@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 {68709, title = {BigNeuron: a resource to benchmark and predict performance of algorithms for automated tracing of neurons in light microscopy datasets.}, journal = {Nature Methods}, volume = {20}, year = {2023}, month = {2023 Jun 01}, abstract = {

BigNeuron is an open community bench-testing platform with the goal of setting open standards for accurate and fast automatic neuron tracing. We gathered a diverse set of image volumes across several species that is representative of the data obtained in many neuroscience laboratories interested in neuron tracing. Here, we report generated gold standard manual annotations for a subset of the available imaging datasets and quantified tracing quality for 35 automatic tracing algorithms. The goal of generating such a hand-curated diverse dataset is to advance the development of tracing algorithms and enable generalizable benchmarking. Together with image quality features, we pooled the data in an interactive web application that enables users and developers to perform principal component analysis, t-distributed stochastic neighbor embedding, correlation and clustering, visualization of imaging and tracing data, and benchmarking of automatic tracing algorithms in user-defined data subsets. The image quality metrics explain most of the variance in the data, followed by neuromorphological features related to neuron size. We observed that diverse algorithms can provide complementary information to obtain accurate results and developed a method to iteratively combine methods and generate consensus reconstructions. The consensus trees obtained provide estimates of the neuron structure ground truth that typically outperform single algorithms in noisy datasets. However, specific algorithms may outperform the consensus tree strategy in specific imaging conditions. Finally, to aid users in predicting the most accurate automatic tracing results without manual annotations for comparison, we used support vector machine regression to predict reconstruction quality given an image volume and a set of automatic tracings.

}, issn = {1548-7105}, doi = {10.1038/s41592-023-01848-5}, author = {Manubens-Gil, Linus and Zhou, Zhi and Chen, Hanbo and Ramanathan, Arvind and Liu, Xiaoxiao and Liu, Yufeng and Bria, Alessandro and Gillette, Todd and Ruan, Zongcai and Yang, Jian and Radojevi{\'c}, Miroslav and Zhao, Ting and Cheng, Li and Qu, Lei and Liu, Siqi and Bouchard, Kristofer E and Gu, Lin and Cai, Weidong and Ji, Shuiwang and Roysam, Badrinath and Wang, Ching-Wei and Yu, Hongchuan and Sironi, Amos and Iascone, Daniel Maxim and Zhou, Jie and Bas, Erhan and Conde-Sousa, Eduardo and Aguiar, Paulo and Li, Xiang and Li, Yujie and Nanda, Sumit and Wang, Yuan and Muresan, Leila and Fua, Pascal and Ye, Bing and He, Hai-Yan and Staiger, Jochen F and Peter, Manuel and Cox, Daniel N and Simonneau, Michel and Oberlaender, Marcel and Jefferis, Gregory and Ito, Kei and Gonzalez-Bellido, Paloma and Kim, Jinhyun and Rubel, Edwin and Cline, Hollis T and Zeng, Hongkui and Nern, Aljoscha and Chiang, Ann-Shyn and Yao, Jianhua and Roskams, Jane and Livesey, Rick and Stevens, Janine and Liu, Tianming and Dang, Chinh and Guo, Yike and Zhong, Ning and Tourassi, Georgia and Hill, Sean and Hawrylycz, Michael and Koch, Christof and Meijering, Erik and Ascoli, Giorgio A and Peng, Hanchuan} } @article {68507, title = {A connectome and analysis of the adult Drosophila central brain.}, journal = {Elife}, volume = {9}, year = {2020}, month = {2020 Sep 07}, abstract = {

The neural circuits responsible for animal behavior remain largely unknown. We summarize new methods and present the circuitry of a large fraction of the brain of the fruit fly . Improved methods include new procedures to prepare, image, align, segment, find synapses in, and proofread such large data sets. We define cell types, refine computational compartments, and provide an exhaustive atlas of cell examples and types, many of them novel. We provide detailed circuits consisting of neurons and their chemical synapses for most of the central brain. We make the data public and simplify access, reducing the effort needed to answer circuit questions, and provide procedures linking the neurons defined by our analysis with genetic reagents. Biologically, we examine distributions of connection strengths, neural motifs on different scales, electrical consequences of compartmentalization, and evidence that maximizing packing density is an important criterion in the evolution of the fly{\textquoteright}s brain.

