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A compounds annotation strategy using targeted molecular networking for offline two-dimensional liquid chromatography-mass spectrometry analysis: Yupingfeng as a case study

文献类型：期刊论文

作者Zhu, Haodong5; He, Liangliang4,5; Wu, Wenyong1,6; Duan, Huifang5; Chen, Jiali5; Xiao, Qiang5; Lin, Pei5; Qin, Zifei2; Dai, Yi5; Wu, Wanying1,3,6
刊名JOURNAL OF CHROMATOGRAPHY A
出版日期2023-08-02
卷号1702页码:13
ISSN号0021-9673
关键词Compounds visual annotation MS2-similarity adjacency matrix Targeted molecular networking Offline two-dimensional liquid chromatography mass spectrometry systems Yupingfeng
DOI10.1016/j.chroma.2023.464045
通讯作者Wu, Wanying(wanyingwu@simm.ac.cn) ; Hu, Liufang(huliufang0404@163.com) ; Yao, Zhihong(yaozhihong_jnu@163.com)
英文摘要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 spectrome-try (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 align-ment 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 de-convoluted 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 MS2 spectrum 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 fragmenta-tions 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 ac-curate 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. (c) 2023 Elsevier B.V. All rights reserved.
WOS关键词NATURAL COMPOUNDS CHARACTERIZATION ; TRADITIONAL CHINESE MEDICINE ; PRECURSOR IONS LIST ; GINSENOSIDES ; METABOLITES ; DISCOVERY ; FRAGMENTATION ; RESOLUTION ; PRODUCTS ; LEAVES
资助项目Guangdong Basic and Applied Basic Research Foundation[2023A1515011144] ; National Natural Science Foundation of China[81974519] ; National Natural Science Foundation of China[82003914] ; Natural Science Foundation of Guangdong Province[2019A1515110391] ; Guangzhou Key Laboratory of Formula-Pattern of Traditional Chinese Medicine[202102010014] ; National Major Scientific and Program of Introducing Talents of Discipline to Universities[B13038]
WOS研究方向Biochemistry & Molecular Biology ; Chemistry
语种英语
出版者ELSEVIER
WOS记录号WOS:001009151300001
源URL[http://119.78.100.183/handle/2S10ELR8/306302]  
专题中国科学院上海药物研究所
通讯作者Wu, Wanying; Hu, Liufang; Yao, Zhihong
作者单位1.Nanjing Univ Chinese Med, Sch Chinese Mat Med, Nanjing 210023, Peoples R China
2.Zhengzhou Univ, Affiliated Hosp 1, Dept Pharm, Zhengzhou 450052, Peoples R China
3.Jinan Univ, Coll Pharm, Guangzhou 510632, Peoples R China
4.Jinan Univ, Sch Tradit Chinese Med, Guangzhou Key Lab Formula Pattern Tradit Chinese M, Guangzhou 510632, Peoples R China
5.Jinan Univ, Inst Tradit Chinese Med & Nat Prod, Guangdong Prov Key Lab Pharmacodynam Constituents, Int Cooperat Lab Tradit Chinese Med Modernizat & I, Guangzhou 510632, Peoples R China
6.Chinese Acad Sci, Shanghai Inst Mat Med, Natl Engn Res Ctr TCM Standardizat Technol, Shanghai 201203, Peoples R China
推荐引用方式
GB/T 7714		 Zhu, Haodong,He, Liangliang,Wu, Wenyong,et al. A compounds annotation strategy using targeted molecular networking for offline two-dimensional liquid chromatography-mass spectrometry analysis: Yupingfeng as a case study[J]. JOURNAL OF CHROMATOGRAPHY A,2023,1702:13.
APA Zhu, Haodong.,He, Liangliang.,Wu, Wenyong.,Duan, Huifang.,Chen, Jiali.,...&Yao, Zhihong.(2023).A compounds annotation strategy using targeted molecular networking for offline two-dimensional liquid chromatography-mass spectrometry analysis: Yupingfeng as a case study.JOURNAL OF CHROMATOGRAPHY A,1702,13.
MLA Zhu, Haodong,et al."A compounds annotation strategy using targeted molecular networking for offline two-dimensional liquid chromatography-mass spectrometry analysis: Yupingfeng as a case study".JOURNAL OF CHROMATOGRAPHY A 1702(2023):13.

入库方式： OAI收割

来源：上海药物研究所

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