GEO series

Modeling SMAD2 mutations in iPSCs provides insights into cardiovascular disease pathogenesis [RNA-seq]

GSE280689 Homo sapiens (human) RNA-seq (gene expression) 21 samples Submitted 2025/10/29 Platform GPL18573

At a glance

RNA-seq (gene expression) · human · 21 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: 21 samples: 21 samples, each with a distinct title — no replicated groupsGroups 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: Modeling SMAD2 Mutations in Induced Pluripotent Stem Cells Provides Insights Into Cardiovascular Disease PathogenesisJournal of the American Heart Association · 2025

Summary (written by the submitters)

Rationale: SMAD2 is a co-regulator that binds a variety of transcription factors and modulates activities during human development. Heterozygous SMAD2 loss-of-function (LoF) variants and missense variants are identified in patients with complex congenital heart disease (CHD), and in patients with arterial aneurysms and dissections. Mechanisms that account for distinct cardiovascular phenotypes caused by SMAD2 variants remain unknown.

Read the full summary ↓

We aimed to define the transcriptional and epigenetic effects of heterozygous SMAD2 loss-of-function (LoF) and missense variants and identify the involvement of transcription factor networks and genes that contribute to CHD. Compared to LoF variants, we assessed the function of rare SMAD2 missense variants of uncertain significance. Methods: We constructed isogenic induced pluripotent stem cells (iPSCs) with heterozygous or homozygous LoF and rare (MAF ≤ 10-5) missense SMAD2 variants identified in CHD probands. Wildtype and mutant iPSCs were studied by bulk RNA sequencing (RNAseq) and chromatin accessibility (ATACseq). Datasets were integrated with published SMAD2/3 chromatin immunoprecipitation (ChIPseq) studies. Cardiomyocyte differentiation and contractility of mutant iPSCs was assessed. Results: SMAD2 haploinsufficiency reduced open chromatin across promoter regions and altered the expression of 385 direct SMAD regulated genes, including ten previously identified as dominant CHD genes. Motif analyses in regions with differential ATAC peaks predicted that SMAD2 haploinsufficiency disrupts interactions with at least five transcription factors, NANOG, ETS, TEAD3/4, CREB1 and AP1. Compared to SMAD2-haploinsufficient cells, iPSCs with heterozygous R114C or W274C variants exhibited shared and distinct patterns of altered chromatin accessibility, transcription factor binding motifs, and dysregulated genes. Conclusions: SMAD2 haploinsufficiency disrupts chromatin interactions and perturbs transcription factor binding, disrupting normal cardiovascular development. Differences in the molecular consequences of LoF and missense variants likely contribute to clinical phenotypic heterogeneity. Additionally, these data indicate opportunities for molecular functional analyses to improve re-classification of SMAD2 variants of uncertain clinical significance.

Published in

Modeling SMAD2 Mutations in Induced Pluripotent Stem Cells Provides Insights Into Cardiovascular Disease Pathogenesis

Ward T, Morton SU, Venturini G et al. · Journal of the American Heart Association 2025 · PMID 40028843 · doi:10.1161/JAHA.124.036860

This dataset

Open GSE280689 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 GSE280689_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 PRJNA1179967 and SRA study SRP542154. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 40028843) ↗

Study design

21 samples, each with a distinct title — no replicated groups

Any grouping that defines a comparison lives in the sample characteristics, which GEO records separately from the titles.

How we read this: Read from 21 sample titles: 21 distinct titles with little repetition. 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

GSM8603745 Wildtype_R1_05, RNAseq
GSM8603746 Wildtype_R2_1A, RNAseq
GSM8603747 Wildtype_R3_1B, RNAseq
GSM8603748 Wildtype_R4_2M, RNAseq
GSM8603749 SMAD2P135*/P135*_R1, RNAseq
GSM8603750 SMAD2P135*/P135*_R3, RNAseq
GSM8603751 SMAD2P135*/K157*_R1, RNAseq
GSM8603752 SMAD2+/P135*(RB9)_R3, RNAseq
GSM8603753 SMAD2+/P135*(RD11)_R2, RNAseq
GSM8603754 SMAD2+/P135*(RD11)_R3, RNAseq
GSM8603755 SMAD2+/R114C*(RA10)_R2, RNAseq
GSM8603756 SMAD2+/R114C*(RA10)_R3, RNAseq
GSM8603757 SMAD2+/R114C*(RF10)_R1, RNAseq
GSM8603758 SMAD2+/R114C*(RF10)_R3, RNAseq
GSM8603759 SMAD2+/W274C*(RB1)_R3, RNAseq
GSM8603760 SMAD2+/W274C*(RG12)_R1, RNAseq
GSM8603761 SMAD2+/W274C*(RG12)_R3, RNAseq
GSM8603762 Wildtype_D30_R1, RNAseq
GSM8603763 Wildtype_D30_R2, RNAseq
GSM8603764 SMAD2+/P135*(RB9)_D30, RNAseq
GSM8603765 SMAD2+/P135*(RD11)_D30, RNAseq

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