GEO series

Single-nucleus chromatin and gene expression profiling across hundreds of skeletal muscle samples reveals context-specific regulation

GSE247455 Homo sapiens (human) Single-nucleus RNA-seq 8 samples Submitted 2025/11/30 Platform GPL24676

At a glance

Single-nucleus RNA-seq · human · 8 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: 8 samples: 4 × gDNA vs 4 × RNAGroups 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.
Paper: Using a modular massively parallel reporter assay to discover context-dependent regulatory activity in type 2 diabetes-linked noncoding regionsHGG advances · 2026

Summary (written by the submitters)

Skeletal muscle is relevant to several polygenic metabolic traits and diseases including type 2 diabetes (T2D). Identifying genetic mechanisms underlying these traits requires pinpointing the relevant cell-types, regulatory elements, target genes, and causal variants. Here, we used genetic multiplexing to generate population-scale single nucleus (sn) chromatin accessibility (snATAC-seq) and transcriptome (snRNA-seq) maps across 287 frozen human skeletal muscle biopsies representing 456,880 nuclei.

Read the full summary ↓

We identified 13 cell-types that collectively represented 983,155 ATAC summits. We integrated genetic information to identify 6,866 expression quantitative trait loci (eQTL) and 100,928 chromatin accessibility QTL (caQTL) (5% FDR) across the five most abundant cell-types, and show atlas-level snATAC maps often miss peaks. We identified 12,046 e-caQTL colocalizations and inferred causality between chromatin accessibility and gene expression through mediation analyses. 3,378 genome-wide association study (GWAS) signals across 43 relevant traits colocalized with sn e/caQTL, 52% in a cell-type-specific manner. 77% (3,616) GWAS signals colocalized with caQTLs and not eQTL, highlighting the importance of sn-caQTL maps for GWAS functional studies. GWAS-e/caQTL colocalization showed distinct cell-type-specific regulatory paradigms. For example, a C2CD4A/B T2D GWAS signal colocalized with caQTLs in muscle fibers and imputed chromatin contacts nominated VPS13C - a glucose uptake gene. The caQTL peak overlapping caSNP rs7163757 showed allelic regulatory activity differences in a human myocyte cell line massively parallel reporter assay. These results illuminate the genetic regulatory architecture of human skeletal muscle at high-resolution epigenomic, transcriptomic, and cell state scales and serve as a template for population-scale multi-omic mapping in complex tissues and traits.

Published in

Using a modular massively parallel reporter assay to discover context-dependent regulatory activity in type 2 diabetes-linked noncoding regions

Tovar A, Kyono Y, Nishino K et al. · HGG advances 2026 · PMID 41952336 · doi:10.1016/j.xhgg.2026.100606

This dataset

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Download · which file do I need?

Direct links to NCBI, 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.

All supplementary files ↗

3 · The original raw files

Whole study · RAW.tar

Everything the authors deposited as one archive (GSE247455_RAW.tar), including the raw array or instrument files. Can be large; the picker in step 2 takes just the samples you need.

Whole study · RAW.tar ↓

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 PRJNA1037581. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 41952336) ↗

Study design

4 × gDNA vs 4 × RNA

Supports a between-group comparison across 8 samples.

How we read this: 2 replicated groups read from 8 sample titles; they account for 8 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

GSM7890201 gDNA_rep1 gDNA
GSM7890202 RNA_rep1 RNA
GSM7890203 gDNA_rep2 gDNA
GSM7890204 RNA_rep2 RNA
GSM7890205 gDNA_rep3 gDNA
GSM7890206 RNA_rep3 RNA
GSM7890207 gDNA_rep4 gDNA
GSM7890208 RNA_rep4 RNA

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