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Updated: Jul 8, 2026

Isolation and Compositional Analysis of Plant Cuticle Lipid Polyester Monomers
Published on: November 22, 2015
Identification and Structural Elucidation of Acylsugars in Tomato Leaves Using Liquid Chromatography-Ion
Kimberly Y Kartowikromo1, Jessica S Pizzo1,2, Thiago Rutz2
1Department of Chemistry and Biochemistry, Auburn University, Auburn, Alabama 36849, United States.
None:
Leaves of tomato plants contain various glandular trichomes that produce a wide range of metabolic products including acylsugars, which may serve as a defense mechanism against various insect pests. Acylsugars exhibit significant structural diversity, differing in their sugar cores, acylated positions, and type of acyl chains. This work demonstrated a comprehensive approach using multidimensional separation techniques, specifically liquid chromatography-ion mobility-tandem mass spectrometry (LC-IM-MS/MS), for structural characterization, and the discrimination of different tomato plants (one cultivar and five accessions) was demonstrated using tomato leaf extracts; six genotypes from five species of Solanum were represented. As a result, we identified 16 acylsugars through their molecular formulas and annotations using LC and MS analyses. The incorporation of ion mobility (IM) analysis revealed an additional 9 isomeric forms, resulting in a comprehensive total of 25 isomeric acylsugars identified. Furthermore, the experimental collision cross section (CCSexp) values agreed reasonably well with the corresponding predicted values (CCSpred), with an overall estimated error of less than 2%. These findings pave the way for research into how the different structural isomers of acylsugars might influence the self-defense mechanism in plants. Moreover, this work demonstrated that the investigated cultivar and accessions of tomatoes can be distinguished from each other based on their metabolite profile, e.g., acylsugars, with principal component analysis (PCA) and linear discriminant analysis (LDA) statistical models, yielding a prediction rate of 98.3%.
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