@article {69997, title = {A connectomic resource for neural cataloguing and circuit dissection of the larval zebrafish brain}, journal = {bioRxiv}, year = {2025}, month = {2025 Jun 16}, abstract = {

We present a correlated light and electron microscopy (CLEM) dataset from a 7-day-old larval zebrafish, integrating confocal imaging of genetically labeled excitatory (vglut2a) and inhibitory (gad1b) neurons with nanometer-resolution serial section EM. The dataset spans the brain and anterior spinal cord, capturing \>180,000 segmented soma, \>40,000 molecularly annotated neurons, and 30 million synapses, most of which were classified as excitatory, inhibitory, or modulatory. To characterize the directional flow of activity across the brain, we leverage the synaptic and cell body annotations to compute region-wise input and output drive indices at single cell resolution. We illustrate the dataset{\textquoteright}s utility by dissecting and validating circuits in three distinct systems: water flow direction encoding in the lateral line, recurrent excitation and contralateral inhibition in a hindbrain motion integrator, and functionally relevant targeted long-range projections from a tegmental excitatory nucleus, demonstrating that this resource enables rigorous hypothesis testing as well as exploratory-driven circuit analysis. The dataset is integrated into an open-access platform optimized to facilitate community reconstruction and discovery efforts throughout the larval zebrafish brain.

Preprint: https://www.biorxiv.org/content/early/2025/06/15/2025.06.10.658982

}, doi = {10.1101/2025.06.10.658982}, url = {https://www.biorxiv.org/content/early/2025/06/15/2025.06.10.658982}, author = {Petkova, Mariela D. and Januszewski, Micha{\l} and Blakely, Tim and Herrera, Kristian J. and Schuhknecht, Gregor F.P. and Tiller, Robert and Choi, Jinhan and Schalek, Richard L. and Boulanger-Weill, Jonathan and Peleg, Adi and Wu, Yuelong and Wang, Shuohong and Troidl, Jakob and Kumar Vohra, Sumit and Wei, Donglai and Lin, Zudi and Bahl, Armin and Tapia, Juan Carlos and Iyer, Nirmala and Miller, Zachary T. and Hebert, Kathryn B. and Pavarino, Elisa C. and Taylor, Milo and Deng, Zixuan and Stingl, Moritz and Hockling, Dana and Hebling, Alina and Wang, Ruohong C. and Zhang, Lauren L. and Dvorak, Sam and Faik, Zainab and King, Kareem I. and Goel, Pallavi and Wagner-Carena, Julian and Aley, David and Chalyshkan, Selimzhan and Contreas, Dominick and Li, Xiong and Muthukumar, Akila V. and Vernaglia, Marina S. and Carrasco, Teodoro Tapia and Melnychuck, Sofia and Yan, TingTing and Dalal, Ananya and DiMartino, James M. and Brown, Sam and Safo-Mensa, Nana and Greenberg, Ethan and Cook, Michael and Finley-May, Samantha and Flynn, Miriam A. and Hopkins, Gary Patrick and Kovalyak, Julie and Leonard, Meghan and Lohff, Alanna and Ordish, Christopher and Scott, Ashley L. and Takemura, Satoko and Walsh, Claire and Walsh, John J. and Berger, Daniel R. and Pfister, Hanspeter and Berg, Stuart and Knecht, Christopher and Meissner, Geoffrey W. and Korff, Wyatt and Ahrens, Misha B. and Jain, Viren and Lichtman, Jeff W. and Engert, Florian} } @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 {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 {69602, title = {Data Release: High-Resolution Imaging and Segmentation of P7 Mouse Tissue Microarchitecture Using FIB-SEM and Machine Learning}, journal = {bioRxiv}, year = {2024}, month = {2024 Sep 07}, abstract = {

This report presents a comprehensive data release exploring the tissue microarchitecture of P7 aged mice using Focused Ion Beam Scanning Electron Microscopy (FIB-SEM) combined with machine learning-based segmentations of nuclei. The study includes high-resolution 3D volumes and nucleus segmentations for seven vital tissues{\textemdash}pancreas, liver, kidney, heart, thymus, hippocampus, and skin{\textemdash}from a single mouse. The detailed datasets are openly accessible on OpenOrganelle.org, providing a valuable resource for the scientific community to support further research and collaboration.

