An online 2D-reversed-phase – Reversed-phase chromatographic method for sensitive and robust plasma protein quantitation
Bibliographic record
Abstract
Offline high-pH reversed-phase fractionation is widely used to reduce sample complexity in proteomic workflows. This is due to the semi-orthogonality and high peak resolution of the two separations. Offline 2D fractionation, however, is low throughput and requires several manual manipulations and is prone to sample losses. To address these issues, we developed an online two dimensional high-pH - low-pH reversed-phase-reversed-phase (2D RPRP) LC-MRM method whereby hundreds of peptides can be quantified in a single LC-MS/MS injection. The method allowed the reproducible and sensitive quantitation of a test panel of 367 peptides (168 proteins) from undepleted and non-enriched human plasma. Of these, we were able to detect and quantify 95 peptides (29 proteins) by 2D-RPRP that were not detectable by 1D LC-MRM-MS. Online 2D RPRP resulted in an average increase of roughly 10-fold in sensitivity compared to traditional 1D low-pH separations, while improving reproducibility and sample throughput relative to offline 2D RPRP by factors of 1.7 and 5, respectively, compared to offline 2D RPRP. This paper serves as proof-of-concept of the feasibility and efficacy of online 2D RPRP at analytical flow rates for highly multiplexed targeted proteomic analyses.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".