@article {67771, title = {A connectome of the central complex reveals network motifs suitable for flexible navigation and context-dependent action selection.}, journal = {eLife}, volume = {10}, year = {2021}, month = {2021 Oct 26}, abstract = {

Flexible behaviors over long timescales are thought to engage recurrent neural networks in deep brain regions, which are experimentally challenging to study. In insects, recurrent circuit dynamics in a brain region called the central complex (CX) enable directed locomotion, sleep, and context- and experience-dependent spatial navigation. We describe the first complete electron-microscopy-based connectome of the CX, including all its neurons and circuits at synaptic resolution. We identified new CX neuron types, novel sensory and motor pathways, and network motifs that likely enable the CX to extract the fly{\textquoteright}s head-direction, maintain it with attractor dynamics, and combine it with other sensorimotor information to perform vector-based navigational computations. We also identified numerous pathways that may facilitate the selection of CX-driven behavioral patterns by context and internal state. The CX connectome provides a comprehensive blueprint necessary for a detailed understanding of network dynamics underlying sleep, flexible navigation, and state-dependent action selection.

}, issn = {2050-084X}, doi = {10.7554/eLife.66039}, author = {Hulse, Brad K and Haberkern, Hannah and Franconville, Romain and Turner-Evans, Daniel B and Takemura, Shin-ya and Wolff, Tanya and Noorman, Marcella and Dreher, Marisa and Dan, Chuntao and Parekh, Ruchi and Hermundstad, Ann M and Rubin, Gerald M and Jayaraman, Vivek} } @article {67753, title = {An open-access volume electron microscopy atlas of whole cells and tissues.}, journal = {Nature}, volume = {599}, year = {2021}, month = {2021 Nov 1}, pages = {147-51}, abstract = {

Understanding cellular architecture is essential for understanding biology. Electron microscopy (EM) uniquely visualizes cellular structures with nanometre resolution. However, traditional methods, such as thin-section EM or EM tomography, have limitations in that they visualize only a single slice or a relatively small volume of the cell, respectively. Focused ion beam-scanning electron microscopy (FIB-SEM) has demonstrated the ability to image small volumes of cellular samples with 4-nm isotropic voxels. Owing to advances in the\ precision and stability of FIB milling, together with enhanced signal detection and faster SEM scanning, we have increased the volume that can be imaged with 4-nm\ voxels by two orders of magnitude. Here we present a volume EM atlas at such resolution comprising ten three-dimensional datasets for whole cells and tissues, including cancer cells, immune cells, mouse pancreatic islets and Drosophila neural tissues. These open access data (via OpenOrganelle) represent the foundation of a field of high-resolution whole-cell volume EM and subsequent analyses, and we invite researchers to explore this atlas and\ pose questions.

}, issn = {1476-4687}, doi = {10.1038/s41586-021-03992-4}, author = {Xu, C Shan and Pang, Song and Shtengel, Gleb and M{\"u}ller, Andreas and Ritter, Alex T and Hoffman, Huxley K and Takemura, Shin-ya and Lu, Zhiyuan and Pasolli, H Amalia and Iyer, Nirmala and Chung, Jeeyun and Bennett, Davis and Weigel, Aubrey V and Freeman, Melanie and Van Engelenburg, Schuyler B and Walther, Tobias C and Farese, Robert V and Lippincott-Schwartz, Jennifer and Mellman, Ira and Solimena, Michele and Hess, Harald F} } @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 {65899, title = {The connectome of the adult mushroom body provides insights into function.}, journal = {eLife}, volume = {9}, year = {2020}, month = {2020 Dec 14}, abstract = {

Making inferences about the computations performed by neuronal circuits from synapse-level connectivity maps is an emerging opportunity in neuroscience. The mushroom body (MB) is well positioned for developing and testing such an approach due to its conserved neuronal architecture, recently completed dense connectome, and extensive prior experimental studies of its roles in learning, memory and activity regulation. Here we identify new components of the MB circuit in , including extensive visual input and MB output neurons (MBONs) with direct connections to descending neurons. We find unexpected structure in sensory inputs, in the transfer of information about different sensory modalities to MBONs, and in the modulation of that transfer by dopaminergic neurons (DANs). We provide insights into the circuitry used to integrate MB outputs, connectivity between the MB and the central complex and inputs to DANs, including feedback from MBONs. Our results provide a foundation for further theoretical and experimental work.

