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Updated: Jun 14, 2026
05:35
An Integrated Workflow of Identification and Quantification on FDR Control-Based Untargeted Metabolome
Published on: September 20, 2022
Balancing robust quantification and identification for iTRAQ: application of UHR-ToF MS
Saw Yen Ow1, Josselin Noirel, Malinda Salim
1ChELSI Institute, Department of Chemical and Process Engineering, University of Sheffield, Sheffield, UK.
Proteomics
|March 31, 2010
Summary
This study optimizes iTRAQ (isobaric tag for relative and absolute quantification) analysis on maXis UHR-Qq-ToF instruments. Adjusting MS/MS ion transmission parameters balances peptide identification and quantification for better proteomic data quality.
Area of Science:
- Proteomics
- Mass Spectrometry
- Analytical Chemistry
Background:
- iTRAQ reagents enable simultaneous multiplex identification and quantification of proteins.
- Effective peptide fragmentation is crucial for generating both sequence and reporter ions.
- Balancing ion transmission across the MS/MS mass range is key for iTRAQ success.
Purpose of the Study:
- To describe an analytical strategy for implementing iTRAQ on maXis UHR-Qq-ToF instruments.
- To discuss the impact of MS/MS ion transmission parameters on data quality.
- To highlight maXis-specific parameters affecting quantification and identification.
Main Methods:
- Implementation of iTRAQ workflow on maXis UHR-Qq-ToF mass spectrometers.
- Systematic adjustment of MS/MS ion transmission parameters.
- Analysis of peptide fragmentation, ion transmission, and detection across the MS/MS range.
Main Results:
- Demonstrated an analytical strategy for iTRAQ on maXis UHR-Qq-ToF.
- Showcased the impact of adjusting MS/MS ion transmission on data quality.
- Identified maXis-specific parameters influencing protein identification and quantification.
Conclusions:
- Optimized iTRAQ implementation on maXis UHR-Qq-ToF instruments is achievable.
- Careful adjustment of MS/MS ion transmission parameters is critical for robust proteomic analysis.
- Understanding maXis-specific parameter interactions enhances proteomic data reliability.
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