ArticleCommunications chemistry2023
Quantifying label enrichment from two mass isotopomers increases proteome coverage for in vivo protein turnover using heavy water metabolic labeling.
Article in Communications chemistry, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed.
- A Computational Model for Determining Labeling Duration in Protein Turnover Studies Using a Single Deuterated Water Labeled Sample.Journal of the American Society for Mass Spectrometry · 2026Article
- Deuterium labeling enables proteome-wide turnover kinetics analysis in cell culture.Cell reports methods · 2025Article
- Improved Method to Determine Protein Turnover Rates with Heavy Water Labeling by Mass Isotopomer Ratio Selection.Journal of proteome research · 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
- Metabolomic and proteomic applications to exercise biomedicine.Translational exercise biomedicine · 2024Review
- In utero pulse injection of isotopic amino acids quantifies protein turnover rates during murine fetal development.Cell reports methods · 2024Article
- Flexible Quality Control for Protein Turnover Rates Using d2ome.International journal of molecular sciences · 2023Article
- A large-scale LC-MS dataset of murine liver proteome from time course of heavy water metabolic labeling.Scientific data · 2023Article
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Authors and funding
9 authors.
Funding
Abstract
Heavy water metabolic labeling followed by liquid chromatography coupled with mass spectrometry is a powerful high throughput technique for measuring the turnover rates of individual proteins in vivo. The turnover rate is obtained from the exponential decay modeling of the depletion of the monoisotopic relative isotope abundance. We provide theoretical formulas for the time course dynamics of six mass isotopomers and use the formulas to introduce a method that utilizes partial isotope profiles, only two mass isotopomers, to compute protein turnover rate. The use of partial isotope profiles alleviates the interferences from co-eluting contaminants in complex proteome mixtures and improves the accuracy of the estimation of label enrichment. In five different datasets, the technique consistently doubles the number of peptides with high goodness-of-fit characteristics of the turnover rate model. We also introduce a software tool, d2ome+, which automates the protein turnover estimation from partial isotope profiles.
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Registered trials
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