Abstract
Human saliva has great potential for clinical disease diagnostics. Constructing a comprehensive catalogue of saliva proteins using proteomic approaches is a necessary first step to identifying potential protein biomarkers of disease. However, because of the challenge presented in cataloguing saliva proteins with widely varying abundance, new proteomic approaches are needed. To this end, we used a newly developed approach coupling peptide separation using free flow electrophoresis with linear ion trap tandem mass spectrometry to identify proteins in whole human saliva. We identified 437 proteins with high confidence (false positive rate below 1%), producing the largest catalogue of proteins from a single saliva sample to date and providing new information on the composition and potential diagnostic utility of this fluid. The statistically validated, transparently presented, and annotated dataset provides a model for presenting large scale proteomic data of this type, which should facilitate better dissemination and easier comparisons of proteomic datasets from future studies in saliva.
Highlights
Human saliva has great potential for clinical disease diagnostics
To filter the sequence matches based upon peptide pI, it is first necessary to confirm the correspondence of peptide pI and measured free flow electrophoresis (FFE) fraction pH for the dataset being analyzed [10]
Overall the close correspondence justifies the use of FFE fraction pH, in addition to p score, as a filtering criterion of peptide sequence matches for this catalogue as we describe below
Summary
Human saliva has great potential for clinical disease diagnostics. Constructing a comprehensive catalogue of saliva proteins using proteomic approaches is a necessary first step to identifying potential protein biomarkers of disease. Because of the challenge presented in cataloguing saliva proteins with widely varying abundance, new proteomic approaches are needed To this end, we used a newly developed approach coupling peptide separation using free flow electrophoresis with linear ion trap tandem mass spectrometry to identify proteins in whole human saliva. We identified 437 proteins with high confidence (false positive rate below 1%), producing the largest catalogue of proteins from a single saliva sample to date and providing new information on the composition and potential diagnostic utility of this fluid. The statistically validated and transparently presented dataset (shown in the supplemental table) provides a model for presenting large, mass spectrometry-based proteomic data that should provide improved dissemination and comparison of datasets in this clinically important biological fluid
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