TJ0113 alleviates ferroptosis by improving mitochondria of renal tubules in the transition from AKI to CKD
RNA-seq (gene expression) · mouse · 9 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
- 9 samples: 3 × C vs 3 × S vs 3 × XGroups 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 GSE286531_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 PRJNA1209871 and SRA study SRP556982. Searching any of these in the dataset finder brings you back here.
Supports a between-group comparison across 9 samples.
How we read this: 3 replicated groups read from 9 sample titles; they account for 9 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.
- GSM8728978 C1 C
- GSM8728979 C2 C
- GSM8728980 C3 C
- GSM8728981 S1 S
- GSM8728982 S2 S
- GSM8728983 S3 S
- GSM8728984 X1 X
- GSM8728985 X2 X
- GSM8728986 X3 X
- GSE329941 Single-cell transcriptomics identifies ergothioneine as a mitochondrial protector to prevent AKI-to-CKD progression 9 samples shares aki, ckd, mitochondria
- GSE268009 Strategic timing of contralateral nephrectomy after ischemic AKI prevents CKD by enhancing epithelial progenitor proliferation while attenuating polyploidization 41 samples shares aki, ckd
- GSE239287 Single-cell transcriptome sequencing of macrophages in acute kidney injury(AKI) to chronic kidney dieases(CKD) 17 samples shares aki, ckd
- GSE284451 Ceramides Drive Mitochondrial Dysfunction and Tubular Injury in AKI 16 samples shares aki, mitochondria
- GSE337704 IL-33 as a component of NETs mediates CIAKI by promoting ferroptosis in tubular epithelial cells 12 samples shares aki, ferroptosis
- GSE302789 Mitochondrial ROS induces metabolic adaptations and maladaptations that differentially regulate ferroptosis sensitivity of the hepatocytes 12 samples shares mitochondria, ferroptosis
- GSE310244 Dynamic Resilience: Stress Phenotypes and Their Impact on Opioid-Taking Behavior, Brain Activity, and Gene Expression 68 samples shares aki
- GSE336380 Knockdown and overexpression of injury-responsive transcription factors in cultures of adult mouse dorsal root ganglia 48 samples shares 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.