The Use of PRV-Bartha to Define Premotor Inputs to Lumbar Motoneurons in the Neonatal Spinal Cord of the Mouse

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.