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Inflammatory intracellular signaling in neurons is influenced by glial soluble factors in iPSC-based cell model of PARK2-associated Parkinson’s disease

GSE272394 Homo sapiens (human) RNA-seq (gene expression) 16 samples Submitted 2024/09/05 Platform GPL18460
At a glance

RNA-seq (gene expression) · human · 16 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
16 samples: 16 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
Inflammatory Intracellular Signaling in Neurons Is Influenced by Glial Soluble Factors in iPSC-Based Cell Model of PARK2-Associated Parkinson's DiseaseInternational journal of molecular sciences · 2024
Summary (written by the submitters)
Neuroinflammation is considered one of the driving factors in Parkinson’s disease (PD). Astrocytes affect the function of neurons in different ways, of which humoral interactions are very important. Here, we used neuronal and glial cell cultures differentiated from iPSC of healthy donors (HD) and PD patients with different PARK2 mutations (PD) in order to evaluate the influence of glial secretome on inflammatory gene network in neurons and to estimate the impact of the lack of Parkin to this interaction.
Published in
Inflammatory Intracellular Signaling in Neurons Is Influenced by Glial Soluble Factors in iPSC-Based Cell Model of PARK2-Associated Parkinson's Disease
Gerasimova T, Poberezhniy D, Nenasheva V et al. · International journal of molecular sciences 2024 · PMID 39273568 · doi:10.3390/ijms25179621
This dataset
Open GSE272394 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 GSE272394_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 PRJNA1136555 and SRA study SRP520282. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 39273568) ↗
Study design
16 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 16 sample titles: 16 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

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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.