{"id":{"repo_id":"bielefeld","oai_identifier":"oai:pub.uni-bielefeld.de:3005107"},"canonical_url":"https://search.dev.ndltd.org/etd/bielefeld/oai:pub.uni-bielefeld.de:3005107","repository":{"repo_id":"bielefeld","name":"Universität Bielefeld","base_url":"https://pub.uni-bielefeld.de/oai"},"display":{"title":"A novel approach to processing and visualizing mass spectrometry imaging data for a new perspective in untargeted analyses","abstract":"Metabolomics plays a crucial role in disease research by enabling the comprehensive analysis of small molecules. Among the key techniques, mass spectrometry imaging (MSI) stands out due to its ability to identify and spatially map metabolites within a single experiment. By combining molecular analysis with morphological visualization, MSI serves as a valuable complement to classical pathology, supporting both targeted and untargeted analyses. However, the complexity of high-dimensional MSI data necessitates robust pre-processing to enhance interpretability and maximize information extraction. This thesis introduces ProViM (Processing for Visualization and Multivariate Analysis of MSI Data), a novel approach to MSI data processing. In addition to standard pre-processing steps like normalisation, ProViM enables the subtraction of interfering signals from matrix and artifacts, which often obscure tissue-specific metabolites and compromise visualization. The application of ProViM results in MSI data that is more interpretable, facilitating more accurate and meaningful visualization. Furthermore, QUIMBI (QUIck Exploration Tool for Multivariate BioImages) is presented as an interactive tool designed for real-time visual exploration of MSI datasets. Unlike conventional methods, QUIMBI processes the entire dataset dynamically and displays co-locations at the pixel level, allowing users to examine both morphological and spectral features simultaneously. The integration of ProViM as a pre-processing step enhances the visibility of tissue-specific signal distributions in QUIMBI visualizations, thus enabling the detection of subtle metabolic patterns that might otherwise remain undetected. To demonstrate the potential of these tools, ProViM and QUIMBI were applied in an untargeted analysis to compare metabolic similarities between Pseudoxanthoma Elasticum (PXE), a rare connective tissue disorder, and atherosclerosis, a chronic inflammatory disease. MSI data from PXE skin was processed using ProViM and visualized with QUIMBI. This approach revealed metabolic signals in PXE skin that were previously hidden in single mass channel image analyses. Comparative analysis identified corresponding masses in atherosclerotic lesions, and high-resolution MSI techniques (MALDI-Orbitrap MSI and timsTOF) confirmed these as known markers of atherosclerosis. The findings highlight ProViM and QUIMBI's effectiveness in interpreting and visualizing MSI data for clinical pathology and medical research. By enhancing metabolic pattern detection, these methods aid targeted and untargeted analyses, deepening the understanding of disease mechanisms. Their application to PXE and atherosclerosis tissues shows potential in revealing molecular similarities, paving the way for research into metabolic biomarkers and pathological processes.","abstract_html":"Metabolomics plays a crucial role in disease research by enabling the comprehensive analysis of small molecules. Among the key techniques, mass spectrometry imaging (MSI) stands out due to its ability to identify and spatially map metabolites within a single experiment. By combining molecular analysis with morphological visualization, MSI serves as a valuable complement to classical pathology, supporting both targeted and untargeted analyses. However, the complexity of high-dimensional MSI data necessitates robust pre-processing to enhance interpretability and maximize information extraction. This thesis introduces ProViM (Processing for Visualization and Multivariate Analysis of MSI Data), a novel approach to MSI data processing. In addition to standard pre-processing steps like normalisation, ProViM enables the subtraction of interfering signals from matrix and artifacts, which often obscure tissue-specific metabolites and compromise visualization. The application of ProViM results in MSI data that is more interpretable, facilitating more accurate and meaningful visualization. Furthermore, QUIMBI (QUIck Exploration Tool for Multivariate BioImages) is presented as an interactive tool designed for real-time visual exploration of MSI datasets. Unlike conventional