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VEGF/ERK activation and PI3K inhibition together drive a vein-to-artery transition in an in vitro model of human angiogenesis

GSE312764 Homo sapiens (human) Single-cell RNA-seq 21 samples Submitted 2025/12/20 Platform GPL24676

At a glance

Single-cell RNA-seq · human · 21 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: 21 samples: 7 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.

Summary (written by the submitters)

Artery endothelial cells (ECs) arise through different pathways, including differentiation from mesodermal cells (vasculogenesis) or from already established vein or capillary plexus ECs (angiogenesis), the latter being most common during embryonic development and regeneration. Understanding the vein-to-artery transition could improve revascularization therapies, but progress is limited by a lack of human models.

Read the full summary ↓

Here, we develop a human pluripotent stem cell (hPSC) differentiation protocol that models the vein-to-artery (v2a) EC conversion. Comparing mesoderm-to-artery (m2a) and v2a transcriptomes with publicly available scRNA-seq data from human embryos showed they reflected vasculogenesis- and angiogenesis-derived artery ECs, respectively. We discovered that full arterial identity could be attained in vein ECs within 48 hours just by activating VEGF signaling while inhibiting PI3K. Overall, we model a critical step in human vascular development and define the minimal signals required for artery differentiation from veins, providing a framework to promote this conversion during revascularization or in therapeutic contexts.

This dataset

Open GSE312764 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 GSE312764_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 PRJNA1375831 and SRA study SRP651851. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗

Study design

7 conditions, mostly in triplicate

aEC ×3 pVEC ×3 vEC ×3 p2a_24h ×3 p2a_48h ×3 v2a_24h ×3 v2a_48h ×3

Supports a between-group comparison across 21 samples.

How we read this: 7 replicated groups read from 21 sample titles; they account for 21 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

GSM9352689 aEC_1 aEC
GSM9352690 aEC_2 aEC
GSM9352691 aEC_3 aEC
GSM9352692 pVEC_1 pVEC
GSM9352693 pVEC_2 pVEC
GSM9352694 pVEC_3 pVEC
GSM9352695 vEC_1 vEC
GSM9352696 vEC_2 vEC
GSM9352697 vEC_3 vEC
GSM9352698 p2a_24h_1 p2a_24h
GSM9352699 p2a_24h_2 p2a_24h
GSM9352700 p2a_24h_3 p2a_24h
GSM9352701 p2a_48h_1 p2a_48h
GSM9352702 p2a_48h_2 p2a_48h
GSM9352703 p2a_48h_3 p2a_48h
GSM9352704 v2a_24h_1 v2a_24h
GSM9352705 v2a_24h_2 v2a_24h
GSM9352706 v2a_24h_3 v2a_24h
GSM9352707 v2a_48h_1 v2a_48h
GSM9352708 v2a_48h_2 v2a_48h
GSM9352709 v2a_48h_3 v2a_48h

Datasets on a similar topic

GSE326938 Discovery of a primed endothelial progenitor that requires VEGF/ERK inhibition to complete vein differentiation [kairos_timecourse_scrnaseq] 10 samples shares vegf, erk, inhibition
GSE290526 VEGFR2 blockade overcomes acquired KRAS G12D inhibitor resistance driven by PI3Kγ activation [scRNA-Seq] 3 samples shares vegf, pi3k, activation
GSE289892 Acetaldehyde Induced Cell Damage and Genotoxic Effects by Modulating PI3K/AKT and MAPK/ERK Pathways in Human Lung Epithelial Cells 17 samples shares pi3k, erk
GSE275078 In vitro drug screening of RAF/MEK/ERK Inhibitors in combination with Autophagy inhibition in the context of two PDA Cell Lines 136 samples shares erk, inhibition
GSE317774 ATR inhibition induces NF-κB-dependent immunogenicity in Merkel cell carcinoma independent of cGAS-STING 48 samples shares erk, inhibition
GSE237512 PI3Kα inhibition blocks osteochondroprogenitor specification and the hyper-inflammatory response to prevent heterotopic ossification 39 samples shares pi3k, inhibition
GSE322526 PI3K-AKT activation determines oncogenic RAS-induced hypertranscription and replication stress 30 samples shares pi3k, activation
GSE269369 LIN28B-mediated activation of the PI3K/AKT pathway promotes metastasis and unravels a new therapeutic vulnerability in colorectal cancer 18 samples shares pi3k, activation

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