Demarcation of Stable Subpopulations within the Pluripotent hESC Compartment
Bibliographic record
Abstract
Heterogeneity is a feature of stem cell populations, resulting from innate cellular hierarchies that govern differentiation capability. How heterogeneity impacts human pluripotent stem cell populations is directly relevant to their efficacious use in regenerative medicine applications. The control of pluripotency is asserted by a core transcription factor network, of which Oct4 is a necessary member. In mouse embryonic stem cells (ESCs), the zinc finger transcription factor Rex1 (Zfp42) closely tracks the undifferentiated state and is capable of segregating Oct4 positive mESCs into metastable populations expressing or lacking Rex1 that are inter-convertible. However, little is currently understood about the extent or function of heterogeneous populations in the human pluripotent compartment. Human ESCs express REX1 transcripts but the distribution and properties of REX1 expressing cells have yet to be described. To address these questions, we used gene targeting in human ESCs to insert the fluorescent protein Venus and an antibiotic selection marker under the control of the endogenous REX1 transcription regulatory elements, generating a sensitive, selectable reporter of pluripotency. REX1 is co-expressed in OCT4 and TRA-1-60 positive hESCs and rapidly lost upon differentiation. Importantly, REX1 expression reveals significant heterogeneity within seemingly homogenous populations of OCT4 and TRA-1-60 hESCs. REX1 expression is extinguished before OCT4 during differentiation, but, in contrast to the mouse, loss of REX1 expression demarcates a stable, OCT4 positive lineage-primed state in pluripotent hESCs that does not revert back to REX1 positivity under normal conditions. We show that loss of REX1 expression correlates with altered patterns of DNA methylation at the REX1 locus, implying that epigenetic mechanisms may interfere with the metastable phenotype commonly found in murine pluripotency.
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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".