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Expression profiling of circular RNAs during differentiation of human pluripotent stem cells into vascular endothelial and smooth muscle cells

GSE210361 Homo sapiens (human) Non-coding RNA profiling 160 samples Submitted 2025/04/01 Platform GPL21697
At a glance

Non-coding RNA profiling · human · 160 samples

What it is
Non-coding RNA profilingLevels of RNAs that do not code for proteins, such as miRNA or lncRNA.
Organism
Homo sapiens (human)
Samples
160 samples: 36 conditions, mostly with about 4 replicates eachGroups 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
Circular RNAs increase during vascular cell differentiation and are biomarkers for vascular diseaseCardiovascular research · 2025
Summary (written by the submitters)
To identify novel circular RNAs (circRNAs) and to investigate their expression levels during human pluripotent stem cells (hiPSC) differentation, we have converted these cells to vascular endothelial cells (ECs) and smooth muscle cells (SMCs). Circular RNAs were de novo identified using RNase R treated samples and quantified in total RNA samples at 13 timepoints per differentiation.
Published in
Circular RNAs increase during vascular cell differentiation and are biomarkers for vascular disease
Northoff BH, Herbst A, Wenk C et al. · Cardiovascular research 2025 · PMID 39901821 · doi:10.1093/cvr/cvaf013
This dataset
Open GSE210361 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 GSE210361_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 ↗

Also filed as BioProject PRJNA865357 and SRA study SRP389509. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 39901821) ↗
Study design
36 conditions, mostly with about 4 replicates each
hiPSC>EC differentiation #1, total RNA, day00… ×4 hiPSC>EC differentiation #1, total RNA, day01… ×4 hiPSC>EC differentiation #1, total RNA, day02… ×4 hiPSC>EC differentiation #1, total RNA, day03… ×4 hiPSC>EC differentiation #1, total RNA, day04… ×4 hiPSC>EC differentiation #1, total RNA, day05… ×4 hiPSC>EC differentiation #1, total RNA, day06… ×4 hiPSC>EC differentiation #1, total RNA, day07… ×4 +54 more

Supports a between-group comparison across 134 samples.

How we read this: 36 replicated groups read from 160 sample titles; they account for 134 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

+ 120 more — browse all 160 samples with per-sample file links →

Datasets on a similar topic

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