GEO series

FOSL2 binding to enhancer elements regulates endothelial cell state in systemic sclerosis pulmonary vascular disease

GSE314055 Mus musculus (mouse) Single-cell RNA-seq 12 samples Submitted 2026/02/20 Platform GPL24247

At a glance

Single-cell RNA-seq · mouse · 12 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: Mus musculus (mouse)
Samples: 12 samples: 2 × WT vs 2 × FRA2 vs 2 × FRA_RAGko vs 2 × hypSugenePVPGroups 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: FOSL2 regulates endothelial cell state and chromatin accessibility in systemic sclerosis pulmonary vascular remodelingJCI insight · 2026

Summary (written by the submitters)

Systemic Sclerosis (SSc) is characterized by fibrosis and vasculopathy affecting the skin and internal organs, leading to multi-organ dysfunction. Injury of microvascular ECs in SSc impairs blood flow and causes tissue ischemia, leading to vascular complications such as Raynaud’s, digital ulcers, and pulmonary hypertension (PH). PH in SSc presents as group 1 pulmonary arterial hypertension (PAH) or as group 3 PH related to hypoxia and interstitial lung disease (ILD), both major causes of mortality.

Read the full summary ↓

Analysis of multiome data from SSc ILD-PH lungs inferred transcription factors regulating EC phenotype, including FOSL2. Overexpression of FOSL2 in transgenic mice (Fosl2tg) leads to vascular changes mirroring human SSc-PH, such as intimal thickening and fibrosis. ScRNA-seq analysis of altered EC gene expression in the Fosl2tg mice showed strong overlap with altered EC gene in SSc-ILD-PH. Overlapping as well as discrete EC gene expression in SuGene/hxpoxia- and hypoxia-treated mice suggested that FOSL2 regulates both hypoxia-dependent and independent pathways in Fosl2tg mice and SSc-ILD-PH. The deep learning model, ChromBPNet, inferred increased AP-1 binding at base pair resolution in SSc-ILDPH ECs and binding to the same motifs was found upon FOSL2 overexpression in primary vascular ECs, highlighting FOSL2’s key role in driving the pathological changes seen in SSc-ILDPH.

Published in

FOSL2 regulates endothelial cell state and chromatin accessibility in systemic sclerosis pulmonary vascular remodeling

Behera R, Zhou Y, Gerges PH et al. · JCI insight 2026 · PMID 42018656 · doi:10.1172/jci.insight.189107

This dataset

Open GSE314055 in finder → Find more mouse 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 GSE314055_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 PRJNA1389889 and SRA study SRP655916. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 42018656) ↗

Study design

2 × WT vs 2 × FRA2 vs 2 × FRA_RAGko vs 2 × hypSugenePVP

Supports a between-group comparison across 8 samples.

How we read this: 4 replicated groups read from 12 sample titles; they account for 8 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

GSM9380766 WT_2 WT
GSM9380767 FRA2_1 FRA2
GSM9380768 FRA2_2 FRA2
GSM9380769 RAGko
GSM9380770 FRA_RAGko1 FRA_RAGko
GSM9380771 FRA_RAGko2 FRA_RAGko
GSM9380772 WT_3 WT
GSM9380773 hypPVP
GSM9380774 hypT5224
GSM9380775 hypSugenePVP1 hypSugenePVP
GSM9380776 hypSugenePVP2 hypSugenePVP
GSM9380777 hypSugeneT5224

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