Microenvironment-mediated reversion of epiblast stem cells by reactivation of repressed JAK–STAT signaling
Bibliographic record
Abstract
Embryonic stem cells (ESC) and epiblast stem cells (EpiSC) are distinct pluripotent stem cell states that require different signaling pathways for their self-renewal. Forward transitions between ESC and EpiSC can be accomplished by changing culture conditions; however reverse transitions between EpiSC and ESC are rare events that require transgene insertion or culture on feeders. We demonstrate that transgene-free reversion of EpiSCs to ESCs can be enhanced by local microenvironmental control and the subsequent reactivation of dormant LIF-STAT3 signaling. Reactivation of LIF responsiveness occurs in regions of colony constraint (high local cell density) typical of culture on feeders, a condition that can be recapitulated using micropatterned (μP) colonies under defined conditions. This increased LIF responsiveness results in a subsequent increase in the frequency of EpiSC reversion. Importantly, the resulting revertant EpiSCs are functionally indistinguishable from naïve mESC. Our findings demonstrate that signaling pathway activation and repression create barriers to cell fate transitions that can be overcome by microenvironmental control.
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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".