Molecular Control of Spinal Accessory Motor Neuron/Axon Development in the Mouse Spinal Cord
Bibliographic record
Abstract
Within the developing vertebrate spinal cord, motor neuron subtypes are distinguished by the settling positions of their cell bodies, patterns of gene expression, and the paths their axons follow to exit the CNS. The inclusive set of cues required to guide a given motor axon subtype from cell body to target has yet to be identified, in any species. This is attributable, in part, to the unavailability of markers that demarcate the complete trajectory followed by a specific class of spinal motor axons. Most spinal motor neurons extend axons out of the CNS through ventral exit points. In contrast, spinal accessory motor neurons (SACMNs) project dorsally directed axons through lateral exit points (LEPs), and these axons assemble into the spinal accessory nerve (SAN). Here we show that an antibody against BEN/ALCAM/SC1/DM-GRASP/MuSC selectively labels mouse SACMNs and can be used to trace the pathfinding of SACMN axons. We use this marker, together with a battery of transcription factor-deficient or guidance cue/receptor-deficient mice to identify molecules required for distinct stages of SACMN development. Specifically, we find that Gli2 is required for the initial extension of axons from SACMN cell bodies, and that netrin-1 and its receptor Dcc are required for the proper dorsal migration of these cells and the dorsally directed extension of SACMN axons toward the LEPs. Furthermore, in the absence of the transcription factor Nkx2.9, SACMN axons fail to exit the CNS. Together, these findings suggest molecular mechanisms that are likely to regulate key steps in SACMN development.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".