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 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 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| 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 | 0.002 |
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".