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

Using Cohesive Zone Model to Simulate the Hydraulic Fracture Interaction with Natural Fracture in Poro-Viscoelastic Formation

Hydraulic fracturing is a widely used production stimulation technology for conventional and unconventional reservoirs. The cohesive element is used to explain the tip fracture process. In this paper, the cohesive zone model was used to simulate hydraulic fracture initiation and propagation at the same time rock deformation and fluid exchange. A numerical model for fracture propagation in poro-viscoelastic formation is considered. In this numerical model, we incorporate the pore-pressure effect by coupling fluid diffusion with shale matrix viscoelasticity. The numerical procedure for hydraulically driven fracture propagation uses a poro-viscoelasticity theory to describe the fluid diffusion and matrix creep in the solid skeleton, in conjunction with pore-pressure cohesive zone model and ABAQUS was used as a platform for the numerical simulation. The simulation results are compared with the available solutions in the literature. The higher the approaching angle, the higher the differential stress, tensile stress difference, injection rate, and injection fluid viscosity, and it will be easier for hydraulic fracture crossing natural fracture. These results could provide theoretical guidance for predicting the generation of fracture network and gain a better understanding of deformational behavior of shale when fracturing

A guide to the BRAIN Initiative Cell Census Network data ecosystem

Characterizing cellular diversity at different levels of biological organization and across data modalities is a prerequisite to understanding the function of cell types in the brain. Classification of neurons is also essential to manipulate cell types in controlled ways and to understand their variation and vulnerability in brain disorders. The BRAIN Initiative Cell Census Network (BICCN) is an integrated network of data-generating centers, data archives, and data standards developers, with the goal of systematic multimodal brain cell type profiling and characterization. Emphasis of the BICCN is on the whole mouse brain with demonstration of prototype feasibility for human and nonhuman primate (NHP) brains. Here, we provide a guide to the cellular and spatial approaches employed by the BICCN, and to accessing and using these data and extensive resources, including the BRAIN Cell Data Center (BCDC), which serves to manage and integrate data across the ecosystem. We illustrate the power of the BICCN data ecosystem through vignettes highlighting several BICCN analysis and visualization tools. Finally, we present emerging standards that have been developed or adopted toward Findable, Accessible, Interoperable, and Reusable (FAIR) neuroscience. The combined BICCN ecosystem provides a comprehensive resource for the exploration and analysis of cell types in the brain