Genome wide expression analysis of senescent BJ fibroblasts in the presence or absence of various SMAD2/3 regulating molecules
RNA-seq (gene expression) · human · 10 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
- 10 samples: 10 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
- Ligand-specific regulation of a binary enhancer code dictating cellular senescenceProceedings of the National Academy of Sciences of the United States of America · 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 GSE146580_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 PRJNA610885 and SRA study SRP251910. Searching any of these in the dataset finder brings you back here.
Any grouping that defines a comparison lives in the sample characteristics, which GEO records separately from the titles.
How we read this: Read from 10 sample titles: 10 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.
- GSM4395662 TSF1_BJ_Senescent_Control_Rep1
- GSM4395663 TSF2_BJ_Senescent_Control_Rep2
- GSM4395664 TSF3_BJ_Senescent_Activin_Rep1
- GSM4395665 TSF4_BJ_Senescent_Activin_Rep2
- GSM4395666 TSF5_BJ_Senescent_Inhibin_Rep1
- GSM4395667 TSF6_BJ_Senescent_Inhibin_Rep2
- GSM4395668 TSF7_BJ_Senescent_TGFB2_Rep1
- GSM4395669 TSF8_BJ_Senescent_TGFB2_Rep2
- GSM4395670 TSF9_BJ_Senescent_Inhibin+TGFB2_Rep1
- GSM4395671 TSF10_BJ_Senescent_Inhibin+TGFB2_Rep2
- GSE335811 Macrophage-Induced Senescent Cancer-Associated Fibroblasts Promote SASP-Mediated Chemoresistance in Colorectal Cancer 12 samples shares fibroblasts, senescent
- GSE235244 Senescent cancer-associated fibroblasts in pancreatic adenocarcinoma suppress CD8+ T cell activation and inhibit response to immune checkpoint therapy [hPDAC] 8 samples shares fibroblasts, senescent
- GSE298598 METTL3/YTHDF2 axis inhibits cell proliferation and fibrosis by degrading the N6-methyladenosine modification of TCF7L2-Smad2/Smad3 mRNA in pterygium 12 samples shares smad2
- GSE314342 Genome-scale perturb-seq in primary human CD4+ T cells reveals genes regulating T cell programs and human immune traits. 577 samples shares regulating
- 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 fibroblasts
- GSE284666 SenCat: Redefining human cell senescence through multiomic profiling of multiple senescent cell types 190 samples shares senescent
- GSE298908 Positionally distinct interferon-stimulated dermal immune-acting fibroblasts promote neutrophil recruitment in Sweet's syndrome [RNA-seq] 113 samples shares fibroblasts
- GSE306317 A high-throughput transcriptomic profiling of fibroblasts treated with 0.0005 - 10 µM Omaveloxolone 72 samples shares fibroblasts
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.