Combination of epigenetic erasing and mechanical cues to generate human epiBlastoids from adult dermal fibroblasts
Bibliographic record
Abstract
Abstract Purpose This study is to develop a new protocol that combines the use of epigenetic cues and mechanical stimuli to assemble 3D spherical structures, arbitrarily defined “epiBlastoids,” whose phenotype is remarkably similar to natural embryos. Methods A 3-step approach is used to generate epiBlastoids. In the first step, adult dermal fibroblasts are converted into trophoblast (TR)-like cells, combining the use of 5-azacytidine, to erase the original phenotype, with an ad hoc induction protocol, to drive cells towards TR lineage. In the second step, epigenetic erasing is applied once again, in combination with mechanosensing-related cues, to generate inner cell mass (ICM)-like organoids. Specifically, erased cells are encapsulated into micro-bioreactors to promote 3D cell rearrangement and boost pluripotency. In the third step, TR-like cells are co-cultured with ICM-like spheroids in the same micro-bioreactors. Subsequently, the newly generated embryoids are transferred to microwells to favor epiBlastoid formation. Results Adult dermal fibroblasts are successfully readdressed towards TR lineage. Cells subjected to epigenetic erasing and encapsulated into micro-bioreactors rearrange in 3D ICM-like structures. Co-culture of TR-like cells and ICM-like spheroids into micro-bioreactors and microwells induces the formation of single structures with uniform shape reminiscent in vivo embryos. CDX2 + cells localized in the out layer of the spheroids, while OCT4 + cells in the inner of the structures. TROP2 + cells display YAP nuclear accumulation and actively transcribed for mature TR markers, while TROP2 − cells showed YAP cytoplasmic compartmentalization and expressed pluripotency-related genes. Conclusion We describe the generation of epiBlastoids that may find useful application in the assisted reproduction field.
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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) | 0.001 | 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 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".