Improved intact peptide and protein quantitation by LC‐MS: Battling the deleterious effects of analyte adsorption
Bibliographic record
Abstract
Peptide and protein quantitation by liquid chromatography-mass spectrometry relies on the assumption of linear signal response with concentration. At low concentrations, analyte adsorption to pipette tips, sample vials and equipment can have significant deleterious effects on signal response. Meanwhile at high concentrations, linearity breaks down due to competitive ionization, signal suppression, and the formation of peptide or protein multimers. These effects result in calibration curves that are more sigmoidal than linear. Linearity at low protein levels for identification and quantitation is of paramount importance in the discovery and validation of biomarker molecules. Herein, we demonstrate the benefits of using commercial low-bind microcentrifuge tubes and LC vials on the response of a 27-mer peptide, Vn96, and the intact proteins apomyoglobin and carbonic anhydrase. Linear curves were acquired for Vn96 while apomyoglobin required the addition of intact carbonic anhydrase as an adsorption competitor to achieve linearity. A linear calibration curve for carbonic anhydrase was also acquired by using the polypeptide ubiquitin as an adsorption competitor and internal standard. Linear response was recorded for approximately two orders of magnitude for apomyoglobin and carbonic anhydrase and three orders of magnitude for Vn96 with detection limits ranging from 0.33 to 19 fmol/µL. Finally, we used low-bind vials for the online enzymatic digestion of apomyoglobin where a high concentration of apomyoglobin acted as an adsorption blocker for the low level trypsin enzyme. Fortunately, the liberated tryptic peptides showed no affinity for the walls of the low-bind vials. In this study, we take a comprehensive approach to combat analyte adsorption by showing the significance of utilizing low-bind vials and adsorption competitors to greatly improve upon signal sensitivity at low concentrations of target molecules. The use of these methodologies should improve the low-level detection of molecules by mass spectrometry.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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 | 0.000 |
| 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".