@article {69881, title = {Comparative connectomics of Drosophila descending and ascending neurons.}, journal = {Nature}, year = {2025}, month = {2025 Apr 30}, abstract = {

In most complex nervous systems there is a clear anatomical separation between the nerve cord, which contains most of the final motor outputs necessary for behaviour, and the brain. In insects, the neck connective is both a physical and an information bottleneck connecting the brain and the ventral nerve cord (an analogue of the spinal cord) and comprises diverse populations of descending neurons (DNs), ascending neurons (ANs) and sensory ascending neurons, which are crucial for sensorimotor signalling and control. Here, by integrating three separate electron microscopy (EM) datasets, we provide a complete connectomic description of the ANs and DNs of the Drosophila female nervous system and compare them with neurons of the male nerve cord. Proofread neuronal reconstructions are matched across hemispheres, datasets and sexes. Crucially, we also match 51\% of DN cell types to light-level data defining specific driver lines, as well as classifying all ascending populations. We use these results to reveal the anatomical and circuit logic of neck connective neurons. We observe connected chains of DNs and ANs spanning the neck, which may subserve motor sequences. We provide a complete description of sexually dimorphic DN and AN populations, with detailed analyses of selected circuits for reproductive behaviours, including male courtship (DNa12; also known as aSP22) and song production (AN neurons from hemilineage 08B) and female ovipositor extrusion (DNp13). Our work provides EM-level circuit analyses that span the entire central nervous system of an adult animal.

}, issn = {1476-4687}, doi = {10.1038/s41586-025-08925-z}, url = {https://doi.org/10.1038/s41586-025-08925-z}, author = {St{\"u}rner, Tomke and Brooks, Paul and Serratosa Capdevila, Laia and Morris, Billy J and Javier, Alexandre and Fang, Siqi and Gkantia, Marina and Cachero, Sebastian and Beckett, Isabella R and Marin, Elizabeth C and Schlegel, Philipp and Champion, Andrew S and Moitra, Ilina and Richards, Alana and Klemm, Finja and Kugel, Leonie and Namiki, Shigehiro and Cheong, Han S J and Kovalyak, Julie and Tenshaw, Emily and Parekh, Ruchi and Phelps, Jasper S and Mark, Brandon and Dorkenwald, Sven and Bates, Alexander S and Matsliah, Arie and Yu, Szi-Chieh and McKellar, Claire E and Sterling, Amy and Seung, H Sebastian and Murthy, Mala and Tuthill, John C and Lee, Wei-Chung Allen and Card, Gwyneth M and Costa, Marta and Jefferis, Gregory S X E and Eichler, Katharina} } @article {69136, title = {Permanent deconstruction of intracellular primary cilia in differentiating granule cell neurons.}, journal = {J Cell Biol.}, volume = {223}, year = {2024}, month = {2024 Aug 13}, pages = {e202404038}, abstract = {

Primary cilia on granule cell neuron progenitors in the developing cerebellum detect sonic hedgehog to facilitate proliferation. Following differentiation, cerebellar granule cells become the most abundant neuronal cell type in the brain. While granule cell cilia are essential during early developmental stages, they become infrequent upon maturation. Here, we provide nanoscopic resolution of cilia in situ using large-scale electron microscopy volumes and immunostaining of mouse cerebella. In many granule cells, we found intracellular cilia, concealed from the external environment. Cilia were disassembled in differentiating granule cell neurons-in a process we call cilia deconstruction-distinct from premitotic cilia resorption in proliferating progenitors. In differentiating granule cells, cilia deconstruction involved unique disassembly intermediates, and, as maturation progressed, mother centriolar docking at the plasma membrane. Unlike ciliated neurons in other brain regions, our results show the deconstruction of concealed cilia in differentiating granule cells, which might prevent mitogenic hedgehog responsiveness. Ciliary deconstruction could be paradigmatic of cilia removal during differentiation in other tissues.

}, doi = {10.1016/j.cub.2024.07.036}, url = {https://doi.org/10.1016/j.cub.2024.07.036 }, author = {Ott, Carolyn M and Constable, Sandii and Nguyen, Tri M and White, Kevin and Lee, Wei-Chung Allen and Lippincott-Schwartz, Jennifer and Mukhopadhyay, Saikat} } @article {68933, title = {Biomechanical origins of proprioceptor feature selectivity and topographic maps in the Drosophila leg.}, journal = {Neuron}, year = {2023}, month = {2023 Aug 04}, abstract = {

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.