}, doi = {10.1101/2024.09.05.611438}, url = {https://www.doi.org/10.1101/2024.09.05.611438}, author = {Ackerman, David and Avetissian, Emma and Bleck, Christopher K. E. and Bogovic, John A. and Innerberger, Michael and Korff, Wyatt and Li, Wei-Ping and Lu, Zhiyuan and Petruncio, Alyson and Preibisch, Stephan and Qiu, Wei and Jeff L Rhoades and Saalfeld, Stephan and Silva, Malan and Trautman, Eric T. and Vorimo, Rebecca and Weigel, Aubrey and Yu, Zhiheng and Zubov, Yurii and ,} } @article {69710, title = {Discovery of neuronal cell types by pairing whole cell reconstructions with RNA expression profiles}, journal = {bioRxiv}, year = {2024}, month = {12/2024}, abstract = {

Effective classification of neuronal cell types requires both molecular and morphological descriptors to be collected in situ at single cell resolution. However, current spatial transcriptomics techniques are not compatible with imaging workflows that successfully reconstruct the morphology of complete axonal projections. Here, we introduce a new methodology that combines tissue clearing, submicron whole-brain two photon imaging, and Expansion-Assisted Iterative Fluorescence In Situ Hybridization (EASI-FISH) to assign molecular identities to fully reconstructed neurons in the mouse brain, which we call morphoFISH. We used morphoFISH to molecularly identify a previously unknown population of cingulate neurons projecting ipsilaterally to the dorsal striatum and contralaterally to higher-order thalamus. By pairing whole-brain morphometry, improved techniques for nucleic acid preservation and spatial gene expression, morphoFISH offers a quantitative solution for discovery of multimodal cell types and complements existing techniques for characterization of increasingly fine-grained cellular heterogeneity in brain circuits.Competing Interest StatementThe authors have declared no competing interest.

}, doi = {10.1101/2024.12.30.630829}, url = {https://www.biorxiv.org/content/early/2024/12/31/2024.12.30.630829}, author = {The MouseLight Project Team and Ferreira, Tiago A. and Eddison, Mark and Copeland, Monique and Lay, Mary and Tenshaw, Emily and Weldon, Monet and Schauder, David and Olbris, Donald J. and Rokicki, Konrad and Spruston, Nelson and Tillberg, Paul W. and Korff, Wyatt and Dudman, Joshua T.} } @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 {68690, title = {Sensitivity optimization of a rhodopsin-based fluorescent voltage indicator}, journal = {Neuron}, volume = {111}, year = {2023}, month = {2023 May 17}, pages = {1547-1563}, abstract = {

The ability to optically image cellular transmembrane voltages at millisecond-timescale resolutions can offer unprecedented insight into the function of living brains in behaving animals. Here, we present a\ point mutation\ that increases the sensitivity of Ace2 opsin-based voltage indicators. We use the mutation to develop Voltron2, an improved chemigeneic voltage indicator that has a 65\% higher sensitivity to single APs and 3-fold higher sensitivity to subthreshold potentials than Voltron. Voltron2 retained the sub-millisecond kinetics and photostability of its predecessor, although with lower baseline fluorescence. In multiple\ in\ vitro\ and\ in\ vivo\ comparisons with its predecessor across multiple species, we found Voltron2 to be more sensitive to APs and subthreshold fluctuations. Finally, we used Voltron2 to study and evaluate the possible mechanisms of\ interneuron\ synchronization in the mouse hippocampus. Overall, we have discovered a generalizable mutation that significantly increases the sensitivity of Ace2 rhodopsin-based sensors, improving their voltage reporting capability.