}, issn = {2050-084X}, doi = {10.7554/eLife.62576}, author = {Li, Feng and Lindsey, Jack W and Marin, Elizabeth C and Otto, Nils and Dreher, Marisa and Dempsey, Georgia and Stark, Ildiko and Bates, Alexander S and Pleijzier, Markus William and Schlegel, Philipp and Nern, Aljoscha and Takemura, Shin-ya and Eckstein, Nils and Yang, Tansy and Francis, Audrey and Braun, Amalia and Parekh, Ruchi and Costa, Marta and Scheffer, Louis K and Aso, Yoshi and Jefferis, Gregory Sxe and Abbott, Larry F and Litwin-Kumar, Ashok and Waddell, Scott and Rubin, Gerald M} } @article {65212, title = {Interactions between Dpr11 and DIP-γ control selection of amacrine neurons in color vision circuits.}, journal = {eLife}, volume = {8}, year = {2019}, month = {2019 Nov 06}, abstract = {

R7 UV photoreceptors (PRs) are divided into yellow (y) and pale (p) subtypes. yR7 PRs express the Dpr11 cell surface protein and are presynaptic to Dm8 amacrine neurons (yDm8) that express Dpr11{\textquoteright}s binding partner DIP-g, while pR7 PRs synapse onto DIP-g-negative pDm8. Dpr11 and DIP-g expression patterns define {\textquoteright}yellow{\textquoteright} and {\textquoteright}pale{\textquoteright} color vision circuits. We examined Dm8 neurons in these circuits by electron microscopic reconstruction and expansion microscopy. and mutations affect the morphologies of yDm8 distal ({\textquoteright}home column{\textquoteright}) dendrites. yDm8 neurons are generated in excess during development and compete for presynaptic yR7 PRs, and interactions between Dpr11 and DIP-g are required for yDm8 survival. These interactions also allow yDm8 neurons to select yR7 PRs as their appropriate home column partners. yDm8 and pDm8 neurons do not normally compete for survival signals or R7 partners, but can be forced to do so by manipulation of R7 subtype fate.

}, issn = {2050-084X}, doi = {10.7554/eLife.48935}, author = {Menon, Kaushiki P and Kulkarni, Vivek and Takemura, Shin-ya and Anaya, Michael and Zinn, Kai} } @article {48436, title = {The comprehensive connectome of a neural substrate for {\textquoteright}ON{\textquoteright} motion detection in Drosophila.}, journal = {eLife}, volume = {6}, year = {2017}, month = {2017 Apr 22}, abstract = {

Analysing computations in neural circuits often uses simplified models because the actual neuronal implementation is not known. For example, a problem in vision, how the eye detects image motion, has long been analysed using Hassenstein-Reichardt (HR) detector or Barlow-Levick (BL) models. These both simulate motion detection well, but the exact neuronal circuits undertaking these tasks remain elusive. We reconstructed a comprehensive connectome of the circuits of Drosophila{\textquoteright}s motion-sensing T4 cells using a novel EM technique. We uncover complex T4 inputs and reveal that putative excitatory inputs cluster at T4{\textquoteright}s dendrite shafts, while inhibitory inputs localize to the bases. Consistent with our previous study, we reveal that Mi1 and Tm3 cells provide most synaptic contacts onto T4. We are, however, unable to reproduce the spatial offset between these cells reported previously. Our comprehensive connectome reveals complex circuits that include candidate anatomical substrates for both HR and BL types of motion detectors.

