Early Cell Fate Decisions of Human Embryonic Stem Cells and Mouse Epiblast Stem Cells Are Controlled by the Same Signalling Pathways
Bibliographic record
Abstract
Human embryonic stem cells have unique value for regenerative medicine, as they are capable of differentiating into a broad variety of cell types. Therefore, defining the signalling pathways that control early cell fate decisions of pluripotent stem cells represents a major task. Moreover, modelling the early steps of embryonic development in vitro may provide the best approach to produce cell types with native properties. Here, we analysed the function of key developmental growth factors such as Activin, FGF and BMP in the control of early cell fate decisions of human pluripotent stem cells. This analysis resulted in the development and validation of chemically defined culture conditions for achieving specification of human embryonic stem cells into neuroectoderm, mesendoderm and into extra-embryonic tissues. Importantly, these defined culture conditions are devoid of factors that could obscure analysis of developmental mechanisms or render the resulting tissues incompatible with future clinical applications. Importantly, the growth factor roles defined using these culture conditions similarly drove differentiation of mouse epiblast stem cells derived from post implantation embryos, thereby reinforcing the hypothesis that epiblast stem cells share a common embryonic identity with human pluripotent stem cells. Therefore the defined growth factor conditions described here represent an essential step toward the production of mature cell types from pluripotent stem cells in conditions fully compatible with clinical use ant also provide a general approach for modelling the early steps of mammalian embryonic development.
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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 | 0.001 | 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".