Statistically correlating NMR spectra and LC-MS data to facilitate the identification of individual metabolites in metabolomics mixtures

doi:10.1007/s00216-019-01600-z

	Statistically correlating NMR spectra and LC-MS data to facilitate the identification of individual metabolites in metabolomics mixtures
	Li, Xing1,2 ; Luo, Huan 1; Huang, Tao 1,2; Xu, Li 1,2; Shi, Xiaohuo 1; Hu, Kaifeng1,3
通讯作者	Hu, Kaifeng(kaifenghu@mail.kib.ac.cn)
	2019-03-01
发表期刊	ANALYTICAL AND BIOANALYTICAL CHEMISTRY
ISSN	1618-2642
卷号	411 期号:7 页码:1301-1309
摘要	NMR and LC-MS are two powerful techniques for metabolomics studies. In NMR spectra and LC-MS data collected on a series of metabolite mixtures, signals of the same individual metabolite are quantitatively correlated, based on the fact that NMR and LC-MS signals are derived from the same metabolite covary. Deconvoluting NMR spectra and LC-MS data of the mixtures through this kind of statistical correlation, NMR and LC-MS spectra of individual metabolites can be obtained as if the specific metabolite is virtually isolated from the mixture. Integrating NMR and LC-MS spectra, more abundant and orthogonal information on the same compound can significantly facilitate the identification of individual metabolites in the mixture. This strategy was demonstrated by deconvoluting 1D C-13, DEPT, HSQC, TOCSY, and LC-MS spectra acquired on 10 mixtures consisting of 6 typical metabolites with varying concentration. Based on statistical correlation analysis, NMR and LC-MS signals of individual metabolites in the mixtures can be extracted as if their spectra are acquired on the purified metabolite, which notably facilitates structure identification. Statistically correlating NMR spectra and LC-MS data (CoNaM) may represent a novel approach to identification of individual compounds in a mixture. The success of this strategy on the synthetic metabolite mixtures encourages application of the proposed strategy of CoNaM to biological samples (such as serum and cell extracts) in metabolomics studies to facilitate identification of potential biomarkers.
关键词	Deconvolution LC-MS NMR Statistical correlation Structure identification
DOI	10.1007/s00216-019-01600-z
收录类别	SCI
语种	英语
WOS记录号	WOS:000463593300001
引用统计	被引频次：11[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://ir.kib.ac.cn/handle/151853/67625
专题	植物化学与西部植物资源持续利用国家重点实验室
通讯作者	Hu, Kaifeng
作者单位	1.Chinese Acad Sci, Kunming Inst Bot, State Key Lab Phytochem & Plant Resources West Ch, 132 Lanhei Rd, Kunming 650201, Yunnan, Peoples R China 2.Univ Chinese Acad Sci, Beijing 100049, Peoples R China 3.Chengdu Univ TCM, Innovat Inst Chinese Med & Pharm, Chengdu 611137, Sichuan, Peoples R China
推荐引用方式 GB/T 7714	Li, Xing,Luo, Huan,Huang, Tao,et al. Statistically correlating NMR spectra and LC-MS data to facilitate the identification of individual metabolites in metabolomics mixtures[J]. ANALYTICAL AND BIOANALYTICAL CHEMISTRY,2019,411(7):1301-1309.
APA	Li, Xing,Luo, Huan,Huang, Tao,Xu, Li,Shi, Xiaohuo,&Hu, Kaifeng.(2019).Statistically correlating NMR spectra and LC-MS data to facilitate the identification of individual metabolites in metabolomics mixtures.ANALYTICAL AND BIOANALYTICAL CHEMISTRY,411(7),1301-1309.
MLA	Li, Xing,et al."Statistically correlating NMR spectra and LC-MS data to facilitate the identification of individual metabolites in metabolomics mixtures".ANALYTICAL AND BIOANALYTICAL CHEMISTRY 411.7(2019):1301-1309.

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