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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.

Specialized mechanosensory end organs within mammalian skin—hair follicle-associated lanceolate complexes, Meissner corpuscles, and Pacinian corpuscles—enable our perception of light, dynamic touch1. In each of these end organs, fast-conducting mechanically sensitive neurons, called Aβ low-threshold mechanoreceptors (Aβ LTMRs), associate with resident glial cells, known as terminal Schwann cells (TSCs) or lamellar cells, to form complex axon ending structures. Lanceolate-forming and corpuscle-innervating Aβ LTMRs share a low threshold for mechanical activation, a rapidly adapting (RA) response to force indentation, and high sensitivity to dynamic stimuli1–6. How mechanical stimuli lead to activation of the requisite mechanotransduction channel Piezo27–15and Aβ RA-LTMR excitation across the morphologically dissimilar mechanosensory end organ structures is not understood. Here, we report the precise subcellular distribution of Piezo2 and high-resolution, isotropic 3D reconstructions of all three end organs formed by Aβ RA-LTMRs determined by large volume enhanced Focused Ion Beam Scanning Electron Microscopy (FIB-SEM) imaging. We found that within each end organ, Piezo2 is enriched along the sensory axon membrane and is minimally or not expressed in TSCs and lamellar cells. We also observed a large number of small cytoplasmic protrusions enriched along the Aβ RA-LTMR axon terminals associated with hair follicles, Meissner corpuscles, and Pacinian corpuscles. These axon protrusions reside within close proximity to axonal Piezo2, occasionally contain the channel, and often form adherens junctions with nearby non-neuronal cells. Our findings support a unified model for Aβ RA-LTMR activation in which axon protrusions anchor Aβ RA-LTMR axon terminals to specialized end organ cells, enabling mechanical stimuli to stretch the axon in hundreds to thousands of sites across an individual end organ and leading to activation of proximal Piezo2 channels and excitation of the neuron.

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.

Our ability to sense and move our bodies relies on proprioceptors, sensory neurons that detect mechanical forces within the body. Proprioceptors are diverse: different subtypes detect different features of joint kinematics, such as position, directional movement, and vibration. However, because they are located within complex and dynamic peripheral tissues, the underlying mechanisms of proprioceptor feature selectivity remain poorly understood. Here, we investigate molecular and biomechanical contributions to proprioceptor diversity in theDrosophilaleg. Using single-nucleus RNA sequencing, we found that different proprioceptor subtypes express similar complements of mechanosensory and other ion channels. However, anatomical reconstruction of the proprioceptive organ and connected tendons revealed major biomechanical differences between proprioceptor subtypes. We constructed a computational model of the proprioceptors and tendons, which identified a putative biomechanical mechanism for joint angle selectivity. The model also predicted the existence of a goniotopic map of joint angle among position-tuned proprioceptors, which we confirmed using calcium imaging. Our findings suggest that biomechanical specialization is a key determinant of proprioceptor feature selectivity inDrosophila. More broadly, our discovery of proprioceptive maps in the fly leg reveals common organizational principles between proprioception and other topographically organized sensory systems.

To effectively control their bodies, animals rely on feedback from proprioceptive mechanosensory neurons. In the Drosophila leg, different proprioceptor subtypes monitor joint position, movement direction, and vibration. Here, we investigate how these diverse sensory signals are integrated by central proprioceptive circuits. We find that signals for leg joint position and directional movement converge in second-order neurons, revealing pathways for local feedback control of leg posture. Distinct populations of second-order neurons integrate tibia vibration signals across pairs of legs, suggesting a role in detecting external substrate vibration. In each pathway, the flow of sensory information is dynamically gated and sculpted by inhibition. Overall, our results reveal parallel pathways for processing of internal and external mechanosensory signals, which we propose mediate feedback control of leg movement and vibration sensing, respectively. The existence of a functional connectivity map also provides a resource for interpreting connectomic reconstruction of neural circuits for leg proprioception.

