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Deep Proteome Profiling by Isobaric Labeling, Extensive Liquid Chromatography, Mass Spectrometry, and Software-assisted Quantification
Published on: November 15, 2017
11.9K
A proximity proteomics pipeline with improved reproducibility and throughput.
Xiaofang Zhong1,2,3, Qiongyu Li1,2,3, Benjamin J Polacco1,2,3
1Quantitative Biosciences Institute (QBI), University of California, San Francisco, San Francisco, CA, 94158, USA.
Molecular Systems Biology
|July 1, 2024
Summary
This study presents a scalable proximity labeling (PL) pipeline for enhanced spatial proteome analysis. The automated workflow improves throughput and reproducibility for mass spectrometry-based proteomics research.
Area of Science:
- Proteomics
- Cell Biology
- Biochemistry
Background:
- Proximity labeling (PL) coupled with mass spectrometry (MS) is crucial for mapping cellular spatial proteomes.
- Existing workflows often require significant hands-on time and can lack quantitative reproducibility for large-scale studies.
- There is a need for streamlined and robust methods to enhance throughput and reliability in PL-based spatial proteomics.
Purpose of the Study:
- To develop and validate a scalable proximity labeling pipeline for high-throughput spatial proteome analysis.
- To improve the reproducibility and efficiency of biotinylated protein enrichment and quantitative mass spectrometry.
- To apply the optimized pipeline for investigating dynamic protein interaction networks in cellular compartments and in response to genetic perturbations.
Main Methods:
- Introduction of a scalable proximity labeling pipeline with automated biotinylated protein enrichment in a 96-well plate format.
- Integration with optimized quantitative mass spectrometry using data-independent acquisition (DIA) for enhanced protein identification and quantification.
- Application to map subcellular proteomes and study temporal interaction network dynamics of the 5HT2A serotonin receptor, including modifications for reduced sample input for CRISPR-based studies.
Main Results:
- The developed pipeline significantly increased sample throughput and improved the reproducibility of protein identification and quantification.
- Successfully delineated subcellular proteomes across various cellular compartments.
- Enabled the study of temporal changes in proximal interaction networks upon receptor activation and assessed network dynamics following gene knockout perturbations.
Conclusions:
- The scalable proximity labeling pipeline enhances throughput and reproducibility for standard biotinylation-based PL proteomics protocols.
- This universally applicable approach facilitates detailed spatial proteome mapping and dynamic interaction network analysis.
- The method is adaptable for various experimental setups, including those with limited sample input for genetic perturbation studies.
