Metabolomics and Mass Spectrometry Studies

STILL-13C: Spatial tracing of isotopically labelled lipids with 13C reveals metabolic heterogeneity in intact tissues

STILL-13C maps lipid metabolic flux in intact human tissues using isotopic labeling and MSI, revealing spatial heterogeneity in lipid pathways and responses to inhibition.

Real-time repository-scale spectral search and global molecular networking with HNSW-MS

HNSW-MS enables fast and accurate spectral search in large metabolomics repositories, allowing rapid integration of new spectra into global molecular networks.

Deep in vivo brain metabolomics maps vulnerability-associated and cocaine-induced neurochemical states

The paper uses in vivo metabolomics to identify neurochemical differences between temperaments linked to SUD vulnerability and shows how cocaine exposure alters these states.

Resolving Challenges in Detection and Quantification of D-2-hydroxyglutarate and L-2-hydroxyglutarate via LC/MS

This paper develops a high-throughput HR-QTOF-LC/MS method to accurately quantify D-2HG and L-2HG enantiomers, enabling better understanding of their roles in disease.

Metabolomic Fingerprinting from Dried Blood Spots Enables Individual Identification Across 1,257 Participants at 94% User-Level Accuracy

Beyond the Default: Optimizing Molecular Networking with arteMIS

The paper introduces arteMIS, a framework for optimizing molecular network parameters in metabolomics, showing improved network quality over default settings.

The Everything Bagel Feature Finder: Ultra-fast automated feature finding for untargeted metabolomics

The Everything Bagel feature finder automates metabolomics data processing, achieving comparable or better performance than existing methods with significantly reduced computational time.

MSMICA: computational metabolite identification in untargeted metabolomics by integrating MS, retention time, and biological evidence

MSMICA improves metabolite identification in untargeted metabolomics by integrating mass spectrometry data, retention time, and biological evidence, achieving high accuracy across mammalian datasets.

UPLC-ESI-MS based lipidomics revealed novel biomarkers in insulin receptor knockdown induced type 2 diabetes model of Drosophila

The paper uses lipidomics to identify novel biomarkers for T2D in a Drosophila model by analyzing lipid profiles and statistical patterns.

A TRANSLATIONAL LC-MS/MS FRAMEWORK FOR LIPID BIOMARKER IDENTIFICATION AND QUANTIFICATION IN HUMAN PLASMA

This paper develops a workflow for identifying and quantifying 48 lipid biomarkers in human plasma to enable breast cancer detection, overcoming challenges in standardization and validation.

Integrated assessment of fatty acid metabolism and cellular energy status using HILIC-MS/MS

The paper introduces a HILIC-MS/MS method to analyze acyl-CoA and acyl-carnitine species, revealing differences in fatty acid oxidation and cellular energy status across cell lines.

Metabolomic and Lipidomic Signatures of Depressive Symptoms in the REasons for Geographic And Racial Differences in Stroke (REGARDS) Study

The paper identifies metabolomic and lipidomic associations with depressive symptoms in the REGARDS study, finding significant longitudinal links between certain lipids and depressive symptoms, with sex-specific differences.

Urinary multi-omics reveal non-invasive diagnostic biomarkers in clear cell renal cell carcinoma

The paper identifies three urinary biomarkers for clear cell renal cell carcinoma using multi-omics analysis and develops a diagnostic score with 96% accuracy.

A Robust Isotope Ratio LC-MS/MS Workflow for High-Throughput Metabolic Profiling of Bacteria

The paper presents a robust LC-MS/MS workflow for high-throughput metabolic profiling of bacteria using stable isotope dilution mass spectrometry.

Using Shared Features Improves Metabolite Effect Estimation

The paper improves metabolite effect estimation by incorporating external biological knowledge through a Bayesian hierarchical framework, showing consistent gains in both simulated and real datasets.

Non-Invasive Embryo Quality Assessment via Matrix-Optimized Untargeted LC-MS Metabolomics of Spent Embryo Culture Media and Weighted Ensemble Machine Learning

This paper develops a non-invasive method for embryo quality assessment using metabolomics of spent culture media and machine learning, identifying metabolic signatures linked to ploidy and morphology.

MassGAT: a graph-based collective learning approach for untargeted detection and annotation of LC-MS data

MassGAT improves LC-HRMS data analysis by combining peak detection and annotation using a graph-based approach, outperforming existing methods on real datasets.

Performance Evaluation of a Quantitative Metabolomics Workflow Incorporating Microchip Capillary Electrophoresis, Indexed Migration Time, and Single-Point External Calibration

This paper evaluates a metabolomics workflow using microchip CE-MS with indexed migration time and single-point external calibration, showing improved accuracy and consistency across biological matrices.

Quantifying per-match Reliability in Library Matching for Untargeted Metabolomics Workflows

This paper introduces a method to assess the reliability of metabolite annotations in untargeted metabolomics by evaluating match reproducibility under spectral perturbation.

Mass Spectrometry-Based Multiomic Profiling Defines Proteome, Lipidome, and Metabolome Remodeling in IFN-γ and LPS-Stimulated BV-2 Microglial Cells

This paper uses mass spectrometry-based multiomic profiling to analyze proteome, lipidome, and metabolome changes in IFN-γ and LPS-stimulated BV-2 microglial cells, revealing coordinated immune, lipid, and metabolic adaptations.

Confronting spurious evaluations of computational methods in small molecule mass spectrometry

The paper exposes a flaw in evaluating small molecule MS/MS models by showing trivial models can perform well due to dataset biases and proposes a new benchmark to assess true model capability.

Enzymes define pathways and metabolic relationships

The paper detects enzymology-defined precursor-product correlations in human plasma LC-MS data, showing consistency across longitudinal and cross-sectional studies.

Integrated metabolomics and proteomics from voxelated cortical hemispheres of adult rhesus monkeys

The paper develops spatially registered metabolomics and proteomics maps of rhesus monkey cortex and integrates them using novel methods to identify conserved molecular gradients and metabolic circuits.

Measurement of a panel of 21 steroids in a quantitative assay in human plasma, adipose tissue, and fecal samples using ultra-high-performance liquid chromatography-tandem mass spectrometry

The paper develops a UHPLC-MS/MS assay to quantify 21 steroids in plasma, adipose tissue, and fecal samples with high sensitivity and accuracy.

Large-scale discovery and annotation of hidden substructure patterns in mass spectrometry profiles

The paper improves MS2LDA for substructure discovery in mass spectrometry by enhancing speed and introducing automated annotation guidance, validated on chemical datasets.

Multimodal molecular profiling of the metabolic penumbra in hyperacute stroke

The paper identifies a metabolically defined penumbra in hyperacute stroke using multimodal molecular profiling in a mouse model.

Metabolite Fraction Libraries for Quantitative NMR Metabolomics

The paper introduces a method using metabolite fraction libraries to improve NMR metabolomics quantification by reducing spectral overlap and enabling accurate metabolite identification.

LipiDetective - a deep learning model for the identification of molecular lipid species in tandem mass spectra

LipiDetective is a transformer model that accurately identifies molecular lipid species in tandem mass spectra using learned fragmentation patterns, improving comparability across experiments.

Kinetic Lipidomics: Quantifying in vivo changes in lipid metabolism using metabolic labeling

This paper develops a method to quantify lipid metabolism kinetics using deuterium labeling and applies it to brain tissue from mice with Alzheimer's risk factors.

Sequential Penta-Omic Extraction Method Using Single Biospecimens of Post-mortem Human Brain

The paper describes a penta-omic sequential extraction method for post-mortem human brain samples, showing minimal omic loss and preserved tissue integrity compared to single-omic methods.