High-throughput quantification of more than 100 primary- and secondary-metabolites, and phytohormones by a single...

Plant metabolites are commonly functionally classified, as defense- or growth-related phytohormones, primary and specialized metabolites, and so forth. Analytical procedures for the quantifications of these metabolites are challenging because the metabolites can vary over several orders of magnitude in concentrations in the same tissues and have very different chemical characteristics. Plants clearly adjust their metabolism to respond to their prevailing circumstances in very sophisticated ways that blur the boundaries among these functional or chemically defined classifications. But if plant biologists want to better understand the processes that are important for a plant's adaptation to its environment, procedures are needed that can provide simultaneous quantifications of the large range of metabolites that have the potential to play central roles in these adjustments in a cost and time effective way and with a low sample consumption.

Here we present a method that combines well-established methods for the targeted analysis of phytohormones, including jasmonates, salicylic acid, abscisic acid, gibberellins, auxins and cytokinins, and extends it to the analysis of inducible and constitutive defense compounds, as well as the primary metabolites involved in the biosynthesis of specialized metabolites and responsible for nutritional quality (e.g., sugars and amino acids). The method is based on a single extraction of 10-100 mg of tissue and allows a broad quantitative screening of metabolites optimized by their chemical characteristics and concentrations, thereby providing a high throughput analysis unbiased by the putative functional attributes of the metabolites. The tissues of Nicotiana attenuata which accumulate high levels of nicotine and diterpene glycosides, provide a challenging matrix that thwarts quantitative analysis; the analysis of various tissues of this plant are used to illustrate the robustness of the procedure.

The method described has the potential to unravel various, until now overlooked interactions among different sectors of plant metabolism in a high throughput manner. Additionally, the method could be particularly beneficial as screening method in forward genetic approaches, as well as for the investigation of plants from natural populations that likely differ in metabolic traits.