A tale of two cousins: Ependymal cells, quiescent neural stem cells and potential mechanisms driving their functional divergence
Bibliographic record
Abstract
Recent work has suggested that stem cells exhibit far greater heterogeneity than initially thought. Indeed, their dynamic nature and shared traits with surrounding niche cells have made prospective identification of adult neural stem cells (NSCs) challenging. Refined fate mapping strategies and single-cell omics techniques have begun to clarify functionally distinct states within the adult NSC pool, the molecular signatures that govern these states, and the functional contributions/interactions with neighboring cells within the subventricular niche. Ependymal cells are the epithelial cells which line the ventricular system and reside in the same niche as NSCs. Our own work has revealed that, despite sharing similar embryonic origins with NSCs and close geographic proximity, ependymal cells are transcriptionally distinct and fail to exhibit stem cell function in vivo, even following injury. Intriguingly, comparison of ependymal cells with qNSCs revealed transcriptional signatures that are largely overlapping, suggesting that post-transcriptional regulation might underlie their divergent phenotypes. Additional analysis of ependymal versus qNSC gene regulatory network activation supports this notion. This Viewpoint summarizes the historical confusion regarding the identity of NSCs within the lateral ventricle niche and describes recent work that provides greater appreciation for the diverse functional states within the NSC niche.
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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 | 0.005 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.005 | 0.026 |
| Scholarly communication | 0.010 | 0.024 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.007 | 0.014 |
| Insufficient payload (model declined to judge) | 0.004 | 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".