}, issn = {08966273}, doi = {10.1016/j.neuron.2023.03.009}, url = {https://linkinghub.elsevier.com/retrieve/pii/S0896627323002052}, author = {Abdelfattah, Ahmed S. and Zheng, Jihong and Singh, Amrita and Huang, Yi-Chieh and Reep, Daniel and Tsegaye, Getahun and Tsang, Arthur and Arthur, Benjamin J. and Rehorova, Monika and Olson, Carl V.L. and Shuai, Yichun and Zhang, Lixia and Fu, Tian-Ming and Milkie, Daniel E. and Moya, Maria V. and Weber, Timothy D. and Lemire, Andrew L. and Baker, Christopher A. and Falco, Natalie and Zheng, Qinsi and Grimm, Jonathan B. and Yip, Mighten C. and Walpita, Deepika and Chase, Martin and Campagnola, Luke and Murphy, Gabe J. and Wong, Allan M. and Forest, Craig R. and Mertz, Jerome and Economo, Michael N. and Turner, Glenn C. and Koyama, Minoru and Lin, Bei-Jung and Betzig, Eric and Novak, Ondrej and Lavis, Luke D. and Svoboda, Karel and Korff, Wyatt and Chen, Tsai-Wen and Schreiter, Eric R. and Hasseman, Jeremy P. and Kolb, Ilya} } @article {68958, title = {Towards Generalizable Organelle Segmentation in Volume Electron Microscopy.}, journal = {Microscopy and Microanalysis}, volume = {29}, year = {2023}, month = {2023 Jul 22}, pages = {975}, keywords = {Organelles, Volume Electron Microscopy}, issn = {1435-8115}, doi = {10.1093/micmic/ozad067.487}, author = {Heinrich, Larissa and Patton, Will and Bennett, Davis and Ackerman, David and Park, Grace and Bogovic, John A and Eckstein, Nils and Petruncio, Alyson and Clements, Jody and Pang, Song and Shan Xu, C and Funke, Jan and Korff, Wyatt and Hess, Harald and Lippincott-Schwartz, Jennifer and Saalfeld, Stephan and Weigel, Aubrey} } @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 {67815, title = {EASI-FISH for thick tissue defines lateral hypothalamus spatio-molecular organization.}, journal = {Cell}, volume = {184}, year = {2021}, month = {2021 Dec 01}, pages = {6361}, abstract = {

Determining the spatial organization and morphological characteristics of molecularly defined cell types is a major bottleneck for characterizing the architecture underpinning brain function. We developed Expansion-Assisted Iterative Fluorescence In Situ Hybridization (EASI-FISH) to survey gene expression in brain tissue, as well as a turnkey computational pipeline to rapidly process large EASI-FISH image datasets. EASI-FISH was optimized for thick brain sections (300\ μm) to facilitate reconstruction of spatio-molecular domains that generalize across brains. Using the EASI-FISH pipeline, we investigated the spatial distribution of dozens of molecularly defined cell types in the lateral hypothalamic area (LHA), a brain region with poorly defined anatomical organization. Mapping cell types in the LHA revealed nine spatially and molecularly defined subregions. EASI-FISH also facilitates iterative reanalysis of scRNA-seq datasets to determine marker-genes that further dissociated spatial and morphological heterogeneity. The EASI-FISH pipeline democratizes mapping molecularly defined cell types, enabling discoveries about brain organization.

}, issn = {1097-4172}, doi = {10.1016/j.cell.2021.11.024}, author = {Wang, Yuhan and Eddison, Mark and Fleishman, Greg and Weigert, Martin and Xu, Shengjin and Wang, Tim and Rokicki, Konrad and Goina, Cristian and Henry, Fredrick E and Lemire, Andrew L and Schmidt, Uwe and Yang, Hui and Svoboda, Karel and Myers, Eugene W and Saalfeld, Stephan and Korff, Wyatt and Sternson, Scott M and Tillberg, Paul W} } @article {67672, title = {jumps with greater velocity and acceleration than previously reported.}, journal = {MicroPublication Biology}, volume = {2021}, year = {2021}, month = {2021 Aug 02}, abstract = {

Infective juveniles of the insect-parastic nematode canjump greater than 6 times their height, a striking evolved novelty in some species of this genus. Using high-speed videography, we observed the kinematics of spontaneousjumping behavior. Our analysis places a lower bound on the velocity and acceleration of these worms.

