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Cell Taxonomies

The Allen Institute for Brain Science builds data-driven classifications of cell types in the mammalian brain. The datasets and publications below provide insight into organization of the cellular and molecular complexity of this amazing biological system. 

We seek input from the community as we continue to develop tools and paradigms to classify cells, and encourage discourse on the Allen Community Forum.

Transcriptional taxonomy of the mouse cortex and hippocampus

The Transcriptomics Explorer is a tool for visualization and analysis of large-scale single-cell RNA-Seq data and associated cell type annotation. Nearly 75,000 single cells from multiple cortical areas and the hippocampus were collected from fine dissections in male and female mice. This dataset reveals the molecular architecture of the neocortex and hippocampal formation, with a wide range of shared and unique cell types across areas. It provides the basis for comparative studies of cellular diversity in development, evolution, and diseases.  This tool will have periodic feature and data content updates.

Gene-level (exon and intron) read counts and metadata for all samples is available at

The RNA-Seq Data Navigator application developed in 2018 to explore transcriptional data in mouse primary visual cortex (VISp) and anterior lateral motor cortex (ALM) is available here.

Transcriptional taxonomy of the human cortex

The Transcriptomics Explorer is also designed to explore RNA-Seq data from human brain, annotated by cell class using a custom taxonomy.  More than 49,000 nuclei were collected from individual layers across middle temporal gyrus (MTG), anterior cingulate gyrus (CgGr), primary visual cortex (V1C), primary motor cortex (M1C), primary somatosensory cortex (S1C), and primary auditory cortex (A1C). This dataset provides the basis for investigating the cellular diversity across human cortex, and allows for direct comparison of cell types with matched regions in mouse.

Gene-level (exon and intron) read counts and metadata for all samples is available at


The RNA-Seq Data Navigator application developed in 2018 to explore transcriptional data in human middle temporal gyrus (MTG) is available here.

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 

Cell Taxonomy Nomenclature

Single cell RNA sequencing (scRNA-Seq) technology has led to an exciting expansion of datasets, tools and approaches for identifying and classifying cell types. We present a working framework for creating brain cell type nomenclature, and include examples using published datasets. Feedback is encouraged on the Allen Community Forum.

Cytosplore Viewer

Cytosplore Viewer is a downloadable application that allows exploration of single cell RNA-Seq data from the Allen Cell Types Database. Currently, Cytosplore Viewer includes sequencing data from over 49,000 nuclei collected from the human cortex and from nearly 75,000 cells collected from mouse cortex and hippocampus, and is back-compatible with previous cell type taxonomies of human MTG and mouse VISp/ALM.  Cytosplore was developed by a team from the Leiden Computational Biology Center, the Division of Image Processing at the Leiden University Medical Center, and the Computer Graphics and Visualization Group at the TU Delft in collaboration with the Allen Institute.


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.

Classification of electrophysiological and morphological neuron types in the mouse visual cortex

To systematically profile morpho-electric properties of mammalian neurons, a single-cell characterization pipeline was built to generate data from patch-clamp recordings in brain slices, and biocytin-based neuronal reconstructions. This article presents the results of that study, with a taxonomy of morphologically and electrophysiologically defined cell types in the mouse cortex: 17 electrophysiological types, 38 morphological types and 46 morpho-electric types.  Data from this publication can be explored in the Allen Cell Types Database

Publication  |  Allen SDK

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.

Publication  |  Provisional 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.

Publication  |  Code

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.

Publication  |  Code