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A precision medicine approach to interpret a GATA4 genetic variant in a paediatric patient with congenital heart disease [RNA-seq]

GSE305730 Homo sapiens (human) RNA-seq (gene expression) 16 samples Submitted 2026/02/11 Platform GPL24676
At a glance

RNA-seq (gene expression) · human · 16 samples

What it is
RNA-seq (gene expression)Gene activity measured by sequencing. The summary does not mention single-cell work, so this is most likely bulk: one average per sample, the usual starting point for comparing groups.
Organism
Homo sapiens (human)
Samples
16 samples: 4 × KOLF cells, HDR, Day 18, biol vs 4 × KOLF cells, WT, Day 18, biol vs 4 × KOLF cells, HDR, Day 0, biol vs 4 × KOLF cells, WT, Day 0, biolGroups 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
A precision medicine approach to interpret a GATA4 genetic variant in a paediatric patient with congenital heart diseaseHuman genomics · 2026
Summary (written by the submitters)
Background. Patients with congenital heart disease are identified in 1% of live births, and with improved surgical intervention many patients now survive to adulthood, the corollary of which is increased mortality in the over-65-year-old CHD population. In the clinic, genetic sequencing increasingly identifies novel genetic variants in genes related to congenital heart disease (CHD).
Read the full summary ↓
Traditional assays for interpreting novel genetic variants are often limited by gene-specificity, whereas animal models are cumbersome and may not accurately reflect human disease. This study investigates CRISPR gene editing in induced pluripotent stem cells and cardiomyocyte-directed differentiation as a human disease model to investigate novel genetic variants identified in association with CHD. Methods and Results. The GATA4 p.Arg284His genetic variant was introduced into iPSCs using high efficiency CRISPR gene editing with homology-directed repair. GATA4 genetic variant and isogenic control cell pairs were selected and differentiated into cardiomyocytes. Expression of the GATA4 p.Arg284His variant resulted in altered calcium transient’s indicative of CHD and consistent with the patient’s clinical phenotype. Transcriptomics revealed molecular and cellular pathway changes in cardiac development, calcium handling, and energy metabolism that contribute to disease aetiology and mechanism, and identified potential treatments that could restore normal cell function. Conclusion. Directed differentiation of iPSCs harbouring the GATA4 p.Arg284His genetic variant recapitulated the CHD phenotype, indicated disease mechanisms, and pointed to potential treatments. The study highlights the utility of transcriptomics for the functional interpretation of cardiac genetic variants and is an exemplar for precision medicine approaches for the investigation of CHD.
Published in
A precision medicine approach to interpret a GATA4 genetic variant in a paediatric patient with congenital heart disease
Forbes CA, Shaw NC, Chen KG et al. · Human genomics 2026 · PMID 41484984 · doi:10.1186/s40246-025-00907-6
This dataset
Open GSE305730 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 GSE305730_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: For RNA-seq the counts are usually a table among the supplementary files (look for 'counts', 'expression', 'FPKM' or 'TPM' in the file name). The series matrix of a sequencing study is sometimes nearly empty.

Also filed as BioProject PRJNA1307511 and SRA study SRP610671. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 41484984) ↗
Study design
4 × KOLF cells, HDR, Day 18, biol vs 4 × KOLF cells, WT, Day 18, biol vs 4 × KOLF cells, HDR, Day 0, biol vs 4 × KOLF cells, WT, Day 0, biol

Supports a between-group comparison across 16 samples.

How we read this: 4 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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