An enhanced chemically defined SILAC culture system for quantitative proteomics study of human embryonic stem cells
Bibliographic record
Abstract
Stable isotope labeling by SILAC-based quantitative proteomics analysis provides an unprecedented tool for the study of mechanisms underlying the self-renewal and differentiation of human embryonic stem cells (hESCs). While we recently reported a chemically defined SILAC culture system specific for a rare cell proteomic reactor (R. Tian et al., Mol. Cell. Proteomics 2011, 10, M110.000679), total hESC yield, prolonged self-renewal capacity (i.e.<12 days), and laborious procedure remain substantial hurdles for its conventional application in hESC studies. Here, we devised an enhanced SILAC culture system consisting of a new chemically defined SILAC-medium and a novel culture protocol. As a result, with much less culture maneuvers, approximately 40-fold greater hESCs were produced than the system reported previously. Moreover, the enhanced SILAC culture system was sufficient to support the self-renewal of hESCs for >60 days and was also highly reproducible. As such, it provides a new platform that can be readily adapted by general laboratory for further comprehensive SILAC-based proteomics analysis of hESCs and induced pluripotent stem cells.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".