Rapid and sensitive separation of trace level protein digests using microfabricated devices coupled to a quadrupole - time-of-flight mass spectrometer
Bibliographic record
Abstract
The application of microfabricated devices coupled to a quadrupole time-of-flight mass spectrometer (Qq-TOF-MS) is presented for the analysis of trace level digests of gel-isolated proteins. In order to enhance the sample loading for proteomics analyses, two different on-chip sample preconcentration techniques were evaluated. First, a sample stacking procedure that used polarity switching to remove the sample buffer prior to zone electrophoresis was easily integrated on the microfabricated devices. With the present chip design, this preconcentration technique provided up to 70 nL sample injection with sub-nM detection limits for most peptide standards. For applications requiring larger sample loading, a disposable adsorption preconcentrator using a C18 membrane is incorporated outside the chip. This preconcentration method yielded lower peptide recoveries than that obtainable with sample stacking, and provided a convenient means of injecting several microL of sample with detection limits of typically 2.5 nM for hydrophobic peptides. The analytical merits of both sample enrichment approaches are described for the identification of bands isolated from two-dimensional (2-D) gel separation of protein extracts from Haemophilus influenzae. Accurate molecular mass measurements (< 5 ppm) in peptide mapping experiments is obtained by introducing an internal standard via a post-separation channel. Rapid identification of trace level peptides is also demonstrated using on-line tandem mass spectrometry and database searching with peptide sequence tags.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 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".