title: Sources of technical variability in quantitative LC-MS proteomics: human brain tissue sample analysis.
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.
authors.affiliation: Biological Sciences Division and Environmental Molecular Sciences Laboratory, Pacific Northwest National Laboratory, Richland, Washington, USA.
authors.forename: Paul D
authors.initials: PD
authors.lastname: Piehowski
authors.forename: Vladislav A
authors.initials: VA
authors.lastname: Petyuk
authors.forename: Daniel J
authors.initials: DJ
authors.lastname: Orton
authors.forename: Fang
authors.initials: F
authors.lastname: Xie
authors.forename: Ronald J
authors.initials: RJ
authors.lastname: Moore
authors.forename: Manuel
authors.initials: M
authors.lastname: Ramirez-Restrepo
authors.forename: Anzhelika
authors.initials: A
authors.lastname: Engel
authors.forename: Andrew P
authors.initials: AP
authors.lastname: Lieberman
authors.forename: Roger L
authors.initials: RL
authors.lastname: Albin
authors.forename: David G
authors.initials: DG
authors.lastname: Camp
authors.forename: Richard D
authors.initials: RD
authors.lastname: Smith
authors.forename: Amanda J
authors.initials: AJ
authors.lastname: Myers
mesh_terms: D000368:Aged; D000465:Algorithms; D000544:Alzheimer Disease / Q000378:metabolism; D016477:Artifacts; D001921:Brain / Q000378:metabolism*; D016022:Case-Control Studies; D006801:Humans; D008297:Male; D008954:Models, Biological; D009419:Nerve Tissue Proteins / Q000378:metabolism*; D020543:Proteome / Q000378:metabolism*; D040901:Proteomics; D012015:Reference Standards; D015203:Reproducibility of Results; D021241:Spectrometry, Mass, Electrospray Ionization / Q000592:standards; D053719:Tandem Mass Spectrometry / Q000592:standards
chemical_list: D009419:Nerve Tissue Proteins; D020543:Proteome