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Localized PD-1 CAR T therapy reprograms neuroinflammation - mouse CAR T data.

GSE327494 Mus musculus (mouse) Single-cell RNA-seq 302 samples Submitted 2026/07/21 Platform GPL24247Platform GPL34328
At a glance

Single-cell RNA-seq · mouse · 302 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
302 samples
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.
Summary (written by the submitters)
B cell-depleting therapies are effective in multiple sclerosis (MS), yet many patients continue to accumulate disability, underscoring the need for more precise interventions. To identify new therapeutic targets, we generated a single-cell RNA-seq atlas of cerebrospinal fluid (CSF), brain, and blood from non-inflammatory controls and patients with MS or other neuroinflammatory diseases.
Read the full summary ↓
We found disease-associated enrichment of class-switched IgG⁺ B cells and plasma cells in the CSF of patients with MS. Unbiased analysis further identified a rare subset of activated, TCR-restricted, PD-1⁺ T follicular helper-like cells with B-cell recruiting features that was enriched in the CSF of patients with MS. To target this population therapeutically, we developed PD-1-directed CAR T cells that selectively depleted pathogenic PD-1⁺ CD4 T cells and, in armored versions, released IL-10 at inflammatory sites. This strategy attenuated CNS inflammation, reprogrammed the local immune milieu, and improved clinical outcomes across murine neuroinflammation models. Together, these findings define a cellular framework for CNS-localized adaptive immunity in MS and nominate a selective strategy to disrupt it.
This dataset
Open GSE327494 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 GSE327494_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 PRJNA1451103 and SRA study SRP690525. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗
Study design
A cohort of 302 individual samples

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

How we read this: Sample titles run AB9068 … AB22023 and carry no group information.

Samples in this study

+ 262 more — browse all 302 samples with per-sample file links →

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.