Summary
Pharmaceutical companies and regulatory agencies are pursuing biomarkers as a means to increase the productivity of drug development. Quantifying differential levels of proteins from complex biological samples like plasma or cerebrospinal fluid is one specific approach being used to identify markers of drug action, efficacy, toxicity, etc. Academic investigators are also interested in markers that are diagnostic or prognostic of disease states. We report a comprehensive, fully automated, and label-free approach to relative protein quantification including: sample preparation, proteolytic protein digestion, LC-MS/MS data acquisition, de-noising, mass and charge state estimation, chromatographic alignment, and peptide quantification via integration of extracted ion chromatograms. Additionally, we describe methods for transformation and normalization of the quantitative peptide levels in multiplexed measurements to improve precision for statistical analysis. Lastly, we outline how the described methods can be used to design and power biomarker discovery studies.
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Acknowledgments
We thank John Saalwaechter and Andrew Kaczorek and the entire scientific computing team for their efforts in developing and maintaining a high-availability grid-computing environment used for this work. We also thank Jude Onyia and the statistical and mathematical sciences management team for supporting us in the development of these methods.
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Higgs, R.E., Knierman, M.D., Gelfanova, V., Butler, J.P., Hale, J.E. (2008). Label-Free LC-MS Method for the Identification of Biomarkers. In: Vlahou, A. (eds) Clinical Proteomics. Methods in Molecular Biology™, vol 428. Humana Press. https://doi.org/10.1007/978-1-59745-117-8_12
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DOI: https://doi.org/10.1007/978-1-59745-117-8_12
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