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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.

Mass spectrometry imaging (MSI) is a powerful tool for spatial metabolomics and biomarker discovery, but molecular identification remains challenging, particularly for low-abundance analytes with poor signal-to-noise ratios. Herein, a novel approach that utilizes a deuterated analog of the reactive matrix FMP-10 to enhance molecular identification is introduced. This isotopically labeled matrix enables precise determination of derivatization patterns, allowing the number and sequence of reactive functional groups in small molecules to be deduced. By observing specific mass shifts, the method provides additional structural information beyond high-resolution MS and MS/MS, addressing key limitations in MSI-based biomarker discovery. This innovative labeling strategy improves identification confidence for neurotransmitters and metabolites, making it a powerful addition to the MSI toolbox for complex tissue analysis. The findings represent a significant advance for spatial metabolomics, with particular advantages for neurochemical mapping in the study of neurodegenerative diseases.

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.

Retention of molecules on immobilized artificial membrane (IAM) chromatography is a key physicochemical property for predictive models of permeability across biological barriers, with applications in drug design and ecotoxicology. Currently, IAM retention is solely experimentally determined, which limits its utility for screening virtual compound libraries or for predictions of yet not synthesized molecules. The present study focuses on developing predictive models of IAM retention factors (logkw(IAM)) for a structurally diverse set of drug compounds, scrutinizing the role of lipophilicity, experimental and calculated, as well as the contribution of additional molecular parameters, selected from a pool of physicochemical, constitutional, topological and 3D descriptors. After obtaining a data overview by principal component analysis, both multiple linear regression (MLR) and partial least squares (PLS) analyses were used to construct lipophilicity-based models and lipophilicity-independent models. Bulk, polarity and fraction of anionic species were common descriptors in all models. It was demonstrated that calculated lipophilicity values introduced additional uncertainty, depending on the software used. On the other hand, lipophilicity-independent MLR and PLS models, which relied solely on computational descriptors, showed comparable performance with lipophilicity-based models, while offering the advantage to more useful for screening large libraries in early drug discovery. The reliability of lipophilicityindependent MLR and PLS models was assessed by external validation as well as by using a blind test set. Error distribution between lipophilicity-based and lipophilicity-independent models was also investigated and found to be comparable, while it was better than the differences between experimental and calculated lipophilicity values.

In an era when population aging is increasing the burden of neurodegenerative conditions, deciphering the mechanisms underlying brain senescence is more important than ever. Here, we present a spatial metabolomics analysis of age-induced neurochemical alterations in the mouse brain using negative ionization mode mass spectrometry imaging. The age-dependent effects of the acetylcholinesterase inhibitor tacrine were simultaneously examined. For ultrahigh mass resolution analysis, we utilized a Fourier-transform ion cyclotron resonance spectrometer. To complement this, a trapped ion mobility spectrometry time-of-flight analyzer provided high speed and lateral resolution. The chosen approach facilitated the detection and identification of a wide range of metabolites, from amino acids to sphingolipids. We reported significant, age-dependent alterations in brain lipids which were most evident for sulfatides and lysophosphatidic acids. Sulfatide species, which are mainly localized to white matter, either increased or decreased with age, depending on the carbon chain length and hydroxylation stage. Lysophosphatidic acids were found to decrease with age in the detailed cortical and hippocampal subregions. An age-dependent increase in the glutamine/glutamate ratio, an indicator of glia-neuron interconnection and neurotoxicity, was detected after tacrine administration. The presented metabolic mapping approach was able to provide visualizations of the lipid signaling and neurotransmission alterations induced by early aging and can thus be beneficial to further elucidating age-related neurochemical pathways.

Ketamine is a common anesthetic used in human and veterinary medicine. This drug has recently received increased medical and scientific attention due to its indications for neurological diseases. Despite being applied for decades, ketamine's entire metabolism and pharmacological profile have not been elucidated yet. Therefore, insights into the metabolism and brain distribution are important toward identification of neurological effects. Herein, we have investigated ketamine and its metabolites in the pig brain, cerebrospinal fluid, and plasma using mass spectrometric and metabolomics analysis. We discovered previously unknown metabolites and validated their chemical structures. Our comprehensive analysis of the brain distribution of ketamine and 30 metabolites describes significant regional differences detected mainly for phase II metabolites. Elevated levels of these metabolites were identified in brain regions linked to clearance through the cerebrospinal fluid. This study provides the foundation for multidisciplinary studies of ketamine metabolism and the elucidation of neurological effects by ketamine.

Given the rapid development of data-driven experimental procedures within the life sciences, it is important to equip students with proper skills and knowledge on how to obtain and interpret complex data. While laboratory exercises have for a long time been well established as a teaching method for life sciences, we consider there is room for improvement in how laboratory exercises are conducted. Hence, we designed a laboratory exercise course in which students at the graduate level in the European education system (M.Sc.) are challenged to pose their own biological question and write their own laboratory protocol for a proteomic study to investigate their hypothesis. Here, students are supported in their task with lectures and seminars that take them through the required details on experimental sample preparation, analysis with LC-MS, and proteomic data evaluation of biological function and relevance. According to student interviews, the inquiry-based learning concept we used here provided a deeper understanding of the laboratory protocols they wrote, according to which they eventually performed their own experiments.

Introduction

Immobilized artificial membrane (IAM) chromatography is widely used in many aspects of drug discovery. It employs stationary phases, which contain phospholipids combining simulation of biological membranes with rapid measurements.

Areas covered

Advances in IAM stationary phases, chromatographic conditions and the underlying retention mechanism are discussed. The potential of IAM chromatography to model permeability and drug-membrane interactions as well as its use to estimate pharmacokinetic properties and toxicity endpoints including ecotoxicity, is outlined. Efforts to construct models for prediction IAM retention factors are presented.

Expert opinion

IAM chromatography, as a border case between partitioning and binding, has broadened its application from permeability studies to encompass processes involving tissue binding. Most IAM-based permeability models are hybrid models incorporating additional molecular descriptors, while for the estimation of pharmacokinetic properties and binding to off targets, IAM retention is combined with other biomimetic properties. However, for its integration into routine drug discovery protocols, reliable IAM prediction models implemented in relevant software should be developed, to enable its use in virtual screening and the design of new molecules. Conversely, preparation of new IAM columns with different phospholipids or mixed monomers offers enhanced flexibility and the potential to tailor the conditions according to the target property.

Myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS) is a chronic, complex illness characterized by severe and often disabling physical and mental fatigue. So far, scientists have not been able to fully pinpoint the biological cause of the illness and yet it affects millions of people worldwide. To gain a better understanding of ME/CFS, we compared the metabolic networks in the plasma of 38 ME/CFS patients to those of 24 healthy control participants. This involved an untargeted metabolomics approach in addition to the measurement of targeted substances including tryptophan and its metabolites, as well as tyrosine, phenylalanine, B vitamins, and hypoxanthine using liquid chromatography coupled to mass spectrometry. We observed significant alterations in several metabolic pathways, including the vitamin B3, arginine-proline, and aspartate-asparagine pathways, in the untargeted analysis. The targeted analysis revealed changes in the levels of 3-hydroxyanthranilic acid, 3-hydroxykynurenine, hypoxanthine, and phenylalanine in ME/CFS patients compared to the control group. These findings suggest potential alterations in immune system response and oxidative stress in ME/CFS patients.

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.