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Parkinson's disease (PD) genetic asymmetry in neuronal nuclei from paired hemisphere samples of prefrontal cortex

GSE264050 Homo sapiens (human) RNA-seq (gene expression) 87 samples Submitted 2026/03/12 Platform GPL18573
At a glance

RNA-seq (gene expression) · human · 87 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
87 samples: 87 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
Gene expression asymmetry in Parkinson's disease: variation of CCT gene expression is correlated with hemisphere specific severityFrontiers in molecular neuroscience · 2025
Summary (written by the submitters)
We measured gene expression in neuronal nuclei from cortex derived from PD (18 male, 10 female) and healthy control (5 male, 6 female) brains. Each brain sample consisted of cortex from both right and left hemispheres. Th side of PD onset, age, duration of disease, as well as technical variables were used to define linear models of expression for each gene. Significant differences in expression due to age, disease, sex, hemisphere, side of PD onset, or combinations were determined
Published in
Gene expression asymmetry in Parkinson's disease: variation of CCT gene expression is correlated with hemisphere specific severity
Pierce SE, van der Schans EJC, Goralski TM et al. · Frontiers in molecular neuroscience 2025 · PMID 41608591 · doi:10.3389/fnmol.2025.1743557
This dataset
Open GSE264050 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 GSE264050_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 PRJNA1100607 and SRA study SRP501908. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 41608591) ↗
Study design
87 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 87 sample titles: 87 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

+ 47 more — browse all 87 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.