Genome wide expression analysis of high passage growth arrested BJ fibroblasts maintained or withdrawn from rapamycin maintenance, in the presence or absence of various SMAD2/3 regulating molecules
RNA-seq (gene expression) · human · 24 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
- 24 samples: 24 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 GSE146579_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 PRJNA610886 and SRA study SRP251909. 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 24 sample titles: 24 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.
- GSM4395638 TSF11_BJ_HPGA_RM_Control_Rep1
- GSM4395639 TSF12_BJ_HPGA_RM_Control_Rep2
- GSM4395640 TSF13_BJ_HPGA_RW_Control_Rep1
- GSM4395641 TSF14_BJ_HPGA_RW_Control_Rep2
- GSM4395642 TSF31_BJ_HPGA_RM_Control_Rep3
- GSM4395643 TSF32_BJ_HPGA_RM_Control_Rep4
- GSM4395644 TSF33_BJ_HPGA_RW_Control_Rep3
- GSM4395645 TSF34_BJ_HPGA_RW_Control_Rep4
- GSM4395646 TSF15_BJ_HPGA_RM_Inhibin_Rep1
- GSM4395647 TSF16_BJ_HPGA_RM_Inhibin_Rep2
- GSM4395648 TSF17_BJ_HPGA_RW_Inhibin_Rep1
- GSM4395649 TSF18_BJ_HPGA_RW_Inhibin_Rep2
- GSM4395650 TSF19_BJ_HPGA_RM_Inhibin+TGFB2_Rep1
- GSM4395651 TSF20_BJ_HPGA_RM_Inhibin+TGFB2_Rep2
- GSM4395652 TSF21_BJ_HPGA_RW_Inhibin+TGFB2_Rep1
- GSM4395653 TSF22_BJ_HPGA_RW_Inhibin+TGFB2_Rep2
- GSM4395654 TSF35_BJ_HPGA_RM_ActivinA_Rep1
- GSM4395655 TSF36_BJ_HPGA_RM_ActivinA_Rep2
- GSM4395656 TSF37_BJ_HPGA_RW_ActivinA_Rep1
- GSM4395657 TSF38_BJ_HPGA_RW_ActivinA_Rep2
- GSM4395658 TSF39_BJ_HPGA_RM_TGFB2_Rep1
- GSM4395659 TSF40_BJ_HPGA_RM_TGFB2_Rep2
- GSM4395660 TSF41_BJ_HPGA_RW_TGFB2_Rep1
- GSM4395661 TSF42_BJ_HPGA_RW_TGFB2_Rep2
- GSE313611 IGF2BP3 promotes progression of head and neck cancers through the circHECTD2/hsa_miR_4310/7157-5p/Smad2 signaling axis in an m6A modification–dependent manner 21 samples shares smad2
- 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
- GSE237690 Lupus dermal fibroblasts hyper-respond to inflammatory cytokines and exhibit a pro-fibrotic phenotype to TGF-β in patients with scarring skin disease 317 samples shares fibroblasts
- 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
- GSE297400 Transcriptome Profiling of Human Dermal Fibroblasts Treated with 4 Phosphodiesterase Inhibitors Across Multiple Dose Gradients 199 samples shares fibroblasts
- 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
- GSE224698 Gene expression profiles of human dermal fibroblasts and human pluripotent stem cells analyzed by bulk RNA-seq 69 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.