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Single Cell Multiomics Profiling for the Mouse Polycystic Kidney Disease

GSE268494 Mus musculus (mouse) Single-cell RNA-seq 32 samples Submitted 2024/10/02 Platform GPL24247
At a glance

Single-cell RNA-seq · mouse · 32 samples

What it is
Single-cell RNA-seqGene activity measured in thousands of individual cells, so cell types can be told apart.
Organism
Mus musculus (mouse)
Samples
32 samples: 6 conditions, mostly in triplicateGroups 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
Multiomics profiling of mouse polycystic kidney disease progression at a single-cell resolutionProceedings of the National Academy of Sciences of the United States of America · 2024
Summary (written by the submitters)
We performed multiomics analysis; single nucleus RNA-seq (snRNA-seq) combined with ATAC (snATAC-seq) with 10XGenomics Multiome platform to generate cell-type-specific gene expression and chromatin accessibility atlas of the mouse polycystic kidney disease on a time course.
Published in
Multiomics profiling of mouse polycystic kidney disease progression at a single-cell resolution
Muto Y, Yoshimura Y, Wu H et al. · Proceedings of the National Academy of Sciences of the United States of America 2024 · PMID 39405347 · doi:10.1073/pnas.2410830121
This dataset
Open GSE268494 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 GSE268494_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 PRJNA1117459 and SRA study SRP510331. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 39405347) ↗
Study design
6 conditions, mostly in triplicate
P100_cont ×3 P100_pkd ×3 P130_pkd ×3 P130_cont ×3 P66_cont ×2 P66_pkd ×2 P66_cont1_atac ×1 P66_pkd1_atac ×1 +14 more

Supports a between-group comparison across 16 samples.

How we read this: 6 replicated groups read from 32 sample titles; they account for 16 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.

Samples in this study
Datasets on a similar topic

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