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.
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.
|Spencer, Sandra E; Corso, Thomas N; Bollinger, James G et al. (2017) Automated Trapping Column Exchanger for High-Throughput Nanoflow Liquid Chromatography. Anal Chem 89:2383-2389|
|Bereman, Michael S; Beri, Joshua; Sharma, Vagisha et al. (2016) An Automated Pipeline to Monitor System Performance in Liquid Chromatography-Tandem Mass Spectrometry Proteomic Experiments. J Proteome Res 15:4763-4769|
|Bollinger, James G; Stergachis, Andrew B; Johnson, Richard S et al. (2016) Selecting Optimal Peptides for Targeted Proteomic Experiments in Human Plasma Using In Vitro Synthesized Proteins as Analytical Standards. Methods Mol Biol 1410:207-21|
|Kawahara, Rebeca; Bollinger, James G; Rivera, César et al. (2016) A targeted proteomic strategy for the measurement of oral cancer candidate biomarkers in human saliva. Proteomics 16:159-73|
|Searle, Brian C; Egertson, Jarrett D; Bollinger, James G et al. (2015) Using Data Independent Acquisition (DIA) to Model High-responding Peptides for Targeted Proteomics Experiments. Mol Cell Proteomics 14:2331-40|
|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|
|Bereman, Michael S; Hsieh, Edward J; Corso, Thomas N et al. (2013) Development and characterization of a novel plug and play liquid chromatography-mass spectrometry (LC-MS) source that automates connections between the capillary trap, column, and emitter. Mol Cell Proteomics 12:1701-8|
|Abbatiello, Susan E; Mani, D R; Schilling, Birgit et al. (2013) Design, implementation and multisite evaluation of a system suitability protocol for the quantitative assessment of instrument performance in liquid chromatography-multiple reaction monitoring-MS (LC-MRM-MS). Mol Cell Proteomics 12:2623-39|