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Cell Taxonomies

The Allen Institute for Brain Science seeks to build a data-driven classification of cell types in the mammalian brain. These data resources and publications provide insight into the cellular and molecular complexity of this amazing biological system.

Transcriptional taxonomy of the mouse cortex

The RNA-Seq Data Navigator is a tool to explore transcriptional data at the level of gene expression, annotated by cell class using a data-driven taxonomy. Transcripts can be viewed based on similarity of expression pattern, or comparisons of cell sample properties.Two areas of the mouse neocortex were analyzed: 14,249 cells were profiled from the primary visual cortex (VISp), and 9,573 cells from the anterior lateral motor cortex (ALM). These two regions represent distant poles of the neocortex and perform distinct functions.

Analysis of these transcriptional profiles reveals 133 transcriptomic cell types, divided into 61 GABAergic neuron types and 16 non-neuronal types, most of which are shared across both cortical areas, and 56 glutamatergic neuron types, most of which segregate by cortical region. 

Gene-level (exon and intron) read counts and metadata for all samples is available at http://celltypes.brain-map.org/rnaseq/

Transcriptional taxonomy of the human cortex

The RNA-Seq Data Navigator is also designed to explore transcriptional data from human brain, annotated by cell class using a custom taxonomy.  Cell nuclei (15,928 ) were analyzed from 8 human tissue donors, ranging in age from 24-66 years. Analysis of these transcriptional profiles reveals approximately 75 transcriptionally distinct cell types covering multiple cortical areas and laminae, subdivided into 45 inhibitory neuron types, 24 excitatory neuron types, and 6 non-neuronal types.

Gene-level (exon and intron) read counts and metadata for all samples is available at http://celltypes.brain-map.org/rnaseq/

Interactive tutorial: Building a cellular taxonomy of the mouse visual cortex

The mammalian brain is composed of various cell populations that differ based on their molecular, morphological, electrophysiological and functional characteristics. Classifying these cells into types is one of the essential approaches to defining the diversity of brain’s building blocks. 

An interactive cellular taxonomy visualization representing diversity in the mouse primary visual cortex allows you to browse this landscape, based on gene expression at the single cell level, using data from over 1,600 cells. This visualization will guide you through some of the data and insights from this study.

Explore  | Publication


In addition to data navigation tools, researchers at the Allen Institute share data via published peer-reviewed articles and pre-review open access services, to provide insight into the data and analysis used to build taxonomies.  Selected studies are below.

Transcriptional landscape of the prenatal human brain

This manuscript provides a comprehensive view of when and where genes are expressed while the brain is developing, providing insight into the cell progenitor groups that give rise to discrete classes. 

Publication  |  Microarray data

Cell type discovery using single-cell transcriptomics: implications for ontological representation

A review of recent studies of cellular diversity in the human brain and immune system using single cell and single nucleus RNA-sequencing. Discussion of a method to identify cell type specific marker genes and a proposal for naming and representing cell types in a structured Cell Ontology.


Shared and distinct transcriptomic cell types across neocortical areas

This work presents a high resolution cellular taxonomy of two regions of mouse cortex using single cell RNA-sequencing. Data in this work is included in the RNA-Seq Data Navigator tool for mouse, described above.


Conserved cell types with divergent features between human and mouse cortex

This manuscript describes a cellular taxonomy of human cortex using single nucleus RNA-sequencing. Identification of matching cell types and cell classes between human and mouse is provided, and analysis of divergent expression between matching types. Data in this work is included in the RNA-Seq Data Navigator tool for human, described above.