}, issn = {2050-084X}, doi = {10.7554/eLife.24394}, author = {Takemura, Shin-ya and Nern, Aljoscha and Chklovskii, Dmitri B and Scheffer, Louis K and Rubin, Gerald M and Meinertzhagen, Ian A} } @article {48658, title = {A connectome of a learning and memory center in the adult Drosophila brain.}, journal = {eLife}, volume = {6}, year = {2017}, month = {2017 Jul 18}, pages = {e26975}, abstract = {

Understanding memory formation, storage and retrieval requires knowledge of the underlying neuronal circuits. In\ Drosophila, the mushroom body (MB) is the major site of associative learning. We reconstructed the morphologies and synaptic connections of all 983 neurons within the three functional units, or compartments, that compose the adult MB{\textquoteright}s α lobe, using a dataset of isotropic 8-nm voxels collected by focused ion-beam milling scanning electron microscopy. We found that Kenyon cells (KCs), whose sparse activity encodes sensory information, each make multiple\ en passant\ synapses to MB output neurons (MBONs) in each compartment. Some MBONs have inputs from all KCs, while others differentially sample sensory modalities. Only six percent of KC\>MBON synapses receive a direct synapse from a dopaminergic neuron (DAN). We identified two unanticipated classes of synapses, KC\>DAN and DAN\>MBON. DAN activation produces a slow depolarization of the MBON in these DAN\>MBON synapses and can weaken memory recall.

}, doi = {10.7554/eLife.26975}, author = {Takemura, Shin-ya and Aso, Yoshinori and Hige, Toshihide and Wong, Allan M and Lu, Zhiyuan and Xu, C Shan and Rivlin, Patricia K and Hess, Harald F and Zhao, Ting and Parag, Toufiq and Berg, Stuart and Huang, Gary and Katz, William T and Olbris, Donald J and Plaza, Stephen M and Umayam, Lowell A and Aniceto, Roxanne and Chang, Lei-Ann and Lauchie, Shirley and et al} } @article {46940, title = {A common evolutionary origin for the ON- and OFF-edge motion detection pathways of the Drosophila visual system.}, journal = {Frontiers in Neural Circuits}, volume = {9}, year = {2015}, month = {2015}, pages = {33}, abstract = {

Synaptic circuits for identified behaviors in the Drosophila brain have typically been considered from either a developmental or functional perspective without reference to how the circuits might have been inherited from ancestral forms. For example, two candidate pathways for ON- and OFF-edge motion detection in the visual system act via circuits that use respectively either T4 or T5, two cell types of the fourth neuropil, or lobula plate (LOP), that exhibit narrow-field direction-selective responses and provide input to wide-field tangential neurons. T4 or T5 both have four subtypes that terminate one each in the four strata of the LOP. Representatives are reported in a wide range of Diptera, and both cell types exhibit various similarities in: (1) the morphology of their dendritic arbors; (2) their four morphological and functional subtypes; (3) their cholinergic profile in Drosophila; (4) their input from the pathways of L3 cells in the first neuropil, or lamina (LA), and by one of a pair of LA cells, L1 (to the T4 pathway) and L2 (to the T5 pathway); and (5) their innervation by a single, wide-field contralateral tangential neuron from the central brain. Progenitors of both also express the gene atonal early in their proliferation from the inner anlage of the developing optic lobe, being alone among many other cell type progeny to do so. Yet T4 receives input in the second neuropil, or medulla (ME), and T5 in the third neuropil or lobula (LO). Here we suggest that these two cell types were originally one, that their ancestral cell population duplicated and split to innervate separate ME and LO neuropils, and that a fiber crossing-the internal chiasma-arose between the two neuropils. The split most plausibly occurred, we suggest, with the formation of the LO as a new neuropil that formed when it separated from its ancestral neuropil to leave the ME, suggesting additionally that ME input neurons to T4 and T5 may also have had a common origin.

}, issn = {1662-5110}, doi = {10.3389/fncir.2015.00033}, author = {Shinomiya, Kazunori and Takemura, Shin-ya and Rivlin, Patricia K and Plaza, Stephen M and Scheffer, Louis K and Meinertzhagen, Ian A} } @article {46263, title = {Connectome of the fly visual circuitry.}, journal = {Microscopy}, volume = {64}, year = {2015}, month = {2015 Feb}, pages = {37-44}, abstract = {

Recent powerful tools for reconstructing connectomes using electron microscopy (EM) have made outstanding contributions to the field of neuroscience. As a prime example, the detection of visual motion is a classic problem of neural computation, yet our understanding of the exact mechanism has been frustrated by our incomplete knowledge of the relevant neurons and synapses. Recent connectomic studies have successfully identified the concrete neuronal circuit in the fly{\textquoteright}s visual system that computes the motion signals. This identification was greatly aided by the comprehensiveness of the EM reconstruction. Compared with light microscopy, which gives estimated connections from arbor overlap, EM gives unequivocal connections with precise synaptic counts. This paper reviews the recent study of connectomics in a brain of the fruit fly Drosophila and highlights how connectomes can provide a foundation for understanding the mechanism of neuronal functions by identifying the underlying neural circuits.

