Systemic comparison of molecular characteristics in different skin fibroblast senescent models
RNA-seq (gene expression) · human · 18 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
- 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.
- Paper
- Systemic comparison of molecular characteristics in different skin fibroblast senescent modelsChinese medical journal · 2025
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.
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.
Supplementary files
The count tables and other files the authors uploaded. Take only the samples you need, or the whole study as GSE262932_RAW.tar (it can run to tens of gigabytes).
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.
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 PRJNA1094942 and SRA study SRP499024. Searching any of these in the dataset finder brings you back here.
Supports a between-group comparison across 18 samples.
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.
- GSM8181669 FB-C1 FB-C
- GSM8181670 FB-C2 FB-C
- GSM8181671 FB-C3 FB-C
- GSM8181672 FB-E1 FB-E
- GSM8181673 FB-E2 FB-E
- GSM8181674 FB-E3 FB-E
- GSM8181675 FB-UVB1 FB-UVB
- GSM8181676 FB-UVB2 FB-UVB
- GSM8181677 FB-UVB3 FB-UVB
- GSM8181678 FB-D-gal1 FB-D-gal
- GSM8181679 FB-D-gal2 FB-D-gal
- GSM8181680 FB-D-gal3 FB-D-gal
- GSM8181681 FB-ATV1 FB-ATV
- GSM8181682 FB-ATV2 FB-ATV
- GSM8181683 FB-ATV3 FB-ATV
- GSM8181684 FB-P301 FB-P
- GSM8181685 FB-P302 FB-P
- GSM8181686 FB-P303 FB-P
- GSE292702 Characterisation of a pathogenic non-migratory fibroblast population in systemic sclerosis skin 12 samples shares fibroblast, systemic, skin
- GSE269317 Reduced WNT4 expression in normal skin fibroblasts leads to ‘Dupuytren-like’ changes in the transcriptome 16 samples shares fibroblast, skin
- GSE335811 Macrophage-Induced Senescent Cancer-Associated Fibroblasts Promote SASP-Mediated Chemoresistance in Colorectal Cancer 12 samples shares fibroblast, senescent
- GSE320020 Macrophage–Fibroblast Signaling Network Identified by Single-Cell RNA Sequencing in Juvenile Systemic Sclerosis 11 samples shares fibroblast, systemic
- GSE235244 Senescent cancer-associated fibroblasts in pancreatic adenocarcinoma suppress CD8+ T cell activation and inhibit response to immune checkpoint therapy [hPDAC] 8 samples shares fibroblast, senescent
- GSE312129 Single-nuclei multiome and spatial analyses of early, untreated SSc skin identify a proinflammatory vascular niche of macrophage-fibroblast signaling 38 samples shares fibroblast, skin
- GSE275647 Role of Cancer-Associated Fibroblasts in Prognostic Stratification of Advanced-Stage Gastric Cancer through Interacting with Endothelial and Malignant Epithelial Cells [AGC RNA-seq] 216 samples shares fibroblast
- GSE306664 Revealing differential type I and type II IFN responses in different immune cells in various human diseases by a new IFNs transcriptomic atlas 131 samples shares different
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.