The BRAIN Initiative Cell Census Network Data Ecosystem: A User’s Guide

Abstract

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.

OME-Zarr: a cloud-optimized bioimaging file format with international community support

“…Institutes with large scale parallel (HPC) file systems like the Pittsburgh Supercomputing Center, home to the Brain Image Library 32 (BIL), are building tools to share OME-Zarr formatted data without object storage and without modifying the existing data repository 33 . Still in early development, these services will make 1000s of whole brain microscopy datasets, including murine, NHP and human, some expected to be over a petabyte in size, from the BICCN (BRAIN Initiative Cell Census Network) (BICCN Data Ecosystem Collaboration et al 2022) and BICAN (BRAIN Initiative Cell Atlas Network) available in OME-Zarr where appropriate starting this year. To achieve this, microservices will automatically provide an OME-Zarr representation of the day on the fly to prevent data duplication.…”

A neuroscientist’s guide to using murine brain atlases for efficient analysis and transparent reporting

“…However, implementations are typically tailored to specific data types (e.g., fluorescent images or 3D data) and may require coding skills. Thus, tools with a graphical user interface that are applicable to a broad range of data types have also been developed, often incorporated as part of analytic workflows ( Tappan et al, 2019 ; Ueda et al, 2020 ; BICCN Data Ecosystem Collaboration et al, 2022 ; Tyson and Margrie, 2022 ). An example of a standalone tool for spatial registration of histological sections to volumetric atlases is QuickNII ( RRID:SCR_017978 ; Puchades et al, 2019 ).…”

“…As these data are spatially coherent and avoid the deformities and damage seen in histological sections, they lend themselves well to volume-to-volume registration with 3D reference atlases. Several groups have developed computational methods for this type of alignment [see review by BICCN Data Ecosystem Collaboration et al (2022) 3 and Tyson and Margrie (2022) ], most often toward the Allen Mouse Brain CCF. The Elastix toolbox ( Klein et al, 2010 ) also offers a collection of algorithms that can be used for 3D image registration.…”

Hippocampome.org v2.0: a knowledge base enabling data-driven spiking neural network simulations of rodent hippocampal circuits

“…The National Institutes of Health launched the BRAIN Initiative Cell Census Network (BICCN) to help establish a comprehensive reference of the cell type diversity in the human, mouse, and non-human primate brains (BRAIN Initiative Cell Census Network, 2021). This multi-institution collaboration is already producing innovative results (Muñoz-Castañeda et al, 2021) and actionable community resources (BICCN Data Ecosystem Collaboration, 2022).…”