Acetic Acid Ion Pairing Additive for Reversed-Phase HPLC Improves Detection Sensitivity in Bottom-up Proteomics Compared to Formic Acid
Bibliographic record
Abstract
Despite the general acceptance of formic acid as the additive of choice for peptide reversed-phase LC-MS/MS applications, some still argue that the selection of acetic acid represents a better option. To settle this debate, we investigated both the difference in MS sensitivity and chromatographic behavior of peptides between these two systems. This interlaboratory study was performed using different MS setups and C18 separation media employing both 0.1% formic and 0.5% acetic acid as ion pairing modifiers. Relative to formic acid, we find an overall ∼2.2-2.5× increase in MS signal and a slight decrease in RP LC retention (-0.7% acetonitrile on average) for acetic acid conditions. While these two features have opposing effects on peptide detectability, we find that acetic acid produces up to 60% higher peptide ID output depending on the type of sample. The drop in RPLC retention increases with peptide net charge at acidic pH. MS signal is dependent on the difference between the charge of the precursor ion and the charge of the peptide in solution, favoring species with a low pI. Lower peptide retention under acetic acid conditions demonstrates its higher hydrophilicity and, as expected, leads to composition and sequence-dependent character of the observed retention shift.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".