}, issn = {2050-5701}, doi = {10.1093/jmicro/dfu102}, author = {Takemura, Shin-ya} } @article {47237, title = {Ig superfamily ligand and receptor pairs expressed in synaptic partners in Drosophila.}, journal = {Cell}, volume = {163}, year = {2015}, month = {2015 Dec 17}, pages = {1756-69}, abstract = {

Information processing relies on precise patterns of synapses between neurons. The cellular recognition mechanisms regulating this specificity are poorly understood. In the medulla of the Drosophila visual system, different neurons form synaptic connections in different layers. Here, we sought to identify candidate cell recognition molecules underlying this specificity. Using RNA sequencing (RNA-seq), we show that neurons with different synaptic specificities express unique combinations of mRNAs encoding hundreds of cell surface and secreted proteins. Using RNA-seq and protein tagging, we demonstrate that 21 paralogs of the Dpr family, a subclass of immunoglobulin (Ig)-domain containing proteins, are expressed in unique combinations in homologous neurons with different layer-specific synaptic connections. Dpr interacting proteins (DIPs), comprising nine paralogs of another subclass of Ig-containing proteins, are expressed in a complementary layer-specific fashion in a subset of synaptic partners. We propose that pairs of Dpr/DIP paralogs contribute to layer-specific patterns of synaptic connectivity.

}, issn = {1097-4172}, doi = {10.1016/j.cell.2015.11.021}, author = {Tan, Liming and Zhang, Kelvin Xi and Pecot, Matthew Y and Nagarkar-Jaiswal, Sonal and Lee, Pei-Tseng and Takemura, Shin-ya and McEwen, Jason M and Nern, Aljoscha and Xu, Shuwa and Tadros, Wael and Chen, Zhenqing and Zinn, Kai and Bellen, Hugo J and Morey, Marta and Zipursky, S Lawrence} } @article {47152, title = {Synaptic circuits and their variations within different columns in the visual system of Drosophila.}, journal = {Proceedings of the National Academy of Sciences of the United States of America}, volume = {112}, year = {2015}, month = {2015 Nov 3}, pages = {13711-6}, abstract = {

We reconstructed the synaptic circuits of seven columns in the second neuropil or medulla behind the fly{\textquoteright}s compound eye. These neurons embody some of the most stereotyped circuits in one of the most miniaturized of animal brains. The reconstructions allow us, for the first time to our knowledge, to study variations between circuits in the medulla{\textquoteright}s neighboring columns. This variation in the number of synapses and the types of their synaptic partners has previously been little addressed because methods that visualize multiple circuits have not resolved detailed connections, and existing connectomic studies, which can see such connections, have not so far examined multiple reconstructions of the same circuit. Here, we address the omission by comparing the circuits common to all seven columns to assess variation in their connection strengths and the resultant rates of several different and distinct types of connection error. Error rates reveal that, overall, \<1\% of contacts are not part of a consensus circuit, and we classify those contacts that supplement (E+) or are missing from it (E-). Autapses, in which the same cell is both presynaptic and postsynaptic at the same synapse, are occasionally seen; two cells in particular, Dm9 and Mi1, form >=20-fold more autapses than do other neurons. These results delimit the accuracy of developmental events that establish and normally maintain synaptic circuits with such precision, and thereby address the operation of such circuits. They also establish a precedent for error rates that will be required in the new science of connectomics.

