Isobaric protein-level labeling strategy for serum glycoprotein quantification analysis by liquid chromatography-tandem mass spectrometry

  • Nie S
  • Lo A
  • Zhu J
 et al. 
  • 12

    Readers

    Mendeley users who have this article in their library.
  • 11

    Citations

    Citations of this article.

Abstract

While peptide-level labeling using isobaric tag reagents has been widely applied for quantitative proteomics experiments, there are comparatively few reports of protein-level labeling. Intact protein labeling could be broadly applied to quantification experiments utilizing protein-level separations or enrichment schemes. Here, protein-level isobaric labeling was explored as an alternative strategy to peptide-level labeling for serum glycoprotein quantification. Labeling and digestion conditions were optimized by comparing different organic solvents and enzymes. Digestions with Asp-N and trypsin were found highly complementary; combining the results enabled quantification of 30% more proteins than either enzyme alone. Three commercial reagents were compared for protein-level labeling. Protein identification rates were highest with iTRAQ 4-plex when compared to TMT 6-plex and iTRAQ 8-plex using higher-energy collisional dissociation on an Orbitrap Elite mass spectrometer. The compatibility of isobaric protein-level labeling with lectin-based glycoprotein enrichment was also investigated. More than 74% of lectin-bound labeled proteins were known glycoproteins, which was similar to results from unlabeled and peptide-level labeled serum samples. Finally, protein-level and peptide-level labeling strategies were compared for serum glycoprotein quantification. Isobaric protein-level labeling gave comparable identification levels and quantitative precision to peptide-level labeling.

Get free article suggestions today

Mendeley saves you time finding and organizing research

Sign up here
Already have an account ?Sign in

Find this document

Get full text

Authors

  • Song Nie

  • Andy Lo

  • Jianhui Zhu

  • Jing Wu

  • MacK T. Ruffin

  • David M. Lubman

Cite this document

Choose a citation style from the tabs below

Save time finding and organizing research with Mendeley

Sign up for free