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Cellular rejuvenation protects neurons from inflammation mediated cell death (RNA-seq).

GSE247173 Mus musculus (mouse) Single-nucleus RNA-seq 14 samples Submitted 2025/01/01 Platform GPL19057

At a glance

Single-nucleus RNA-seq · mouse · 14 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: 14 samples: 4 × Sample_naive_n vs 4 × Sample_onset_n vs 4 × Sample_pre_n vs 2 × Sample_peak_nGroups 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.

Summary (written by the submitters)

In multiple sclerosis (MS), invasion of the central nervous system by peripheral immune cells, activation of resident microglia and astrocytes, and demyelination occur as a cascade of events that trigger neuronal damage and death. The molecular signals in neurons that activate damage in response to the complex immune environment have not been fully characterized.

Read the full summary ↓

Retinal ganglion cell neurons (RGCs) of the central nervous system (CNS) undergo axonal injury and cell death in MS and this is recapitulated in the experimental autoimmune encephalomyelitis (EAE) mouse model of MS. To profile the molecular landscape of pathologically inflamed neurons, we isolated RGCs from mice that were subjected to the EAE protocol and performed RNA-sequencing and ATAC-sequencing analysis. Pathway analysis of the RNA-sequencing data revealed that RGCs exhibited a molecular signature reminiscent of aged neurons with features of senescence. Pathway level analysis of single-nucleus RNA-sequencing analysis of human MS neurons also demonstrated a similar senescence-like phenotype. Immunofluorescence analyses confirmed alterations to various markers of senescence in RGCs in EAE mice, including alterations to the nuclear envelope, changes to chromatin marks, and accumulation of DNA damage. Transduction of RGCs with an Oct4-Sox2-Klf4 transgene to rejuvenate neurons to a younger epigenetic and transcriptomic state in the EAE model was sufficient to increase survival of RGCs. Together, this work reveals a novel senescent like phenotype in neurons in a model of pathological inflammation and in neurons from MS patients. Rejuvenation of the aged transcriptome improves visual acuity and neuronal survival in the EAE model supporting the notion that anti-aging therapies and/or senotherapeutic agents may directly target neurons for neuroprotection in autoimmune disorders.

This dataset

Open GSE247173 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 GSE247173_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 PRJNA1036568 and SRA study SRP470538. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗

Study design

4 × Sample_naive_n vs 4 × Sample_onset_n vs 4 × Sample_pre_n vs 2 × Sample_peak_n

Supports a between-group comparison across 14 samples.

How we read this: 4 replicated groups read from 14 sample titles; they account for 14 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

GSM7884729 Sample_naive_n1 Sample_naive_n
GSM7884730 Sample_naive_n2 Sample_naive_n
GSM7884731 Sample_naive_n4 Sample_naive_n
GSM7884732 Sample_naive_n5 Sample_naive_n
GSM7884733 Sample_onset_n1 Sample_onset_n
GSM7884734 Sample_onset_n2 Sample_onset_n
GSM7884735 Sample_onset_n3 Sample_onset_n
GSM7884736 Sample_onset_n4 Sample_onset_n
GSM7884737 Sample_peak_n1 Sample_peak_n
GSM7884738 Sample_peak_n2 Sample_peak_n
GSM7884739 Sample_pre_n1 Sample_pre_n
GSM7884740 Sample_pre_n2 Sample_pre_n
GSM7884741 Sample_pre_n3 Sample_pre_n
GSM7884742 Sample_pre_n4 Sample_pre_n

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