Spinal Inhibitory Interneuron Diversity Delineates Variant Motor Microcircuits

Abstract

SUMMARY Animals generate movement by engaging spinal circuits that direct precise sequences of muscle contraction, but the identity and organizational logic of local interneurons that lie at the core of these circuits remain unresolved. Here we show that V1 interneurons, a major inhibitory population that controls motor output, fractionate into highly diverse subsets on the basis of the expression of nineteen transcription factors. Transcriptionally defined V1 subsets exhibit distinct physiological signatures and highly structured spatial distributions with mediolateral and dorsoventral positional biases. These positional distinctions constrain patterns of input from sensory and motor neurons, arguing that interneuron position is a determinant of microcircuit organization. Moreover, V1 diversity indicates that different inhibitory microcircuits exist for motor pools controlling hip, ankle, and foot muscles, revealing a variable circuit architecture for interneurons that control limb movement.

Bayesian Sparse Regression Analysis Documents the Diversity of Spinal Inhibitory Interneurons

“…The companion paper (Bikoff et al , 2016) used comparative microarray screening to identify and map the expression of 19 TFs in V1 interneurons. We used three sets of data to infer V1 neuronal diversity: (i) the fraction of neurons within the parental V1 population that express each of the 19 TFs (Figure 1A), (ii) the fractions of neurons co-expressing various pairs of TFs (Figure 1B), and (iii) the position of V1 interneurons expressing each of the 19 TFs (Figure 3A).…”

“…A third issue is the impact of V1 cell type spatial segregation on synaptic input specificity (Bikoff et al ., 2016). An inordinate diversity in spatially-restricted cell types may be necessary to satisfy highly diverse spinal circuit computations.…”

“…Yet these two physiologically defined subtypes represent only a small fraction of the parental V1 population (Alvarez et al , 2005), implying a greater diversity of V1 neurons. Indeed, the V1 population has recently been subdivided on the basis of the expression of 19 TFs (Bikoff et al , 2016). …”

Direct neuronal reprogramming: learning from and for development

“…Generation of the diverse array of neurons by direct reprogramming has relied on key findings from development; but, at the same time, generating precise neuronal subtypes is still in its infancy and shows us the limitations of our knowledge in this regard. As revealed by recent single-cell transcriptome analysis, neuronal diversity is as large or even larger at the transcriptional level than expected from the diversity of morphology, connectivity and electrophysiological properties (Bikoff et al, 2016;Telley et al, 2016;Zeisel et al, 2015). However, it is still not well understood how this level of neuronal complexity is generated during development.…”

“…Thus, much more work is required in vivo, with a particular focus on the connectivity of the reprogrammed neurons. In addition, specific transcriptional signatures are becoming available for specific neuronal subtypes, as shown recently in pioneering work identifying molecular signatures of neurons at the single-cell level (Bikoff et al, 2016;Fuzik et al, 2016;Zeisel et al, 2015). These transcriptional signatures could provide a comprehensive molecular map of specific neuronal identities that could be used to classify reprogrammed neurons.…”