methods, QUIMBI processes the entire dataset dynamically and displays co-locations at the pixel level, allowing users to examine both morphological and spectral features simultaneously. The integration of ProViM as a pre-processing step enhances the visibility of tissue-specific signal distributions in QUIMBI visualizations, thus enabling the detection of subtle metabolic patterns that might otherwise remain undetected. To demonstrate the potential of these tools, ProViM and QUIMBI were applied in an untargeted analysis to compare metabolic similarities between Pseudoxanthoma Elasticum (PXE), a rare connective tissue disorder, and atherosclerosis, a chronic inflammatory disease. MSI data from PXE skin was processed using ProViM and visualized with QUIMBI. This approach revealed metabolic signals in PXE skin that were previously hidden in single mass channel image analyses. Comparative analysis identified corresponding masses in atherosclerotic lesions, and high-resolution MSI techniques (MALDI-Orbitrap MSI and timsTOF) confirmed these as known markers of atherosclerosis. The findings highlight ProViM and QUIMBI's effectiveness in interpreting and visualizing MSI data for clinical pathology and medical research. By enhancing metabolic pattern detection, these methods aid targeted and untargeted analyses, deepening the understanding of disease mechanisms. Their application to PXE and atherosclerosis tissues shows potential in revealing molecular similarities, paving the way for research into metabolic biomarkers and pathological processes.","abstract_has_math":false,"creators":["Zurowietz, Annika"],"institution":"Universität Bielefeld","degree_name":null,"degree_level":"thesis.doctoral","degree_discipline":null,"degree_department":null,"school":null,"contributors":[],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2025,"date_issued":"2025-07-07","date_published":"2025-07-07","updated_at":"2026-07-27T18:50:12Z","subjects":[],"languages":[],"rights":[],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"https://pub.uni-bielefeld.de/record/3005107","outbound_label":"Repository record","outbound_source":"source_url"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:creator","label":"Author","values":["Zurowietz, Annika"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:publisher","label":"Institution","values":["Universitätsbibliothek Bielefeld"]},{"key":"dc:type","label":"Dc Type","values":["doctoralThesis"]},{"key":"thesis:degree_level","label":"Degree Level","values":["thesis.doctoral"]},{"key":"thesis:institution_name","label":"Thesis Institution Name","values":["Universität Bielefeld"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description.abstract","label":"Abstract","values":["Metabolomics plays a crucial role in disease research by enabling the comprehensive analysis of small molecules. Among the key techniques, mass spectrometry imaging (MSI) stands out due to its ability to identify and spatially map metabolites within a single experiment. By combining molecular analysis with morphological visualization, MSI serves as a valuable complement to classical pathology, supporting both targeted and untargeted analyses. However, the complexity of high-dimensional MSI data necessitates robust pre-processing to enhance interpretability and maximize information extraction. This thesis introduces ProViM (Processing for Visualization and Multivariate Analysis of MSI Data), a novel approach to MSI data processing. In addition to standard pre-processing steps like normalisation, ProViM enables the subtraction of interfering signals from matrix and artifacts, which often obscure tissue-specific metabolites and compromise visualization. The application of ProViM results in MSI data that is more interpretable, facilitating more accurate and meaningful visualization. Furthermore, QUIMBI (QUIck Exploration Tool for Multivariate BioImages) is presented as an interactive tool designed for real-time visual exploration of MSI datasets. Unlike conventional methods, QUIMBI processes the entire dataset dynamically and displays co-locations at the pixel level, allowing users to examine both morphological and spectral features simultaneously. The integration of ProViM as a pre-processing step enhances the visibility of tissue-specific signal distributions in QUIMBI visualizations, thus enabling the detection of subtle metabolic patterns that might otherwise remain undetected. To demonstrate the potential of these tools, ProViM and QUIMBI were applied in an untargeted analysis to compare metabolic similarities