A guide to the BRAIN Initiative Cell Census Network data ecosystem

Michael Hawrylycz(Allen Institute for Brain Science), Maryann E. Martone(San Francisco VA Medical Center), Giorgio A. Ascoli(George Mason University), Jan G. Bjaalie(University of Oslo), Hong‐Wei Dong(University of California, Los Angeles), Satrajit Ghosh(McGovern Institute for Brain Research), Jesse Gillis(University of Toronto), Ronna Hertzano(University of Maryland, Baltimore), David R. Haynor(University of Washington), Patrick R. Hof(Allen Institute for Brain Science), Yongsoo Kim(Pennsylvania State University), Ed S. Lein(Allen Institute for Brain Science), Yufeng Liu(Southeast University), Jeremy A. Miller(Allen Institute for Brain Science), Partha P. Mitra(Cold Spring Harbor Laboratory), Eran A. Mukamel(University of California San Diego), Lydia Ng(Allen Institute for Brain Science), David Osumi-Sutherland(European Bioinformatics Institute), Hanchuan Peng(Southeast University), Patrick L. Ray(Allen Institute for Brain Science), Raymond Sanchez(Allen Institute for Brain Science), Aviv Regev, Alex Ropelewski(Pittsburgh Supercomputing Center), Richard H. Scheuermann(J. Craig Venter Institute), Shawn Zheng Kai Tan(European Bioinformatics Institute), Carol L. Thompson(Allen Institute for Brain Science), Timothy L. Tickle(Broad Institute), Hagen Tilgner(Cornell University), Merina Varghese(Allen Institute for Brain Science), Brock A. Wester(Johns Hopkins University Applied Physics Laboratory), Owen White(University of Maryland, Baltimore), Hongkui Zeng(Allen Institute for Brain Science), Brian D. Aevermann(Chan Zuckerberg Initiative (United States)), David Allemang, Seth A. Ament(University of Maryland, Baltimore), Thomas L. Athey(Johns Hopkins University), C L Baker, Katherine Baker(Allen Institute for Brain Science), Pamela Baker(Allen Institute for Brain Science), Anita Bandrowski(University of California San Diego), Samik Banerjee(Cold Spring Harbor Laboratory), Prajal Bishwakarma(Allen Institute for Brain Science), Ambrose Carr(Chan Zuckerberg Initiative (United States)), Min Chen(University of Pennsylvania), Roni Choudhury, Jonah Cool(Chan Zuckerberg Initiative (United States)), Heather H. Creasy(University of Maryland, Baltimore), Florence D. D’Orazi(Chan Zuckerberg Initiative (United States)), Kylee Degatano(Broad Institute), Ben Dichter, Song‐Lin Ding(Allen Institute for Brain Science), Tim Dolbeare(Allen Institute for Brain Science), Joseph R. Ecker(Salk Institute for Biological Studies), Rongxin Fang(University of California San Diego), Jean‐Christophe Fillion‐Robin, Timothy P. Fliss(Allen Institute for Brain Science), James C. Gee(University of Pennsylvania), Tom Gillespie(University of California San Diego), Nathan W. Gouwens(Allen Institute for Brain Science), Guo‐Qiang Zhang(The University of Texas Health Science Center at Houston), Yaroslav O. Halchenko(Dartmouth College), Nomi L. Harris(Lawrence Berkeley National Laboratory), Brian R. Herb(University of Maryland, Baltimore), Houri Hintiryan(University of California, Los Angeles), Gregory Hood(Pittsburgh Supercomputing Center), S. Horvath, Bing‐Xing Huo(Cold Spring Harbor Laboratory), Dorota Jarecka(McGovern Institute for Brain Research), Shengdian Jiang(Southeast University), Farzaneh Khajouei(Broad Institute), Elizabeth Kiernan(Broad Institute), Hüseyin Kır(European Bioinformatics Institute), Lauren Kruse(Allen Institute for Brain Science), Changkyu Lee(Allen Institute for Brain Science), Boudewijn P. F. Lelieveldt(Leiden University Medical Center), Yang Eric Li(University of California San Diego), Hanqing Liu(Salk Institute for Biological Studies), Lijuan Liu(Southeast University), Anup Markuhar(University of Maryland, Baltimore), James C. Mathews(Allen Institute for Brain Science), Kaylee L. Mathews(Broad Institute), Christopher Mezias(Cold Spring Harbor Laboratory), Michael I. Miller(Johns Hopkins University), Tyler Mollenkopf(Allen Institute for Brain Science), Shoaib Mufti(Allen Institute for Brain Science), Chris Mungall(Lawrence Berkeley National Laboratory), Joshua Orvis(University of Maryland, Baltimore), Maja Puchades(University of Oslo), Lei Qu(Southeast University), Joseph P. Receveur(University of Maryland, Baltimore), Bing Ren(University of California San Diego), Nathan Sjoquist(Microsoft (United States)), Brian Staats(Allen Institute for Brain Science), Daniel J. Tward(University of California, Los Angeles), Cindy T. J. van Velthoven(Allen Institute for Brain Science), Quanxin Wang(Allen Institute for Brain Science), Fangming Xie(University of California, Los Angeles), Hua Xu(The University of Texas Health Science Center at Houston), Zizhen Yao(Allen Institute for Brain Science), Zhixi Yun(Southeast University), Yun Renee Zhang(J. Craig Venter Institute), W. Jim Zheng(The University of Texas Health Science Center at Houston), Brian Zingg(University of California, Los Angeles)
PLoS Biology
June 30, 2023
Cited by 52Open Access
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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.


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