The overall goal of this proposal is to improve the quality, reliability, and interlaboratory comparability of peptide mass spectrometry data. Mass spectrometry (MS) has become a fundamental technology for the identification and quantitative analysis of proteins, protein interactions, and protein post-translational modifications. These analyses are an important part of solving biological problems that involve changes in protein abundance in response to disease, drug treatment, and genetic or environmental perturbations. Unfortunately, the application of protein mass spectrometry measurements in the clinical laboratory has been limited. Unlike most clinical assays by mass spectrometry, which use microflow liquid chromatography, peptide measurements are commonly performed using a nanoflow liquid chromatograph interface to the mass spectrometer (nanoflow LC-MS). Despite their analytical power, these nanoflow LC-MS methods have been difficult to apply robustly in quantitative assays involving large numbers of samples from a challenging sample matrix. The successful completion of our project will result in a peptide analysis platform that can automatically assess problems with the nanoflow LC-MS system and correct the problem during an analytical run and will significantly improve the robustness and reproducibility of peptide mass spectrometry measurements.

Public Health Relevance

Mass spectrometry has been a fundamental technology for the analysis of proteins in health and disease. However, despite the analytical power of conventional mass spectrometry methods, they have not been well-suited for the comparative analysis of very large numbers of samples acquired under a large number of conditions. Thus, the continued development of novel mass spectrometry technology is essential to understanding complex biological systems so that can be characterized that have a change in abundance in response to disease, drug treatment, and genetic or environmental perturbation.

Agency
National Institute of Health (NIH)
Institute
National Institute of General Medical Sciences (NIGMS)
Type
Research Project (R01)
Project #
1R01GM107142-01
Application #
8558710
Study Section
Special Emphasis Panel (ZRG1-BCMB-B (02))
Program Officer
Sheeley, Douglas
Project Start
2013-09-01
Project End
2017-04-30
Budget Start
2013-09-01
Budget End
2014-04-30
Support Year
1
Fiscal Year
2013
Total Cost
$355,930
Indirect Cost
$84,579
Name
University of Washington
Department
Genetics
Type
Schools of Medicine
DUNS #
605799469
City
Seattle
State
WA
Country
United States
Zip Code
98195
Bereman, Michael S; Johnson, Richard; Bollinger, James et al. (2014) Implementation of statistical process control for proteomic experiments via LC MS/MS. J Am Soc Mass Spectrom 25:581-7