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Data-Independent Acquisition: A Milestone and Prospect in Clinical Mass Spectrometry–Based Proteomics

蛋白质组学 数据科学 软件 标准化 领域(数学) 化学 计算机科学 生物化学 数学 基因 操作系统 程序设计语言 纯数学
作者
Klemens Fröhlich,Matthias Fahrner,Eva Brombacher,Adrianna Seredynska,Maximilian Maldacker,Clemens Kreutz,Alexander Schmidt,Oliver Schilling
出处
期刊:Molecular & Cellular Proteomics [Elsevier BV]
卷期号:23 (8): 100800-100800 被引量:103
标识
DOI:10.1016/j.mcpro.2024.100800
摘要

Data-independent acquisition (DIA) has revolutionized the field of mass spectrometry (MS)-based proteomics over the past few years. DIA stands out for its ability to systematically sample all peptides in a given m/z range, allowing an unbiased acquisition of proteomics data. This greatly mitigates the issue of missing values and significantly enhances quantitative accuracy, precision, and reproducibility compared to many traditional methods. This review focuses on the critical role of DIA analysis software tools, primarily focusing on their capabilities and the challenges they address in proteomic research. Advances in MS technology, such as trapped ion mobility spectrometry, or high field asymmetric waveform ion mobility spectrometry require sophisticated analysis software capable of handling the increased data complexity and exploiting the full potential of DIA. We identify and critically evaluate leading software tools in the DIA landscape, discussing their unique features, and the reliability of their quantitative and qualitative outputs. We present the biological and clinical relevance of DIA-MS and discuss crucial publications that paved the way for in-depth proteomic characterization in patient-derived specimens. Furthermore, we provide a perspective on emerging trends in clinical applications and present upcoming challenges including standardization and certification of MS-based acquisition strategies in molecular diagnostics. While we emphasize the need for continuous development of software tools to keep pace with evolving technologies, we advise researchers against uncritically accepting the results from DIA software tools. Each tool may have its own biases, and some may not be as sensitive or reliable as others. Our overarching recommendation for both researchers and clinicians is to employ multiple DIA analysis tools, utilizing orthogonal analysis approaches to enhance the robustness and reliability of their findings.
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