Prevention of Amino Acid Conversion in SILAC Experiments with Embryonic Stem Cells
Bibliographic record
Abstract
Recent studies using stable isotope labeling with amino acids in culture (SILAC) in quantitative proteomics have made mention of the problematic conversion of isotope-coded arginine to proline in cells. The resulting converted proline peptide divides the heavy peptide ion signal causing inaccuracy when compared with the light peptide ion signal. This is of particular concern as it can effect up to half of all peptides in a proteomic experiment. Strategies to both compensate for and limit the inadvertent conversion have been demonstrated, but none have been shown to prevent it. Additionally, these methods combined with SILAC labeling in general have proven problematic in their large scale application to sensitive cell types including embryonic stem cells (ESCs) from the mouse and human. Here, we show that by providing as little as 200 mg/liter L-proline in SILAC media, the conversion of arginine to proline can be rendered completely undetectable. At the same time, there was no compromise in labeling with isotope-coded arginine, indicating there is no observable back conversion from the proline supplement. As a result, when supplemented with proline, correct interpretation of "light" and "heavy" peptide ratios could be achieved even in the worst cases of conversion. By extending these principles to ESC culture protocols and reagents we were able to routinely SILAC label both mouse and human ESCs in the absence of feeder cells and without compromising the pluripotent phenotype. This study provides the simplest protocol to prevent proline artifacts in SILAC labeling experiments with arginine. Moreover, it presents a robust, feeder cell-free, protocol for performing SILAC experiments on ESCs from both the mouse and the human.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".