Optimizing MS Parameters for Data-Independent Acquisition (DIA) to Enhance Untargeted Metabolomics

Data-Independent Acquisition (DIA) has emerged as a powerful
mass
spectrometry (MS) strategy for comprehensive metabolomics. This study
presents a novel short gradient (13 min) nanosensitive analytical
method for human plasma analysis using DIA LC-MS/MS, focusing on in-depth
optimization of MS parameters to maximize data quality and metabolite
coverage. Key MS parameters, including scan speed, isolation window
width, resolution, automatic gain control, and collision energy, were
systematically tuned to balance the sensitivity and specificity while
minimizing interferences. The optimized method enabled the detection
of 2,907 features with 675 annotated compounds, leveraging recent
progress in nano-LC-MS/MS for multiomics applications and showcasing
the possibility of combining proteomics and metabolomics within a
single chromatographic system. Ultimately, a comparison was performed
between the data acquired through the DIA and DDA MS approaches in
the context of untargeted metabolomics. This optimized analytical
method yields more robust and reproducible results, thereby expanding
the potential for meaningful discoveries across diverse biological
fields.

数据非依赖性采集（Data-Independent Acquisition, DIA）已成为支撑全面代谢组学研究的高性能质谱（Mass Spectrometry, MS）策略。本研究提出一种基于DIA液相色谱-串联质谱（LC-MS/MS）的新型纳级灵敏度短梯度（13分钟）分析方法，用于人体血浆样本分析，重点对质谱参数进行深度优化，以最大化数据质量与代谢物覆盖范围。研究人员系统调控了扫描速度、隔离窗口宽度、分辨率、自动增益控制及碰撞能量等关键质谱参数，在平衡灵敏度与特异性的同时最大限度抑制干扰。依托近年来面向多组学应用的纳升LC-MS/MS技术进展，经优化后的方法可检测到2907个特征峰，并注释出675种化合物，同时展现了在单一色谱系统中同时开展蛋白质组学与代谢组学研究的可行性。最终，本研究在非靶向代谢组学场景下，对比了DIA与数据依赖性采集（Data-Dependent Acquisition, DDA）两种质谱采集方式所得的实验数据。经优化的分析方法可获得更稳定且可重复的实验结果，进而为跨多样生物领域的有价值科学发现拓展了应用潜力。