Vimentin intermediate filaments (VIFs) form complex, tightly packed networks; due to this density, traditional imaging approaches cannot discern single-filament behavior. To address this, we developed and validated a sparse vimentin-SunTag labeling strategy, enabling single-particle tracking of individual VIFs and providing a sensitive, unbiased, and quantitative method for measuring global VIF motility. Using this approach, we define the steady-state VIF motility rate, showing a constant ∼8% of VIFs undergo directed microtubule-based motion irrespective of subcellular location or local filament density. Significantly, our single-particle tracking approach revealed uncorrelated motion of individual VIFs within bundles, an observation seemingly at odds with conventional models of tightly cross-linked bundles. To address this, we acquired high-resolution focused ion beam scanning electron microscopy volumes of vitreously frozen cells and reconstructed three-dimensional VIF bundles, finding that they form only loosely organized, semi-coherent structures from which single VIFs frequently emerge to locally engage neighboring microtubules. Overall, this work demonstrates single VIF dynamics and organization in the cellular milieu for the first time.

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—pancreas, liver, kidney, heart, thymus, hippocampus, and skin—from a single mouse. The detailed datasets are openly accessible onOpenOrganelle.org, providing a valuable resource for the scientific community to support further research and collaboration. ### Competing Interest Statement The authors have declared no competing interest.

DaCapo is a specialized deep learning library tailored to expedite the training and application of existing machine learning approaches on large, near-isotropic image data. In this correspondence, we introduce DaCapo's unique features optimized for this specific domain, highlighting its modular structure, efficient experiment management tools, and scalable deployment capabilities. We discuss its potential to improve access to large-scale, isotropic image segmentation and invite the community to explore and contribute to this open-source initiative.

Single-particle tracking demonstrates that individual filaments in bundles of vimentin intermediate filaments are transported in the cytoplasm by motor proteins along microtubules. Furthermore, using 3D FIB-SEM the authors showed that vimentin filament bundles are loosely packed and coaligned with microtubules. Vimentin intermediate filaments (VIFs) form complex, tight-packed networks; due to this density, traditional ensemble labeling and imaging approaches cannot accurately discern single filament behavior. To address this, we introduce a sparse vimentin-SunTag labeling strategy to unambiguously visualize individual filament dynamics. This technique confirmed known long-range dynein and kinesin transport of peripheral VIFs and uncovered extensive bidirectional VIF motion within the perinuclear vimentin network, a region we had thought too densely bundled to permit such motility. To examine the nanoscale organization of perinuclear vimentin, we acquired high-resolution electron microscopy volumes of a vitreously frozen cell and reconstructed VIFs and microtubules within a ~50 μm3 window. Of 583 VIFs identified, most were integrated into long, semi-coherent bundles that fluctuated in width and filament packing density. Unexpectedly, VIFs displayed minimal local co-alignment with microtubules, save for sporadic cross-over sites that we predict facilitate cytoskeletal crosstalk. Overall, this work demonstrates single VIF dynamics and organization in the cellular milieu for the first time.

Dopamine is a key chemical neuromodulator that plays vital roles in various brain functions. Traditionally, neuromodulators like dopamine are believed to be released in a diffuse manner and are not commonly associated with synaptic structures where pre- and postsynaptic processes are closely aligned. Our findings challenge this conventional view. Using single-bouton optical measurements of dopamine release, we discovered that dopamine is predominantly released from varicosities that are juxtaposed against the processes of their target neurons. Dopamine axons specifically target neurons expressing dopamine receptors, forming synapses to release dopamine. Interestingly, varicosities that were not directly apposed to dopamine receptor-expressing processes or associated with neurons lacking dopamine receptors did not release dopamine, regardless of their vesicle content. The ultrastructure of dopamine release sites share common features of classical synapses. We further show that the dopamine released at these contact sites induces a precise, dopamine-gated biochemical response in the target processes. Our results indicate that dopamine release sites share key characteristics of conventional synapses that enable relatively precise and efficient neuromodulation of their targets. ### Competing Interest Statement The authors have declared no competing interest.

The endoplasmic reticulum (ER) forms a dynamic network that contacts other cellular membranes to regulate stress responses, calcium signalling and lipid transfer. Here, using high-resolution volume electron microscopy, we find that the ER forms a previously unknown association with keratin intermediate filaments and desmosomal cell–cell junctions. Peripheral ER assembles into mirror image-like arrangements at desmosomes and exhibits nanometre proximity to keratin filaments and the desmosome cytoplasmic plaque. ER tubules exhibit stable associations with desmosomes, and perturbation of desmosomes or keratin filaments alters ER organization, mobility and expression of ER stress transcripts. These findings indicate that desmosomes and the keratin cytoskeleton regulate the distribution, function and dynamics of the ER network. Overall, this study reveals a previously unknown subcellular architecture defined by the structural integration of ER tubules with an epithelial intercellular junction.