}, issn = {2578-9430}, doi = {10.17912/micropub.biology.000435}, author = {Dillman, Adler R and Korff, Wyatt and Dickinson, Michael H and Sternberg, Paul W} } @article {67750, title = {Whole-cell organelle segmentation in volume electron microscopy.}, journal = {Nature}, volume = {599}, year = {2021}, month = {2021 Nov 01}, pages = {141-46}, abstract = {

Cells contain hundreds of organelles and macromolecular assemblies. Obtaining a complete understanding of their intricate organization requires the nanometre-level, three-dimensional reconstruction of whole cells, which is only feasible with robust and scalable automatic methods. Here, to support the development of such methods, we annotated up to 35 different cellular organelle classes-ranging from endoplasmic reticulum to microtubules to ribosomes-in diverse sample volumes from multiple cell types imaged at a near-isotropic resolution of 4 nm per voxel with focused ion beam scanning electron microscopy (FIB-SEM). We trained deep learning architectures to segment these structures in 4 nm and 8 nm per voxel FIB-SEM volumes, validated their performance and showed that automatic reconstructions can be used to directly quantify previously inaccessible metrics including spatial interactions between cellular components. We also show that such reconstructions can be used to automatically register light and electron microscopy images for correlative studies. We have created an open data and open-source web repository, {\textquoteright}OpenOrganelle{\textquoteright}, to share the data, computer code and trained models, which will enable scientists everywhere to query and further improve automatic reconstruction of these datasets.

}, issn = {1476-4687}, doi = {10.1038/s41586-021-03977-3}, author = {Heinrich, Larissa and Bennett, Davis and Ackerman, David and Park, Woohyun and Bogovic, John and Eckstein, Nils and Petruncio, Alyson and Clements, Jody and Pang, Song and Xu, C Shan and Funke, Jan and Korff, Wyatt and Hess, Harald F and Lippincott-Schwartz, Jennifer and Saalfeld, Stephan and Weigel, Aubrey V} } @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 {65138, title = {Reconstruction of 1,000 projection neurons reveals new cell types and organization of long-range connectivity in the mouse brain.}, journal = {Cell}, volume = {179}, year = {2019}, month = {2019 Sep 19}, pages = {268-81}, abstract = {

Neuronal\ cell\ types\ are the nodes of neural circuits that determine the flow of information within the\ brain. Neuronal morphology, especially the shape of the axonal arbor, provides an essential descriptor of\ cell\ type and\ reveals\ how individual\ neurons\ route their output across the\ brain. Despite the importance of morphology, few\ projection\ neurons\ in the\ mouse\ brain\ have been reconstructed in their entirety. Here we present a robust and efficient platform for imaging and reconstructing complete neuronal morphologies, including axonal arbors that span substantial portions of the\ brain. We used this platform to reconstruct more than\ 1,000\ projection\ neurons\ in the motor cortex, thalamus, subiculum, and hypothalamus. Together, the reconstructed\ neurons\ constitute more than 85\ meters of axonal length and are available in a searchable online database. Axonal shapes revealed previously unknown subtypes of\ projection\ neurons\ and suggest organizational principles of\ long-range\ connectivity.

}, issn = {00928674}, doi = {10.1016/j.cell.2019.07.042}, author = {Winnubst, Johan and Bas, Erhan and Ferreira, Tiago A. and Wu, Zhuhao and Economo, Michael N. and Edson, Patrick and Arthur, Ben J. and Bruns, Christopher and Rokicki, Konrad and Schauder, David and Olbris, Donald J. and Murphy, Sean D. and Ackerman, David G. and Arshadi, Cameron and Baldwin, Perry and Blake, Regina and Elsayed, Ahmad and Hasan, Mashtura and Ramirez, Daniel and Dos Santos, Bruno and Weldon, Monet and Zafar, Amina and Dudman, Joshua T. and Gerfen, Charles R. and Hantman, Adam W. and Korff, Wyatt and Sternson, Scott M. and Spruston, Nelson and Svoboda, Karel and Chandrashekar, Jayaram} } @article {65159, title = {A repeated molecular architecture across thalamic pathways.}, journal = {Nature Neuroscience}, volume = {22}, year = {2019}, month = {2019 Sep 16}, pages = {1925-35}, abstract = {