}, issn = {1097-4199}, doi = {10.1016/j.neuron.2023.07.009}, author = {Mamiya, Akira and Sustar, Anne and Siwanowicz, Igor and Qi, Yanyan and Lu, Tzu-Chiao and Gurung, Pralaksha and Chen, Chenghao and Phelps, Jasper S and Kuan, Aaron T and Pacureanu, Alexandra and Lee, Wei-Chung Allen and Li, Hongjie and Mhatre, Natasha and Tuthill, John C} } @article {68564, title = {Local shape descriptors for neuron segmentation.}, journal = {Nature Methods}, volume = {20}, year = {2023}, month = {2023 Feb 01}, pages = {295-303}, abstract = {

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.

}, issn = {1548-7105}, doi = {10.1038/s41592-022-01711-z}, author = {Sheridan, Arlo and Nguyen, Tri M and Deb, Diptodip and Lee, Wei-Chung Allen and Saalfeld, Stephan and Turaga, Srinivas C and Manor, Uri and Funke, Jan} } @article {68517, title = {Structured cerebellar connectivity supports resilient pattern separation.}, journal = {Nature}, volume = {613}, year = {2023}, month = {2023 Jan 01}, pages = {543-549}, abstract = {

The cerebellum is thought to help\ detect and correct errors between intended and executed commands and is critical for social behaviours, cognition and emotion. 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 noise. Influential theories of cerebellar information processing have largely assumed random network connectivity, which increases the encoding capacity of the network{\textquoteright}s first layer. However, maximizing encoding capacity reduces the resilience to noise. 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.

}, issn = {1476-4687}, doi = {10.1038/s41586-022-05471-w}, author = {Nguyen, Tri M and Thomas, Logan A and Jeff L Rhoades and Ricchi, Ilaria and Yuan, Xintong Cindy and Sheridan, Arlo and Hildebrand, David G C and Funke, Jan and Regehr, Wade G and Lee, Wei-Chung Allen} } @article {65832, title = {Automatic Detection of Synaptic Partners in a Whole-Brain Drosophila EM Dataset}, journal = {Nature Methods}, volume = {18}, year = {2021}, month = {2021 Jul 1}, pages = {771-4}, abstract = {

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 requires 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: 96\% of edges between 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.

}, doi = {10.1038/s41592-021-01183-7}, author = {Buhmann, Julia and Sheridan, Arlo and Gerhard, Stephan and Krause, Renate and Nguyen, Tri and Heinrich, Larissa and Schlegel, Philipp and Lee, Wei-Chung Allen and Wilson, Rachel and Saalfeld, Stephan and Jefferis, Gregory and Bock, Davi and Turaga, Srinivas and Cook, Matthew and Funke, Jan} } @article {67751, title = {Functional architecture of neural circuits for leg proprioception in Drosophila.}, journal = {Current Biology}, volume = {31}, year = {2021}, month = {2021 Dec 06}, pages = {5163}, abstract = {

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.

}, issn = {1879-0445}, doi = {10.1016/j.cub.2021.09.035}, author = {Chen, Chenghao and Agrawal, Sweta and Mark, Brandon and Mamiya, Akira and Sustar, Anne and Phelps, Jasper S and Lee, Wei-Chung Allen and Dickson, Barry J and Card, Gwyneth M and Tuthill, John C} } @article {67228, title = {Local Shape Descriptors for Neuron Segmentation}, journal = {bioRxiv}, year = {2021}, month = {2021/01}, abstract = {

We present a simple, yet effective, 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 are designed to capture local statistics about the neuron to be segmented, such as diameter, elongation, and direction. On a large study comparing several existing methods across various specimen, imaging techniques, and resolutions, we find that auxiliary learning of LSDs consistently increases segmentation accuracy of affinity-based methods over a range of metrics. Furthermore, the addition of LSDs promotes affinitybased segmentation methods to be on par with the current state of the art for neuron segmentation (Flood-Filling Networks, FFN), while being two orders of magnitudes more efficient{\textemdash}a critical requirement for the processing of future petabyte-sized datasets. Implementations of the new auxiliary learning task, network architectures, training, prediction, and evaluation code, as well as the datasets used in this study are publicly available as a benchmark for future method contributions.Competing Interest StatementThe authors have declared no competing interest.

}, doi = {10.1101/2021.01.18.427039}, url = {https://www.biorxiv.org/content/early/2021/01/18/2021.01.18.427039}, author = {Sheridan, Arlo and Nguyen, Tri and Deb, Diptodip and Lee, Wei-Chung Allen and Saalfeld, Stephan and Turaga, Srini and Manor, Uri and Funke, Jan} } @article {65833, title = {Reconstruction of motor control circuits in adult Drosophila using automated transmission electron microscopy}, journal = {Cell}, volume = {184}, year = {2021}, month = {2021 Feb 04}, abstract = {

Many 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 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 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.

