BRAIN Initiative Cell Census Network data catalog. Provides access to all major data sets of the BICCN with descriptions.

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.

Data for the MetaNeighbor notebook presented at the BICCN workshop (22/06/15).

The development of the human neocortex is a highly dynamic process and involves complex cellular trajectories controlled by cell-type-specific gene regulation. Here, we collected paired single-nucleus chromatin accessibility and transcriptome data from 38 human neocortical samples encompassing both the prefrontal cortex and primary visual cortex. These samples span five main developmental stages, ranging from the first trimester to adolescence. In parallel, we performed spatial transcriptomic analysis on a subset of the samples to illustrate spatial organization and intercellular communication. This atlas enables us to catalog cell type-, age-, and area-specific gene regulatory networks underlying neural differentiation. Moreover, combining single-cell profiling, progenitor purification, and lineage-tracing experiments, we have untangled the complex lineage relationships among progenitor subtypes during the transition from neurogenesis to gliogenesis in the human neocortex. We identifie..., , , # Data from: Molecular and cellular dynamics of the developing human neocortex at single-cell resolution

https://doi.org/10.5061/dryad.2280gb612

We collected paired single-nucleus chromatin accessibility and transcriptome data (10x Multiome) from the developing human neocortex.

We collected MERFISH data (Vizgen MERSCOPE) from the developing human neocortex.

We collected scRNA-seq data (10x Flex) from cultured slices treated with and without somatostatin receptor (SSTR) agonists.

We collected scRNA-seq data (10x 3' v3.1) from FACS-isolated glial progenitor cells (GPCs) during in vitro differentiation.

Description of the data and file structure

For 10x Multiome data:

• The 10x Genomics Cellranger ARC output files, including atac_fragments.tsv.gz and atac_fragments.tsv.gz.tbi, and filtered_feature_bc_matrix.h5 for each sample (38 samples in total). We used GRCh38 Reference - 2020-A-2.0.0 as the reference file. Detailed information and f...

We extracted replicable markers for neuron cell types from a compendium of 7 scRNAseq datasets generated by the BICCN in the mouse primary motor cortex (https://doi.org/10.1101/2020.02.29.970558). The markers were extracted using the MetaMarkers package (https://github.com/gillislab/MetaMarkers) using default parameters and keeping the top 1000 markers for each cell type.We present markers for cell types at each level of the hierarchy defined by the BICCN. - biccn_class_markers.csv: highest level with only 3 cell types (excitatory neurons, inhibitory neurons, non-neurons). - biccn_subclass_markers.csv: intermediate level containing 13 cell types (e.g. PV+ interneurons, L6b excitatory neurons). - biccn_cluster_markers.csv: high-resolution level containing 86 cell types (e.g. Chandelier cells).The number of informative markers varies by cell type. To find the best number of markers, we looked for optimal annotation performance for each cell type. The results are summarized in "optimal_number_markers.csv". For each cell type, performance is reported in column "f1" (0.75 indicates good performance, 1 is perfect performance). To read this table, pick a cell type of interest, find optimal performance, then the range of genes that lead to optimal performance. For example, Chandelier cells (Pvalb Vipr2_2) are perfectly characterized by 50 to 200 markers (F1>0.99).

Data repository specifically focused on storage and dissemination of omic data generated from BRAIN Initiative and related brain research projects. Data repository and archive for BCDC and BICCN project, among others. NeMO data include genomic regions associated with brain abnormalities and disease, transcription factor binding sites and other regulatory elements, transcription activity, levels of cytosine modification, histone modification profiles and chromatin accessibility.

