Significant impact of consumable material and buffer composition for low-cell number proteomic sample preparation

12 July 2024, Version 2
This content is a preprint and has not undergone peer review at the time of posting.

Abstract

Proteomics, essential for understanding gene and cell functions, faces challenges with peptide loss due to adsorption onto vial surfaces, especially in samples with low peptide quantities. Using HeLa tryptic digested standard solutions, we demon-strate preferential adsorption of peptides, particularly hydrophobic ones, onto polypropylene (PP) vials, leading to non-uniform signal loss. This phenomenon can alter protein quantification (e.g., Label-Free Quantification, LFQ) if no appropri-ated data processing is applied. Our study is based on understanding this adsorption phenomenon to establish recommenda-tions for minimizing peptide loss. To address this issue, we evaluated the nature of surface material and buffer additives to reduce peptide-surface non-covalent binding. Here, we report that using vials made of polymer containing polar monomeric units such as polymethylmethacrylate (PMMA) or polyethylene terephthalate (PET) drastically reduces the hydrophobic peptide loss, increasing the global proteomics performances (fourfold increase in identified peptides for the single-cell equiv-alent peptide content range). Additionally, the incorporation of non-ionic detergents like polyethylene oxide (PEO) and n-Dodecyl-Beta-Maltoside (DDM) at optimized concentrations (0.0001% and 0.0075% respectively) improves overall prote-omic performance and consistency, even when different vial materials are used. Implementing these recommendations on 0.2 ng/µL HeLa tryptic digest results in a tenfold increase in terms of peptide signal. Application to True Single Cell sample preparation without specialized instrumentation dramatically improves performance, allowing for the identification of ap-proximately 650 proteins, compared to none with classical protocols.

Keywords

Proteomics
Single-cell Proteomics
Low-cell number proteomics
Peptide loss
Peptide adsorption
Sample preparation
DDM

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