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Microglial mechanisms drive amyloid-β clearance in immunized patients with Alzheimer’s disease [dataset3]

GSE263079 Homo sapiens (human) Single-cell RNA-seq 32 samples Submitted 2025/03/03 Platform GPL34284Platform GPL24676
At a glance

Single-cell RNA-seq · human · 32 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
Homo sapiens (human)
Samples
32 samples: 32 samples, each with a distinct title — no replicated groupsGroups 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
Microglial mechanisms drive amyloid-β clearance in immunized patients with Alzheimer's diseaseNature medicine · 2025
Summary (written by the submitters)
Alzheimer’s disease (AD) therapies utilizing amyloid-β (Aβ) immunization have shown potential in clinical trials. Yet, the mechanisms driving Aβ clearance in the immunized AD brain remain unclear. Here, we use spatial transcriptomics to explore the effects of both active and passive Aβ immunization in the AD brain. We compare actively immunized AD patients with nonimmunized AD patients and neurologically healthy controls, identifying distinct microglial states associated with Aβ clearance.
Read the full summary ↓
Using high-resolution spatial transcriptomics alongside single-cell RNA sequencing, we delve deeper into the transcriptional pathways involved in Aβ removal after lecanemab treatment. We uncover spatially distinct microglial responses that vary by brain region. Our analysis reveals upregulation of the triggering receptor expressed on myeloid cells 2 (TREM2) and apolipoprotein E (APOE) in microglia across immunization approaches, which correlate positively with antibody responses and Aβ removal. Furthermore, we show that complement signaling in brain myeloid cells contributes to Aβ clearance after immunization. These findings provide new insights into the transcriptional mechanisms orchestrating Aβ removal and shed light on the role of microglia in immune-mediated Aβ clearance. Importantly, our work uncovers potential molecular targets that could enhance Aβ-targeted immunotherapies, offering new avenues for developing more effective therapeutic strategies to combat AD.
Published in
Microglial mechanisms drive amyloid-β clearance in immunized patients with Alzheimer's disease
van Olst L, Simonton B, Edwards AJ et al. · Nature medicine 2025 · PMID 40050704 · doi:10.1038/s41591-025-03574-1
This dataset
Open GSE263079 in finder → Find more human 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 GSE263079_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 PRJNA1095549 and SRA study SRP499392. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 40050704) ↗
Study design
32 samples, each with a distinct title — no replicated groups

Any grouping that defines a comparison lives in the sample characteristics, which GEO records separately from the titles.

How we read this: Read from 32 sample titles: 32 distinct titles with little repetition. 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
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Metadata from NCBI GEO, cached and refreshed periodically — the NCBI page above is authoritative. Downloads link straight to NCBI/ENA; nothing is proxied through BioTransfer.