Titel:
Spatial Probabilistic Mapping of Biomolecular Ensembles in Tissue via Mass Spectrometry Imaging
Beteiligte:
Abu Sammour, Denis
[VerfasserIn]
Erschienen:
Heidelberg University: HeiDok, 2023
Sprache:
Englisch
DOI:
https://doi.org/10.11588/heidok.00034101
Entstehung:
Anmerkungen:
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Beschreibung:
Tissues are a major focus of clinical research and histopathological diagnosis for a wide range of diseases. Understanding the complex biomolecular manifestations of disease within tissues by characterizing its morphology and biomolecular information content paves the way for exploring the fundamental mechanisms of pathogenesis and for identifying diagnostic and prognostic biomarkers and potential therapeutic targets. Among the many tissue-investigation techniques, matrix-assisted laser desorption/ionization mass spectrometry imaging (MALDI-MSI) has evolved into a label-free core technology for visualization and spatially-resolved ex vivo analysis of biomolecules directly from tissue samples. Ion images, i.e. false color renderings of mass-to-charge ratio (m/z) intervals of interest, are used as the fundamental investigation tool in MALDI-MSI for conveying the spatial distribution of molecules-of-interest (MOIs, e.g., metabolites, drugs, lipids or proteins) within biological tissues that are often compared to external histopathology annotations. They are considered as the gold-standard by MSI researchers against which the biomarker discovery methods are validated. However, the conversion of raw MSI data into ion images for visualization, spatial interpretation and molecular analysis, has not changed since the inception of the technology. Moreover, the generated ion images can be prone to technical artifacts, user input- and user perception-bias. This work introduces a computational framework, moleculaR, which proposes a coherent spatial probabilistic approach for mapping tissue MOIs and allowing for a user-independent spatial visualization and interpretation of MOIs' distribution in tissue samples via MSI. moleculaR uses user-independent assignment of m/z intervals for capturing MOIs based on the device- and measurement-dependent mass resolving power along with Gaussian-weighting of observed peak intensities for improved reliability of metabolite/lipid/drug signals in MSI. Instead of relying on a subjective ...