Detection and Identification of Sub-nanogram Levels of Protein in a NanoLC-Trypsin-MS System
Bibliographic record
Abstract
Proteomic workflows involving liquid-based protein separations are an alternative to gel-based protein analysis, however the trypsin digestion procedure is usually difficult to implement, particularly when processing low abundance proteins from capillary column effluent. To convert the protein to peptides for the purpose of identification, current protocols require several sample handling steps, and sample losses become an issue. In this study, we present an improved system that conducts reversed-phase protein chromatography and rapid on-line tryptic digestion requiring sub-nanogram quantities of protein. This system employs a novel mirror-gradient concept that allows for dynamic titration of the column effluent to create optimal conditions for real-time tryptic digestion. The purpose behind this development was to improve the limits of detection of the online concept, to support flow-based alternatives to gel-based proteomics and to simplify the characterization of low abundance proteins. Using test mixtures of proteins, we show that peptide mass fingerprinting with high sequence representation can be easily achieved at the 20 fmol level, with detection limits down to 5 fmol (85 pg myoglobin). Limits of identification using standard data-dependent MS/MS experiments are as low as 10 fmol. These results suggest that the nanoLC-trypsin-MS/MS system could represent an alternative to the conventional "1D-gel to MS" proteomic strategy.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".