The endoplasmic reticulum (ER) forms a dynamic network that contacts other cellular membranes to regulate stress responses, calcium signaling, and lipid transfer. Using high-resolution volume electron microscopy, we find that the ER forms a previously unknown association with keratin intermediate filaments and desmosomal cell-cell junctions. Peripheral ER assembles into mirror image-like arrangements at desmosomes and exhibits nanometer proximity to keratin filaments and the desmosome cytoplasmic plaque. ER tubules exhibit stable associations with desmosomes, and perturbation of desmosomes or keratin filaments alters ER organization and mobility. These findings indicate that desmosomes and the keratin cytoskeleton pattern the distribution of the ER network. Overall, this study reveals a previously unknown subcellular architecture defined by the structural integration of ER tubules with an epithelial intercellular junction. One-Sentence Summary The desmosome adhesive junction regulates the organization and dynamics of the endoplasmic reticulum network.

Cytotoxic T lymphocytes (CTLs) and natural killer cells kill virus-infected and tumor cells through the polarized release of perforin and granzymes. Perforin is a pore-forming toxin that creates a lesion in the plasma membrane of the target cell through which granzymes enter the cytosol and initiate apoptosis. Endosomal sorting complexes required for transport (ESCRT) proteins are involved in the repair of small membrane wounds. We found that ESCRT proteins were precisely recruited in target cells to sites of CTL engagement immediately after perforin release. Inhibition of ESCRT machinery in cancer-derived cells enhanced their susceptibility to CTL-mediated killing. Thus, repair of perforin pores by ESCRT machinery limits granzyme entry into the cytosol, potentially enabling target cells to resist cytolytic attack.

Cytotoxic T lymphocytes (CTLs) kill virus-infected and tumorigenic cells through the polarized release of perforin and granzymes, which are housed in modified lysosomes called lytic granules. Perforin is a pore-forming toxin that creates an ephemeral portal in the plasma membrane of the target through which granzymes enter the cytosol and initiate apoptosis. This conjugate was captured moments after perforin secretion toward the target. Sample: OT-I mouse cytotoxic T lymphocyte attacking an ID8 ovarian cancer cell; fixed and imaged moments after the release of perforin/granzyme from lytic granules. The ID8 murine ovarian cancer cell is stably expressing mEmerald-Chmp4b and TOMM20-SNAP. Protocol: High pressure freezing, freeze-substitution resin embedding with 2% OsO4 0.1% UA 3% H2O in acetone; resin embedding in Eponate 12.Contributions: Sample provided by Alex Ritter (Genentech), prepared for imaging by Gleb Shtengel (HHMI/Janelia), with imaging and post-processing by C. Shan Xu (HHMI/Janelia).Dataset ID: jrc_ctl-id8-5Final voxel size (nm): 8 x 8 x 8 (X, Y, Z)Dimensions (µm): 41.6 x 12 x 35.17 (X, Y, Z)Acquisition date: 2020-01-29Dataset URL: https://data.janelia.org/MgvbAVisualization Website: https://openorganelle.janelia.org/datasets/jrc_ctl-id8-5Publication: Ritter et al., ESCRT-mediated membrane repair at the immunological synapse protects tumor cells against T cell attack, Science (2022).

Chemical neurotransmission constitutes one of the fundamental modalities of communication between neurons. Monitoring release of these chemicals has traditionally been difficult to carry out at spatial and temporal scales relevant to neuron function. To understand chemical neurotransmission more fully, we need to improve the spatial and temporal resolutions of measurements for neurotransmitter release. To address this, we engineered a chemi-sensitive, two-dimensional composite nanofilm that facilitates visualization of the release and diffusion of the neurochemical dopamine with synaptic resolution, quantal sensitivity, and simultaneously from hundreds of release sites. Using this technology, we were able to monitor the spatiotemporal dynamics of dopamine release in dendritic processes, a poorly understood phenomenon. We found that dopamine release is broadcast from a subset of dendritic processes as hotspots that have a mean spatial spread of ≈ 3.2 µm (full width at half maximum [FWHM]) and are observed with a mean spatial frequency of one hotspot per ≈ 7.5 µm of dendritic length. Major dendrites of dopamine neurons and fine dendritic processes, as well as dendritic arbors and dendrites with no apparent varicose morphology participated in dopamine release. Remarkably, these release hotspots co-localized with Bassoon, suggesting that Bassoon may contribute to organizing active zones in dendrites, similar to its role in axon terminals.

