4.6 Article

An Optimized SPME-GC-MS Method for Volatile Metabolite Profiling of Different Alfalfa (Medicago sativa L.) Tissues

Journal

MOLECULES
Volume 26, Issue 21, Pages -

Publisher

MDPI
DOI: 10.3390/molecules26216473

Keywords

Medicago sativa; alfalfa; SPME; GC-MS; volatiles

Funding

  1. NSF Plant Genome Research Program [0605033]
  2. Samuel Roberts Noble Foundation
  3. University of Missouri
  4. Division Of Integrative Organismal Systems
  5. Direct For Biological Sciences [0605033] Funding Source: National Science Foundation

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The optimized SPME-GC-MS method was used to quantify and qualify volatile metabolites in alfalfa glandular trichomes, leading to the detection of tissue-specific volatile metabolites. HCA and PCA analysis demonstrated that the method effectively differentiated metabolic profiles of different tissues.
Solid-phase microextraction (SPME) was coupled to gas chromatography mass spectrometry (GC-MS) and a method optimized to quantitatively and qualitatively measure a large array of volatile metabolites in alfalfa glandular trichomes isolated from stems, trichome-free stems, and leaves as part of a non-targeted metabolomics approach. Major SPME extraction parameters optimized included SPME fiber composition, extraction temperature, and extraction time. The optimized SPME method provided the most chemically diverse coverage of alfalfa volatile and semi-volatile metabolites using a DVB/CAR/PDMS fiber, extraction temperature of 60 & DEG;C, and an extraction time of 20 min. Alfalfa SPME-GC-MS profiles were processed using automated peak deconvolution and identification (AMDIS) and quantitative data extraction software (MET-IDEA). A total of 87 trichome, 59 stem, and 99 leaf volatile metabolites were detected after background subtraction which removed contaminants present in ambient air and associated with the fibers and NaOH/EDTA buffer solution containing CaCl2. Thirty-seven volatile metabolites were detected in all samples, while 15 volatile metabolites were uniquely detected only in glandular trichomes, 9 only in stems, and 33 specifically in leaves as tissue specific volatile metabolites. Hierarchical cluster analysis (HCA) and principal component analysis (PCA) of glandular trichomes, stems, and leaves showed that the volatile metabolic profiles obtained from the optimized SPME-GC-MS method clearly differentiated the three tissues (glandular trichomes, stems, and leaves), and the biochemical basis for this differentiation is discussed. Although optimized using plant tissues, the method can be applied to other types of samples including fruits and other foods.

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