}, keywords = {Animals, Brain, Connectome, Drosophila melanogaster, Female, Male, Neurons, Synapses}, issn = {2050-084X}, doi = {10.7554/eLife.57443}, author = {Scheffer, Louis K and Xu, C Shan and Januszewski, Michal and Lu, Zhiyuan and Takemura, Shin-ya and Hayworth, Kenneth J and Huang, Gary B and Shinomiya, Kazunori and Maitlin-Shepard, Jeremy and Berg, Stuart and Clements, Jody and Hubbard, Philip M and Katz, William T and Umayam, Lowell and Zhao, Ting and Ackerman, David and Blakely, Tim and Bogovic, John and Dolafi, Tom and Kainmueller, Dagmar and Kawase, Takashi and Khairy, Khaled A and Leavitt, Laramie and Li, Peter H and Lindsey, Larry and Neubarth, Nicole and Olbris, Donald J and Otsuna, Hideo and Trautman, Eric T and Ito, Masayoshi and Bates, Alexander S and Goldammer, Jens and Wolff, Tanya and Svirskas, Robert and Schlegel, Philipp and Neace, Erika and Knecht, Christopher J and Alvarado, Chelsea X and Bailey, Dennis A and Ballinger, Samantha and Borycz, Jolanta A and Canino, Brandon S and Cheatham, Natasha and Cook, Michael and Dreher, Marisa and Duclos, Octave and Eubanks, Bryon and Fairbanks, Kelli and Finley, Samantha and Forknall, Nora and Francis, Audrey and Hopkins, Gary Patrick and Joyce, Emily M and Kim, SungJin and Kirk, Nicole A and Kovalyak, Julie and Lauchie, Shirley A and Lohff, Alanna and Maldonado, Charli and Manley, Emily A and McLin, Sari and Mooney, Caroline and Ndama, Miatta and Ogundeyi, Omotara and Okeoma, Nneoma and Ordish, Christopher and Padilla, Nicholas and Patrick, Christopher M and Paterson, Tyler and Phillips, Elliott E and Phillips, Emily M and Rampally, Neha and Ribeiro, Caitlin and Robertson, Madelaine K and Rymer, Jon Thomson and Ryan, Sean M and Sammons, Megan and Scott, Anne K and Scott, Ashley L and Shinomiya, Aya and Smith, Claire and Smith, Kelsey and Smith, Natalie L and Sobeski, Margaret A and Suleiman, Alia and Swift, Jackie and Takemura, Satoko and Talebi, Iris and Tarnogorska, Dorota and Tenshaw, Emily and Tokhi, Temour and Walsh, John J and Yang, Tansy and Horne, Jane Anne and Li, Feng and Parekh, Ruchi and Rivlin, Patricia K and Jayaraman, Vivek and Costa, Marta and Jefferis, Gregory Sxe and Ito, Kei and Saalfeld, Stephan and George, Reed and Meinertzhagen, Ian A and Rubin, Gerald M and Hess, Harald F and Jain, Viren and Plaza, Stephen M} } @article {65382, title = {Conservation and divergence of related neuronal lineages in the central brain.}, journal = {eLife}, volume = {9}, year = {2020}, month = {2020 Apr 07}, abstract = {

Wiring a complex brain requires many neurons with intricate cell specificity, generated by a limited number of neural stem cells. central brain lineages are a predetermined series of neurons, born in a specific order. To understand how lineage identity translates to neuron morphology, we mapped 18 central brain lineages. While we found large aggregate differences between lineages, we also discovered shared patterns of morphological diversification. Lineage identity plus Notch-mediated sister fate govern primary neuron trajectories, whereas temporal fate diversifies terminal elaborations. Further, morphological neuron types may arise repeatedly, interspersed with other types. Despite the complexity, related lineages produce similar neuron types in comparable temporal patterns. Different stem cells even yield two identical series of dopaminergic neuron types, but with unrelated sister neurons. Together, these phenomena suggest that straightforward rules drive incredible neuronal complexity, and that large changes in morphology can result from relatively simple fating mechanisms.

}, issn = {2050-084X}, doi = {10.7554/eLife.53518}, author = {Lee, Ying-Jou and Yang, Ching-Po and Miyares, Rosa L and Huang, Yu-Fen and He, Yisheng and Ren, Qingzhong and Chen, Hui-Min and Kawase, Takashi and Ito, Masayoshi and Otsuna, Hideo and Sugino, Ken and Aso, Yoshi and Ito, Kei and Lee, Tzumin} } @article {65314, title = {Input connectivity reveals additional heterogeneity of dopaminergic reinforcement in Drosophila}, journal = {Current Biology}, volume = {30}, year = {2020}, month = {2020 Aug 17}, pages = {3200-11}, abstract = {

