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A deep cellular atlas of the human ventral substantia nigra in Parkinson's identifies a genetic and molecular overlap with insulin resistance

GSE329625 Homo sapiens (human) Single-nucleus RNA-seq 20 samples Submitted 2026/05/10 Platform GPL24676
At a glance

Single-nucleus RNA-seq · human · 20 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
Homo sapiens (human)
Samples
20 samples: 5 conditions, mostly in duplicateGroups 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.
Summary (written by the submitters)
Parkinson’s disease (PD) is a complex neurodegenerative disorder characterised by selective neuronal loss. We integrate deep full-length single-nuclei sequencing of the human substantia nigra with novel genome-wide association studies (GWAS) identifying genetic and cellular drivers of PD. Genetic risk converges on AGTR1+ dopaminergic neurons and perineuronal oligodendrocytes (pODCs), both reduced in PD, as well as oligodendrocyte precursor cells, enriched among disease-disrupted intercellular interactions.
Read the full summary ↓
AGTR1+ neurons represent a metabolically stressed state, characterised by renin-angiotensin system (RAS) and MAPK activation, oxidative stress, and mitochondrial dysfunction, rather than a distinct subtype. AGTR1+ neurons and pODCs link PD risk to metabolic traits; in pODCs, this association reflects insulin resistance with downregulated PI3K–AKT signalling.
This dataset
Open GSE329625 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 GSE329625_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 PRJNA1460344 and SRA study SRP696356. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗
Study design
5 conditions, mostly in duplicate
SN, control, ×7 SN, PD_B5-6, ×6 SN, PD_B3-4, ×3 SN, ILBD_B1-2, ×2 SN, ILBD_B3-4, ×2

Supports a between-group comparison across 20 samples.

How we read this: 5 replicated groups read from 20 sample titles; they account for 20 of them. 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.