SIGNIFICANT IMPACT OF CONSUMABLE MATERIAL AND BUFFER COMPOSITION FOR LOW-CELL NUMBER PROTEOMIC SAMPLE PREPARATION
Bibliographic record
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 demonstrate preferential adsorption of peptides, particularly hydrophobic ones, onto polypropylene (PP) vials, leading to nonuniform signal loss. This phenomenon can alter protein quantification (e.g., Label-Free Quantification, LFQ) if no appropriate data processing is applied. Our study is based on understanding this adsorption phenomenon to establish recommendations for minimizing peptide loss. To address this issue, we evaluated the nature of surface material and buffer additives to reduce peptide-surface noncovalent binding. Here, we report that using vials made from polymer containing polar monomeric units such as poly(methyl methacrylate) (PMMA) or polyethylene terephthalate (PET) drastically reduces the hydrophobic peptide loss, increasing the global proteomics performance (4-fold increase in identified peptides for the single-cell equivalent peptide content range). Additionally, the incorporation of nonionic detergents like poly(ethylene oxide) (PEO) and n-Dodecyl-Beta-Maltoside (DDM) at optimized concentrations (0.0001% and 0.0075%, respectively) improves the overall proteomic performance and consistency, even across different vial materials. Implementing these recommendations on 0.2 ng/μL HeLa tryptic digest results in a 10-fold increase in terms of peptide signal. Application to True Single-Cell sample preparation without specialized instrumentation dramatically improves the performance, allowing for the identification of approximately 650 proteins, a stark contrast to none detected with classical protocols.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".