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Here, we introduce Met-ID, a graphical user interface software designed to efficiently identify metabolites from MALDI-MSI data sets. Met-ID enables annotation of m/z features from any type of MALDI-MSI experiment, involving either derivatizing or conventional matrices. It utilizes structural information for derivatizing matrices to generate a subset of targets that contain only functional groups specific to the derivatization agent. The software is able to identify multiple derivatization sites on the same molecule, facilitating identification of the derivatized compound. This ability is exemplified by FMP-10, a reactive matrix that assists the covalent charge-tagging of molecules containing phenolic hydroxyl and/or primary or secondary amine groups. Met-ID also permits users to recalibrate data with known m/z ratios, boosting confidence in mass match results. Furthermore, Met-ID includes a database featuring MS2 spectra of numerous chemical standards, consisting of neurotransmitters and metabolites derivatized with FMP-10, alongside peaks for FMP-10 itself, all accessible directly through the software. The MS2 spectral database supports user-uploaded spectra and enables comparison of these spectra with user-provided tissue MS2 spectra for similarity assessment. Although initially installed with basic data, Met-ID is designed to be customizable, encouraging users to tailor the software to their specific needs. While several MSI-oriented software solutions exist, Met-ID combines both MS1 and MS2 functionalities. Developed in alignment with the FAIR Guiding Principles for scientific software, Met-ID is freely available as an open-source tool on GitHub, ensuring wide accessibility and collaboration.

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.