Segmentation of mouse brain magnetic resonance images (MRI) based on anatomical and/or functional features is an important step towards morphogenetic brain structure characterization of murine models in neurobiological studies. State-of-the-art image segmentation methods register image volumes to standard presegmented templates or well-characterized highly detailed image atlases. Performance of these methods depends critically on the quality of skull-stripping, which is the digital removal of tissue signal exterior to the brain. This is, however, tedious to do manually and challenging to automate. Registration-based segmentation, in addition, performs poorly on small structures, low resolution images, weak signals, or faint boundaries, intrinsic to in vivo MRI scans. To address these issues, we developed an automated end-to-end pipeline called DeepBrainIPP (deep learning-based brain image processing pipeline) for 1) isolating brain volumes by stripping skull and tissue from T2w MRI images using an improved deep learning-based skull-stripping and data augmentation strategy, which enables segmentation of large brain regions by atlas or template registration, and 2) address segmentation of small brain structures, such as the paraflocculus, a small lobule of the cerebellum, for which DeepBrainIPP performs direct segmentation with a dedicated model, producing results superior to the skull-stripping/atlas-registration paradigm. We demonstrate our approach on data from both in vivo and ex vivo samples, using an in-house dataset of 172 images, expanded to 4,040 samples through data augmentation. Our skull stripping model produced an average Dice score of 0.96 and residual volume of 2.18%. This facilitated automatic registration of the skull-stripped brain to an atlas yielding an average cross-correlation of 0.98. For small brain structures, direct segmentation yielded an average Dice score of 0.89 and 5.32% residual volume error, well below the tolerance threshold for phenotype detection. Full pipeline execution is provided to non-expert users via a Web-based interface, which exposes analysis parameters, and is powered by a service that manages job submission, monitors job status and provides job history. Usability, reliability, and user experience of DeepBrainIPP was measured using the Customer Satisfaction Score (CSAT) and a modified PYTHEIA Scale, with a rating of excellent. DeepBrainIPP code, documentation and network weights are freely available to the research community.

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) 1 . 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, ‘OpenOrganelle’, to share the data, computer code and trained models, which will enable scientists everywhere to query and further improve automatic reconstruction of these datasets.

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 Drosophila melanogaster. 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's brain.

Cells contain hundreds of different organelle and macromolecular assemblies intricately organized relative to each other to meet any cellular demands. Obtaining a complete understanding of their organization is challenging and requires nanometer-level, threedimensional reconstruction of whole cells. Even then, the immense size of datasets and large number of structures to be characterized requires generalizable, automatic methods. To meet this challenge, we developed an analysis pipeline for comprehensively reconstructing and analyzing the cellular organelles in entire cells imaged by focused ion beam scanning electron microscopy (FIB-SEM) at a near-isotropic size of 4 or 8 nm per voxel. The pipeline involved deep learning architectures trained on diverse samples for automatic reconstruction of 35 different cellular organelle classes - ranging from endoplasmic reticulum to microtubules to ribosomes - from multiple cell types.Automatic reconstructions were used to directly quantify various previously inaccessible metrics about these structures, including their spatial interactions. We show that automatic organelle reconstructions can also be used to automatically register light and electron microscopy images for correlative studies. We created an open data and open source web repository, OpenOrganelle, to share the data, computer code, and trained models, enabling scientists everywhere to query and further reconstruct the datasets.

