Embryos Derived from Porcine Skin-Derived Stem Cells Exhibit Enhanced Preimplantation Development1
Bibliographic record
Abstract
Ongoing research to identify the most suitable type of donor cell for nuclear transfer (NT) has suggested that less differentiated stem cells may be better donors than other somatic cell types. Recently, we have reported the isolation and characterization of porcine skin-originated sphere (PSOS) stem cells from fetal skin, making it possible to test this hypothesis in a nonrodent animal model. In the present study, we have investigated and compared the feasibility and preimplantation developmental efficiency of using fetal PSOS cells and fibroblasts as nuclear-transfer donors. The majority of fetal PSOS cells are in the G1/ G0 stage of the cell cycle, which is desirable for NT. During long-term in vitro culture, fetal PSOS cells had greater genome stability, with a lower frequency of abnormal karyotypes than fetal fibroblast cells. Embryos cloned from PSOS cells showed enhanced preimplantation development compared with fibroblast cloned embryos, which is indicated by an increased rate of blastocyst development and a higher total cell number in Day 7 blastocysts. The gene expression profile of genes critical for early development from eight-cell-stage PSOS NT embryos more closely resembled the pattern observed from in vivo-produced embryos compared with that of fibroblast-cloned embryos. Cumulatively, our data suggest that fetal PSOS cells may be better donor cells for NT in the pig.
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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) | 0.000 | 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) | 0.002 | 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".