The placenta: epigenetic insights into trophoblast developmental models of a generation-bridging organ with long-lasting impact on lifelong health
Bibliographic record
Abstract
The placenta is a unique organ system that functionally combines both maternal and fetal cell types with distinct lineage origins. Normal placentation is critical for developmental progression and reproductive success. Although the placenta is best known for its nutrient supply function to the fetus, genetic experiments in mice highlight that the placenta is also pivotal for directing the proper formation of specific fetal organs. These roles underscore the importance of the placenta for pregnancy outcome and lifelong health span, which makes it essential to better understand the molecular processes governing placental development and function and to find adequate models to study it. In this review, we provide an overview of placental development and highlight the instructional role of the epigenome in dictating cell fate decisions specifically in the placental trophoblast cell lineage. We then focus on recent advances in exploring stem cell and organoid models reflecting the feto-maternal interface in mice and humans that provide much-improved tools to study events in early development. We discuss stem cells derived from the placenta as well as those artificially induced to resemble the placenta, and how they can be combined with embryonic stem cells and with endometrial cell types of the uterus to reconstitute the early implantation site. We then allude to the exciting prospects of how these models can be harnessed in biomedicine to enhance our understanding of the pathological underpinnings of pregnancy complications in a patient-specific manner, and ultimately to facilitate therapeutic approaches of tissue- and organ-based regenerative medicine.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".