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Critical role for the TGF-β1/mTORC1 signalling axis in defining the transcriptional identity of CTHRC1+ pathologic fibroblasts in idiopathic pulmonary fibrosis

GSE272972 Homo sapiens (human) Single-cell RNA-seq 18 samples Submitted 2026/07/21 Platform GPL18573
At a glance

Single-cell RNA-seq · human · 18 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
18 samples: 6 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)
Fibroblasts represent key effector cells responsible for fibrogenesis during wound healing and pathological fibrosis, including idiopathic pulmonary fibrosis (IPF), the most rapidly progressive and fatal fibrotic lung disease. Single-cell RNA sequencing of fibrotic lung tissue has identified a novel population of high collagen-producing fibroblasts not present in non-diseased control lung, characterised by high expression of the marker, collagen triple helix repeat containing 1 (CTHRC1).
Read the full summary ↓
The cardinal pro-fibrotic mediator TGF-β1 has been widely implicated in promoting fibrogenesis in multiple fibrotic conditions. In addition to the canonical Smad signalling pathway, TGF-β1-induced collagen I production is under critical regulatory control by the mTORC1/4E-BP1 signalling hub. Using pharmacological inhibition (dual ATP-competitive mTOR inhibitors and rapamycin) in combination with gene-editing approaches, we now demonstrate that the role of the mTORC1 axis extends to the regulation of over a third of all TGF-β1 regulated matrisome genes. We further show that the global transcriptome of TGF-β1-stimulated fibroblasts matches that of CTHRC1+ pathological fibroblast population in the IPF lung. In contrast, the TGF-β1 induced transcriptome of fibroblasts in which mTORC1-signalling is disrupted (by RPTOR gene editing using CRISPR-Cas9) does not map to any known fibroblast population. These data define, for the first time, a critical role for both TGF-β1 and the mTORC1 signalling hub in determining the transcriptional identity of CTHRC1+ pathological fibroblasts and provide strong scientific support for targeting mTORC1 as a therapeutic strategy in IPF and potentially other fibrotic conditions associated with dysregulated TGF-β1 profibrotic signalling.
This dataset
Open GSE272972 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 GSE272972_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 PRJNA1139735 and SRA study SRP521916. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗
Study design
6 conditions, mostly in triplicate
CRISPR control not treated ×3 CRISPR control with TGF-β1 treatment ×3 RPTOR knock down not treated ×3 RPTOR knock down with TGF-β1 treatment ×3 RICTOR knock down not treated ×3 RICTOR knock down with TGF-β1 treatment ×3

Supports a case/control comparison: 6 samples read as cases, 3 as controls.

How we read this: 6 replicated groups read from 18 sample titles; they account for 18 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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