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Robust differentiation of human pluripotent stem cells into mural progenitor cells via transient activation of NKX3.1

GSE251718 Homo sapiens (human) RNA-seq (gene expression) 22 samples Submitted 2024/08/26 Platform GPL20301
At a glance

RNA-seq (gene expression) · human · 22 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
22 samples: 22 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
Robust differentiation of human pluripotent stem cells into mural progenitor cells via transient activation of NKX3.1Nature communications · 2024
Summary (written by the submitters)
Mural cells are central to vascular integrity and function. In this study, we demonstrate the innovative use of the transcription factor NKX3.1 to guide the differentiation of human induced pluripotent stem cells into mural progenitor cells (iMPCs). By transiently activating NKX3.1 in mesodermal intermediates, we developed a method that diverges from traditional growth factor-based differentiation techniques.
Read the full summary ↓
This approach efficiently generates a robust iMPC population capable of maturing into diverse functional mural cell subtypes, including smooth muscle cells and pericytes. These iMPCs exhibit key mural cell functionalities such as contractility, deposition of extracellular matrix, and the ability to support endothelial cell-mediated vascular network formation in vivo.
Published in
Robust differentiation of human pluripotent stem cells into mural progenitor cells via transient activation of NKX3.1
Lee U, Zhang Y, Zhu Y et al. · Nature communications 2024 · PMID 39349465 · doi:10.1038/s41467-024-52678-8
This dataset
Open GSE251718 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 GSE251718_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 PRJNA1055110 and SRA study SRP479264. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 39349465) ↗
Study design
22 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 22 sample titles: 22 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
Datasets on a similar topic

Search all human RNA-seq datasets in GEO →

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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.