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Updated: Jun 28, 2026
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Deep Proteome Profiling by Isobaric Labeling, Extensive Liquid Chromatography, Mass Spectrometry, and Software-assisted Quantification
Published on: November 15, 2017
Science, marketing and wishful thinking in quantitative proteomics
1Department of Chemical Engineering, University of Washington, Seattle, WA 98195, USA. mhackett@u.washington.edu
Proteomics
|October 22, 2008
Summary
Ensuring good analytical practice in quantitative proteomics is crucial. This involves addressing challenges in mass spectrometry (MS)-based sampling depth and data management for reliable large-scale studies.
Area of Science:
- Proteomics
- Analytical Chemistry
- Biotechnology
Background:
- Concerns exist regarding the reporting of quantitative results in proteomics.
- Good analytical practice is often overlooked in large-scale proteomic studies.
Purpose of the Study:
- To discuss challenges in achieving adequate sampling for mass spectrometry (MS)-based proteomics.
- To highlight the importance of sampling depth for detecting protein abundance changes.
- To explore strategies for overcoming data management obstacles in large-scale proteomics.
Main Methods:
- Discussion based on existing literature and expert commentary.
- Analysis of the relationship between sampling depth and statistical power.
- Consideration of data transfer and storage solutions.
Main Results:
- Inadequate sampling can limit the ability to detect meaningful protein abundance changes.
- Bureaucratic hurdles can hinder the adoption of efficient data sharing technologies.
- Conflict of interest may influence reporting practices.
Conclusions:
- Improving analytical practices in quantitative proteomics is essential for data validity.
- Addressing sampling strategies and data management is key for large-scale MS-based studies.
- Overcoming obstacles to peer-to-peer data sharing is necessary for scientific advancement.
