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Cysteine-Directed Isobaric Labeling Combined with GeLC-FAIMS-MS for Quantitative Top-Down Proteomics
Theo Matzanke1, Philipp T Kaulich1, Kyowon Jeong2,3
1Systematic Proteome Research & Bioanalytics, Institute for Experimental Medicine, Christian-Albrechts-Universität zu Kiel, 24105 Kiel, Germany.
Journal of Proteome Research
|January 31, 2025
Summary
This study introduces an optimized proteoform quantification method using iodoTMT labeling and GeLC-FAIMS-MS. The improved workflow enhances proteoform identification and analysis, revealing significant proteome differences in E. coli.
Area of Science:
- Proteomics
- Biochemistry
- Analytical Chemistry
Background:
- Proteoform quantification is crucial for understanding biological processes.
- Isobaric labeling and multidimensional separations enable deep proteome analysis.
- Top-down proteomics offers comprehensive proteoform insights.
Purpose of the Study:
- To develop and optimize an iodoTMT-based, gel- and gas-phase fractionation (GeLC-FAIMS-MS) workflow for in-depth quantitative proteoform analysis.
- To enhance proteoform identification and reduce ratio compression through workflow optimization.
- To implement a mass feature-based quantification strategy for improved data analysis.
Main Methods:
- Cysteine-directed isobaric labeling with iodoTMT.
- Gel and gas-phase fractionation (GeLC-FAIMS-MS) coupled with mass spectrometry.
- Optimization of FAIMS compensation voltages, isolation windows, acquisition strategy, and fragmentation.
- Implementation of mass feature-based quantification in FLASHDeconv.
Main Results:
- The optimized iodoTMT GeLC-FAIMS-MS workflow significantly increased the number of quantified proteoforms.
- Ratio compression was reduced, leading to more accurate quantification.
- Application to *Escherichia coli* revealed 726 differentially abundant proteoforms between glucose and acetate growth conditions.
Conclusions:
- The developed iodoTMT GeLC-FAIMS-MS workflow provides a robust platform for quantitative proteoform analysis.
- This method facilitates deeper insights into biological systems by enabling comprehensive proteome profiling.
- The findings highlight significant proteome-level adaptations in *E. coli* based on carbon source metabolism.
