Automated detection and segmentation of synaptic contacts in nearly isotropic serial electron microscopy images

AimSeg: A machine-learning-aided tool for axon, inner tongue and myelin segmentation

Electron microscopy (EM) images of axons and their ensheathing myelin from both the central and peripheral nervous system are used for assessing myelin formation, degeneration (demyelination) and regeneration (remyelination). The g-ratio is the gold standard measure of assessing myelin thickness and quality, and traditionally is determined from measurements made manually from EM images-a time-consuming endeavour with limited reproducibility. These measurements have also historically neglected the innermost uncompacted myelin sheath, known as the inner tongue. Nonetheless, the inner tongue has been shown to be important for myelin growth and some studies have reported that certain conditions can elicit its enlargement. Ignoring this fact may bias the standard g-ratio analysis, whereas quantifying the uncompacted myelin has the potential to provide novel insights in the myelin field. In this regard, we have developed AimSeg, a bioimage analysis tool for axon, inner tongue and myelin segmentation. Aided by machine learning classifiers trained on transmission EM (TEM) images of tissue undergoing remyelination, AimSeg can be used either as an automated workflow or as a user-assisted segmentation tool. Validation results on TEM data from both healthy and remyelinating samples show good performance in segmenting all three fibre components, with the assisted segmentation showing the potential for further improvement with minimal user intervention. This results in a considerable reduction in time for analysis compared with manual annotation. AimSeg could also be used to build larger, high quality ground truth datasets to train novel deep learning models. Implemented in Fiji, AimSeg can use machine learning classifiers trained in ilastik. This, combined with a user-friendly interface and the ability to quantify uncompacted myelin, makes AimSeg a unique tool to assess myelin growth.

CellRemorph: A Toolkit for Transforming, Selecting, and Slicing 3D Cell Structures on the Road to Morphologically Detailed Astrocyte Simulations

Understanding functions of astrocytes can be greatly enhanced by building and simulating computational models that capture their morphological details. Novel computational tools enable utilization of existing morphological data of astrocytes and building models that have appropriate level of details for specific simulation purposes. In addition to analyzing existing computational tools for constructing, transforming, and assessing astrocyte morphologies, we present here the CellRemorph toolkit implemented as an add-on for Blender, a 3D modeling platform increasingly recognized for its utility for manipulating 3D biological data. To our knowledge, CellRemorph is the first toolkit for transforming astrocyte morphologies from polygonal surface meshes into adjustable surface point clouds and vice versa, precisely selecting nanoprocesses, and slicing morphologies into segments with equal surface areas or volumes. CellRemorph is an open-source toolkit under the GNU General Public License and easily accessible via an intuitive graphical user interface. CellRemorph will be a valuable addition to other Blender add-ons, providing novel functionality that facilitates the creation of realistic astrocyte morphologies for different types of morphologically detailed simulations elucidating the role of astrocytes both in health and disease.

Three-dimensional ultrastructure analysis of organelles in injured motor neuron

Morphological analysis of organelles is one of the important clues for understanding the cellular conditions and mechanisms occurring in cells. In particular, nanoscale information within crowded intracellular organelles of tissues provide more direct implications when compared to analyses of cells in culture or isolation. However, there are some difficulties in detecting individual shape using light microscopy, including super-resolution microscopy. Transmission electron microscopy (TEM), wherein the ultrastructure can be imaged at the membrane level, cannot determine the whole structure, and analyze it quantitatively. Volume EM, such as focused ion beam/scanning electron microscopy (FIB/SEM), can be a powerful tool to explore the details of three-dimensional ultrastructures even within a certain volume, and to measure several parameters from them. In this review, the advantages of FIB/SEM analysis in organelle studies are highlighted along with the introduction of mitochondrial analysis in injured motor neurons. This would aid in understanding the morphological details of mitochondria, especially those distributed in the cell bodies as well as in the axon initial segment (AIS) in mouse tissues. These regions have not been explored thus far due to the difficulties encountered in accessing their images by conditional microscopies. Some mechanisms of nerve regeneration have also been discussed with reference to the obtained findings. Finally, future perspectives on FIB/SEM are introduced. The combination of biochemical and genetic understanding of organelle structures and a nanoscale understanding of their three-dimensional distribution and morphology will help to match achievements in genomics and structural biology.

Deep learning-based synapse counting and synaptic ultrastructure analysis of electron microscopy images

BACKGROUND

Synapses are the connections between neurons in the central nervous system (CNS) or between neurons and other excitable cells in the peripheral nervous system (PNS), where electrical or chemical signals rapidly travel through one cell to another with high spatial precision. Synaptic analysis, based on synapse numbers and fine morphology, is the basis for understanding neurological functions and diseases. Manual analysis of synaptic structures in electron microscopy (EM) images is often limited by low efficiency and subjective bias.

NEW METHOD

We developed a multifunctional synaptic analysis system based on several advanced deep learning (DL) models. The system achieved synapse counting in low-magnification EM images and synaptic ultrastructure analysis in high-magnification EM images.

RESULTS

The synapse counting system based on ResNet18 and a Faster R-CNN model had a mean average precision (mAP) of 92.55%. For synaptic ultrastructure analysis, the Faster R-CNN model based on ResNet50 achieved a mAP of 91.60%, the DeepLab v3 + model based on ResNet50 enabled high performance in presynaptic and postsynaptic membrane segmentation with a global accuracy of 0.9811, and the Faster R-CNN model based on ResNet18 achieved a mAP of 91.41% for synaptic vesicle detection.

CONCLUSIONS

The proposed multifunctional synaptic analysis system may help to overcome the experimental bias inherent in manual analysis, thereby facilitating EM image-based synaptic function studies.

How innovations in methodology offer new prospects for volume electron microscopy

Detailed knowledge of biological structure has been key in understanding biology at several levels of organisation, from organs to cells and proteins. Volume electron microscopy (volume EM) provides high resolution 3D structural information about tissues on the nanometre scale. However, the throughput rate of conventional electron microscopes has limited the volume size and number of samples that can be imaged. Recent improvements in methodology are currently driving a revolution in volume EM, making possible the structural imaging of whole organs and small organisms. In turn, these recent developments in image acquisition have created or stressed bottlenecks in other parts of the pipeline, like sample preparation, image analysis and data management. While the progress in image analysis is stunning due to the advent of automatic segmentation and server-based annotation tools, several challenges remain. Here we discuss recent trends in volume EM, emerging methods for increasing throughput and implications for sample preparation, image analysis and data management.

