1. Learn from our Executive Vice President & Director of Brain Science, Hongkui Zeng, on the overview of cell types from the roots in evolution & development to the approaches in how to characterize, while also providing a roadmap for the future.

2. Cell Types 101 Webinar focuses on an introduction to the study of cell types! It covers evolving cell type definitions, cell types across species, the types of data used to define cell types, and the importance of having standard definitions for cell types.

This webinar is tutorial on Allen Cell Types Database. Starting at 4:13, we give an overview of single cell or nucleus transcriptomics. (Note: Webinar is from 2021; the taxonomies presented in the webinar are not the latest taxonomies from the Institute, as newer taxonomies have been created since 2021.)‍

This guide book describes how 10x single cell sequencing works with easy-to-understand language and graphics.

1. This user-friendly tutorial goes step by step on how cell type taxonomies are created, with linking to data from Allen Cell Types Database in the end. This is a great web-interface that is perfect for students to experts to begin exploring what a taxonomy is.

2. 'What is a taxonomy?' webinar is focused on the systematic classification of cell types and their hierarchical relationships. Much like species taxonomy (family, genus, species, etc.), researchers at the Allen Institute and their collaborators are working to create a standard taxonomy for cell types.

A UMAP is a common way to visualize cell types taxonomies. Learn how to interpret and analyze these graphs in this user guide.

1. Patch-Seq was developed around late 2010s, with Allen Institute helping to optimize the technique. Here is a 2016 Allen Institute team talk giving an overview of Patch-Seq.

2. This webinar from 2024 gives an overview of what Patch-Seq is and how that data is used in the Cell Type Knowledge Explorer tool.

1. Understand the Allen Institute framework for developing nomenclatures called the Common Cell Type Nomenclature, or CCN.

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.

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.

These resources below are used in multiple tools and data sets on Allen Brain Map:

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.

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.

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.

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.

6. Protocol to generate full-length cDNA from single cells, or nuclei, using Takara SMARTer V4.

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.

1. This page includes protocols for SMART-Seq and Nextera XT, FACs, and tissue preparation and analysis & clustering links.

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.

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.

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.

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.

3. Read the science behind some of the data sets included in the ABC Atlas in peer-reviewed publications.

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.

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.

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.

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).

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'.

‍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).

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.

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).