A compounds annotation strategy using targeted molecular networking for offline two-dimensional liquid chromatography-mass spectrometry analysis: Yupingfeng as a case study
A compounds annotation strategy using targeted molecular networking for offline two-dimensional liquid chromatography-mass spectrometry analysis: Yupingfeng as a case study
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DOI：
10.1016/j.chroma.2023.464045
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发表时间：
2023-05-24
影响因子：
4.1
通讯作者：
Yao，Zhihong
中科院分区：
文献类型：
--
作者：
Zhu，Haodong;He，Liangliang;Yao，Zhihong
离线二维液相色谱质谱（offline 2D-LC MS）系统存在组分重叠和长时间消耗等问题，阻碍了数据处理。虽然分子网络技术已广泛应用于液相色谱-质谱（LC-MS）的数据处理中，但其在离线2D-LC MS中的应用受到数据量大和冗余的挑战。鉴于此，本研究首次开发了一种结合手拉手比对和靶向分子网络（TMN）的数据删除和可视化策略，用于离线2D-LC MS数据的化合物注释，并将其应用于中药经典处方玉平风（YPF）的化学特征分析。首先，构建脱机2D-LC质谱系统，对YPF提取物进行分离和数据采集。然后对YPF提取的12个组分的数据进行反卷积和手拉手比对，使组分重叠减少49.2%（从17951个离子减少到9112个离子），提高了前驱离子的ms2谱质量。随后，通过自构建Python脚本计算聚焦母离子的ms2相似度邻接矩阵，实现了创新性TMN的构建。有趣的是，TMN被发现能够有效地区分和可视化聚类网络中的共洗脱、源内碎片和多类型加合离子。结果表明，仅依靠7种基于产物离子过滤（PIF）和中性损失过滤（NLF）的TMN分析，就能成功鉴定出497个目标化合物。该集成策略提高了离线2D-LC MS数据中靶向化合物发现的效率，在复杂样品的精确化合物标注方面也显示出巨大的可扩展性。总之，我们的研究开发了可用的概念和工具，同时为中药处方等复杂样品的高效快速化合物注释提供了一个研究范式，以YPF为例。
Component overlapping and long-time consumption hinder the data processing of offline two-dimensional liquid chromatography mass spectrometry (offline 2D-LC MS) system. Although molecular networking has been commonly employed in data processing of liquid chromatography mass spectrometry (LC-MS), its application in offline 2D-LC MS is challenged by voluminous and redundant data. In light of this, for the first time, a data deduplication and visualization strategy combining hand-in-hand alignment with targeted molecular networking (TMN) for compounds annotation of offline 2D-LC MS data was developed and applied to the chemical profile of Yupingfeng (YPF), a classical traditional Chinese medicine (TCM) prescription, as a case study. Firstly, an offline 2D-LC MS system was constructed for the separation and data acquisition of YPF extract. Then the data of 12 fractions derived from YPF were deconvoluted and aligned as a whole data file by hand-in-hand alignment, resulting in a 49.2% reduction in component overlapping (from 17951 to 9112 ions) and an improvement in the MS2spectrum quality of precursor ions. Subsequently, the MS2-similarity adjacency matrix of focused parent ions was computed by a self-building Python script, which realized the construction of an innovative TMN. Interestingly, the TMN was found to be able to efficiently distinguish and visualize the co-elution, in-source fragmentations and multi-type adduct ions in a clustering network. Consequently, a total of 497 compounds were successfully identified depending on only seven TMN analysis guided by product ions filtering (PIF) and neutral loss filtering (NLF) for the targeted compounds in YPF. This integrated strategy improved the efficiency of targeted compound discovery in offline 2D-LC MS data, also shown a huge scalability in accurate compound annotation of complex samples. In conclusion, our study developed available concepts and tools while providing a research paradigm for efficient and rapid compound annotation in complex samples such as TCM prescriptions, with YPF as an example.