GEO series

CITEseq analysis of human CSF cells in Multiple sclerosis

GSE286068 Homo sapiens (human) Single-cell RNA-seq 26 samples Submitted 2025/02/01 Platform GPL11154

At a glance

Single-cell RNA-seq · human · 26 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: 26 samples: 11 × multiplexed CSF cells vs 5 × CSF cells, PP-MS vs 4 × CSF cells, HD vs 4 × CSF cells, RR-MSGroups 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: Migration arrest and transendothelial trafficking of human pathogenic-like Th17 cells are mediated by differentially positioned chemokinesNature communications · 2025

Summary (written by the submitters)

In CSF bulk RNA sequence, patients with MS (pwMS) exhibited higher T cell receprot (TCR) clonality and specificity to EBV lytic proteins compared to controls. TCRs from pwMS showed greater clustering coefficient (i.e. TCR similarity) in complementarity-determining regions 3 (CDR3), particularly those predicted to target EBV antigens. Importantly, TCR clustering coefficient was associated with the expression of cytotoxic CD8 module genes in WGCNA, such as CD8A, GZMH, GZMK, and NKG7 and with interferon signaling module.

Read the full summary ↓

scRNA-seq revealed a subpopulation of GZMK+GZMH+ double positive (DP) CD8 T cells expressing genes related to identified cytotoxicity and interferon signaling modules. These DP CD8 T cells had significantly higher predicted specificity for EBV proteins than other CD8 cells.

Published in

Migration arrest and transendothelial trafficking of human pathogenic-like Th17 cells are mediated by differentially positioned chemokines

Parween F, Singh SP, Kathuria N et al. · Nature communications 2025 · PMID 40000641 · doi:10.1038/s41467-025-57002-6

This dataset

Open GSE286068 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 GSE286068_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 PRJNA1207574 and SRA study SRP555878. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 40000641) ↗

Study design

11 × multiplexed CSF cells vs 5 × CSF cells, PP-MS vs 4 × CSF cells, HD vs 4 × CSF cells, RR-MS

Supports a between-group comparison across 24 samples.

How we read this: 4 replicated groups read from 26 sample titles; they account for 24 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

GSM8717331 CSF cells, SP-MS1
GSM8717332 CSF cells, HD1 CSF cells, HD
GSM8717333 CSF cells, PP-MS1 CSF cells, PP-MS
GSM8717334 CSF cells, RR-MS1 CSF cells, RR-MS
GSM8717335 CSF cells, RR-MS2 CSF cells, RR-MS
GSM8717336 CSF cells, PP-MS2 CSF cells, PP-MS
GSM8717337 CSF cells, NIND1
GSM8717338 CSF cells, HD2 CSF cells, HD
GSM8717339 CSF cells, RR-MS3 CSF cells, RR-MS
GSM8717340 CSF cells, HD3 CSF cells, HD
GSM8717341 CSF cells, PP-MS3 CSF cells, PP-MS
GSM8717342 CSF cells, PP-MS4 CSF cells, PP-MS
GSM8717343 CSF cells, PP-MS5 CSF cells, PP-MS
GSM8717344 CSF cells, HD4 CSF cells, HD
GSM8717345 CSF cells, RR-MS4 CSF cells, RR-MS
GSM8717346 multiplexed CSF cells1 multiplexed CSF cells
GSM8717347 multiplexed CSF cells2 multiplexed CSF cells
GSM8717348 multiplexed CSF cells3 multiplexed CSF cells
GSM8717349 multiplexed CSF cells4 multiplexed CSF cells
GSM8717350 multiplexed CSF cells5 multiplexed CSF cells
GSM8717351 multiplexed CSF cells6 multiplexed CSF cells
GSM8717352 multiplexed CSF cells7 multiplexed CSF cells
GSM8717353 multiplexed CSF cells8 multiplexed CSF cells
GSM8804804 multiplexed CSF cells9 multiplexed CSF cells
GSM8804805 multiplexed CSF cells10 multiplexed CSF cells
GSM8804806 multiplexed CSF cells11 multiplexed CSF cells

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