RationaleMass spectrometry imaging of young seedlings is an invaluable tool in understanding how mutations affect metabolite accumulation in plant development. However, due to numerous biological considerations, established methods for the relative quantification of analytes using infrared matrix‐assisted laser desorption electrospray ionization (IR‐MALDESI) mass spectrometry imaging are not viable options. In this study, we report a method for the quantification of auxin‐related compounds using stable‐isotope‐labelled (SIL) indole‐3‐acetic acid (IAA) doped into agarose substrate. MethodsWild‐typeArabidopsis thalianaseedlings,sur2andwei8 tar2loss‐of‐function mutants, andYUC1gain‐of‐function line were grown for 3 days in the dark in standard growth medium. SIL‐IAA was doped into a 1% low‐melting‐point agarose gel and seedlings were gently laid on top for IR‐MALDESI imaging with Orbitrap mass spectrometry analysis. Relative quantification was performed post‐acquisition by normalization of auxin‐related compounds to SIL‐IAA in the agarose. Amounts of auxin‐related compounds were compared between genotypes to distinguish the effects of the mutations on the accumulation of indolic metabolites of interest. ResultsIAA added to agarose was found to remain stable, with repeatability and abundance features of IAA comparable with those of other compounds used in other methods for relative quantification in IR‐MALDESI analyses. Indole‐3‐acetaldoxime was increased insur2mutants compared with wild‐type and other mutants. Other auxin‐related metabolites were either below the limits of quantification or successfully quantified but showing little difference among mutants. ConclusionsAgarose was shown to be an appropriate sampling surface for IR‐MALDESI mass spectrometry imaging ofArabidopsisseedlings. SIL‐IAA doping of agarose was demonstrated as a viable technique for relative quantification of metabolites in live seedlings or tissues with similar biological considerations.
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Staring into the void: demystifying microbial metabolomics
ABSTRACT Metabolites give us a window into the chemistry of microbes and are split into two subclasses: primary and secondary. Primary metabolites are required for life whereas secondary metabolites have historically been classified as those appearing after exponential growth and are not necessarily needed for survival. Many microbial species are estimated to produce hundreds of metabolites and can be affected by differing nutrients. Using various analytical techniques, metabolites can be directly detected in order to elucidate their biological significance. Currently, a single experiment can produce anywhere from megabytes to terabytes of data. This big data has motivated scientists to develop informatics tools to help target specific metabolites or sets of metabolites. Broadly, it is imperative to identify clear biological questions before embarking on a study of metabolites (metabolomics). For instance, studying the effect of a transposon insertion on phenazine biosynthesis in Pseudomonas is a very different from asking what molecules are present in a specific banana-derived strain of Pseudomonas. This review is meant to serve as a primer for a ‘choose your own adventure’ approach for microbiologists with limited mass spectrometry expertise, with a strong focus on liquid chromatography mass spectrometry based workflows developed or optimized within the past five years.
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- Award ID(s):
- 1817955
- PAR ID:
- 10101311
- Date Published:
- Journal Name:
- FEMS Microbiology Letters
- Volume:
- 366
- Issue:
- 11
- ISSN:
- 1574-6968
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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