Automated Synapse Detection Method for Cerebellar Connectomics

The connectomic analyses of large-scale volumetric electron microscope (EM) images enable the discovery of hidden neural connectivity. While the technologies for neuronal reconstruction of EM images are under rapid progress, the technologies for synapse detection are lagging behind. Here, we propose a method that automatically detects the synapses in the 3D EM images, specifically for the mouse cerebellar molecular layer (CML). The method aims to accurately detect the synapses between the reconstructed neuronal fragments whose types can be identified. It extracts the contacts between the reconstructed neuronal fragments and classifies them as synaptic or non-synaptic with the help of type information and two deep learning artificial intelligences (AIs). The method can also assign the pre- and postsynaptic sides of a synapse and determine excitatory and inhibitory synapse types. The accuracy of this method is estimated to be 0.955 in F1-score for a test volume of CML containing 508 synapses. To demonstrate the usability, we measured the size and number of the synapses in the volume and investigated the subcellular connectivity between the CML neuronal fragments. The basic idea of the method to exploit tissue-specific properties can be extended to other brain regions.

Primate gastrulation and early organogenesis at single-cell resolution

Our understanding of human early development is severely hampered by limited access to embryonic tissues. Due to their close evolutionary relationship with humans, nonhuman primates are often used as surrogates to understand human development but currently suffer from a lack of in vivo datasets, especially from gastrulation to early organogenesis during which the major embryonic cell types are dynamically specified. To fill this gap, we collected six Carnegie stage 8-11 cynomolgus monkey (Macaca fascicularis) embryos and performed in-depth transcriptomic analyses of 56,636 single cells. Our analyses show transcriptomic features of major perigastrulation cell types, which help shed light on morphogenetic events including primitive streak development, somitogenesis, gut tube formation, neural tube patterning and neural crest differentiation in primates. In addition, comparative analyses with mouse embryos and human embryoids uncovered conserved and divergent features of perigastrulation development across species-for example, species-specific dependency on Hippo signalling during presomitic mesoderm differentiation-and provide an initial assessment of relevant stem cell models of human early organogenesis. This comprehensive single-cell transcriptome atlas not only fills the knowledge gap in the nonhuman primate research field but also serves as an invaluable resource for understanding human embryogenesis and developmental disorders.

Efficient and Accurate Synapse Detection With Selective Structured Illumination Microscopy on the Putative Regions of Interest of Ultrathin Serial Sections

Critical determinants of synaptic functions include subcellular locations, input sources, and specific molecular characteristics. However, there is not yet a reliable and efficient method that can detect synapses. Electron microscopy is a gold-standard method to detect synapses due to its exceedingly high spatial resolution. However, it requires laborious and time-consuming sample preparation and lengthy imaging time with limited labeling methods. Recent advances in various fluorescence microscopy methods have highlighted fluorescence microscopy as a substitute for electron microscopy in reliable synapse detection in a large volume of neural circuits. In particular, array tomography has been verified as a useful tool for neural circuit reconstruction. To further improve array tomography, we developed a novel imaging method, called “structured illumination microscopy on the putative region of interest on ultrathin sections”, which enables efficient and accurate detection of synapses-of-interest. Briefly, based on low-magnification conventional fluorescence microscopy images, synapse candidacy was determined. Subsequently, the coordinates of the regions with candidate synapses were imaged using super-resolution structured illumination microscopy. Using this system, synapses from the high-order thalamic nucleus, the posterior medial nucleus in the barrel cortex were rapidly and accurately imaged.

Whole-cell organelle segmentation in volume electron microscopy

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.

Cellular connectomes as arbiters of local circuit models in the cerebral cortex

With the availability of cellular-resolution connectivity maps, connectomes, from the mammalian nervous system, it is in question how informative such massive connectomic data can be for the distinction of local circuit models in the mammalian cerebral cortex. Here, we investigated whether cellular-resolution connectomic data can in principle allow model discrimination for local circuit modules in layer 4 of mouse primary somatosensory cortex. We used approximate Bayesian model selection based on a set of simple connectome statistics to compute the posterior probability over proposed models given a to-be-measured connectome. We find that the distinction of the investigated local cortical models is faithfully possible based on purely structural connectomic data with an accuracy of more than 90%, and that such distinction is stable against substantial errors in the connectome measurement. Furthermore, mapping a fraction of only 10% of the local connectome is sufficient for connectome-based model distinction under realistic experimental constraints. Together, these results show for a concrete local circuit example that connectomic data allows model selection in the cerebral cortex and define the experimental strategy for obtaining such connectomic data.

Upscaling X-ray nanoimaging to macroscopic specimens

Upscaling X-ray nanoimaging to macroscopic specimens has the potential for providing insights across multiple length scales, but its feasibility has long been an open question. By combining the imaging requirements and existing proof-of-principle examples in large-specimen preparation, data acquisition and reconstruction algorithms, the authors provide imaging time estimates for howX-ray nanoimaging can be scaled to macroscopic specimens. To arrive at this estimate, a phase contrast imaging model that includes plural scattering effects is used to calculate the required exposure and corresponding radiation dose. The coherent X-ray flux anticipated from upcoming diffraction-limited light sources is then considered. This imaging time estimation is in particular applied to the case of the connectomes of whole mouse brains. To image the connectome of the whole mouse brain, electron microscopy connectomics might require years, whereas optimized X-ray microscopy connectomics could reduce this to one week. Furthermore, this analysis points to challenges that need to be overcome (such as increased X-ray detector frame rate) and opportunities that advances in artificial-intelligence-based 'smart' scanning might provide. While the technical advances required are daunting, it is shown that X-ray microscopy is indeed potentially applicable to nanoimaging of millimetre- or even centimetre-size specimens.

