数据独立采集质谱法作为宏蛋白质组学的工具:使用模型微生物组的实验室间比较

IF 3.9 4区 生物学 Q2 BIOCHEMICAL RESEARCH METHODS
Proteomics Pub Date : 2025-04-10 DOI:10.1002/pmic.202400187
Andrew T. Rajczewski, J. Alfredo Blakeley-Ruiz, Annaliese Meyer, Simina Vintila, Matthew R. McIlvin, Tim Van Den Bossche, Brian C. Searle, Timothy J. Griffin, Mak A. Saito, Manuel Kleiner, Pratik D. Jagtap
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引用次数: 0

摘要

基于质谱(MS)的宏蛋白质组学用于鉴定和定量微生物组样品中的蛋白质,常用的方法是数据依赖获取质谱(DDA-MS)。然而,DDA-MS在可重复性鉴定和定量低丰度肽和蛋白质的能力方面受到限制。为了解决DDA-MS的不足,蛋白质组学研究人员已经开始使用数据独立获取质谱法(DIA-MS)对肽和蛋白质进行重复性检测和定量。我们试图利用已知分类组成的模拟群落来评估DIA-MS相对于DDA-MS的元蛋白质组学测量的再现性和准确性。采用DDA-和DIA-MS采集方法,对已知组成的人工微生物群落进行独立分析。在本研究中,由于所选择的特定仪器和软件参数,在每个实验室中,DIA-MS比DDA-MS鉴定出更多的蛋白质和肽。此外,在所有实验室中,蛋白质和肽鉴定的可重复性更高,并提供了样品中蛋白质和分类群的准确定量。我们还发现了当前DIA工具在应用于元蛋白质组学数据时的一些局限性,强调了改进DIA工具以分析来自复杂微生物组的元蛋白质组学数据集的具体需求。最终,由于其大量检测到的蛋白质和肽,可重复性,深度测序能力和准确的定量,DIA-MS代表了基于ms的宏蛋白质组学的有前途的策略。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Data-Independent Acquisition Mass Spectrometry as a Tool for Metaproteomics: Interlaboratory Comparison Using a Model Microbiome

Data-Independent Acquisition Mass Spectrometry as a Tool for Metaproteomics: Interlaboratory Comparison Using a Model Microbiome

Data-Independent Acquisition Mass Spectrometry as a Tool for Metaproteomics: Interlaboratory Comparison Using a Model Microbiome

Data-Independent Acquisition Mass Spectrometry as a Tool for Metaproteomics: Interlaboratory Comparison Using a Model Microbiome

Mass spectrometry (MS)-based metaproteomics is used to identify and quantify proteins in microbiome samples, with the frequently used methodology being data-dependent acquisition mass spectrometry (DDA-MS). However, DDA-MS is limited in its ability to reproducibly identify and quantify lower abundant peptides and proteins. To address DDA-MS deficiencies, proteomics researchers have started using Data-independent acquisition mass spectrometry (DIA-MS) for reproducible detection and quantification of peptides and proteins. We sought to evaluate the reproducibility and accuracy of DIA-MS metaproteomic measurements relative to DDA-MS using a mock community of known taxonomic composition. Artificial microbial communities of known composition were analyzed independently in three laboratories using DDA- and DIA-MS acquisition methods. In this study, DIA-MS yielded more protein and peptide identifications than DDA-MS in each laboratory for the particular instruments and software parameters chosen. In addition, the protein and peptide identifications were more reproducible in all laboratories and provided an accurate quantification of proteins and taxonomic groups in the samples. We also identified some limitations of current DIA tools when applied to metaproteomic data, highlighting specific needs to improve DIA tools enabling analysis of metaproteomic datasets from complex microbiomes. Ultimately, DIA-MS represents a promising strategy for MS-based metaproteomics due to its large number of detected proteins and peptides, reproducibility, deep sequencing capabilities, and accurate quantitation.

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来源期刊
Proteomics
Proteomics 生物-生化研究方法
CiteScore
6.30
自引率
5.90%
发文量
193
审稿时长
3 months
期刊介绍: PROTEOMICS is the premier international source for information on all aspects of applications and technologies, including software, in proteomics and other "omics". The journal includes but is not limited to proteomics, genomics, transcriptomics, metabolomics and lipidomics, and systems biology approaches. Papers describing novel applications of proteomics and integration of multi-omics data and approaches are especially welcome.
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