Metabolomics and Mass Spectrometry Studies

The paper introduces an open-source platform for reference data-driven metabolomics analysis, demonstrating its ability to extract dietary patterns from stool metabolomics data.

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.

TidyMass2 improves untargeted metabolomics by inferring metabolite origins and analyzing functional modules, increasing biologically interpretable features from 5.8% to 58.8% in pregnancy urine data.

This study characterizes the lipidome of specific neuronal populations in mouse brain nuclei using imaging mass spectrometry, revealing sex-based differences in lipid profiles.

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

ChemEmbed improves metabolite identification by combining enhanced MS/MS data with multidimensional molecular embeddings, achieving high accuracy in benchmark tests.

The paper develops a multidimensional bile acid reference library and evaluates workflows for accurate bile acid measurement in biological samples.

MRMhub automates processing of large-scale targeted metabolomics data, offering scalability and quality control tools, and is validated on lipidomics datasets.

MetAlyzer streamlines and visualizes targeted metabolomics data from biocrates, enabling interactive analysis without coding.

The paper measures hundreds of amines and peptides in freely moving mice using microdialysis and mass spectrometry, revealing high-dimensional physiological dynamics and state transitions.

MRH improves peak detection in HRMS data by using a hierarchical, multi-resolution approach, achieving higher accuracy and lower ambiguity than existing methods.

This study compares XCMS and MZmine 2 for peak picking in untargeted metabolomics and finds significant discrepancies in biomarker identification across different sample types and parameters.

The paper uses synthetic multiplexing to annotate uncharacterized metabolites, identifying ibuprofen-carnitine as a metabolite linked to muscle toxicity.

The paper updates the metaboprep R package to support proteomics data, enhancing its utility for multi-omics preprocessing in epidemiological research.

The paper uses a large-language model to simulate tandem mass spectra for small molecules, bypassing the need for domain-specific training.

The paper develops a method using HPLC-DAD and Gaussian Mixture Models to analyze plant metabolomics, showing improved classification of chemical classes compared to traditional methods.

MetaboFM is a foundation model for spatial metabolomics that uses Vision Transformers to create embeddings from MSI data, achieving high accuracy in metadata prediction and enabling interpretable, multimodal interactions.

The paper integrates metabolomic and transcriptomic data to identify dynamic changes in placental metabolism across trimesters in human pregnancy.

This study maps spatial metabolic gradients in murine liver and intestine using integrated experimental-computational methods, revealing distinct metabolic patterns and fructose-induced metabolic derangements.

The paper integrates multi-modal omics data to identify therapeutic pathways in atherosclerosis for coronary heart disease, highlighting amino acid metabolism and 16 relevant proteins.

This study introduces MP-GNN, a graph neural network integrating metabolic pathways to predict hypercholesterolemia risk from serum metabolomics data, achieving high accuracy and biological interpretability.

The paper introduces a microfluidic NMR platform to monitor metabolic fluxes in live tissue slices with high temporal resolution and broad metabolite coverage.

MS2extract is an R package that automates MS/MS library creation, enabling efficient spectral database generation for metabolomics applications.