Examining the NEUROG2 lineage and associated gene expression in human cortical organoids
Bibliographic record
Abstract
Proneural genes are conserved drivers of neurogenesis across the animal kingdom. How their functions have adapted to guide human-specific neurodevelopmental features is poorly understood. Here, we mined transcriptomic data from human fetal cortices and generated from human embryonic stem cell-derived cortical organoids (COs) to show that NEUROG1 and NEUROG2 are most highly expressed in basal neural progenitor cells, with pseudotime trajectory analyses indicating that NEUROG1-derived lineages predominate early and NEUROG2 lineages later. Using ChIP-qPCR, gene silencing and overexpression studies in COs, we show that NEUROG2 is necessary and sufficient to directly transactivate known target genes (NEUROD1, EOMES, RND2). To identify new targets, we engineered NEUROG2-mCherry knock-in human embryonic stem cells for CO generation. The mCherry-high CO cell transcriptome is enriched in extracellular matrix-associated genes, and two genes associated with human-accelerated regions: PPP1R17 and FZD8. We show that NEUROG2 binds COL1A1, COL3A1 and PPP1R17 regulatory elements, and induces their ectopic expression in COs, although NEUROG2 is not required for this expression. Neurog2 similarly induces Col3a1 and Ppp1r17 in murine P19 cells. These data are consistent with a conservation of NEUROG2 function across mammalian species.
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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 | 0.001 |
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".