A Statistical Procedure to Selectively Detect Metabolite Signals in LC-MS Data Based on Using Variable Isotope Ratios

• Published in: Journal of the American Society for Mass Spectrometry Vol. 21; no. 2; pp. 232 - 241
• Main Authors: Lin, Lung-Cheng, Wu, Hsin-Yi, Tseng, Vincent Shin-Mu, Chen, Lien-Chin, Chang, Yu-Chen, Liao, Pao-Chi
• Format: Journal Article
• Language: English
• Published: New York Elsevier Inc 01.02.2010; Springer-Verlag; Elsevier; Springer Nature B.V
• Subjects: Algorithms; Analytical Chemistry; Animals; Bioinformatics; Biotechnology; Chemistry; Chemistry and Materials Science; Chromatographic methods and physical methods associated with chromatography; Chromatography, Liquid - methods; Computational Biology - methods; Confidence; Confidence intervals; Data mining; Diethylhexyl Phthalate - chemistry; Exact sciences and technology; Filtering; Filtration; Isotope ratios; Isotopes - chemistry; Liver - metabolism; Male; Mass spectrometry; Mass Spectrometry - methods; Metabolites; Mining; Models, Statistical; Organic Chemistry; Other chromatographic methods; Precursors; Proteomics; Rats; Rats, Wistar; Solid Phase Extraction; Retention Time Shift; Phthalate; Phthalic Anhydride; DEHP; Metabolite Signal; Pollutant; Tandem mass spectrometry; Chemical analysis; Statistical analysis; Metabolite; Coupled method; HPLC chromatography; Qualitative analysis; Isotopic composition; Mass spectrometry; Electrospray; Phthalates
• ISSN: 1044-0305; 1879-1123
• DOI: 10.1016/j.jasms.2009.10.002

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. A statistical procedure was proposed and developed to selectively detect metabolite signals in LC-MS data based on using variable isotope ratios.