The thalamus is the central communication hub of the forebrain and provides the cerebral cortex with inputs from sensory organs, subcortical systems and the cortex itself. Multiple thalamic regions send convergent information to each cortical region, but the organizational logic of thalamic projections has remained elusive. Through comprehensive transcriptional analyses of retrogradely labeled thalamic neurons in adult mice, we identify three major profiles of thalamic pathways. These profiles exist along a continuum that is repeated across all major projection systems, such as those for vision, motor control and cognition. The largest component of gene expression variation in the mouse thalamus is topographically organized, with features conserved in humans. Transcriptional differences between these thalamic neuronal identities are tied to cellular features that are critical for function, such as axonal morphology and membrane properties. Molecular profiling therefore reveals covariation in the properties of thalamic pathways serving all major input modalities and output targets, thus establishing a molecular framework for understanding the thalamus.

}, issn = {1546-1726}, doi = {10.1038/s41593-019-0483-3}, author = {Phillips, James W and Schulmann, Anton and Hara, Erina and Winnubst, Johan and Liu, Chenghao and Valakh, Vera and Wang, Lihua and Shields, Brenda C and Korff, Wyatt and Chandrashekar, Jayaram and Lemire, Andrew L and Mensh, Brett and Dudman, Joshua T and Nelson, Sacha B and Hantman, Adam W} } @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 {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 {48646, title = {Mapping the neural substrates of behavior.}, journal = {Cell}, volume = {170}, year = {2017}, month = {2017-07-13}, pages = {393-406}, abstract = {

Assigning behavioral functions to neural structures has long been a central goal in neuroscience and is a necessary first step toward a circuit-level understanding of how the brain generates behavior. Here, we map the neural substrates of locomotion and social behaviors for Drosophila melanogaster using automated machine-vision and machine-learning techniques. From videos of 400,000 flies, we quantified the behavioral effects of activating 2,204 genetically targeted populations of neurons. We combined a novel quantification of anatomy with our behavioral analysis to create brain-behavior correlation maps, which are shared as browsable web pages and interactive software. Based on these maps, we generated hypotheses of regions of the brain causally related to sensory processing, locomotor control, courtship, aggression, and sleep. Our maps directly specify genetic tools to target these regions, which we used to identify a small population of neurons with a role in the control of walking.

{\textbullet}We developed machine-vision methods to broadly and precisely quantify fly behavior{\textbullet}We measured effects of activating 2,204 genetically targeted neuronal populations{\textbullet}We created whole-brain maps of neural substrates of locomotor and social behaviors{\textbullet}We created resources for exploring our results and enabling further investigation

Machine-vision analyses of large behavior and neuroanatomy data reveal whole-brain maps of regions associated with numerous complex behaviors.

}, doi = {10.1016/j.cell.2017.06.032}, url = {http://dx.doi.org/10.1016/j.cell.2017.06.032}, author = {Robie, Alice A and Hirokawa, Jonathan and Edwards, Austin W and Umayam, Lowell A and Lee, Allen and Phillips, Mary L and Card, Gwyneth M and Korff, Wyatt and Rubin, Gerald M and Simpson, Julie H and Reiser, Michael B and Branson, Kristin 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 {48985, title = {A topographic axis of transcriptional identity in thalamus.}, journal = {bioRxiv}, year = {2017}, month = {2017 Dec 31}, pages = {241315}, abstract = {

