@article {69882, title = {Molecular organization of central cholinergic synapses.}, journal = {Proc Natl Acad Sci U S A}, volume = {122}, year = {2025}, month = {2025 Apr 29}, pages = {e2422173122}, abstract = {

Synapses have undergone significant diversification and adaptation, contributing to the complexity of the central nervous system. Understanding their molecular architecture is essential for deciphering the brain{\textquoteright}s functional evolution. While nicotinic acetylcholine receptors (nAchRs) are widely distributed across metazoan brains, their associated protein networks remain poorly characterized. Using in vivo proximity labeling, we generated proteomic maps of subunit-specific nAchR interactomes in developing and mature brains. Our findings reveal a developmental expansion and reconfiguration of the nAchR interactome. Proteome profiling with genetic perturbations showed that removing individual nAchR subunits consistently triggers compensatory shifts in receptor subtypes, highlighting mechanisms of synaptic plasticity. We also identified the Rho-GTPase regulator Still life (Sif) as a key organizer of cholinergic synapses, with loss of Sif disrupting their molecular composition and structural integrity. These results provide molecular insights into the development and plasticity of central cholinergic synapses, advancing our understanding of synaptic identity conservation and divergence.

}, keywords = {Animals, Brain, Cholinergic Neurons, Drosophila melanogaster, Drosophila Proteins, Neuronal Plasticity, Proteome, Proteomics, Receptors, Nicotinic, Synapses}, issn = {1091-6490}, doi = {10.1073/pnas.2422173122}, url = {https://doi.org/10.1073/pnas.2422173122}, author = {Rosenthal, Justin S and Zhang, Dean and Yin, Jun and Long, Caixia and Yang, George and Li, Yan and Lu, Zhiyuan and Li, Wei-Ping and Yu, Zhiheng and Li, Jiefu and Yuan, Quan} } @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 {68805, title = {Comparative connectomics and escape behavior in larvae of closely related Drosophila species.}, journal = {Current Biology}, year = {2023}, month = {2023 May 31}, abstract = {

Evolution has generated an enormous variety of morphological, physiological, and behavioral traits in animals. How do behaviors evolve in different directions in species equipped with similar neurons and molecular components? Here we adopted a comparative approach to investigate the similarities and differences of escape behaviors in response to noxious stimuli and their underlying neural circuits between closely related drosophilid species. Drosophilids show a wide range of escape behaviors in response to noxious cues, including escape crawling, stopping, head casting, and rolling. Here we find that D.\ santomea, compared with its close relative D.\ melanogaster, shows a higher probability of rolling in response to noxious stimulation. To assess whether this behavioral difference could be attributed to differences in neural circuitry, we\ generated focused ion beam-scanning electron microscope volumes of the ventral nerve cord of D.\ santomea to reconstruct the downstream partners of mdIV, a nociceptive sensory neuron in D.\ melanogaster. Along with partner interneurons of mdVI (including Basin-2, a multisensory integration neuron necessary for rolling) previously identified in D.\ melanogaster, we identified two additional partners of mdVI in D.\ santomea. Finally, we showed that joint activation of one of the partners (Basin-1) and a common partner (Basin-2) in D.\ melanogaster increased rolling probability, suggesting that the high rolling probability in D.\ santomea is mediated by the additional activation of Basin-1 by mdIV. These results provide a plausible mechanistic explanation for how closely related species exhibit quantitative differences in the likelihood of expressing the same behavior.

