Peak annotation and data analysis software tools for mass spectrometry imaging

Identificador:  TDX:4204

Handle: https://hdl.handle.net/20.500.11797/TDX4204

Autores:  Sementé Fernández, Lluc

Resumen:
Spatial metabolomics is the discipline that studies the images of the distributions of low weight chemical compounds (metabolites) on the surface of biological tissues to unveil interactions between molecules. Mass spectrometry imaging (MSI) is currently the principal technique to get molecular imaging information for spatial metabolomics. MSI is a labelfree molecular imaging technology that produces mass spectra preserving the spatial structures of tissue samples. This is achieved by ionizing small portions of a sample (a pixel) in a defined raster through all its surface, which results in a collection of ion distribution images (registered as mass-to-charge ratios (m/z)) over the sample. This thesis is aimed to develop computational tools for peak annotation in MSI and in the design of workflows for the statistical and multivariate analysis of MSI data, including spatial segmentation. The work carried out in this thesis can be clearly separated in two parts. Firstly, the development of an isotope and adduct peak annotation tool suited to facilitate the identification of the low mass range compounds. We can now easily find monoisotopic ions in our MSI datasets thanks to the rMSIannotation software package. Secondly, the development of software tools for data analysis and spatial segmentation based on soft clustering for MSI data. In this thesis, we have developed tools and methodologies to search for significant ions (rMSIKeyIon software package) and for the soft clustering of tissues (Fuzzy c-means algorithm).