Phd in CHEMICAL SCIENCES

Thesis title: Development of innovative analytical methods based on omics sciences for the characterization of complex matrices

In the post-genomic era, the omics sciences have revolutionized the approach to system biology, opening up unraveled paths in clinical, pharmaceutical, and food research. The omics sciences are based on multidisciplinary expertise and advanced technologies for aiming at the comprehensive characterization of all compounds within a macro-class, e.g., DNA, proteins, or metabolites. The introduction of high-resolution mass spectrometry, which allows measuring accurate mass to charge ratios, allowed to overcome the traditional targeted approaches, in which a limited number of compounds are analyzed, in favor of untargeted proteomics or metabolomics studies, in which no previous knowledge of the sample composition is required. Whether it is the characterization of bioactive compounds in food matrices or the research of possible biomarkers related to the insurgence of a pathology, untargeted metabolomics is the prime resource for maximum identification. In the case of metabolomics, however, a simultaneous tout-court analysis suffers from many limitations related to the structural inconsistency of the metabolome. In general, no metabolomics approach would have the same performance throughout all classes of metabolites. Common workflows, in fact, avoid sample pretreatment to prevent losses of metabolite classes and keep data acquisition and processing to the simplest. As a result, many low-abundance compounds that could possibly be significant to the study are commonly neglected. For dealing with the structural inconsistency of metabolites, several branches of metabolomics have emerged in the last few years, such as lipidomics, which focuses on lipids, or glycomics, that studies sugars and carbohydrates. In the present thesis, several different approaches to the untargeted characterization of structurally-related classes of compounds will be presented that intervene in the three main steps of untargeted metabolomics, i.e., sample preparation, data acquisition, and data analysis. The doctoral project was articulated in three main research lines: (i) short peptidomics, (ii) major and minor phytocompounds, and (iii) phosphocholine-containing lipids. After the method development, the analytical workflow was eventually employed for one or more applications in the clinical, food, and plant research fields. Short peptides are a class of peptides that is neglected by common proteomics and peptidomics studies, despite their enhanced biological activities and their possible significance in clinical research. A dedicated workflow was optimized, based on a carbon-based sorbent for purification and enrichment, and suspect screening mass spectrometric data acquisition and data analysis. Following the promising results obtained with short peptides, untargeted phytochemical analyses were approached. Despite being of great scientific interest for their broad range of biological activities, the study of phenolic compounds and phytocannabinoids has usually been limited to the major constituents. By setting up proper mass spectrometric and data analysis methods, a comprehensive characterization of phenolic compounds and phytocannabinoids in cannabis sativa was achieved. Minor phytocannabinoids were then proven to play a crucial role in the classification of cannabis accessions in chemotypes. The last part of the doctoral project was aimed at solving the isomeric mass overlaps that complicate the untargeted identification of phosphocholine-containing lipids. For this purpose, a buffer modification strategy was optimized by experimental design followed by a data processing workflow that was customized specifically for the purpose. The results presented in this thesis demonstrated the role of proper method optimization in untargeted mass spectrometry for gaining knowledge on structurally-related classes of compounds of significant scientific interest.