Quantitative intravital Ca2+ imaging maps single cell behavior to kidney tubular structure

Ca2+ is an important second messenger that translates extracellular stimuli into intracellular responses. Although there has been significant progress in understanding Ca2+ dynamics in organs such as the brain, the nature of Ca2+ signals in the kidney is still poorly understood. Here, we show that by using a genetically expressed highly sensitive reporter (GCaMP6s), it is possible to perform imaging of Ca2+ signals at high resolution in the mouse kidney in vivo. Moreover, by applying machine learning-based automated analysis using a Ca2+-independent signal, quantitative data can be extracted in an unbiased manner. By projecting the resulting data onto the structure of the kidney, we show that different tubular segments display highly distinct spatiotemporal patterns of Ca2+ signals. Furthermore, we provide evidence that Ca2+ activity in the proximal tubule decreases with increasing distance from the glomerulus. Finally, we demonstrate that substantial changes in intracellular Ca2+ can be detected in proximal tubules in a cisplatin model of acute kidney injury, which can be linked to alterations in cell structure and transport function. In summary, we describe a powerful new tool to investigate how single cell behavior is integrated with whole organ structure and function and how it is altered in disease states relevant to humans.

Two Stream Active Query Suggestion for Active Learning in Connectomics

For large-scale vision tasks in biomedical images, the labeled data is often limited to train effective deep models. Active learning is a common solution, where a query suggestion method selects representative unlabeled samples for annotation, and the new labels are used to improve the base model. However, most query suggestion models optimize their learnable parameters only on the limited labeled data and consequently become less effective for the more challenging unlabeled data. To tackle this, we propose a two-stream active query suggestion approach. In addition to the supervised feature extractor, we introduce an unsupervised one optimized on all raw images to capture diverse image features, which can later be improved by fine-tuning on new labels. As a use case, we build an end-to-end active learning framework with our query suggestion method for 3D synapse detection and mitochondria segmentation in connectomics. With the framework, we curate, to our best knowledge, the largest connectomics dataset with dense synapses and mitochondria annotation. On this new dataset, our method outperforms previous state-of-the-art methods by 3.1% for synapse and 3.8% for mitochondria in terms of region-of-interest proposal accuracy. We also apply our method to image classification, where it outperforms previous approaches on CIFAR-10 under the same limited annotation budget. The project page is https://zudi-lin.github.io/projects/#two_stream_active.

Multiparametric imaging reveals that mitochondria-rich intercalated cells in the kidney collecting duct have a very high glycolytic capacity

Alpha intercalated cells (αICs) in the kidney collecting duct (CD) belong to a family of mitochondria rich cells (MRCs) and have a crucial role in acidifying the urine via apical V-ATPase pumps. The nature of metabolism in αICs and its relationship to transport was not well-understood. Here, using multiphoton live cell imaging in mouse kidney tissue, FIB-SEM, and other complementary techniques, we provide new insights into mitochondrial structure and function in αICs. We show that αIC mitochondria have a rounded structure and are not located in close proximity to V-ATPase containing vesicles. They display a bright NAD(P)H fluorescence signal and low uptake of voltage-dependent dyes, but are energized by a pH gradient. However, expression of complex V (ATP synthase) is relatively low in αICs, even when stimulated by metabolic acidosis. In contrast, anaerobic glycolytic capacity is surprisingly high, and sufficient to maintain intracellular calcium homeostasis in the presence of complete aerobic inhibition. Moreover, glycolysis is essential for V-ATPase-mediated proton pumping. Key findings were replicated in narrow/clear cells in the epididymis, also part of the MRC family. In summary, using a range of cutting-edge techniques to investigate αIC metabolism in situ, we have discovered that these mitochondria dense cells have a high glycolytic capacity.

Oxygen Tension and the VHL-Hif1α Pathway Determine Onset of Neuronal Polarization and Cerebellar Germinal Zone Exit

Postnatal brain circuit assembly is driven by temporally regulated intrinsic and cell-extrinsic cues that organize neurogenesis, migration, and axo-dendritic specification in post-mitotic neurons. While cell polarity is an intrinsic organizer of morphogenic events, environmental cues in the germinal zone (GZ) instructing neuron polarization and their coupling during postnatal development are unclear. We report that oxygen tension, which rises at birth, and the von Hippel-Lindau (VHL)-hypoxia-inducible factor 1α (Hif1α) pathway regulate polarization and maturation of post-mitotic cerebellar granule neurons (CGNs). At early postnatal stages with low GZ vascularization, Hif1α restrains CGN-progenitor cell-cycle exit. Unexpectedly, cell-intrinsic VHL-Hif1α pathway activation also delays the timing of CGN differentiation, germinal zone exit, and migration initiation through transcriptional repression of the partitioning-defective (Pard) complex. As vascularization proceeds, these inhibitory mechanisms are downregulated, implicating increasing oxygen tension as a critical switch for neuronal polarization and cerebellar GZ exit.

Development of protocols for the first serial block-face scanning electron microscopy (SBF SEM) studies of bone tissue

There is an unmet need for a high-resolution three-dimensional (3D) technique to simultaneously image osteocytes and the matrix in which these cells reside. In serial block-face scanning electron microscopy (SBF SEM), an ultramicrotome mounted within the vacuum chamber of a microscope repeatedly sections a resin-embedded block of tissue. Backscattered electron scans of the block face provide a stack of high-resolution two-dimensional images, which can be used to visualise and quantify cells and organelles in 3D. High-resolution 3D images of biological tissues from SBF SEM have been exploited considerably to date in the neuroscience field. However, non-brain samples, in particular hard biological tissues, have appeared more challenging to image by SBF SEM due to the difficulties of sectioning and rendering the samples conductive. We have developed and propose protocols for bone tissue preparation using SBF SEM, for imaging simultaneously soft and hard bone tissue components in 3D. We review the state of the art in high-resolution imaging of osteocytes, provide a historical perspective of SBF SEM, and we present first SBF SEM proof-of-concept studies for murine and human tissue. The application of SBF SEM to hard tissues will facilitate qualitative and quantitative 3D studies of tissue microstructure and ultrastructure in bone development, ageing and pathologies such as osteoporosis and osteoarthritis.

