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Liver single-nucleus multiome profiling reveals cell-type mechanisms for cardiometabolic traits

GSE296875 Homo sapiens (human) Single-nucleus RNA-seq 16 samples Submitted 2025/10/01 Platform GPL24676
At a glance

Single-nucleus RNA-seq · human · 16 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
16 samples: 8 × liverTissue_scRNA-seq_well vs 8 × liverTissue_scATAC-seq_wellGroups 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.
Paper
Liver single-nucleus multiome profiling reveals cell-type mechanisms for cardiometabolic traitsAmerican journal of human genetics · 2026
Summary (written by the submitters)
Genome-wide association studies (GWAS) have identified numerous genetic variants associated with cardiometabolic traits, yet their mechanisms in relevant liver cell types remain unclear. Using multiome single-nucleus RNA and ATAC sequencing on liver samples from 39 individuals, we profiled gene expression and chromatin accessibility in 68,398 nuclei across six primary liver cell types.
Read the full summary ↓
We identified 306,706 accessible chromatin regions, including 70,884 regions undetected in bulk tissue analyses, predominantly representing less abundant cell types. By mapping quantitative trait loci (QTLs), we detected 1,885 chromatin accessibility QTLs (caQTLs) and 67 expression QTLs (eQTLs), highlighting genetic regulation in the different liver cell types. We integrated cell-type QTLs with GWAS signals and revealed cell-types, genes, and chromatin regulatory elements involved in cardiometabolic traits, such as liver enzymes and cholesterol levels. Importantly, non-hepatocyte cell-type QTL analyses elucidated previously obscured mechanisms, such as an eQTL for ADAMTS12 in liver sinusoidal endothelial cells potentially involved in liver fibrosis, demonstrating how single-nucleus approaches capture regulatory events missed in bulk analyses. Furthermore, we annotated bulk liver caQTLs colocalized with GWAS signals to liver cell types, enhancing functional interpretations and predicting cell-type of action for complex trait associations. Our findings provide a comprehensive, high-resolution map of the hepatic regulatory landscape, advancing the understanding of cellular contexts and molecular mechanisms underlying cardiometabolic diseases.
Published in
Liver single-nucleus multiome profiling reveals cell-type mechanisms for cardiometabolic traits
Alkhawaja AA, Currin KW, Perrin HJ et al. · American journal of human genetics 2026 · PMID 41338218 · doi:10.1016/j.ajhg.2025.11.009
This dataset
Open GSE296875 in finder → Find more human ChIP / ATAC / CUT&Tag datasets →
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.

Series matrix ↓
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 GSE296875_RAW.tar (it can run to tens of gigabytes).

All supplementary files ↗ Whole study · RAW.tar ↓
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.

Raw reads on ENA ↗

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 PRJNA1261871 and SRA study SRP584786. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 41338218) ↗
Study design
8 × liverTissue_scRNA-seq_well vs 8 × liverTissue_scATAC-seq_well

Supports a between-group comparison across 16 samples.

How we read this: 2 replicated groups read from 16 sample titles; they account for 16 of them. 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
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