Article Figures and data Abstract eLife digest Introduction Appendix 1 Data availability References Decision letter Author response Article and author information Metrics 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 Drosophila melanogaster. 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’s brain. eLife digest Animal brains of all sizes, from the smallest to the largest, work in broadly similar ways. Studying the brain of any one animal in depth can thus reveal the general principles behind the workings of all brains. The fruit fly Drosophila is a popular choice for such research. With about 100,000 neurons – compared to some 86 billion in humans – the fly brain is small enough to study at the level of individual cells. But it nevertheless supports a range of complex behaviors, including navigation, courtship and learning. Thanks to decades of research, scientists now have a good understanding of which parts of the fruit fly brain support particular behaviors. But exactly how they do this is often unclear. This is because previous studies showing the connections between cells only covered small areas of the brain. This is like trying to understand a novel when all you can see is a few isolated paragraphs. To solve this problem, Scheffer, Xu, Januszewski, Lu, Takemura, Hayworth, Huang, Shinomiya et al. prepared the first complete map of the entire central region of the fruit fly brain. The central brain consists of approximately 25,000 neurons and around 20 million connections. To prepare the map – or connectome – the brain was cut into very thin 8nm slices and photographed with an electron microscope. A three-dimensional map of the neurons and connections in the brain was then reconstructed from these images using machine learning algorithms. Finally, Scheffer et al. used the new connectome to obtain further insights into the circuits that support specific fruit fly behaviors. The central brain connectome is freely available online for anyone to access. When used in combination with existing methods, the map will make it easier to understand how the fly brain works, and how and why it can fail to work correctly. Many of these findings will likely apply to larger brains, including our own. In the long run, studying the fly connectome may therefore lead to a better understanding of the human brain and its disorders. Performing a similar analysis on the brain of a small mammal, by scaling up the methods here, will be a likely next step along this path. Introduction The connectome we present is a dense reconstruction of a portion of the central brain (referred to here as the hemibrain) of the fruit fly, Drosophila melanogaster, as shown in Figure 1. This region was chosen since it contains all the circuits of the central brain (assuming bilateral symmetry), and in particular contains circuits critical to unlocking mysteries involving associative learning in the mushroom body, navigation and sleep in the central complex, and circadian rhythms among clock circuits. The largest dense reconstruction to date, it contains around 25,000 neurons, most of which were rigorously clustered and named, with about 20 million chemical synapses between them, plus portions of many other neurons truncated by the boundary of the data set (details in Figure 1). Each neuron is documented at many levels - the detailed voxels that constitute it, a skeleton with segment diameters, its synaptic partners and the location of most of their synapses. Figure 1 Download asset Open asset The hemibrain and some basic statistics. The highlighted area shows the portion of the central brain that was imaged and reconstructed, superimposed on a grayscale representation of the entire Drosophila brain. For the table, a neuron is traced if all its main branches within the volume are reconstructed. A neuron is considered uncropped if most arbors (though perhaps not the soma) are contained in the volume. Others are considered cropped. Note: (1) our definition of cropped is somewhat subjective; (2) the usefulness of a cropped neuron depends on the application; and (3) some small fragments are known to be distinct neurons. For simplicity, we will often state that the hemibrain contains ≈25K neurons. Producing this data set required advances in sample preparation, imaging, image alignment, machine segmentation of cells, synapse detection, data storage, proofreading software, and protocols to arbitrate each decision. A number of new tests for estimating the completeness and accuracy were required and therefore developed, in order to verify the correctness of the connectome. These data describe whole-brain properties and circuits, as well as contain new methods to classify cell types based on connectivity. Computational compartments are now more carefully defined, we conclusively identify synaptic circuits, and each neuron is annotated by name and putative cell type, making this the first complete census of neuropils, tracts, cells, and connections in this portion of the brain. We compare the statistics and structure of different brain regions, and for the brain as a whole, without the confounds introduced by studying different circuitry in different animals. All data are publicly available through web interfaces. This includes a browser interface, NeuPrint (Clements et al., 2020), designed so that any interested user can query the hemibrain connectome even without specific training. NeuPrint can query the connectivity, partners, connection strengths and morphologies of all specified neurons, thus making identification of upstream and downstream partners both orders of magnitude easier, and significantly more confident, compared to existing genetic methods. In addition, for those who are willing to program, the full data set - the gray scale voxels, the segmentation and proofreading results, skeletons, and graph model of connectivity, are also available through publicly accessible application program interfaces (APIs). This effort differs from previous EM reconstructions in its social and collaborative aspects. Previous reconstructions were either dense in much smaller EM volumes (such as Meinertzhagen and O'Neil, 1991; Helmstaedter et al., 2013; Takemura et al., 2017) or sparse in larger volumes (such as Eichler et al., 2017 or Zheng et al., 2018). All have concentrated on the reconstruction of specific circuits to answer specific questions. When the same EM volume is used for many such efforts, as has occurred in the Drosophila larva and the full adult fly brain, this leads to an overall reconstruction that is the union of many individual efforts (Saalfeld et al., 2009). The result is inconsistent coverage of the brain, with some regions well reconstructed and others missing entirely. In contrast, here we have analyzed the entire volume, not just the subsets of interest to specific groups of researchers with the expertise to tackle EM reconstruction. We are making these data available without restriction, with only the requirement to cite the source. This allows the benefits of known circuits and connectivity to accrue to the field as a whole, a much larger audience than those with expertise in EM reconstruction. This is analogous