Different types of\ Drosophila\ dopaminergic neurons (DANs) reinforce memories of unique valence and provide state-dependent motivational control [1]. Prior studies suggest that the compartment architecture of the mushroom body (MB) is the relevant resolution for distinct DAN functions [2,\ 3]. Here we used a recent electron microscope volume of the fly brain [4] to reconstruct the fine anatomy of individual DANs within three MB compartments. We find the 20 DANs of the γ5 compartment, at least some of which provide reward teaching signals, can be clustered into 5 anatomical subtypes that innervate different regions within γ5. Reconstructing 821 upstream neurons reveals input selectivity, supporting the functional relevance of DAN sub-classification. Only one PAM-γ5 DAN subtype (γ5fb) receives direct recurrent input from γ5β{\textquoteright}2a mushroom body output neurons (MBONs) and behavioral experiments distinguish a role for these DANs in memory revaluation from those reinforcing sugar memory. Other DAN subtypes receive major, and potentially reinforcing, inputs from putative gustatory interneurons or lateral horn neurons, which can also relay indirect feedback from the MB. We similarly reconstructed the single aversively reinforcing PPL1-γ1pedc DAN. The γ1pedc DAN inputs are mostly different to those of γ5 DANs and are clustered onto distinct branches of its dendritic tree, presumably separating its established roles in aversive reinforcement and appetitive motivation [5,\ 6]. Additional tracing identified neurons that provide broad input to γ5, β{\textquoteright}2a and γ1pedc DANs suggesting that distributed DAN populations can be coordinately regulated. These connectomic and behavioral analyses therefore reveal additional complexity of dopaminergic reinforcement circuits between and within MB compartments.

}, author = {Otto, Nils and Pleijzier, Markus W. and Morgan, Isabel C. and Edmondson-Stait, Amelia J and Heinz, Konrad J. and Stark, Ildiko and Dempsey, Georgia and Ito, Masayoshi and Kapoor, Ishaan and Hsu, Joseph and Schlegel, Philipp M. and Bates, Alexander S. and Costa, Marta and Ito, Kei and Bock, Davi D. and Rubin, Gerald M. and Jefferis, Gregory S.X.E. and Waddell, Scott} } @article {65910, title = {An unbiased template of the Drosophila brain and ventral nerve cord.}, journal = {PLoS One}, volume = {15}, year = {2020}, month = {2020 Dec 31}, pages = {e0236495}, abstract = {

The fruit fly Drosophila melanogaster is an important model organism for neuroscience with a wide array of genetic tools that enable the mapping of individual neurons and neural subtypes. Brain templates are essential for comparative biological studies because they enable analyzing many individuals in a common reference space. Several central brain templates exist for Drosophila, but every one is either biased, uses sub-optimal tissue preparation, is imaged at low resolution, or does not account for artifacts. No publicly available Drosophila ventral nerve cord template currently exists. In this work, we created high-resolution templates of the Drosophila brain and ventral nerve cord using the best-available technologies for imaging, artifact correction, stitching, and template construction using groupwise registration. We evaluated our central brain template against the four most competitive, publicly available brain templates and demonstrate that ours enables more accurate registration with fewer local deformations in shorter time.

}, issn = {1932-6203}, doi = {10.1371/journal.pone.0236495}, author = {Bogovic, John A and Otsuna, Hideo and Heinrich, Larissa and Ito, Masayoshi and Jeter, Jennifer and Meissner, Geoffrey and Nern, Aljoscha and Colonell, Jennifer and Malkesman, Oz and Ito, Kei and Saalfeld, Stephan} } @article {48921, title = {Topological and modality-specific representation of somatosensory information in the fly brain.}, journal = {Science (New York, N.Y.)}, volume = {358}, year = {2017}, month = {2017 11 03}, pages = {615-623}, abstract = {

Insects and mammals share similarities of neural organization underlying the perception of odors, taste, vision, sound, and gravity. We observed that insect somatosensation also corresponds to that of mammals. In Drosophila, the projections of all the somatosensory neuron types to the insect{\textquoteright}s equivalent of the spinal cord segregated into modality-specific layers comparable to those in mammals. Some sensory neurons innervate the ventral brain directly to form modality-specific and topological somatosensory maps. Ascending interneurons with dendrites in matching layers of the nerve cord send axons that converge to respective brain regions. Pathways arising from leg somatosensory neurons encode distinct qualities of leg movement information and play different roles in ground detection. Establishment of the ground pattern and genetic tools for neuronal manipulation should provide the basis for elucidating the mechanisms underlying somatosensation.

