4.8 Article

MS-IDF: A Software Tool for Nontargeted Identification of Endogenous Metabolites after Chemical Isotope Labeling Based on a Narrow Mass Defect Filter

Journal

ANALYTICAL CHEMISTRY
Volume 94, Issue 7, Pages 3194-3202

Publisher

AMER CHEMICAL SOC
DOI: 10.1021/acs.analchem.1c04719

Keywords

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Funding

  1. Natural Science Foundation of China [82173779, 81871445, U1903119, 81773688]
  2. Fujian Province Science and Technology Project [2020Y0013]
  3. National Major Scientific and Technological Special Project for Significant New Drugs Development [2020ZX09201005]
  4. Fundamental Research Funds for the Central Universities [20720200052]
  5. Xiamen Municipal Bureau of Science and Technology Planning Project [3502Z20184051, 3502Z20194080]

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Chemical isotope labeling liquid chromatography mass spectrometry (LC-MS) is a new metabolomic strategy for quantifying and characterizing small molecular compounds in biological samples. However, data analysis is challenging due to the large amount of data and interference from biological matrices. To improve efficiency, a new software tool called MS-IDF was developed based on a narrow mass defect filter.
Chemical isotope labeling liquid chromatography mass spectrometry (LC-MS) is an emerging metabolomic strategy for the quantification and characterization of small molecular compounds in biological samples. However, its subsequent data analysis is not straightforward due to a large amount of data produced and interference of biological matrices. In order to improve the efficiency of searching and identification of target endogenous metabolites, a new software tool for nontargeted metabolomics data processing called MS-IDF was developed based on the principle of a narrow mass defect filter. The developed tool provided two function modules, including IsoFinder and MDFinder. The IsoFinder function module applied a conventional peak extraction method by using a fixed mass differences between the heavy and light labels and by the alignment of chromatographic retention time (RT). On the other hand, MDFinder was designed to incorporate the accurate mass defect differences between or among stable isotopes in the peak extraction process. By setting an appropriate filter interval, the target metabolites can be efficiently screened out while eliminating interference. Notably, the present results showed that the efficiency in compound identification using the new MDFinder module was nearly doubled as compared to the conventional IsoFinder method (an increase from 259 to 423 compounds). The Matlab codes of the developed MS-IDF software are available from github at https://github.com/jydong2018/MS_IDF. Based on the MS-IDF software tool, a novel and effective approach from nontargeted to targeted metabolomics research was developed and applied to the exploration of potential primary amine biomarkers in patients with schizophrenia. With this approach, potential biomarkers, including N,N-dimethylglycine, S-adenosine-L-methionine, DL-homocysteine, and spermidine, were discovered.

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