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Single-nucleus RNA sequencing revealed the impact of post-mortem interval on the cellular component and gene expression analysis of mouse brains

GSE285936 Mus musculus (mouse) Single-nucleus RNA-seq 16 samples Submitted 2025/12/08 Platform GPL24247

At a glance

Single-nucleus RNA-seq · mouse · 16 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: Mus musculus (mouse)
Samples: 16 samples: 5 conditions, mostly in triplicateGroups 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: Single-nucleus RNA sequencing revealed ribosomal genes expression defines the cellular state of oligodendrocytes and precursor cells in post-mortem ischemia mouse brainsInternational journal of biological macromolecules · 2025

Summary (written by the submitters)

Accurate analysis of cell atlas and gene expression in biological tissues using single-nucleus RNA sequencing (snRNA-seq) is closely related to the quality of their RNA quality, and post-mortem interval (PMI) is one of the major origins of RNA variation. Although the use of RNA-degraded tissues in transcriptome analysis remains controversial, such samples are sometimes the sole means to address specific questions.

Read the full summary ↓

Current studies on the impact of PMI on transcriptome data are limited to large-scale RNA-seq, which ignores cellular heterogeneity. Thus, deciphering the non-cell-autonomous effects caused by PMI is imperative for understanding the cellular and molecular disruption it elicits. Here, we investigated the impact of PMI on cellular components and gene expression using snRNA-seq data from mouse brain tissues of post-mortem. We collected samples that were allowed to decay for varying amounts of time at 25 ̊C prior to snRNA-seq, covering the entire range of RIN values. The different effects on the PMI to the degradation rate of mRNA and rRNA within nuclei, and the mRNA presented a more stable state. Multi-channel analysis revealed the preferential transient depletion oligodendrocytes and OPCs with increasing PMI. In addition, a rapid widespread overregulation of ribosomal transient recruitment to protein (RP) genes in various cells, and reached a plateau at PMI of 36 h. Although state depletion of neuronal cells was not detected, we reported significant upregulation of PMI-dependent RP genes in its subpopulations and their cell loss. Moreover, RP genes showed the greatest differential expression in the subpopulations with greater cell perturbation, and we speculated that aberrant expression of these genes might be associated with cell death. In this study, we systematically investigated the changes in the transcriptome profile of brain tissue induced by PMI at single-cell resolution, and revealed one of the important factors that might be responsible for the changes. In addition, our data complemented a possible explanation for the changes in the cellular state of brain tissue induced by postmortem hypoxia-ischemia, and provided a reference for transcriptome studies of RNA degradation samples.

Published in

Single-nucleus RNA sequencing revealed ribosomal genes expression defines the cellular state of oligodendrocytes and precursor cells in post-mortem ischemia mouse brains

Guo Y, Ma J, Ma X et al. · International journal of biological macromolecules 2025 · PMID 40967535 · doi:10.1016/j.ijbiomac.2025.147708

This dataset

Open GSE285936 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 GSE285936_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 PRJNA1207017 and SRA study SRP555513. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 40967535) ↗

Study design

5 conditions, mostly in triplicate

Sample 5, 54 hour, ×4 Sample 1, 0 hour, ×3 Sample 3, 36 hour, ×3 Sample 4, 48 hour, ×3 Sample 2, 24 hour, ×2 Sample 2, 24 hour ×1

Supports a between-group comparison across 15 samples.

How we read this: 5 replicated groups read from 16 sample titles; they account for 15 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

GSM8713119 Sample 1, 0 hour, replicate 1 Sample 1, 0 hour,
GSM8713120 Sample 1, 0 hour, replicate 2 Sample 1, 0 hour,
GSM8713121 Sample 1, 0 hour, replicate 3 Sample 1, 0 hour,
GSM8713122 Sample 2, 24 hour, replicate 1 Sample 2, 24 hour,
GSM8713123 Sample 2, 24 hour replicate 2
GSM8713124 Sample 2, 24 hour, replicate 3 Sample 2, 24 hour,
GSM8713125 Sample 3, 36 hour, replicate 1 Sample 3, 36 hour,
GSM8713126 Sample 3, 36 hour, replicate 2 Sample 3, 36 hour,
GSM8713127 Sample 3, 36 hour, replicate 3 Sample 3, 36 hour,
GSM8713128 Sample 4, 48 hour, replicate 1 Sample 4, 48 hour,
GSM8713129 Sample 4, 48 hour, replicate 2 Sample 4, 48 hour,
GSM8713130 Sample 4, 48 hour, replicate 3 Sample 4, 48 hour,
GSM8713131 Sample 5, 54 hour, replicate 1 Sample 5, 54 hour,
GSM8713132 Sample 5, 54 hour, replicate 2 Sample 5, 54 hour,
GSM8713133 Sample 5, 54 hour, replicate 3 Sample 5, 54 hour,
GSM8713134 Sample 5, 54 hour, replicate 4 Sample 5, 54 hour,

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