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Mass spectrometry imaging (MSI) is an emerging technique for spatially resolving the molecular composition of biological samples. MSI frequently relies on matrix-assisted laser desorption/ionization (MALDI), in which a pulsed laser beam and chemical matrices are used to facilitate desorption/ionization of molecular species from the sample surface. MALDI matrices can be divided into two broad groups: conventional matrices that promote ionization by protonation/deprotonation or cationization, and derivatizing matrices that target specific chemical functionalities. Derivatizing matrices such as FMP-10 are charged molecules that react with specific chemical structures on target analytes to form covalent matrix-analyte conjugates, enhancing ionization and detectability but limiting chemical coverage. Derivatizing matrices may also create multiple derivatization products through serial reactions with single analytes, complicating annotation. This prompted development of Met-ID, a software tool for automatic annotation of MSI data with an emphasis on derivatization-based workflows. Met-ID incorporates matrix-specific chemistry to enumerate plausible derivative products and filter chemically implausible annotations. It includes a database of in-house acquired tandem mass spectrometry (MS2) spectra of FMP-10-derivatized chemical standards to support MS2 spectral matching. The use of ion mobility (IM) spectrometry in MSI enables collision cross section (CCS) values to be used for annotation. This motivated the development of CCSSim, an in-silico CCS prediction method implemented in Met-ID together with a mixture-model framework to increase annotation confidence by integrating m/z and CCS data. To improve spatial correlations between mass spectrometric and transcriptomic data, a method was developed to enable sequential MSI and spatially resolved transcriptomics (SRT) analysis of one tissue section rather than using consecutive sections. This spatial multimodal analysis can be performed on non-conductive Visium slides without appreciable degradation of MSI metabolite signal or SRT RNA signal. Finally, MALDI-MSI was evaluated as a sample-efficient approach for distinguishing de novo Parkinson’s disease patients from controls using limited patient material and minimal sample preparation, reducing analytical time compared to more sample-intensive workflows. In conclusion, this thesis introduces new high-throughput computational methods for automated metabolite annotation in tissue sections, demonstrates the compatibility of MALDI-MSI with SRT, and highlights the versatility of MSI for analyzing sample-limited clinical biofluids.

L-DOPA-induced dyskinesia (LID) is a significant and treatment-limiting complication in Parkinson's disease (PD) therapy, yet its mechanisms remain poorly understood. We used high-resolution mass spectrometry imaging to map brain-region-specific alterations of glycerophospholipids and sphingolipids in a female macaque model of PD with and without LID following chronic L-DOPA treatment. LID was associated with depletion of antioxidant plasmalogen phosphatidylcholines in the globus pallidus interna, claustrum, and precentral gyrus-regions critical for motor function-and elevations of polyunsaturated fatty acid-containing glycerophospholipids, indicative of increased membrane fluidity. This lipid profile differed from similarly treated non-dyskinetic animals, suggesting lipid composition mediates differential susceptibility to LID. Lipid alterations correlated strongly with dyskinesia severity, dopamine, and L-DOPA concentrations, supporting a mechanistic link between lipid metabolism, neurotransmitter dysregulation, and LID. This comprehensive spatial lipidomic analysis identifies region-specific lipid dysregulation as a novel aspect of LID pathology, highlighting lipid pathways as potential therapeutic targets for mitigating dyskinesia.

We present a spatial omics approach that combines histology, mass spectrometry imaging and spatial transcriptomics to facilitate precise measurements of mRNA transcripts and low-molecular-weight metabolites across tissue regions. The workflow is compatible with commercially available Visium glass slides. We demonstrate the potential of our method using mouse and human brain samples in the context of dopamine and Parkinson's disease. Metabolites and RNA in a tissue section are profiled simultaneously.

