Sources of Technical Variability in Quantitative LC鈥揗S Proteomics: Human Brain Tissue Sample Analysis
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- 作者：Paul D. Piehowski ; Vladislav A. Petyuk ; Daniel J. Orton ; Fang Xie ; Ronald J. Moore ; Manuel Ramirez-Restrepo ; Anzhelika Engel ; Andrew P. Lieberman ; Roger L. Albin ; David G. Camp ; Richard D. Smith ; Amanda J. Myers
- 刊名：Journal of Proteome Research
- 出版年：2013
- 出版时间：May 3, 2013
- 年：2013
- 卷：12
- 期：5
- 页码：2128-2137
- 全文大小：383K
- 年卷期：v.12,no.5(May 3, 2013)
- ISSN：1535-3907
文摘
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鈥揗S 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鈥揗S/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.