The Use of PRV-Bartha to Define Premotor Inputs to Lumbar Motoneurons in the Neonatal Spinal Cord of the Mouse
Bibliographic record
Abstract
BACKGROUND: The neonatal mouse has become a model system for studying the locomotor function of the lumbar spinal cord. However, information about the synaptic connectivity within the governing neural network remains scarce. A neurotropic pseudorabies virus (PRV) Bartha has been used to map neuronal connectivity in other parts of the nervous system, due to its ability to travel trans-neuronally. Its use in spinal circuits regulating locomotion has been limited and no study has defined the time course of labelling for neurons known to project monosynaptically to motoneurons. METHODOLOGY/PRINCIPAL FINDINGS: Here we investigated the ability of PRV Bartha, expressing green and/or red fluorescence, to label spinal neurons projecting monosynaptically to motoneurons of two principal hindlimb muscles, the tibialis anterior (TA) and gastrocnemius (GC). As revealed by combined immunocytochemistry and confocal microscopy, 24-32 h after the viral muscle injection the label was restricted to the motoneuron pool while at 32-40 h the fluorescence was seen in interneurons throughout the medial and lateral ventral grey matter. Two classes of ipsilateral interneurons known to project monosynaptically to motoneurons (Renshaw cells and cells of origin of C-terminals) were consistently labeled at 40 h post-injection but also a group in the ventral grey matter contralaterally. Our results suggest that the labeling of last order interneurons occurred 8-12 h after motoneuron labeling and we presume this is the time taken by the virus to cross one synapse, to travel retrogradely and to replicate in the labeled cells. CONCLUSIONS/SIGNIFICANCE: The study establishes the time window for virally-labelling monosynaptic projections to lumbar motoneurons following viral injection into hindlimb muscles. Moreover, it provides a good foundation for intracellular targeting of the labeled neurons in future physiological studies and better understanding the functional organization of the lumbar neural networks.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".