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

We report the generation of a multimodal cell census and atlas of the mammalian primary motor cortex (MOp or M1) 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. Together, our results advance the collective knowledge and understanding of brain cell type organization: First, our study reveals a unified molecular genetic landscape of cortical cell types that congruently integrates their transcriptome, open chromatin and DNA methylation maps. Second, cross-species analysis achieves a unified taxonomy of transcriptomic types and their hierarchical organization that are conserved from mouse to marmoset and human. Third, cross-modal analysis provides compelling evidence for the epigenomic, transcriptomic, and gene regulatory basis of neuronal phenotypes such as their physiological and anatomical properties, demonstrating the biological validity and genomic underpinning of neuron types and subtypes. Fourth, in situ single-cell transcriptomics provides a spatially-resolved cell type atlas of the motor cortex. Fifth, integrated transcriptomic, epigenomic and anatomical analyses reveal the correspondence between neural circuits and transcriptomic cell types. We further present an extensive genetic toolset for targeting and fate mapping glutamatergic projection neuron types toward linking their developmental trajectory to their circuit function. Together, our results establish a unified and mechanistic framework of neuronal cell type organization that integrates multi-layered molecular genetic and spatial information with multi-faceted phenotypic properties.

It is crucial to develop accurate and reliable algorithms for fine reconstruction of neural morphology from whole-brain image datasets. Even though the involvement of human experts in the reconstruction process can help to ensure the quality and accuracy of the reconstructions, automated refinement algorithms are necessary to handle substantial deviations problems of reconstructed branches and bifurcation points from the large-scale and high-dimensional nature of the image data. Our proposed Neuron Reconstruction Refinement Strategy (NRRS) is a novel approach to address the problem of deviation errors in neuron morphology reconstruction. Our method partitions the reconstruction into fixed-size segments and resolves the deviation problems by re-tracing in two steps. We also validate the performance of our method using a synthetic dataset. Our results show that NRRS outperforms existing solutions and can handle most deviation errors. We apply our method to SEU-ALLEN/BICCN dataset containing 1741 complete neuron reconstructions and achieve remarkable improvements in the accuracy of the neuron skeleton representation, the task of radius estimation and axonal bouton detection. Our findings demonstrate the critical role of NRRS in refining neuron morphology reconstruction. Availability and implementation The proposed refinement method is implemented as a Vaa3D plugin and the source code are available under the repository of vaa3d_tools/hackathon/Levy/refinement. The original fMOST images of mouse brains can be found at the BICCN’s Brain Image Library (BIL) (https://www.brainimagelibrary.org). The synthetic dataset is hosted on GitHub (https://github.com/Vaa3D/vaa3d_tools/tree/master/hackathon/Levy/refinement). Supplementary information Supplementary data are available at Bioinformatics Advances online.