}, issn = {1095-9203}, doi = {10.1126/science.aan4428}, author = {Tsubouchi, Asako and Yano, Tomoko and Yokoyama, Takeshi K and Murtin, Chlo{\'e} and Otsuna, Hideo and Ito, Kei} } @article {6884, title = {A systematic nomenclature for the insect brain.}, journal = {Neuron}, volume = {81}, year = {2014}, month = {2014 Feb 19}, pages = {755-65}, abstract = {

Despite the importance of the insect nervous system for functional and developmental neuroscience, descriptions of insect brains have suffered from a lack of uniform nomenclature. Ambiguous definitions of brain regions and fiber bundles have contributed to the variation of names used to describe the same structure. The lack of clearly determined neuropil boundaries has made it difficult to document precise locations of neuronal projections for connectomics study. To address such issues, a consortium of\ neurobiologists studying arthropod brains, the Insect Brain Name Working Group, has established the present hierarchical nomenclature system, using the brain of Drosophila melanogaster as the reference framework, while taking the brains of other taxa into careful consideration for maximum consistency and expandability. The following summarizes the consortium{\textquoteright}s nomenclature system and highlights examples of existing ambiguities and remedies for them. This nomenclature is intended to serve as a\ standard of reference for the study of the brain of Drosophila and other insects.

}, issn = {1097-4199}, doi = {10.1016/j.neuron.2013.12.017}, author = {Ito, Kei and Shinomiya, Kazunori and Ito, Masayoshi and Armstrong, J Douglas and Boyan, George and Hartenstein, Volker and Harzsch, Steffen and Heisenberg, Martin and Homberg, Uwe and Jenett, Arnim and Keshishian, Haig and Restifo, Linda L and R{\"o}ssler, Wolfgang and Simpson, Julie H and Strausfeld, Nicholas J and Strauss, Roland and Vosshall, Leslie B} } @article {5291, title = {Mushroom body efferent neurons responsible for aversive olfactory memory retrieval in Drosophila.}, journal = {Nature Neuroscience}, volume = {14}, year = {2011}, month = {2011 Jun 19}, pages = {903-10}, abstract = {

Aversive olfactory memory is formed in the mushroom bodies in Drosophila melanogaster. Memory retrieval requires mushroom body output, but the manner in which a memory trace in the mushroom body drives conditioned avoidance of a learned odor remains unknown. To identify neurons that are involved in olfactory memory retrieval, we performed an anatomical and functional screen of defined sets of mushroom body output neurons. We found that MB-V2 neurons were essential for retrieval of both short- and long-lasting memory, but not for memory formation or memory consolidation. MB-V2 neurons are cholinergic efferent neurons that project from the mushroom body vertical lobes to the middle superiormedial protocerebrum and the lateral horn. Notably, the odor response of MB-V2 neurons was modified after conditioning. As the lateral horn has been implicated in innate responses to repellent odorants, we propose that MB-V2 neurons recruit the olfactory pathway involved in innate odor avoidance during memory retrieval.

}, keywords = {Afferent Pathways, Analysis of Variance, Animal, Animals, Antigens, Avoidance Learning, Behavior, Biological, CD8, Choline O-Acetyltransferase, Conditioning (Psychology), Drosophila melanogaster, Drosophila Proteins, Gene Expression Regulation, Genetically Modified, Glutamate Decarboxylase, Green Fluorescent Proteins, Memory, Mental Recall, Models, Motor Neurons, Mushroom Bodies, Odors, Olfactory Pathways, Short-Term, Smell, Synapsins, Temperature, Time Factors, Transcription Factors, Tyrosine 3-Monooxygenase, Vesicular Glutamate Transport Proteins}, issn = {1546-1726}, doi = {10.1038/nn.2846}, author = {S{\'e}journ{\'e}, Julien and Placais, Pierre-Yves and Aso, Yoshinori and Siwanowicz, Igor and Trannoy, S{\'e}verine and Thoma, Vladimiros and Tedjakumala, Stevanus R and Rubin, Gerald M and Tch{\'e}nio, Paul and Ito, Kei and Isabel, Guillaume and Tanimoto, Hiromu and Preat, Thomas} } @article {794, title = {Clonal analysis of Drosophila antennal lobe neurons: diverse neuronal architectures in the lateral neuroblast lineage.}, journal = {Development}, volume = {135}, year = {2008}, month = {2008 Sep}, pages = {2883-93}, abstract = {

