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Deep Proteome Profiling by Isobaric Labeling, Extensive Liquid Chromatography, Mass Spectrometry, and Software-assisted Quantification
Published on: November 15, 2017
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Isobaric Matching between Runs and Novel PSM-Level Normalization in MaxQuant Strongly Improve Reporter Ion-Based
Sung-Huan Yu1, Pelagia Kyriakidou1, Jürgen Cox1,2
1Computational Systems Biochemistry, Max-Planck Institute of Biochemistry, Am Klopferspitz 18, Martinsried 82152, Germany.
Journal of Proteome Research
|September 7, 2020
Summary
New algorithms in MaxQuant improve isobaric labeling quantification for multiple samples. These tools enhance data analysis by increasing quantifiable spectra and removing batch effects for more accurate results.
Area of Science:
- Proteomics
- Mass Spectrometry
- Bioinformatics
Background:
- Isobaric labeling offers high sample multiplexing and precise quantification in proteomics.
- However, normalization challenges and missing values limit its application across multiple experimental batches (n-plexes).
Purpose of the Study:
- To introduce novel algorithms in MaxQuant to improve quantitative proteomics analysis with multiple n-plexes.
- To address limitations in data analysis, specifically normalization and missing value problems.
Main Methods:
- Development of isobaric matching between runs (IMBR) utilizing MS1 features to transfer identifications across liquid chromatography-mass spectrometry (LC-MS) runs.
- Implementation of a novel PSM-level normalization method, a weighted median approach, for data with or without a common reference channel.
Main Results:
- IMBR significantly increases the number of MS/MS spectra available for quantification.
- The PSM-level normalization effectively removes batch effects, highlighting biological sample variations.
- Enhanced data processing and normalization options are integrated into MaxQuant and Perseus.
Conclusions:
- The novel MaxQuant algorithms substantially improve the accuracy and scope of quantitative proteomics using isobaric labeling across multiple n-plexes.
- These advancements facilitate more robust data analysis and reliable biological insights from complex proteomic datasets.
