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APOE genotype modifies the gene signature of microglial and astrocytic responses to aging

GSE285471 Mus musculus (mouse) Single-cell RNA-seq 24 samples Submitted 2026/08/08 Platform GPL21103

At a glance

Single-cell RNA-seq · mouse · 24 samples

What it is: Single-cell RNA-seqGene activity measured in thousands of individual cells, so cell types can be told apart. 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: 24 samples: 12 conditions, mostly in duplicateGroups 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)

To determine how APOE genotypes impact the gene signature of microglia and astrocytes during aging at single cell level, we isolated brain cells from young (3 month of age) and aged (24 month of age) APOE2 and APOE3 and APOE4 mice, and conducted single cell RNA sequencing (scRNAseq) (4 mice/age/genotype). We annotated all the cells based on their gene markers and then focused on microglia and astrocytes for further downstream analysis.

Read the full summary ↓

We functionally annotated each subcluster with microglia and astrocytes, based on the expression pattern of the widely accepted typical maker genes from multiple publications. Further, we identified the differentially expressed genes (DEG) in the comparison between young and aged mice for each subcluster in each APOE genotype and characterized the pathways related to these DEGs. Taken together, our dataset provided comprehensive resources to understand the APOE-regulated microglial and astrocytic responses to aging.

This dataset

Open GSE285471 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 GSE285471_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 PRJNA1203890 and SRA study SRP554196. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗

Study design

12 conditions, mostly in duplicate

APOE2, Male, 3M, # ×2 APOE3, Male, 3M, # ×2 APOE4, Male, 3M, # ×2 APOE2, Male, 24M, # ×2 APOE3, Male, 24M, # ×2 APOE4, Male, 24M, # ×2 APOE2, Female, 3M, # ×2 APOE3, Female, 3M, # ×2 +4 more

Supports a between-group comparison across 24 samples.

How we read this: 12 replicated groups read from 24 sample titles; they account for 24 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

GSM8702983 APOE2, Male, 3M, #1 APOE2, Male, 3M, #
GSM8702984 APOE3, Male, 3M, #1 APOE3, Male, 3M, #
GSM8702985 APOE4, Male, 3M, #1 APOE4, Male, 3M, #
GSM8702986 APOE2, Male, 24M, #1 APOE2, Male, 24M, #
GSM8702987 APOE3, Male, 24M, #1 APOE3, Male, 24M, #
GSM8702988 APOE4, Male, 24M, #1 APOE4, Male, 24M, #
GSM8702989 APOE2, Female, 3M, #1 APOE2, Female, 3M, #
GSM8702990 APOE3, Female, 3M, #1 APOE3, Female, 3M, #
GSM8702991 APOE4, Female, 3M, #1 APOE4, Female, 3M, #
GSM8702992 APOE2, Female, 24M, #1 APOE2, Female, 24M, #
GSM8702993 APOE3, Female, 24M, #1 APOE3, Female, 24M, #
GSM8702994 APOE4, Female, 24M, #1 APOE4, Female, 24M, #
GSM8702995 APOE2, Female, 3M, #2 APOE2, Female, 3M, #
GSM8702996 APOE3, Female, 3M, #2 APOE3, Female, 3M, #
GSM8702997 APOE4, Female, 3M, #2 APOE4, Female, 3M, #
GSM8702998 APOE2, Female, 24M, #2 APOE2, Female, 24M, #
GSM8702999 APOE3, Female, 24M, #2 APOE3, Female, 24M, #
GSM8703000 APOE4, Female, 24M, #2 APOE4, Female, 24M, #
GSM8703001 APOE2, Male, 3M, #2 APOE2, Male, 3M, #
GSM8703002 APOE3, Male, 3M, #2 APOE3, Male, 3M, #
GSM8703003 APOE4, Male, 3M, #2 APOE4, Male, 3M, #
GSM8703004 APOE2, Male, 24M, #2 APOE2, Male, 24M, #
GSM8703005 APOE3, Male, 24M, #2 APOE3, Male, 24M, #
GSM8703006 APOE4, Male, 24M, #2 APOE4, Male, 24M, #

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