@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 {69721, title = {A split-GAL4 driver line resource for Drosophila neuron types}, year = {2025}, month = {01/2025}, doi = {10.7554/elife.98405.3}, url = {http://dx.doi.org/10.7554/eLife.98405.3}, author = {Meissner, Geoffrey W and Vannan, Allison and Jeter, Jennifer and Close, Kari and Depasquale, Gina M and Dorman, Zachary and Forster, Kaitlyn and Beringer, Jaye Anne and Gibney, Theresa V and Hausenfluck, Joanna H and He, Yisheng and Henderson, Kristin and Johnson, Lauren and Johnston, Rebecca M and Ihrke, Gudrun and Iyer, Nirmala and Lazarus, Rachel and Lee, Kelley and Li, Hsing-Hsi and Liaw, Hua-Peng and Melton, Brian and Miller, Scott and Motaher, Reeham and Novak, Alexandra and Ogundeyi, Omotara and Petruncio, Alyson and Price, Jacquelyn and Protopapas, Sophia and Tae, Susana and Taylor, Jennifer and Vorimo, Rebecca and Yarbrough, Brianna and Zeng, Kevin Xiankun and Zugates, Christopher T and Dionne, Heather and Angstadt, Claire and Ashley, Kelly and Cavallaro, Amanda and Dang, Tam and Gonzalez, Guillermo A and Hibbard, Karen L and Huang, Cuizhen and Kao, Jui-Chun and Laverty, Todd and Mercer, Monti and Perez, Brenda and Pitts, Scarlett and Ruiz, Danielle and Vallanadu, Viruthika and Zheng, Grace Zhiyu and Goina, Cristian and Otsuna, Hideo and Rokicki, Konrad and Svirskas, Robert R and Cheong, Han Sj and Dolan, Michael-John and Ehrhardt, Erica and Feng, Kai and El Galfi, Basel and Goldammer, Jens and Huston, Stephen J and Hu, Nan and Ito, Masayoshi and McKellar, Claire and minegishi, ryo and Namiki, Shigehiro and Nern, Aljoscha and Schretter, Catherine E and Sterne, Gabriella R and Venkatasubramanian, Lalanti and Wang, Kaiyu and Wolff, Tanya and Wu, Ming and George, Reed and Malkesman, Oz and Aso, Yoshinori and Card, Gwyneth M and Dickson, Barry J and Korff, Wyatt and Ito, Kei and Truman, James W and Zlatic, Marta and Rubin, Gerald M} } @article {70030, title = {Transforming descending input into motor output: An analysis of the Drosophila Male Adult Nerve Cord connectome}, journal = {eLife}, year = {2025}, month = {2025 Jul 21}, abstract = {

In most animals, a relatively small number of descending neurons (DNs) connect higher brain centers in the animal{\textquoteright}s head to circuits and motor neurons (MNs) in the nerve cord of the animal{\textquoteright}s body that effect movement of the limbs. To understand how brain signals generate behavior, it is critical to understand how these descending pathways are organized onto the body MNs. In the fly,\ Drosophila melanogaster, MNs controlling muscles in the leg, wing, and other motor systems reside in a ventral nerve cord (VNC), analogous to the mammalian spinal cord. In companion papers, we introduced a densely-reconstructed connectome of the\ Drosophila\ Male Adult Nerve Cord (MANC, (Takemura et al., 2024)), including cell type and developmental lineage annotation (Marin et al., 2024), which provides complete VNC connectivity at synaptic resolution. Here, we present a first look at the organization of the VNC networks connecting DNs to MNs based on this new connectome information. We proofread and curated all DNs and MNs to ensure accuracy and reliability, then systematically matched DN axon terminals and MN dendrites with light microscopy data to link their VNC morphology with their brain inputs or muscle targets. We report both broad organizational patterns of the entire network and fine-scale analysis of selected circuits of interest. We discover that direct DN-MN connections are infrequent and identify communities of intrinsic neurons linked to control of different motor systems, including putative ventral circuits for walking, dorsal circuits for flight steering and power generation, and intermediate circuits in the lower tectulum for coordinated action of wings and legs. Our analysis generates hypotheses for future functional experiments and, together with the MANC connectome, empowers others to investigate these and other circuits of the\ Drosophila\ ventral nerve cord in richer mechanistic detail.