Tunable and Reversible Substrate Stiffness Reveals a Dynamic Mechanosensitivity of Cardiomyocytes

New directions in material applications have allowed for the fresh insight into the coordination of biophysical cues and regulators. Although the role of the mechanical microenvironment on cell responses and mechanics is often studied, most analyses only consider static environments and behavior, however, cells and tissues are themselves dynamic materials that adapt in myriad ways to alterations in their environment. Here, we introduce an approach, through the addition of magnetic inclusions into a soft poly(dimethylsiloxane) elastomer, to fabricate a substrate that can be stiffened nearly instantaneously in the presence of cells through the use of a magnetic gradient to investigate short-term cellular responses to dynamic stiffening or softening. This substrate allows us to observe time-dependent changes, such as spreading, stress fiber formation, Yes-associated protein translocation, and sarcomere organization. The identification of temporal dynamic changes on a short time scale suggests that this technology can be more broadly applied to study targeted mechanisms of diverse biologic processes, including cell division, differentiation, tissue repair, pathological adaptations, and cell-death pathways. Our method provides a unique in vitro platform for studying the dynamic cell behavior by better mimicking more complex and realistic microenvironments. This platform will be amenable to future studies aimed at elucidating the mechanisms underlying mechanical sensing and signaling that influence cellular behaviors and interactions.

Supervised learning to quantify amyloidosis in whole brains of an Alzheimer's disease mouse model acquired with optical projection tomography

Alzheimer's disease (AD) is characterized by amyloidosis of brain tissues. This phenomenon is studied with genetically-modified mouse models. We propose a method to quantify amyloidosis in whole 5xFAD mouse brains, a model of AD. We use optical projection tomography (OPT) and a random forest voxel classifier to segment and measure amyloid plaques. We validate our method in a preliminary cross-sectional study, where we measure 6136 ± 1637, 8477 ± 3438, and 17267 ± 4241 plaques (AVG ± SD) at 11, 17, and 31 weeks. Overall, this method can be used in the evaluation of new treatments against AD.

QuBiT: a quantitative tool for analyzing epithelial tubes reveals unexpected patterns of organization in the Drosophila trachea

Biological tubes are essential for animal survival, and their functions are dependent on tube shape. Analyzing the contributions of cell shape and organization to the morphogenesis of small tubes has been hampered by the limitations of existing programs in quantifying cell geometry on highly curved tubular surfaces and calculating tube-specific parameters. We therefore developed QuBiT (Quantitative Tool for Biological Tubes) and used it to analyze morphogenesis of the embryonic Drosophila trachea (airway). In the main tube, we find previously unknown anterior-to-posterior (A-P) gradients of cell apical orientation and aspect ratio, and periodicity in the organization of apical cell surfaces. Inferred cell intercalation during development dampens an A-P gradient of the number of cells per cross-section of the tube, but does not change the patterns of cell connectivity. Computationally 'unrolling' the apical surface of wild-type trachea and the hindgut reveals previously unrecognized spatial patterns of the apical marker Uninflatable and a non-redundant role for the Na⁺/K⁺ ATPase in apical marker organization. These unexpected findings demonstrate the importance of a computational tool for analyzing small diameter biological tubes.

DoGNet: A deep architecture for synapse detection in multiplexed fluorescence images

Neuronal synapses transmit electrochemical signals between cells through the coordinated action of presynaptic vesicles, ion channels, scaffolding and adapter proteins, and membrane receptors. In situ structural characterization of numerous synaptic proteins simultaneously through multiplexed imaging facilitates a bottom-up approach to synapse classification and phenotypic description. Objective automation of efficient and reliable synapse detection within these datasets is essential for the high-throughput investigation of synaptic features. Convolutional neural networks can solve this generalized problem of synapse detection, however, these architectures require large numbers of training examples to optimize their thousands of parameters. We propose DoGNet, a neural network architecture that closes the gap between classical computer vision blob detectors, such as Difference of Gaussians (DoG) filters, and modern convolutional networks. DoGNet is optimized to analyze highly multiplexed microscopy data. Its small number of training parameters allows DoGNet to be trained with few examples, which facilitates its application to new datasets without overfitting. We evaluate the method on multiplexed fluorescence imaging data from both primary mouse neuronal cultures and mouse cortex tissue slices. We show that DoGNet outperforms convolutional networks with a low-to-moderate number of training examples, and DoGNet is efficiently transferred between datasets collected from separate research groups. DoGNet synapse localizations can then be used to guide the segmentation of individual synaptic protein locations and spatial extents, revealing their spatial organization and relative abundances within individual synapses. The source code is publicly available: https://github.com/kulikovv/dognet.

DeepBouton: Automated Identification of Single-Neuron Axonal Boutons at the Brain-Wide Scale

Fine morphological reconstruction of individual neurons across the entire brain is essential for mapping brain circuits. Inference of presynaptic axonal boutons, as a key part of single-neuron fine reconstruction, is critical for interpreting the patterns of neural circuit wiring schemes. However, automated bouton identification remains challenging for current neuron reconstruction tools, as they focus mainly on neurite skeleton drawing and have difficulties accurately quantifying bouton morphology. Here, we developed an automated method for recognizing single-neuron axonal boutons in whole-brain fluorescence microscopy datasets. The method is based on deep convolutional neural networks and density-peak clustering. High-dimensional feature representations of bouton morphology can be learned adaptively through convolutional networks and used for bouton recognition and subtype classification. We demonstrate that the approach is effective for detecting single-neuron boutons at the brain-wide scale for both long-range pyramidal projection neurons and local interneurons.

Single-Cell RNA Sequencing of Microglia throughout the Mouse Lifespan and in the Injured Brain Reveals Complex Cell-State Changes

Microglia, the resident immune cells of the brain, rapidly change states in response to their environment, but we lack molecular and functional signatures of different microglial populations. Here, we analyzed the RNA expression patterns of more than 76,000 individual microglia in mice during development, in old age, and after brain injury. Our analysis uncovered at least nine transcriptionally distinct microglial states, which expressed unique sets of genes and were localized in the brain using specific markers. The greatest microglial heterogeneity was found at young ages; however, several states-including chemokine-enriched inflammatory microglia-persisted throughout the lifespan or increased in the aged brain. Multiple reactive microglial subtypes were also found following demyelinating injury in mice, at least one of which was also found in human multiple sclerosis lesions. These distinct microglia signatures can be used to better understand microglia function and to identify and manipulate specific subpopulations in health and disease.

