ArticleAnalytical chemistry2021
JUMPt: Comprehensive Protein Turnover Modeling of In Vivo Pulse SILAC Data by Ordinary Differential Equations.
Article in Analytical chemistry, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
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Who cites it
13 citing papers in PubMed.
- Age-based approach to characterize the dynamics of cellular processes.Proceedings of the National Academy of Sciences of the United States of America · 2026Article
- A Deep Quantitative Proteome Turnover Platform for Human iPSC-derived Neurons.bioRxiv : the preprint server for biology · 2026Article
- A robust multiplex-DIA workflow profiles protein turnover regulations associated with cisplatin resistance and aneuploidy.Nature communications · 2025Article
- Article
- Human and mouse proteomics reveals the shared pathways in Alzheimer's disease and delayed protein turnover in the amyloidome.Nature communications · 2025Article
- Human-mouse proteomics reveals the shared pathways in Alzheimer's disease and delayed protein turnover in the amyloidome.bioRxiv : the preprint server for biology · 2024Article
- An Extensive Atlas of Proteome and Phosphoproteome Turnover Across Mouse Tissues and Brain Regions.bioRxiv : the preprint server for biology · 2024Article
- Multi-modal proteomic characterization of lysosomal function and proteostasis in progranulin-deficient neurons.Molecular neurodegeneration · 2023Article
- Quantifying label enrichment from two mass isotopomers increases proteome coverage for in vivo protein turnover using heavy water metabolic labeling.Communications chemistry · 2023Article
- Toward a hypothesis-free understanding of how phosphorylation dynamically impacts protein turnover.Proteomics · 2023Article
- Determining and interpreting protein lifetimes in mammalian tissues.Trends in biochemical sciences · 2023Review
- Software Tool for Visualization and Validation of Protein Turnover Rates Using Heavy Water Metabolic Labeling and LC-MS.International journal of molecular sciences · 2022Article
- Protein turnover models for LC-MS data of heavy water metabolic labeling.Briefings in bioinformatics · 2022Article
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11 authors.
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Abstract
Recent advances in mass spectrometry (MS)-based proteomics allow the measurement of turnover rates of thousands of proteins using dynamic labeling methods, such as pulse stable isotope labeling by amino acids in cell culture (pSILAC). However, when applying the pSILAC strategy to multicellular animals (e.g., mice), the labeling process is significantly delayed by native amino acids recycled from protein degradation in vivo, raising a challenge of defining accurate protein turnover rates. Here, we report JUMPt, a software package using a novel ordinary differential equation (ODE)-based mathematical model to determine reliable rates of protein degradation. The uniqueness of JUMPt is to consider amino acid recycling and fit the kinetics of the labeling amino acid (e.g., Lys) and whole proteome simultaneously to derive half-lives of individual proteins. Multiple settings in the software are designed to enable simple to comprehensive data inputs for precise analysis of half-lives with flexibility. We examined the software by studying the turnover of thousands of proteins in the pSILAC brain and liver tissues. The results were largely consistent with the proteome turnover measurements from previous studies. The long-lived proteins are enriched in the integral membrane, myelin sheath, and mitochondrion in the brain. In summary, the ODE-based JUMPt software is an effective proteomics tool for analyzing large-scale protein turnover, and the software is publicly available on GitHub (https://github.com/JUMPSuite/JUMPt) to the research community.
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