Like the vertebrate spinal cord, the insect ventral nerve cord (VNC) mediates limb sensation and motor control. Here, we applied automated tools for electron microscopy (EM) volume alignment, neuron reconstruction, and synapse prediction to create a draft connectome of theDrosophilaVNC. To interpret the VNC connectome, it is crucial to know its relationship with the rest of the body. We therefore mapped the muscle targets of leg and wing motor neurons in the connectome by comparing their morphology to genetic driver lines, dye fills, and x-ray holographic nano-tomography volumes of the fly leg and wing. Knowing the outputs of the connectome allowed us to identify neural circuits that coordinate the wings with the middle and front legs during escape takeoff. We provide the draft VNC connectome and motor neuron atlas, along with tools for programmatic and interactive access, as community resources.

Animal movement is controlled by motor neurons (MNs), which project out of the central nervous system to activate muscles. Because individual muscles may be used in many different behaviors, MN activity must be flexibly coordinated by dedicated premotor circuitry, the organization of which remains largely unknown. Here, we use comprehensive reconstruction of neuron anatomy and synaptic connectivity from volumetric electron microscopy (i.e., connectomics) to analyze the wiring logic of motor circuits controlling theDrosophilaleg and wing. We find that both leg and wing premotor networks are organized into modules that link MNs innervating muscles with related functions. However, the connectivity patterns within leg and wing motor modules are distinct. Leg premotor neurons exhibit proportional gradients of synaptic input onto MNs within each module, revealing a novel circuit basis for hierarchical MN recruitment. In comparison, wing premotor neurons lack proportional synaptic connectivity, which may allow muscles to be recruited in different combinations or with different relative timing. By comparing the architecture of distinct limb motor control systems within the same animal, we identify common principles of premotor network organization and specializations that reflect the unique biomechanical constraints and evolutionary origins of leg and wing motor control.

A prevailing challenge in neuroscience is understanding how diverse neuronal cell types select their synaptic partners to form circuits. In the neocortex, major classes of excitatory projection neurons and inhibitory interneurons are conserved across functionally distinct regions. There is evidence these classes form canonical circuit motifs that depend primarily on their identity; however, regional cues likely also influence their choice of synaptic partners. We mined the Allen Institute’s single-cell RNA-sequencing database of mouse cortical neurons to study the expression of genes necessary for synaptic connectivity and physiology in two regions: the anterior lateral motor cortex (ALM) and the primary visual cortex (VISp). We used the Allen’s metadata to parse cells by clusters representing major excitatory and inhibitory classes that are common to both ALM and VISp. We then performed two types of pairwise differential gene expression analysis: (1) between different neuronal classes within the same brain region (ALM or VISp), and (2) between the same neuronal class in ALM and VISp. We filtered our results for differentially expressed genes related to circuit connectivity and developed a novel bioinformatic approach to determine the sets uniquely enriched in each neuronal class in ALM, VISp, or both. This analysis provides an organized set of genes that may regulate synaptic connectivity and physiology in a cell-type-specific manner. Furthermore, it identifies candidate mechanisms for circuit organization that are conserved across functionally distinct cortical regions or that are region dependent. Finally, we used the SFARI Human Gene Module to identify genes from this analysis that are related to risk for autism spectrum disorder (ASD). Our analysis provides clear molecular targets for future studies to understand neocortical circuit organization and abnormalities that underlie autistic phenotypes.

A prevailing challenge in neuroscience is understanding how diverse neuronal cell types select their synaptic partners to form circuits. In the neocortex, major subclasses of excitatory projection neurons and inhibitory interneurons are conserved across functionally distinct regions. There is evidence these subclasses form circuits that depend primarily on their identity; however, regional cues likely also influence their choice of synaptic partners. We mined the Allen Brain Institute's single-cell RNA-sequencing database of mouse cortical neurons to study the expression of cellular adhesion molecules (CAMs) necessary for synapse formation in two regions: the anterior lateral motor cortex (ALM) and the primary visual cortex (VISp). We used the Allen's metadata to parse cells by clusters representing major excitatory and inhibitory subclasses that are common to both ALM and VISp. We then performed two types of pairwise differential gene expression analysis: 1) between different neuronal subclasses within the same brain region (ALM or VISp), and 2) between the same neuronal subclass in ALM and VISp. We filtered our results for differentially expressed genes encoding CAMs and developed a novel bioinformatic approach to determine the sets uniquely enriched in each neuronal subclass in ALM, VISp, or both. This analysis provides an organized set of genes that may regulate circuit formation in a cell-type specific manner. Furthermore, it identifies candidate mechanisms for the formation of circuits that are conserved across functionally distinct cortical regions or that are region dependent. Finally, we used the SFARI Human Gene Module to identify CAMs from our analysis that are related to risk for autism spectrum disorder (ASD). From over 3,000 differentially expressed genes, we found 40 ASD-associated CAMs that are enriched in specific neuronal subclasses in both ALM and VISp. Our analysis provides clear molecular targets for future studies to understand neocortical circuit organization and abnormalities that underly autistic phenotypes.