ArticleFrontiers in plant science2013
Systematic evaluation of reference protein normalization in proteomic experiments.
Article in Frontiers in plant science, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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6 citing papers in PubMed.
- Absolute quantitative proteomics guides patient-stratified drug repurposing in clear cell and papillary renal cell carcinoma.bioRxiv : the preprint server for biology · 2026Article
- Leveraging ML for profiling lipidomic alterations in breast cancer tissues: a methodological perspective.Scientific reports · 2024Article
- Does filter-aided sample preparation provide sufficient method linearity for quantitative plant shotgun proteomics?Frontiers in plant science · 2022Article
- Article
- Quantification of Changes in Protein Expression Using SWATH Proteomics.Methods in molecular biology (Clifton, N.J.) · 2021Article
- Molecular Signatures of Membrane Protein Complexes Underlying Muscular Dystrophy.Molecular & cellular proteomics : MCP · 2016Article
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3 authors.
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Abstract
Quantitative comparative analyses of protein abundances using peptide ion intensities and their modifications have become a widely used technique in studying various biological questions. In the past years, several methods for quantitative proteomics were established using stable-isotope labeling and label-free approaches. We systematically evaluated the application of reference protein normalization (RPN) for proteomic experiments using a high mass accuracy LC-MS/MS platform. In RPN all sample peptide intensities were normalized to an average protein intensity of a spiked reference protein. The main advantage of this method is that it avoids fraction of total based relative analysis of proteomic data, which is often very much dependent on sample complexity. We could show that reference protein ion intensity sums are sufficiently reproducible to ensure a reliable normalization. We validated the RPN strategy by analyzing changes in protein abundances induced by nutrient starvation in Arabidopsis thaliana. Beyond that, we provide a principle guideline for determining optimal combination of sample protein and reference protein load on individual LC-MS/MS systems.
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