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Single-cell multiomic integration identifies widespread, cell-type resolved fetal reactivation in the diseased human heart

GSE290367 Homo sapiens (human) Single-cell RNA-seq 90 samples Submitted 2026/03/15 Platform GPL21697
At a glance

Single-cell RNA-seq · human · 90 samples

What it is
Single-cell RNA-seqGene activity measured in thousands of individual cells, so cell types can be told apart.
Organism
Homo sapiens (human)
Samples
90 samples: 35 conditions, mostly in duplicateGroups 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
An integrative single-nucleus multiomic atlas of the human left ventricle identifies gene regulatory network dynamics across cardiac development, aging, and diseaseGenome biology · 2026
Summary (written by the submitters)
Background: As the first organ to develop in utero, the human heart undergoes extensive molecular, structural and metabolic remodeling during development and must sustain its function throughout life. Results: We generate an integrated multiomic atlas of human cardiac cells, combining newly generated and publicly available single-nucleus RNA sequencing datasets from 299 donors and single nucleus ATAC-seq datasets from 106 donors.
Read the full summary ↓
Developmental and disease-associated processes drive far more extensive molecular remodeling than sex-associated or aging-dependent effects. Across nearly all cardiac cell types, developmental and disease-driven changes exhibit strong overlap at both the transcriptomic and epigenomic levels, revealing widespread reactivation of fetal-associated gene programs beyond cardiomyocytes. Both cardiac development and disease show convergent shifts in intercellular communication, including increased TGFβ signaling. Integration of gene expression and chromatin accessibility data reveals putative cell-type–specific transcriptional factors driving fetal reactivation in major cardiac diseases. Spatial transcriptomics data orthogonally identifies localization of this fetal reactivation signature within spatially distinct niches in ischemic and fibrotic zones of acute myocardial infarction. Finally, we construct a cell-type–resolved enhancer-to-gene linkage map that refines the association of dilated and hypertrophic cardiomyopathy genetic risk loci to downstream target genes. Conclusions: This study presents a comprehensive multimodal, cell-type–resolved atlas of the human heart, providing a foundation for understanding human cardiac gene regulation across the human lifespan and in cardiac diseases.
Published in
An integrative single-nucleus multiomic atlas of the human left ventricle identifies gene regulatory network dynamics across cardiac development, aging, and disease
Gao W, Hu P, Wick B et al. · Genome biology 2026 · PMID 41937210 · doi:10.1186/s13059-026-04061-7
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 GSE290367_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 PRJNA1227589 and SRA study SRP566068. Searching any of these in the dataset finder brings you back here.

Study design
35 conditions, mostly in duplicate
Penn_F1 donor, female, gestational week 18, n… ×5 K1783 donor, male, age 69, non-diseased, left… ×4 Penn_F2 donor, male, gestational week 18, non… ×3 K1430 donor, male, age 62, DCM, left ventricl… ×3 K1584 donor, male, age 22, non-diseased, left… ×3 ND15755 donor, male, age 65, non-diseased, ri… ×3 ND15840 donor, female, age 91, non-diseased, … ×3 ND15840 donor, female, age 91, non-diseased, … ×3 +35 more

Supports a between-group comparison across 82 samples.

How we read this: 35 replicated groups read from 90 sample titles; they account for 82 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

+ 50 more — browse all 90 samples with per-sample file links →

Datasets on a similar topic

Search all human ChIP / ATAC / CUT&Tag datasets in GEO →

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