GEO series

Fibroblast starvation activates ECM remodeling gene transcription and putative enhancers despite inducing quiescence [chipseq]

GSE272507 Homo sapiens (human) ChIP-seq / ATAC-seq (genome binding and open chromatin) 12 samples Submitted 2025/06/01 Platform GPL20301

At a glance

ChIP-seq / ATAC-seq (genome binding and open chromatin) · human · 12 samples

What it is: ChIP-seq / ATAC-seq (genome binding and open chromatin)Where proteins bind the genome, or which regions are open and active.
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: Starvation activates ECM-remodeling gene transcription and putative enhancers in fibroblasts despite inducing quiescenceCell reports · 2025

Summary (written by the submitters)

Depletion of growth factors and nutrients induces cellular quiescence, which often accompanies transcriptional silencing and chromatin compaction. Paradoxically, such depletion occurs in pathological microenvironments in which fibroblasts are activated to orchestrate tissue remodeling. The relationship between fibroblast activation and growth factor and nutrient depletion remains unclear.

Read the full summary ↓

Here, we report that serum depletion in cell culture, a model for growth factor and nutrient depletions, extensively activates transcription in fibroblasts, despite inducing quiescence. Activated genes were enriched for extracellular matrix (ECM) structural components and proteases. The ECM-related transcription accompanied activation of putative distal enhancers, but not promoters. The activated putative enhancers were enriched for non-coding variants associated with inflammatory bowel disease (IBD) risk, suggesting an alteration in the ECM remodeling gene regulatory network in IBD. This study implicates nutrient and growth factor depletion in activating the ECM remodeling gene program in fibroblasts, challenging the prevailing view linking such depletion to transcriptional dormancy.

Published in

Starvation activates ECM-remodeling gene transcription and putative enhancers in fibroblasts despite inducing quiescence

Secchia S, Beilinson V, Chen X et al. · Cell reports 2025 · PMID 40560730 · doi:10.1016/j.celrep.2025.115896

This dataset

Open GSE272507 in finder → Find more human ChIP / ATAC / CUT&Tag 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 GSE272507_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: Look for peak files (.bed, .narrowPeak) and signal tracks (.bigWig) among the supplementary files; the raw reads are on ENA.

Also filed as BioProject PRJNA1137060 and SRA study SRP520620. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 40560730) ↗

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

GSM8403719 KI396_Input_starvation_GM07492
GSM8403720 KI397_Input_recovery_GM07492
GSM8403721 KI374_K27ac_starvation_GM07492
GSM8403722 KI401_K27ac_starvation_GM07492
GSM8403723 KI379_K27ac_recovery_GM07492
GSM8403724 KI402_K27ac_recovery_GM07492
GSM8403725 KI353_K4me3_proliferation_GM07492
GSM8403726 KI403_K4me3_proliferation_GM07492
GSM8403727 KI376_K4me3_starvation_GM07492
GSM8403728 KI404_K4me3_starvation_GM07492
GSM8403729 KI381_K4me3_recovery_GM07492
GSM8403730 KI405_K4me3_recovery_GM07492

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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.