}, doi = {10.1101/2020.01.10.902478}, author = {Maniates-Selvin, Jasper T. and Hildebrand, David Grant Colburn and Graham, Brett J. and Kuan, Aaron T. and Thomas, Logan A. and Nguyen, Tri and Buhmann, Julia and Azevedo, Anthony W. and Shanny, Brendan L. and Funke, Jan and Tuthill, John C. and Lee, Wei-Chung Allen} } @article {65725, title = {Dense neuronal reconstruction through X-ray holographic nano-tomography.}, journal = {Nature Neuroscience}, volume = {23}, year = {2020}, month = {2020 Dec -1}, pages = {1637-43}, abstract = {

Imaging neuronal networks provides a foundation for understanding the nervous system, but resolving dense nanometer-scale structures over large volumes remains challenging for light microscopy (LM) and electron microscopy (EM). Here we show that X-ray holographic nano-tomography (XNH) can image millimeter-scale volumes with sub-100-nm resolution, enabling reconstruction of dense wiring in Drosophila melanogaster and mouse nervous tissue. We performed correlative XNH and EM to reconstruct hundreds of cortical pyramidal cells and show that more superficial cells receive stronger synaptic inhibition on their apical dendrites. By combining multiple XNH scans, we imaged an adult Drosophila leg with sufficient resolution to comprehensively catalog mechanosensory neurons and trace individual motor axons from muscles to the central nervous system. To accelerate neuronal reconstructions, we trained a convolutional neural network to automatically segment neurons from XNH volumes. Thus, XNH bridges a key gap between LM and EM, providing a new avenue for neural circuit discovery.

}, issn = {1546-1726}, doi = {10.1038/s41593-020-0704-9}, author = {Kuan, Aaron T and Phelps, Jasper S and Thomas, Logan A and Nguyen, Tri M and Han, Julie and Chen, Chiao-Lin and Azevedo, Anthony W and Tuthill, John C and Funke, Jan and Cloetens, Peter and Pacureanu, Alexandra and Lee, Wei-Chung Allen} } @article {49342, title = {The ESCRT-III protein CHMP1A mediates secretion of sonic hedgehog on a distinctive subtype of extracellular vesicles.}, journal = {Cell Reports}, volume = {24}, year = {2018}, month = {2018 Jul 24}, pages = {973-986.e8}, abstract = {

Endosomal sorting complex required for transport (ESCRT) complex proteins regulate biogenesis and release of extracellular vesicles (EVs), which enable cell-to-cell communication in the nervous system essential for development and adult function. We recently showed human loss-of-function (LOF) mutations in ESCRT-III member CHMP1A cause autosomal recessive microcephaly with pontocerebellar hypoplasia, but its mechanism was unclear. Here, we show Chmp1a is required for progenitor proliferation in mouse cortex and cerebellum and progenitor maintenance in human cerebral organoids. In Chmp1a null mice, this defect is associated with impaired sonic hedgehog (Shh) secretion and intraluminal vesicle (ILV) formation in multivesicular bodies (MVBs). Furthermore, we show CHMP1A is important for release of an EV subtype that contains AXL, RAB18, and TMED10 (ART) and SHH. Our findings show CHMP1A loss impairs secretion of SHH on ART-EVs, providing molecular mechanistic insights into the role of ESCRT proteins and EVs in the brain.

}, issn = {2211-1247}, doi = {10.1016/j.celrep.2018.06.100}, author = {Coulter, Michael E and Dorobantu, Cristina M and Lodewijk, Gerrald A and Delalande, Fran{\c c}ois and Cianferani, Sarah and Ganesh, Vijay S and Smith, Richard S and Lim, Elaine T and Xu, C Shan and Pang, Song and Wong, Eric T and Lidov, Hart G W and Calicchio, Monica L and Yang, Edward and Gonzalez, Dilenny M and Schlaeger, Thorsten M and Mochida, Ganeshwaran H and Hess, Harald and Lee, Wei-Chung Allen and Lehtinen, Maria K and Kirchhausen, Tomas and Haussler, David and Jacobs, Frank M J and Gaudin, Rapha{\"e}l and Walsh, Christopher A} } @article {48497, title = {Whole-brain serial-section electron microscopy in larval zebrafish.}, journal = {Nature}, volume = {545}, year = {2017}, month = {2017 May 18}, pages = {345-349}, abstract = {