A fundamental goal in neuroscience is to uncover common principles by which different modalities of information are processed. In the mammalian brain, thalamus acts as the essential hub for forebrain circuits handling inputs from sensory, motor, limbic, and cognitive pathways. Whether thalamus imposes common transformations on each of these modalities is unknown. Molecular characterization offers a principled approach to revealing the organization of thalamus. Using near-comprehensive and projection-specific transcriptomic sequencing, we found that almost all thalamic nuclei fit into one of three profiles. These profiles lie on a single axis of genetic variance which is aligned with the mediolateral spatial axis of thalamus. Genes defining this axis of variance include receptors and ion channels, providing a systematic diversification of input/output transformations across the topography of thalamus. Single cell transcriptional profiling revealed graded heterogeneity within individual thalamic nuclei, demonstrating that a spectrum of cell types and potentially diverse input/output transforms exist within a given thalamic nucleus. Together, our data argue for an archetypal organization of pathways serving diverse input modalities, and provides a comprehensive organizational scheme for thalamus.

}, doi = {10.1101/241315}, author = {Phillips, James W and Schulman, Anton and Hara, Erina and Liu, Chenghao and Shields, Brenda C and Korff, Wyatt and Lemire, Andrew and Dudman, Joshua T and Nelson, Sacha B and Hantman, Adam W} } @article {47538, title = {A screen for constituents of motor control and decision making in Drosophila reveals visual distance-estimation neurons.}, journal = {Scientific Reports}, volume = {6}, year = {2016}, month = {2016}, pages = {27000}, abstract = {

Climbing over chasms larger than step size is vital to fruit flies, since foraging and mating are achieved while walking. Flies avoid futile climbing attempts by processing parallax-motion vision to estimate gap width. To identify neuronal substrates of climbing control, we screened a large collection of fly lines with temporarily inactivated neuronal populations in a novel high-throughput assay described here. The observed climbing phenotypes were classified; lines in each group are reported. Selected lines were further analysed by high-resolution video cinematography. One striking class of flies attempts to climb chasms of unsurmountable width; expression analysis guided us to C2 optic-lobe interneurons. Inactivation of C2 or the closely related C3 neurons with highly specific intersectional driver lines consistently reproduced hyperactive climbing whereas strong or weak artificial depolarization of C2/C3 neurons strongly or mildly decreased climbing frequency. Contrast-manipulation experiments support our conclusion that C2/C3 neurons are part of the distance-evaluation system.

}, issn = {2045-2322}, doi = {10.1038/srep27000}, author = {Triphan, Tilman and Nern, Aljoscha and Roberts, Sonia F and Korff, Wyatt and Naiman, Daniel Q and Strauss, Roland} } @article {45811, title = {Mushroom body output neurons encode valence and guide memory-based action selection in Drosophila.}, journal = {eLife}, volume = {4}, year = {2014}, month = {12/2014}, abstract = {

Animals discriminate stimuli, learn their predictive value and use this knowledge to modify their behavior. In Drosophila, the mushroom body (MB) plays a key role in these processes. Sensory stimuli are sparsely represented by \~{}2000 Kenyon cells, which converge onto 34 output neurons (MBONs) of 21 types. We studied the role of MBONs in several associative learning tasks and in sleep regulation, revealing the extent to which information flow is segregated into distinct channels and suggesting possible roles for the multi-layered MBON network. We also show that optogenetic activation of MBONs can, depending on cell type, induce repulsion or attraction in flies. The behavioral effects of MBON perturbation are combinatorial, suggesting that the MBON ensemble collectively represents valence. We propose that local, stimulus-specific dopaminergic modulation selectively alters the balance within the MBON network for those stimuli. Our results suggest that valence encoded by the MBON ensemble biases memory-based action selection.

}, issn = {2050-084X}, doi = {10.7554/eLife.04580}, author = {Aso, Yoshinori and Sitaraman, Divya and Ichinose, Toshiharu and Kaun, Karla R and Vogt, Katrin and Belliart-Gu{\'e}rin, Ghislain and Placais, Pierre-Yves and Robie, Alice A and Yamagata, Nobuhiro and Schnaitmann, Christopher and Rowell, William J and Johnston, Rebecca M and Ngo, Teri-T B and Chen, Nan and Korff, Wyatt and Nitabach, Michael N and Heberlein, Ulrike and Preat, Thomas and Branson, Kristin M and Tanimoto, Hiromu and Rubin, Gerald M} }