An ultrastructural map of a spinal sensorimotor circuit reveals the potential of astroglia modulation
Zachary M. Koh, Ricky Avalos Arceo, Jacob Hammer, Khang Chau, Sarah E.W. LightiD, Antonio Dolojan, Michał JanuszewskiiD, Fabian SvaraiD, Cody J. SmithiD
Abstract
Information flow through circuits is dictated by the precise connectivity of neurons and glia. While a single astrocyte can contact many synapses, how glial-synaptic interactions are arranged within a single circuit to impact information flow remains understudied. Here, we use the local spinal sensorimotor circuit in zebrafish as a model to understand how neurons and astroglia are connected in a vertebrate circuit. With semi-automated cellular reconstructions and automated approaches to map all the synaptic connections, we identified the precise synaptic connections of the local sensorimotor circuit, from dorsal root ganglia neurons to spinal interneurons and finally to motor neurons. This revealed a complex network of interneurons that interact in the local sensorimotor circuit. We then mapped the glial processes within tripartite synapses in the circuit. We demonstrate that tripartite synapses are equally distributed across the circuit, supporting the idea that glia can modulate information flow through the circuit at different levels. We show that multiple astroglia, including bona fide astrocytes, contact synapses within a single sensory neurons circuit and that each of these astroglia can contact multiple parts of the circuit. This detailed map reveals an extensive network of connected neurons and astroglia that process sensory stimuli in a vertebrate. We then utilized this ultrastructural map to model how synaptic thresholding and glial modulation could alter information flow in circuits. We validated this circuit map with GCaMP6s imaging of dorsal root ganglia, spinal neurons and astroglia. This work provides a foundational resource detailing the ultrastructural organization of neurons and glia in a vertebrate circuit, offering insights in how glia could influence information flow in complex neural networks.
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