}, doi = {10.7554/elife.96084.2}, url = {http://dx.doi.org/10.7554/eLife.96084.2}, author = {Cheong, Han Sj and Eichler, Katharina and St{\"u}rner, Tomke and Asinof, Samuel K and Champion, Andrew S and Marin, Elizabeth C and Oram, Tess B and Sumathipala, Marissa and Venkatasubramanian, Lalanti and Namiki, Shigehiro and Siwanowicz, Igor and Costa, Marta and Berg, Stuart and Jefferis, Gregory Sxe and Card, Gwyneth M} } @article {69790, title = {An updated catalogue of split-GAL4 driver lines for descending neurons in Drosophila melanogaster}, journal = {bioRxiv}, year = {2025}, month = {2025 Feb 24}, abstract = {

Descending neurons (DNs) occupy a key position in the sensorimotor hierarchy, conveying signals from the brain to the rest of the body below the neck. In Drosophila melanogaster flies, approximately 480 DN cell types have been described from electron-microscopy image datasets. Genetic access to these cell types is crucial for further investigation of their role in generating behaviour. We previously conducted the first large-scale survey of Drosophila melanogaster DNs, describing 98 unique cell types from light microscopy and generating cell-type-specific split-Gal4 driver lines for 65 of them. Here, we extend our previous work, describing the morphology of 137 additional DN types from light microscopy, bringing the total number DN types identified in light microscopy datasets to 235, or nearly 50\%. In addition, we produced 500 new sparse split-Gal4 driver lines and compiled a list of previously published DN lines from the literature for a combined list of 738 split-Gal4 driver lines targeting 171 DN types.

}, doi = {10.1101/2025.02.22.639679}, url = {https://www.biorxiv.org/content/early/2025/02/24/2025.02.22.639679}, author = {Zung, Jessica L. and Namiki, Shigehiro and Meissner, Geoffrey W. and Costa, Marta and Eichler, Katharina and St{\"u}rner, Tomke and Jefferis, Gregory S.X.E. and Managan, Claire and FlyLight Project Team and Korff, Wyatt and Card, Gwyneth M.} } @article {70123, title = {An updated catalogue of split-GAL4 driver lines for descending neurons in Drosophila melanogaster}, journal = {eLife}, year = {2025}, month = {2025 Sept 19}, abstract = {

Descending neurons (DNs) occupy a key position in the sensorimotor hierarchy, conveying signals from the brain to the rest of the body below the neck. In\ Drosophila melanogaster\ flies, approximately 480 DN cell types have been described from electron-microscopy image datasets. Genetic access to these cell types is crucial for further investigation of their role in generating behaviour. We previously conducted the first large-scale survey of\ Drosophila melanogaster\ DNs, describing 98 unique cell types from light microscopy and generating cell-type-specific split-Gal4 driver lines for 65 of them. Here, we extend our previous work, describing the morphology of 146 additional DN types from light microscopy, bringing the total number DN types identified in light microscopy datasets to 244, or roughly 50\% of all DN types. In addition, we produced 500 new sparse split-Gal4 driver lines and compiled a list of previously published DN lines from the literature for a combined list of 806 split-Gal4 driver lines targeting 190 DN types.

