Tag you're it: Application of stable isotope labeling and LC-MS to identify the precursors of specialized metabolites in plants
Untargeted liquid chromatography/mass spectrometry (LC-MS) can contribute a comprehensive and unbiased picture of the metabolic space of plants. These data can be used to quantify natural metabolite variation for genome wide association studies, to compare global metabolic responses from environmental or genetic perturbations, and to identify previously undescribed metabolites in Nature. A major limitation with untargeted metabolomics is the classification and identification of the thousands of metabolite features that can be detected in a single analytical run. Isotopic labeling improves the informational value of these datasets by categorizing metabolites as being derived from specific upstream precursors and/or to known metabolic pathways. When a 13C-labeled precursor is fed to either a plant or tissue, the downstream metabolites produced from it have a higher m/z value than the molecules in the pre-existing pool, generating an m/z peak pair that can be specifically identified within the MS data. In this paper, we outline methods and principles to consider when supplementing untargeted MS data with isotopic labeling, including how to choose the appropriate isotopic label, grow and feed plant tissues to maximize label uptake and incorporation into derivatives, optimize LC-MS methods, and interpret the resulting labeling data. Although the focus here is on annotation of amino acid-derived metabolites using LC-MS, we anticipate that the methods are generally adaptable to other precursors, plant species, and chromatographic approaches.
- Research Organization:
- Purdue Univ., West Lafayette, IN (United States)
- Sponsoring Organization:
- USDOE Office of Science (SC), Biological and Environmental Research (BER). Biological Systems Science Division; USDA
- Grant/Contract Number:
- SC0020368; 2018-08121/1019231
- OSTI ID:
- 1905560
- Journal Information:
- Methods in Enzymology, Vol. 676; ISSN 0076-6879
- Publisher:
- ElsevierCopyright Statement
- Country of Publication:
- United States
- Language:
- English
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