Kitata Reta Birhanu, Yang Jhih-Ci, Chen Yu-Ju
Institute of Chemistry, Academia Sinica, Taipei, Taiwan.
Sustainable Chemical Science and Technology, Taiwan International Graduate Program, Academia Sinica and National Yang Ming Chiao Tung University, Taipei, Taiwan.
Mass Spectrom Rev. 2023 Nov-Dec;42(6):2324-2348. doi: 10.1002/mas.21781. Epub 2022 May 29.
The data-independent acquisition mass spectrometry (DIA-MS) has rapidly evolved as a powerful alternative for highly reproducible proteome profiling with a unique strength of generating permanent digital maps for retrospective analysis of biological systems. Recent advancements in data analysis software tools for the complex DIA-MS/MS spectra coupled to fast MS scanning speed and high mass accuracy have greatly expanded the sensitivity and coverage of DIA-based proteomics profiling. Here, we review the evolution of the DIA-MS techniques, from earlier proof-of-principle of parallel fragmentation of all-ions or ions in selected m/z range, the sequential window acquisition of all theoretical mass spectra (SWATH-MS) to latest innovations, recent development in computation algorithms for data informatics, and auxiliary tools and advanced instrumentation to enhance the performance of DIA-MS. We further summarize recent applications of DIA-MS and experimentally-derived as well as in silico spectra library resources for large-scale profiling to facilitate biomarker discovery and drug development in human diseases with emphasis on the proteomic profiling coverage. Toward next-generation DIA-MS for clinical proteomics, we outline the challenges in processing multi-dimensional DIA data set and large-scale clinical proteomics, and continuing need in higher profiling coverage and sensitivity.
数据非依赖采集质谱法(DIA-MS)已迅速发展成为一种强大的替代方法,用于进行高度可重复的蛋白质组分析,其独特优势在于能够生成永久数字图谱,用于生物系统的回顾性分析。与快速质谱扫描速度和高质量精度相结合的复杂DIA-MS/MS谱数据分析软件工具的最新进展,极大地扩展了基于DIA的蛋白质组学分析的灵敏度和覆盖范围。在此,我们回顾DIA-MS技术的发展历程,从早期对所有离子或选定m/z范围内离子进行平行碎裂的原理验证、全理论质谱的顺序窗口采集(SWATH-MS)到最新创新、数据信息学计算算法的最新发展,以及增强DIA-MS性能的辅助工具和先进仪器。我们进一步总结了DIA-MS的近期应用,以及用于大规模分析的实验衍生和虚拟谱图库资源,以促进人类疾病中的生物标志物发现和药物开发,重点关注蛋白质组分析覆盖范围。对于面向临床蛋白质组学的下一代DIA-MS,我们概述了处理多维DIA数据集和大规模临床蛋白质组学时面临的挑战,以及对更高分析覆盖范围和灵敏度的持续需求。