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SIGNIFICANT IMPACT OF CONSUMABLE MATERIAL AND BUFFER COMPOSITION FOR LOW-CELL NUMBER PROTEOMIC SAMPLE PREPARATION
Christopher Kune1, Sylvia Tielens2, Dominique Baiwir3
1Mass Spectrometry Laboratory, MolSys Research Unit, University of Liege, Liege B-4000, Belgium.
Analytical Chemistry
|February 11, 2025
Summary
Peptide loss in proteomics is reduced by using polar vial materials and nonionic detergents. These methods improve protein quantification and increase identified peptides, especially for low-quantity samples like single-cell proteomics.
Area of Science:
- Proteomics
- Analytical Chemistry
- Biochemistry
Background:
- Peptide adsorption to polypropylene vials causes significant loss, particularly for hydrophobic peptides.
- This loss impacts protein quantification accuracy, especially in low-volume samples.
- Current methods lack strategies to mitigate peptide adsorption during sample handling.
Purpose of the Study:
- To investigate peptide adsorption onto vial surfaces and its impact on proteomics.
- To identify vial materials and buffer additives that minimize peptide loss.
- To improve proteomic data quality and consistency, particularly for low-input samples.
Main Methods:
- Evaluation of different vial materials (PP, PMMA, PET) for peptide adsorption.
- Testing nonionic detergents (PEO, DDM) as additives to reduce peptide-surface binding.
- Quantification of peptide signal and identification of proteins using Label-Free Quantification (LFQ).
Main Results:
- Polar vial materials (PMMA, PET) significantly reduced hydrophobic peptide loss compared to PP.
- Nonionic detergents (PEO, DDM) at optimized concentrations enhanced proteomic performance and consistency.
- Recommendations led to a 10-fold increase in peptide signal for low-input digests and enabled detection in single-cell proteomics.
Conclusions:
- Utilizing polar vial materials and specific nonionic detergents is crucial for minimizing peptide loss in proteomics.
- These strategies substantially improve proteomic data quality and enable reliable analysis of low-input samples.
- The findings offer practical solutions for enhancing proteomics workflows, including single-cell applications.
