Metabolic Labeling of Model Organisms Using Heavy Nitrogen (15N)
Bibliographic record
Abstract
Quantitative proteomics aims to identify and quantify proteins in cells or organisms that have been obtained from different biological origin (e.g., "healthy vs. diseased"), that have received different treatments, or that have different genetic backgrounds. Protein expression levels can be quantified by labeling proteins with stable isotopes, followed by mass spectrometric analysis. Stable isotopes can be introduced in vitro by reacting proteins or peptides with isotope-coded reagents (e.g., iTRAQ, reductive methylation). A preferred way, however, is the metabolic incorporation of heavy isotopes into cells or organisms by providing the label, in the form of amino acids (such as in SILAC) or salts, in the growth media. The advantage of in vivo labeling is that it does not suffer from side reactions or incomplete labeling that might occur in chemical derivatization. In addition, metabolic labeling occurs at the earliest possible moment in the sample preparation process, thereby minimizing the error in quantitation. Labeling with the heavy stable isotope of nitrogen (i.e., (15)N) provides an efficient way for accurate protein quantitation. Where the application of SILAC is mostly restricted to cell culture, (15)N labeling can be used for micro-organisms as well as a number of higher (multicellular) organisms. The most prominent examples of the latter are Caenorhabditis elegans and Drosophila (fruit fly), two important model organisms for a range of regulatory processes underlying developmental biology. Here we describe in detail the labeling with (15)N atoms, with a particular focus on fruit flies and C. elegans. We also describe methods for the identification and quantitation of (15)N-labeled proteins by mass spectrometry and bioinformatic 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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".