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Impact of severe traumatic brain injury (TBI) on cerebral blood flow and the endothelial cell phenotype

GSE271769 Mus musculus (mouse) Single-cell RNA-seq 16 samples Submitted 2024/12/02 Platform GPL21626
At a glance

Single-cell RNA-seq · mouse · 16 samples

What it is
Single-cell RNA-seqGene activity measured in thousands of individual cells, so cell types can be told apart. 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
16 samples: 16 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
Severe traumatic brain injury temporally affects cerebral blood flow, endothelial cell phenotype, and ciliabioRxiv : the preprint server for biology · 2024
Summary (written by the submitters)
TBI causes the disruption of blood vessels in brain leading to hemorrhage, edema, and changes in cerebral blood flow. How such altered vascular and flow condition affect endothelial cells, the cellular unit of blood vessels, is not known. We used single cell RNA sequencing (scRNAseq) to analyze the heterogenity in endothelial cell clusters in brain, and how key cellular and signaling signatures of these clusters are temporally changed post-TBI at the site of injury.
Published in
Severe traumatic brain injury temporally affects cerebral blood flow, endothelial cell phenotype, and cilia
Gupta A, Bice Z, Chen V et al. · bioRxiv : the preprint server for biology 2024 · PMID 39605741 · doi:10.1101/2024.11.19.623875
This dataset
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.

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 GSE271769_RAW.tar (it can run to tens of gigabytes).

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.

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 PRJNA1133059 and SRA study SRP518641. Searching any of these in the dataset finder brings you back here.

Study design
16 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 16 sample titles: 16 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
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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.