← BioTransfer GEO Dataset Finder
GEO series

LRRK2-G2019S Impairs Astrocyte Differentiation and Triggers a senescence-like phenotype in Parkinson's Disease Models - Midbrain organoids

GSE313031 Homo sapiens (human) Single-cell RNA-seq 18 samples Submitted 2026/05/19 Platform GPL24676
At a glance

Single-cell RNA-seq · human · 18 samples

What it is
Single-cell RNA-seqGene activity measured in thousands of individual cells, so cell types can be told apart. 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
18 samples: 18 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.
Summary (written by the submitters)
Astrocytes are increasingly recognised as essential contributors to both physiological brain function and neurodegenerative diseases. Here, we describe how the Parkinson’s disease (PD)-associated mutation LRRK2-G2019S affects astrocytes using autoptic brain samples and PD patient-specific 3D midbrain organoids and 2D astrocytes derived from induced pluripotent stem cells.
Read the full summary ↓
In autoptic midbrain samples from LRRK2-G2019S patients, we observed a reduction in GFAP⁺ astrocytes but increased branching, together with transcriptional signatures consistent with altered astrocyte function. We also observed delayed astrocyte differentiation in PD patient-specific midbrain organoids, accompanied by altered astrocyte transcriptomic profiles revealed by single-cell RNA sequencing. This defective differentiation contributes to the acquisition of a senescent-like phenotype. In 2D cultures, astrocyte differentiation from LRRK2-G2019S precursor cells was associated with early apoptosis and altered Wnt/β-catenin and TGFβ signalling compared to LRRK2-WT cultures. Notably, pharmacological activation of the developmental transcription factor NR2F1, downregulated in LRRK2-G2019S models, reduced astrocyte cell death and senescence-like phenotypes. Together, these data show that LRRK2-G2019S impairs astrocyte specification and predisposes to a senescent phenotype.
This dataset
Open GSE313031 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 GSE313031_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 PRJNA1378160 and SRA study SRP653782. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗
Study design
18 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 18 sample titles: 18 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.