← BioTransfer GEO Dataset Finder
GEO series

A single-cell atlas deconstructs heterogeneity across multiple models in murine traumatic brain injury and identifies novel cell-specific targets

GSE269748 Mus musculus (mouse) Single-cell RNA-seq 36 samples Submitted 2024/07/02 Platform GPL24247
At a glance

Single-cell RNA-seq · mouse · 36 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
36 samples: 7 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
A single-cell atlas deconstructs heterogeneity across multiple models in murine traumatic brain injury and identifies novel cell-specific targetsNeuron · 2024
Summary (written by the submitters)
The problem of traumatic brain injury (TBI) heterogeneity has been a critical barrier to successful translation of therapies in the field. TBI heterogeneity exists in the patient substrate pre-injury (genetics, sex, comorbidities), external injury characteristics (severity, mechanism), and resultant post-injury host response that is responsible for deleterious secondary injury processes (seizures, neuroinflammation, neurodegeneration) and repair/regeneration.
Read the full summary ↓
Identification of final common molecular pathways and signatures that integrate this vast heterogeneity could be valuable for guiding biomarkers, therapeutic targets, and predictive enrichment. In this study, we present the first large-scale searchable murine single-cell atlas of the transcriptomic response to TBI in 339,357 cells as a foundational step in molecularly deconstructing TBI heterogeneity. We identify 23 cell types with massive heterogeneity in the single-cell response across extrinsicand intrinsic factors, that has been underestimated. Majority of response to TBI was unique to individual cell populations with minimal overlap even within a single injury-model thus highlighting the importance of cell-level resolution. Through this effort, we report novel cell-specific targets and a previously unrecognized role for specific microglial and ependymal subtypes in post-TBI pathophysiology that is highly variable depending on the extrinsic and intrinsic factors studied. One ependymal subtype was a hub of neuroinflammatory signaling after contusional-TBI, particularly related to Il-1b. A single microglial-lineage along pseudotime (comprising 3 microglial subtypes) was a key mediator of host-response after TBI, and shared features with disease associated microglia noted in Alzheimer’s disease and other neurodegenerative disorders, potentially providing a link between TBI and accelerated neurodegeneration. One microglial subtype within this lineage emerged as a key target – it was the only cell type of all 23 that retained persistent and marked gene expression changes 6 months post contusional-TBI. We identify sexually dimorphic gene expression and pathway vulnerabilities with cell-specific differences in both immune and non-immune biological processes. These likely contribute to sex-based outcome and warrant further study to facilitate discovery of cell- and sex-specific druggable targets. Active changes in brain regions distal from the site of primary TBI impact included infiltration of specific microglial populations as well as cell-specific transcriptomic changes in several genes and inflammatory processes distinct from both the peri-contusional and naïve signatures. This atlas validates several known contributors in TBI pathophysiology, and also identifies previously unrecognized targets and avenues for further research. Beyond our presented exemplar analyses (including pathways of clinical interest like sulfonylurea-receptor-1), the companion searchable atlas serves as a foundation for countless future efforts to understand cell-specific heterogeneity after TBI (https://shiny.crc.pitt.edu/cerebri/) as well as numerous other neurological diseases with overlapping pathophysiology.
Published in
A single-cell atlas deconstructs heterogeneity across multiple models in murine traumatic brain injury and identifies novel cell-specific targets
Jha RM, Rajasundaram D, Sneiderman C et al. · Neuron 2024 · PMID 39019041 · doi:10.1016/j.neuron.2024.06.021
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 GSE269748_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 PRJNA1123602 and SRA study SRP513725. Searching any of these in the dataset finder brings you back here.

Study design
7 conditions, mostly in triplicate
24 hours male Naïve ×6 6 month male CCI ×4 7 day male CCI ×4 24 hours male repetitive closed head tbi ×3 24 hours female CCI ipsilateral ×3 24 hours female Naïve ×3 24 hours male CCI replicate (ipsilateral) ×2 24 hours male CCI+HS ×1 +10 more

Supports a between-group comparison across 25 samples.

How we read this: 7 replicated groups read from 36 sample titles; they account for 25 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

Search all mouse RNA-seq datasets in GEO →

Share this dataset

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.