ArticleNature protocols2019
A mass spectrometry workflow for measuring protein turnover rates in vivo.
Article in Nature protocols, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers.
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Who cites it
21 citing papers in PubMed.
- Ageing impacts extracellular matrix turnover and remodelling in the kidney.Matrix biology plus · 2026Article
- Accelerated basement membrane remodeling and serum matrix fragments as biomarkers of fibrosis in Alport syndrome.Cell reports · 2026Article
- DELTA: a method for brain-wide measurement of synaptic protein turnover reveals localized plasticity during learning.Nature neuroscience · 2025Article
- Article
- Measuring HSD17β13 protein turnover in mouse liver with DBioanalysis · 2025Article
- Morphological correlates of synaptic protein turnover in the mouse brain.Life science alliance · 2024Article
- An Extensive Atlas of Proteome and Phosphoproteome Turnover Across Mouse Tissues and Brain Regions.bioRxiv : the preprint server for biology · 2024Article
- In utero pulse injection of isotopic amino acids quantifies protein turnover rates during murine fetal development.Cell reports methods · 2024Article
- Conditional Fragment Ion Probabilities Improve Database Searching for Nonmonoisotopic Precursors.Journal of proteome research · 2023Article
- TurnoveR: A Skyline External Tool for Analysis of Protein Turnover in Metabolic Labeling Studies.Journal of proteome research · 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
- Protein lifetimes in aged brains reveal a proteostatic adaptation linking physiological aging to neurodegeneration.Science advances · 2022Article
- Understanding the "SMART" features of hematopoietic stem cells and beyond.Science China. Life sciences · 2021Review
- An atlas of protein turnover rates in mouse tissues.Nature communications · 2021Article
- JUMPt: Comprehensive Protein Turnover Modeling of In Vivo Pulse SILAC Data by Ordinary Differential Equations.Analytical chemistry · 2021Article
- Article
- Article
- NanoSIMS observations of mouse retinal cells reveal strict metabolic controls on nitrogen turnover.BMC molecular and cell biology · 2021Article
- Proteome Turnover in the Spotlight: Approaches, Applications, and Perspectives.Molecular & cellular proteomics : MCP · 2021Review
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Proteins are continually produced and degraded, to avoid the accumulation of old or damaged molecules and to maintain the efficiency of physiological processes. Despite its importance, protein turnover has been difficult to measure in vivo. Previous approaches to evaluating turnover in vivo have required custom labeling approaches, involved complex mass spectrometry (MS) analyses, or used comparative strategies that do not allow direct quantitative measurements. Here, we describe a robust protocol for quantitative proteome turnover analysis in mice that is based on a commercially available diet for stable isotope labeling of amino acids in mammals (SILAM). We start by discussing fundamental concepts of protein turnover, including different methodological approaches. We then cover in detail the practical aspects of metabolic labeling and explain both the experimental and computational steps that must be taken to obtain accurate in vivo results. Finally, we present a simple experimental workflow that enables measurement of precise turnover rates in a time frame of ~4-5 weeks, including the labeling time. We also provide all the scripts needed for the interpretation of the MS results and for comparing turnover across different conditions. Overall, the workflow presented here comprises several improvements in the determination of protein lifetimes with respect to other available methods, including a minimally invasive labeling strategy and a robust interpretation of MS results, thus enhancing reproducibility across laboratories.
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