Quantifying terpenes in tomato leaf extracts from different species using gas chromatography-mass spectrometry (GC-MS).

Terpenes play a vital role in plant defense; tomato plants produce a diverse range of terpenes within specialized glandular trichomes, influencing interactions with herbivores, predators, and pollinators. This study employed two distinct methods, namely leaf dip and maceration, to extract trichomes from tomato leaves. Terpene quantification was carried out using Gas Chromatography-Mass Spectrometry (GC-MS). The leaf dip method proved effective in selectively targeting trichome content, revealing unique extraction patterns compared to maceration. The GC-MS method demonstrated high linearity, accuracy, sensitivity, and low limits of detection and quantification. Application of the method to different tomato species (Solanum pennellii, Solanum pimpinellifolium, Solanum galapagense, Solanum habrochaites, and Solanum lycopersicum) identified significant variation in terpene content among these species, highlighting the potential of specific accessions for breeding programs. Notably, the terpene α-zingiberene, known for its repellency against whiteflies, was found in high quantities (211.90-9155.13 µg g-1) in Solanum habrochaites accession PI209978. These findings provide valuable insights into terpenoid diversity for plant defense mechanisms, guiding future research on developing pest-resistant tomato cultivars. Additionally, the study underscores the broader applications of terpenes in agriculture.

Collision cross section measurement and prediction methods in omics.

Omics studies such as metabolomics, lipidomics, and proteomics have become important for understanding the mechanisms in living organisms. However, the compounds detected are structurally different and contain isomers, with each structure or isomer leading to a different result in terms of the role they play in the cell or tissue in the organism. Therefore, it is important to detect, characterize, and elucidate the structures of these compounds. Liquid chromatography and mass spectrometry have been utilized for decades in the structure elucidation of key compounds. While prediction models of parameters (such as retention time and fragmentation pattern) have also been developed for these separation techniques, they have some limitations. Moreover, ion mobility has become one of the most promising techniques to give a fingerprint to these compounds by determining their collision cross section (CCS) values, which reflect their shape and size. Obtaining accurate CCS enables its use as a filter for potential analyte structures. These CCS values can be measured experimentally using calibrant-independent and calibrant-dependent approaches. Identification of compounds based on experimental CCS values in untargeted analysis typically requires CCS references from standards, which are currently limited and, if available, would require a large amount of time for experimental measurements. Therefore, researchers use theoretical tools to predict CCS values for untargeted and targeted analysis. In this review, an overview of the different methods for the experimental and theoretical estimation of CCS values is given where theoretical prediction tools include computational and machine modeling type approaches. Moreover, the limitations of the current experimental and theoretical approaches and their potential mitigation methods were discussed.