}, issn = {1879-0445}, doi = {10.1016/j.cub.2023.05.043}, author = {Zhu, Jiayi and Boivin, Jean-Christophe and Pang, Song and Xu, C Shan and Lu, Zhiyuan and Saalfeld, Stephan and Hess, Harald F and Ohyama, Tomoko} } @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 {51252, title = {Comparisons between the ON- and OFF-edge motion pathways in the brain.}, journal = {eLife}, volume = {8}, year = {2019}, month = {2019 Jan 09}, abstract = {

Understanding the circuit mechanisms behind motion detection is a long-standing question in visual neuroscience. In , recent synapse-level connectomes in the optic lobe, particularly in ON-pathway (T4) receptive-field circuits, in concert with physiological studies, suggest an increasingly intricate motion model compared with the ubiquitous Hassenstein-Reichardt model, while our knowledge of OFF-pathway (T5) has been incomplete. Here we present a conclusive and comprehensive connectome that for the first time integrates detailed connectivity information for inputs to both T4 and T5 pathways in a single EM dataset covering the entire optic lobe. With novel reconstruction methods using automated synapse prediction suited to such a large connectome, we successfully corroborate previous findings in the T4 pathway and comprehensively identify inputs and receptive fields for T5. While the two pathways are likely evolutionarily linked and indeed exhibit many similarities, we uncover interesting differences and interactions that may underlie their distinct functional properties.

}, issn = {2050-084X}, doi = {10.7554/eLife.40025}, author = {Shinomiya, Kazunori and Huang, Gary and Lu, Zhiyuan and Parag, Toufiq and Xu, C Shan and Aniceto, Roxanne and Ansari, Namra and Cheatham, Natasha and Lauchie, Shirley and Neace, Erika and Ogundeyi, Omotara and Ordish, Christopher and Peel, David and Shinomiya, Aya and Smith, Claire and Takemura, Satoko and Talebi, Iris and Rivlin, Patricia K and Nern, Aljoscha and Scheffer, Louis K and Plaza, Stephen M and Meinertzhagen, Ian A} } @article {65224, title = {Gas cluster ion beam SEM for imaging of large tissue samples with 10 nm isotropic resolution.}, journal = {Nature Methods}, volume = {17}, year = {2019}, month = {2020 Jan 01}, pages = {68-71}, abstract = {

We demonstrate gas cluster ion beam scanning electron microscopy (SEM), in which wide-area ion milling is performed on a series of thick tissue sections. This three-dimensional electron microscopy technique acquires datasets with \<10 nm isotropic resolution of each section, and these can then be stitched together to span the sectioned volume. Incorporating gas cluster ion beam SEM into existing single-beam and multibeam SEM workflows should be straightforward, increasing reliability while improving z resolution by a factor of three or more.

}, issn = {1548-7105}, doi = {10.1038/s41592-019-0641-2}, author = {Hayworth, Kenneth J and Peale, David and Januszewski, Micha{\l} and Knott, Graham W and Lu, Zhiyuan and Xu, C Shan and Hess, Harald F} } @article {64823, title = {GCIB-SEM: A path to 10 nm isotropic imaging of cubic millimeter volumes.}, journal = {bioRxiv}, year = {2019}, month = {2019 Feb 28}, abstract = {Focused Ion Beam Scanning Electron Microscopy (FIB-SEM) generates 3D datasets optimally suited for segmentation of cell ultrastructure and automated connectome tracing but is limited to small fields of view and is therefore incompatible with the new generation of ultrafast multibeam SEMs. In contrast, section-based techniques are multibeam-compatible but are limited in z-resolution making automatic segmentation of cellular ultrastructure difficult. Here we demonstrate a novel 3D electron microscopy technique, Gas Cluster Ion Beam SEM (GCIB-SEM), in which top-down, wide-area ion milling is performed on a series of thick sections, acquiring < 10 nm isotropic datasets of each which are then stitched together to span the full sectioned volume. Based on our results, incorporating GCIB-SEM into existing single beam and multibeam SEM workflows should be straightforward and should dramatically increase reliability while simultaneously improving z-resolution by a factor of 3 or more.}, doi = {10.1101/563239}, author = {Hayworth, Kenneth J and Peale, David R and Januszewski, Michal and Knott, Graham and Lu, Zhiyuan and Xu, C. Shan and Hess, Harald F} } @article {49497, title = {A resource for the antennal lobe provided by the connectome of glomerulus VA1v.}, journal = {eLife}, volume = {7}, year = {2018}, month = {2018 Nov 01}, abstract = {

