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Updated: Jun 27, 2025

TMT Sample Preparation for Proteomics Facility Submission and Subsequent Data Analysis
Published on: June 8, 2020
An Efficient, Amine-Specific, and Cost-Effective Method for TMT 6/11-plex Labeling Improves the Proteome Coverage,
Yan Cai1, Chenchen Chang1, Qin Yang1
1Shanghai Institute of Precision Medicine, Ninth People's Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 200125, China.
This study introduces an acidic method to reduce Tandem Mass Tag (TMT) overlabeling in proteomics. The new approach enhances peptide identification and improves quantitative accuracy, leading to more statistically significant protein discoveries.
Area of Science:
- Proteomics
- Analytical Chemistry
- Biochemistry
Background:
- Tandem Mass Tags (TMT) are essential for multiplexed quantitative proteomics.
- TMT reagents can react with hydroxyl groups (serine, threonine, tyrosine) under alkaline conditions, causing overlabeling.
- Overlabeling compromises analytical sensitivity, precision, and protein identification rates.
Purpose of the Study:
- To develop a novel method to suppress TMT overlabeling.
- To improve peptide/protein identification and quantitative accuracy in proteomics.
- To enhance statistical power for detecting significant protein changes.
Main Methods:
- A new TMT labeling method was developed using acidic conditions.
- This method utilizes significantly reduced TMT reagent amounts (one-fifth of manufacturer's recommendation).
- The new method was systematically evaluated against standard and TMT-reduced methods using yeast/human proteome samples.
Main Results:
- The acidic method reduced overlabeled peptides by 9-fold compared to the standard method and 6-fold compared to the TMT-reduced method.
- Significant enhancement in peptide and protein identification rates was observed.
- Quantitative accuracy and precision were improved, leading to a 42% and 12% increase in statistically significant yeast protein detection, respectively.
Conclusions:
- The developed acidic TMT labeling method effectively suppresses overlabeling.
- This approach substantially improves quantitative proteomics, enhancing data reliability and statistical power.
- The method offers a more sensitive and accurate alternative for TMT-based proteomic analyses.
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