to progress in genomics, which transitioned from individual groups studying subsets of genes, to publicly available genomes that can be queried for information about genes of choice (Altschul et al., 1990). One major benefit to this effort is to facilitate research into the circuits of the fly’s brain. A common question among researchers, for example, is the identity of upstream and downstream (respectively input and output) partners of specific neurons. Previously, this could only be addressed by genetic trans-synaptic labeling, such as trans-Tango (Talay et al., 2017), or by sparse tracing in previously imaged EM volumes (Zheng et al., 2018). However, the genetic methods may give false positives and negatives, and both alternatives require specialized expertise and are time consuming, often taking months of effort. Now, for any circuits contained in our volume, a researcher can obtain the same answers in seconds by querying a publicly available database. Another major benefit of dense reconstruction is its exhaustive nature. Genetic methods such as stochastic labeling may miss certain cell types, and counts of cells of a given type are dependent on expression levels, which are always uncertain. Previous dense reconstructions have demonstrated that existing catalogs of cell types are incomplete, even in well-covered regions (Takemura et al., 2017). In our hemibrain sample, we have identified all the cells within the reconstructed volume, thus providing a complete and unbiased census of all cell types in the fly’s central brain (at least in this single female), and a precise count of the cells of each type. Another scientific benefit lies in an analysis without the uncertainty of pooling data obtained from different animals. The detailed circuitry of the fly’s brain is known to depend on nutritional history, age, and circadian rhythm. Here, these factors are held constant, as are the experimental methods, facilitating comparison between different fly brain regions in this single animal. Evaluating stereotypy across animals will of course eventually require additional connectomes. Previous reconstructions of compartmentalized brains have concentrated on particular regions and circuits. The mammalian retina (Helmstaedter et al., 2013) and cortex (Kasthuri et al., 2015), and insect mushroom bodies (Eichler et al., 2017; Takemura et al., 2017) and optic lobes (Takemura et al., 2015) have all been popular targets. Additional studies have examined circuits that cross regions, such as those for sensory integration (Ohyama et al., 2015) or motion vision (Shinomiya et al., 2019). So far lacking are systematic studies of the statistical properties of computational compartments and their connections. Neural circuit motifs have been studied (Song et al., 2005), but only those restricted to small motifs and at most a few cell types, usually in a single portion of the brain. Many of these results are in mammals, leading to questions of whether they also apply to invertebrates, and whether they extend to other regions of the brain. While there have been efforts to build reduced, but still accurate, electrical models of neurons (Marasco et al., 2012), none of these to our knowledge have used the compartment structure of the brain. What is included Table 1 shows the hierarchy of the named brain regions that are included in the hemibrain. Table 2 shows the primary regions that are at least 50% included in the hemibrain sample, their approximate size, and their completion percentage. Our names for brain regions follow the conventions of Ito et al., 2014 with the addition of ‘(L)’ or ‘(R)’ to indicate whether the region (most of which occur on both sides of the fly) has its cell bodies in the left or right, respectively. The mushroom body (Tanaka et al., 2008; Aso et al., 2014) and central complex (Wolff et al., 2015; Wolff and Rubin, 2018) are further divided into finer compartments. Table 1 Brain regions contained and defined in the hemibrain, following the naming conventions of Ito et al., 2014 with the addition of (R) and (L) to specify the side of the soma for that region. Italics indicate master regions not explicitly defined in the hemibrain. Region LA is not included in the volume. The regions are hierarchical, with the more indented regions forming subsets of the less indented. The only exceptions are dACA, lACA, and vACA which are considered part of the mushroom body but are not contained in the master region MB. OL(R)Optic lobeCXCentral complexLH(R)Lateral horn LAlamina FBFan-shaped body ME(R)Medula FBl1Fan-shaped body layer 1SNP(R)/(L)Superior neuropils AME(R)Accessory medulla FBl2Fan-shaped body layer 2 SLP(R)Superior lateral protocerebrum LO(R)Lobula FBl3Fan-shaped body layer 4 SIP(R)/(L)Superior intermediate protocerebrum LOP(R)Lobula plate FBl4Fan-shaped body layer 4 SMP(R)(L)Superior medial protocerebrum FBl5Fan-shaped body layer 5MB(R)/(L)Mushroom body FBl6Fan-shaped body layer 6INPInferior neuropils CA(R)/(L)Calyx FBl7Fan-shaped body layer 7 CRE(R)/(L)Crepine dACA(R)Dorsal accessory calyx FBl8Fan-shaped body layer 8 RUB(R)/(L)Rubu lACA(R)Lateral accessory calyx FBl9Fan-shaped body layer 9 ROB(R)Round body vACA(R)Ventral accessory calyx EBEllipsoid body SCL(R)/(L)Superior clamp PED(R)Pedunculus EBr1Ellipsoid body zone r1 ICL(R)/(L)Inferior clamp a’L(R)/(L)Alpha prime lobe EBr2r4Ellipsoid body zone r2r4 IBInferior bridge a’1(R)Alpha prime lobe compartment 1 EBr3amEllipsoid body zone r3am ATL(R)/(L)Antler a’2(R)Alpha prime lobe compartment 2 EBr3dEllipsoid body zone r3d a’3(R)Alpha prime lobe compartment 3 EBr3pwEllipsoid body zone r3pwAL(R)/(L)Antennal lobe aL(R)/(L)Alpha lobe EBr5Ellipsoid body zone r5 a1(R)Alpha lobe compartment 1 EBr6Ellipsoid body zone r6VMNPVentromedial neuropils a2(R)Alpha lobe compartment 2 AB(R)/(L)Asymmetrical body VES(R)/(L)Vest a3(R)Alpha lobe compartment 3 PBProtocerebral bridge EPA(R)/(L)Epaulette gL(R)/(L)Gamma lobe PB(R1)PB glomerulus R1 GOR(R)/(L)Gorget g1(R)Gamma lobe compartment 1 PB(R2)PB glomerulus R2 SPS(R)/(L)Superior posterior slope g2(R)Gamma lobe compartment 2 PB(R3)PB glomerulus R3 IPS(R)/(L)Inferior posterior slope g3(R)Gamma lobe compartment 3 PB(R4)PB glomerulus R4 g4(R)Gamma lobe compartment 4 PB(R5)PB glomerulus R5PENPPariesophageal neuropils g5(R)Gamma lobe compartment 5 PB(R6)PB glomerulus R6 SADSaddle b’L(R)/(L)Beta prime lobe PB(R7)PB glomerulus R7 AMMCAntennal mechanosensory and motor center b’1(R)Beta prime lobe compartment 1 PB(R8)PB glomerulus R8 FLA(R)Flange b’2(R)Beta prime lobe compartment 2 PB(R9)PB glomerulus R9 CAN(R)Cantle bL(R)/(L)Beta lobe PB(L1)PB glomerulus L1 PRWprow b1(R)Beta lobe compartment 1 PB(L2)PB glomerulus L2 b2(R)Beta lobe compartment 2 PB(L3)PB glomerulus L3GNGGnathal ganglia PB(L4)PB glomerulus L4LX(R)/(L)Lateral complex PB(L5)PB glomerulus L5Major Fiber bundles BU(R)/(L)Bulb PB(L6)PB glomerulus L6 AOT(R)Anterior optic tract LAL(R)/(L)Lateral accessory lobe PB(L7)PB glomerulus L7 GCGreat commissure GA(R)Gall PB(L8)PB glomerulus L8 GF(R)Giant Fiber (single neuron) PB(L9)PB glomerulus L9 mALT(R)/(L)Medial antennal lobe tractVLNP(R)Ventrolateral neuropils NONoduli POCPosterior optic commissure AOTU(R)Anterior optic