Proneural genes form a combinatorial code to diversify neocortical neural progenitor cells
Bibliographic record
Abstract
ABSTRACT Neocortical neural progenitor cells (NPCs) are molecularly heterogeneous, yet the genes that confer distinct neuronal morphologies and connectivities during development are poorly understood. Here, we determined that a proneural gene combinatorial code diversifies cortical NPCs. By mining scRNA-seq data from murine embryonic and early postnatal cortices and generating trajectory inference models, we found that Neurog2 is predominant, and is transiently co-expressed with Ascl1 and/or Neurog1 during an apical-to-basal NPC transition state in NPCs with early pseudotime identities. To assess whether proneural gene pairs confer distinct properties, we first used Neurog2/Ascl1 reporter mice expressing unique reporters, revealing that NPCs have distinct cell division modes and cell cycle dynamics dependent on their proneural gene profile. To assess Neurog2/Neurog1 interactions, we used double knock-out mice and novel split-Cre transgenics crossed to a Rosa-diptheria-toxin-A line to delete double + cells, showing Neurog1/Neurog2 are specifically required to generate early-born neurons and to maintain NPCs. Finally, in silico mutation of a cortical Neurog2-gene regulatory network and validation using Neurog1/Neurog2 mutant and ‘deleter’ mice, identified Bclllb and Nhlh2, expressed in early-born neurons, as dependent on Neurog1/Neurog2. Our data explains how proneural genes act combinatorically to diversify gene regulatory networks, thereby lineage restricting NPCs and creating cortical neuronal diversity.
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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".