Submitted on August 14, 2006
Revised on October 23, 2006
Accepted on October 30, 2006
Shotgun glycopeptide-capture approach coupled with mass spectrometry for comprehensive glycoproteomics
Bingyun Sun, Jeffrey A. Ranish, Angelita G. Utleg, James T. White, Xiaowei Yan, Biaoyang Lin, and Leroy Hood
The Institute for Systems Biology, Seattle, WA 98103
Corresponding Author: blin{at}systemsbiology.org
We present a robust and general shotgun glycoproteomics approach to comprehensively profile glycoproteins in complex biological mixtures. In this approach, glycopeptides derived from glycoproteins are enriched by selective capture onto a solid support using hydrazide chemistry, followed by enzymatic release of the peptides and subsequent analysis by tandem mass spectrometry. The approach was validated using standard protein mixtures which resulted in a close to 100 % capture efficiency. Our capture approach was then applied to microsomal fractions of the cisplatin-resistant ovarian-cancer cell line IGROV-1/CP. With a protein-prophet probability value greater than 0.9, we identified a total of 302 proteins with an average protein identification rate of 136±19 (n=4) in a single LTQ nanoLC-MS experiment, and a selectivity of 91±1.6 % (n=4) for the N-linked glyco-consensus sequence. Our method has several advantages: 1) the utility of sodium-sulphite as a quencher in our capture approach to replace the SPE (solid phase extraction) step in earlier glycoprotein chemical-capture approach for removing excess sodium periodate allows the overall capture procedure to be completed in a single vessel. This improvement minimizes sample loss and increases sensitivity, and makes our protocol amenable for high throughput implementation, a feature that is essential for biomarker identification and validation of large number of clinical samples; 2) digestion of proteins initially into peptides improves solubility of large membrane proteins and exposes all of the glycosylation sites to ensure equal accessibility to capture reagents; 3) capturing glycosylated peptides can effectively reduce sample complexity and at the same time increase the confidence of MS-based protein identifications (more potential peptide identifications per protein); 4) the approach is demonstrated here on the analysis of N-linked glycopeptides, however, it can be applied equally well to O-glycoprotein analysis.