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Related Experiment Video

Updated: May 30, 2026

Deep Proteome Profiling by Isobaric Labeling, Extensive Liquid Chromatography, Mass Spectrometry, and Software-assisted Quantification
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Deep Proteome Profiling by Isobaric Labeling, Extensive Liquid Chromatography, Mass Spectrometry, and Software-assisted Quantification

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

Xin Huang1, Miao Liu, Michael J Nold

  • 1Department of Pathology and Microbiology, University of Nebraska Medical Center, Omaha, Nebraska 68198, United States.

Analytical Chemistry
|August 13, 2011
PubMed
Summary
This summary is machine-generated.

A new software tool analyzes stable isotope labeling (SIL) quantitative proteomics data from data independent acquisition (DIA) mass spectrometry. This DIA method offers high quantitation accuracy across peptide intensities, expanding proteomic analysis capabilities.

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Area of Science:

  • Proteomics
  • Mass Spectrometry
  • Bioinformatics

Background:

  • Stable isotope labeling (SIL) is crucial for quantitative proteomics.
  • Existing software primarily supports data dependent acquisition (DDA).
  • Data independent acquisition (DIA) offers advantages but lacks dedicated SIL analysis tools.

Purpose of the Study:

  • To develop novel software for analyzing SIL-based quantitative proteomics data acquired via DIA.
  • To evaluate the performance of DIA on SYNAPT G2MS for SIL quantitation.
  • To compare DIA performance with DDA on LTQ-Orbitrap MS.

Main Methods:

  • Development of a new software tool for DIA-SIL quantitative proteomics.
  • Performance evaluation using SIL-labeled complex proteome mixtures with known heavy/light ratios (H/L = 1:1, 1:5, 1:10).
  • Comparison of DIA (SYNAPT G2MS) with DDA (LTQ-Orbitrap MS).
  • Application of the software to compare mouse embryonic fibroblasts (MEFs) and induced pluripotent stem cells (iPSCs) using (16)O/(18)O labeling.

Main Results:

  • DIA demonstrated high quantitation accuracy across all intensity regions.
  • DDA exhibited an intensity-dependent distribution of H/L ratios.
  • DIA showed a stepwise drop in detected SIL-peptide pairs and dynamic range with increasing H/L ratios.
  • DDA showed no significant changes in these metrics across different H/L ratios.
  • The new software successfully investigated proteome differences between MEFs and iPSCs.

Conclusions:

  • The developed software expands the UNiquant pipeline for DIA-SIL quantitative proteomics.
  • DIA offers robust quantitation accuracy for SIL-based proteomics.
  • The study provides insights into the comparative performance of DIA and DDA platforms for SIL analysis.