We’ve recently expanded our mass spectrometry and imaging capabilities with the installation of Bruker’s rapifleX® MALDI Tissuetyper®, making IRBM the first CRO in Italy to adopt this advanced MALDI-TOF/TOF platform.

La investigación de Maldita.es desvela el modus operandi de los pederastas en la red social más consumida por los adolescentes españoles. La plataforma obtiene una parte del beneficio de los regalos d...

AbstractAccurate metabolite annotation remains a major bottleneck in spatial metabolomics because MALDI mass spectrometry imaging (MALDI-MSI) lacks retention time and other orthogonal descriptors. We investigated whether reproducible adduct ion formation could provide complementary evidence for annotation. Profiling 753 authentic standards under fixed matrix conditions identified 10 prevalent adduct types in negative mode and 8 in positive mode, including often-overlooked matrix-derived ions involving 1,5-diaminonaphthalene (DAN). We developed DeepGCN-FP, a transfer-learning framework that extracts graph convolutional network fingerprints (GCN-FPs) from a pretrained DeepGCN model and combines them with machine learning classifiers to predict adduct formation. GCN-FP models with gradient-boosted decision trees outperformed extended-connectivity fingerprints, achieving cross-validated AUCs of 0.86−0.95 for all 10 negative-mode adducts and 0.80−0.86 for 6 of 8 positive-mode adducts. Atom-level interpretation highlighted functional groups associated with deprotonation, while on-slice experiments showed that adduct profiles were preserved on tissue, and titration experiments found that low-concentration adduct repertoires were subsets of those at higher concentrations. On 19 independent standards, predictions achieved 87% precision. Embedded in an adduct-aware scoring framework for metabolite annotation, adduct prediction routinely elevated the rank of correct metabolites in both standard mixtures and real biological samples and yielded pathway-level enrichments in mouse skeletal muscle that match the known biochemical differences between slow-twitch and fast-twitch myofibers, including carnosine- and pyruvate-related pathways that mass-only annotation failed to recover. Adduct prediction thus provides a practical, chemically interpretable aid for metabolite annotation in spatial metabolomics. Our code is available at: https://github.com/dadazhetang/DeepGCN-FP.

To investigate differences in cerebrospinal fluid (CSF) N-glycosylation profiles among children with purulent meningitis (PM), viral meningitis (VM), and d

AbstractModern imaging mass spectrometry (MSI) technologies, such as matrix-assisted laser desorption/ionization (MALDI), provide unprecedented spatial resolution for mapping tissue biomolecules. However, experimental variations frequently cause mass-to-charge (m/z) shifts across different data batches, misaligning identical biological substances. Existing alignment algorithms often fail to project matched spectra onto the exact same m/z coordinates as the reference data, hindering downstream comparative analyses. Here, we propose PGAlign (Peak-Group Alignment), a novel peak-group-based algorithm that aligns unknown spectra directly to a reference, producing matched data sets defined on a shared m/z grid. Operating on representative mean spectra, PGAlign executes four discrete steps: peak calling, peak grouping, peak-group pairing, and fine alignment assessment. Using simulated mouse pancreatic MALDI data and metabolomics data from a macrophage efferocytosis study, we show that PGAlign achieves lower alignment error than other methods. In canine sarcoma MALDI data, PGAlign preserves biologically meaningful differences between cancer and normal tissues and supports downstream classification, suggesting that alignment onto a common m/z grid can improve the reliability of MS-based biomarker analyses.

1概要 MALDI-TOF MS（タンパク質量分析）による微生物種の迅速同定法は、シーケンスよりも低…

PI: Marcin Czerwiński (Hirszfeld Institute of Immunology and Experimental Therapy, PAS) Keywords: SSR4-CDG CDG N-glycans plasma MALDI Data related to N-glycomic and glycolipidomic case studies of SSR4-CDG patients....

La investigación de Maldita.es desvela el modus operandi de los pederastas en la red social más consumida por los adolescentes españoles. La plataforma obtiene una parte del beneficio de los regalos d...

ALT: Playful Dog Chases Laser Light

ALT: Panda Attacks Office Workers

AbstractUnderstanding the stability of the active pharmaceutical ingredient (API) and drug−excipient interactions is critical for ensuring the efficacy and shelf life of pharmaceutical formulations. This study applies matrix-assisted laser desorption/ionization mass spectrometry imaging (MALDI-MSI) to investigate two-dimensional (2D) and three-dimensional (3D) spatial profiling of degradants in Losartan potassium tablets that are subjected to temperature- and humidity-induced stress conditions. This approach allows for in situ visualization of API degradation and excipient compatibility within solid dosage forms. Upon stress, Losartan tablets formed dimeric and trimeric species as major degradation products. Spatial mapping revealed a gradual growth of these degradants from the control (T0) through four weeks of stress. Ion images showed distinct heterogeneity in the formation of dimer and trimer species across the tablet depth and plane; however, no preferential pattern was observed at the tablet periphery. The random distribution of degradants suggests that alongside humidity, localized microenvironments drive the degradation process. MALDI-MSI demonstrates strong potential as a high-throughput and sensitive alternative to traditional liquid chromatography-based procedures for early-stage formulation screening. Furthermore, this work suggests that when integrated with orthogonal techniques to coregister excipient domains, MALDI-MSI can serve as an emerging tool to uniquely characterize drug−excipient interactions during early-stage drug development. This provides spatially resolved insight that enhances accelerated stability assessment programs and streamline the selection of compatible excipients.