Global Quantitative Proteomic Profiling through <sup>18</sup>O-Labeling in Combination with MS/MS Spectra Analysis
Bibliographic record
Abstract
Several stable-isotope-based peptide labeling methods have been developed to support large-scale relative quantitation, through mass spectrometry, of proteins present in two different biological samples. In one of these, trypsin-catalyzed 18O-based labeling, quantitation is typically performed at the full scan (MS) level by comparing the peak intensities of sister precursor ions corresponding to the labeled and unlabeled forms of an intact peptide as they co-elute during liquid chromatography (LC) separations. We show here that measuring relative abundance at the product ion (MS/MS) level after fragmentation provides excellent accuracy, sensitivity and signal-to-noise, while combining quantitation with global shotgun protein identification. To facilitate routine data analysis using this approach, we have developed two specialized software programs, ySelect and yRatios, which draw upon database search results for 18O-based data sets and combine fragmentation spectra peak lists to (1) accurately determine protein ratios between two samples while applying a correction for incomplete labeling and (2) tabulate these results in both intuitive summary reports and in formats amenable to systematic pathway level analysis. To validate our process, we subjected simple and complex test protein mixtures to single-step and multistep LC-MS/MS profiling experiments. Ratio distributions approached the expected means, allowing empirical derivation of confidence level cutoffs for determining statistically significant fold-changes in protein abundance. A set of stringent criteria for detecting spurious ratios based on consistency checking between unlabeled and labeled y-ion pairs was found to highlight putative false positive identifications. In summary, this toolkit facilitates comparative proteomic quantitation under conditions that are optimized for making reliable protein inferences.
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How this classification was reachedexpand
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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".