GEO series

Transcriptome sequencing of cavernous fibroblasts cultured on substrates with different stiffness.

GSE329316 Homo sapiens (human) RNA-seq (gene expression) 9 samples Submitted 2026/04/28 Platform GPL24676

At a glance

RNA-seq (gene expression) · human · 9 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: 9 samples: 9 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)

Fibrotic diseases involve increased mechanical stress due to extracellular matrix deposition, significantly altering cellular metabolism. However, a systematic understanding of how mechanical force regulates lipid metabolism across different molecular layers remains limited. We employed an integrated multi-omics approach, including transcriptomics, Ribo-seq, proteomics, and lipidomics, to comprehensively assess the effects of mechanical stretch on lipid metabolism in human cavernous fibroblasts.

Read the full summary ↓

Our analysis revealed that while mechanical force elicits complex multi-layered responses, post-translational regulation predominantly drives this lipid metabolic reprogramming. Lipidomics identified a significant reduction in fatty acids and an upregulation of ganglioside. Key genes central to this mechanosensitive metabolic shift were pinpointed. This study demonstrates that mechanical force hierarchically reprograms lipid metabolism. The shift from fatty acids to ganglioside underscores a key metabolic adaptation in fibroblasts. Targeting the identified critical genes may offer a promising therapeutic strategy for fibrosis-related diseases.

This dataset

Open GSE329316 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 GSE329316_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 PRJNA1458654 and SRA study SRP695558. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗

Study design

9 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 9 sample titles: 9 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

GSM9701648 HD1-m
GSM9701649 HD2-m
GSM9701650 HD3-m
GSM9701651 MD1-m
GSM9701652 MD2-m
GSM9701653 MD3-m
GSM9701654 SO1-m
GSM9701655 SO2-m
GSM9701656 SO3-m

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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.