Covert actions of epidural stimulation on spinal locomotor circuits.

Spinal circuitry produces the rhythm and patterning of locomotion. However, both descending and sensory inputs are required to initiate and adapt locomotion to the environment. Spinal cord injury (SCI) disrupts descending controls of the spinal cord, producing paralysis. Epidural stimulation (ES) is a promising clinical therapy for motor control recovery and is capable of reactivating the lumbar spinal locomotor networks, yet little is known about the effects of ES on locomotor neurons. Previously, we found that both sensory afferent pathways and serotonin exert mixed excitatory and inhibitory actions on lumbar interneurons involved in the generation of the locomotor rhythm, identified by the transcription factor Shox2. However, after chronic complete SCI, sensory afferent inputs to Shox2 interneurons become almost exclusively excitatory and Shox2 interneurons are supersensitive to serotonin. Here, we investigated the effects of ES on these SCI-induced changes. Inhibitory input from sensory pathways to Shox2 interneurons was maintained and serotonin supersensitivity was not observed in SCI mice that received daily sub-motor threshold ES. Interestingly, the effects of ES were maintained for at least three weeks after the ES was discontinued. In contrast, the effects of ES were not observed in Shox2 interneurons from mice that received ES after the establishment of the SCI-induced changes. Our results demonstrate mechanistic actions of ES at the level of identified spinal locomotor circuit neurons and the effectiveness of early treatment with ES on preservation of spinal locomotor circuitry after SCI, suggesting possible therapeutic benefits prior to the onset of motor rehabilitation.

Identification of adult spinal Shox2 neuronal subpopulations based on unbiased computational clustering of electrophysiological properties.

Spinal cord neurons integrate sensory and descending information to produce motor output. The expression of transcription factors has been used to dissect out the neuronal components of circuits underlying behaviors. However, most of the canonical populations of interneurons are heterogeneous and require additional criteria to determine functional subpopulations. Neurons expressing the transcription factor Shox2 can be subclassified based on the co-expression of the transcription factor Chx10 and each subpopulation is proposed to have a distinct connectivity and different role in locomotion. Adult Shox2 neurons have recently been shown to be diverse based on their firing properties. Here, in order to subclassify adult mouse Shox2 neurons, we performed multiple analyses of data collected from whole-cell patch clamp recordings of visually-identified Shox2 neurons from lumbar spinal slices. A smaller set of Chx10 neurons was included in the analyses for validation. We performed k-means and hierarchical unbiased clustering approaches, considering electrophysiological variables. Unlike the categorizations by firing type, the clusters displayed electrophysiological properties that could differentiate between clusters of Shox2 neurons. The presence of clusters consisting exclusively of Shox2 neurons in both clustering techniques suggests that it is possible to distinguish Shox2+Chx10- neurons from Shox2+Chx10+ neurons by electrophysiological properties alone. Computational clusters were further validated by immunohistochemistry with accuracy in a small subset of neurons. Thus, unbiased cluster analysis using electrophysiological properties is a tool that can enhance current interneuronal subclassifications and can complement groupings based on transcription factor and molecular expression.