Mass Spectrometric Analysis of Protein Mixtures at Low Levels Using Cleavable 13C-Isotope-coded Affinity Tag and Multidimensional Chromatography
Bibliographic record
Abstract
In order to identify and compare the protein content of very low quantity samples of high complexity, a protocol has been established that combines the differential profiling strength of a new cleavable 13C isotope-coded affinity tag (cICAT) reagent with the high sequence coverage provided by multidimensional liquid chromatography and two modes of tandem mass spectrometry. Major objectives during protocol optimization were to minimize sample losses and establish a robust procedure that employs volatile buffer systems that are highly compatible with mass spectrometry. Cleavable ICAT-labeled tryptic peptides were separated from nonlabeled peptides by avidin affinity chromatography. Subsequently, peptide samples were analyzed by nanoflow liquid chromatography electrospray ionization tandem mass spectrometry and liquid chromatography matrix-assisted laser desorption/ionization tandem mass spectrometry. The use of two ionization/instrumental configurations led to complementary peptide identifications that increased the confidence of protein assignments. Examples that illustrate the power of this strategy are taken from two different projects: i) immunoaffinity purified complexes containing the prion protein from the murine brain, and ii) human tracheal epithelium gland secretions. In these studies, a large number of novel proteins were identified using stringent match criteria, in addition to many that had been identified in previous experiments. In the latter case, the ICAT method produced significant new information on changes that occur in protein expression levels in a patient suffering from cystic fibrosis.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".