Department of Chemistry, Faculty of Science, University of British Columbia, Vancouver Campus, 2036 Main Mall, Vancouver V6T 1Z1, British Columbia, Canada.
Anal Chem. 2021 Feb 2;93(4):2669-2677. doi: 10.1021/acs.analchem.0c05022. Epub 2021 Jan 19.
Existing data acquisition modes such as full-scan, data-dependent (DDA), and data-independent acquisition (DIA) often present limited capabilities in capturing metabolic information in liquid chromatography-mass spectrometry (LC-MS)-based metabolomics. In this work, we proposed a novel metabolomic data acquisition workflow that combines DDA and DIA analyses to achieve better metabolomic data quality, including enhanced metabolome coverage, tandem mass spectrometry (MS) coverage, and MS quality. This workflow, named data-dependent-assisted data-independent acquisition (DaDIA), performs untargeted metabolomic analysis of individual biological samples using DIA mode and the pooled quality control (QC) samples using DDA mode. This combination takes advantage of the high-feature number and MS spectral coverage of the DIA data and the high MS spectral quality of the DDA data. To analyze the heterogeneous DDA and DIA data, we further developed a computational program, DaDIA.R, to automatically extract metabolic features and perform streamlined metabolite annotation of DaDIA data set. Using human urine samples, we demonstrated that the DaDIA workflow delivers remarkably improved data quality when compared to conventional DDA or DIA metabolomics. In particular, both the number of detected features and annotated metabolites were greatly increased. Further biological demonstration using a leukemia metabolomics study also proved that the DaDIA workflow can efficiently detect and annotate around 4 times more significant metabolites than DDA workflow with broad MS coverage and high MS spectral quality for downstream statistical analysis and biological interpretation. Overall, this work represents a critical development of data acquisition mode in untargeted metabolomics, which can greatly benefit untargeted metabolomics for a wide range of biological applications.
现有的数据采集模式，如全扫描、数据依赖（DDA）和数据非依赖（DIA），在基于液相色谱-质谱（LC-MS）的代谢组学中获取代谢信息方面往往具有有限的能力。在这项工作中，我们提出了一种新的代谢组学数据采集工作流程，该工作流程结合了 DDA 和 DIA 分析，以实现更好的代谢组学数据质量，包括增强代谢组覆盖度、串联质谱（MS）覆盖度和 MS 质量。该工作流程命名为数据依赖辅助数据非依赖采集（DaDIA），使用 DIA 模式对单个生物样本进行非靶向代谢组学分析，使用 DDA 模式对混合的质量控制（QC）样本进行分析。这种组合利用了 DIA 数据的高特征数量和 MS 光谱覆盖度，以及 DDA 数据的高 MS 光谱质量。为了分析异质的 DDA 和 DIA 数据，我们进一步开发了一个计算程序 DaDIA.R，以自动提取代谢特征，并对 DaDIA 数据集进行简化的代谢物注释。使用人尿样本来验证，与传统的 DDA 或 DIA 代谢组学相比，DaDIA 工作流程可显著提高数据质量。特别是，检测到的特征和注释代谢物的数量都大大增加。使用白血病代谢组学研究进行的进一步生物学验证也证明，与具有广泛 MS 覆盖度和高 MS 光谱质量的 DDA 工作流程相比，DaDIA 工作流程可以有效地检测和注释多达 4 倍的显著代谢物，为下游统计分析和生物学解释提供了便利。总的来说，这项工作代表了非靶向代谢组学中数据采集模式的重要发展，可为广泛的生物学应用带来极大的益处。
