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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 human and mouse forebrain

The Transcriptomics Explorer is a tool for visualization and analysis of large-scale single-cell or single nucleus RNA-Seq data and associated cell type annotation. This tool will have periodic feature and data content updates.  Currently, four taxonomies are available for exploration:

  • Mouse Whole Cortex and Hippocampus (10x Chromium 3’) - This taxonomy includes single-cell transcriptomes from multiple cortical areas and the hippocampal formation, including 1,093,785 total cells. Samples were collected from dissections of brain regions from ~8-week-old male and female mice, from pan-neuronal transgenic lines. A cell type taxonomy of 379 transcriptomic clusters was derived jointly using 10x and SMART-seq data.

  • Mouse Whole Cortex and Hippocampus (SMART-seq) - A complementary set of 76,533 total cells were collected from dissections of brain regions from ~8-week-old male and female mice. These samples were primarily collected from pan-GABAergic, pan-glutamatergic, and pan-neuronal transgenic lines, with the addition of more specific transgenic lines and some retrogradely-labeled cells in VISp and ALM. This taxonomy replaces the “Mouse – Whole Cortex + Hippocampus” released in October 2019.

  • Human Primary Motor Cortex (10x Chromium 3’) - This taxonomy includes single-nucleus transcriptomes from 76,533 nuclei derived from two post-mortem human brain specimens, to survey cell type diversity in the primary motor cortex (M1C or M1). The data were generated as part of a BICCN collaboration to characterize cell type diversity in M1 across species and data modalities. In total, 127 clusters were identified.

  • Human Multiple Cortical Areas (SMART-seq) - This taxonomy includes single-nucleus transcriptomes from 49,495 nuclei across six cortical areas, including middle temporal gyrus (MTG) and primary motor cortex (M1C or M1), sampled from post-mortem and neurosurgical (MTG only) donor brains. Individual layers of cortex were dissected from tissues covering these brain regions and nuclei were dissociated and sorted using the neuronal marker NeuN. This taxonomy was released in October 2019 and contains 120 clusters.

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, and in human middle temporal gyrus (MTG) is available here.

Multimodal characterization of individual mouse and human cortical neurons

The number of estimated transcriptomic and morpho-electrical cell types in mouse visual cortex differ by at least a factor of two. To explore the basis for this difference we carried out experiments to capture transcriptomic, electrophysiological, and morphological characteristics of single neurons using a modified Patch-seq recording technique. We also applied this Patch-seq approach to characterize the multimodal features of human cortical neurons for the purpose of cross-species comparison and to delineate shared and distinctive features relative to mouse cortical neurons.

Methodological details and background information, as well as access to multimodal data (and metadata) from 4,435 mouse and 318 human cortical neurons, are currently available. Additional data, metadata, and a data browser will be available in late 2020.

Learn More and Download Data

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.  A preprint describing an updated version of this convention in more detail with added context is available on bioRxiv.

Overview  |  Publication  |  Code  |  Feedback 

Cytosplore Viewer

Cytosplore Viewer is a downloadable application that allows exploration of single cell RNA-Seq data from the Allen Cell Types Database and from associated Allen Institute and BICCN publications. 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 Comparison Viewer (new!) is tailored to cross-species comparison of cell types in the motor cortex between human, marmoset and mouse. 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.

Toward an integrated classification of neuronal cell types: morphoelectric and transcriptomic characterization of individual GABAergic cortical neurons

To better constrain the definition of neuronal cell types, we characterized the transcriptomes and intrinsic physiological properties of over 3,700 GABAergic mouse visual cortical neurons and reconstructed the local morphologies of 350 of those neurons. The data support the presence of at least 20 interneuron types that have congruent morphological, electrophysiological, and transcriptomic properties. 

Publication  |  Allen SDK

A taxonomy of transcriptomic cell types across the isocortex and hippocampal formation

This manuscript used two complementary single-cell RNA-sequencing approaches, SMART-seq and 10x, to profile ∼1.2 million cells covering all regions in the adult mouse isocortex and hippocampal formation and derived a hierarchical cell type taxonomy comprising 379 transcriptomic types. Data in this work is included in the Transcriptomics Explorer tool for mouse, described above.

Publication  |  Code  |  Cluster summaries (shinyapps)

Evolution of cellular diversity in primary motor cortex of human, marmoset monkey, and mouse

This manuscript uses high-throughput transcriptomic and epigenomic profiling of over 450,000 single nuclei in human, marmoset monkey, and mouse to demonstrate a broadly conserved cellular makeup of primary motor cortex (M1). It defines a cross-species consensus cell type classification, allowing inference of conserved cell type properties across species, and characterization of species specializations.

Publication  |  BICCN  |  Cytosplore

Human cortical expansion involves diversification and specialization of supragranular intratelencephalic-projecting neurons

To probe the functional and anatomical correlates of the diverse set of transcriptomic cell types described in human cortex, a robust Patch-seq platform using neurosurgically-resected human tissues was built. This manuscript characterizes the morphological and physiological properties of five transcriptomically defined glutamatergic neuron types in layers 2 and 3 of human cortex and compares these properties with matched neuron types in mouse.

Publication  |  Allen SDK  |  NeMO archive  |  Code

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

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