Strategic timing of contralateral nephrectomy after ischemic AKI prevents CKD by enhancing epithelial progenitor proliferation while attenuating polyploidization
RNA-seq (gene expression) · mouse · 41 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
- 41 samples: 7 conditions, mostly with about 6 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.
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 GSE268009_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 PRJNA1114039 and SRA study SRP508940. Searching any of these in the dataset finder brings you back here.
Supports a between-group comparison across 40 samples.
How we read this: 7 replicated groups read from 41 sample titles; they account for 40 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.
- GSM8283822 Control_1 Control
- GSM8283823 Control_2 Control
- GSM8283824 Control_3 Control
- GSM8283825 Control_4 Control
- GSM8283826 UIRI d3_1 UIRI d3
- GSM8283827 UIRI d3_2 UIRI d3
- GSM8283828 UIRI d3_3 UIRI d3
- GSM8283829 UIRI d7_1 UIRI d7
- GSM8283830 UIRI d7_2 UIRI d7
- GSM8283831 UIRI d7_3 UIRI d7
- GSM8283832 UIRI d7_4 UIRI d7
- GSM8283833 UIRI d7_5 UIRI d7
- GSM8283834 UIRI d7_6 UIRI d7
- GSM8283835 UIRI+Nx d7_1 UIRI+Nx d7
- GSM8283836 UIRI+Nx d7_2 UIRI+Nx d7
- GSM8283837 UIRI+Nx d7_3 UIRI+Nx d7
- GSM8283838 UIRI+Nx d7_4 UIRI+Nx d7
- GSM8283839 UIRI d3_4 UIRI d3
- GSM8283840 Control_5 Control
- GSM8283841 Control_6 Control
- GSM8283842 UIRI d3_5 UIRI d3
- GSM8283843 UIRI d3_6 UIRI d3
- GSM8283844 UIRI+Nx d7_5 UIRI+Nx d7
- GSM8283845 UIRI+Nx d7_6 UIRI+Nx d7
- GSM9426739 WT_IRNxd7_1 WT_IRNxd7
- GSM9426740 WT_IRNxd7_2 WT_IRNxd7
- GSM9426741 WT_IRNxd7_3 WT_IRNxd7
- GSM9426742 WT_IRNxd7_4 WT_IRNxd7
- GSM9426743 WT_IRd7_1 WT_IRd7
- GSM9426744 WT_IRd7_2 WT_IRd7
- GSM9426745 WT_IRd7_3 WT_IRd7
- GSM9426746 WT_IRd7_4 WT_IRd7
- GSM9426747 WT_IRd7_5 WT_IRd7
- GSM9426748 WT_IRd7_6 WT_IRd7
- GSM9426749 WT_IRd3_1 WT_IRd3
- GSM9426750 WT_IRd3_2 WT_IRd3
- GSM9426751 WT_IRd3_3 WT_IRd3
- GSM9426752 WT_IRd3_4 WT_IRd3
- GSM9426753 WT_IRd3_5 WT_IRd3
- GSM9426754 WT_IRd3_6 WT_IRd3
+ 1 more — browse all 41 samples with per-sample file links →
- GSE40670 Microbiota facilitates dietary heme-induced epithelial hyperproliferation and hyperplasia by breaking the mucus barrier 18 samples shares aki, proliferation, epithelial
- GSE239287 Single-cell transcriptome sequencing of macrophages in acute kidney injury(AKI) to chronic kidney dieases(CKD) 17 samples shares aki, ckd
- GSE304219 Increased Elmo1 Expression worsens the AKI-CKD transition via ROS 11 samples shares aki, ckd
- GSE332889 Quinolinic acid metabolism may mitigate AKI to CKD transition 10 samples shares aki, ckd
- GSE329941 Single-cell transcriptomics identifies ergothioneine as a mitochondrial protector to prevent AKI-to-CKD progression 9 samples shares aki, ckd
- GSE286531 TJ0113 alleviates ferroptosis by improving mitochondria of renal tubules in the transition from AKI to CKD 9 samples shares aki, ckd
- GSE280033 Heparin prevents AKI to CKD transition via HDAC1-mediated anti-inflammatory effect independent from its anticoagulant properties 9 samples shares aki, ckd
- GSE281245 Single-cell RNA sequencing identifies a unique population of macrophages in the Aristolochic acid I (AAI)-induced AKI-to-CKD transition murine model 5 samples shares aki, ckd
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.