Bulk RNA-seq of multiple organs in LmnaG609G progeria mice with inducible Δ133p53α
RNA-seq (gene expression) · mouse · 99 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
- Mus musculus (mouse)
- Samples
- 99 samples: 99 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
- Senescence-inhibitory Δ133p53α counteracts accelerated ageing and mortalitybioRxiv : the preprint server for biology · 2026
Read the full summary ↓
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 GSE315211_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 PRJNA1395132 and SRA study SRP658711. 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 99 sample titles: 99 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.
- GSM9423035 Heart_WT_173, Female
- GSM9423036 Heart_WT_355, Female
- GSM9423037 Heart_WT_528, Female
- GSM9423038 Heart_WT_545, Male
- GSM9423039 Heart_WT_653, Male
- GSM9423040 Heart_G1_129, Female
- GSM9423041 Heart_G1_267, Female
- GSM9423042 Heart_G1_269, Male
- GSM9423043 Heart_G1_272, Male
- GSM9423044 Heart_G1_311, Female
- GSM9423045 Heart_G2_169, Female
- GSM9423046 Heart_G2_278, Male
- GSM9423047 Heart_G2_279, Male
- GSM9423048 Heart_G2_281, Female
- GSM9423049 Heart_G2_312, Female
- GSM9423050 Heart_G3_130, Female
- GSM9423051 Heart_G3_273, Female
- GSM9423052 Heart_G3_309, Female
- GSM9423053 Heart_G3_385, Male
- GSM9423054 Heart_G3_404, Male
- GSM9423055 Heart_G4_172, Female
- GSM9423056 Heart_G4_275, Female
- GSM9423057 Heart_G4_277, Male
- GSM9423058 Heart_G4_282, Male
- GSM9423059 Heart_G4_310, Female
- GSM9423060 Kidney_WT_173, Female
- GSM9423061 Kidney_WT_355, Female
- GSM9423062 Kidney_WT_528, Female
- GSM9423063 Kidney_WT_545, Male
- GSM9423064 Kidney_WT_653, Male
- GSM9423065 Kidney_G1_129, Female
- GSM9423066 Kidney_G1_267, Female
- GSM9423067 Kidney_G1_269, Male
- GSM9423068 Kidney_G1_311, Female
- GSM9423069 Kidney_G2_169, Female
- GSM9423070 Kidney_G2_278, Male
- GSM9423071 Kidney_G2_279, Male
- GSM9423072 Kidney_G2_281, Female
- GSM9423073 Kidney_G2_312, Female
- GSM9423074 Kidney_G3_130, Female
+ 59 more — browse all 99 samples with per-sample file links →
- GSE332646 Bulk RNA-seq of juvenile mouse liver following NDMA or saline treatment in WT and Mgmt-/- genotypes across multiple timepoints 214 samples shares multiple, rna-seq, bulk
- GSE205410 Multiple subpopulations in medullary thymic epithelial cells are regulated by Aire with distinctive modes of gene regulations [bulkRNA-seq] 47 samples shares multiple, rna-seq, bulk
- GSE205991 Multiple subpopulations in medullary thymic epithelial cells are regulated by Aire with distinctive modes of gene regulations [deep single-cell RNA-seq] 2148 samples shares multiple, rna-seq
- GSE330728 Single-nucleus RNA-seq atlas of 17 ASD-risk mouse lines under early-postnatal fluoxetine or lithium exposure (4-plex On-Chip Multiplexed) 53 samples shares multiple, rna-seq
- GSE289677 Fidelity in lineage determination and stimulus-inducible gene expression requires ISWI chromatin remodeling activity [RNA-Seq] 39 samples shares inducible, rna-seq
- GSE288269 Hypoxia-inducible factor 2α promotes protective Th2 cell responses during intestinal helminth infection [RNA-seq] 24 samples shares inducible, rna-seq
- GSE241629 Inhibition of Notch4 with Novel Neutralizing Antibodies Reduces Tumor Growth in Multiple Tumor Types and Targets Endothelium and Tumor Associated Macrophages in a Mouse Breast Cancer Model [RiboTag RNA-seq] 20 samples shares multiple, rna-seq
- GSE281907 Spinal cord bulk RNA sequencing of an EAE mouse model of multiple sclerosis 16 samples shares multiple, bulk
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.