Proteomics Core Facility, Biozentrum Basel, University of Basel, Basel, Switzerland.
Institute for Surgical Pathology, Medical Center - University of Freiburg, Faculty of Medicine, University of Freiburg, Freiburg, Germany; German Cancer Consortium (DKTK) and Cancer Research Center (DKFZ), Freiburg, Germany.
Mol Cell Proteomics. 2024 Aug;23(8):100800. doi: 10.1016/j.mcpro.2024.100800. Epub 2024 Jun 15.
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.
数据非依赖性采集(DIA)在过去几年中彻底改变了基于质谱(MS)的蛋白质组学领域。DIA 的突出特点是能够系统地采样给定 m/z 范围内的所有肽,从而能够无偏地获取蛋白质组学数据。与许多传统方法相比,这极大地减少了缺失值的问题,并显著提高了定量的准确性、精密度和重现性。
本综述重点介绍了 DIA 分析软件工具的关键作用,主要关注它们的功能以及它们在蛋白质组学研究中解决的挑战。MS 技术的进步,如离子阱淌度质谱或高场非对称波形离子淌度质谱,需要能够处理增加的数据复杂性并充分利用 DIA 潜力的复杂分析软件。
我们确定并批判性地评估了 DIA 领域中的领先软件工具,讨论了它们的独特功能以及其定量和定性输出的可靠性。我们介绍了 DIA-MS 的生物学和临床相关性,并讨论了为患者来源标本中深入的蛋白质组学特征分析铺平道路的关键出版物。此外,我们还介绍了临床应用中的新兴趋势,并提出了即将面临的挑战,包括基于 MS 的采集策略在分子诊断中的标准化和认证。虽然我们强调需要不断开发软件工具以跟上不断发展的技术,但我们建议研究人员不要不加批判地接受 DIA 软件工具的结果。每个工具都可能有其自身的偏差,并且有些工具可能不如其他工具敏感或可靠。我们对研究人员和临床医生的总体建议是使用多种 DIA 分析工具,利用正交分析方法来增强他们发现的稳健性和可靠性。