}, doi = {10.7554/elife.107450.1}, url = {http://dx.doi.org/10.7554/eLife.107450.1}, author = {Zung, Jessica L and Namiki, Shigehiro and Meissner, Geoffrey W and Cheong, Han Sj and Costa, Marta and Eichler, Katharina and St{\"u}rner, Tomke and Jefferis, Gregory Sxe and Managan, Claire and Korff, Wyatt and Card, Gwyneth M} } @article {69489, title = {Comparative connectomics of the descending and ascending neurons of the Drosophila nervous system: stereotypy and sexual dimorphism}, journal = {bioRxiv}, year = {2024}, month = {2024 Jun 06}, 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 information bottleneck connecting the brain and the ventral nerve cord (VNC, spinal cord analogue) and comprises diverse populations of descending (DN), ascending (AN) and sensory ascending neurons, which are crucial for sensorimotor signalling and control.Integrating three separate EM datasets, we now provide a complete connectomic description of the ascending and descending neurons of the female nervous system of Drosophila and compare them with neurons of the male nerve cord. Proofread neuronal reconstructions have been matched across hemispheres, datasets and sexes. Crucially, we have also matched 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 general architecture, tracts, neuropil innervation and connectivity of neck connective neurons. We observe connected chains of descending and ascending neurons spanning the neck, which may subserve motor sequences. We provide a complete description of sexually dimorphic DN and AN populations, with detailed analysis of circuits implicated in sex-related behaviours, including female ovipositor extrusion (DNp13), male courtship (DNa12/aSP22) and song production (AN hemilineage 08B). Our work represents the first EM-level circuit analyses spanning the entire central nervous system of an adult animal.

}, doi = {10.1101/2024.06.04.596633}, url = {https://www.biorxiv.org/content/early/2024/06/06/2024.06.04.596633}, 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 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 Schlegel, Philipp 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, Sebastian and Murthy, Mala and Tuthill, John and Lee, Wei-Chung A. and Card, Gwyneth M. and Costa, Marta and Jefferis, Gregory S.X.E. and Eichler, Katharina} } @article {69301, title = {Motor neurons generate pose-targeted movements via proprioceptive sculpting.}, journal = {Nature}, year = {2024}, month = {2024 Mar 20}, abstract = {

Motor neurons are the final common pathway through which the brain controls movement of the body, forming the basic elements from which all movement is composed. Yet how a single motor neuron contributes to control during natural movement remains unclear. Here we anatomically and functionally characterize the individual roles of the motor neurons that control head movement in the fly, Drosophila melanogaster. Counterintuitively, we find that activity in a single motor neuron rotates the head in different directions, depending on the starting posture of the head, such that the head converges towards a pose determined by the identity of the stimulated motor neuron. A feedback model predicts that this convergent behaviour results from motor neuron drive interacting with proprioceptive feedback. We identify and genetically suppress a single class of proprioceptive neuron that changes the motor neuron-induced convergence as predicted by the feedback model. These data suggest a framework for how the brain controls movements: instead of directly generating movement in a given direction by activating a fixed set of motor neurons, the brain controls movements by adding bias to a continuing proprioceptive-motor loop.

}, issn = {1476-4687}, doi = {10.1038/s41586-024-07222-5}, author = {Gorko, Benjamin and Siwanowicz, Igor and Close, Kari and Christoforou, Christina and Hibbard, Karen L and Kabra, Mayank and Lee, Allen and Park, Jin-Yong and Li, Si Ying and Chen, Alex B and Namiki, Shigehiro and Chen, Chenghao and Tuthill, John C and Bock, Davi D and Rouault, Herv{\'e} and Branson, Kristin and Ihrke, Gudrun and Huston, Stephen J} } @article {68629, title = {A searchable image resource of Drosophila GAL4-driver expression patterns with single neuron resolution.}, journal = {eLife}, volume = {12}, year = {2023}, month = {2023 Feb 23}, abstract = {

Precise, repeatable genetic access to specific neurons via GAL4/UAS and related methods is a key advantage of Drosophila neuroscience. Neuronal targeting is typically documented using light microscopy of full GAL4 expression patterns, which generally lack the single-cell resolution required for reliable cell type identification. Here we use stochastic GAL4 labeling with the MultiColor FlpOut approach to generate cellular resolution confocal images at large scale. We are releasing aligned images of 74,000 such adult central nervous systems. An anticipated use of this resource is to bridge the gap between neurons identified by electron or light microscopy. Identifying individual neurons that make up each GAL4 expression pattern improves the prediction of split-GAL4 combinations targeting particular neurons. To this end we have made the images searchable on the NeuronBridge website. We demonstrate the potential of NeuronBridge to rapidly and effectively identify neuron matches based on morphology across imaging modalities and datasets.

