Morphological diversity of single neurons in molecularly defined cell types.

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

Automatic 3D Neuron Tracing from Optical Microscopy Images.

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