Improved Sensitivity in Low-Input Proteomics using Micro-Pillar Array-based Chromatography
Bibliographic record
Abstract
ABSTRACT Capitalizing on the massive increase in sample concentrations which are produced by extremely low elution volumes, nano-LC-ESI-MS/MS is currently one of the most sensitive analytical technologies for the comprehensive characterization of complex protein samples. However, despite tremendous technological improvements made in the production and the packing of monodisperse spherical particles for nano-flow HPLC, current state-of-the-art systems still suffer from limits in operation at the maximum potential of the technology. With the recent introduction of the µPAC system, which provides perfectly ordered micro-pillar array based chromatographic support materials, completely new chromatographic concepts for optimization towards the needs of ultra-sensitive proteomics become available. Here we report on a series of benchmarking experiments comparing the performance of a commercially available 50 cm micro-pillar array column to a widely used nano-flow HPLC column for the proteomics analysis of 10 ng tryptic HeLa cell digest. Comparative analysis of LC-MS/MS-data corroborated that micro-pillar array cartridges provide outstanding chromatographic performance, excellent retention time stability, increase sensitivity in the analysis of low-input proteomics samples, and thus repeatedly yielded almost twice as many unique peptide and unique protein group identifications when compared to conventional nano-flow HPLC columns.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| 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".