These are Allen Institute resources to explain how to use our datasets, replicate our experiments with protocols, and links to tables with important metadata and features definitions. These sections are designed for scientists who are interested in more technical support in their cell types research.
Identifying and naming brain cells has been an integral part of neuroscience for a century, including Allen Institute cell typing efforts. This means many cell types have multiple names, and tracking this information requires standards.
1. Understand the Allen Institute framework for developing nomenclatures called the Common Cell Type Nomenclature, or CCN.
Related publication (Miller et al 2020)
2. Learn about the schema the Allen Institute has developed for defining cell type taxonomy components, such as nomenclature, annotations, metadata, and the underlying transcriptomic data, or use this format for your own data.
Allen Institute Taxonomies in AIT Format
3. Compare how cell type names have changed as the Allen Institute collects more data from across the brain, and see how brain region, age, gene counts, and other cell features relate to cell types.
Annotation Comparison Explorer
Adopting the Patch-seq technique in a lab can be daunting, but these protocols and resources can help. The Allen Institute has adopted and extended multiple experimental and computational protocols to make Patch-Seq is a useful method for understand the structure and function of brain defined cell types.
These resources below are used in multiple tools and data sets on Allen Brain Map:
Related tool: Allen Cell Types database
Related tool: Mouse PatchSeq VISp viewer
Related tool: Cell Type Knowledge Explorer
1. This GitHub provides a starting point for labs interested in using the Patch-seq technique or refining their existing technique. Specifically, this resource consists of three components: (1) a step-by-step optimized Patch-seq protocol (2) the Multichannel Igor Electrophysiology Suite (MIES) software package and (3) an R library that uses a modified workflow.
Related publication (Lee et al 2021)
2. Overview of the various steps and tools to generate data across species and brain regions in Patch-Seq.
3. From our electrophysiology team, this is detailed protocol to obtain electrophysiological recordings and cellular contents from neurons in postnatal mouse and/or human brain slices.
Related publication (Lee et al 2021)
4. IPFX is a Python package for computing intrinsic cell features from electrophysiology data. That can perform cell data quality control (e.g. resting potential stability), detect action potentials and their features (e.g. threshold time and voltage), calculate features of spike trains (e.g., adaptation index), and calculate stimulus-specific cell features.
Automatic computing for electrophysiological cell features
5. Here is your one-stop shop for the Allen Institute’s free, open-source neuron reconstruction software, protocols, and analysis scripts for generating and analyzing image-based, quantitative, 3D morphologies for your own research.
Protocol, Software, Analysis: Reconstruct neuron morphology
6. Protocol to generate full-length cDNA from single cells, or nuclei, using Takara SMARTer V4.
Takara SMARTer V4 (Protocols.io protocol)
7. The electrophysiology and morphology feature definitions are used for all our Patch-Seq datasets. This is from “NIHMS1691616-supplement-Supplementary_Figures” in Gouwens, Sorensen, Berg, et al. 2019, starts at page 73 for electrophysiology and page 75 for morphology.
Electrophsyiology & Morphology Features Definition
Related publication (Gouwens et al 2019)
We define cell types based on which genes are turned on and which genes are turned off in a cell. These resources detail how we do this robustly for millions of cells.
1. This page includes protocols for SMART-Seq and Nextera XT, FACs, and tissue preparation and analysis & clustering links.
SOPs: RNA-Seq mouse whole cortex & hippocampus
SOPs: RNA-seq human multiple cortex areas
2. To monitor for a consistent, high-quality sampling of single-cell and single-nucleus RNA-Seq data, we have controls used in each application sample. These controls for mouse and human data are available to download for you to use in your own experiments.
The Cell Type Knowledge Explorer is a scientific and educational tool for exploration of human, marmoset, and mouse primary motor cortex cell types and the features that make them distinct.
Explore the Cell Type Knowledge Explorer
1. Learn from the scientists behind our Cell Types Knowledge Explorer about why it is made, key findings, and a walkthrough of the tool itself.
2. Read the science behind the cell type knowledge included in the Cell Type Knowledge Explorer in peer-reviewed publications.
Mouse Patch-seq (Scala et al 2021)
Mouse transcriptomics and epigenetics (Yao et al 2021)
Aligning cell types across species (Bakken et al 2021)
3. These use cases were designed to show researchers how to use the mouse data in the Cell Type Knowledge Explorer for their own research questions. The case titled “Experimental Design” shows how the Cell Type Knowledge Explorer can be used to guide research questions.
4. Python code for you to your own generate data visualizations that was used in the Cell Type Knowledge Explorer.
Replicate our data visualization
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.
