~(18)O-Labeled Proteome Reference as Global Internal Standards for Targeted Quantification by Selected Reaction Monitoring-Mass Spectrometry

摘要

Selected reaction monitoring (SRM)-MS is an emerging technology for high throughput targeted protein quantification and verification in biomarker discovery studies; however, the cost associated with the application of stable isotope-labeled synthetic peptides as internal standards can be prohibitive for screening a large number of candidate proteins as often required in the preverification phase of discovery studies. Herein we present a proof of concept study using an ~(18)O-labeled proteome reference as global internal standards (GIS) for SRM-based relative quantification. The ~(18)O-labeled proteome reference (or GIS) can be readily prepared and contains a heavy isotope (~(18)O)-labeled internal standard for every possible tryptic peptide. Our results showed that the percentage of heavy isotope (~(18)O) incorporation applying an improved protocol was >99.5% for most peptides investigated. The accuracy, reproducibility, and linear dynamic range of quantification were further assessed based on known ratios of standard proteins spiked into the labeled mouse plasma reference. Reliable quantification was observed with high reproducibility (i.e. coefficient of variance <10%) for ana-lyte concentrations that were set at 100-fold higher or lower than those of the GIS based on the light (~(16)O)/heavy (~(18)O) peak area ratios. The utility of ~(18)O-labeled GIS was further illustrated by accurate relative quantification of 45 major human plasma proteins. Moreover, quantification of the concentrations of C-reactive protein and prostate-specific antigen was illustrated by coupling the GIS with standard additions of purified protein standards. Collectively, our results demonstrated that the use of ~(18)O-la-beled proteome reference as GIS provides a convenient, low cost, and effective strategy for relative quantification of a large number of candidate proteins in biological or clinical samples using SRM.