Proneural genes define ground state rules to regulate neurogenic patterning and cortical folding
Bibliographic record
Abstract
SUMMARY Transition from smooth, lissencephalic brains to highly-folded, gyrencephalic structures is associated with neuronal expansion and breaks in neurogenic symmetry. Here we show that Neurog2 and Ascl1 proneural genes regulate cortical progenitor cell differentiation through cross-repressive interactions to sustain neurogenic continuity in a lissencephalic rodent brain. Using in vivo lineage tracing, we found that Neurog2 and Ascl1 expression defines a lineage continuum of four progenitor pools, with ‘double + progenitors’ displaying several unique features (least lineage-restricted, complex gene regulatory network, G 2 pausing). Strikingly, selective killing of double + progenitors using split-Cre; Rosa-DTA transgenics breaks neurogenic symmetry by locally disrupting Notch signaling, leading to cortical folding. Finally, consistent with NEUROG2 and ASCL1 driving discontinuous neurogenesis and folding in gyrencephalic species, their transcripts are modular in folded macaque cortices and pseudo-folded human cerebral organoids. Neurog2 / Ascl1 double + progenitors are thus Notch-ligand expressing ‘niche’ cells that control neurogenic periodicity to determine cortical gyrification. HIGHLIGHTS Neurog2 and Ascl1 expression defines four distinct transitional progenitor states Double + NPCs are transcriptionally complex and mark a lineage branch point Double + NPCs control neurogenic patterning and cortical folding via Notch signaling Neurog2 and Ascl1 expression is modular in folded and not lissencephalic cortices eTOC BLURB Emergence of a gyrencephalic cortex is associated with a break in neurogenic continuity across the cortical germinal zone. Han et al. identify a pool of unbiased neural progenitors at a lineage bifurcation point that co-express Neurog2 and Ascl1 and produce Notch ligands to control neurogenic periodicity and cortical folding.
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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".