Improvement of the Quantification Accuracy and Throughput for Phosphoproteome Analysis by a Pseudo Triplex Stable Isotope Dimethyl Labeling Approach
Bibliographic record
Abstract
Accurately quantifying the changes of phosphorylation level on specific sites is crucial to understand the role of protein phosphorylation in physiological and pathological processes. Here, a pseudo triplex stable isotope dimethyl labeling approach was developed to improve the accuracy and the throughput of comprehensive quantitative phosphoproteome analyses. In this strategy, two identical samples are labeled with light and heavy isotopes, respectively, while another comparative sample is labeled with an intermediate isotope. Two replicated quantification results were achieved in just one experiment, and the relative standard deviation (RSD) criterion was used to control the quantification accuracy. Compared with the conventional duplex labeling approach, the number of quantified phosphopeptides increased nearly 50% and the experimental time was reduced by 50% under the same quantification accuracy. Combined with the automated online reversed phase-strong cation exchange-reversed phase (RP-SCX-RP) multidimensional separation system, a comparative phosphoproteome analysis of hepatocellular carcinoma (HCC) and normal human liver tissues was performed. Over 1800 phosphopeptides corresponding to ~2000 phosphorylation sites were quantified reliably in a 42 h multidimensional analysis. The pro-directed motifs, which were mainly associated with the extracellular signal-regulated kinases (ERKs), were observed as being overrepresented in the regulated phosphorylation sites, and some quantification results of phosphorylation sites were validated by the other studies. Therefore, this pseudo triplex labeling approach was demonstrated as a promising alternative for the comprehensive quantitative phosphoproteome analysis.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".