Journal
JOURNAL OF PROTEOME RESEARCH
Volume 12, Issue 5, Pages 2128-2137Publisher
AMER CHEMICAL SOC
DOI: 10.1021/pr301146m
Keywords
label-free quantification; technical variation; sample preparation; reproducibility; study design; tissue analysis
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Funding
- National Center for Research Resources [5 P41 RR018522-10]
- National Institute of General Medical Sciences from the National Institutes of Health [8 P41 GM103493-10]
- NIH EUREKA grant [R01-AG-034504]
- NIH [P50-AG08671]
- National Institute of Allergy and Infectious Diseases [Y1-AI-8401]
- Department of Energy Office of Biological and Environmental Research Genome Sciences Program under the Pan-omics project
- DOE [DE-AC05-76RLO01830]
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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-tern; 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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