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Deep Proteome Profiling by Isobaric Labeling, Extensive Liquid Chromatography, Mass Spectrometry, and Software-assisted Quantification
Published on: November 15, 2017
NHS-Ester Tandem Labeling in One Pot Enables 48-Plex Quantitative Proteomics.
Sansi Xing1, Akshat Pai1, Ruilin Wu1
1Department of Biochemistry and Biomedical Sciences, McMaster University, Michael G. DeGroote Centre for Learning and Centre, Room 5033, 1280 Main Street West, Hamilton, ON L8S 4K1, Canada.
We developed a novel hybrid-isotope labeling method, NETLOP, for highly multiplexed quantitative proteomics. This approach enhances sample multiplexity and accuracy without sample cleanup, enabling deeper proteomic insights.
Area of Science:
- Proteomics
- Mass Spectrometry
- Biochemistry
Background:
- Stable-isotope labeling is crucial for quantitative proteomics.
- Hybrid strategies combining isotopic and isobaric tags increase multiplexity.
- Existing methods can involve sample loss and quantification inaccuracies.
Purpose of the Study:
- To introduce a novel hybrid-isotope labeling method, NETLOP (NHS-ester tandem labeling in one pot).
- To demonstrate NETLOP's utility for high-plex quantitative proteomic analysis.
- To improve accuracy and reduce sample loss in multiplexed proteomics.
Main Methods:
- Optimized 16-plex TMTpro labeling of lysine residues.
- Performed 2- or 3-plex mTRAQ labeling of peptide N-termini using NHS-ester reagents.
- Applied NETLOP to HeLa cell samples for LC-MS/MS analysis.
Main Results:
- Successfully performed proof-of-principle 32-plex and 48-plex quantitative proteomic analyses in single experiments.
- NETLOP requires no intermediate sample cleanup, minimizing sample loss.
- NETLOP avoids retention time shifts, ensuring quantification accuracy.
Conclusions:
- NETLOP offers a robust and efficient hybrid-isotope labeling strategy for high-plex quantitative proteomics.
- The method is adaptable to other NHS-ester reagents for even greater multiplexity.
- NETLOP is suitable for diverse biological and clinical proteomics applications.
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