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Age-dependent alterations in the human striatum identify a PPP1R15B-miR-196a node as a modifier of Huntington’s disease phenotypes

GSE264237 Homo sapiens (human) RNA-seq (gene expression) 30 samples Submitted 2026/08/04 Platform GPL24676
At a glance

RNA-seq (gene expression) · human · 30 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
30 samples: 10 conditions, mostly in triplicateGroups 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)
Polyglutamine expansion in Huntingtin (HTT) causes its aggregation and progressive loss of striatal neurons in Huntington’s disease (HD). HD is a mostly adult-onset neurodegenerative disorder with no disease-modifying therapies. Here we found that human striatal aging is associated with a global upregulation of genes involved in translation, including the translation and proteostasis regulator PPP1R15B (R15B).
Read the full summary ↓
We used the R15B inhibitor Raphin1 to investigate if the age-associated changes could modify HD pathology. R15B inhibition rescued early learning and late motor deficits in HDYAC128 mice. In striatal medium spiny neurons directly reprogrammed from fibroblasts of symptomatic HD patients (HD-MSNs), Raphin1 reduced the formation of mutant HTT aggregates and neuronal death. Genetic knockdown of R15B also protected HD-MSNs from neurodegeneration whereas its overexpression exacerbated disease phenotypes. Moreover, both human striatum and reprogrammed MSNs exhibited age-dependent decline of miR-196a, a microRNA that directly targets non-conserved sites in human R15B 3’UTR and overexpressing miR-196a lowered mutant HTT aggregation. This work identifies age-dependent alterations in miR-196a and its target R15B and demonstrates the therapeutic potential of reversing these changes in diverse models and readouts of HD. We propose miR-196a and R15B as disease-modifying targets in HD.
This dataset
Open GSE264237 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 GSE264237_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 PRJNA1101519 and SRA study SRP502410. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗
Study design
10 conditions, mostly in triplicate
AG08379, Healthy Control, DMSO ×3 GM02171, Healthy Control, DMSO ×3 GM04194, HD, Raphin1 ×3 GM04194, HD, DMSO ×3 GM04196, HD, Raphin1 ×3 GM04196, HD, DMSO ×3 GM04198, HD, Raphin1 ×3 GM04198, HD, DMSO ×3 +2 more

Supports a between-group comparison across 30 samples.

How we read this: 10 replicated groups read from 30 sample titles; they account for 30 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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