Parkinson's disease (PD) is a highly prevalent neurodegenerative disorder affecting the motor system. However, the correct diagnosis of PD and atypical parkinsonism may be difficult with high clinical uncertainty. There is an urgent need to identify reliable biomarkers using high-throughput, molecular-specific methods to improve current diagnostics. Here, we present a matrix-assisted laser desorption/ionization mass spectrometry imaging method that requires minimal sample preparation and only 1 mu L of crude cerebrospinal fluid (CSF). The method enables analysis of hundreds of samples in a single experiment while simultaneously detecting numerous metabolites with subppm mass accuracy. To test the method, we analyzed CSF samples from 12 de novo PD patients (that is, newly diagnosed and previously untreated) and 12 age-matched controls. Within the identified molecules, we found neurotransmitters and their metabolites such as gamma-aminobutyric acid, 3-methoxytyramine, homovanillic acid, serotonin, histamine, amino acids, and metabolic intermediates. Limits of detection were estimated for multiple neurotransmitters with high linearity (R-2 > 0.99) and sensitivity (as low as 16 pg/mu L). Application of multivariate classification led to a highly significant (P < 0.001) model of PD prediction with a 100% classification rate, which was further thoroughly validated with a permutation test and univariate analysis. Molecules related to the neuromelanin pathway were found to be significantly increased in the PD group, indicated by their elevated relative intensities compared to the control group. Our method enables rapid detection of PD-related biomarkers in low sample volumes and could serve as a valuable tool in the development of robust PD diagnostics.

Metabolism of MPTP (1-methyl-4-phenyl-1,2,3,6-tetrahydropyridine) to the neurotoxin MPP+ in the brain causes permanent Parkinson's disease-like symptoms by destroying dopaminergic neurons in the pars compacta of the substantia nigra in humans and non-human primates. However, the complete molecular pathology underlying MPTP-induced parkinsonism remains poorly understood. We used dual polarity matrix-assisted laser desorption/ionization mass spectrometry imaging to thoroughly image numerous glycerophospholipids and sphingolipids in coronal brain tissue sections of MPTP-lesioned and control non-human primate brains (Macaca mulatta). The results revealed specific distributions of several sulfatide lipid molecules based on chain-length, number of double bonds, and importantly, hydroxylation stage. More specifically, certain long-chain hydroxylated sulfatides with polyunsaturated chains in the molecular structure were depleted within motor-related brain regions in the MPTP-lesioned animals, e.g., external and internal segments of globus pallidus and substantia nigra pars reticulata. In contrast, certain long-chain non-hydroxylated sulfatides were found to be elevated within the same brain regions. These findings demonstrate region-specific dysregulation of sulfatide metabolism within the MPTP-lesioned macaque brain. The depletion of long-chain hydroxylated sulfatides in the MPTP-induced pathology indicates oxidative stress and oligodendrocyte/myelin damage within the pathologically relevant brain regions. Hence, the presented findings improve our current understanding of the molecular pathology of MPTP-induced parkinsonism within primate brains, and provide a basis for further research regarding the role of dysregulated sulfatide metabolism in PD.

Parkinson’s disease (PD) is a highly prevalent neurodegenerative disorder affecting the motor system. However, the correct diagnosis of PD and atypical parkinsonism may be difficult, with high clinical uncertainty. The disease is diagnosed solely based on the presence of clinical symptoms, and there is an urgent need to identify reliable biomarkers using high-throughput, molecular-specific methods to improve current diagnostics. Here, we present a matrix-assisted laser desorption/ionization mass spectrometry imaging (MALDI-MSI) method that requires minimal sample preparation and only one µl of crude cerebrospinal fluid (CSF). The method enables analysis of hundreds of samples in a single experiment while simultaneously detecting numerous metabolites with sub-ppm mass accuracy. To test the method, we analyzed CSF samples from 12 de novo PD patients (that is, newly diagnosed and previously untreated) and 12 age-matched controls. Within the identified molecules, we found neurotransmitters and their metabolites, such as γ-aminobutyric acid, 3-methoxytyramine, homovanillic acid, serotonin, histamine, amino acids, and metabolic intermediates. Limits of detection were estimated for multiple neurotransmitters with high linearity (R2 > 0.99) and sensitivity (as low as 16 pg/µl). Application of multivariate classification led to a highly significant (P <0.001) model of PD prediction with 100% classification rate, which was further thoroughly validated with permutation test and univariate analysis. Molecules related to the neuromelanin pathway were found to be increased in the PD group. Our method enables rapid detection of PD-related biomarkers in low sample volumes and could serve as a valuable tool in the development of robust PD diagnostics.