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TMT Labeling under Acidic pH Overcomes Detrimental Overlabeling and Improves Peptide Identification Rates
Rijing Liao1, Pu You2, Kai Weng1
1Shanghai Institute of Precision Medicine, Ninth People's Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 200125, China.
This study reveals Tandem Mass Tags (TMT) overlabeling is caused by histidine catalysis. A novel acidic pH TMT labeling method prevents this, significantly increasing peptide and protein identification in proteomics.
Area of Science:
- Proteomics
- Analytical Chemistry
- Biochemistry
Background:
- Tandem Mass Tags (TMT) enable high-throughput proteomics quantification by multiplexing samples.
- Standard TMT labeling targets primary amines but can also label hydroxyl groups, reducing sensitivity and peptide identification.
- Overlabeling, particularly on serine, threonine, and tyrosine residues, compromises TMT-based proteomic analyses.
Purpose of the Study:
- To investigate the chemical mechanism behind TMT overlabeling.
- To develop an improved TMT labeling strategy to mitigate overlabeling.
- To enhance peptide and protein identification rates in quantitative proteomics.
Main Methods:
- Investigated the chemical nature of TMT overlabeling, identifying intramolecular catalysis by histidine residues.
- Developed a modified TMT labeling protocol utilizing acidic pH conditions.
- Compared the novel method against the standard TMT labeling procedure using proteomic analysis.
Main Results:
- Identified histidine-mediated intramolecular catalysis as the cause of TMT overlabeling on hydroxyl groups.
- The novel acidic pH TMT labeling method effectively prevented overlabeling.
- Achieved comparable labeling efficiency to the standard method while significantly reducing overlabeled peptides.
- Led to a 33.9% increase in unique peptide and a 20.9% increase in protein identifications.
Conclusions:
- Understanding the chemical mechanism of TMT overlabeling is crucial for method development.
- The developed acidic pH TMT labeling method offers a significant improvement for quantitative proteomics.
- This optimized TMT approach enhances analytical sensitivity and data quality, enabling deeper proteome coverage.
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