}, issn = {1091-6490}, doi = {10.1073/pnas.1509820112}, author = {Takemura, Shin-ya and Xu, C Shan and Lu, Zhiyuan and Rivlin, Patricia K and Parag, Toufiq and Olbris, Donald J and Plaza, Stephen and Zhao, Ting and Katz, William T and Umayam, Lowell and Weaver, Charlotte and Hess, Harald F and Horne, Jane Anne and Nunez-Iglesias, Juan and Aniceto, Roxanne and Chang, Lei-Ann and Lauchie, Shirley and Nasca, Ashley and Ogundeyi, Omotara and Sigmund, Christopher and Takemura, Satoko and Tran, Julie and Langille, Carlie and Le Lacheur, Kelsey and McLin, Sari and Shinomiya, Aya and Chklovskii, Dmitri B and Meinertzhagen, Ian A and Scheffer, Louis K} } @article {6178, title = {A visual motion detection circuit suggested by Drosophila connectomics.}, journal = {Nature}, volume = {500}, year = {2013}, month = {2013 Aug 7}, pages = {175{\textendash}81}, abstract = {

Animal behaviour arises from computations in neuronal circuits, but our understanding of these computations has been frustrated by the lack of detailed synaptic connection maps, or connectomes. For example, despite intensive investigations over half a century, the neuronal implementation of local motion detection in the insect visual system remains elusive. Here we develop a semi-automated pipeline using electron microscopy to reconstruct a connectome, containing 379 neurons and 8,637 chemical synaptic contacts, within the Drosophila optic medulla. By matching reconstructed neurons to examples from light microscopy, we assigned neurons to cell types and assembled a connectome of the repeating module of the medulla. Within this module, we identified cell types constituting a motion detection circuit, and showed that the connections onto individual motion-sensitive neurons in this circuit were consistent with their direction selectivity. Our results identify cellular targets for future functional investigations, and demonstrate that connectomes can provide key insights into neuronal computations.

}, issn = {1476-4687}, doi = {doi:10.1038/nature12450}, url = {http://www.nature.com/nature/journal/v500/n7461/full/nature12450.html}, author = {Takemura, Shin-ya and Bharioke, Arjun and Lu, Zhiyuan and Nern, Aljoscha and Vitaladevuni, Shiv and Rivlin, Patricia K. and Katz, William T. and Olbris, Donald J. and Plaza, Stephen M and Winston, Philip and Zhao, Ting and Horne, Jane Anne and Fetter, Richard D and Takemura, Satoko and Blazek, Katerina and Chang, Lei-Ann and Ogundeyi, Omotara and Saunders, Mathew A. and Shapiro, Victor and Sigmund, Christopher and Rubin, Gerald M and Scheffer, Louis K and Meinertzhagen, Ian A. and Chklovskii, Dmitri B} } @article {4327, title = {Wiring economy and volume exclusion determine neuronal placement in the Drosophila brain.}, journal = {Current Biology}, volume = {21}, year = {2011}, month = {2011 Dec}, pages = {2000-5}, abstract = {

Wiring economy has successfully explained the individual placement of neurons in simple nervous systems like that of Caenorhabditis elegans [1-3] and the locations of coarser structures like cortical areas in complex vertebrate brains [4]. However, it remains unclear whether wiring economy can explain the placement of individual neurons in brains larger than that of C.\ elegans. Indeed, given the greater number of neuronal interconnections in larger brains, simply minimizing the length of connections results in unrealistic configurations, with multiple neurons occupying the same position in space. Avoiding such configurations, or volume exclusion, repels neurons from each other, thus counteracting wiring economy. Here we test whether wiring economy together with volume exclusion can explain the placement of neurons in a module of the Drosophila melanogaster brain known as lamina cartridge [5-13]. We used newly developed techniques for semiautomated reconstruction from serial electron microscopy (EM) [14] to obtain the shapes of neurons, the location of synapses, and the resultant synaptic connectivity. We show that wiring length minimization and volume exclusion together can explain the structure of the lamina microcircuit. Therefore, even in brains larger than that of C.\ elegans, at least for some circuits, optimization can play an important role in individual neuron placement.

}, issn = {1879-0445}, doi = {10.1016/j.cub.2011.10.022}, author = {Rivera-Alba, Marta and Vitaladevuni, Shiv N. and Mischenko, Yuriy and Lu, Zhiyuan and Takemura, Shin-ya and Scheffer, Lou and Meinertzhagen, Ian and Chklovskii, Dmitri and Polavieja, Gonzalo} }