Our ability to sense and move our bodies relies on proprioceptors, sensory neurons that detect mechanical forces within the body. Different subtypes of proprioceptors detect different kinematic features, such as joint position, movement, and vibration, but the mechanisms that underlie proprioceptor feature selectivity remain poorly understood. Using single-nucleus RNA sequencing (RNA-seq), we found that proprioceptor subtypes in the Drosophila leg lack differential expression of mechanosensitive ion channels. However, anatomical reconstruction of the proprioceptors and connected tendons revealed major biomechanical differences between subtypes. We built a model of the proprioceptors and tendons that identified a biomechanical mechanism for joint angle selectivity and predicted the existence of a topographic map of joint angle, which we confirmed using calcium imaging. Our findings suggest that biomechanical specialization is a key determinant of proprioceptor feature selectivity in Drosophila. More broadly, the discovery of proprioceptive maps reveals common organizational principles between proprioception and other topographically organized sensory systems.
Publications & Preprints
2023
Mamiya, Akira, Anne Sustar, Igor Siwanowicz, Yanyan Qi, Tzu-Chiao Lu, Pralaksha Gurung, Chenghao Chen, et al. (2023) 2023. “Biomechanical Origins of Proprioceptor Feature Selectivity and Topographic Maps in the Drosophila Leg.”. Neuron 111 (20): 3230-3243.e14. https://doi.org/10.1016/j.neuron.2023.07.009.
Sheridan, Arlo, Tri M Nguyen, Diptodip Deb, Wei-Chung Allen Lee, Stephan Saalfeld, Srinivas C Turaga, Uri Manor, and Jan Funke. (2023) 2023. “Local Shape Descriptors for Neuron Segmentation.”. Nature Methods 20 (2): 295-303. https://doi.org/10.1038/s41592-022-01711-z.
We present an auxiliary learning task for the problem of neuron segmentation in electron microscopy volumes. The auxiliary task consists of the prediction of local shape descriptors (LSDs), which we combine with conventional voxel-wise direct neighbor affinities for neuron boundary detection. The shape descriptors capture local statistics about the neuron to be segmented, such as diameter, elongation, and direction. On a study comparing several existing methods across various specimen, imaging techniques, and resolutions, auxiliary learning of LSDs consistently increases segmentation accuracy of affinity-based methods over a range of metrics. Furthermore, the addition of LSDs promotes affinity-based segmentation methods to be on par with the current state of the art for neuron segmentation (flood-filling networks), while being two orders of magnitudes more efficient-a critical requirement for the processing of future petabyte-sized datasets.
2022
Nguyen, Tri, Logan Thomas, Jeff Rhoades, Ilaria Ricchi, Xintong Cindy Yuan, Arlo Sheridan, David Hildebrand, Jan Funke, Wade Regehr, and Wei-Chung Allen Lee. 2022. “Structured Cerebellar Connectivity Supports Resilient Pattern Separation”. Nature. https://doi.org/10.1038/s41586-022-05471-w.
The cerebellum is thought to help detect and correct errors between intended and executed commands1,2 and is critical for social behaviours, cognition and emotion3-6. Computations for motor control must be performed quickly to correct errors in real time and should be sensitive to small differences between patterns for fine error correction while being resilient to noise7. Influential theories of cerebellar information processing have largely assumed random network connectivity, which increases the encoding capacity of the network's first layer8-13. However, maximizing encoding capacity reduces the resilience to noise7. To understand how neuronal circuits address this fundamental trade-off, we mapped the feedforward connectivity in the mouse cerebellar cortex using automated large-scale transmission electron microscopy and convolutional neural network-based image segmentation. We found that both the input and output layers of the circuit exhibit redundant and selective connectivity motifs, which contrast with prevailing models. Numerical simulations suggest that these redundant, non-random connectivity motifs increase the resilience to noise at a negligible cost to the overall encoding capacity. This work reveals how neuronal network structure can support a trade-off between encoding capacity and redundancy, unveiling principles of biological network architecture with implications for the design of artificial neural networks.
Lu, Jenny, Amir Behbahani, Lydia Hamburg, Elena Westeinde, Paul Dawson, Cheng Lyu, Gaby Maimon, Michael Dickinson, Shaul Druckmann, and Rachel Wilson. 2022. “Transforming Representations of Movement from Body- to World-Centric Space”. Nature 601 (7891): 98-104. https://doi.org/10.1038/s41586-021-04191-x.
When an animal moves through the world, its brain receives a stream of information about the body's translational velocity from motor commands and sensory feedback signals. These incoming signals are referenced to the body, but ultimately, they must be transformed into world-centric coordinates for navigation1,2. Here we show that this computation occurs in the fan-shaped body in the brain of Drosophila melanogaster. We identify two cell types, PFNd and PFNv3-5, that conjunctively encode translational velocity and heading as a fly walks. In these cells, velocity signals are acquired from locomotor brain regions6 and are multiplied with heading signals from the compass system. PFNd neurons prefer forward-ipsilateral movement, whereas PFNv neurons prefer backward-contralateral movement, and perturbing PFNd neurons disrupts idiothetic path integration in walking flies7. Downstream, PFNd and PFNv neurons converge onto hΔB neurons, with a connectivity pattern that pools together heading and translation direction combinations corresponding to the same movement in world-centric space. This network motif effectively performs a rotation of the brain's representation of body-centric translational velocity according to the current heading direction. Consistent with our predictions, we observe that hΔB neurons form a representation of translational velocity in world-centric coordinates. By integrating this representation over time, it should be possible for the brain to form a working memory of the path travelled through the environment8-10.