}, issn = {2050-084X}, doi = {10.7554/eLife.80660}, author = {Meissner, Geoffrey Wilson and Nern, Aljoscha and Dorman, Zachary and Depasquale, Gina M and Forster, Kaitlyn and Gibney, Theresa and Hausenfluck, Joanna H and He, Yisheng and Iyer, Nirmala A and Jeter, Jennifer and Johnson, Lauren and Johnston, Rebecca M and Lee, Kelley and Melton, Brian and Yarbrough, Brianna and Zugates, Christopher T and Clements, Jody and Goina, Cristian and Otsuna, Hideo and Rokicki, Konrad and Svirskas, Robert R and Aso, Yoshinori and Card, Gwyneth M and Dickson, Barry J and Ehrhardt, Erica and Goldammer, Jens and Ito, Masayoshi and Kainmueller, Dagmar and Korff, Wyatt and Mais, Lisa and minegishi, ryo and Namiki, Shigehiro and Rubin, Gerald M and Sterne, Gabriella R and Wolff, Tanya and Malkesman, Oz} } @article {68566, title = {Synaptic gradients transform object location to action.}, journal = {Nature}, volume = {613}, year = {2023}, month = {2023 Jan 04}, pages = {534-42}, abstract = {

To survive, animals must convert sensory information into appropriate behaviours. Vision is a common sense for locating ethologically relevant stimuli and guiding motor responses. How circuitry converts object location in retinal coordinates to movement direction in body coordinates remains largely unknown. Here we show through behaviour, physiology, anatomy and connectomics in Drosophila that visuomotor transformation occurs by conversion of topographic maps formed by the dendrites of feature-detecting visual projection neurons (VPNs) into synaptic weight gradients of VPN outputs onto central brain neurons. We demonstrate how this gradient motif transforms the anteroposterior location of a visual looming stimulus into the fly{\textquoteright}s directional escape. Specifically, we discover that two neurons postsynaptic to a looming-responsive VPN type promote opposite takeoff directions. Opposite synaptic weight gradients onto these neurons from looming VPNs in different visual field regions convert localized looming threats into correctly oriented escapes. For a second looming-responsive VPN type, we demonstrate graded responses along the dorsoventral axis. We show that this synaptic gradient motif generalizes across all 20 primary VPN cell types and most often arises without VPN axon topography. Synaptic gradients may thus be a general mechanism for conveying spatial features of sensory information into directed motor outputs.

}, issn = {1476-4687}, doi = {10.1038/s41586-022-05562-8}, author = {Dombrovski, Mark and Peek, Martin Y and Park, Jin-Yong and Vaccari, Andrea and Sumathipala, Marissa and Morrow, Carmen and Breads, Patrick and Zhao, Arthur and Kurmangaliyev, Yerbol Z and Sanfilippo, Piero and Rehan, Aadil and Polsky, Jason and Alghailani, Shada and Tenshaw, Emily and Namiki, Shigehiro and Zipursky, S Lawrence and Card, Gwyneth M} } @article {67892, title = {A population of descending neurons that regulates the flight motor of Drosophila.}, journal = {Current Biology}, volume = {32}, year = {2022}, month = {2022 Mar 14}, pages = {1189-1196}, abstract = {

