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Kv1.3 inhibition alleviates neuropathology via neuroinflammatory and resilience pathways in a mouse model of Aβ pathology

GSE338689 Mus musculus (mouse) Single-nucleus RNA-seq 12 samples Submitted 2026/07/15 Platform GPL34290
At a glance

Single-nucleus RNA-seq · mouse · 12 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
12 samples: 12 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
Kv1.3 inhibition alleviates neuropathology via neuroinflammatory and resilience pathways in a mouse model of Aβ pathologybioRxiv : the preprint server for biology · 2025
Summary (written by the submitters)
Abstract Inhibition of voltage-gated potassium channel Kv1.3 is a therapeutic strategy to curb microglia-mediated neuroinflammation in neurodegeneration, although the cellular and signaling mechanisms of disease-modification by Kv1.3 blockers are unclear. In this study, we delineate protective mechanisms of Kv1.3 blockade in a mouse model of Alzheimer's disease (AD) pathology using comprehensive transcriptomics and proteomics profiling of brain, corresponding with neuropathological effects of two translationally relevant Kv1.3 blockers, namely small molecule PAP-1 and peptide ShK-223.
Read the full summary ↓
Following 3 months of treatment, both molecules reduced Ab plaque burden. Single nuclear RNA seq (snRNA seq) of brain nuclei showed that PAP-1 disproportionately impacted oligodendrocytes and microglia and increased crosstalk between neurons and astrocytes with endothelial cells. In contrast, ShK-223 had pronounced effects on glutamatergic neurons and astrocytes. Both blockers increased expression of myelination genes in oligodendrocytes and synaptic genes in neurons. Neuroprotective effects of PAP-1 were further confirmed by bulk brain transcriptomics and proteomics whereby PAP-1 increased levels of synaptic, cognitive resilience and mitochondrial proteins, while decreasing glial and immune pathways including STAT1/3 phosphorylation. Using proximity labeling and co-immunoprecipitation, we found that Kv1.3 interacts with STAT1/3 in microglia. Using microglial cell lines and primary microglia, we discovered a preferential functional coupling between Kv1.3 and type 2 but not type 1 IFN signaling. Brain-level disease modification by Kv1.3 blockade was reflected in the cerebrospinal fluid (CSF) via reduced levels of neurofilament-light (NEFL) and resilience protein RPH3A, both of which are increased in human AD CSF. Together, this study demonstrates functional links between Kv1.3 channels and type 2 IFN signaling and reveals distinct cellular effects of Kv1.3 blockers in AD pathology that correspond with reduced neuropathology and neuroinflammation, augmentation of resilience and neuro-vascular pathways, along with biomarkers of therapeutic effect.
Published in
Kv1.3 inhibition alleviates neuropathology via neuroinflammatory and resilience pathways in a mouse model of Aβ pathology
Kumari R, Bowen C, Srivastava U et al. · bioRxiv : the preprint server for biology 2025 · PMID 41509242 · doi:10.64898/2025.12.25.696456
This dataset
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.

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 GSE338689_RAW.tar (it can run to tens of gigabytes).

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.

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 PRJNA1495533 and SRA study SRP718179. Searching any of these in the dataset finder brings you back here.

Study design
12 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 12 sample titles: 12 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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