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Proteomics for Low Cell Numbers: How to Optimize the Sample Preparation Workflow for Mass Spectrometry Analysis
Sara Kassem1, Kyra van der Pan1, Anniek L de Jager1
1Department of Immunology, Leiden University Medical Center (LUMC), Albinusdreef 2, 2333ZA Leiden, Netherlands.
Journal of Proteome Research
|July 30, 2021
Summary
Proteomics on limited cell samples is challenging due to sample loss during multistep workflows. Recent innovations now enable comprehensive protein analysis on paucicellular samples, advancing clinical proteomics.
Area of Science:
- Biochemistry
- Molecular Biology
- Proteomics
Background:
- Single-cell genomics and transcriptomics are advanced, but single-cell proteomics remains challenging.
- Multistep proteomics workflows often cause significant sample loss, especially with limited cell numbers (paucicellular samples).
- Clinical research frequently encounters limited sample availability, hindering protein-level analysis.
Purpose of the Study:
- To critically evaluate existing and novel sample preparation protocols for mass spectrometry-based proteomics.
- To identify methods that improve protein and peptide recovery from paucicellular samples.
- To assess the feasibility of comprehensive proteomics on limited clinical samples.
Main Methods:
- Comparison of multistep sample preparation techniques including cell lysis, protein quantification, electrophoresis, staining, protein digestion, and peptide recovery.
- Evaluation of sample preparation workflows for mass spectrometry (MS) studies.
- Analysis of recent technological innovations in proteomics.
Main Results:
- Despite challenges, sample loss can be minimized through careful protocol selection and management.
- Recent technological advancements enhance the feasibility of proteomics on paucicellular samples.
- Optimized workflows demonstrate improved protein and peptide recovery.
Conclusions:
- Comprehensive proteomics on paucicellular samples is now achievable.
- Innovations in sample preparation and high-quality sample management are key.
- This advancement has significant implications for clinical proteomics research.
