Evidence for chronological diversification of spinal neuron subtypes by a shared sequence of transcription factors
Bibliographic record
Abstract
Abstract The mechanisms underlying the generation of the immense diversity of neuronal cell types remains a fundamental question of developmental biology. In the spinal cord, different “cardinal classes” of neurons that share a common molecular identity are produced from spatially segregated progenitor domains. Within many such classes, a stereotyped sequence of divergent neuronal types of related function is generated over time, raising the question of the molecular mechanisms that control this process. Here, we show that the successive expression of mouse transcription factors Onecut2, Pou2f2 and Pou3f1 within the cardinal classes giving rise to motor and sensory circuits, correlates with the emergence of sequentially generated subpopulations of neurons within those domains. We demonstrate that the genetic loss of Pou2f2 results in impaired development of two early-born motor neuron columns and re-specification of anterolateral system projection neurons as a later-born subset. Similarly, we show that Pou3f1 expression is required for the normal development of later-born subsets of motor neurons and anterolateral system projection neurons. Together, our observations provide functional evidence that horologic diversification of output neurons of spinal motor and sensory circuits are driven by a conserved sequential order of expression of transcription factors.
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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".