GEO series

Distinct transcriptional changes distinguish efficient and poor remyelination in multiple sclerosis

GSE283092 Homo sapiens (human) RNA-seq (gene expression) 96 samples Submitted 2025/01/02 Platform GPL24676

At a glance

RNA-seq (gene expression) · human · 96 samples

What it is: RNA-seq (gene expression)Gene activity measured by sequencing. The summary does not mention single-cell work, so this is most likely bulk: one average per sample, the usual starting point for comparing groups.
Organism: Homo sapiens (human)
Samples: 96 samples: 12 conditions, mostly with about 7 replicates eachGroups 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: Distinct transcriptional changes distinguish efficient and poor remyelination in multiple sclerosisBrain : a journal of neurology · 2025

Summary (written by the submitters)

Multiple sclerosis (MS) is a highly heterogeneous disease with varying remyelination potential across individuals and between lesions. However, the molecular mechanisms underlying the potential to remyelinate remain poorly understood. In this study, we aimed to take advantage of the intrinsic heterogeneity in remyelinating capacity between MS donors and lesions to uncover known and novel pro-remyelinating molecules for MS therapies.

Read the full summary ↓

To elucidate distinct molecular signatures underlying the potential to remyelinate, we stratified MS donors from the Netherlands Brain Bank cohort (n=239) based on proportions of remyelinated lesions (RLs) into efficiently remyelinating donors (ERDs; n=21) and poorly remyelinating donors (PRDs; n=19). We performed bulk RNA sequencing of RLs, active lesions with ramified and amoeboid microglia/macrophage morphology (ALs non-foamy), active lesions with foamy microglia/macrophage morphology (ALs foamy), and normal-appearing white matter (NAWM) from ERDs and PRDs. We found that ALs non-foamy were positively correlated with remyelination, whereas ALs foamy were not, indicating a role for microglia/macrophage state in influencing remyelination potential. Bioinformatics analyses were performed to identify key pathways and molecules implicated in the remyelination process. We found distinct differences between the donors with differing remyelination potential in comparable MS lesion types. RLs and ALs non-foamy of ERDs versus PRDs showed upregulation of epithelial–mesenchymal transition pathway, while in ALs foamy of PRDs, inflammation and damage-associated pathways (i.e. MTORC1 signaling, TNF signaling and oxidative phosphorylation) were upregulated compared to ALs foamy of ERDs, suggesting that these latter pathways may counteract remyelination. We found genes significantly upregulated in RLs and/or ALs non-foamy of ERDs that have previously been associated with remyelination, including CXCL12, EGF, HGF, IGF2, IL10, PDGFB, PPARG, and TREM2, illustrating the strength of our donor and lesion stratification. TGFB1, TGFB2, EGF, and IGF1 were determined as key upstream regulators of genes upregulated in RLs and ALs non-foamy of ERDs. We further identified potential novel pro-remyelinating molecules, such as BTC, GDF10, GDF15, CCN1, CCN4, FGF5, FGF10, and INHBB. Our study identified both known and novel genes associated with efficient remyelination that may facilitate the development of therapeutic strategies to promote tissue repair and clinical recovery in MS.

Published in

Distinct transcriptional changes distinguish efficient and poor remyelination in multiple sclerosis

Chen JQA, McNamara NB, Engelenburg HJ et al. · Brain : a journal of neurology 2025 · PMID 39718981 · doi:10.1093/brain/awae414

This dataset

Open GSE283092 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 GSE283092_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: For RNA-seq the counts are usually a table among the supplementary files (look for 'counts', 'expression', 'FPKM' or 'TPM' in the file name). The series matrix of a sequencing study is sometimes nearly empty.

Also filed as BioProject PRJNA1191847 and SRA study SRP548238. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 39718981) ↗

Study design

12 conditions, mostly with about 7 replicates each

ERD, RL, s ×14 ERD, RL_NAWM, s ×13 PRD, RL, s ×7 ERD, AL ram-amo, s ×7 PRD, AL ram-amo, s ×7 PRD, AL ram-amo_NAWM, s ×7 ERD, AL foamy, s ×7 ERD, AL foamy_NAWM, s ×7 +6 more

Supports a between-group comparison across 94 samples.

How we read this: 12 replicated groups read from 96 sample titles; they account for 94 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

GSM8655822 ERD, RL, s001 ERD, RL, s
GSM8655823 ERD, RL, s002 ERD, RL, s
GSM8655824 ERD, RL, s003 ERD, RL, s
GSM8655825 ERD, RL, s004 ERD, RL, s
GSM8655826 ERD, RL, s005 ERD, RL, s
GSM8655827 ERD, RL, s006 ERD, RL, s
GSM8655828 ERD, RL, s007 ERD, RL, s
GSM8655829 ERD, RL_NAWM, AL ram-amo_NAWM, s008
GSM8655830 ERD, RL_NAWM, s009 ERD, RL_NAWM, s
GSM8655831 ERD, RL_NAWM, s010 ERD, RL_NAWM, s
GSM8655832 ERD, RL_NAWM, s011 ERD, RL_NAWM, s
GSM8655833 ERD, RL_NAWM, s012 ERD, RL_NAWM, s
GSM8655834 ERD, RL_NAWM, s013 ERD, RL_NAWM, s
GSM8655835 ERD, RL_NAWM, s014 ERD, RL_NAWM, s
GSM8655836 ERD, RL, s015 ERD, RL, s
GSM8655837 ERD, RL, s016 ERD, RL, s
GSM8655838 ERD, RL, s017 ERD, RL, s
GSM8655839 ERD, RL, s018 ERD, RL, s
GSM8655840 ERD, RL, s019 ERD, RL, s
GSM8655841 ERD, RL, s020 ERD, RL, s
GSM8655842 ERD, RL, s021 ERD, RL, s
GSM8655843 ERD, RL_NAWM, s022 ERD, RL_NAWM, s
GSM8655844 ERD, RL_NAWM, s023 ERD, RL_NAWM, s
GSM8655845 ERD, RL_NAWM, s024 ERD, RL_NAWM, s
GSM8655846 ERD, RL_NAWM, s025 ERD, RL_NAWM, s
GSM8655847 ERD, RL_NAWM, s026 ERD, RL_NAWM, s
GSM8655848 ERD, RL_NAWM, s027 ERD, RL_NAWM, s
GSM8655849 ERD, RL_NAWM, s028 ERD, RL_NAWM, s
GSM8655850 PRD, RL, s029 PRD, RL, s
GSM8655851 PRD, RL, s030 PRD, RL, s
GSM8655852 PRD, RL, s031 PRD, RL, s
GSM8655853 PRD, RL, s032 PRD, RL, s
GSM8655854 PRD, RL, s033 PRD, RL, s
GSM8655855 PRD, RL, s034 PRD, RL, s
GSM8655856 PRD, RL, s035 PRD, RL, s
GSM8655857 PRD, RL_NAWM, s036 PRD, RL_NAWM, s
GSM8655858 PRD, RL_NAWM, s037 PRD, RL_NAWM, s
GSM8655859 PRD, RL_NAWM, s038 PRD, RL_NAWM, s
GSM8655860 PRD, RL_NAWM, AL foamy_NAWM, s039
GSM8655861 PRD, RL_NAWM, s040 PRD, RL_NAWM, s

+ 56 more — browse all 96 samples with per-sample file links →

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