A comparison of MS/MS‐based, stable‐isotope‐labeled, quantitation performance on ESI‐quadrupole TOF and MALDI‐TOF/TOF mass spectrometers
Why this work is in the frame
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Bibliographic record
Abstract
The peptide-based quantitation accuracy and precision of LC-ESI (QSTAR Elite) and LC-MALDI (4800 MALDI TOF/TOF) were compared by analyzing identical Escherichia coli tryptic digests containing iTRAQ-labeled peptides of defined abundances (1:1, 2.5:1, 5:1, and 10:1). Only 51.4% of QSTAR spectra were used for quantitation by ProteinPilot Software versus 66.7% of LC-MALDI spectra. The average protein sequence coverages for LC-ESI and LC-MALDI were 24.0 and 18.2% (14.9 and 8.4 peptides per protein), respectively. The iTRAQ-based expression ratios determined by ProteinPilot from the 57 467 ESI-MS/MS and 26 085 MALDI-MS/MS spectra were analyzed for measurement accuracy and reproducibility. When the relative abundances of peptides within a sample were increased from 1:1 to 10:1, the mean ratios calculated on both instruments differed by only 0.7-6.7% between platforms. In the 10:1 experiment, up to 64.7% of iTRAQ ratios from LC-ESI MS/MS spectra failed S/N thresholds and were excluded from quantitation, while only 0.1% of the equivalent LC-MALDI iTRAQ ratios were rejected. Re-analysis of an archived LC-MALDI sample set stored for 5 months generated 3715 MS/MS spectra for quantitation, compared with 3845 acquired originally, and the average ratios differed by only 3.1%. Overall, MS/MS-based peptide quantitation performance of offline LC-MALDI was comparable with on-line LC-ESI, which required threefold less time. However, offline LC-MALDI allows the re-analysis of archived HPLC-separated samples.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it