Metabolomics and Mass Spectrometry Studies

This study identifies systemic metabolic differences between mice with high and low responsiveness to environmental changes, linking behavior to metabolic signatures.

The paper introduces NMLP, a module within LipidOne that translates plasma lipidomics data into interpretable nutritional-metabolic indices, revealing metabolic shifts and phenotypes across different dietary and health conditions.

The paper identifies alkamines as a previously hidden class of metabolites formed via reductive amination, revealing significant gaps in metabolomics and drug metabolism understanding.

This paper introduces MT-GEM and STEP to predict discrete structural transformations in metabolites from MS/MS spectra, achieving significant improvements over existing methods.

The paper maps spatial metabolome coherence across mouse brain regions using MALDI imaging and optimal transport, showing preserved coherence in Alzheimer's model despite metabolic changes and demonstrating intervention effects.

The paper presents a Python Dash App and cPanel workflow to automate metabolomics data analysis and visualization, enabling interactive statistical analysis and result sharing.

SMEW provides a code-free, interactive platform for analyzing spatial metabolomics data across multiple scales and conditions to identify metabolic patterns and biological pathways.

MSAgent improves molecular identification from mass spectrometry data by integrating LLMs with domain tools, achieving higher accuracy and reliability than existing methods.

The paper collects and standardizes metabolic fingerprints of 17 Brassicaceae species across three tissues using LC-MS/MS, enabling integration with genomic and transcriptomic data for natural product discovery.

SIMO integrates spatial metabolomics, lipidomics, and proteomics from a single tissue section to enable comprehensive multi-omics analysis across spatial regions.

The paper develops a workflow to map metabolic neighborhoods in neural rosettes using MALDI imaging and single-cell analysis, enabling spatial and metabolic characterization of differentiating stem cells.

This study identifies metabolic signatures in COPD patients using saliva metabolomics, revealing distinct patterns in smokers and ex-smokers compared to healthy controls.

This paper develops [13C6]scyllo-inositol as an isotopic tracer to study its metabolism and shows its incorporation into phosphatidylinositols in mammalian cells.

The paper uses in vivo isotope tracing and AI to identify biosynthetic origins of unknown metabolites, revealing new metabolic pathways and age-related changes.

This paper develops and validates a mass spectrometry-based method to measure insulin-stimulated tissue-specific glucose clearance in mice without radiolabels or catheters.

The paper identifies serum metabolic signatures associated with cognitive resilience in a longitudinal aging cohort using untargeted LC-MS/MS profiling.

The paper systematically extracts and catalogs acylcarnitine metabolites from multiple repositories, identifying previously unknown carnitines and providing a resource for further metabolic research.

The paper uses cell-specific isotope labeling to identify myo-inositol transfer from neurons to oligodendrocyte progenitor cells, promoting myelin repair and revealing a metabolic target for neurodegenerative disease therapy.

This paper introduces BertMS, a transformer-based spectral similarity framework that improves compound identification in metabolomics by better capturing structural relationships in MS/MS data.

The paper develops a rapid reversed-phase LC-MS method for profiling polar metabolites, achieving high coverage and stability for 123 compounds in E. coli extracts.

This study uses bibliometric analysis to identify trends in metabolomics research related to prediabetes and type 2 diabetes, highlighting key topics and influential journals.

MassID provides near-complete metabolite annotation with probabilistic identification and FDR control in metabolomics data.

This study maps conjugated metabolites by analyzing MS/MS spectra from public repositories, identifying potential conjugation events and providing a resource for structural discovery.

Intelliwaste uses 13C-labeled metabolites and NMR to profile carbon metabolism in stem cells and model neuropathology, revealing metabolic differences in LIS1 mutants.

The paper characterizes the metabolomes of microglia, astrocytes, and neurons in aging and Alzheimer's brains, identifying cell-type-specific metabolic pathways and their changes with age and disease.

The paper introduces microbiomeMASST, a metadata network graph linking metabolites across datasets, enabling cross-study analysis of microbial metabolites and their biological context.

The paper introduces a collaborative platform for curating microbial metabolite knowledge to enhance metabolomics data interpretation.

FLARE improves metabolite annotation by aligning spectra and molecular structures through contrastive learning, achieving state-of-the-art performance on MassSpecGym.

The paper develops a deep learning model to predict MS2 identifiability using MS1 data and acquisition parameters, achieving 90% recovery of known identifiable spectra.

The paper uses a multi-property fine-tuned language model to predict lipid retention times from peptide data, improving accuracy in data-sparse scenarios.