FIB-SEM imaging properties of Drosophila melanogaster tissues embedded in Lowicryl HM20

Lowicryl resins enable processing of biological material for electron microscopy at the lowest temperatures compatible with resin embedding. When combined with high-pressure freezing and freeze-substitution, Lowicryl embedding supports preservation of fine structural details and fluorescent markers. Here, we analysed the applicability of Lowicryl HM20 embedding for focused ion beam (FIB) scanning electron microscopy (SEM) tomography of Drosophila melanogaster embryonic and larval model systems. We show that the freeze-substitution with per-mill concentrations of uranyl acetate provided sufficient contrast and an image quality of SEM imaging in the range of similar samples analysed by transmission electron microscopy (TEM). Preservation of genetically encoded fluorescent proteins allowed correlative localization of regions of interest (ROI) within the embedded tissue block. TEM on sections cut from the block face enabled evaluation of structural preservation to allow ROI ranking and thus targeted, time-efficient FIB-SEM tomography data collection. The versatility of Lowicryl embedding opens new perspectives for designing hybrid SEM-TEM workflows to comprehensively analyse biological structures. LAY DESCRIPTION: Focused ion beam scanning electron microscopy is becoming a widely used technique for the three-dimensional analysis of biological samples at fine structural details beyond levels feasible for light microscopy. To withstand the abrasion of material by the ion beam and the imaging by the scanning electron beam, biological samples have to be embedded into resins, most commonly these are very dense epoxy-based plastics. However, dense resins generate electron scattering which interferes with the signal from the biological specimen. Furthermore, to improve the imaging contrast, epoxy embedding requires chemical treatments with e.g. heavy metals, which deteriorate the ultrastructure of the biological specimen. In this study we explored the applicability of an electron lucent resin, Lowicryl HM 20, for focused ion beam scanning electron microscopy. The Lowicryl embedding workflow operates at milder chemical treatments and lower temperatures, thus preserving the sub-cellular and sub-organellar organization, as well as fluorescent markers visible by light microscopy. Here we show that focus ion beam scanning electron microscopy of Lowicryl-embedded fruit flies tissues provides reliable imaging revealing fine structural details. Our workflow benefited from use of transmission electron microscopy for the quality control of the ultrastructural preservation and fluorescent light microscopy for localization of regions of interest. The versatility of Lowicryl embedding opens up new perspectives for designing hybrid workflows combining fluorescent light, scanning, and transmission electron microscopy techniques to comprehensively analyze biological structures.

A resource for the Drosophila antennal lobe provided by the connectome of glomerulus VA1v

Using FIB-SEM we report the entire synaptic connectome of glomerulus VA1v of the right antennal lobe in Drosophila melanogaster. Within the glomerulus we densely reconstructed all neurons, including hitherto elusive local interneurons. The fruitless-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' pre/postsynaptic sites.

Fully-Automatic Synapse Prediction and Validation on a Large Data Set

Extracting a connectome from an electron microscopy (EM) data set requires identification of neurons and determination of connections (synapses) between neurons. As manual extraction of this information is very time-consuming, there has been extensive research efforts to automatically segment the neurons to help guide and eventually replace manual tracing. Until recently, there has been comparatively little research on automatic detection of the actual synapses between neurons. This discrepancy can, in part, be attributed to several factors: obtaining neuronal shapes is a prerequisite for the first step in extracting a connectome, manual tracing is much more time-consuming than annotating synapses, and neuronal contact area can be used as a proxy for synapses in determining connections. However, recent research has demonstrated that contact area alone is not a sufficient predictor of a synaptic connection. Moreover, as segmentation improved, we observed that synapse annotation consumes a more significant fraction of overall reconstruction time (upwards of 50% of total effort). This ratio will only get worse as segmentation improves, gating the overall possible speed-up. Therefore, we address this problem by developing algorithms that automatically detect presynaptic neurons and their postsynaptic partners. In particular, presynaptic structures are detected using a U-Net convolutional neural network (CNN), and postsynaptic partners are detected using a multilayer perceptron (MLP) with features conditioned on the local segmentation. This work is novel because it requires minimal amount of training, leverages advances in image segmentation directly, and provides a complete solution for polyadic synapse detection. We further introduce novel metrics to evaluate our algorithm on connectomes of meaningful size. When applied to the output of our method on EM data from Drosphila, these metrics demonstrate that a completely automatic prediction can be used to effectively characterize most of the connectivity correctly.

Excitatory and inhibitory synapse reorganization immediately after critical sensory experience in a vocal learner

Excitatory and inhibitory synapses are the brain’s most abundant synapse types. However, little is known about their formation during critical periods of motor skill learning, when sensory experience defines a motor target that animals strive to imitate. In songbirds, we find that exposure to tutor song leads to elimination of excitatory synapses in HVC (used here as a proper name), a key song generating brain area. A similar pruning is associated with song maturation, because juvenile birds have fewer excitatory synapses, the better their song imitations. In contrast, tutoring is associated with rapid insertion of inhibitory synapses, but the tutoring-induced structural imbalance between excitation and inhibition is eliminated during subsequent song maturation. Our work suggests that sensory exposure triggers the developmental onset of goal-specific motor circuits by increasing the relative strength of inhibition and it suggests a synapse-elimination model of song memorization.

A wide range of species use complex sounds to communicate, including humans and songbirds like zebra finches. During a critical period of learning, infants and young animals learn how to remember and discriminate this ‘language’ from other sounds. However, the changes that happen in the brain during this learning period are not well understood.

The process of learning forms new connections between neurons in the brain and prunes away old connections. These connections, known as synapses, come in different types. Signals sent across excitatory synapses increase the activity of the receiving neuron, while signals sent across inhibitory synapses reduce neuron activity.

What happens to the synapses in the brain during the critical period? To find out, Huang et al. used electron microscopy to examine the brains of young zebra finches that either had never heard birdsong, or had just heard birdsong for the first time. A single day of hearing song dramatically shifted the balance of excitatory and inhibitory synapses in the main vocal control area of the young birds’ brains. The number of excitatory synapses decreased, and the number of inhibitory synapses increased.

The balance between excitation and inhibition is important for the brain to work correctly. Therefore, as well as helping us to understand how infants learn their first language, the results presented by Huang et al. could also help us to improve treatments for conditions where this balance goes wrong, such as mood disorders. For example, tailoring the time point of medication intake in combination with sensory exposure therapies could improve how effectively either one works.

