Metabolomics and Mass Spectrometry Studies

Chemistry-based vectors map the chemical space of natural biomes from untargeted mass spectrometry data

The paper uses chemistry-based vectors to analyze untargeted mass spectrometry data and identifies compound classes that differentiate natural biomes.

Shared Molecular Landscape of Human Brain Structure and Systemic Metabolism

The paper identifies extensive phenotypic and genetic links between systemic metabolism and brain morphology using UK Biobank data.

Integral-Omics: serial extraction and profiling of metabolome, lipidome, genome, transcriptome, whole proteome and phosphoproteome using biopsy tissue

Integral-Oms enables sequential extraction and profiling of multiple omics layers from biopsy tissue, revealing suppressed ferroptosis in colorectal cancer tissues.

Genome-wide characterization of 54 urinary metabolites reveals molecular impact of kidney function

The paper identifies 52 genome-wide associations for urinary metabolites and finds causal relationships between eGFR and 13 metabolites.

Lipidomics Reveals Cell Specific Changes During Pluripotent Differentiation to Neural and Mesodermal Lineages

The paper uses lipidomics to analyze changes in lipid composition during pluripotent stem cell differentiation toward neural and mesodermal lineages, identifying shared and unique lipid fluctuations.

Defining the limits of plant chemical space: challenges and estimations

The paper estimates the size of plant chemical space using metabolomics and literature data, suggesting it may span millions of unique compounds, most of which remain unexplored.

Insights into Heart Failure Metabolite Markersthrough Explainable Machine Learning

This study uses machine learning and explainable AI to identify metabolite markers for heart failure, finding that lignoceric acid is a significant discriminator.

Enrichment analysis for spatial and single-cell metabolomics accounting for molecular ambiguity

The paper introduces S2IsoMEr and METASPACE tools to handle molecular ambiguity in spatial and single-cell metabolomics through bootstrapping for enrichment analysis.

Imputation for Lipidomics and Metabolomics (ImpLiMet): Online application for optimization and method selection for missing data imputation

ImpLiMet is a platform that helps users select the best missing data imputation method by testing eight approaches and evaluating their impact through statistical and visualization tools.

Introducing identification probability for automated and transferable assessment of metabolite identification confidence in metabolomics and related studies

The paper proposes identification probability as an automated and transferable method to assess metabolite identification confidence in metabolomics.

Differential metabolic biomarkers between coronary heart disease and diabetes mellitus

The paper identifies disease-specific metabolic biomarkers for coronary heart disease, diabetes mellitus, and their co-occurrence using metabolomics profiling and statistical analysis.

Normalization strategies for lipidome data in cell line panels

This study evaluates normalization strategies for lipidomic data in cell line panels, finding that data-based approaches improve replicate similarity but may introduce artifacts.

MS-DIAL 5 multimodal mass spectrometry data mining unveils lipidome complexities

The paper introduces MS-DIAL 5, a tool for analyzing multimodal mass spectrometry data to elucidate lipid structures and localization using structural annotations.

PyINETA: Open-source platform for INADEQUATE-JRES integration in NMR metabolomics

PyINETA integrates INADEQUATE and 13C-JRES NMR data to improve compound annotation in metabolomics, showing tissue-specific metabolite distribution and handling unknown compounds.

Language model-guided anticipation and discovery of unknown metabolites

This paper introduces DeepMet, a chemical language model that predicts and discovers unknown metabolites using mass spectrometry data.

Interlaboratory comparison of standardised metabolomics and lipidomics analyses in human and rodent blood using the MxP(R) Quant 500 kit

This study evaluates the interlaboratory reproducibility of the MxP(R) Quant 500 metabolomics/lipidomics kit across human and rodent blood samples, showing high consistency and reliability.

MS2Lipid: a lipid subclass prediction program using machine learning and curated tandem mass spectral data

MS2Lipid predicts lipid subclasses from MS/MS data using machine learning, achieving high accuracy and outperforming existing tools.

The microbiome diversifies N-acyl lipid pools - including short-chain fatty acid-derived compounds

The paper creates a spectral library of N-acyl lipids from metabolomics data, identifies novel compounds, and links them to health conditions like HIV and cognitive impairment.

Quantifying acyl-chain diversity in isobaric compound lipids containing monomethyl branched-chain fatty acids

The paper develops a method to quantify acyl-chain diversity in isobaric compound lipids using UHPLC-MS and validates it with metabolically engineered adipocytes.

Composition of saliva metabolome is significantly associated with SARS-CoV2 infection and with severity of COVID-19 disease

The study identifies saliva metabolite changes associated with SARS-CoV-2 infection and disease severity, highlighting potential non-invasive biomarkers for COVID-19.

METASPACE-ML: Context-specific metabolite annotation for imaging mass spectrometry using machine learning

METASPACE-ML improves metabolite annotation in imaging mass spectrometry by using machine learning, achieving higher precision and throughput than a rule-based method.

Introducing Molecular Hypernetworks forDiscovery in MultidimensionalMetabolomics Data

This paper introduces molecular hypernetworks to improve annotation in metabolomics data by representing complex relationships beyond traditional molecular networks.

Targeted and Untargeted Amine Metabolite Quantitation in Single Cells with Isobaric Multiplexing

The paper develops a single-cell amine analysis method using isobaric multiplexing and mass spectrometry, achieving absolute quantification of metabolites and modest cell type separation.

Structural annotation of full-scan MS data: A unified solution for LC-MS and MS imaging analyses

The paper introduces ms1-id, a tool for structural annotation of MS1 data in LC-MS and MS imaging, enabling level 2/3 annotation and uncovering previously overlooked metabolites.

Empirically establishing drug exposure records directly from untargeted metabolomics data

The paper develops a drug exposure resource from untargeted metabolomics data to identify medications without clinical records.

Utility of an untargeted metabolomics approach using a 2D GC-GC-MS platform to distinguish relapsing and progressive multiple sclerosis

This study uses untargeted metabolomics to identify altered serum metabolites distinguishing relapsing-remitting MS, progressive MS, and healthy subjects.

Exploring Metabolic Anomalies in COVID-19 and Post-COVID-19: A Machine Learning Approach with Explainable Artificial Intelligence

This study uses machine learning and explainable AI to identify metabolomic signatures and biomarkers in COVID-19 and Post-COVID-19 patients, revealing heterogeneous metabolic responses.

A scalable approach to absolute quantitation in metabolomics

This paper introduces Pyxis, a machine learning tool for absolute metabolite quantification in metabolomics, demonstrating its scalability and efficiency compared to traditional methods.

Sex-dependent metabolic remodeling of kidneys revealed by arteriovenous metabolomics

This study identifies sex-dependent metabolic differences in kidneys using arteriovenous metabolomics and transcriptomics, revealing distinct metabolic responses to a ketogenic diet.

Inferring super-resolved spatial metabolomics from microscopy

MetaLens uses deep learning to enhance spatial metabolomics resolution from microscopy, identifying subcellular metabolic domains without labels.