Metabolomics and Mass Spectrometry Studies

Machine learning and data-driven inverse modeling of metabolomics unveil key process of active aging

The paper uses machine learning and COVRECON to identify metabolomics biomarkers and causal processes related to fitness in elderly individuals.

Optimizing the detection of biological signals through a semi-automated feature selection tool

The paper introduces RegFilter, a tool that enhances metabolite detection in DDA mode by dynamically selecting precursor ions and creating custom spectral libraries.

Automated preparation of plasma lipids, metabolites, and proteins for LC/MS-based analysis of a high-fat diet in mice

The paper describes an automated LLE platform for plasma sample preparation and analyzes metabolomic, lipidomic, and proteomic changes in mice on a high-fat diet.

Metabolic profiling using benchtop NMR identifies the metabolomic signature of persistent CAR-T cells

The paper uses benchtop NMR and PCA to identify metabolomic differences in CAR-T cells based on intracellular domain combinations.

PANAMA enabled high sensitivity dual nanoflow LC/MS metabolomics and proteomics analysis

PANAMA enables high sensitivity joint metabolomics and proteomics analysis using nanoflow LC/MS without instrument duplication, improving resolution and detection sensitivity.

Insights into the metabolic consequences of type 2 diabetes

This study identifies metabolites causally linked to type 2 diabetes and its complications, revealing genetic regulation effects and metabolic pathways involved.

MargheRita: streamlining MS-DIAL output analysis and metabolite identification in R

MargheRita streamlines MS-DIAL output analysis and metabolite identification in R by providing a comprehensive pipeline for LC-MS/MS data, enhancing fragment matching accuracy with a curated spectral library.

Data processing of product ion spectra: Methods to control false discovery rate in compound search results for non-targeted metabolomics

This study introduces two methods to control false discovery rate in non-targeted metabolomics by improving compound search reliability and enabling novel metabolite identification.

Common data models to streamline metabolomics processing and annotation, and implementation in a Python pipeline

The paper introduces a Python pipeline and data structures for metabolomics data processing, annotation, and standardization, demonstrating its utility with large-scale datasets.

Spatial Lipidomics Maps Brain Alterations Associated with Mild Traumatic Brain Injury

This study uses spatial lipidomics to identify brain lipid alterations in rats with mild traumatic brain injury and repetitive mild traumatic brain injury.

Chemical Space Localization for Unknown Metabolite Annotation via Semantic Similarity of Mass Spectral Language

The paper introduces a deep learning method for annotating unknown metabolites using semantic similarity of mass spectral data, showing improved performance over existing methods.

plantMASST - Community-driven chemotaxonomic digitization of plants

The paper curates LC-MS/MS data from plant extracts to create a taxonomically focused database for exploring plant-derived molecules.

Characterizing human postprandial metabolic response using multiway data analysis

This study uses multiway data analysis to characterize postprandial metabolic responses in humans, revealing BMI-related differences in metabolite groups and temporal profiles between males.

Predicting The Pathway Involvement Of Metabolites Based on Combined Metabolite and Pathway Features

The paper predicts metabolite pathway involvement using a single binary classifier with combined metabolite and pathway features, outperforming previous methods.

Automated machine learning and explainable AI (AutoML-XAI) for metabolomics: improving cancer diagnostics

This paper develops an AutoML-XAI pipeline for metabolomics data, improving cancer diagnostics by combining automated machine learning with explainable AI techniques.

Annotation of DOM Metabolomes with an Ultrahigh Resolution Mass Spectrometry Molecular Formula Library

The paper annotates DOM metabolomes using a molecular formula library from FT-ICR MS, achieving higher annotation rates than conventional MS/MS methods.

How much storage precision can be lost: Guidance for near-lossless compression of untargeted metabolomics mass spectrometry data

The paper evaluates precision loss in metabolomics mass spectrometry data compression and provides thresholds to minimize impact on downstream analysis.

omicsMIC: a Comprehensive Benchmarking Platform for Robust Comparison of Imputation Methods in Mass Spectrometry-based Omics Data

omicsMIC benchmarks 28 imputation methods for mass spectrometry-based omics data, enabling researchers to evaluate and select methods based on real-time visualizations.

Comprehensive Metabolomic Analysis of Human Heart Tissue Enabled by Parallel Metabolite Extraction and High-Resolution Mass Spectrometry

This paper presents a comprehensive metabolomic analysis of human heart tissue using parallel extraction methods and high-resolution mass spectrometry to identify 2276 metabolites across a wide range of polarities.

Tailored mass spectral data exploration using the specXplore interactive dashboard

The paper introduces specXplore, an interactive dashboard for exploring mass spectral data, enabling structural hypothesis generation through visualization and spectral similarity analysis.

Bridging polarities in metabolomics: Cross-ionization mode chemical similarity prediction between tandem mass spectra

MS2DeepScore 2.0 predicts chemical similarity between tandem mass spectra across ionization modes, improving metabolite annotation and data exploration in untargeted metabolomics.

Enhancing the Robustness of OPLS Modelling in Small Cohorts by Leveraging Permutation Analysis Prior to Variable Selection

This paper introduces a permutation-based approach to improve OPLS model robustness in small cohorts by adjusting Q2 values and using variable selection with Elastic Net.

Salivary metabolomics in the family environment: A large-scale study investigating oral metabolomes in children and their parental caregivers

The paper uses metabolomics to analyze saliva samples from children and caregivers, finding similar metabolomes between dyads and distinct population-level metabotypes.

Optimized Mass Spectrometry Detection of Thyroid Hormones and Polar Metabolites in Rodent Cerebrospinal Fluid

The paper develops an optimized LC-MS method to measure thyroid hormones and metabolites in rodent CSF, revealing differences between pregnant and non-pregnant animals and across developmental stages.

Using data-dependent and independent hybrid acquisitions for fast liquid chromatography-based untargeted lipidomics

The paper proposes a hybrid LC-MS/MS approach combining data-dependent and data-independent acquisitions to improve lipid annotation in fast untargeted lipidomics.

Machine learning of cellular metabolic rewiring

This paper introduces MetaboLiteLearner, a machine learning framework that predicts metabolic changes in cells using GC/MS fragmentation patterns, demonstrating its ability to identify organ-specific metabolic adaptations in breast cancer metastasis.

Open Access Repository-Scale Propagated Nearest Neighbor Suspect Spectral Library for Untargeted Metabolomics

The paper creates a suspect spectral library for untargeted metabolomics by propagating annotations from public MS/MS spectra to improve interpretation of unannotated spectra.

MetaVision3D: Automated framework for the Generation of Spatial Metabolome Atlas in 3D

MetaVision3D converts serial 2D MALDI mass spectrometry imaging sections into a 3D spatial metabolome atlas using computer vision techniques.

A chemical structure and machine learning approach to assess the potential bioactivity of endogenous metabolites and their association with early-childhood hs-CRP levels

The paper uses metabolomics and machine learning to identify metabolites associated with early childhood hs-CRP levels, finding 52 metabolites with potential bioactivity linked to systemic inflammation.

Picky with peakpicking: assessing chromatographic peak quality with simple metrics in metabolomics

The paper evaluates peak quality metrics in metabolomics to improve automated LC-MS workflows by reducing false positives and enhancing statistical power.