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Anal Methods. 2017 Apr 21;9(15):2275-2283. doi: 10.1039/C7AY00291B. Epub 2017 Mar 10.

Analysis of Stable Isotope Assisted Metabolomics Data Acquired by High Resolution Mass Spectrometry.

Wei X1,2,3,4, Lorkiewicz PK2,5,6, Shi B1,2,3,4, Salabei JK7,6, Hill BG7,5,6, Kim S8, McClain CJ9,7,6,3,4,10, Zhang X1,9,2,3,4.

Author information

1
Department of Chemistry, University of Louisville, Louisville, KY 40292, United States.
2
Center for Regulatory and Environmental Analytical Metabolomics, University of Louisville, Louisville, KY 40292, United States.
3
Alcohol Research Center, University of Louisville, Louisville, KY 40292, United States.
4
Hepatobiology & Toxicology Program, University of Louisville, Louisville, KY 40292, United States.
5
Institute of Molecular Cardiology, University of Louisville, Louisville, KY 40292, United States.
6
Diabetes and Obesity Center, University of Louisville, Louisville, KY 40292, United States.
7
Medicine, University of Louisville, Louisville, KY 40292, United States.
8
Biostatistics Core, Karmanos Cancer Institute, Department of Oncology, School of Medicine, Wayne State University, Detroit, MI 48201, United States.
9
Pharmacology & Toxicology, University of Louisville, Louisville, KY 40292, United States.
10
Robley Rex Louisville VAMC, Louisville, Kentucky 40292, United States.

Abstract

Stable isotope assisted metabolomics (SIAM) uses stable isotope tracers to support studies of biochemical mechanisms. We report a suite of data analysis algorithms for automatic analysis of SIAM data acquired on a high resolution mass spectrometer. To increase the accuracy of isotopologue assignment, metabolites detected in the unlabeled samples were used as reference metabolites to generate possible isotopologue candidates for analysis of peaks detected in the labeled samples. An iterative linear regression model was developed to deconvolute the overlapping isotopic peaks of isotopologues present in a full MS spectrum, where the threshold for the weight factor was determined by a simulation study assuming different levels of Gaussian white noise contamination. A normalization method enabling isotope ratio-based normalization was implemented to study the difference of isotopologue abundance distribution between sample groups. The developed method can analyze SIAM data acquired by direct infusion MS and LC-MS, and can handle metabolite tracers containing different tracer elements. Analysis of SIAM data acquired from mixtures of known compounds showed that the developed algorithms accurately identify metabolites and quantify stable isotope enrichment. Application of SIAM data acquired from a biological study further demonstrated the effectiveness and accuracy of the developed method for analysis of complex samples.

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