Integration of solid-phase extraction membranes for sample multiplexing: Application to rapid protein identification from gel-isolated protein extracts
Bibliographic record
Abstract
The present report describes the design and application of a dual sprayer system for high-throughput proteome analysis. This system comprises parallel solid-phase extraction cartridges used for preconcentration and desalting of proteolytic digests prior to nanoelectrospray mass spectrometry analyses. Tryptic peptides from in-gel digest of protein bands/spots are first adsorbed on styrene divinyl benzene membrane and subsequently eluted with a short plug of organic buffer prior to infusion to the mass spectrometer at a flow rate of typically 500 nL/min. Tryptic peptide eluting from the membrane are analyzed by the mass spectrometer by moving in turn each sprayer in front of the sampling orifice. Sequential injection, preconcentration and analyses of tryptic digests are typically achieved with a throughput of up to 3.5 min/sample and a detection limit of approximately 8-80 fmol per injection. Replicate injections of peptide mixtures indicated that reproducibility of peak areas ranged from relative standard deviations (RSD) of 1.1% to 4.5%. The application of this device is demonstrated for digests of gel-isolated proteins obtained from sodium dodecyl sulfate-polyacrylamide gel electrophoresis (SDS-PAGE) separation of rat liver plasma membrane and from two-dimensional gel electrophoresis of total cell lysate extracts from human prostatic cancer cell.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 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".