Ages included:Embryonic day (E)11.5, 13.5, 15.5, 18.5, Postnatal day (P)4, P14, P56See DevCCFv1_ReadMe.md forData ElementsAbbreviationsContents DescriptionLicenseConditions for UseCitation InformationSupportRelated LinksRelease Notes.Content OrganizationData folders are labeled by ageExample Data Folder Contents:MRI template from at least 4 contrasts (ADC, DWI, FA, T2w)LSFM template registered to MRI morphologyMasks designating Brain from background (and head if relevant)DevCCF Annotation filesExtras Folder containing additional files (e.g. additional MRI contrast template, epDevCCF templates aligned to MRI)Data files within each 'Age' folder are labeled by Age, Template, Modality, and Isotropic Resolution and can be overlayed in the same physical morphology.Additional information in ReadMe fileConditions for UseThe DevCCF is a Brain Initiative Cell Census Network (BICCN) resource.BICCN data, tools, and resources, including the DevCCF are generally released under the Creative Commons Attribution 4.0 International Public License (CC BY 4.0, https://creativecommons.org/licenses/by/4.0/legalcode) (“CC BY 4.0 License”). Under the CC BY 4.0 License external data users may freely download, analyze and publish results based on any BICCN open-access data and tools as soon as they are released, provided they give appropriate credit, a link to the license, and indicate if changes were made. The CC BY 4.0 License applies to all open-access datasets generated by individual members of the Network, regardless of type or size.Researchers using unpublished BICCN data are encouraged to contact the data producers to discuss possible coordinated publications; however, this is optional. The Network will continue to publish the results of its own analysis efforts in independent publications.Citation InformationPlease cite both the DevCCF Manuscript in Nature Communications and this dataset if using the DevCCF in your work.We require that researchers who use BICCN datasets (published or unpublished) in any tools, web applications, presentations, and publications cite and acknowledge the BICCN and BICCN production laboratory(s) for referenced dataset(s).Release NotesDevCCFv1 (current release):This update is paired with the Kronman et al (2024) DevCCF Nature Communications ManuscriptDevCCF_OntologyStructure_v4 has columns for each age denoting included ROIs.Addition of P56, P14, and P04 stereotaxic coordinates filesITK-SNAP Labels for DevCCF, CCFv3 updated to include segmentation abbreviation and full nameAddition of MolecularAtlas Annotations in DevCCF spaceDevCCF Ontology minor cell formatting updatesWhole brain DevCCF Annotations (from hemisphere)Annotation voxel resolution corrected from MRI resolution to 20umCCFv3 Annotations updated to 16bit labels from Kim LabE11.5 Templates & Labels centeredAdded Symmetric Version of Annotations

The Serial Two-Photon (STP) dataset presented in this paper was collected as a part of Brain Initiative Cell Census Network (BICCN) and downloaded from Brain Image Library (BIL). One STP dataset was involvedin the development and demonstration of methods in this paper. The dual-color fluorescent micro-optical sectioning tomography(fMOST) data presented in this paper , both raw images and sin-gle neuron reconstruction data (“ground truth”), were collectedas a part of BICCN and downloaded from BIL. One fMOST dataset was involved in the de-velopment and demonstration of methods in this paper.

Complete set of software and tools resources generated by the BICCN, including type, RRID, name of resource, location for access and description. (XLSX)

Definition of data levels defined by the BICCN. Columns are detailed definition for each specific modality profiled. (XLSX)

We published 3 protocols illustrating how MetaNeighbor can be used to quantify cell type replicability across single cell transcriptomic datasets.The data files included here are needed to run the R version of the protocols available on Github (https://github.com/gillislab/MetaNeighbor-Protocol) in RMarkdown (.Rmd) and Jupyter (.ipynb) notebook format. To run the protocols, download the protocols on Github, download the data on Figshare, place the data and protocol files in the same directory, then run the notebooks in Rstudio or Jupyter.The scripts used to generate the data are included in the Github directory. Briefly: - full_biccn_hvg.rds contains a single cell transcriptomic dataset published by the Brain Initiative Cell Census Network (in SingleCellExperiment format). It combines data from 7 datasets obtained in the mouse primary motor cortex (https://www.biorxiv.org/content/10.1101/2020.02.29.970558v2). Note that this dataset only contains highly variable genes. - biccn_hvgs.txt: highly variable genes from the BICCN dataset described above (computed with the MetaNeighbor library). - biccn_gaba.rds: same dataset as full_biccn_hvg.rds, but restricted to GABAergic neurons. The dataset contains all genes common to the 7 BICCN datasets (not just highly variable genes). - go_mouse.rds: gene ontology annotations, stored as a list of gene symbols (one element per gene set).- functional_aurocs.txt: results of the MetaNeighbor functional analysis in protocol 3.

Data objects processed by scQuint.