High-resolution serial-section electron microscopy (ssEM) makes it possible to investigate the dense meshwork of axons, dendrites, and synapses that form neuronal circuits. However, the imaging scale required to comprehensively reconstruct these structures is more than ten orders of magnitude smaller than the spatial extents occupied by networks of interconnected neurons, some of which span nearly the entire brain. Difficulties in generating and handling data for large volumes at nanoscale resolution have thus restricted vertebrate studies to fragments of circuits. These efforts were recently transformed by advances in computing, sample handling, and imaging techniques, but high-resolution examination of entire brains remains a challenge. Here, we present ssEM data for the complete brain of a larval zebrafish (Danio rerio) at 5.5 days post-fertilization. Our approach utilizes multiple rounds of targeted imaging at different scales to reduce acquisition time and data management requirements. The resulting dataset can be analysed to reconstruct neuronal processes, permitting us to survey all myelinated axons (the projectome). These reconstructions enable precise investigations of neuronal morphology, which reveal remarkable bilateral symmetry in myelinated reticulospinal and lateral line afferent axons. We further set the stage for whole-brain structure-function comparisons by co-registering functional reference atlases and in vivo two-photon fluorescence microscopy data from the same specimen. All obtained images and reconstructions are provided as an open-access resource.

}, issn = {1476-4687}, doi = {10.1038/nature22356}, author = {Hildebrand, David Grant Colburn and Cicconet, Marcelo and Torres, Russel Miguel and Choi, Woohyuk and Quan, Tran Minh and Moon, Jungmin and Wetzel, Arthur Willis and Scott Champion, Andrew and Graham, Brett Jesse and Randlett, Owen and Plummer, George Scott and Portugues, Ruben and Bianco, Isaac Henry and Saalfeld, Stephan and Baden, Alexander David and Lillaney, Kunal and Burns, Randal and Vogelstein, Joshua Tzvi and Schier, Alexander Franz and Lee, Wei-Chung Allen and Jeong, Won-Ki and Lichtman, Jeff William and Engert, Florian} } @article {4326, title = {Large-scale automated histology in the pursuit of connectomes.}, journal = {The Journal of Neuroscience: The Official Journal of the Society for Neuroscience}, volume = {31}, year = {2011}, month = {2011 Nov 9}, pages = {16125-38}, abstract = {

How does the brain compute? Answering this question necessitates neuronal connectomes, annotated graphs of all synaptic connections within defined brain areas. Further, understanding the energetics of the brain{\textquoteright}s computations requires vascular graphs. The assembly of a connectome requires sensitive hardware tools to measure neuronal and neurovascular features in all three dimensions, as well as software and machine learning for data analysis and visualization. We present the state of the art on the reconstruction of circuits and vasculature that link brain anatomy and function. Analysis at the scale of tens of nanometers yields connections between identified neurons, while analysis at the micrometer scale yields probabilistic rules of connection between neurons and exact vascular connectivity.

}, issn = {1529-2401}, doi = {10.1523/JNEUROSCI.4077-11.2011}, author = {Kleinfeld, David and Bharioke, Arjun and Blinder, Pablo and Bock, Davi D and Briggman, Kevin L and Chklovskii, Dmitri B and Denk, Winfried and Helmstaedter, Moritz and Kaufhold, John P and Lee, Wei-Chung Allen and Meyer, Hanno S and Micheva, Kristina D and Oberlaender, Marcel and Prohaska, Steffen and Reid, R Clay and Smith, Stephen J and Takemura, Shinya and Tsai, Philbert S and Sakmann, Bert} } @article {2164, title = {Network anatomy and in vivo physiology of visual cortical neurons.}, journal = {Nature}, volume = {471}, year = {2011}, month = {2011 Mar 10}, pages = {177-82}, abstract = {

In the cerebral cortex, local circuits consist of tens of thousands of neurons, each of which makes thousands of synaptic connections. Perhaps the biggest impediment to understanding these networks is that we have no wiring diagrams of their interconnections. Even if we had a partial or complete wiring diagram, however, understanding the network would also require information about each neuron{\textquoteright}s function. Here we show that the relationship between structure and function can be studied in the cortex with a combination of in vivo physiology and network anatomy. We used two-photon calcium imaging to characterize a functional property{\textendash}the preferred stimulus orientation{\textendash}of a group of neurons in the mouse primary visual cortex. Large-scale electron microscopy of serial thin sections was then used to trace a portion of these neurons{\textquoteright} local network. Consistent with a prediction from recent physiological experiments, inhibitory interneurons received convergent anatomical input from nearby excitatory neurons with a broad range of preferred orientations, although weak biases could not be rejected.

}, keywords = {Animals, Calcium Signaling, Electron, Fluorescence, Interneurons, Male, Mice, Microscopy, Microtomy, Nerve Net, Neural Inhibition, Neurons, Pyramidal Cells, Synapses, Transmission, Visual Cortex}, issn = {1476-4687}, doi = {10.1038/nature09802}, author = {Bock, Davi D and Lee, Wei-Chung Allen and Kerlin, Aaron M and Andermann, Mark L and Hood, Greg and Wetzel, Arthur W and Yurgenson, Sergey and Soucy, Edward R and Kim, Hyon Suk and Reid, R Clay} }