<i>NEUROG2</i> regulates a human-specific neurodevelopmental gene regulatory program
Bibliographic record
Abstract
ABSTRACT Unique hallmarks of human neocortical development include slower rates of neurogenesis and the establishment of an extracellular matrix-rich, outer-subventricular zone that supports basal neural progenitor cell expansion. How gene regulatory networks have evolved to support these human-specific neurodevelopmental features is poorly understood. Mining single cell data from cerebral organoids and human fetal cortices, we found that NEUROG2 expression is enriched in basal neural progenitor cells. To identify and purify NEUROG2 -expressing cells and trace their short-term lineage, we engineered two NEUROG2-mCherry knock-in human embryonic stem cell lines to produce cerebral organoids. Transcriptomic profiling of mCherry-high organoid cells revealed elevated expression of PPP1R17 , associated with a fast-evolving human-accelerated regulatory region, oligodendrocyte precursor cell and extracellular matrix-associated gene transcripts. Conversely, only neurogenic gene transcripts were enriched in mCherry-high cortical cells from Neurog2:mCherry knock-in mice. Finally, we show that Neurog2 is sufficient to induce Ppp1r17 , which slows human neural progenitor cell division, and Col13a1 , an extracellular matrix gene, in P19 cells. NEUROG2 thus regulates a human neurodevelopmental gene regulatory program implicated in supporting a pro-proliferative basal progenitor cell niche and tempering the neurogenic pace. SUMMARY STATEMENT Transcriptomic analyses of NEUROG2-mCherry knock-in human embryonic stem cell-derived cerebral organoids reveal a link between NEUROG2 and extracellular matrix remodeling during human cortical development.
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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".