tubercle NO1(R)/(L)Nodulus 1 AVLP(R)Anterior ventrolateral protocerebrum NO2(R)/(L)Nodulus 2 PVLP(R)Posterior ventrolateral protocerebrum NO3(R)/(L)Nodulus 3 PLP(R)Posterior lateral cerebrum WED(R)Wedge Table 2 Regions with ≥50% included in the hemibrain, sorted by completion percentage. The approximate percentage of the region included in the hemibrain volume is shown as ‘%inV’. ‘T-bars’ gives a rough estimate of the size of the region. ‘comp%’ is the fraction of the post-synaptic densities (PSDs) contained in the brain region for which both the PSD and the corresponding T-bar are in neurons marked ‘Traced’. Name%inVT-barscomp%Name%inVT-barscomp%PED(R)100%5480585%aL(R)100%9537584%b’L(R)100%6769583%bL(R)100%7111283%gL(R)100%17678583%a’L(R)100%3909182%EB100%16428681%bL(L)56%5879981%NO100%3672279%b’L(L)88%5780278%gL(L)55%13325676%CA(R)100%6951773%AB(R)100%273465%aL(L)51%4480362%FB100%45103162%AL(R)83%50100459%AB(L)100%57257%PB100%4655755%AME(R)100%604551%BU(R)100%938546%CRE(R)100%13794640%AOTU(R)100%9257838%LAL(R)100%23438838%SMP(R)100%51093734%PVLP(R)100%47521930%ATL(R)100%2547229%SPS(R)100%25381829%ATL(L)100%2815329%VES(R)84%15716829%IB100%20044728%CRE(L)90%13265628%SIP(R)100%18749326%BU(L)52%701426%GOR(R)100%2714026%WED(R)100%23289825%SMP(L)100%46078426%EPA(R)100%3143826%PLP(R)100%42994926%AVLP(R)100%63053823%ICL(R)100%20254923%SLP(R)100%48779523%LO(R)64%85525122%SCL(R)100%18956922%GOR(L)60%1955821%LH(R)100%23166219%CAN(R)68%651216% Appendix 1—table 6 provide the list of identified neuron types and their naming schemes. These include newly identified sensory inputs and motor outputs. The nature of the proofreading process allows us to improve the data even after their initial publication. Our initial data release was version v1.0 (Xu et al., 2020c). Version v1.1 is now available, including improvements such as better accuracy, more consistent cell naming and typing, and inclusion of anatomical names for central complex neurons. The old version(s) remain online and available, to allow reproducibility of older analyses, but we strongly recommend all new analyses use the latest version. The analyses in this article, and in the corresponding articles on the mushroom body and central complex, are based on version v1.1, unless otherwise noted. What is not included This research focused on the neurons of the brain and the chemical synapses between them. Every step in our process, from staining and sample preparation through segmentation and proofreading, has been optimized with this goal in mind. While neurons and their chemical synapses are critical to brain operation, they are far from the full story. Other contributors, known to be important, could not be included in our study, largely for technical reasons. Among these are gap junctions, glia, and structures internal to the cell such as mitochondria. Gap junctions, or electrical connections between neurons, are difficult to reliably detect by FIB-SEM under the best of circumstances and not detectable at the low (for EM) resolution needed to complete this study in a reasonable amount of time. Their contribution to the connectome will need to be established through other means - see the section on future research. Glial cells were difficult to segment, due to both staining differences and convoluted morphologies. We identified the volumes where they exist (a glia ’mask’, which allows these regions to be color-coded when viewed in NeuroGlancer) but did not separate them into cells. Structures internal to the neurons, except for synapses, are not considered here even though many are visible in our EM preparation. The most obvious example is mitochondria. Again, we have identified many of them so we could evaluate their effect on segmentation, but they are not included in our connectome. Finally, autapses (synapses from a neuron onto itself) are known to exist in Drosophila, but are sufficiently rare that they fall well below the rate of false positives in our automated synapse detection. Therefore most of the putative autapses are false positives, and we do not include them in our connectivity data. Differences from connectomes of vertebrates Most accounts of neurobiology define the operation of the mammalian nervous system with, at most, only passing reference to invertebrate brains. Fly (or other insect) nervous systems differ from those of vertebrates in several aspects (Meinertzhagen, 2016b). Some main differences include: Most synapses are polyadic. Each synapse structure comprises a single presynaptic release site and, adjacent to this, several neurites expressing neurotransmitter receptors. An element, T-shaped and typically called a T-bar in flies, marks the site of transmitter release into the cleft between cells. This site typically abuts the neurites of several other cells, where a postsynaptic density (PSD) marks the receptor location. Most neurites are neither purely axonic nor dendritic, but have both pre- and postsynaptic partners, a feature that may be more prominent in mammalian brains than recognized (Morgan and Lichtman, 2020). Within a single brain region, however, neurites are frequently predominantly dendritic (postsynaptic) or axonic (presynaptic). Unlike some synapses in mammals, EM imagery (at least as we have acquired and analyzed it here) fails to reveal obvious information about whether a synapse is excitatory or inhibitory. The soma or cell body of each fly neuron resides in a rind (the cell body layer) on the periphery of the brain, mostly disjoint from the main neurites innervating the internal neuropil. As a result, unlike vertebrate neurons, no synapses form directly on the soma. The neuronal process between the soma and the first branch point is called the cell body fiber (CBF), which is likewise not involved in the synaptic transmission of information. Synapse sizes are much more uniform than those of mammals. Stronger connections are formed by increasing the number of synapses in parallel, not by forming larger synapses, as in vertebrates. In this paper, we will refer to the ‘strength’ of a connection as the synapse count, even though we acknowledge that we lack information on the relative activity and strength of the synapses, and thus a true measure of their coupling strength. The brain is small, about 250 μm per side, and has roughly the same size as the dendritic arbor of a single pyramidal neuron in the mammalian cortex. Axons of fly neurons are not myelinated. Some fly neurons rely on graded transmission (as opposed to spiking), without obvious anatomical distinction. Some neurons even switch between graded and spiking operation (Pimentel et al., 2016). Connectome reconstruction Producing a connectome comprising reconstructed neurons and the chemical synapses between them required several steps. The first step, preparing a fly brain and imaging half of its center, produced a dataset consisting of 26 teravoxels of data, each with 8 bits of grayscale information. We applied numerous machine-learning algorithms and over 50 person-years of proofreading effort over ≈2 calendar years to extract a variety of more compact and useful representations, such as neuron skeletons, synapse locations, and connectivity