Using FIB-SEM we report the entire synaptic connectome of glomerulus VA1v of the right antennal lobe in . Within the glomerulus we densely reconstructed all neurons, including hitherto elusive local interneurons. The -positive, sexually dimorphic VA1v included \>11,140 presynaptic sites with ~38,050 postsynaptic dendrites. These connected input olfactory receptor neurons (ORNs, 51 ipsilateral, 56 contralateral), output projection neurons (18 PNs), and local interneurons (56 of \>150 previously reported LNs). ORNs are predominantly presynaptic and PNs predominantly postsynaptic; newly reported LN circuits are largely an equal mixture and confer extensive synaptic reciprocity, except the newly reported LN2V with input from ORNs and outputs mostly to monoglomerular PNs, however. PNs were more numerous than previously reported from genetic screens, suggesting that the latter failed to reach saturation. We report a matrix of 192 bodies each having 50 connections; these form 88\% of the glomerulus{\textquoteright} pre/postsynaptic sites.

}, issn = {2050-084X}, doi = {10.7554/eLife.37550}, author = {Horne, Jane Anne and Langille, Carlie and McLin, Sari and Wiederman, Meagan and Lu, Zhiyuan and Xu, C Shan and Plaza, Stephen M and Scheffer, Louis K and Hess, Harald F 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 {48490, title = {Enhanced FIB-SEM systems for large-volume 3D imaging.}, journal = {eLife}, volume = {6}, year = {2017}, month = {2017 May 13}, abstract = {

Focused Ion Beam Scanning Electron Microscopy (FIB-SEM) can automatically generate 3D images with superior z-axis resolution, yielding data that needs minimal image registration and related post-processing. Obstacles blocking wider adoption of FIB-SEM include slow imaging speed and lack of long-term system stability, which caps the maximum possible acquisition volume. Here we present techniques that accelerate image acquisition while greatly improving FIB-SEM reliability, allowing the system to operate for months and generating continuously imaged volumes \> 10(6) {\textmu}m(3). These volumes are large enough for connectomics, where the excellent z resolution can help in tracing of small neuronal processes and accelerate the tedious and time-consuming human proofreading effort. Even higher resolution can be achieved on smaller volumes. We present example data sets from mammalian neural tissue, Drosophila brain, and Chlamydomonas reinhardtii to illustrate the power of this novel high-resolution technique to address questions in both connectomics and cell biology.

}, issn = {2050-084X}, doi = {10.7554/eLife.25916}, author = {Xu, C Shan and Hayworth, Kenneth J and Lu, Zhiyuan and Grob, Patricia and Hassan, Ahmed M and Garc{\'\i}a-Cerd{\'a}n, Jos{\'e} G and Niyogi, Krishna K and Nogales, Eva and Weinberg, Richard J and Hess, Harald F} } @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 {46325, title = {Ultrastructurally smooth thick partitioning and volume stitching for large-scale connectomics.}, journal = {Nature Methods}, volume = {12}, year = {2015}, month = {2015 Feb 16}, pages = {319-22}, abstract = {

Focused-ion-beam scanning electron microscopy (FIB-SEM) has become an essential tool for studying neural tissue at resolutions below 10 nm {\texttimes} 10 nm {\texttimes} 10 nm, producing data sets optimized for automatic connectome tracing. We present a technical advance, ultrathick sectioning, which reliably subdivides embedded tissue samples into chunks (20 μm thick) optimally sized and mounted for efficient, parallel FIB-SEM imaging. These chunks are imaged separately and then {\textquoteright}volume stitched{\textquoteright} back together, producing a final three-dimensional data set suitable for connectome tracing.

}, issn = {1548-7105}, doi = {10.1038/nmeth.3292}, author = {Hayworth, Kenneth J and Xu, C Shan and Lu, Zhiyuan and Knott, Graham W and Fetter, Richard D and Tapia, Juan Carlos and Lichtman, Jeff W and Hess, Harald F} } @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} }