between Pseudoxanthoma Elasticum (PXE), a rare connective tissue disorder, and atherosclerosis, a chronic inflammatory disease. MSI data from PXE skin was processed using ProViM and visualized with QUIMBI. This approach revealed metabolic signals in PXE skin that were previously hidden in single mass channel image analyses. Comparative analysis identified corresponding masses in atherosclerotic lesions, and high-resolution MSI techniques (MALDI-Orbitrap MSI and timsTOF) confirmed these as known markers of atherosclerosis. The findings highlight ProViM and QUIMBI's effectiveness in interpreting and visualizing MSI data for clinical pathology and medical research. By enhancing metabolic pattern detection, these methods aid targeted and untargeted analyses, deepening the understanding of disease mechanisms. Their application to PXE and atherosclerosis tissues shows potential in revealing molecular similarities, paving the way for research into metabolic biomarkers and pathological processes."]},{"key":"dc:format.medium","label":"Dc Format Medium","values":["application/pdf"]},{"key":"dc:title","label":"Title","values":["A novel approach to processing and visualizing mass spectrometry imaging data for a new perspective in untargeted analyses"]}]}],"canonical_facts":{"dc:creator":["Zurowietz, Annika"],"dc:description.abstract":["Metabolomics plays a crucial role in disease research by enabling the comprehensive analysis of small molecules. Among the key techniques, mass spectrometry imaging (MSI) stands out due to its ability to identify and spatially map metabolites within a single experiment. By combining molecular analysis with morphological visualization, MSI serves as a valuable complement to classical pathology, supporting both targeted and untargeted analyses. However, the complexity of high-dimensional MSI data necessitates robust pre-processing to enhance interpretability and maximize information extraction. This thesis introduces ProViM (Processing for Visualization and Multivariate Analysis of MSI Data), a novel approach to MSI data processing. In addition to standard pre-processing steps like normalisation, ProViM enables the subtraction of interfering signals from matrix and artifacts, which often obscure tissue-specific metabolites and compromise visualization. The application of ProViM results in MSI data that is more interpretable, facilitating more accurate and meaningful visualization. Furthermore, QUIMBI (QUIck Exploration Tool for Multivariate BioImages) is presented as an interactive tool designed for real-time visual exploration of MSI datasets. Unlike conventional methods, QUIMBI processes the entire dataset dynamically and displays co-locations at the pixel level, allowing users to examine both morphological and spectral features simultaneously. The integration of ProViM as a pre-processing step enhances the visibility of tissue-specific signal distributions in QUIMBI visualizations, thus enabling the detection of subtle metabolic patterns that might otherwise remain undetected. To demonstrate the potential of these tools, ProViM and QUIMBI were applied in an untargeted analysis to compare metabolic similarities between Pseudoxanthoma Elasticum (PXE), a rare connective tissue disorder, and atherosclerosis, a chronic inflammatory disease. MSI data from PXE skin was processed using ProViM and visualized with QUIMBI. This approach revealed metabolic signals in PXE skin that were previously hidden in single mass channel image analyses. Comparative analysis identified corresponding masses in atherosclerotic lesions, and high-resolution MSI techniques (MALDI-Orbitrap MSI and timsTOF) confirmed these as known markers of atherosclerosis. The findings highlight ProViM and QUIMBI's effectiveness in interpreting and visualizing MSI data for clinical pathology and medical research. By enhancing metabolic pattern detection, these methods aid targeted and untargeted analyses, deepening the understanding of disease mechanisms. Their application to PXE and atherosclerosis tissues shows potential in revealing molecular similarities, paving the way for research into metabolic biomarkers and pathological processes."],"dc:format.medium":["application/pdf"],"dc:publisher":["Universitätsbibliothek Bielefeld"],"dc:title":["A novel approach to processing and visualizing mass spectrometry imaging data for a new perspective in untargeted analyses"],"dc:type":["doctoralThesis"],"thesis:degree_level":["thesis.doctoral"],"thesis:institution_name":["Universität Bielefeld"]},"updated_at":"2026-07-27T18:50:12Z"}