GEO series

Single-cell profiling of limbic excitatory neurons reveals dysregulation of metabolic pathways in Parkinson’s disease

GSE295998 Homo sapiens (human) Single-cell RNA-seq 24 samples Submitted 2026/04/29 Platform GPL24676

At a glance

Single-cell RNA-seq · human · 24 samples

What it is: Single-cell RNA-seqGene activity measured in thousands of individual cells, so cell types can be told apart.
Organism: Homo sapiens (human)
Samples: 24 samples: 22 × Subject vs 2 × Subject 01Groups 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 common age-related neurodegenerative disorder that is pathologically characterized by the presence of Lewy bodies within neurons across several brain regions. Although the severe, selective degeneration of pigmented dopaminergic neurons in the midbrain results in a characteristic constellation of motor symptoms, how additional neuronal populations are affected in disease and contribute to neuropsychiatric symptoms remains unclear.

Read the full summary ↓

The amygdala is one of the most frequent brain regions to harbor Lewy bodies in both PD and Alzheimer’s disease with co-existing alpha-synuclein pathology. To understand how neurons and glial cells are altered in this subcortical limbic area in PD, we performed single-nucleus RNA-sequencing (snRNA-seq) of patients and age-matched controls that yielded 168,270 transcriptomes following quality control measures. Through stringent differential gene expression analysis, we found transcriptional perturbations of genes linked to metabolic pathways in excitatory neurons, including upregulation of TFEB, but not inhibitory neurons or neural precursors. Additionally, we observed increased microglia with shifts towards activated states. Through bioinformatic integration of snRNA-seq with genetics studies, our findings suggest that PD risk factors act in cell-autonomous processes through both limbic excitatory neurons and midbrain dopaminergic neurons. Our findings define the selective vulnerability of excitatory neurons in the amygdala and may have implications for the understanding and treatment of non-movement symptoms in PD.

This dataset

Open GSE295998 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 GSE295998_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 PRJNA1257284. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗

Study design

22 × Subject vs 2 × Subject 01

Supports a between-group comparison across 24 samples.

How we read this: 2 replicated groups read from 24 sample titles; they account for 24 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

GSM8963184 Subject 01-1 Subject 01
GSM8963185 Subject 02 Subject
GSM8963186 Subject 03 Subject
GSM8963187 Subject 04 Subject
GSM8963188 Subject 05 Subject
GSM8963189 Subject 06 Subject
GSM8963190 Subject 07 Subject
GSM8963191 Subject 08 Subject
GSM8963192 Subject 01-2 Subject 01
GSM8963193 Subject 09 Subject
GSM8963194 Subject 10 Subject
GSM8963195 Subject 11 Subject
GSM8963196 Subject 12 Subject
GSM8963197 Subject 13 Subject
GSM8963198 Subject 14 Subject
GSM8963199 Subject 15 Subject
GSM8963200 Subject 16 Subject
GSM8963201 Subject 17 Subject
GSM8963202 Subject 18 Subject
GSM8963203 Subject 19 Subject
GSM8963204 Subject 20 Subject
GSM8963205 Subject 21 Subject
GSM8963206 Subject 22 Subject
GSM8963207 Subject 23 Subject

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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.