Dual LC–MS Platform for High-Throughput Proteome Analysis
Bibliographic record
Abstract
We describe a dual-column interface for parallel chromatography to improve throughput during LC-MS experimentation. The system employs a high-voltage switch to operate two capillary column/nanospray emitters fixed at the MS orifice. Sequentially loading one column while operating the second nearly doubles the LC-MS duty cycle. Given the innate run-to-run variation of a nanospray LC-MS (12% RSD peak area; 2% retention time), the intercolumn variability of the platform showed no meaningful difference for proteome analysis, with equal numbers of proteins and peptides identified per column. Applied to GeLC analysis of an E. coli extract, throughput was increased using one of three methods: doubling the number of replicates, increasing the LC gradient length, or sectioning the gel into twice as many fractions. Each method increased the total number of identifications as well as detection throughput (number of peptides/proteins identified per hour). The greatest improvement was achieved by doubling the number of gel slices (10 vs 5). Analysis on the dual column platform provided a 26% increase in peptides identified per hour (24% proteins). This translates into ~50% more total proteins and peptides identified in the experiment using the dual LC-MS platform.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.002 | 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 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.008 |
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".