Dr. Alessia Staropoli has extensive experience in the field of classical and advanced biochemistry methodologies, with particular emphasis on the analytical methodologies most commonly used in protein characterization and the definition of post-translational changes. She also has specific expertise in Bioinformatics applications, biochemical methodologies and advanced mass spectrometry techniques; finally, she has experience in the field of organic and inorganic pollutants analysis from various waste products, using advanced mass spectrometry.
The study of chemical compounds, associated with cellular processes occurring in a system, is called metabolomic: through this methodology it is possible to identify the unique fingerprint derived from these processes and to characterize the metabolic pattern of biological systems.
The presence (or absence) of specific metabolites provides important information on the physiological and functional status of the biological system or sample in question. This study, applied to agronomic interest plant species allows not only the metabolomic characterization, but also pathologies evaluation that occur in metabolic changes.
The aim of this research project is to characterize the metabolome of plant tissues and beneficial microorganisms by using mass spectrometry techniques.
In fact, the analysis aims at characterizing microorganisms employed in specific treatments activity and the plant response.
The metabolomic analysis will be carried out both on the treated vegetable tissues and on cultured filtration samples of the selected beneficial microorganisms.
The experimental approach, that is intended to be used, involves plants treatment with selected strains with beneficial effect.
Subsequently, the samples will be prepared for instrumental analysis, that will be conducted using high-performance reverse phase liquid chromatography (RP-HPLC) coupled to mass spectrometry methodologies. This will allow to obtain all the matrix components and simultaneous identification and quantification. The analysis of the data obtained from the instrument will be carried out using software for identification, by comparing with specific databases, and for the quantification of the most interesting metabolites.