Biological variation not only provides insight into the molecular machinery of disease progression, but accurately informs clinicians about a patient's health status, both current and future. Researchers discover biological variation by conducting large scale comparative studies aimed at detecting differences in the molecular makeup (biomarkers) of samples in different states. Ideally suited for biomarkers are proteins because their cellular composition (proteome) and their degraded parts, endogenous peptides (peptidome), change in response to their environment and disease progression. For comparative proteomic studies, researchers commonly employ high performance liquid chromatography, coupled with electrospray ionization tandem mass spectrometry (HPLC-ESI-MS/MS) and labeled quantification. However, intensity-based label free relative quantification (iLFRQ) is more desirable than labeled quantification because iLFRQ is more cost effective and does not limit the number of samples in a study. Unfortunately, iLFRQ for proteins, and especially peptides, is challenging. Here, I highlight three challenges. 1) I contend that the current relative abundance paradigm is ill-suited to detect biological variation using iLFRQ. 2) HPLC-ESI-MS/ MS analyses produce poorly repeatable and reproducible results, and current normalization methods fail to mitigate localized extraneous variability (complex variability in measurements) from transient stochastic events occurring during an HPLC-ESI-MS/MS run. 3) Current software frameworks report protein level quantification rather than peptide level quantification. To overcome these challenges, I offer three contributions. 1) I propose to use the proportionality paradigm for iLFRQ instead of the relative abundance paradigm. 2) Proximity-based Intensity Normalization (PIN), an embodiment of the proportionality paradigm, normalizes a peptide's signal intensity by constructing its temporal neighborhood and computing its relative proportion within that neighborhood. 3) RIPPER, a new software framework that reports normalized peptide signal intensities rather than protein intensities. Evaluation results demonstrate that PIN dominates current normalization methods in reducing systematic bias and complex variability. Furthermore, RIPPER/PIN finds statistically significant biological variation which is now falsely reported or missed. I expect the proportionality paradigm for iLFRQ, embodied in PIN, and implemented in RIPPER, to change the way researchers analyze HPLC-ESI-MS/MS experimental data. The upshot will, I expect, will be reproducibility and repeatability improved, and otherwise falsely reported or missed, statistically significant biological variation discovered.
University of Minnesota Ph.D. dissertation. May 2013. Major: Biomedical Informatics and Computational Biology. Advisors: Dr. John V. Carlis & Dr. Timothy J. Griffin. 1 computer file (PDF); xiv, 242 pages, appendices A-C.
Van Riper, Susan Kaye.
The importance of being proportional: a paradigm shift for intensity-based label free relative quantification in mass spectrometry proteomics.
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