Multiplexed Proteomic Reactor for the Processing of Proteomic Samples
Bibliographic record
Abstract
We report the development of a 96-well plate proteomic reactor for gel-free processing of minute amounts of complex proteomic samples. The device performs multiplexed trapping, enrichment, and biochemical processing of proteins, resulting in concentrated peptide solutions ready for mass spectrometric analysis. Individual wells on the reactor can process up to 2 microg of protein. We also report the coupling of the plate proteomic reactor with protein fractionation using size-exclusion chromatography for large-scale identification of proteins. To illustrate the potential of this approach, we separated 400 microg of MCF7 cell lysate using size-exclusion chromatography and processed 35 protein fractions on the reactor plate. Using stringent criteria when searching the data, a total of 875 unique proteins were identified. More relaxed searching conditions associated with a 1% false positive rate led to the identification of 2683 unique proteins, meaning that one protein was identified per 3-10 ng of total protein lysate loaded on the reactor plate.
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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 | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.003 |
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".