A statistical procedure to selectively detect metabolite signals in LC-MS data based on using variable isotope ratios |
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Authors: | Lung-Cheng Lin Hsin-Yi Wu Vincent Shin-Mu Tseng Lien-Chin Chen Yu-Chen Chang Pao-Chi Liao |
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Affiliation: | 1.Department of Environmental and Occupational Health, College of Medicine,National Cheng Kung University,Tainan,Taiwan;2.Department of Computer Science and Information Engineering,National Cheng Kung University,Tainan,Taiwan;3.Sustainable Environment Research Center,National Cheng Kung University,Tainan,Taiwan |
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Abstract: | The tracing of metabolite signals in LC-MS data using stable isotope-labeled compounds has been described in the literature. However, the filtering efficiency and confidence when mining metabolite signals in complex LC-MS datasets can be improved. Here, we propose an additional statistical procedure to increase the compound-derived signal mining efficiency. This method also provides a highly confident approach to screen out metabolite signals because the correlation of varying concentration ratios of native/stable isotope-labeled compounds and their instrumental response ratio is used. An in-house computational program [signal mining algorithm with isotope tracing (SMAIT)] was developed to perform the statistical procedure. To illustrate the SMAIT concept and its effectiveness for mining metabolite signals in LC-MS data, the plasticizer, di-(2-ethylhexyl) phthalate (DEHP), was used as an example. The statistical procedure effectively filtered 15 probable metabolite signals from 3617 peaks in the LC-MS data. These probable metabolite signals were considered structurally related to DEHP. Results obtained here suggest that the statistical procedure could be used to confidently facilitate the detection of probable metabolites from a compound-derived precursor presented in a complex LC-MS dataset. |
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