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Allen Brain Cell Atlas

The Allen Brain Cell Atlas

The Allen Brain Cell (ABC) Atlas provides a platform for visualizing multimodal single cell data across the mammalian brain and aims to empower researchers to explore and analyze multiple whole-brain datasets simultaneously. This open science resource, developed by the Allen Institute as part of the Brain Knowledge Platform, allows unprecedented insights into the enormous diversity of cell types in the brain and where they are. 

As the Allen Institute and its collaborators continue to add new modalities, species, and insights to the ABC Atlas, this groundbreaking platform will keep growing, opening up endless possibilities for discoveries and breakthroughs in neuroscience. 

The ABC Atlas enables the neuroscience community to 

•   Identify more cell types in the brain 
•   Investigate the spatial location of cell types 
•   Investigate gene expression and co-expression patterns in cell types 
•   Refine boundaries and knowledge of brain regions defined by gene expression 

Go to ABC Atlas

The Brain Knowledge Platform

With funding from the National Institutes of Health (NIH) and technology from Amazon Web Services (AWS), the Allen Institute is leading an effort to build the Brain Knowledge Platform (BKP) – soon to be the largest open-source database of brain cell data in the world. The BKP will host the ABC Atlas including single-cell resolution neuroscience datasets from collaborative teams worldwide. It will be a premier resource to compile and standardize massive datasets on the structure and function of mammalian brains.  

The ultimate goal of the BKP is to enable better diagnosis and treatment of the mental and neurological disorders and diseases. 

 

Explore the data

Mouse whole-brain transcriptomic cell type atlas

Hongkui Zeng, Allen Institute for Brain Science

The Mouse Whole Brain Atlas is a high-resolution transcriptomic and spatial cell-type atlas across the entire mouse brain, integrating several whole-brain single-cell RNA-sequencing (scRNA-seq) datasets. The datasets contain a total of ~4 million cells passing rigorous quality-control (QC) criteria. The integrated transcriptomic taxonomy contains 5,322 clusters that are organized in a hierarchical manner with nested groupings of 34 classes, 338 subclasses, 1,201 supertypes and 5,322 types/clusters. The scRNA-seq data reveal transcriptome-wide gene expression and co-expression patterns for each cell type. The anatomical location of each cell type has been annotated using a comprehensive brain-wide MERFISH dataset with a total of ~4 million segmented and QC-passed cells, probed with a 500-gene panel and registered to the Allen Mouse Brain Common Coordinate Framework (CCF v3). The MERFISH data not only provide accurate spatial annotation of cell types at subclass, supertype and cluster levels, but also reveal fine-resolution spatial distinctions or gradients for cell types.

The combination of scRNA-seq and MERFISH data reveals a high degree of correspondence between transcriptomic identity and spatial specificity for each cell type, as well as unique features of cell type organization in different brain regions.

A detailed cell type annotation table is also provided along with data downloads (GitHub), detailing information about the hierarchical membership, anatomical annotation, neurotransmitter type, cell type marker genes, transcription factor and neuropeptide markers, and other metadata types for each cluster. Learn more...

Explore the data in the ABC Atlas

Quick start guides

Use Case 1: Experimental Design

 

MERFISH whole mouse brain

Xiaowei Zhuang, Harvard University

This spatially resolved cell atlas of the whole mouse brain provides a systematic characterization of the spatial organization of transcriptomically defined cell types across the entire adult mouses brain using in situ, single-cell transcriptomic profiling with multiplexed error-robust fluorescence in situ hybridization (MERFISH). Zhuang imaged ~9 million cells with a ~1,100-gene panel using MERFISH and performed spatially resolved, single-cell expression profiling at the whole-transcriptome scale by integrating our MERFISH data with the whole-brain single-cell RNA-sequencing (scRNA-seq) dataset generated by the Allen Institute. This allowed them to generate a comprehensive cell atlas of >5,000 transcriptionally distinct cell clusters, belonging to ∼300 major cell types (subclasses), in the whole mouse brain with high molecular and spatial resolution. The cell atlas was further registered to the Allen Mouse Brain Common Coordinate Framework (CCF v3), which allows systematic quantifications of the cell composition and organization in individual brain regions as anatomically delineated in the CCF v3. This cell atlas also reveals molecularly defined brain regions characterized by distinct cell-type compositions, spatial gradients featuring gradual changes in the gene-expression profiles of cells, as well as cell-type-specific cell-cell interactions and the molecular basis and functional implications of these cell-cell interactions. Learn more...

Explore the data in the ABC Atlas
 

 

The Seattle Alzheimer’s Disease Brain Cell Atlas

Seattle Alzheimer’s Disease Brain Cell Atlas

The Seattle Alzheimer’s Disease Brain Cell Atlas (SEA-AD) is a consortium focused on gaining a deep molecular and cellular understanding of the early pathogenesis of Alzheimer’s Disease. SEA-AD is a collaboration of the Allen Institute for Brain Science, the University of Washington Alzheimer’s Disease Research Center, and Kaiser Permanente Washington Research Institute.

Using the ABC Atlas, users can explore the largest cell-resolution spatial transcriptomics (MERFISH) data set in human brain to date, which provides accurate spatial annotation and colocalization of cell types in the middle temporal gyrus (MTG) of 24 donors from the SEA-AD cohort. This can be viewed in parallel with the 2.78 million cells collected from MTG and dorsolateral prefrontal cortex (DFC) using single nucleus transcriptomics and multiomics (from 84 donors), and organized into a joint taxonomy to allow exploration of gene expression in the context of cell types and donor metrics. Learn more...

Seattle Alzheimer’s Disease Brain Cell Atlas (SEA-AD) - 10x single nucleus RNAseq

Processed cell by gene count matrices from 10x single nucleus RNAseq dataset from human middle temporal gyrus (MTG) as part of the Seattle Alzheimer’s Disease Brain Cell Atlas (SEA-AD) project. Data is derived from an aged cohort of donors who span the full spectrum of Alzheimer’s disease severity.

Seattle Alzheimer’s Disease Brain Cell Atlas (SEA-AD) - Spatial transcriptomics - MERFISH

This project contains spatial transcriptomics MERFISH datasets from human brain as part of the Seattle Alzheimer’s Disease Brain Cell Atlas (SEA-AD) project. Data is derived from an aged cohort of donors who span the full spectrum of Alzheimer’s disease severity.

Explore the data in the ABC Atlas

 

Transcriptomic diversity of cell types in adult human brain

This dataset includes more than three million cells sampled from the adult human brain. Samples were isolated from ~100 dissections from three donors and assayed using single-nucleus RNA sequencing. The resulting cells were clustered into hierarchical groups of 31 superclusters, 461 clusters, and 3313 subclusters. Additionally, categorical neurotransmitter type annotations were assigned to clusters based on expression (or lack thereof) of one or more neurotransmitter-associated marker genes. Included in this dataset includes two tSNE plots - one plot contains approximately 900k non-neuronal cells and the other contains approximately 2.5m neuronal cells... Learn more...

Explore the data in the ABC Atlas

Quick start guides

Use Case 1: Experimental Design  Use Case 2: Scientific Knowledge   Use Case 3: Experimental Design with Coding

Related resources

Community Forum Documentation and help 

GitHub Data downloads and notebooks with tutorials and examples

Support Comprehensive catalog of the Allen Institute's resources for understanding cell types

Map My Cells Apply the cell types taxonomy to your data

What is a UMAP