Xie, Yajun, Aaron Kuan, Wengang Wang, Zachary Herbert, Olivia Mosto, Olubusola Olukoya, Manal Adam, et al. 2022. “Astrocyte-Neuron Crosstalk through Hedgehog Signaling Mediates Cortical Synapse Development”. Cell Rep 38 (8): 110416. https://doi.org/10.1016/j.celrep.2022.110416.
Neuron-glia interactions play a critical role in the regulation of synapse formation and circuit assembly. Here we demonstrate that canonical Sonic hedgehog (Shh) pathway signaling in cortical astrocytes acts to coordinate layer-specific synaptic connectivity. We show that the Shh receptor Ptch1 is expressed by cortical astrocytes during development and that Shh signaling is necessary and sufficient to promote the expression of genes involved in regulating synaptic development and layer-enriched astrocyte molecular identity. Loss of Shh in layer V neurons reduces astrocyte complexity and coverage by astrocytic processes in tripartite synapses; conversely, cell-autonomous activation of Shh signaling in astrocytes promotes cortical excitatory synapse formation. Furthermore, Shh-dependent genes Lrig1 and Sparc distinctively contribute to astrocyte morphology and 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.
Osorno, Tomas, Stephanie Rudolph, Tri Nguyen, Velina Kozareva, Naeem Nadaf, Aliya Norton, Evan Macosko, Wei-Chung Allen Lee, and Wade Regehr. 2022. “Candelabrum Cells Are Ubiquitous Cerebellar Cortex Interneurons With Specialized Circuit Properties”. Nat Neurosci 25 (6): 702-13. https://doi.org/10.1038/s41593-022-01057-x.
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, single-nucleus RNA sequencing, in situ hybridization and serial electron microscopy in mice. 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 are strongly inhibited by PCs. CCs in turn primarily inhibit molecular layer interneurons, which leads to PC disinhibition. Thus, inputs, outputs and local signals converge onto CCs to allow them to assume a unique role in controlling cerebellar output.
2021
Chen, Chenghao, Sweta Agrawal, Brandon Mark, Akira Mamiya, Anne Sustar, Jasper Phelps, Wei-Chung Allen Lee, Barry Dickson, Gwyneth Card, and John Tuthill. 2021. “Functional Architecture of Neural Circuits for Leg Proprioception in Drosophila”. Curr Biol 31 (23): 5163-5175.e7. https://doi.org/10.1016/j.cub.2021.09.035.
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.
Phelps, Jasper S., David Grant Colburn Hildebrand, Brett J. Graham, Aaron T. Kuan, Logan A. Thomas, Tri Nguyen, Julia Buhmann, et al. 2021. “Reconstruction of Motor Control Circuits in Adult Drosophila Using Automated Transmission Electron Microscopy”. Cell 184 (3): 759–774.
To investigate circuit mechanisms underlying locomotor behavior, we used serial-section electron microscopy (EM) to acquire a synapse-resolution dataset containing the ventral nerve cord (VNC) of an adult female Drosophila 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 studied neuronal networks that control leg and wing movements by reconstructing all 507 motor neurons that control the limbs. We show that a specific class of leg sensory neurons synapses directly onto motor neurons with the largest-caliber axons on both sides of the body, representing a unique 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 more accessible and affordable to the scientific community.
Buhmann, Julia, Arlo Sheridan, Stephan Gerhard, Renate Krause, Tri Nguyen, Larissa Heinrich, Philipp Schlegel, et al. 2021. “Automatic Detection of Synaptic Partners in a Whole-Brain Drosophila EM Dataset.”. Nature Methods, no. 18: 711-74.
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.
2020
Aleksandr Rayshubskiy, Stephen L. Holtz, Isabel D’Alessandro, Anna A. Li, Quinn X. Vanderbeck, Isabel S. Haber, Peter W. Gibb, and Rachel I. Wilson. 2020. “Neural Circuit Mechanisms for Steering Control in Walking Drosophila”. BioRxiv.
Navigation can be directed toward distant targets represented within the brain’s spatial maps; alternatively, navigation can be directed toward objects in the local environment. Here we identify neurons in the Drosophila brain that integrate these two types of navigation drives. These neurons send axonal projections to the ventral nerve cord, and their activity predicts and influences steering during walking. Meanwhile, their dendrites integrate steering signals from the compass in the brain’s spatial memory center, as well as stimulus-directed steering signals from multimodal sensory pathways that bypass the compass. Using a computational model, we show how the specific connectivity of this network can generate steering behavior directed toward internal (remembered) goals, and we show how environmental cues can dynamically alter the balance of stimulus- and memory-directed steering. Our results suggest a framework where motor dynamics emerge from the integration of parallel feedback loops that drive steering toward internal versus external goals.