Mouse kidney ischemia-reperfusion injury time course
RNA-seq (gene expression) · mouse · 57 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
- 57 samples: 12 conditions, mostly with about 5 replicates eachGroups 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
- Inflammation primes the murine kidney for recovery by activating AZIN1 adenosine-to-inosine editingThe Journal of clinical investigation · 2024
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 GSE267650_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 PRJNA1112333 and SRA study SRP508128. Searching any of these in the dataset finder brings you back here.
Supports a between-group comparison across 57 samples.
How we read this: 12 replicated groups read from 57 sample titles; they account for 57 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.
- GSM8271629 WT_Ctl-No-IRI_1 WT_Ctl-No-IRI
- GSM8271630 WT_Ctl-No-IRI_2 WT_Ctl-No-IRI
- GSM8271631 WT_Ctl-No-IRI_3 WT_Ctl-No-IRI
- GSM8271632 WT_Ctl-No-IRI_4 WT_Ctl-No-IRI
- GSM8271633 WT_Ctl-No-IRI_5 WT_Ctl-No-IRI
- GSM8271634 WT_Ischemia_20min_without_reperfusion_1 WT_Ischemia_20min_witho…
- GSM8271635 WT_Ischemia_20min_without_reperfusion_2 WT_Ischemia_20min_witho…
- GSM8271636 WT_Ischemia_20min_without_reperfusion_3 WT_Ischemia_20min_witho…
- GSM8271637 WT_Ischemia_20min_without_reperfusion_4 WT_Ischemia_20min_witho…
- GSM8271638 WT_Ischemia_20min_without_reperfusion_5 WT_Ischemia_20min_witho…
- GSM8271639 WT_IRI_4h_1 WT_IRI_4h
- GSM8271640 WT_IRI_4h_2 WT_IRI_4h
- GSM8271641 WT_IRI_4h_3 WT_IRI_4h
- GSM8271642 WT_IRI_4h_4 WT_IRI_4h
- GSM8271643 WT_IRI_4h_5 WT_IRI_4h
- GSM8271644 WT_IRI_16h_1 WT_IRI_16h
- GSM8271645 WT_IRI_16h_2 WT_IRI_16h
- GSM8271646 WT_IRI_16h_3 WT_IRI_16h
- GSM8271647 WT_IRI_16h_4 WT_IRI_16h
- GSM8271648 WT_IRI_16h_5 WT_IRI_16h
- GSM8271649 WT_IRI_24h_1 WT_IRI_24h
- GSM8271650 WT_IRI_24h_2 WT_IRI_24h
- GSM8271651 WT_IRI_24h_3 WT_IRI_24h
- GSM8271652 WT_IRI_24h_4 WT_IRI_24h
- GSM8271653 WT_IRI_24h_5 WT_IRI_24h
- GSM8271654 WT_IRI_36h_1 WT_IRI_36h
- GSM8271655 WT_IRI_36h_2 WT_IRI_36h
- GSM8271656 WT_IRI_36h_3 WT_IRI_36h
- GSM8271657 WT_IRI_36h_4 WT_IRI_36h
- GSM8271658 WT_IRI_36h_5 WT_IRI_36h
- GSM8271659 WT_IRI_48h_1 WT_IRI_48h
- GSM8271660 WT_IRI_48h_2 WT_IRI_48h
- GSM8271661 WT_IRI_48h_3 WT_IRI_48h
- GSM8271662 WT_IRI_48h_4 WT_IRI_48h
- GSM8271663 WT_IRI_48h_5 WT_IRI_48h
- GSM8271664 WT_IRI_72h_1 WT_IRI_72h
- GSM8271665 WT_IRI_72h_2 WT_IRI_72h
- GSM8271666 WT_IRI_72h_3 WT_IRI_72h
- GSM8271667 WT_IRI_72h_4 WT_IRI_72h
- GSM8271668 WT_IRI_72h_5 WT_IRI_72h
+ 17 more — browse all 57 samples with per-sample file links →
- GSE254162 Dynamic fibroblast-immune interactions shape recovery after brain injury [snRNASeq_Timecourse] 12 samples shares injury, course, time
- GSE274819 Single-cell RNA sequencing of murine kidneys for ischemia-reperfusion induced renal injury and fibrosis 10 samples shares ischemia-reperfusion, kidney, injury
- GSE319684 Timecourse of FACS sorted Ly6C+ monocytes, tissue resident alveolar macrophages (trAMs), monocyte derived alveolar macrophages (bmAMs) and epithelial cells from SARS-CoV-2 (maVie16) vs. IAV (PR8) infected mouse lungs 176 samples shares course, time
- GSE277487 Single-nucleus RNA-seq of mouse cortex following CCI-induced traumatic brain injury across multiple time points 12 samples shares injury, time
- GSE279693 Transcriptional analysis on ischemia-reperfusion injury of mouse normal or steatotic livers 12 samples shares ischemia-reperfusion, injury
- GSE319035 P21+TREM2+-Senescent Macrophages Fuel Inflammaging and Metabolic Dysfunction-Associated Steatotic Liver Disease [NAFLD_time_course] 77 samples shares course, time
- GSE276647 Gene expression profile of microglia following traumatic brain injury and nasal anti-CD3 treatment at acute and chronic timepoints 54 samples shares injury, time
- GSE264278 TIme-cource single-cell RNA-seq analysis of bleomycin and LPS-induced lung injury and fibrosis 18 samples shares injury, time
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.