challenges. While data-independent acquisition (DIA) has revolutionized bottom-up proteomics and metabolomics, they are rarely employed in TDP. The unique feature of protein ions in an electrospray mass spectrum as well as the data complexity require the development of new DIA strategies. This study introduces a machine learning-assisted Full Window DIA (FW-DIA) method that eliminates precursor ion isolation, making it compatible with a wide range of commercial mass spectrometers. Moreover, FW-DIA leverages all precursor protein ions to generate high-quality tandem mass spectra, enhancing signal intensities by ∼50-fold and protein sequence coverage by 3-fold in a modular protein analysis. The method was successfully applied to the analysis of a five-protein mixture under native conditions and Escherichia coli ribosomal proteoform characterization.

求助全文

通过发布文献求助，成功后即可免费获取论文全文。去求助

来源期刊

Analytical Chemistry 化学-分析化学

CiteScore

12.10

自引率

12.20%

发文量

1949

审稿时长

1.4 months

期刊介绍： Analytical Chemistry, a peer-reviewed research journal, focuses on disseminating new and original knowledge across all branches of analytical chemistry. Fundamental articles may explore general principles of chemical measurement science and need not directly address existing or potential analytical methodology. They can be entirely theoretical or report experimental results. Contributions may cover various phases of analytical operations, including sampling, bioanalysis, electrochemistry, mass spectrometry, microscale and nanoscale systems, environmental analysis, separations, spectroscopy, chemical reactions and selectivity, instrumentation, imaging, surface analysis, and data processing. Papers discussing known analytical methods should present a significant, original application of the method, a notable improvement, or results on an important analyte.