Metabolomic fingerprinting of plants.

Metabolomic fingerprinting is a high-throughput technology which mostly uses advanced methods to characterize and quantify hundreds to thousands of low molecular weight analytes simultaneously, using targeted or untargeted approaches. In metabolomics studies, mass spectrometry (MS) is widely used being a very effective methodology for identifying, characterizing and quantifying unknowns. High sensitivity, high selectivity and high specificity are some of its main features. Modern MS offers an array of technologies that differ in operational principles and performance, ranging from a wide range of ionization techniques, to high resolution and many methods for inducing dissociations of ions. A typical strategy in metabolomics is coupling MS with chromatography. So GC-MS and LC-MS are used for studying mixtures of volatile or polar compounds, respectively. Another approach consists of direct MS analyses of sample crude mixtures without chromatographic separations. This approach, even if less informative, provides a high throughput screening tool and allows a direct comparison between different samples. Direct MS applicability in metabolomics is broadened by advanced instrumentation capable of high resolution, accurate mass measurements, and tandem mass spectrometry methods.In this communication, a direct MS strategy for obtaining metabolomic fingerprinting of plants is presented and applied for characterizing polar metabolites in seeds, sprouts and young leaves of Brassica oleracea var. acephala (black cabbage). Electrospray ionization in positive and negative ion mode has been used together with tandem MS and high resolution measurements. Databases have been also used for identification of unknowns. Different classes of compounds, ranging from glucosinolates, hydroxycinnamic acids and galactolipids have been identified and their variations in seeds and in different developmental stages of plantlets have been determined. A correct interpretation of the data is mandatory for avoiding errors in assigning structures