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Updated: Jan 31, 2026

Enhanced Sample Multiplexing of Tissues Using Combined Precursor Isotopic Labeling and Isobaric Tagging cPILOT
Published on: May 1, 2017
Evaluation of a Dual Isolation Width Acquisition Method for Isobaric Labeling Ratio Decompression.
Theodoros I Roumeliotis1,2, Hendrik Weisser3,2, Jyoti S Choudhary1,2
1The Institute of Cancer Research , Chester Beatty Laboratories, London SW3 6JB , U.K.
Isobaric labeling for protein quantification faces interference issues. A new dual isolation width acquisition (DIWA) method improves accuracy by using both standard and narrow isolation widths for better results.
Area of Science:
- Proteomics
- Analytical Chemistry
- Biochemistry
Background:
- Isobaric labeling techniques offer precise protein quantification.
- Isolation interference at the MS2 level causes ratio underestimation in isobaric tagging.
- Narrow isolation widths reduce interference but limit proteome coverage.
Purpose of the Study:
- To address the isolation interference problem in isobaric tagging.
- To develop a method that enhances quantification accuracy without compromising proteome coverage.
- To improve the reliability of protein quantification in complex biological samples.
Main Methods:
- Designed a dual isolation width acquisition (DIWA) method.
- Employed two MS2 HCD scans: one standard width for identification/preliminary quantification, and one narrow width for reduced interference quantification.
- Utilized linear regression models to decompress fold-changes from standard scans using narrow scan data.
Main Results:
- The DIWA method was evaluated using a nested two species/gene knockout TMT-6plex design.
- Demonstrated the retention of reporter ion signals even with narrow isolation windows.
- Showcased the ability to decompress fold-changes, indicating improved quantification accuracy.
Conclusions:
- The DIWA method effectively mitigates isolation interference in isobaric labeling.
- This approach offers a promising solution for accurate protein quantification in proteomics.
- DIWA enhances the precision of quantitative proteomics by reducing ratio underestimation.
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