Functional and multi-omic aging rejuvenation with GLP-1R agonism [bulk RNA-seq]
RNA-seq (gene expression) · mouse · 620 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
- 620 samples: 620 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
- Body-wide multi-omic counteraction of aging with GLP-1R agonismCell metabolism · 2025
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 GSE280382_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 PRJNA1177532 and SRA study SRP540954. 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 the first 400 of 620 sample titles: 400 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.
- GSM8595888 LT_Y_ctrl, Animal A51, Skeletal_muscle
- GSM8595889 LT_Y_ctrl, Animal A52, Frontal_cortex
- GSM8595890 LT_Y_ctrl, Animal A52, Hypothalamus
- GSM8595891 LT_Y_ctrl, Animal A52, Hippocampus
- GSM8595892 LT_Y_ctrl, Animal A52, Kidney
- GSM8595893 LT_Y_ctrl, Animal A52, Skeletal_muscle
- GSM8595894 LT_Y_ctrl, Animal A52, Circulating_WBCs
- GSM8595895 LT_Y_ctrl, Animal A53, Skeletal_muscle
- GSM8595896 LT_Y_ctrl, Animal A53, Circulating_WBCs
- GSM8595897 LT_Y_ctrl, Animal A55, Frontal_cortex
- GSM8595898 LT_Y_ctrl, Animal A55, Hypothalamus
- GSM8595899 LT_Y_ctrl, Animal A55, Hippocampus
- GSM8595900 LT_Y_ctrl, Animal A55, Kidney
- GSM8595901 LT_Y_ctrl, Animal A55, Skeletal_muscle
- GSM8595902 LT_Y_ctrl, Animal A56, Frontal_cortex
- GSM8595903 LT_Y_ctrl, Animal A56, Hypothalamus
- GSM8595904 LT_Y_ctrl, Animal A56, Hippocampus
- GSM8595905 LT_Y_ctrl, Animal A56, Kidney
- GSM8595906 LT_Y_ctrl, Animal A56, Circulating_WBCs
- GSM8595907 LT_Y_ctrl, Animal A57, Frontal_cortex
- GSM8595908 LT_Y_ctrl, Animal A57, Hypothalamus
- GSM8595909 LT_Y_ctrl, Animal A57, Hippocampus
- GSM8595910 LT_Y_ctrl, Animal A57, Kidney
- GSM8595911 LT_Y_ctrl, Animal A57, Circulating_WBCs
- GSM8595912 LT_Y_ctrl, Animal A58, Frontal_cortex
- GSM8595913 LT_Y_ctrl, Animal A58, Hypothalamus
- GSM8595914 LT_Y_ctrl, Animal A58, Hippocampus
- GSM8595915 LT_Y_ctrl, Animal A58, Kidney
- GSM8595916 LT_Y_ctrl, Animal A58, Skeletal_muscle
- GSM8595917 LT_Y_ctrl, Animal A58, Circulating_WBCs
- GSM8595918 LT_A_Ex, Animal B13, Frontal_cortex
- GSM8595919 LT_A_Ex, Animal B13, Hypothalamus
- GSM8595920 LT_A_Ex, Animal B13, Hippocampus
- GSM8595921 LT_A_Ex, Animal B13, Kidney
- GSM8595922 LT_A_Ex, Animal B13, Skeletal_muscle
- GSM8595923 LT_A_Ex, Animal B13, Circulating_WBCs
- GSM8595924 LT_A_Ex, Animal B14, Frontal_cortex
- GSM8595925 LT_A_Ex, Animal B14, Hypothalamus
- GSM8595926 LT_A_Ex, Animal B14, Hippocampus
- GSM8595927 LT_A_Ex, Animal B14, Kidney
+ 580 more — browse all 620 samples with per-sample file links →
- GSE272841 Life-long microbiome rejuvenation improves intestinal metabolism and inflammaging in mice [bulk RNA-seq] 39 samples shares rejuvenation, rna-seq, aging, bulk
- GSE246954 Multi-omics characterization of partial chemical reprogramming reveals evidence of cell rejuvenation [RNA-seq] 24 samples shares rejuvenation, multi-omic, rna-seq
- GSE272842 Life-long microbiome rejuvenation improves intestinal metabolism and inflammaging in mice [scRNA-seq] 12 samples shares rejuvenation, rna-seq, aging
- GSE292885 Transcriptomic Hallmarks of Mortality Reveal Universal and Specific Mechanisms of Aging, Chronic Disease, and Rejuvenation 210 samples shares rejuvenation, aging
- GSE302188 Multi-omics analysis highlights the link of aging-related cognitive decline with systemic inflammation and alterations of tissue-maintenance 165 samples shares multi-omic, aging
- GSE301299 Multi-omics analysis identifies intrinsic Trp53 driven metastatic breast cancer subtypes [RNA-seq] 51 samples shares multi-omic, rna-seq
- GSE325692 Multi-omic analysis reveals brain-blood concordance and persistent neuroimmune reprogramming with stress and toxicant exposure in a mouse model of Gulf War Illness [RNA-Seq] 30 samples shares multi-omic, rna-seq
- GSE247173 Cellular rejuvenation protects neurons from inflammation mediated cell death (RNA-seq). 14 samples shares rejuvenation, rna-seq
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.