@article {69229, title = {Organization of an ascending circuit that conveys flight motor state in Drosophila.}, journal = {Current Biology}, year = {2024}, month = {2024 Feb 13}, abstract = {

Natural behaviors are a coordinated symphony of motor acts that drive reafferent (self-induced) sensory activation. Individual sensors cannot disambiguate exafferent (externally induced) from reafferent sources. Nevertheless, animals readily differentiate between these sources of sensory signals to carry out adaptive behaviors through corollary discharge circuits (CDCs), which provide predictive motor signals from motor pathways to sensory processing and other motor pathways. Yet, how CDCs comprehensively integrate into the nervous system remains unexplored. Here, we use connectomics, neuroanatomical, physiological, and behavioral approaches to resolve the network architecture of two pairs of ascending histaminergic neurons (AHNs) in Drosophila, which function as a predictive CDC in other insects. Both AHN pairs receive input primarily from a partially overlapping population of descending neurons, especially from DNg02, which controls wing motor output. Using Ca imaging and behavioral recordings, we show that AHN activation is correlated to flight behavior and precedes wing motion. Optogenetic activation of DNg02 is sufficient to activate AHNs, indicating that AHNs are activated by descending commands in advance of behavior and not as a consequence of sensory input. Downstream, each AHN pair targets predominantly non-overlapping networks, including those that process visual, auditory, and mechanosensory information, as well as networks controlling wing, haltere, and leg sensorimotor control. These results support the conclusion that the AHNs provide a predictive motor signal about wing motor state to mostly non-overlapping sensory and motor networks. Future work will determine how AHN signaling is driven by other descending neurons and interpreted by AHN downstream targets to maintain adaptive sensorimotor performance.

}, issn = {1879-0445}, doi = {10.1016/j.cub.2024.01.071}, author = {Cheong, Han S J and Boone, Kaitlyn N and Bennett, Marryn M and Salman, Farzaan and Ralston, Jacob D and Hatch, Kaleb and Allen, Raven F and Phelps, Alec M and Cook, Andrew P and Phelps, Jasper S and Erginkaya, Mert and Lee, Wei-Chung A and Card, Gwyneth M and Daly, Kevin C and Dacks, Andrew M} } @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 {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 {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} }