SUMMARYThe blood-brain barrier (BBB) is critical for protecting the brain and maintaining neuronal homeostasis. Although the BBB is a unique feature of the central nervous system (CNS) vasculature, not all brain regions have the same degree of impermeability. Differences in BBB permeability are important for controlling the local extracellular environment of specific brain regions to regulate the function and plasticity of particular neural circuits. However, how BBB heterogeneity occurs is poorly understood. Here, we demonstrate how regional specialization of the BBB is achieved. With unbiased cell profiling in small, defined brain regions, we compare the median eminence, which has a naturally leaky BBB, with the cortex, which has an impermeable BBB. We identify hundreds of molecular differences in endothelial cells (ECs) and demonstrate the existence of differences in perivascular astrocytes and pericytes in these regions, finding 3 previously unknown subtypes of astrocytes and several key differences in pericytes. By serial electron microscopy reconstruction and a novel, aqueous-based tissue clearing imaging method, we further reveal previously unknown anatomical specializations of these perivascular cells and their unique physical interactions with neighboring ECs. Finally, we identify ligand-receptor pairs between ECs and perivascular cells that may regulate regional BBB integrity in ECs. Using a bioinformatic approach we identified 26 and 26 ligand-receptor pairs underlying EC-pericyte and EC-astrocyte interactions, respectively. Our results demonstrate that differences in ECs, together with region-specific physical and molecular interactions with local perivascular cells, contribute to BBB functional heterogeneity. These regional cell inventories serve as a platform for further investigation of the dynamic and heterogeneous nature of the BBB in other brain regions. Identification of local BBB specializations provides insight into the function of different brain regions and will permit the development of region-specific drug delivery in the CNS.

To understand how the cerebellar cortex transforms mossy fiber (MF) inputs into Purkinje cell (PC) outputs, it is vital to delineate the elements of this circuit. Candelabrum cells (CCs) are enigmatic interneurons of the cerebellar cortex that have been identified based on their morphology, but their electrophysiological properties, synaptic connections, and function remain unknown. Here we clarify these properties using electrophysiology, snRNA sequencing, in situ hybridization, and serial electron microscopy. We find that CCs are the most abundant PC layer interneuron. They are GABAergic, molecularly distinct, and present in all cerebellar lobules. Their high resistance renders CC firing highly sensitive to synaptic inputs. CCs are excited by MFs and granule cells, and strongly inhibited by PCs. CCs in turn inhibit molecular layer interneurons, which leads to PC disinhibition. Thus, inputs, outputs and local signals all converge onto CCs to allow them to assume a unique role in controlling cerebellar output.

SUMMARYNeuron-glia relationships play a critical role in the regulation of synapse formation and neuronal specification. The cellular and molecular mechanisms by which neurons and astrocytes communicate and coordinate are not well understood. Here we demonstrate that the canonical Sonic hedgehog (Shh) pathway is active in cortical astrocytes, where it acts to coordinate layer-specific synaptic connectivity and functional circuit development. We show that Ptch1 is a Shh receptor that is expressed by cortical astrocytes during development and that Shh signaling is necessary and sufficient to promote the expression of layer-specific astrocyte genes involved in regulating synapse formation and function. Loss of Shh in layer V neurons reduces astrocyte complexity and coverage by astrocytic processes in tripartite synapses, moreover, cell-autonomous activation of Shh signaling in astrocytes promotes cortical excitatory synapse formation. Together, these results suggest that Shh secreted from deep layer cortical neurons acts to specialize the molecular and functional features of astrocytes during development to shape circuit assembly and function.

