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Brain pericytes and Wnt/β-catenin signaling induce functional blood-brain barrier phenotype in human iPSC-derived model

GSE305015 Homo sapiens (human) RNA-seq (gene expression) 40 samples Submitted 2025/08/28 Platform GPL34284
At a glance

RNA-seq (gene expression) · human · 40 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
40 samples: 14 conditions, mostly in triplicateGroups 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
Brain Pericytes and Wnt/β-Catenin Signaling Induce Functional Blood-Brain Barrier Phenotype in Human iPSC-Derived ModelSmall methods · 2026
Summary (written by the submitters)
The blood-brain barrier (BBB), formed by brain microvascular endothelial cells (BMECs), restricts vascular permeability by establishing tight junctions, reducing vesicular transport, and regulating molecular trafficking via dedicated transporters. In disease, BBB dysfunction leads to barrier disruption and neuroinflammation. Existing human in vitro BBB models recapitulate some but not all of these key BMEC features.
Read the full summary ↓
Here, we present a method to differentiate human induced pluripotent stem cells (hiPSCs) into BMECs (hiBMECs) by co-culturing hiPSC-derived endothelial cells with brain pericytes (hiBPCs) and activating the Wnt/β-catenin pathway. The resulting hiBMECs exhibit barrier properties, functional efflux transporters, and appropriate inflammatory responses. RNA sequencing revealed a transcriptomic profile in hiBMECs closely matching the adult human BBB. Mechanistically, hiBPCs and Wnt/β-catenin signaling provided complementary cues that converged on ETS1, SMAD3/4, and PPARγ transcriptional networks. These findings reveal a synergistic mechanism by which brain pericytes and Wnt/β-catenin signaling orchestrate human BMEC differentiation and function, providing mechanistic insight into human BBB development and an improved hiPSC-derived BBB model for future drug screening and disease modeling.
Published in
Brain Pericytes and Wnt/β-Catenin Signaling Induce Functional Blood-Brain Barrier Phenotype in Human iPSC-Derived Model
Nogueira Pinto H, Kok NR, Hauger PC et al. · Small methods 2026 · PMID 41601407 · doi:10.1002/smtd.202502114
This dataset
Open GSE305015 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 GSE305015_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 PRJNA1303708 and SRA study SRP607727. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 41601407) ↗
Study design
14 conditions, mostly in triplicate
hiBPC ×3 hiBPC_inf ×3 hiBPC+hiEC ×3 hiBPC+hiBMEC ×3 hiBPC+hiEC_inf ×3 hiEC ×3 hiEC_CHI ×3 hiEC_inf ×3 +6 more

Supports a between-group comparison across 40 samples.

How we read this: 14 replicated groups read from 40 sample titles; they account for 40 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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