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Advancing spatial-omics through Pyxis, vendor-neutral softwarefor ion mobility mass spectrometry imaging data analysis
Advancing spatial-omics through Pyxis, vendor-neutral software for ion mobility mass spectrometry imaging data analysis
November 25, 2025
Abstract
Mass spectrometry imaging (MSI) is a powerful analytical technique suited for simultaneously measuring and assigning functional roles of multiple analytes directly from intact tissue sections. MSI acquisitions often lead to highly complex datasets with numerous isobaric species. Ion mobility (IM) spectrometry crucially helps to unravel these datasets by providing an orthogonal separation thus supplementing the lack of chromatographic separation in MSI. However, the rich, multidimensional data produced by IM-MSI investigations, combined with the lack of comprehensive software solutions that support the entire data analysis workflow, poses a major challenge preventing IM-MSI full exploitation. Here, we discuss the benefits and challenges of IM-MSI data analysis in metabolomic applications. Finally, Pyxis, a novel, vendor-neutral IT solution for IM-MSI data analysis, is introduced and its capabilities demonstrated on mouse kidney tissue and THP-1 monocytes data.
MassMetasite 4.8.0 release note
We are glad to announce that we recently released MassMetasite 4.8.0!
Machine Learning-Assisted False Positive Detection in Metabolite Identification Workflows
Machine Learning-Assisted False Positive Detection in Metabolite Identification Workflows
December 10, 2025
Abstract
Metabolite identification is a pivotal step in drug discovery and development, enabling the comprehensive analysis of drug-derived compounds within biological systems. However, the complexity of liquid chromatography–mass spectrometry data often results in numerous false positives, complicating the identification of true metabolites. This study introduces a machine-learning-based approach to improve the accuracy of false positive detection in metabolite identification workflows. By incorporating expert knowledge, we develop a feature set for metabolite-related chromatographic peaks that characterizes true and false positives with high accuracy, integrating data from mass spectra, chromatographic signals, and kinetic profiles. We validate this method via gradient boosting decision tree classifiers on both publicly available and proprietary “real-world” data sets, including small molecules and new modalities. Our findings demonstrate that machine learning-assisted techniques significantly reduce false positive identifications, thereby increasing the efficiency and accuracy of metabolite identification processes.