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Rapid generation of functional vascular organoids via simultaneous transcription factor activation of endothelial and mural lineages

GSE281002 Homo sapiens (human) Single-cell RNA-seq 15 samples Submitted 2025/06/13 Platform GPL20301
At a glance

Single-cell RNA-seq · human · 15 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
15 samples: 3 × 2D-iEC vs 3 × 2D-iMC vs 3 × VO-iEC vs 3 × VO-iMCGroups 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
Rapid generation of functional vascular organoids via simultaneous transcription factor activation of endothelial and mural lineagesCell stem cell · 2025
Summary (written by the submitters)
Vascular organoids (VOs) are valuable tools for studying vascular development, disease, and regenerative medicine. However, controlling endothelial and mural compartments independently remains challenging. Here, we present a streamlined method to generate VOs from induced pluripotent stem cells (iPSCs) via orthogonal activation of the transcription factors ETV2 and NKX3.1 using Dox-inducible or modRNA systems.
Read the full summary ↓
This approach enables efficient co-differentiation of endothelial (iECs) and mural cells (iMCs), producing functional 3D VOs in five days without ECM embedding. VOs matured further upon ECM exposure, forming larger, structured vessels. Single-cell RNA sequencing revealed vascular heterogeneity, and temporal regulation of transcription factor expression allowed modulation of arterial and angiogenic iEC phenotypes. In vivo, VOs engrafted into immunodeficient mice, formed perfused vasculature, and promoted revascularization in models of hind limb ischemia and pancreatic islet transplantation. These findings establish a rapid and versatile VO platform with broad potential for vascular modeling, disease studies, and regenerative cell therapy.
Published in
Rapid generation of functional vascular organoids via simultaneous transcription factor activation of endothelial and mural lineages
Gong L, Zhang Y, Zhu Y et al. · Cell stem cell 2025 · PMID 40516530 · doi:10.1016/j.stem.2025.05.014
This dataset
Open GSE281002 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 GSE281002_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 PRJNA1181813 and SRA study SRP543024. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 40516530) ↗
Study design
3 × 2D-iEC vs 3 × 2D-iMC vs 3 × VO-iEC vs 3 × VO-iMC

Supports a between-group comparison across 12 samples.

How we read this: 4 replicated groups read from 15 sample titles; they account for 12 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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