GEO series

Alzheimer's disease risk ABCA7 p.A696S variant disturbs the microglial response to amyloid pathology in mice

GSE288170 Mus musculus (mouse) RNA-seq (gene expression) 20 samples Submitted 2025/03/13 Platform GPL21103

At a glance

RNA-seq (gene expression) · mouse · 20 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: Mus musculus (mouse)
Samples: 20 samples: 5 × 10M-WT-M vs 5 × 10M-KI-M vs 5 × 3M-WT-M vs 5 × 3M-KI-MGroups 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: Alzheimer's disease risk ABCA7 p.A696S variant disturbs the microglial response to amyloid pathology in miceNeurobiology of disease · 2025

Summary (written by the submitters)

The adenosine triphosphate–binding cassette transporter A7 (ABCA7) gene is ranked as one of the top susceptibility loci for Alzheimer’s disease (AD). While ABCA7 mediates lipid transport across cellular membranes, ABCA7 loss of function has been shown to exacerbate amyloid-β (Aβ) pathology and compromise microglial function. Our family-based study uncovered an extremely rare ABCA7 p.A696S variant that was substantially segregated with the development of AD in 3 African American families.

Read the full summary ↓

Using the knockin mouse model, we investigated the effects of ABCA7-A696S substitution on amyloid pathology and brain immune response in 5xFAD transgenic mice. Importantly, our study demonstrated that ABCA7-A696S substitution reduces amyloid plaque–associated microgliosis and increases dystrophic neurites around amyloid deposits compared to control mice. We also found increased X-34–positive amyloid plaque burden in 5xFAD mice with ABCA7-A696S substitution, while there was no evident difference in insoluble Aβ levels between mouse groups. Thus, ABCA7-A696S substitution may disrupt amyloid compaction resulting in aggravated neuritic dystrophy due to insufficient microglia barrier function. In addition, we observed that ABCA7-A696S substitution disturbs the induction of proinflammatory cytokines interleukin 1β and interferon γ in the brains of 5xFAD mice, although some disease-associated microglia gene expression, including Trem2 and Tyrobp, are upregulated. Lipidomics also detected higher total lysophosphatidylethanolamine levels in the brains of 5xFAD mice with ABCA7-A696S substitution than controls. These results suggest that ABCA7-A696S substitution might compromise the adequate innate immune response to amyloid pathology in AD by modulating brain lipid metabolism, providing novel insight into the pathogenic mechanisms mediated by ABCA7.

Published in

Alzheimer's disease risk ABCA7 p.A696S variant disturbs the microglial response to amyloid pathology in mice

Ma X, Prokopenko D, Wang N et al. · Neurobiology of disease 2025 · PMID 39880319 · doi:10.1016/j.nbd.2025.106813

This dataset

Open GSE288170 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 GSE288170_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 PRJNA1216322 and SRA study SRP560267. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 39880319) ↗

Study design

5 × 10M-WT-M vs 5 × 10M-KI-M vs 5 × 3M-WT-M vs 5 × 3M-KI-M

Supports a between-group comparison across 20 samples.

How we read this: 4 replicated groups read from 20 sample titles; they account for 20 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

GSM8760308 10M-WT-M6 10M-WT-M
GSM8760309 10M-WT-M7 10M-WT-M
GSM8760310 10M-WT-M8 10M-WT-M
GSM8760311 10M-WT-M9 10M-WT-M
GSM8760312 10M-WT-M10 10M-WT-M
GSM8760313 10M-KI-M03 10M-KI-M
GSM8760314 10M-KI-M04 10M-KI-M
GSM8760315 10M-KI-M05 10M-KI-M
GSM8760316 10M-KI-M06 10M-KI-M
GSM8760317 10M-KI-M07 10M-KI-M
GSM8760318 3M-WT-M6 3M-WT-M
GSM8760319 3M-WT-M7 3M-WT-M
GSM8760320 3M-WT-M8 3M-WT-M
GSM8760321 3M-WT-M9 3M-WT-M
GSM8760322 3M-WT-M10 3M-WT-M
GSM8760323 3M-KI-M03 3M-KI-M
GSM8760324 3M-KI-M04 3M-KI-M
GSM8760325 3M-KI-M05 3M-KI-M
GSM8760326 3M-KI-M06 3M-KI-M
GSM8760327 3M-KI-M07 3M-KI-M

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