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LRRK2-mediated neuroinflammation induced neuronal dysfunctions in a Parkinson’s and Alzheimer’s disease cellular model

GSE308143 Homo sapiens (human) RNA-seq (gene expression) 37 samples Submitted 2025/10/02 Platform GPL30173
At a glance

RNA-seq (gene expression) · human · 37 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
37 samples: 5 conditions, mostly with about 4 replicates eachGroups 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
LRRK2-Mediated Neuroinflammation-Induced Neuronal Dysfunctions in a Parkinson's and Alzheimer's Disease Cellular ModelBiomolecules · 2025
Summary (written by the submitters)
Dissect the transcriptional consequences of neuroinflammation and the role of the kinase LRRK2 in regulating neuronal responses in human induced pluripotent stem cell (hiPSC)-derived neurons exposed to pro-inflammatory conditions relevant to Parkinson’s disease (PD) and Alzheimer’s disease (AD).Dopaminergic neurons (PD context) and cholinergic neurons (AD context) were differentiated from hiPSCs and subsequently cultured in glial-conditioned medium containing either α-synuclein pre-formed fibrils (α-syn pffs) or amyloid-β (Aβ) fibrils, for PD and aAD respectively.
Read the full summary ↓
Neuronal cultures were exposed to inflammed media to model chronic inflammatory stress.
Published in
LRRK2-Mediated Neuroinflammation-Induced Neuronal Dysfunctions in a Parkinson's and Alzheimer's Disease Cellular Model
Mutti V, Carini G, Marizzoni M et al. · Biomolecules 2025 · PMID 41008629 · doi:10.3390/biom15091322
This dataset
Open GSE308143 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 GSE308143_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 PRJNA1329028 and SRA study SRP620653. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 41008629) ↗
Study design
5 conditions, mostly with about 4 replicates each
PF ×5 AB ×5 ASYN ×4 MLI2 ×4 NT ×4 MLI2_1_C ×1 MLI2_2_C ×1 MLI2_3_C ×1 +12 more

Supports a between-group comparison across 22 samples.

How we read this: 5 replicated groups read from 37 sample titles; they account for 22 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
Datasets on a similar topic

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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.