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Localized PD-1 CAR T therapy reprograms neuroinflammation - human CSF data.

GSE308973 Homo sapiens (human) Single-cell RNA-seq 88 samples Submitted 2026/07/21 Platform GPL34281
At a glance

Single-cell RNA-seq · human · 88 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
Homo sapiens (human)
Samples
88 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 progress toward disability, highlighting the need for improved 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 revealed a rare subset of activated PD-1⁺, TCR-restricted, B-cell recruiting T follicular helper-like cells enriched in MS CSF. To therapeutically target this population, we developed PD-1-directed CAR T cells that selectively depleted pathogenic PD-1⁺ CD4 T cells. In a murine model of neuroinflammation, this approach attenuated CNS inflammation and improved clinical outcomes, particularly when using an IL-10-armored CAR construct. These findings establish a cellular framework for CNS-localized adaptive immunity in MS and outline a strategy for its selective disruption.
This dataset
Open GSE308973 in finder → Find more human 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 GSE308973_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 PRJNA1333511 and SRA study SRP625653. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗
Study design
A cohort of 88 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 AB10361 … AB13488 and carry no group information.

Samples in this study

+ 48 more — browse all 88 samples with per-sample file links →

Datasets on a similar topic

Search all human 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.