SUMMARYMany animals use coordinated limb movements to interact with and navigate through the environment. To investigate circuit mechanisms underlying locomotor behavior, we used serial-section electron microscopy (EM) to map synaptic connectivity within a neuronal network that controls limb movements. We present a synapse-resolution EM dataset containing the ventral nerve cord (VNC) of an adult femaleDrosophila melanogaster. To generate this dataset, we developed GridTape, a technology that combines automated serial-section collection with automated high-throughput transmission EM. Using this dataset, we reconstructed 507 motor neurons, including all those that control the legs and wings. We show that a specific class of leg sensory neurons directly synapse onto the largest-caliber motor neuron axons on both sides of the body, representing a unique feedback pathway for fast limb control. We provide open access to the dataset and reconstructions registered to a standard atlas to permit matching of cells between EM and light microscopy data. We also provide GridTape instrumentation designs and software to make large-scale EM data acquisition more accessible and affordable to the scientific community.

The study of neural circuits requires the reconstruction of neurons and the identification of synaptic connections between them. To scale the reconstruction to the size of whole-brain datasets, semi-automatic methods are needed to solve those tasks. Here, we present an automatic method for synaptic partner identification in insect brains, which uses convolutional neural networks to identify post-synaptic sites and their pre-synaptic partners. The networks can be trained from human generated point annotations alone and require only simple post-processing to obtain final predictions. We used our method to extract 244 million putative synaptic partners in the fifty-teravoxel full adult fly brain (FAFB) electron microscopy (EM) dataset and evaluated its accuracy on 146,643 synapses from 702 neurons with a total cable length of 312 mm in four different brain regions. The predicted synaptic connections can be used together with a neuron segmentation to infer a connectivity graph with high accuracy: between 92% and 96% of edges linking connected neurons are correctly classified as weakly connected (less than five synapses) and strongly connected (at least five synapses). Our synaptic partner predictions for the FAFB dataset are publicly available, together with a query library allowing automatic retrieval of up- and downstream neurons.

Serial-section electron microscopy is the method of choice for studying cellular structure and network connectivity in the brain. We have built a pipeline of parallel imaging using transmission electron automated microscopes (piTEAM) that scales this technology and enables the acquisition of petascale datasets containing local cortical microcircuits. The distributed platform is composed of multiple transmission electron microscopes that image, in parallel, different sections from the same block of tissue, all under control of a custom acquisition software (pyTEM) that implements 24/7 continuous autonomous imaging. The suitability of this architecture for large scale electron microscopy imaging was demonstrated by acquiring a volume of more than 1 mm3 of mouse neocortex spanning four different visual areas. Over 26,500 ultrathin tissue sections were imaged, yielding a dataset of more than 2 petabytes. Our current burst imaging rate is 500 Mpixel/s (image capture only) per microscope and net imaging rate is 100 Mpixel/s (including stage movement, image capture, quality control, and post processing). This brings the combined burst acquisition rate of the pipeline to 3 Gpixel/s and the net rate to 600 Mpixel/s with six microscopes running acquisition in parallel, which allowed imaging a cubic millimeter of mouse visual cortex at synaptic resolution in less than 6 months.

Transmission electron microscopy (TEM) is an essential tool for studying cells and molecules. We present a tape-based, reel-to-reel pipeline that combines automated serial sectioning with automated high-throughput TEM imaging. This acquisition platform provides nanometer-resolution imaging at fast rates for a fraction of the cost of alternative approaches. We demonstrate the utility of this imaging platform for generating datasets of biological tissues with a focus on examining brain circuits.

Elucidating the structure of neuronal networks provides a foundation for understanding how the nervous system processes information to generate behavior. Despite technological breakthroughs in visible light and electron microscopy, imaging dense nanometer-scale neuronal structures over millimeter-scale tissue volumes remains a challenge. Here, we demonstrate that X-ray holographic nano-tomography is capable of imaging large tissue volumes with sufficient resolution to disentangle dense neuronal circuitry in Drosophila melanogaster and mammalian central and peripheral nervous tissue. Furthermore, we show that automatic segmentation using convolutional neural networks enables rapid extraction of neuronal morphologies from these volumetric datasets. The technique we present allows rapid data collection and analysis of multiple specimens, and can be used correlatively with light microscopy and electron microscopy on the same samples. Thus, X-ray holographic nano-tomography provides a new avenue for discoveries in neuroscience and life sciences in general.