Abstract

Mass spectrometry imaging (MSI) is a MS-based technique. It provides a way of ascertaining both spatial distribution and relative abundance of a large variety of analytes from various biological sample surfaces. MSI is able to generate distribution maps of multiple analytes simultaneously without any labeling and does not require a prior knowledge of the target analytes, thus it has become an attractive molecular histology tool. MSI has been widely used in medicine and pharmaceutical fields, while its application in plants is recent although information regarding the spatial organization of metabolic processes in plants is of great value for understanding biological questions such as plant development, plant environment interactions, gene function and regulatory processes. The application of MSI to these studies, however, is not straightforward due to the inherent complexity of the technique. In this thesis, the issues of plant sample preparation, surface properties heterogeneity, fast MSI analysis for spatially resolved population studies and data analysis are addressed. More specifically, two MSI approaches, namely matrix assisted laser desorption ionization (MALDI) imaging and desorption electrospray ionization (DESI) imaging, have been evaluated and compared by mapping the localization of a range of secondary and primary metabolites in apple and grapes, respectively. The work based on MALDI has been focused on the optimization of sample preparation for apple tissues to preserve the true quantitative localization of metabolites and on the development of specific data analysis tool to enhance the chemical identification in untargeted MSI (chapter 3). MALDI imaging allows high-spatial localization analysis of metabolites, but it is not suitable for applications where rapid and high throughput analysis is required when the absolute quantitative information is not necessary as in the case of screening a large number of lines in genomic or plant breeding programs. DESI imaging, in contrast, is suitable for high throughput applications with the potential of obtaining statistically robust results. However, DESI is still in its infancy and there are several fundamental aspects which have to be investigated before using it as a reliable technique in extensive imaging applications. With this in mind, we investigated how DESI imaging can be used to map the distribution of the major organic acids in different grapevine tissue parts, aiming at statistically comparing their distribution differences among various grapevine tissues and gaining insights into their metabolic pathways in grapevine. Our study demonstrated that this class of molecules can be successfully detected in grapevine stem sections, but the surface property differences within the structurally heterogeneous grapevine tissues can strongly affect their semi-quantitative detection in DESI, thereby masking their true distribution. Then we decided to investigate this phenomenon in details, in a series of dedicated imaging studies, and the results have been presented in chapter 4. At the same time, during DESI experiments we have observed the production of the dianions of small dicarboxylates acids. We further studied the mechanism of formation of such species in the ion source proposing the use of doubly charged anions as a possible proxy to visualize the distributions of organic acid salts directly in plant tissues (chapter 5). The structural organization of the PhD thesis is as below: Chapter one and Chapter two describe the general MSI principle, compare the most widely used MSI ion sources, and discuss the current status in MSI data pre-processing and statistical methods. Due to the importance of sample preparation in MSI, sample handling for plant samples is independently reviewed in chapter two, with all the essential steps being fully discussed. The first two chapters describe the comprehensive picture regarding to MSI in plants. Chapter three presents high spatial and high mass resolution MALDI imaging of flavonols and dihydrochalcones in apple. Besides its importance in plant research, our results demonstrate that how data analysis as such Intensity Correlation Analysis could benefit untargeted MSI analysis. Chapter four discusses how sample surface property differences in a structurally/biologically heterogeneous sample affect the quantitative mapping of analytes in the DESI imaging of organic acids in grapevine tissue sections. Chapter five discusses the mechanism of formation of dicarboxylate dianions in DESI and ESI Chapter six summarizes the work in the thesis and discusses the future perspectives.