Aggregation of cloned embryos in empty zona pellucida improves derivation efficiency of pig ES-like cells
Bibliographic record
Abstract
The development of embryonic stem cells (ESCs) from large animal species has become an important model for therapeutic cloning using ESCs derived by somatic cell nuclear transfer (SCNT). However, poor embryo quality and blastocyst formation have been major limitations for derivation of cloned ESCs (ntESCs). In this study, we have tried to overcome these problems by treating these cells with histone deacetylase inhibitors (HDACi) and aggregating porcine embryos. First, cloned embryos were treated with Scriptaid to confirm the effect of HDACi on cloned embryo quality. The Scriptaid-treated blastocysts showed significantly higher total cell numbers (29.50 ± 2.10) than non-treated blastocysts (22.29 ± 1.50, P < 0.05). Next, cloned embryo quality and blastocyst formation were analyzed in aggregates. Three zona-free, reconstructed, four-cell-stage SCNT embryos were injected into the empty zona of hatched parthenogenetic (PA) blastocysts. Blastocyst formation and total cell number of cloned blastocysts increased significantly for all aggregates (76.4% and 83.18 ± 8.33) compared with non-aggregates (25.5% and 27.11 ± 1.67, P < 0.05). Finally, aggregated blastocysts were cultured on a feeder layer to examine the efficiency of porcine ES-like cell derivation. Aggregated blastocysts showed a higher primary colony formation rate than non-aggregated cloned blastocysts (17.6 ± 12.3% vs. 2.2 ± 1.35%, respectively, P < 0.05). In addition, derived ES-like cells showed typical characters of ESCs. In conclusion, the aggregation of porcine SCNT embryos at the four-cell stage could be a useful technique for improving the development rate and quality of porcine-cloned blastocysts and the derivation efficiency of porcine ntESCs.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".