graphs. These are both more useful and much smaller than the raw grayscale data. For example, the connectivity could be reasonably summarized by a graph with ≈25,000 nodes and ≈3 million edges. Even when the connections were assigned to different brain regions, such a graph took only 26 MB, still large but roughly a million fold reduction in data size. Many of the supporting methods for this reconstruction have been recently published. Here, we briefly survey each major area, with more details reported in the companion papers. Major advances include: New methods to fix and stain the sample, preparing a whole fly brain with well-preserved subcellular detail particularly suitable for machine analysis. Methods that have enabled us to collect the largest EM dataset yet using Focused Ion Beam Scanning Electron Microscopy (FIB-SEM), resulting in isotropic data with few artifacts, features that significantly sped up reconstruction. A coarse-to-fine, automated flood-filling network segmentation pipeline applied to image data normalized with cycle-consistent generative adversarial networks, and an aggressive automated agglomeration regime enabled by advances in proofreading. A new hybrid synapse prediction method, using two differing underlying techniques, for accurate synapse prediction throughout the volume. New top-down proofreading methods that utilize visualization and machine learning to achieve orders of magnitude faster reconstruction compared with previous approaches in the fly’s brain. Each of these is explained in more detail in the following sections and, where necessary, in the appendix. The companion papers are ‘The connectome of the Drosophila melanogaster mushroom body: implications for function’ (Li et al., 2020) and ‘A complete synaptic-resolution connectome of the Drosophila melanogaster central complex’ by Jayaraman, et al. Image stack collection The first steps, fixing and staining the specimen, have been accomplished taking advantage of three new developments. These improved methods allow us to fix and stain a full fly’s brain but nevertheless recover neurons as round profiles with darkly stained synapses, suitable for machine segmentation and automatic synapse detection. We started with a 5-day-old female of wild-type Canton S strain G1 x w1118, raised on a 12 hr day/night cycle. 1.5 hr after lights-on, we used a custom-made jig to microdissect the brain, which was then fixed and embedded in Epon, an epoxy resin. We then enhanced the electron contrast by staining with heavy metals, and progressively lowered the temperature during dehydration of the sample. Collectively, these methods optimize morphological preservation, allow full-brain preparation without distortion (unlike fast freezing methods), and provide increased staining intensity that speeds the rate of FIB-SEM imaging (Lu et al., 2019). The hemibrain sample is roughly 250 × 250 × 250 μm, larger than we can FIB-SEM without introducing milling artifacts. Therefore, we subdivided our epoxy-embedded samples into 20-μm-thick slabs, both to avoid artifacts and allow imaging in parallel (each slab can be imaged in a different FIB machine) for increased throughput. To be effective, the cut surfaces of the slabs must be smooth at the ultrastructural level and have only minimal material loss. Specifically, for connectomic research, all long-distance processes must remain traceable across sequential slabs. We used an improved version of our previously published ‘hot-knife’ ultrathick sectioning procedure (Hayworth et al., 2015) which uses a heated, oil-lubricated diamond knife, to section the Drosophila brain into 37 sagittal slabs of 20 μm thickness with an estimated material loss between consecutive slabs of only ∼30 nm – sufficiently small to allow tracing of long-distance neurites. Each slab was re-embedded, mounted, and trimmed, then examined in 3D with X-ray tomography to check for sample quality and establish a scale factor for Z-axis cutting by FIB. The resulting slabs were FIB-SEM imaged separately (often in parallel, in different FIB-SEM machines), and the resulting volume datasets were stitched together computationally. Connectome studies come with clearly defined resolution requirements – the finest neurites must be traceable by humans and should be reliably segmented by automated algorithms (Januszewski et al., 2018). In Drosophila, the very finest neural processes are usually 50 nm but can be as little as 15 nm (Meinertzhagen, 2016a). This fundamental biological dimension determines the minimum isotropic resolution requirements for tracing neural circuits. To meet the demand for high isotropic resolution and large volume imaging, we chose the FIB-SEM imaging platform, which offers high isotropic resolution (<10 nm in x, y, and z), minimal artifacts, and robust image alignment. The high-resolution and isotropic dataset possible with FIB-SEM has substantially expedited the Drosophila connectome pipeline. Compared to serial-section imaging, with its sectioning artifacts and inferior Z-axis resolution, FIB-SEM offers high-quality image alignment, a smaller number of artifacts, and isotropic resolution. This allows higher quality automated segmentation and makes manual proofreading and correction easier and faster. At the beginning, deficiencies in imaging speed and system reliability of any commercial FIB-SEM system capped the maximum possible image volume to less than 0.01% of a full fly brain, problems that persist even now. To remedy them, we redesigned the entire control system, improved the imaging speed more than 10x, and created innovative solutions addressing all known failure modes, which thereby expanded the practical imaging volume of conventional FIB-SEM by more than four orders of magnitude from 103μm3 to 3⋅107 μm3, while maintaining an isotropic resolution of 8 × 8 × 8 nm voxels (Xu et al., 2017; Xu et al., 2020a). In order to overcome the aberration of a large field of view (up to 300 μm wide), we developed a novel tiling approach without sample stage movement, in which the imaging parameters of each tile are individually optimized through an in-line auto focus routine without overhead (Xu et al., 2020b). After numerous improvements, we have transformed the conventional FIB-SEM from a laboratory tool that is unreliable for more than a few days of imaging to a robust volume EM platform with effective long-term reliability, able to perform years of continuous imaging without defects in the final image stack. Imaging time, rather than FIB-SEM reliability, is now the main impediment to obtaining even larger volumes. In our study here, the Drosophila 'hemibrain', 13 consecutive hot-knifed slabs were imaged using two customized enhanced FIB-SEM systems, in which an FEI Magnum FIB column was mounted at 90° upon a Zeiss Merlin SEM. After data collection, streaking artifacts generated by secondary electrons along the FIB milling direction were computationally removed using a mask in the frequency domain. The image stacks were then aligned using a customized version of the software platform developed for serial section transmission electron microscopy (Zheng et al., 2018; Khairy et al., 2018), followed by binning