Methods for Mapping Neuronal Activity to Synaptic Connectivity: Lessons From Larval Zebrafish

For a mechanistic understanding of neuronal circuits in the brain, a detailed description of information flow is necessary. Thereby it is crucial to link neuron function to the underlying circuit structure. Multiphoton calcium imaging is the standard technique to record the activity of hundreds of neurons simultaneously. Similarly, recent advances in high-throughput electron microscopy techniques allow for the reconstruction of synaptic resolution wiring diagrams. These two methods can be combined to study both function and structure in the same specimen. Due to its small size and optical transparency, the larval zebrafish brain is one of the very few vertebrate systems where both, activity and connectivity of all neurons from entire, anatomically defined brain regions, can be analyzed. Here, we describe different methods and the tools required for combining multiphoton microscopy with dense circuit reconstruction from electron microscopy stacks of entire brain regions in the larval zebrafish.

From Light Microscopy to Analytical Scanning Electron Microscopy (SEM) and Focused Ion Beam (FIB)/SEM in Biology: Fixed Coordinates, Flat Embedding, Absolute References

Correlative light and electron microscopy (CLEM) has been in use for several years, however it has remained a costly method with difficult sample preparation. Here, we report a series of technical improvements developed for precise and cost-effective correlative light and scanning electron microscopy (SEM) and focused ion beam (FIB)/SEM microscopy of single cells, as well as large tissue sections. Customized coordinate systems for both slides and coverslips were established for thin and ultra-thin embedding of a wide range of biological specimens. Immobilization of biological samples was examined with a variety of adhesives. For histological sections, a filter system for flat embedding was developed. We validated ultra-thin embedding on laser marked slides for efficient, high-resolution CLEM. Target cells can be re-located within minutes in SEM without protracted searching and correlative investigations were reduced to a minimum of preparation steps, while still reaching highest resolution. The FIB/SEM milling procedure is facilitated and significantly accelerated as: (i) milling a ramp becomes needless, (ii) significant re-deposition of milled material does not occur; and (iii) charging effects are markedly reduced. By optimizing all technical parameters FIB/SEM stacks with 2 nm iso-voxels were achieved over thousands of sections, in a wide range of biological samples.

Multi-class segmentation of neuronal structures in electron microscopy images

BACKGROUND

Serial block face scanning electron microscopy (SBFEM) is becoming a popular technology in neuroscience. We have seen in the last years an increasing number of works addressing the problem of segmenting cellular structures in SBFEM images of brain tissue. The vast majority of them is designed to segment one specific structure, typically membranes, synapses and mitochondria. Our hypothesis is that the performance of these algorithms can be improved by concurrently segmenting more than one structure using image descriptions obtained at different scales.

RESULTS

We consider the simultaneous segmentation of two structures, namely, synapses with mitochondria, and mitochondra with membranes. To this end we select three image stacks encompassing different SBFEM acquisition technologies and image resolutions. We introduce both a new Boosting algorithm to perform feature scale selection and the Jaccard Curve as a tool compare several segmentation results. We then experimentally study the gains in performance obtained when simultaneously segmenting two structures with properly selected image descriptor scales. The results show that by doing so we achieve significant gains in segmentation accuracy when compared to the best results in the literature.

CONCLUSIONS

Simultaneously segmenting several neuronal structures described at different scales provides voxel classification algorithms with highly discriminating features that significantly improve segmentation accuracy.

A Review of User Interface Design for Interactive Machine Learning

Interactive Machine Learning (IML) seeks to complement human perception and intelligence by tightly integrating these strengths with the computational power and speed of computers. The interactive process is designed to involve input from the user but does not require the background knowledge or experience that might be necessary to work with more traditional machine learning techniques. Under the IML process, non-experts can apply their domain knowledge and insight over otherwise unwieldy datasets to find patterns of interest or develop complex data-driven applications. This process is co-adaptive in nature and relies on careful management of the interaction between human and machine. User interface design is fundamental to the success of this approach, yet there is a lack of consolidated principles on how such an interface should be implemented. This article presents a detailed review and characterisation of Interactive Machine Learning from an interactive systems perspective. We propose and describe a structural and behavioural model of a generalised IML system and identify solution principles for building effective interfaces for IML. Where possible, these emergent solution principles are contextualised by reference to the broader human-computer interaction literature. Finally, we identify strands of user interface research key to unlocking more efficient and productive non-expert interactive machine learning applications.

Lymphatic exosomes promote dendritic cell migration along guidance cues

Inflammation stimulates lymphatic endothelial cells to release exosomes, which accumulate in the perivascular stroma. Brown et al. show that these exosomes promote the directional migration of dendritic cells along guidance cues in complex environments by enhancing dynamic cellular protrusions in a CX3CL1-dependent manner.

Lymphatic endothelial cells (LECs) release extracellular chemokines to guide the migration of dendritic cells. In this study, we report that LECs also release basolateral exosome-rich endothelial vesicles (EEVs) that are secreted in greater numbers in the presence of inflammatory cytokines and accumulate in the perivascular stroma of small lymphatic vessels in human chronic inflammatory diseases. Proteomic analyses of EEV fractions identified >1,700 cargo proteins and revealed a dominant motility-promoting protein signature. In vitro and ex vivo EEV fractions augmented cellular protrusion formation in a CX3CL1/fractalkine-dependent fashion and enhanced the directional migratory response of human dendritic cells along guidance cues. We conclude that perilymphatic LEC exosomes enhance exploratory behavior and thus promote directional migration of CX3CR1-expressing cells in complex tissue environments.

Neuron Segmentation With High-Level Biological Priors

We present a novel approach to the problem of neuron segmentation in image volumes acquired by an electron microscopy. Existing methods, such as agglomerative or correlation clustering, rely solely on boundary evidence and have problems where such an evidence is lacking (e.g., incomplete staining) or ambiguous (e.g., co-located cell and mitochondria membranes). We investigate if these difficulties can be overcome by means of sparse region appearance cues that differentiate between pre- and postsynaptic neuron segments in mammalian neural tissue. We combine these cues with the traditional boundary evidence in the asymmetric multiway cut (AMWC) model, which simultaneously solves the partitioning and the semantic region labeling problems. We show that AMWC problems over superpixel graphs can be solved to global optimality with a cutting plane approach, and that the introduction of semantic class priors leads to significantly better segmentations.

