GEO series

Dissecting the evolving transcriptional landscape of the remyelinating spinal cord

GSE278643 Mus musculus (mouse) Single-nucleus RNA-seq 13 samples Submitted 2025/02/03 Platform GPL24247

At a glance

Single-nucleus RNA-seq · mouse · 13 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). 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: 13 samples: 3 × Les, 5dpl, vs 3 × Les, 20dpl, vs 2 × Les, 10dpl, vs 2 × Naïve,Groups 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: Senescent-like microglia limit remyelination through the senescence associated secretory phenotypeNature communications · 2025

Summary (written by the submitters)

Demyelination, or the loss of myelin in the central nervous system (CNS) is a hallmark of multiple sclerosis (MS) and occurs in various forms of CNS injury and neurodegenerative diseases. The replenishing of myelin, termed remyelination, also occurs spontaneously following demyelination. The lysolecithin-induced focal demyelination model enables the investigation into the mechanisms of spontaneous remyelination, providing insight into the molecular basis underlying an evolving remyelinating microenvironment.

Read the full summary ↓

Here, we present a detailed analysis using high-resolution single-nucleus RNA sequencing to investigate gene expression dynamics across all cell types involved in the remyelination process. We examine three specific time points following focal demyelinating injury. By delineating activation states within the heterogeneous cell populations of demyelinated lesions, we highlight changes in gene expression within subclusters of each cell type from the early stages of injury response to the initiation and maintenance of remyelination. Our findings reveal how shifts in microglial, astrocytic and fibroblast activities within lesions are associated with efficient oligodendrocyte differentiation during remyelination.

Published in

Senescent-like microglia limit remyelination through the senescence associated secretory phenotype

Gross PS, Durán-Laforet V, Ho LT et al. · Nature communications 2025 · PMID 40055369 · doi:10.1038/s41467-025-57632-w

This dataset

Open GSE278643 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 GSE278643_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 PRJNA1168135 and SRA study SRP536271. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 40055369) ↗

Study design

3 × Les, 5dpl, vs 3 × Les, 20dpl, vs 2 × Les, 10dpl, vs 2 × Naïve,

Supports a between-group comparison across 10 samples.

How we read this: 4 replicated groups read from 13 sample titles; they account for 10 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

GSM8551845 Les, 5dpl, rep1 Les, 5dpl,
GSM8551846 Les, 5dpl, rep2 Les, 5dpl,
GSM8551847 Les, 5dpl, rep3 Les, 5dpl,
GSM8551848 NL, 5dpl, rep 1
GSM8551849 Les, 10dpl, rep 1 Les, 10dpl,
GSM8551850 Les, 10dpl, rep 2 Les, 10dpl,
GSM8551851 NL, 10dpl, rep 1
GSM8551852 Les, 20dpl, rep 1 Les, 20dpl,
GSM8551853 Les, 20dpl, rep 2 Les, 20dpl,
GSM8551854 Les, 20dpl, rep 3 Les, 20dpl,
GSM8551855 NL, 20dpl, rep 1
GSM8551856 Naïve, rep 1 Naïve,
GSM8551857 Naïve, rep 2 Naïve,

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