Systems biology approaches applied in mass spectrometry imaging

At the core of this research lies the technology of Mass Spectrometry Imaging (MSI). While MSI is a novel, powerful research tool for mapping the molecular composition directly from tissue whilst preserving spatial morphology, it does not hold all the answers to the complex (clinical) biological questions. Thereto, this thesis presents various efforts to integrate MSI data with other valuable, complementary, imaging or non-imaging data sources in order to investigate systematically the complex tissue biology in health and disease. For instance, this work delivers workflows that accelerate histological annotation for rapid correlation with the molecular information provided by high-spatial-resolution MSI and as such lays out the future of high throughput, automated digital pathology-based diagnosis. The highlight of this PhD is a study conducted in collaboration with Johns Hopkins, where the aforementioned multi-disciplinary strategies were employed to investigate molecular signature of metastatic breast cancer in a unique multi-organ sample set from several breast cancer patients. The findings emphasize the clinical importance and benefits of spatial tissue analysis by MSI, which combined with extensive pathology annotation leads to research opportunities with unprecedented translational potential

A patch-based super resolution algorithm for improving image resolution in clinical mass spectrometry

Abstract Mass spectrometry imaging (MSI) and histology are complementary analytical tools. Integration of the two imaging modalities can enhance the spatial resolution of the MSI beyond its experimental limits. Patch-based super resolution (PBSR) is a method where high spatial resolution features from one image modality guide the reconstruction of a low resolution image from a second modality. The principle of PBSR lies in image redundancy and aims at finding similar pixels in the neighborhood of a central pixel that are then used to guide reconstruction of the central pixel. In this work, we employed PBSR to increase the resolution of MSI. We validated the proposed pipeline by using a phantom image (micro-dissected logo within a tissue) and mouse cerebellum samples. We compared the performance of the PBSR with other well-known methods: linear interpolation (LI) and image fusion (IF). Quantitative and qualitative assessment showed advantage over the former and comparability with the latter. Furthermore, we demonstrated the potential applicability of PBSR in a clinical setting by accurately integrating structural (i.e., histological) and molecular (i.e., MSI) information from a case study of a dog liver

Spatial Systems Lipidomics Reveals Nonalcoholic Fatty Liver Disease Heterogeneity

Hepatocellular lipid accumulation characterizes nonalcoholic fatty liver disease (NAFLD). However, the types of lipids associated with disease progression are debated, as is the impact of their localization. Traditional lipidomics analysis using liver homogenates or plasma dilutes and averages lipid concentrations, and does not provide spatial information about lipid distribution. We aimed to characterize the distribution of specific lipid species related to NAFLD severity by performing label-free molecular analysis by mass spectrometry imaging (MSI). Fresh frozen liver biopsies from obese subjects undergoing bariatric surgery (n = 23) with various degrees of NAFLD were cryosectioned and analyzed by matrix-assisted laser desorption/ionization (MALDI)-MSI. Molecular identification was verified by tandem MS. Tissue sections were histopathologically stained, annotated according to the Kleiner classification, and coregistered with the MSI data set. Lipid pathway analysis was performed and linked to local proteome networks. Spatially resolved lipid profiles showed pronounced differences between nonsteatotic and steatotic tissues. Lipid identification and network analyses revealed phosphatidylinositols and arachidonic acid metabolism in nonsteatotic regions, whereas low-density lipoprotein (LDL) and very low-density lipoprotein (VLDL) metabolism was associated with steatotic tissue. Supervised and unsupervised discriminant analysis using lipid based classifiers outperformed simulated analysis of liver tissue homogenates in predicting steatosis severity. We conclude that lipid composition of steatotic and nonsteatotic tissue is highly distinct, implying that spatial context is important for understanding the mechanisms of lipid accumulation in NAFLD. MSI combined with principal component-linear discriminant analysis linking lipid and protein pathways represents a novel tool enabling detailed, comprehensive studies of the heterogeneity of NAFLD