The Neuroscience Multi-Omic Archive: a BRAIN Initiative resource for single-cell transcriptomic and epigenomic data from the mammalian brain

Abstract

Scalable technologies to sequence the transcriptomes and epigenomes of single cells are transforming our understanding of cell types and cell states. The Brain Research through Advancing Innovative Neurotechnologies (BRAIN) Initiative Cell Census Network (BICCN) is applying these technologies at unprecedented scale to map the cell types in the mammalian brain. In an effort to increase data FAIRness (Findable, Accessible, Interoperable, Reusable), the NIH has established repositories to make data generated by the BICCN and related BRAIN Initiative projects accessible to the broader research community. Here, we describe the Neuroscience Multi-Omic Archive (NeMO Archive; nemoarchive.org), which serves as the primary repository for genomics data from the BRAIN Initiative. Working closely with other BRAIN Initiative researchers, we have organized these data into a continually expanding, curated repository, which contains transcriptomic and epigenomic data from over 50 million brain cells, including single-cell genomic data from all of the major regions of the adult and prenatal human and mouse brains, as well as substantial single-cell genomic data from non-human primates. We make available several tools for accessing these data, including a searchable web portal, a cloud-computing interface for large-scale data processing (implemented on Terra, terra.bio), and a visualization and analysis platform, NeMO Analytics (nemoanalytics.org).

Figure 1.

NeMO Archive data inventory. (A) Growth of data storage in the NeMO Archive over time. (B) Counts of cells assayed by each transcriptomic or epigenomic technology. (C) Counts of cells for each species. Y-axes in panels B and C are on a log-scale.

Figure 2.

Data processing workflow. Flow of data from ingest through validation and release to NeMO resources.

Figure 3.

Accessing BICCN data through the NeMO Portal. (A) Users can find and access data in the NeMO Archive through a website and portal (www.nemoarchive.org). (B) The landing page allows users to get information about the project, look at summary of data available at the archive, growth of data over time at the archive, access documentation on tools and data downloads, and find links to other related resources. (C) The data discovery and search page of the data portal provides a filtering interface (left) and summaries based on different metadata properties (right). Users use the faceted search interface to filter the data by selecting or deselecting the check boxes next to the facets. (D) Each data resource is described on a landing page, which provides pertinent metadata and links to each of the corresponding files.

Figure 4.

Visualization of BICCN data with NeMO Analytics. Expression of Cux2, a layer-specific marker of excitatory neurons, in BICCN data from the primary motor cortex, visualized with NeMO Analytics. (A) Clustering of transcriptomic cell types with 10× Genomics snRNA-seq (https://nemoanalytics.org/index.html?&layout_id=4e8f6c00&gene_symbol=Cux2). (B) Comparison of cell type-specific gene expression in the primary motor cortex of humans, marmosets, and mice (https://nemoanalytics.org/index.html?&gene_symbol_exact_match=1&multigene_plots=0&layout_id=4e8f6c00&gene_symbol=Cux2). (C) Multimodal characterization of gene expression, morphology, and electrophysiology with Patch-seq (https://nemoanalytics.org/index.html?&layout_id=1206f7ce&gene_symbol=Cux2). (D) Spatial transcriptomics with MERFISH (https://nemoanalytics.org/index.html?&layout_id=7e14ad94&gene_symbol=Cux2). (E) Chromatin accessibility profiling with scATAC-seq (https://nemoanalytics.org/index.html?&layout_id=2c72ff02&gene_symbol=Cux2).