The Calamistrum of the Feather-Legged Spider Uloborus plumipes Investigated by Focused Ion Beam and Scanning Electron Microscopy (FIB-SEM) Tomography

Spiders are natural specialists in fiber processing. In particular, cribellate spiders manifest this ability as they produce a wool of nanofibers to capture prey. During its production they deploy a sophisticated movement of their spinnerets to darn in the fibers as well as a comb-like row of setae, termed calamistrum, on the metatarsus which plays a key role in nanofiber processing. In comparison to the elaborate nanofiber extraction and handling process by the spider's calamistrum, the human endeavors of spinning and handling of artificial nanofibers is still a primitive technical process. An implementation of biomimetics in spinning technology could lead to new materials and applications. Despite the general progress in related fields of nanoscience, the expected leap forward in spinning technology depends on a better understanding of the specific shapes and surfaces that control the forces at the nanoscale and that are involved in the mechanical processing of the nanofibers, respectively. In this study, the authors investigated the morphology of the calamistrum of the cribellate spider Uloborus plumipes. Focused ion beam and scanning electron microscopy tomography provided a good image contrast and the best trade-off between investigation volume and spatial resolution. A comprehensive three-dimensional model is presented and the putative role of the calamistrum in nanofiber processing is discussed.

Knockout of Amyloid β Protein Precursor (APP) Expression Alters Synaptogenesis, Neurite Branching and Axonal Morphology of Hippocampal Neurons

The function of the β-A4 amyloid protein precursor (APP) of Alzheimer's disease (AD) remains unclear. APP has a number of putative roles in neuronal differentiation, survival, synaptogenesis and cell adhesion. In this study, we examined the development of axons, dendrites and synapses in cultures of hippocampus neutrons derived from APP knockout (KO) mice. We report that loss of APP function reduces the branching of cultured hippocampal neurons, resulting in reduced synapse formation. Using a compartmentalised culture approach, we found reduced axonal outgrowth in cultured hippocampal neurons and we also identified abnormal growth characteristics of isolated hippocampal neuron axons. Although APP has previously been suggested to play an important role in promoting cell adhesion, we surprisingly found that APPKO hippocampal neurons adhered more strongly to a poly-L-lysine substrate and their neurites displayed an increased density of focal adhesion puncta. The findings suggest that the function of APP has an important role in both dendritic and axonal growth and that endogenous APP may regulate substrate adhesion of hippocampal neurons. The results may explain neuronal and synaptic morphological abnormalities in APPKO mice and the presence of abnormal APP expression in dystrophic neurites around amyloid deposits in AD.

Comparison of 3D cellular imaging techniques based on scanned electron probes: Serial block face SEM vs. Axial bright-field STEM tomography

Microscopies based on focused electron probes allow the cell biologist to image the 3D ultrastructure of eukaryotic cells and tissues extending over large volumes, thus providing new insight into the relationship between cellular architecture and function of organelles. Here we compare two such techniques: electron tomography in conjunction with axial bright-field scanning transmission electron microscopy (BF-STEM), and serial block face scanning electron microscopy (SBF-SEM). The advantages and limitations of each technique are illustrated by their application to determining the 3D ultrastructure of human blood platelets, by considering specimen geometry, specimen preparation, beam damage and image processing methods. Many features of the complex membranes composing the platelet organelles can be determined from both approaches, although STEM tomography offers a higher ∼3 nm isotropic pixel size, compared with ∼5 nm for SBF-SEM in the plane of the block face and ∼30 nm in the perpendicular direction. In this regard, we demonstrate that STEM tomography is advantageous for visualizing the platelet canalicular system, which consists of an interconnected network of narrow (∼50-100 nm) membranous cisternae. In contrast, SBF-SEM enables visualization of complete platelets, each of which extends ∼2 µm in minimum dimension, whereas BF-STEM tomography can typically only visualize approximately half of the platelet volume due to a rapid non-linear loss of signal in specimens of thickness greater than ∼1.5 µm. We also show that the limitations of each approach can be ameliorated by combining 3D and 2D measurements using a stereological approach.

3D Electron Microscopy (EM) and Correlative Light Electron Microscopy (CLEM) Methods to Study Virus-Host Interactions

Viruses use different strategies to interact with their host and perform a successful viral infection that results in the formation of new infectious viral particles and their propagation to new hosts. Understanding how viruses interact with their hosts requires the use of high-resolution techniques for the direct visualization of these interactions. Here electron microscopy (EM) methods are described that allow the 3D ultrastructural analysis of virus-infected cells. These methods can be implemented with light microscopy (LM) to certainly allocate virus-infected cells or cells displaying a specific/interesting phenotype caused by the interaction of viral proteins with the cellular machinery. Some sample preparation procedures where LM is integrated, known as correlative light electron microscopy (CLEM), are also explained in this chapter. All of these methods are applicable to any kind of cultured cells, including influenza virus-infected cells.

Load Adaptation of Lamellipodial Actin Networks

Actin filaments polymerizing against membranes power endocytosis, vesicular traffic, and cell motility. In vitro reconstitution studies suggest that the structure and the dynamics of actin networks respond to mechanical forces. We demonstrate that lamellipodial actin of migrating cells responds to mechanical load when membrane tension is modulated. In a steady state, migrating cell filaments assume the canonical dendritic geometry, defined by Arp2/3-generated 70° branch points. Increased tension triggers a dense network with a broadened range of angles, whereas decreased tension causes a shift to a sparse configuration dominated by filaments growing perpendicularly to the plasma membrane. We show that these responses emerge from the geometry of branched actin: when load per filament decreases, elongation speed increases and perpendicular filaments gradually outcompete others because they polymerize the shortest distance to the membrane, where they are protected from capping. This network-intrinsic geometrical adaptation mechanism tunes protrusive force in response to mechanical load.

