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Transcriptional profiling of striatum and cortex from a mouse model of Huntington's disease (Q140) crossed with Tcerg1 heterozygous mice [Tcerg1]

GSE229832 Mus musculus (mouse) RNA-seq (gene expression) 64 samples Submitted 2025/02/10 Platform GPL24247
At a glance

RNA-seq (gene expression) · mouse · 64 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
Mus musculus (mouse)
Samples
64 samples: 64 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
Distinct mismatch-repair complex genes set neuronal CAG-repeat expansion rate to drive selective pathogenesis in HD miceCell · 2025
Summary (written by the submitters)
As part of collaboration between UCLA and CHDI, UCLA is creating knockouts (KOs) of 123 genes, implicated in Huntington’s disease (HD) through various computational modeling efforts. Striatum and cortex were isolated from 6-month-old Tcerg1+/- mice crossed with the Q140 knock-in (KI) HD mice and their respective controls. Transcriptomic analysis (RNASeq) was performed on 4 genotypes: WT, Tcerg1+/-, Q140, and Q140 X Tcerg1+/-, 8 replicates per genotype.
Published in
Distinct mismatch-repair complex genes set neuronal CAG-repeat expansion rate to drive selective pathogenesis in HD mice
Wang N, Zhang S, Langfelder P et al. · Cell 2025 · PMID 39938516 · doi:10.1016/j.cell.2025.01.031
This dataset
Open GSE229832 in finder → Find more mouse 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 GSE229832_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 PRJNA956283 and SRA study SRP432880. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 39938516) ↗
Study design
64 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 64 sample titles: 64 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

+ 24 more — browse all 64 samples with per-sample file links →

Datasets on a similar topic

Search all mouse 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.