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Cardiac differentiation roadmap for analysis of plasticity and balanced lineage commitment

GSE263325 Homo sapiens (human) Single-cell RNA-seq 24 samples Submitted 2025/01/27 Platform GPL18573
At a glance

Single-cell RNA-seq · human · 24 samples

What it is
Single-cell RNA-seqGene activity measured in thousands of individual cells, so cell types can be told apart. Read from the summary: GEO's own type field only says 'expression profiling by sequencing', so confirm it in the paper.
Organism
Homo sapiens (human)
Samples
24 samples: 8 conditions, mostly in triplicateGroups 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)
Stem cell-based models of human heart tissue and cardiac differentiation employ monolayer and 3D organoid cultures with different properties, cell type composition, and maturity. Here we show how cardiac monolayer, embryoid body, and engineered heart tissue trajectories compare in a single-cell roadmap of atrial and ventricular differentiation conditions.
Read the full summary ↓
Using a multiomic approach and gene-regulatory network inference, we identified regulators of the epicardial, atrial and ventricular cardiomyocyte lineages. We identified ZNF711 as a regulatory switch and safeguard for cardiomyocyte commitment. We show that ZNF711 ablation prevents cardiomyocyte differentiation in the absence of retinoic acid, causing progenitors to be diverted more prominently to epicardial and other lineages. Retinoic acid rescues this shift in lineage commitment and promotes atrial cardiomyocyte differentiation by regulation of shared and complementary target genes, showing an interplay between ZNF711 and retinoic acid in cardiac lineage commitment.
This dataset
Open GSE263325 in finder → Find more human RNA-seq 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 GSE263325_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 PRJNA1096710 and SRA study SRP500041. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗
Study design
8 conditions, mostly in triplicate
AM_LHX2 ×3 VM_LHX2 ×3 AM_ZNF503 ×3 VM_ZNF503 ×3 AM_TRAC ×3 VM_TRAC ×3 AM_ZNF711 ×3 VM_ZNF711 ×3

Supports a between-group comparison across 24 samples.

How we read this: 8 replicated groups read from 24 sample titles; they account for 24 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
Datasets on a similar topic

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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.