Computational tools for the annotation of in-source fragments and matrix-related signals in MALDI Mass Spectrometry Imaging

Identificador:  TDX:4154

Handle: https://hdl.handle.net/20.500.11797/TDX4154

Autores:  Baquer Gómez, Gerard

Resumen:
Matrix-Assisted Laser Desorption Ionization Mass Spectrometry Imaging (MALDI-MSI) is an analytical technique used in biochemical and clinical studies to reveal the chemical composition and spatial information of organic tissues. Insource fragments and matrix-related signals clutter the spectra making the identification of each mass-to-charge (m/z) a challenge. In this thesis we develop two computational tools (rMSIfragment & rMSIcleanup) for FDR-controlled annotation of in-source fragments and matrix-related signals. We also present a full experimental and computational protocol based on a Stable Isotope Labeled MALDI matrix to discover matrix-related signals. We demonstrate high performance of our tools and protocols across multiple sample types, MALDI matrices and MS analyzers. We also find that the removal of these signals allows dimensionality reduction techniques to better focus on biologically relevant spatial features and improves metabolite annotation. Collectively, these results indicate that the annotation of insource fragments and matrix-related signals should be included in rigorous untargeted metabolomics studies using MSI.