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Gene expression profile at bulk (24h, 48h & 72h) and single nucleus (24h) level of mouse right kidney post sham operation and unilateral nephrectomy.

GSE241213 Mus musculus (mouse) Single-nucleus RNA-seq 40 samples Submitted 2024/07/18 Platform GPL28457Platform GPL24247
At a glance

Single-nucleus RNA-seq · mouse · 40 samples

What it is
Single-nucleus RNA-seqGene activity measured in individual nuclei (used when whole cells are hard to isolate, for example from frozen tissue). Read from the summary: GEO's own type field only says 'expression profiling by sequencing', so confirm it in the paper.
Organism
Mus musculus (mouse)
Samples
40 samples: 40 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
The transcriptome of early compensatory kidney growth reveals cell and time specific responsesiScience · 2024
Summary (written by the submitters)
The mechanism driving the remarkable ability of the remaining kidney to enlarge and increase its function following removal of its contralateral pair remains elusive. To explore the pathways driving compensatory renal hypertrophy, comprehensive RNA-seq transcriptional studies in the kidneys of mice undergoing hypertrophy 24, 48 and 72 hours following nephrectomy have been undertaken and compared with mice undergoing sham operations.
Read the full summary ↓
The results reveal substantial time dependent enhancement of cholesterol biosynthesis pathways, increases in mitochondrial gene expression and cell cycle perturbations. Single nuclei RNA-Seq 24 hours post nephrectomy was used to further explore cholesterol biosynthesis signature and its driver SREBP2. In a cell specific manner, snRNA-seq demonstrated that SREBP2 activity increases in proximal tubules and medullary thick ascending limb and is responsible for cell size regulation following IGF-1 stimulation. These results suggest a previously undescribed role for SREBP2 in the mechanism underlying compensatory renal hypertrophy. This mechanism might be amenable to therapeutic manipulation to enhance kidney size and function.
Published in
The transcriptome of early compensatory kidney growth reveals cell and time specific responses
Rojas-Canales DM, Wong SW, Tucker EJ et al. · iScience 2024 · PMID 39220259 · doi:10.1016/j.isci.2024.110608
This dataset
Open GSE241213 in finder → Find more mouse RNA-seq datasets →
Download · which file do I need?

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.

1 · Just have a look

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.

Series matrix ↓
2 · Re-analyse it

Supplementary files

The count tables and other files the authors uploaded. Take only the samples you need, or the whole study as GSE241213_RAW.tar (it can run to tens of gigabytes).

All supplementary files ↗ Whole study · RAW.tar ↓
3 · Start from the raw reads

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.

Raw reads on ENA ↗

What the files usually look like: Single-cell studies usually ship their counts as 10x files (matrix.mtx, barcodes.tsv, features.tsv), an .h5 file, or an .rds / .h5ad object that Seurat or Scanpy can open. Look for those among the supplementary files.

Also filed as BioProject PRJNA1006732 and SRA study SRP455859. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 39220259) ↗
Study design
40 samples, each with a distinct title — no replicated groups

Any grouping that defines a comparison lives in the sample characteristics, which GEO records separately from the titles.

How we read this: Read from 40 sample titles: 40 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.

Samples in this study
Datasets on a similar topic

Search all mouse RNA-seq datasets in GEO →

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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.