Directed differentiation of human hindbrain neuroepithelial stem cells recapitulates cerebellar granule neurogenesis
Bibliographic record
Abstract
Cerebellar granule neurons (CGNs) are the most abundant neurons in the human brain. Dysregulation of their development underlies movement disorders and medulloblastomas. It is suspected that these disorders arise in progenitor states of the CGN lineage, for which human models are lacking. Here, we have differentiated human hindbrain neuroepithelial stem (hbNES) cells to CGNs in vitro using soluble growth factors, recapitulating key progenitor states in the lineage. We show that hbNES cells are not lineage committed and retain rhombomere 1 regional identity. Upon differentiation, hbNES cells transit through a rhombic lip (RL) progenitor state at day 7, demonstrating human specific sub-ventricular cell identities. This RL state is followed by an ATOH1+ CGN progenitor state at day 14. By the end of a 56-day differentiation procedure, we obtain functional neurons expressing CGN markers GABAARα6 and vGLUT2. We show that sonic hedgehog promotes GABAergic lineage specification and CGN progenitor proliferation. Our work presents a new model with which to study development and diseases of the CGN lineage in a human context.
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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".