Similar to many insect species, Drosophila melanogaster is capable of maintaining a stable flight trajectory for periods lasting up to several hours. Because aerodynamic torque is roughly proportional to the fifth power of wing length, even small asymmetries in wing size require the maintenance of subtle bilateral differences in flapping motion to maintain a stable path. Flies can even fly straight after losing half of a wing, a feat they accomplish via very large, sustained kinematic changes to both the damaged and intact wings. Thus, the neural network responsible for stable flight must be capable of sustaining fine-scaled control over wing motion across a large dynamic range. In this study, we describe an unusual type of descending neuron (DNg02) that projects directly from visual output regions of the brain to the dorsal flight neuropil of the ventral nerve cord. Unlike many descending neurons, which exist as single bilateral pairs with unique morphology, there is a population of at least 15 DNg02 cell pairs with nearly identical shape. By optogenetically activating different numbers of DNg02 cells, we demonstrate that these neurons regulate wingbeat amplitude over a wide dynamic range via a population code. Using two-photon functional imaging, we show that DNg02 cells are responsive to visual motion during flight in a manner that would make them well suited to continuously regulate bilateral changes in wing kinematics. Collectively, we have identified a critical set of descending neurons that provides the sensitivity and dynamic range required for flight control.

}, issn = {1879-0445}, doi = {10.1016/j.cub.2022.01.008}, author = {Namiki, Shigehiro and Ros, Ivo G and Morrow, Carmen and Rowell, William J and Card, Gwyneth M and Korff, Wyatt and Dickinson, Michael H} } @article {65723, title = {A systematic nomenclature for the Drosophila ventral nerve cord.}, journal = {Neuron}, volume = {107}, year = {2020}, month = {2020 Sep 14}, pages = {1071-79}, abstract = {

Drosophila melanogaster is an established model for neuroscience research with relevance in biology and medicine. Until recently, research on the Drosophila brain was hindered by the lack of a complete and uniform nomenclature. Recognizing this, Ito et\ al. (2014) produced an authoritative nomenclature for the adult insect brain, using Drosophila as the reference. Here, we extend this nomenclature to the adult thoracic and abdominal neuromeres, the ventral nerve cord (VNC), to provide an anatomical description of this major component of the Drosophila nervous system. The VNC is the locus for the reception and integration of sensory information and involved in generating most of the locomotor actions that underlie fly behaviors. The aim is to create a nomenclature, definitions, and spatial boundaries for the Drosophila VNC that are consistent with other insects. The work establishes an anatomical framework that provides a powerful tool for analyzing the functional organization of the VNC.

}, issn = {1097-4199}, doi = {10.1016/j.neuron.2020.08.005}, author = {Court, Robert and Namiki, Shigehiro and Armstrong, J Douglas and Borner, Jana and Card, Gwyneth and Costa, Marta and Dickinson, Michael and Duch, Carsten and Korff, Wyatt and Mann, Richard and Merritt, David and Murphey, Rod K and Seeds, Andrew M and Shirangi, Troy and Simpson, Julie H and Truman, James W and Tuthill, John C and Williams, Darren W and Shepherd, David} } @article {65050, title = {State-dependent decoupling of sensory and motor circuits underlies behavioral flexibility in Drosophila.}, journal = {Nature Neuroscience}, volume = {22}, year = {2019}, month = {2019 Jul 01}, pages = {1132-1139}, abstract = {

An approaching predator and self-motion toward an object can generate similar looming patterns on the retina, but these situations demand different rapid responses. How central circuits flexibly process visual cues to activate appropriate, fast motor pathways remains unclear. Here we identify two descending neuron (DN) types that control landing and contribute to visuomotor flexibility in Drosophila. For each, silencing impairs visually evoked landing, activation drives landing, and spike rate determines leg extension amplitude. Critically, visual responses of both DNs are severely attenuated during non-flight periods, effectively decoupling visual stimuli from the landing motor pathway when landing is inappropriate. The flight-dependence mechanism differs between DN types. Octopamine exposure mimics flight effects in one, whereas the other probably receives neuronal feedback from flight motor circuits. Thus, this sensorimotor flexibility arises from distinct mechanisms for gating action-specific descending pathways, such that sensory and motor networks are coupled or decoupled according to the behavioral state.

