GEO series

Single-cell transcriptomics reveal diverging pathobiology and opportunities for precision targeting in scleroderma -associated versus idiopathic PAH

GSE293580 Homo sapiens (human) Single-cell RNA-seq 12 samples Submitted 2025/06/06 Platform GPL24676

At a glance

Single-cell RNA-seq · human · 12 samples

What it is: Single-cell RNA-seqGene activity measured in thousands of individual cells, so cell types can be told apart.
Organism: Homo sapiens (human)
Samples: 12 samples: 12 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: Lung Single-Cell Transcriptomics Reveal Diverging Pathobiology and Opportunities for Precision Targeting in Scleroderma-Associated Versus Idiopathic Pulmonary Arterial HypertensionCirculation. Genomic and precision medicine · 2025

Summary (written by the submitters)

Pulmonary arterial hypertension (PAH) is characterized by progressive cellular and molecular changes within the pulmonary vasculature that lead to structural remodeling, rising pulmonary vascular resistance and, if untreated, right ventricular failure. In clinical practice, PAH treatments have evolved little over the last two decades, and until recently, all approved therapies have targeted one of three molecular pathways: the nitric oxide (NO) signaling pathway, the endothelin pathway, or the prostacyclin pathway.

Read the full summary ↓

With these therapies, treatment responses have been modest and variable, with patients experiencing a heterogeneity of treatment effects. In particular, patients with PAH related to systemic sclerosis (SSc-PAH) have derived less benefit from PAH-specific therapies and have experienced worse clinical outcomes compared to other PAH subgroups. We and others have shown that in SSc-PAH, pathophysiologic differences at the molecular, cellular, and tissue levels underlie observed differences in clinical outcomes. A detailed understanding of the molecular aberration present in SSc-PAH versus idiopathic PAH (IPAH) might allow for exploitation of aberrant signaling pathways that are unique to SSc-PAH (and/or present in both of these two common PAH subtypes) for drug targeting. Recently, the approval of sotatercept, a novel ligand-trap molecule that targets TGF-beta signaling in PAH, was based on randomized controlled trial data demonstrating clinical improvement in patients already treated with drugs targeting NO, endothelin, and/or prostacyclin signaling. The success of sotatercept therefore lends credence to the notion that targeting as-yet unaddressed molecular aberrancies can bring about additional clinical benefit. Most previous omics investigations in PAH have examined circulating plasma or serum. With this study, we sought to identify distinct and overlapping gene expression patterns in SSc-PAH versus IPAH human lung tissue as a means of implicating abnormal biological processes, including inferred gene regulation and cell signaling, unique to the organ manifesting disease. To maximize biologic insight, we took a single-cell approach to RNA sequencing, in contrast to the bulk sequencing performed in most previous PAH studies. Finally, we leveraged cell-specific gene expression patterns and established computational methods to identify small molecule compounds with high potential to reverse disease-specific expression signatures, as a means of prioritizing candidates for drug repositioning. We posit that generation of phenotype-specific high-dimensional omics datasets, such as this one, will enable a precision-targeted approach to molecular aberration that might improve therapeutic responses and resolve heterogeneous treatment effects, thereby improving clinical outcomes across PAH subtypes.

Published in

Lung Single-Cell Transcriptomics Reveal Diverging Pathobiology and Opportunities for Precision Targeting in Scleroderma-Associated Versus Idiopathic Pulmonary Arterial Hypertension

Tuhy T, Coursen JC, Graves T et al. · Circulation. Genomic and precision medicine 2025 · PMID 40686216 · doi:10.1161/CIRCGEN.124.004936

This dataset

Open GSE293580 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 GSE293580_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 PRJNA1245292. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 40686216) ↗

Study design

12 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 12 sample titles: 12 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

GSM8885793 BA-044, Failed Donor, scRNA-seq
GSM8885794 BA-048, Failed Donor, scRNA-seq
GSM8885795 BA-053, Failed Donor, scRNA-seq
GSM8885796 BA-058, Failed Donor, scRNA-seq
GSM8885797 BA-024, SSc-PAH, scRNA-seq
GSM8885798 BA-025, SSc-PAH, scRNA-seq
GSM8885799 ST-005, SSc-PAH, scRNA-seq
GSM8885800 ST-059, SSc-PAH, scRNA-seq
GSM8885801 BA-008, IPAH, scRNA-seq
GSM8885802 BA-014, IPAH, scRNA-seq
GSM8885803 CC-030, IPAH, scRNA-seq
GSM8885804 ST-042, IPAH, scRNA-seq

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