The antennal lobe (AL) is the primary structure in the Drosophila brain that relays odor information from the antennae to higher brain centers. The characterization of uniglomerular projection neurons (PNs) and some local interneurons has facilitated our understanding of olfaction; however, many other AL neurons remain unidentified. Because neuron types are mostly specified by lineage and temporal origins, we use the MARCM techniques with a set of enhancer-trap GAL4 lines to perform systematical lineage analysis to characterize neuron morphologies, lineage origin and birth timing in the three AL neuron lineages that contain GAL4-GH146-positive PNs: anterodorsal, lateral and ventral lineages. The results show that the anterodorsal lineage is composed of pure uniglomerular PNs that project through the inner antennocerebral tract. The ventral lineage produces uniglomerular and multiglomerular PNs that project through the middle antennocerebral tract. The lateral lineage generates multiple types of neurons, including uniglomeurlar PNs, diverse atypical PNs, various types of AL local interneurons and the neurons that make no connection within the ALs. Specific neuron types in all three lineages are produced in specific time windows, although multiple neuron types in the lateral lineage are made simultaneously. These systematic cell lineage analyses have not only filled gaps in the olfactory map, but have also exemplified additional strategies used in the brain to increase neuronal diversity.

}, keywords = {Animal Structures, Animals, Axons, Cell Lineage, Clone Cells, Drosophila melanogaster, Enhancer Elements, Genetic, Mosaicism, Neurons}, issn = {0950-1991}, doi = {10.1242/dev.024380}, author = {Lai, Sen-Lin and Awasaki, Takeshi and Ito, Kei and Lee, Tzumin} } @article {795, title = {Organization and postembryonic development of glial cells in the adult central brain of Drosophila.}, journal = {The Journal of Neuroscience: The Official Journal of the Society for Neuroscience}, volume = {28}, year = {2008}, month = {2008 Dec 17}, pages = {13742-53}, abstract = {

Glial cells exist throughout the nervous system, and play essential roles in various aspects of neural development and function. Distinct types of glia may govern diverse glial functions. To determine the roles of glia requires systematic characterization of glia diversity and development. In the adult Drosophila central brain, we identify five different types of glia based on its location, morphology, marker expression, and development. Perineurial and subperineurial glia reside in two separate single-cell layers on the brain surface, cortex glia form a glial mesh in the brain cortex where neuronal cell bodies reside, while ensheathing and astrocyte-like glia enwrap and infiltrate into neuropils, respectively. Clonal analysis reveals that distinct glial types derive from different precursors, and that most adult perineurial, ensheathing, and astrocyte-like glia are produced after embryogenesis. Notably, perineurial glial cells are made locally on the brain surface without the involvement of gcm (glial cell missing). In contrast, the widespread ensheathing and astrocyte-like glia derive from specific brain regions in a gcm-dependent manner. This study documents glia diversity in the adult fly brain and demonstrates involvement of different developmental programs in the derivation of distinct types of glia. It lays an essential foundation for studying glia development and function in the Drosophila brain.

}, keywords = {Animals, Antigens, Brain, Cell Count, Cell Differentiation, Cell Lineage, Clone Cells, Differentiation, DNA-Binding Proteins, Drosophila, Drosophila Proteins, Embryo, Genetically Modified, Homeodomain Proteins, Larva, Neuroglia, Neurons, Neuropil, Nonmammalian, Transcription Factors}, issn = {1529-2401}, doi = {10.1523/JNEUROSCI.4844-08.2008}, author = {Awasaki, Takeshi and Lai, Sen-Lin and Ito, Kei and Lee, Tzumin} } @article {4979, title = {Multiple memory traces for olfactory reward learning in Drosophila.}, journal = {The Journal of Neuroscience: The Official Journal of the Society for Neuroscience}, volume = {27}, year = {2007}, month = {2007 Oct 10}, pages = {11132-8}, abstract = {

Physical traces underlying simple memories can be confined to a single group of cells in the brain. In the fly Drosophila melanogaster, the Kenyon cells of the mushroom bodies house traces for both appetitive and aversive odor memories. The adenylate cyclase protein, Rutabaga, has been shown to mediate both traces. Here, we show that, for appetitive learning, another group of cells can additionally accommodate a Rutabaga-dependent memory trace. Localized expression of rutabaga in either projection neurons, the first-order olfactory interneurons, or in Kenyon cells, the second-order interneurons, is sufficient for rescuing the mutant defect in appetitive short-term memory. Thus, appetitive learning may induce multiple memory traces in the first- and second-order olfactory interneurons using the same plasticity mechanism. In contrast, aversive odor memory of rutabaga is rescued selectively in the Kenyon cells, but not in the projection neurons. This difference in the organization of memory traces is consistent with the internal representation of reward and punishment.

}, keywords = {Animals, Drosophila melanogaster, Female, Learning, Male, Nerve Net, Olfactory Pathways, Reward, Smell}, issn = {1529-2401}, doi = {10.1523/JNEUROSCI.2712-07.2007}, author = {Thum, Andreas S and Jenett, Arnim and Ito, Kei and Heisenberg, Martin and Tanimoto, Hiromu} }