Improved Annotation of Untargeted Metabolomics Data through Buffer Modifications That Shift Adduct Mass and Intensity
Abstract
Annotation of untargeted high-resolution full-scan LC-MS metabolomics data remains challenging due to individual metabolites generating multiple LC-MS peaks arising from isotopes, adducts and fragments. Adduct annotation is a particular challenge, as the same mass difference between peaks can arise from adduct formation, fragmentation, or different biological species. To address this, here we describe a Buffer Modification Workflow (BMW), in which the same sample is run by LC-MS in both liquid chromatography solvent with 14NH3-acetate buffer, and in solvent with the buffer modified with 15NH3-formate. Buffer switching results in characteristic mass and signal intensity changes for adduct peaks, facilitating their annotation. This relatively simple and convenient chromatography modification annotated yeast metabolomics data with similar effectiveness to growing the yeast in isotope-labeled media. Application to mouse liver data annotated both known metabolite and known adduct peaks with 95% accuracy. Altogether, it identified 26% of ~ 27,000 liver LC-MS features as putative metabolites, of which ~ 2600 showed HMDB or KEGG database formula match. This workflow is well-suited to biological samples that cannot be readily isotope labeled, including plants, mammalian tissues, and tumors.
- Publication Date:
- Research Org.:
- Center for Advanced Bioenergy and Bioproducts Innovation (CABBI), Urbana, IL (United States)
- Sponsoring Org.:
- USDOE Office of Science (SC), Biological and Environmental Research (BER); National Institutes of Health (NIH)
- OSTI Identifier:
- 1807696
- Grant/Contract Number:
- SC0018420; DP1DK113643; R50CA211437
- Resource Type:
- Accepted Manuscript
- Journal Name:
- Analytical Chemistry
- Additional Journal Information:
- Journal Volume: 92; Journal Issue: 17; Journal ID: ISSN 0003-2700
- Publisher:
- American Chemical Society (ACS)
- Country of Publication:
- United States
- Language:
- English
- Subject:
- 37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CHEMISTRY; Anatomy; Adducts; Metabolism; Ions; Organic compounds
Citation Formats
Lu, Wenyun, Xing, Xi, Wang, Lin, Chen, Li, Zhang, Sisi, McReynolds, Melanie R., and Rabinowitz, Joshua D. Improved Annotation of Untargeted Metabolomics Data through Buffer Modifications That Shift Adduct Mass and Intensity. United States: N. p., 2020.
Web. doi:10.1021/acs.analchem.0c00985.
Lu, Wenyun, Xing, Xi, Wang, Lin, Chen, Li, Zhang, Sisi, McReynolds, Melanie R., & Rabinowitz, Joshua D. Improved Annotation of Untargeted Metabolomics Data through Buffer Modifications That Shift Adduct Mass and Intensity. United States. https://doi.org/10.1021/acs.analchem.0c00985
Lu, Wenyun, Xing, Xi, Wang, Lin, Chen, Li, Zhang, Sisi, McReynolds, Melanie R., and Rabinowitz, Joshua D. Thu .
"Improved Annotation of Untargeted Metabolomics Data through Buffer Modifications That Shift Adduct Mass and Intensity". United States. https://doi.org/10.1021/acs.analchem.0c00985. https://www.osti.gov/servlets/purl/1807696.
@article{osti_1807696,
title = {Improved Annotation of Untargeted Metabolomics Data through Buffer Modifications That Shift Adduct Mass and Intensity},
author = {Lu, Wenyun and Xing, Xi and Wang, Lin and Chen, Li and Zhang, Sisi and McReynolds, Melanie R. and Rabinowitz, Joshua D.},
abstractNote = {Annotation of untargeted high-resolution full-scan LC-MS metabolomics data remains challenging due to individual metabolites generating multiple LC-MS peaks arising from isotopes, adducts and fragments. Adduct annotation is a particular challenge, as the same mass difference between peaks can arise from adduct formation, fragmentation, or different biological species. To address this, here we describe a Buffer Modification Workflow (BMW), in which the same sample is run by LC-MS in both liquid chromatography solvent with 14NH3-acetate buffer, and in solvent with the buffer modified with 15NH3-formate. Buffer switching results in characteristic mass and signal intensity changes for adduct peaks, facilitating their annotation. This relatively simple and convenient chromatography modification annotated yeast metabolomics data with similar effectiveness to growing the yeast in isotope-labeled media. Application to mouse liver data annotated both known metabolite and known adduct peaks with 95% accuracy. Altogether, it identified 26% of ~ 27,000 liver LC-MS features as putative metabolites, of which ~ 2600 showed HMDB or KEGG database formula match. This workflow is well-suited to biological samples that cannot be readily isotope labeled, including plants, mammalian tissues, and tumors.},
doi = {10.1021/acs.analchem.0c00985},
journal = {Analytical Chemistry},
number = 17,
volume = 92,
place = {United States},
year = {Thu Jul 02 00:00:00 EDT 2020},
month = {Thu Jul 02 00:00:00 EDT 2020}
}
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