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Central amygdala single-nucleus atlas reveals chromatin and gene transcription dynamics in human alcohol use disorder

GSE336954 Homo sapiens (human) Single-nucleus RNA-seq 126 samples Submitted 2026/07/01 Platform GPL24676Platform GPL24247
At a glance

Single-nucleus RNA-seq · human · 126 samples

What it is
Single-nucleus RNA-seqGene activity measured in individual nuclei (used when whole cells are hard to isolate, for example from frozen tissue).
Organism
Homo sapiens (human)
Samples
126 samples: 126 samples, each with a distinct title — no replicated groupsGroups are read from the sample names, not from the paper. Check the list below.
Data you can get
A processed table (series matrix) and supplementary files from NCBI; the raw sequencing reads from ENA. Which of these hold counts depends on the study, so check the file list.
Summary (written by the submitters)
Regulation of gene expression is a highly coordinated process in both the healthy and pathological brain with unique patterns across a multitude of cell types. Here we present a multi-omic single nucleus study of ~175,000 nuclei from 50 donors with alcohol use disorder (AUD) and control donors without AUD, profiling cell type specific gene expression and chromatin accessibility in the human central amygdala.
Read the full summary ↓
We identify all major CNS cell types and neuronal subtypes and find inhibitory neurons are particularly affected by AUD. We find high numbers of differentially expressed genes (DEGs) including GABRA2, GRM8, and NCAM1 and show significant enrichment for AUD risk genes within these DEGs. We identified 51,431 cell type-specific, disease associated candidate cis-regulatory elements including an interneuron-associated set of chromatin loops at the AUD risk gene CALN1. Transcription factor footprinting identified Kruppel-like factors upstream of AUD GWAS genes and DEGs. Finally, we also perform cell type-specific fine mapping for AUD GWAS to prioritize variants within functional genomic elements.
This dataset
Download · which file do I need?

Direct links to NCBI and ENA, no account and no request form. Pick by what you want to do: take the small processed table to look, the supplementary files to re-analyse, or the original raw data to start over.

1 · Just have a look

Processed table

The series matrix: one table of values per sample as the submitters processed it. The smallest download, and it opens in Excel or R. Start here to see what the data look like.

2 · Re-analyse it

Supplementary files

The count tables and other files the authors uploaded. Take only the samples you need, or the whole study as GSE336954_RAW.tar (it can run to tens of gigabytes).

3 · Start from the raw reads

Raw sequencing reads

The original FASTQ files, straight from the sequencer, held at ENA. Large, and you align them yourself, so only choose this if you need to re-process the study from scratch.

What the files usually look like: Single-cell studies usually ship their counts as 10x files (matrix.mtx, barcodes.tsv, features.tsv), an .h5 file, or an .rds / .h5ad object that Seurat or Scanpy can open. Look for those among the supplementary files.

Also filed as BioProject PRJNA1483640 and SRA study SRP713623. Searching any of these in the dataset finder brings you back here.

Study design
126 samples, each with a distinct title — no replicated groups

Any grouping that defines a comparison lives in the sample characteristics, which GEO records separately from the titles.

How we read this: Read from 126 sample titles: 126 distinct titles with little repetition. These groups are guessed from the sample names, not taken from the paper, so check them against the sample list below before relying on them.

Samples in this study

+ 86 more — browse all 126 samples with per-sample file links →

Datasets on a similar topic

Search all RNA-seq datasets in GEO →

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Metadata from NCBI GEO, cached and refreshed periodically — the NCBI page above is authoritative. Downloads link straight to NCBI/ENA; nothing is proxied through BioTransfer.