ArticleJournal of proteome research2013
Sources of technical variability in quantitative LC-MS proteomics: human brain tissue sample analysis.
Article in Journal of proteome research, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 106 papers, 2 of them syntheses that pooled it.
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Who cites it
106 citing papers in PubMed, 2 syntheses or guidelines pooled it.
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- Insights into the changes in the proteome of Alzheimer disease elucidated by a meta-analysis.Scientific data · 2021Pooled it
- Protein Variability Patterns in Ovarian Serous Carcinoma.International journal of molecular sciences · 2026Article
- Bioprocess Design and Optimization of Extracellular Vesicles Derived from Mesenchymal Stromal Cells.ACS nano · 2026Article
- Consensus statement on mass spectrometry-based proteomic analysis of cardiac tissue.Nature cardiovascular research · 2026Article
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- Cross-platform clinical proteomics using the Charité open standard for plasma proteomics (OSPP).Nature communications · 2025Article
- Alternative Ion-Pairing Modifiers Should Be Investigated in Low-Input and Single-Cell Proteomics.Journal of proteome research · 2025Article
- Designing a Comparative Proteomics Experiment: Retention-Time Alignment and Imputation Algorithms Affect Statistical Comparisons between Samples.Journal of proteome research · 2025Article
- Molecular flexibility of hyaluronic acid has a profound effect on invasion of cancer cells.Royal Society open science · 2025Article
- Automated sparse feature selection in high-dimensional proteomics data via 1-bit compressed sensing and K-Medoids clustering.BMC bioinformatics · 2025Article
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- Data Interoperability and Harmonization in Cardiovascular Genomic and Precision Medicine.Circulation. Genomic and precision medicine · 2025Review
- Quality Control Standards for Batch Effect Evaluation and Correction in Mass Spectrometry Imaging.Analytical chemistry · 2025Article
- To Impute or Not To Impute in Untargeted Metabolomics─That is the Compositional Question.Journal of the American Society for Mass Spectrometry · 2025Article
- A curated tissue-specific proteome, phosphoproteome, and kinome map of Drosophila melanogaster with an integrated outlook in circadian physiology.Functional & integrative genomics · 2025Article
- Data from a multi-year targeted proteomics study of a longitudinal birth cohort of type 1 diabetes.Scientific data · 2025Article
- UrinaryJournal of cellular immunology · 2025Article
- Forewarned is forearmed: rice plants develop tolerance to post-anoxia during anoxic conditions by proteomic changes.Frontiers in plant science · 2025Article
46 more citing papers are in PubMed but not listed here.
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Authors and funding
12 authors.
Funding
Abstract
To design a robust quantitative proteomics study, an understanding of both the inherent heterogeneity of the biological samples being studied as well as the technical variability of the proteomics methods and platform is needed. Additionally, accurately identifying the technical steps associated with the largest variability would provide valuable information for the improvement and design of future processing pipelines. We present an experimental strategy that allows for a detailed examination of the variability of the quantitative LC-MS proteomics measurements. By replicating analyses at different stages of processing, various technical components can be estimated and their individual contribution to technical variability can be dissected. This design can be easily adapted to other quantitative proteomics pipelines. Herein, we applied this methodology to our label-free workflow for the processing of human brain tissue. For this application, the pipeline was divided into four critical components: Tissue dissection and homogenization (extraction), protein denaturation followed by trypsin digestion and SPE cleanup (digestion), short-term run-to-run instrumental response fluctuation (instrumental variance), and long-term drift of the quantitative response of the LC-MS/MS platform over the 2 week period of continuous analysis (instrumental stability). From this analysis, we found the following contributions to variability: extraction (72%) >> instrumental variance (16%) > instrumental stability (8.4%) > digestion (3.1%). Furthermore, the stability of the platform and its suitability for discovery proteomics studies is demonstrated.
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