Volume electron microscopy of the distribution of synapses in the neuropil of the juvenile rat somatosensory cortex

Knowing the proportions of asymmetric (excitatory) and symmetric (inhibitory) synapses in the neuropil is critical for understanding the design of cortical circuits. We used focused ion beam milling and scanning electron microscopy (FIB/SEM) to obtain stacks of serial sections from the six layers of the juvenile rat (postnatal day 14) somatosensory cortex (hindlimb representation). We segmented in three-dimensions 6184 synaptic junctions and determined whether they were established on dendritic spines or dendritic shafts. Of all these synapses, 87–94% were asymmetric and 6–13% were symmetric. Asymmetric synapses were preferentially located on dendritic spines in all layers (80–91%) while symmetric synapses were mainly located on dendritic shafts (62–86%). Furthermore, we found that less than 6% of the dendritic spines establish more than one synapse. The vast majority of axospinous synapses were established on the spine head. Synapses on the spine neck were scarce, although they were more common when the dendritic spine established multiple synapses. This study provides a new large quantitative dataset that may contribute not only to the knowledge of the ultrastructure of the cortex, but also towards defining the connectivity patterns through all cortical layers.

SynEM, automated synapse detection for connectomics

Nerve tissue contains a high density of chemical synapses, about 1 per µm3 in the mammalian cerebral cortex. Thus, even for small blocks of nerve tissue, dense connectomic mapping requires the identification of millions to billions of synapses. While the focus of connectomic data analysis has been on neurite reconstruction, synapse detection becomes limiting when datasets grow in size and dense mapping is required. Here, we report SynEM, a method for automated detection of synapses from conventionally en-bloc stained 3D electron microscopy image stacks. The approach is based on a segmentation of the image data and focuses on classifying borders between neuronal processes as synaptic or non-synaptic. SynEM yields 97% precision and recall in binary cortical connectomes with no user interaction. It scales to large volumes of cortical neuropil, plausibly even whole-brain datasets. SynEM removes the burden of manual synapse annotation for large densely mapped connectomes.

Automated gold particle quantification of immunogold labeled micrographs

BACKGROUND

Immunogold cytochemistry is the method of choice for precise localization of antigens on a subcellular scale. The process of immunogold quantification in electron micrographs is laborious, especially for proteins with a dense distribution pattern.

NEW METHODS

Here I present a MATLAB based toolbox that is optimized for a typical immunogold analysis workflow. It combines automatic detection of gold particles through a multi-threshold algorithm with manual segmentation of cell membranes and regions of interests.

RESULTS

The automated particle detection algorithm was applied to a typical immunogold dataset of neural tissue, and was able to detect particles with a high degree of precision. Without manual correction, the algorithm detected 97% of all gold particles, with merely a 0.1% false-positive rate.

COMPARISONS WITH EXISTING METHOD(S)

To my knowledge, this is the first free and publicly available software custom made for immunogold analyses. The proposed particle detection method compares favorably to previously published algorithms.

CONCLUSIONS

The software presented here will be valuable tool for researchers in neuroscience working with immunogold cytochemistry.

Automated synaptic connectivity inference for volume electron microscopy

Teravoxel volume electron microscopy data sets from neural tissue can now be acquired in weeks, but data analysis requires years of manual labor. We developed the SyConn framework, which uses deep convolutional neural networks and random forest classifiers to infer a richly annotated synaptic connectivity matrix from manual neurite skeleton reconstructions by automatically identifying mitochondria, synapses and their types, axons, dendrites, spines, myelin, somata and cell types. We tested our approach on serial block-face electron microscopy data sets from zebrafish, mouse and zebra finch, and computed the synaptic wiring of songbird basal ganglia. We found that, for example, basal-ganglia cell types with high firing rates in vivo had higher densities of mitochondria and vesicles and that synapse sizes and quantities scaled systematically, depending on the innervated postsynaptic cell types.

Genetically targeted 3D visualisation of Drosophila neurons under Electron Microscopy and X-Ray Microscopy using miniSOG

Large dimension, high-resolution imaging is important for neural circuit visualisation as neurons have both long- and short-range patterns: from axons and dendrites to the numerous synapses at terminal endings. Electron Microscopy (EM) is the favoured approach for synaptic resolution imaging but how such structures can be segmented from high-density images within large volume datasets remains challenging. Fluorescent probes are widely used to localise synapses, identify cell-types and in tracing studies. The equivalent EM approach would benefit visualising such labelled structures from within sub-cellular, cellular, tissue and neuroanatomical contexts. Here we developed genetically-encoded, electron-dense markers using miniSOG. We demonstrate their ability in 1) labelling cellular sub-compartments of genetically-targeted neurons, 2) generating contrast under different EM modalities, and 3) segmenting labelled structures from EM volumes using computer-assisted strategies. We also tested non-destructive X-ray imaging on whole Drosophila brains to evaluate contrast staining. This enabled us to target specific regions for EM volume acquisition.

A framework for feedback-based segmentation of 3D image stacks

3D segmentation has become a widely used technique. However, automatic segmentation does not deliver high accuracy in optically dense images and manual segmentation lowers the throughput drastically. Therefore, we present a workflow for 3D segmentation being able to forecast segments based on a user-given ground truth. We provide the possibility to correct wrong forecasts and to repeatedly insert ground truth in the process. Our aim is to combine automated and manual segmentation and therefore to improve accuracy by a tunable amount of manual input.

Progress Towards Mammalian Whole-Brain Cellular Connectomics

Neurons are the fundamental structural units of the nervous system-i.e., the Neuron Doctrine-as the pioneering work of Santiago Ramón y Cajal in the 1880's clearly demonstrated through careful observation of Golgi-stained neuronal morphologies. However, at that time sample preparation, imaging methods and computational tools were either nonexistent or insufficiently developed to permit the precise mapping of an entire brain with all of its neurons and their connections. Some measure of the "mesoscopic" connectional organization of the mammalian brain has been obtained over the past decade by alignment of sparse subsets of labeled neurons onto a reference atlas or via MRI-based diffusion tensor imaging. Neither method, however, provides data on the complete connectivity of all neurons comprising an individual brain. Fortunately, whole-brain cellular connectomics now appears within reach due to recent advances in whole-brain sample preparation and high-throughput electron microscopy (EM), though substantial obstacles remain with respect to large volume electron microscopic acquisitions and automated neurite reconstructions. This perspective examines the current status and problems associated with generating a mammalian whole-brain cellular connectome and argues that the time is right to launch a concerted connectomic attack on a small mammalian whole-brain.