α-Tanycytes of the adult hypothalamic third ventricle include distinct populations of FGF-responsive neural progenitors
Bibliographic record
Abstract
Emerging evidence suggests that new cells, including neurons, can be generated within the adult hypothalamus, suggesting the existence of a local neural stem/progenitor cell niche. Here, we identify α-tanycytes as key components of a hypothalamic niche in the adult mouse. Long-term lineage tracing in vivo using a GLAST::CreER(T2) conditional driver indicates that α-tanycytes are self-renewing cells that constitutively give rise to new tanycytes, astrocytes and sparse numbers of neurons. In vitro studies demonstrate that α-tanycytes, but not β-tanycytes or parenchymal cells, are neurospherogenic. Distinct subpopulations of α-tanycytes exist, amongst which only GFAP-positive dorsal α2-tanycytes possess stem-like neurospherogenic activity. Fgf-10 and Fgf-18 are expressed specifically within ventral tanycyte subpopulations; α-tanycytes require fibroblast growth factor signalling to maintain their proliferation ex vivo and elevated fibroblast growth factor levels lead to enhanced proliferation of α-tanycytes in vivo. Our results suggest that α-tanycytes form the critical component of a hypothalamic stem cell niche, and that local fibroblast growth factor signalling governs their proliferation.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".