GEO series

INFLAMMATION-INDUCED SENESCENT-LIKE GLIA REWIRE CELLULAR NETWORKS IN MULTIPLE SCLEROSIS [snRNAseq Brain]

GSE277430 Homo sapiens (human) Single-nucleus RNA-seq 23 samples Submitted 2025/07/31 Platform GPL24676

At a glance

Single-nucleus RNA-seq · human · 23 samples

What it is: Single-nucleus RNA-seqGene activity measured in individual nuclei (used when whole cells are hard to isolate, for example from frozen tissue).
Organism: Homo sapiens (human)
Samples: 23 samples: 19 × Cortex, s vs 4 × WM, sGroups 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: Spatially-restricted inflammation-induced senescent-like glia in multiple sclerosis and patient-derived organoidsNature communications · 2025

Summary (written by the submitters)

In multiple sclerosis (MS), chronic compartmentalized inflammation is thought to drive relentless clinical deterioration. Here, we investigate the link between unresolved parenchymal inflammation and cellular senescence in MS progression. Single-cell transcriptomic analysis of human brain tissue reveals an accumulation of senescent-like glial cells in diseased white matter, especially in chronic active lesions, and to a lesser extent in the cortex.

Read the full summary ↓

Spatial transcriptomics show gradients of senescence-like signatures extending from lesion cores to periplaque regions, alongside rewired cellular networks. Experimental induction of senescence in MS hiPSC-derived neural organoids demonstrates that microglia are especially vulnerable to inflammation-induced senescence, which can be partially rescued by CNS-penetrant anti-inflammatory drugs. At the patient level (n=500), increased 3T MRI-estimated brain-age is observed, especially in individuals with more than four chronic active lesions. These findings suggest that chronic inflammation might accelerate senescence-like processes, potentially contributing to disease progression, and that its modulation might help limit further propagation.

Published in

Spatially-restricted inflammation-induced senescent-like glia in multiple sclerosis and patient-derived organoids

Fagiani F, Pedrini E, Martire MS et al. · Nature communications 2025 · PMID 41006208 · doi:10.1038/s41467-025-63371-9

This dataset

Open GSE277430 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 GSE277430_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 PRJNA1162155 and SRA study SRP533228. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 41006208) ↗

Study design

19 × Cortex, s vs 4 × WM, s

Supports a between-group comparison across 23 samples.

How we read this: 2 replicated groups read from 23 sample titles; they account for 23 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

GSM8522352 WM, s25 WM, s
GSM8522353 WM, s26 WM, s
GSM8522354 WM, s27 WM, s
GSM8522355 WM, s31 WM, s
GSM8522356 Cortex, s35 Cortex, s
GSM8522357 Cortex, s39 Cortex, s
GSM8522358 Cortex, s41 Cortex, s
GSM8522359 Cortex, s42 Cortex, s
GSM8522360 Cortex, s43 Cortex, s
GSM8522361 Cortex, s44 Cortex, s
GSM8522362 Cortex, s45 Cortex, s
GSM8522363 Cortex, s46 Cortex, s
GSM8522364 Cortex, s47 Cortex, s
GSM8522365 Cortex, s48 Cortex, s
GSM8522366 Cortex, s49 Cortex, s
GSM8522367 Cortex, s51 Cortex, s
GSM8522368 Cortex, s53 Cortex, s
GSM8522369 Cortex, s54 Cortex, s
GSM8522370 Cortex, s55 Cortex, s
GSM8522371 Cortex, s57 Cortex, s
GSM8522372 Cortex, s60 Cortex, s
GSM8522373 Cortex, s65 Cortex, s
GSM8522374 Cortex, s69 Cortex, s

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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.