No abstract

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.

Characterizing cellular diversity at different levels of biological organization across data modalities is a prerequisite to understanding the function of cell types in the brain. Classification of neurons is also required 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 and demonstration of prototypes for human and non-human primate (NHP) brains. Here, we provide a guide to the cellular and spatial approaches employed, and to accessing and using the BICCN data and its 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 by the BICCN toward FAIR (Wilkinson et al. 2016a) neuroscience. The combined BICCN ecosystem provides a comprehensive resource for the exploration and analysis of cell types in the brain.

BackgroundNeuron morphology analysis is an essential component of neuron cell-type definition. Morphology reconstruction represents a bottleneck in high-throughput morphology analysis workflow, and erroneous extra reconstruction owing to noise and entanglements in dense neuron regions restricts the usability of automated reconstruction results. We propose SNAP, a structure-based neuron morphology reconstruction pruning pipeline, to improve the usability of results by reducing erroneous extra reconstruction and splitting entangled neurons.MethodsFor the four different types of erroneous extra segments in reconstruction (caused by noise in the background, entanglement with dendrites of close-by neurons, entanglement with axons of other neurons, and entanglement within the same neuron), SNAP incorporates specific statistical structure information into rules for erroneous extra segment detection and achieves pruning and multiple dendrite splitting.ResultsExperimental results show that this pipeline accomplishes pruning with satisfactory precision and recall. It also demonstrates good multiple neuron-splitting performance. As an effective tool for post-processing reconstruction, SNAP can facilitate neuron morphology analysis.

We conducted a large-scale study of whole-brain morphometry, analyzing 3.7 peta-voxels of mouse brain images at the single-cell resolution, producing one of the largest multi-morphometry databases of mammalian brains to date. We spatially registered 205 mouse brains and associated data from six Brain Initiative Cell Census Network (BICCN) data sources covering three major imaging modalities from five collaborative projects to the Allen Common Coordinate Framework (CCF) atlas, annotated 3D locations of cell bodies of 227,581 neurons, modeled 15,441 dendritic microenvironments, characterized the full morphology of 1,891 neurons along with their axonal motifs, and detected 2.58 million putative synaptic boutons. Our analysis covers six levels of information related to neuronal populations, dendritic microenvironments, single-cell full morphology, sub-neuronal dendritic and axonal arborization, axonal boutons, and structural motifs, along with a quantitative characterization of the diversity and stereotypy of patterns at each level. We identified 16 modules consisting of highly intercorrelated brain regions in 13 functional brain areas corresponding to 314 anatomical regions in CCF. Our analysis revealed the dendritic microenvironment as a powerful method for delineating brain regions of cell types and potential subtypes. We also found that full neuronal morphologies can be categorized into four distinct classes based on spatially tuned morphological features, with substantial cross-areal diversity in apical dendrites, basal dendrites, and axonal arbors, along with quantified stereotypy within cortical, thalamic and striatal regions. The lamination of somas was found to be more effective in differentiating neuron arbors within the cortex. Further analysis of diverging and converging projections of individual neurons in 25 regions throughout the brain reveals branching preferences in the brain-wide and local distributions of axonal boutons. Overall, our study provides a comprehensive description of key anatomical structures of neurons and their types, covering a wide range of scales and features, and contributes to our understanding of neuronal diversity and its function in the mammalian brain.