1. From our product managers of ABC Atlas, here is a user guide to help you navigate all the features of ABC Atlas.
2. The ABC Atlas is under active development! See all the updates & patches to ABC Atlas on the Allen Brain Map Community Forum post that is updated regularly.
ABC Atlas channel on the Community Forum
3. Read the science behind some of the data sets included in the ABC Atlas in peer-reviewed publications.
Mouse Whole Brain (Yao et al 2023)
Mouse Whole Brain (Zhang et al 2023)
Human Whole Brain (Siletti et al 2023)
Human Alzheimer's disease (Gabitto et al 2021)
4. These use cases were designed to show researchers how to use the Whole Human Brain data in the ABC Atlas for their own research questions. The two use cases titled “Experimental Design” show how the ABC Atlas can be used to guide research questions, while the use case titled “Scientific Knowledge” shows how the ABC Atlas can be used studying and/or writing a literature review. The “coding” in the third use case shows users how to access the raw data for the Whole Human Brain using Jupyter notebooks.
Use Case: Scientific Knowledge
Use Case: Experimental Design with Coding
5. Learn from the scientists behind the new collection of studies from the BRAIN Initiative Cell Atlas Network (biccn.org) and published in Nature on Dec 14, 2023.
Whole Mouse Brain Paper Package Highlights Webinar
Related Collection of Scientific Publications
6. List of the 500 Gene panel used in the whole brain mouse. This is from the supplemental table 6 in Yao, et al. 2023. Also, the set of genes where expression in the spatial transcriptomics data is estimated (or "imputed") using information from the single cell RNA-seq data.
Whole Mouse Brain, Spatial Gene Panel List
Whole Mouse Brain, Spatial Imputed Genes
7. Tables including detailed information for each cluster, including neurotransmitter information, overall marker genes, main dissection region, and more. The first two buttons correspond to clusters in whole mouse brain (supplemental table 7 in Yao et al 2023) as published, and updated to include a comparison with clusters in a previous study of mouse cortex and hippocampus (Yao et al 2023). The third button relates to subclusters in whole human brain (unpublished supplemental information from Siletti et al 2023).
Whole Mouse Brain, Published Cluster Annotation Table
Whole Mouse Brain, Extended Cluster Annotation Table
Whole Human Brain, Subcluster Annotation Table
8. Download Excel files of the acronyms found in ABC Atlas with their corresponding full name, type of acronym (“types”), and the identifiers (“primary identifier”, “secondary identifier”, and “tertiary identifier”). See the table below for list of the types and identifiers used in the Excel file. Note that acronyms are now defined directly in the ABC Atlas in 'nomenclature cards'.
Whole Mouse Brain Cluster Annotations
Whole Mouse Brain Anatomical Annotations
Whole Human Brain Cluster Annotations
An ever-growing number of data sets are included in the ABC Atlas. This table lists the names, relevant publication, names and number of data points for every data sets available on the ABC Atlas (as of September 2025).
MapMyCells allows you discover what cell types your transcriptomics and spatial data corresponds with by comparing your data to our massive, high-quality reference datasets.
1. Learn from one of our scientists behind MapMyCells on how to use it, what type of data it accepts, what taxonomies are built of fit, which algorithms to select, and peek under the hood on how it works.
2. MapMyCells needs cell by gene matrix where rows are “cells” and columns are “genes”, which need to be in either a csv, csv.gz, or h5ad file format. This guide provides more details about how to prepare your file, including input file limits.
Input creation, file requirements, and limits
3. Unsure what algorithm or taxonomy to select in MapMyCells? These guides are for you.
4. The online version of MapMyCells will provide accurate cell type assignments for user-inputted data in most cases. However, there are some situations when using the direct scripts may be more appropriate: (1) if the reference taxonomy you are interested in is not one currently included in MapMyCells, (2) if the data set you have is quite large, (3) if you'd like to include these algorithms as part of an analysis pipeline, or (4) if you need to select a different set of genes for mapping (e.g., for mapping MERFISH data).
Run MapMyCells in python (cell_type_mapper)
Run MapMyCells in R (scrattch.mapping)
Most of these resources are part of the Brain Initiative Cell Census Network (BICCN) and/or Brain Initiative Cell Atlas Network (BICAN). The Allen Institute serves as the coordinating member of this network.
Discover all pages in this series:
This third section "Putting it all together" is a key to link research focus to related scientific tool to primary paper to taxonomy used.
This first section "What is..." lists introductory information on cell type topics.
Learn about Rna Seq Process Controls with comprehensive guides and examples from Allen Institute for Brain Science.
