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Bottom-up and Shotgun Proteomics to Identify a Comprehensive Cochlear Proteome
Published on: March 7, 2014
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Mass Spectrometry-Based Bottom-Up Proteomics: Sample Preparation, LC-MS/MS Analysis, and Database Query Strategies
Matthew J Wither1, Kirk C Hansen1, Julie A Reisz1
1Biological Mass Spectrometry Core, Department of Biochemistry and Molecular Genetics, University of Colorado Denver, Aurora, Colorado.
Current Protocols in Protein Science
|November 2, 2016
Summary
This study presents a robust workflow for tissue proteomic analysis using mass spectrometry (MS). Optimized sample preparation and peptide fractionation significantly enhance protein identification in complex biological samples.
Area of Science:
- Proteomics
- Biomolecular analysis
- Mass spectrometry (MS)
Background:
- Technological advances in MS enable sensitive detection of biomolecules.
- High sample purity is crucial for accurate biomolecule identification in MS.
- Tissue heterogeneity and complexity pose significant challenges in proteomic studies.
Purpose of the Study:
- To detail a comprehensive workflow for tissue sample preparation for proteomic analysis.
- To improve peptide resolution and protein identification in complex biological samples.
- To describe optimized instrument setup and data analysis for MS-based proteomics.
Main Methods:
- Tissue sample preparation including protein extraction, proteolysis, and purification.
- Peptide-level polarity-based fractionation using C18 resin.
- Nanoscale liquid chromatography coupled with quadrupole Orbitrap mass spectrometry.
- Database searching for protein identification.
Main Results:
- Achieved high sensitivity detection of proteins and peptides at low femtomole quantities.
- Demonstrated increased peptide resolution through C18 fractionation.
- Showcased enhanced protein identification rates in complex tissue samples.
- Described optimized MS instrument parameters and data processing strategies.
Conclusions:
- The presented workflow effectively addresses challenges in tissue proteomic analysis.
- Peptide fractionation is critical for maximizing protein identification in MS.
- Optimized MS techniques and data analysis improve the depth and accuracy of proteomic studies.
