Software for quantitative proteomic analysis using stable isotope labeling and data independent acquisition

Anal Chem. 2011 Sep 15;83(18):6971-9. doi: 10.1021/ac201555m. Epub 2011 Aug 23.


Many software tools have been developed for analyzing stable isotope labeling (SIL)-based quantitative proteomic data using data dependent acquisition (DDA). However, programs for analyzing SIL-based quantitative proteomics data obtained with data independent acquisition (DIA) have yet to be reported. Here, we demonstrated the development of a new software for analyzing SIL data using the DIA method. Performance of the DIA on SYNAPT G2MS was evaluated using SIL-labeled complex proteome mixtures with known heavy/light ratios (H/L = 1:1, 1:5, and 1:10) and compared with the DDA on linear ion trap (LTQ)-Orbitrap MS. The DIA displays relatively high quantitation accuracy for peptides cross all intensity regions, while the DDA shows an intensity dependent distribution of H/L ratios. For the three proteome mixtures, the number of detected SIL-peptide pairs and dynamic range of protein intensities using DIA drop stepwise, whereas no significant changes in these aspects using DDA were observed. The new software was applied to investigate the proteome difference between mouse embryonic fibroblasts (MEFs) and MEF-derived induced pluripotent stem cells (iPSCs) using (16)O/(18)O labeling. Our study expanded the capacities of our UNiquant software pipeline and provided valuable insight into the performance of the two cutting-edge MS platforms for SIL-based quantitative proteomic analysis today.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Animals
  • Cell Line
  • Chromatography, High Pressure Liquid / methods
  • Fibroblasts / metabolism
  • Humans
  • Induced Pluripotent Stem Cells / metabolism
  • Isotope Labeling / methods*
  • Mass Spectrometry / methods
  • Mice
  • Oxygen Isotopes / chemistry
  • Peptides / analysis
  • Proteome / analysis*
  • Proteomics / methods*
  • Software*


  • Oxygen Isotopes
  • Peptides
  • Proteome