Quantitative Protein Profiling by Mass Spectrometry Using Isotope-Coded Affinity Tags
Bibliographic record
Abstract
A key issue in proteomics is to quantify changes in protein levels in complex biological samples under different conditions. Traditional two-dimensional gel (2-DE) electrophoresis-based proteomic approaches are tedious and suffer from several limitations, including difficulties in detecting low abundant and insoluble proteins. Isotope-coded affinity tagging (ICAT), one of the most employed chemical isotope labeling methods, can address many of the shortcomings of 2-DE. ICAT relies on the sensitivity of mass spectrometry (MS) to quantify relative protein abundance in a mixture of two differentially labeled protein samples. We describe here a detailed protocol for ICAT-based quantification of proteins in two or more biological samples, including sample preparation, ICAT labeling, fractionation and purification, and analysis by MS. For the MS analysis, we describe a "targeted" approach, which includes quantification of the samples using MS followed by selective identification of only the differentially expressed ICAT pairs using tandem MS (MS/MS). This approach gives more biologically relevant information than a data-dependent MS/MS analysis. We also describe the steps in data analysis, statistical analysis, and protein database searching.
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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 | 0.000 | 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 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".