}, issn = {1546-1726}, doi = {10.1038/s41593-019-0413-4}, author = {Ache, Jan M and Namiki, Shigehiro and Lee, Allen and Branson, Kristin and Card, Gwyneth M} } @article {48976, title = {Optogenetic dissection of descending behavioral control in Drosophila.}, journal = {eLife}, year = {2018}, month = {2018}, pages = {e34275}, abstract = {

In most animals, the brain makes behavioral decisions that are transmitted by descending neurons to the nerve cord circuitry that produces behaviors. In insects, only a few descending neurons have been associated with specific behaviors. To explore how descending neurons control an insect{\textquoteright}s movements, we developed a novel method to systematically assay the behavioral effects of activating individual neurons on freely behaving terrestrial\ D. melanogaster. We calculated a two-dimensional representation of the entire behavior space explored by these flies and we associated descending neurons with specific behaviors by identifying regions of this space that were visited with increased frequency during optogenetic activation. Applying this approach across a large collection of descending neurons, we found that (1) activation of most of the descending neurons drove stereotyped behaviors, (2) in many cases multiple descending neurons activated similar behaviors, and (3) optogenetically-activated behaviors were often dependent on the behavioral state prior to activation.

}, doi = {10.7554/eLife.34275}, author = {Cande, Jessica and Namiki, Shigehiro and Qiu, Jirui and Korff, Wyatt and Card, Gwyneth M and Shaevitz, Joshua W and Stern, David L and Berman, Gordon J} } @article {49418, title = {Speed dependent descending control of freezing behavior in Drosophila melanogaster.}, journal = {Nature Communications}, volume = {9}, year = {2018}, month = {2018 Sep 12}, pages = {3697}, abstract = {

The most fundamental choice an animal has to make when it detects a threat is whether to freeze, reducing its chances of being noticed, or to flee to safety. Here we show that Drosophila melanogaster exposed to looming stimuli in a confined arena either freeze or flee. The probability of freezing versus fleeing is modulated by the fly{\textquoteright}s walking speed at the time of threat, demonstrating that freeze/flee decisions depend on behavioral state. We describe a pair of descending neurons crucially implicated in freezing. Genetic silencing of DNp09 descending neurons disrupts freezing yet does not prevent fleeing. Optogenetic activation of both DNp09 neurons induces running and freezing in a state-dependent manner. Our findings establish walking speed as a key factor in defensive response choices and reveal a pair of descending neurons as a critical component in the circuitry mediating selection and execution of freezing or fleeing behaviors.

}, issn = {2041-1723}, doi = {10.1038/s41467-018-05875-1}, author = {Zacarias, Ricardo and Namiki, Shigehiro and Card, Gwyneth M and Vasconcelos, Maria Lu{\'\i}sa and Moita, Marta A} } @article {48600, title = {Feature integration drives probabilistic behavior in the Drosophila escape response.}, journal = {Neuron}, volume = {94}, year = {2017}, month = {2017 Jun 21}, pages = {1190-204}, abstract = {

Animals rely on dedicated sensory circuits to extract and encode environmental features. How individual neurons integrate and translate these features into behavioral responses remains a major question. Here, we identify a visual\ projection neuron\ type that conveys predator approach information to the\ Drosophila\ giant fiber (GF) escape circuit. Genetic removal of this input during looming stimuli reveals that it encodes angular expansion velocity, whereas other input cell type(s) encode angular size. Motor program selection and timing emerge from\ linear\ integration of these two features within the GF. Linear integration improves size detection invariance over prior models and appropriately biases motor selection to rapid,\ GF-mediated\ escapes during fast looms. Our findings suggest\ feature integration, and motor control may occur as simultaneous operations within the same neuron and establish the\ Drosophila\ escape circuit as a model system in which these computations may be further dissected at the circuit level.