The neural circuits responsible for behavior remain largely unknown. Previous efforts have reconstructed the complete circuits of small animals, with hundreds of neurons, and selected circuits for larger animals. Here we (the FlyEM project at Janelia and collaborators at Google) summarize new methods and present the complete circuitry of a large fraction of the brain of a much more complex animal, the fruit fly Drosophila melanogaster . Improved methods include new procedures to prepare, image, align, segment, find synapses, and proofread such large data sets; new methods that define cell types based on connectivity in addition to morphology; and new methods to simplify access to a large and evolving data set. From the resulting data we derive a better definition of computational compartments and their connections; an exhaustive atlas of cell examples and types, many of them novel; detailed circuits for most of the central brain; and exploration of the statistics and structure of different brain compartments, and the brain as a whole. We make the data public, with a web site and resources specifically designed to make it easy to explore, for all levels of expertise from the expert to the merely curious. The public availability of these data, and the simplified means to access it, dramatically reduces the effort needed to answer typical circuit questions, such as the identity of upstream and downstream neural partners, the circuitry of brain regions, and to link the neurons defined by our analysis with genetic reagents that can be used to study their functions.Note: In the next few weeks, we will release a series of papers with more involved discussions. One paper will detail the hemibrain reconstruction with more extensive analysis and interpretation made possible by this dense connectome. Another paper will explore the central complex, a brain region involved in navigation, motor control, and sleep. A final paper will present insights from the mushroom body, a center of multimodal associative learning in the fly brain.

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 comprise more than 75 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.