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Whole-brain imaging has become an increasingly important approach to investigate neural structures, such as somata distribution, dendritic morphology, and axonal projection patterns. Different structures require whole-brain imaging at different resolutions. Thus, it is highly desirable to perform whole-brain imaging at multiple scales. Imaging a complete mammalian brain at synaptic resolution is especially challenging, as it requires continuous imaging from days to weeks because of the large number of voxels to sample, and it is difficult to acquire a constant quality of imaging because of light scattering during in toto imaging. Here, we reveal that light-sheet microscopy has a unique advantage over wide-field microscopy in multi-scale imaging because of its decoupling of illumination and detection. Based on this observation, we have developed a multi-scale light-sheet microscope that combines tiling of light-sheet, automatic zooming, periodic sectioning, and tissue expansion to achieve a constant quality of brain-wide imaging from cellular (3 μm × 3 μm × 8 μm) to sub-micron (0.3 μm × 0.3 μm × 1 μm) spatial resolution rapidly (all within a few hours). We demonstrated the strength of the system by testing it using mouse brains prepared using different clearing approaches. We were able to track electrode tracks as well as axonal projections at sub-micron resolution to trace the full morphology of single medial prefrontal cortex (mPFC) neurons that have remarkable diversity in long-range projections.

Dendritic and axonal morphology reflects the input and output of neurons and is a defining feature of neuronal types 1 , 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. Sparse labelling and whole-brain imaging are used to reconstruct and classify brain-wide complete morphologies of 1,741 individual neurons in the mouse brain, revealing a dependence on both brain region and transcriptomic profile.

Neuron tracing is the process of reconstructing three-dimensional morphology of neurons from microscopy images. It is essential for delivering more comprehensive understanding of the relationship between neuronal structure and function, which is the fundamental to know how the brain works. However, currently neuron tracing remains a challenging task, due to the natural complexity of neuronal structure, inadequate available data and computational limitation. In recent years, many automatic neuron tracing methods have been developed in the research field, with limited success on specific issues. The lack of a robust neuron tracing method with more general applicability greatly restrains systematic characterisation and analysis on neuronal morphology. To address aforementioned challenges, we first establish a pipeline to generate more standard data, in which we specifically propose a novel approach for automatic refinement on semi-manual reconstruction. Following the pipeline, we manage to generate more than 1000 full morphology data. Second, based on the generated standard reconstruction, we conduct a systematic and quantitative analysis to identify the most critical obstacles in neuron tracing. Third, we propose a novel neuron tracing method by embedding occupancy learning with curve skeleton extraction, which tackles the major issues of weak and punctuated signal, as concluded from the previous analysis. We curated a large dataset to train and test the model. The experimental results show it exceeds other counterpart approaches in most terms of evaluation metrics. At last, we propose a novel learning model for automatic neuron tracing, which learns to directly extracts the skeleton from a raw image. It addresses the main issue of close but irrelevant signal, as concluded previously. We train and bench test it on the curated dataset, as well as a public dataset. Experiments show it achieves state-of-the-art performances in all cases.

Ever since the seminal findings of Ramon y Cajal, dendritic and axonal morphology has been recognized as a defining feature of neuronal types. Yet our knowledge concerning the diversity of neuronal morphologies, in particular distal axonal projection patterns, is extremely limited. To systematically obtain single neuron full morphology on a brain-wide scale, we established a platform with five major components: sparse labeling, whole-brain imaging, reconstruction, registration, and classification. We achieved sparse, robust and consistent fluorescent labeling of a wide range of neuronal types by combining transgenic or viral Cre delivery with novel transgenic reporter lines. We acquired high-resolution whole-brain fluorescent images from a large set of sparsely labeled brains using fluorescence micro-optical sectioning tomography (fMOST). We developed a set of software tools for efficient large-volume image data processing, registration to the Allen Mouse Brain Common Coordinate Framework (CCF), and computer-assisted morphological reconstruction. We reconstructed and analyzed the complete morphologies of 1,708 neurons from the striatum, thalamus, cortex and claustrum. Finally, we classified these cells into multiple morphological and projection types and identified a set of region-specific organizational rules of long-range axonal projections at the single cell level. Specifically, different neuron types from different regions follow highly distinct rules in convergent or divergent projection, feedforward or feedback axon termination patterns, and between-cell homogeneity or heterogeneity. Major molecularly defined classes or types of neurons have correspondingly distinct morphological and projection patterns, however, we also identify further remarkably extensive morphological and projection diversity at more fine-grained levels within the major types that cannot presently be accounted for by preexisting transcriptomic subtypes. These insights reinforce the importance of full morphological characterization of brain cell types and suggest a plethora of ways different cell types and individual neurons may contribute to the function of their respective circuits.

ABSTRACT Ever since the seminal findings of Ramon y Cajal, dendritic and axonal morphology has been recognized as a defining feature of neuronal types. Yet our knowledge concerning the diversity of neuronal morphologies, in particular distal axonal projection patterns, is extremely limited. To systematically obtain single neuron full morphology on a brain-wide scale, we established a platform with five major components: sparse labeling, whole-brain imaging, reconstruction, registration, and classification. We achieved sparse, robust and consistent fluorescent labeling of a wide range of neuronal types by combining transgenic or viral Cre delivery with novel transgenic reporter lines. We acquired high-resolution whole-brain fluorescent images from a large set of sparsely labeled brains using fluorescence micro-optical sectioning tomography (fMOST). We developed a set of software tools for efficient large-volume image data processing, registration to the Allen Mouse Brain Common Coordinate Framework (CCF), and computer-assisted morphological reconstruction. We reconstructed and analyzed the complete morphologies of 1,708 neurons from the striatum, thalamus, cortex and claustrum. Finally, we classified these cells into multiple morphological and projection types and identified a set of region-specific organizational rules of long-range axonal projections at the single cell level. Specifically, different neuron types from different regions follow highly distinct rules in convergent or divergent projection, feedforward or feedback axon termination patterns, and between-cell homogeneity or heterogeneity. Major molecularly defined classes or types of neurons have correspondingly distinct morphological and projection patterns, however, we also identify further remarkably extensive morphological and projection diversity at more fine-grained levels within the major types that cannot presently be accounted for by preexisting transcriptomic subtypes. These insights reinforce the importance of full morphological characterization of brain cell types and suggest a plethora of ways different cell types and individual neurons may contribute to the function of their respective circuits. Competing Interest Statement The authors have declared no competing interest. Footnotes * This version of the manuscript has been revised to correct a typo in the title.