Thomas Metz

This study evaluates plasma extracellular vesicle proteomics for identifying predictive biomarkers of type 1 diabetes development, finding 448 differentially abundant proteins and a 0.81 AUC in predicting islet autoimmunity.

This paper identifies unknown molecules without reference libraries by combining computational predictions with spectrometric data, achieving exact identifications in a fentanyl case study.

The paper develops a comprehensive metabolomics database for Pseudomonas putida to support untargeted metabolomics studies and enable molecular-level analysis.

This paper benchmarks feature screening methods for omics data classification, finding BcorSIS to be the most effective and efficient across various datasets.

This study uses multi-omics analysis to show that dysregulated lung epithelial cell homeostasis and immune responses drive severe MERS-CoV disease in mice.

This study shows that maternal obesity alters lipid metabolism, metabolomics, and gene expression in bone marrow myeloid cells of offspring, potentially contributing to adult metabolic and cardiovascular disease risk.

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

This study investigates changes in human milk composition after SARS-CoV-2 infection or vaccination using multi-omics approaches, finding greater compositional changes post-infection than post-vaccination.

The paper improves compound identification and inter-laboratory reproducibility in untargeted lipidomics using harmonized methods and spectral analysis.

The paper develops a machine learning workflow for auto-deconvolution of GC-MS data within GNPS, enabling molecular networking and applications in breathomics and volatilome mapping.

This paper introduces a generalizable method for estimating false discovery rates in mass spectrometry-based lipidomics to enable automated data analysis.

This study shows that pro-inflammatory cytokines reveal plasticity in β-cell regulatory landscapes and identify T1D-associated variants disrupting cytokine-responsive enhancers.

MetFish enables LC-MS-based exo-metabolomics analysis in hypersaline samples, quantifying metabolites with high sensitivity and reproducibility in various environments.

Q4SRM is a software tool that rapidly assesses the quality of targeted proteomics experiments by monitoring stable-isotope labeled standards.

This study investigates how Lactobacillus acidophilus affects microbial interactions in bile acid metabolism, revealing it inhibits the production of ursocholate, which correlates with weight loss after gastric bypass.