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Transcriptome study of Alzheimer's disease on post-mortem human brain

GSE261050 Homo sapiens (human) RNA-seq (gene expression) 101 samples Submitted 2025/05/14 Platform GPL34281
At a glance

RNA-seq (gene expression) · human · 101 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
101 samples: 101 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
Association of RDoC dimensions with post mortem brain transcriptional profiles in Alzheimer's diseaseAlzheimer's & dementia (Amsterdam, Netherlands) · 2025
Summary (written by the submitters)
INTRODUCTION: Neuropsychiatric symptoms are common in people with Alzheimer’s disease (AD) across all severity stages. Their heterogeneous presentation and variable temporal association with cognitive decline suggest shared and distinct biological mechanisms. We hypothesized that specific patterns of gene expression associate with distinct NIMH Research Domain Criteria (RDoC) domains in AD.
Read the full summary ↓
METHODS: Post-mortem bulk RNAseq on the insula and anterior cingulate cortex from 60 brain donors representing the spectrum of canonical AD neuropathology combined with natural language processing approaches based on the RDoC Clinical Domains.  RESULTS: Distinct sets of >100 genes (pFDR<0.05) were specifically associated with at least one clinical domain (Cognitive, Social, Negative, Positive, Arousal). In addition, dysregulation of immune response pathways was shared across domains and brain regions. DISCUSSION: Our findings provide evidence for distinct transcriptional profiles associated with RDoC domains suggesting that each dimension is characterized by specific sets of genes providing insight into the underlying mechanisms.
Published in
Association of RDoC dimensions with post mortem brain transcriptional profiles in Alzheimer's disease
Jiang W, Vogelgsang J, Dan S et al. · Alzheimer's & dementia (Amsterdam, Netherlands) 2025 · PMID 40352682 · doi:10.1002/dad2.70103
This dataset
Open GSE261050 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 GSE261050_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 PRJNA1082265 and SRA study SRP492575. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 40352682) ↗
Study design
101 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 101 sample titles: 101 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

+ 61 more — browse all 101 samples with per-sample file links →

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.