ArticleScientific data2023
A large-scale LC-MS dataset of murine liver proteome from time course of heavy water metabolic labeling.
Article in Scientific data, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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Who cites it
12 citing papers in PubMed.
- Role of dual specificity phosphatase 1 in influencing inflammatory pathways in macrophages modulated byFrontiers in immunology · 2026Article
- Nascent liver proteome reveals enzymes and transcription regulators under physiological and alcohol exposure conditions.Nature communications · 2025Article
- Amniotic Fluid Reduces Liver Fibrosis By Attenuating Hepatic Stellate Cell Activation.bioRxiv : the preprint server for biology · 2025Article
- Deuterium labeling enables proteome-wide turnover kinetics analysis in cell culture.Cell reports methods · 2025Article
- Article
- Role of Dual Specificity Phosphatase 1 (DUSP1) in influencing inflammatory pathways in macrophages modulated bybioRxiv : the preprint server for biology · 2025Article
- Turnover Rates and Numbers of Exchangeable Hydrogens in Deuterated Water Labeled Samples.International journal of molecular sciences · 2025Article
- Improved Method to Determine Protein Turnover Rates with Heavy Water Labeling by Mass Isotopomer Ratio Selection.Journal of proteome research · 2025Article
- Measuring HSD17β13 protein turnover in mouse liver with DBioanalysis · 2025Article
- Numbers of Exchangeable Hydrogens from LC-MS Data of Heavy Water Metabolically Labeled Samples.Journal of the American Society for Mass Spectrometry · 2024Article
- Exact Integral Formulas for False Discovery Rate and the Variance of False Discovery Proportion.Journal of proteome research · 2024Article
- Flexible Quality Control for Protein Turnover Rates Using d2ome.International journal of molecular sciences · 2023Article
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Authors and funding
9 authors.
Funding
Abstract
Metabolic stable isotope labeling with heavy water followed by liquid chromatography coupled with mass spectrometry (LC-MS) is a powerful tool for in vivo protein turnover studies. Several algorithms and tools have been developed to determine the turnover rates of peptides and proteins from time-course stable isotope labeling experiments. The availability of benchmark mass spectrometry data is crucial to compare and validate the effectiveness of newly developed techniques and algorithms. In this work, we report a heavy water-labeled LC-MS dataset from the murine liver for protein turnover rate analysis. The dataset contains eighteen mass spectral data with their corresponding database search results from nine different labeling durations and quantification outputs from d2ome+ software. The dataset also contains eight mass spectral data from two-dimensional fractionation experiments on unlabeled samples.
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Registered trials
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