}, doi = {10.1016/j.neuron.2017.05.036}, author = {von Reyn, Catherine R and Nern, Aljoscha and Williamson, W Ryan and Breads, Patrick and Wu, Ming and Namiki, Shigehiro and Card, Gwyneth M} } @article {48975, title = {The functional organization of descending sensory-motor pathways in Drosophila.}, journal = {eLife}, year = {2017}, month = {2018 Jun 26}, pages = {e34272}, abstract = {

In most animals, the brain controls the body via a set of descending neurons (DNs) that traverse the neck. DN activity activates, maintains or modulates locomotion and other behaviors. Individual DNs have been well-studied in species from insects to primates, but little is known about overall connectivity patterns across the DN population. We systematically investigated DN anatomy in\ Drosophila melanogaster\ and created over 100 transgenic lines targeting individual cell types. We identified roughly half of all\ Drosophila\ DNs and comprehensively map connectivity between sensory and motor neuropils in the brain and nerve cord, respectively. We find the nerve cord is a layered system of neuropils reflecting the fly{\textquoteright}s capability for two largely independent means of locomotion -- walking and flight -- using distinct sets of appendages. Our results reveal the basic functional map of descending pathways in flies and provide tools for systematic interrogation of neural circuits.

}, doi = {10.7554/eLife.34272}, author = {Namiki, Shigehiro and Dickinson, Michael H and Wong, Allan M and Korff, Wyatt and Card, Gwyneth M} } @article {48657, title = {A systematic nomenclature for the Drosophila ventral nervous system.}, journal = {bioRxiv}, year = {2017}, month = {2017 Apr 26}, abstract = {

Insect nervous systems are proven and powerful model systems for neuroscience research with wide relevance in biology and medicine. However, descriptions of insect brains have suffered from a lack of a complete and uniform nomenclature. Recognising this problem the Insect Brain Name Working Group produced the first agreed hierarchical nomenclature system for the adult insect brain, using Drosophila melanogaster as the reference framework, with other insect taxa considered to ensure greater consistency and expandability (Ito et al., 2014). Ito et al. (2014) purposely focused on the gnathal regions that account for approximately 50\% of the adult CNS. We extend this nomenclature system to the sub-gnathal regions of the adult Drosophila nervous system to provide a nomenclature of the so-called ventral nervous system (VNS), which includes the thoracic and abdominal neuromeres that was not included in the original work and contains the neurons that play critical roles underpinning most fly behaviours.

}, doi = {10.1101/122952}, author = {Court, Robert Christopher and Armstrong, James Douglas and Borner, Jana and Card, Gwyneth M and Costa, Marta and Dickinson, Michael H and Duch, Carsten and Korff, Wyatt and Mann, Richard S and Merritt, David and Murphey, Rodney K and Namiki, Shigehiro and Seeds, Andrew M and Shepherd, David and Shirangi, Troy R and Simpson, Julie H and Truman, James W and Tuthill, John C and Williams, Darren W} } @article {46297, title = {Novel cell-based odorant sensor elements based on insect odorant receptors.}, journal = {Biosensors \& Bioelectronics}, volume = {65C}, year = {2014}, month = {2014 Oct 17}, pages = {287-294}, abstract = {

Development of cell-based odorant sensor elements combined not only high degree of sensitivity and selectivity but also long-term stability is crucial for their practical applications. Here we report the development of a novel cell-based odorant sensor element that sensitively and selectively detects odorants and displays increased fluorescent intensities over a long period of time. Our odorant sensor elements, based on Sf21 cell lines expressing insect odorant receptors, are sensitive to the level of several tens of parts per billion in solution, can selectively distinguish between different types of odorants based on the odorant selectivity intrinsic to the expressed receptors, and have response times of approximately 13s. Specifically, with the use of Sf21 cells and insect odorant receptors, we demonstrated that the established cell lines stably expressing insect odorant receptors are able to detect odorants with consistent responsiveness for at least 2 months, thus exceeding the short life-span normally associated with cell-based sensors. We also demonstrated the development of a compact odorant sensor chip by integrating the established insect cell lines into a microfluidic chip. The methodology we established in this study, in conjunction with the large repertoire of insect odorant receptors, will aid in the development of practical cell-based odorant sensors for various applications, including food administration and health management.

}, issn = {1873-4235}, doi = {10.1016/j.bios.2014.10.026}, author = {Mitsuno, Hidefumi and Sakurai, Takeshi and Namiki, Shigehiro and Mitsuhashi, Hiroyuki and Kanzaki, Ryohei} }