The V3D system provides three-dimensional (3D) visualization of gigabyte-sized microscopy image stacks in real time on current laptops and desktops. Combined with highly ergonomic features for selecting an X, Y, Z location of an image directly in 3D space and for visualizing overlays of a variety of surface objects, V3D streamlines the on-line analysis, measurement, and proofreading of complicated image patterns. V3D is cross-platform and can be enhanced by plug-ins. We built V3D-Neuron on top of V3D to reconstruct complex 3D neuronal structures from large brain images. V3D-Neuron enables us to precisely digitize the morphology of a single neuron in a fruit fly brain in minutes, with about 17-fold improvement in reliability and 10-fold savings in time compared to other neuron reconstruction tools. Using V3D-Neuron, we demonstrated the feasibility of building a 3D digital atlas of neurite tracts in the fruit fly brain.

Dendritic and axonal morphology reflects the input and output of neurons and is a defining feature of neuronal types1,2, yet our knowledge of its diversity remains limited. Here, to systematically examine complete single-neuron morphologies on a brain-wide scale, we established a pipeline encompassing sparse labelling, whole-brain imaging, reconstruction, registration and analysis. We fully reconstructed 1,741 neurons from cortex, claustrum, thalamus, striatum and other brain regions in mice. We identified 11 major projection neuron types with distinct morphological features and corresponding transcriptomic identities. Extensive projectional diversity was found within each of these major types, on the basis of which some types were clustered into more refined subtypes. This diversity follows a set of generalizable principles that govern long-range axonal projections at different levels, including molecular correspondence, divergent or convergent projection, axon termination pattern, regional specificity, topography, and individual cell variability. Although clear concordance with transcriptomic profiles is evident at the level of major projection type, fine-grained morphological diversity often does not readily correlate with transcriptomic subtypes derived from unsupervised clustering, highlighting the need for single-cell cross-modality studies. Overall, our study demonstrates the crucial need for quantitative description of complete single-cell anatomy in cell-type classification, as single-cell morphological diversity reveals a plethora of ways in which different cell types and their individual members may contribute to the configuration and function of their respective circuits.

Here we report the generation of a multimodal cell census and atlas of the mammalian primary motor cortex as the initial product of the BRAIN Initiative Cell Census Network (BICCN). This was achieved by coordinated large-scale analyses of single-cell transcriptomes, chromatin accessibility, DNA methylomes, spatially resolved single-cell transcriptomes, morphological and electrophysiological properties and cellular resolution input–output mapping, integrated through cross-modal computational analysis. Our results advance the collective knowledge and understanding of brain cell-type organization1–5. First, our study reveals a unified molecular genetic landscape of cortical cell types that integrates their transcriptome, open chromatin and DNA methylation maps. Second, cross-species analysis achieves a consensus taxonomy of transcriptomic types and their hierarchical organization that is conserved from mouse to marmoset and human. Third, in situ single-cell transcriptomics provides a spatially resolved cell-type atlas of the motor cortex. Fourth, cross-modal analysis provides compelling evidence for the transcriptomic, epigenomic and gene regulatory basis of neuronal phenotypes such as their physiological and anatomical properties, demonstrating the biological validity and genomic underpinning of neuron types. We further present an extensive genetic toolset for targeting glutamatergic neuron types towards linking their molecular and developmental identity to their circuit function. Together, our results establish a unifying and mechanistic framework of neuronal cell-type organization that integrates multi-layered molecular genetic and spatial information with multi-faceted phenotypic properties.

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Motivation: Digital reconstruction of 3D neuron structures is an important step toward reverse engineering the wiring and functions of a brain. However, despite a number of existing studies, this task is still challenging, especially when a 3D microscopic image has low single-to-noise ratio and discontinued segments of neurite patterns.Results: We developed a graph-augmented deformable model (GD) to reconstruct (trace) the 3D structure of a neuron when it has a broken structure and/or fuzzy boundary. We formulated a variational problem using the geodesic shortest path, which is defined as a combination of Euclidean distance, exponent of inverse intensity of pixels along the path and closeness to local centers of image intensity distribution. We solved it in two steps. We first used a shortest path graph algorithm to guarantee that we find the global optimal solution of this step. Then we optimized a discrete deformable curve model to achieve visually more satisfactory reconstructions. Within our framework, it is also easy to define an optional prior curve that reflects the domain knowledge of a user. We investigated the performance of our method using a number of challenging 3D neuronal image datasets of different model organisms including fruit fly, Caenorhabditis elegans, and mouse. In our experiments, the GD method outperformed several comparison methods in reconstruction accuracy, consistency, robustness and speed. We further used GD in two real applications, namely cataloging neurite morphology of fruit fly to build a 3D ‘standard’ digital neurite atlas, and estimating the synaptic bouton density along the axons for a mouse brain.Availability: The software is provided as part of the V3D-Neuron 1.0 package freely available at http://penglab.janelia.org/proj/v3dContact: pengh@janelia.hhmi.org

Earlier studies have suggested abnormal brain volumes in autism, but inconsistencies exist. Using voxel-based morphometry, we compared global and regional brain volumes in 17 high-functioning autistic children with 15 matched controls. We identified significant reduction in left white matter volume and white/gray matter ratio in autism. Regional brain volume reductions were detected for right anterior cingulate, left superior parietal lobule white matter volumes, and right parahippocampal gyrus gray matter volume, whereas enlargements in bilateral supramarginal gyrus, right postcentral gyrus, right medial frontal gyrus, and right posterior lobe of cerebellum gray matter in autism. Our findings showed global and regional brain volumes abnormality in high-functioning autism.

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32Ever since the seminal findings of Ramon y Cajal, dendritic and axonal morphology has been 33 recognized as a defining feature of neuronal types and their connectivity. Yet our knowledge 34 about the diversity of neuronal morphology, in particular its distant axonal projections, is still 35 extremely limited. To systematically obtain single neuron full morphology on a brain-wide scale 36in mice, we established a pipeline that encompasses five major components: sparse labeling, 37whole-brain imaging, reconstruction, registration, and classification. We achieved sparse, robust 38and consistent fluorescent labeling of a wide range of neuronal types across the mouse brain in 39 an efficient way by combining transgenic or viral Cre delivery with novel transgenic reporter 40 lines, and generated a large set of high-resolution whole-brain fluorescent imaging datasets 41containing thousands of reconstructable neurons using the fluorescence micro-optical sectioning 42 tomography (fMOST) system. We developed a set of software tools based on the visualization 43 and analysis suite, Vaa3D, for large-volume image data processing and computation-assisted 44 morphological reconstruction. In a proof-of-principle case, we reconstructed full morphologies 45 of 96 neurons from the claustrum and cortex that belong to a single transcriptomically-defined 46 neuronal subclass. We developed a data-driven clustering approach to classify them into multiple 47 morphological and projection types, suggesting that these neurons work in a targeted and 48coordinated manner to process cortical information. Imaging data and the new computational 49 reconstruction tools are publicly available to enable community-based efforts towards large-scale 50 full morphology reconstruction of neurons throughout the entire mouse brain. 51 52 53