High-flow Nano-chromatography Columns Facilitate Rapid and High-quality Proteome Analysis on Standard Nano-LC Hardware
Bibliographic record
Abstract
Abstract Optimizing the acquisition of proteomics data collected from a mass spectrometer (MS) requires careful selection of processed material quantities, liquid-chromatography (LC) setup, and data acquisition parameters. The small internal diameter (ID) columns standardly used in nano-chromatography coupled MS result in long per injection overhead times that require sacrifices in design of offline-fractionation and data acquisition schemes. As cohort sizes and the numbers of samples to be analyzed continue to increase, there is a need to investigate methods for improving the efficiency and time of an acquisition (LC + MS). In this work, the ability to improve throughput in single runs or as part of an in-depth proteome analysis of a fractionated sample using standard LC hardware is investigated. Capitalizing on the increased loading capacity of nanochromatography columns with larger IDs, substantially improved throughput with no reduction in detection sensitivity is achieved in single-injection proteome analyses. An optimized 150 μm ID column setup is paired with an offline fractionation-concatenation scheme to demonstrate the ability to perform in-depth proteome analysis on-par with current state-of-the-art studies, while minimizing sample loading overhead. Together, these data demonstrate an easy and effective means to improve sample analysis throughput with no reduction in data quality using an approach that is applicable to any standard nano-LC hardware.
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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 | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.006 |
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".