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Transcriptomics-based Screening of Repurposing Drug for Idiopathic Pulmonary Fibrosis

GSE291190 Homo sapiens (human) RNA-seq (gene expression) 12 samples Submitted 2025/12/16 Platform GPL24676
At a glance

RNA-seq (gene expression) · human · 12 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
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.
Summary (written by the submitters)
IPF is a pathological condition resulting from injury to the lungs and an ensuing fibrotic response leading to thickening of the alveolar walls and the obliteration of the alveolar space. The etiology of IPF and the role of the microenvironment in disease progression are largely unknown. We first used the repurposing pipeline to explore disease biology for the identification of relevant cell populations and then applied the screening pipeline to discover novel compounds.
Read the full summary ↓
Using various types of RNAseq data from multiple datasets, we constructed signatures that capture different aspects of IPF biology, such as those related to cell type-specific transcriptional changes and those associated with disease development in animal models. We predicted and tested drugs targeting various IPF signatures and identified Pyrithyldione as our top candidate for reduction of fibrosis in the human precision-cut lung slices PCLS model and RNAseq was conducted on treated and untreated paired adjacent patient samples to determine its the effects on gene expression.
This dataset
Open GSE291190 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 GSE291190_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 PRJNA1232167 and SRA study SRP568133. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗
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
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.