Comparison of Protein Expression by Isotope-Coded Affinity Tag Labeling
Bibliographic record
Abstract
Isotope-coded affinity tag (ICAT) labeling, in combination with mass spectrometry (MS), has been widely adopted as an effective method for comparing protein abundance levels. This chapter describes the ICAT labeling procedure in search for the celecoxib-regulated proteins in a colon cancer cell line. Celecoxib, a cyclooxygenase-2 (COX-2) specific inhibitor, is used as a colorectal cancer preventative drug in clinical trials. Here, celecoxib is used to inhibit the expression of COX-2 in a colon cancer cell line HT-29. To elucidate the proteomic changes induced by celecoxib, the protein lysates from the treated and control cells are prepared. The cysteine-containing proteins are labeled with the heavy and light ICAT reagents, respectively. The labeled proteins are then combined and digested with trypsin. The ICAT-labeled peptides are subject to the purification through an avidin column and eventually the cleavage of the biotin tags. This chapter focuses on the ICAT labeling procedure itself, because sample preparation is the most critical step of an ICAT-based protein expression comparison experiment. Other related procedures such as the cation exchange high performance liquid chromatography separation of peptides and MS analysis are detailed elsewhere in this book.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.003 |
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".