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High-throughput quantitative analysis of phytohormones in sorghum leaf and root tissue by ultra-performance liquid chromatography-mass spectrometry
- Sheflin, Amy M., Kirkwood, Jay S., Wolfe, Lisa M., Jahn, Courtney E., Broeckling, Corey D., Schachtman, Daniel P., Prenni, Jessica E.
- Analytical and bioanalytical chemistry 2019 v.411 no.19 pp. 4839-4848
- abscisic acid, auxins, brassinosteroids, cytokinins, data collection, environmental factors, genetic factors, genome, genotype, gibberellins, jasmonic acid, leaves, liquid-liquid extraction, mass spectrometry, metabolome, microbiome, plant development, plant tissues, quantitative analysis, salicylates, soil, solid phase extraction, transcriptome, ultra-performance liquid chromatography
- Plant development, growth, and adaptation to stress are regulated by phytohormones, which can influence physiology even at low concentrations. Phytohormones are chemically grouped according to both structure and function as auxins, cytokinins, abscisic acid, jasmonates, salicylates, gibberellins, and brassinosteroids, among others. This chemical diversity and requirement for highly sensitive detection in complex matrices create unique challenges for comprehensive phytohormone analysis. Here, we present a robust and efficient quantitative UPLC-MS/MS assay for 17 phytohormones, including jasmonates, salicylates, abscisic acid, gibberellins, cytokinins, and auxins. Using this assay, 12 phytohormones were detected and quantified in sorghum plant tissue without the need for solid phase extraction (SPE) or liquid-liquid extraction. Variation of phytohormone profiles was explored in both root and leaf tissues between three genotypes, harvested at two different developmental time points. The results highlight the importance of tissue type, sampling time, and genetic factors when designing experiments that involve phytohormone analysis of sorghum. This research lays the groundwork for future studies, which can combine phytohormone profiling with other datasets such as transcriptome, soil microbiome, genome, and metabolome data, to provide important functional information about adaptation to stress and other environmental variables.