← BioTransfer GEO Dataset Finder
GEO series

Chromatin accessibility mapping for functional GWAS risk variants of neuropsychiatric and neurodegenerative disorders in hiPSC-derived neural cells

GSE263809 Homo sapiens (human) RNA-seq (gene expression) 21 samples Submitted 2025/01/30 Platform GPL11154
At a glance

RNA-seq (gene expression) · human · 21 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
Homo sapiens (human)
Samples
21 samples: 21 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
PICALM Alzheimer's risk allele causes aberrant lipid droplets in microgliaNature · 2025
Summary (written by the submitters)
Despite genome-wide association studies (GWAS) of late-onset Alzheimer’s disease (LOAD) have identified 75 genetic risk loci 2-7, the disease causal mechanism underlying most GWAS risk loci remains largely unknown. A major challenge is how to identify a putative causal disease variant among multiple risk variants at individual GWAS risk locus and functionally link the risk allele to LOAD-relevant cellular phenotypes.
Read the full summary ↓
Leveraging our recent approach for identifying functional noncoding GWAS risk variants that show allele-specific open chromatin (ASoC), we systematically identified putative causal LOAD risk variants in human iPSC-derived neurons and glia cells, and established a causal link between PICALM risk allele to unappreciated microglia (MG)-specific role of PICALM in lipid droplet (LD) accumulation and phagocytosis. Our ASoC mapping identified functional risk variants for 26 AD risk loci, most of which are MG-specific. At the PICALM locus, the LOAD risk allele of the ASoC SNP rs10792832 altered chromatin accessibility to PU.1 binding and reduced PICALM expression in MG, thereby leading to impaired phagocytosis of Amyloid beta (Aβ) and myelin debris. Interestingly, transcriptomic profiling of MG carrying PICALM risk allele not only supported abnormal phagocytosis but also revealed enrichment of gene pathways related to cholesterol synthesis and LD formation. Further genetic and pharmacological perturbations in MG established the causal link of the PICALM risk allele with PICALM reduction, LD accumulation and phagocytosis deficits. Our work elucidates an MG-specific role of PICALM in regulating lipid/cholesterol accumulation that is potentially linked to AD pathophysiology, providing a functional neurobiological basis for developing novel clinical interventions.
Published in
PICALM Alzheimer's risk allele causes aberrant lipid droplets in microglia
Kozlova A, Zhang S, Sudwarts A et al. · Nature 2025 · PMID 40903578 · doi:10.1038/s41586-025-09486-x
This dataset
Open GSE263809 in finder → Find more human 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 GSE263809_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 PRJNA1099279 and SRA study SRP501294. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 40903578) ↗
Study design
21 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 21 sample titles: 21 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
Datasets on a similar topic

Search all human RNA-seq datasets in GEO →

Share this dataset

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.