期刊
ANALYTICA CHIMICA ACTA
卷 614, 期 2, 页码 127-133出版社
ELSEVIER SCIENCE BV
DOI: 10.1016/j.aca.2008.03.024
关键词
metabolomics; metabolite identification; quantitative; two-dimensional; heteronuclear single quantum; coherence; nuclear magnetic resonance
资金
- Natural Environment Research Council [NER/J/S/2002/00618] Funding Source: researchfish
- NHGRI NIH HHS [R01 HG003352-01A2, R01 HG003352] Funding Source: Medline
- NIEHS NIH HHS [5 P42 ES04699, P42 ES004699, P42 ES004699-199005] Funding Source: Medline
The automated and robust identification of metabolites in a complex biological sample remains one of the greatest challenges in metabolomics. In our experiments, HSQC carbon-proton correlation NMR data with a model that takes intensity information into account improves upon the identification of metabolites that was achieved using COSY proton-proton correlation NMR data with the binary model of [Y. Xi, J.S. de Ropp, M.R. Viant, D.L. Woodruff, P. Yu, Metabolornics, 2 (2006) 221-233]. in addition, using intensity information results in easier-to-interpret grey areas for cases where it is not clear if the compound might be present. We report on highly successful experiments that identify compounds in chemically defined mixtures as well as in biological samples, and compare our two-dimensional HSQC analyses against quantification of metabolites in the corresponding one-dimensional proton NMR spectra. We show that our approach successfully employs a fully automated algorithm for identifying the presence or absence of predefined compounds (held within a library) in biological HSQC spectra, and in addition calculates upper bounds on the compound intensities. (c) 2008 Elsevier B.V. All rights reserved.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据