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Functional divergence of Capicua isoforms explains differential tissue vulnerability in neurological disease

GSE310453 Mus musculus (mouse) RNA-seq (gene expression) 70 samples Submitted 2026/07/29 Platform GPL34328
At a glance

RNA-seq (gene expression) · mouse · 70 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
70 samples: 9 conditions, mostly with about 8 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
Functional divergence of Capicua isoforms explains differential tissue vulnerability in neurological diseaseGenes & development · 2026
Summary (written by the submitters)
Many neurological diseases attack specific brain regions despite widespread expression of the disease-related protein. Spinocerebellar ataxia type 1 (SCA1) primarily affects the cerebellum, though Ataxin-1 (ATXN1) is widely expressed. We previously showed that intensified interaction between mutant ATXN1 and Capicua (CIC) drives SCA1 pathogenesis in the cerebellum, whereas ATXN1 loss augments amyloid beta production in the hippocampus and cortex.
Read the full summary ↓
CIC, however, forms a complex with ATXN1 and its paralog, Ataxin-1-like (ATXN1L)—yet knockout of either yields completely different phenotypes. To determine whether this could be due to CIC having two isoforms, we generated mice bearing either the long (CIC-L) or short (CIC-S) isoform. Loss of CIC-L led to cognitive deficits, whereas loss of CIC-S caused perinatal lethality, phenocopying, ATXN1 and ATXN1L knockout mice, respectively. Furthermore, CIC-L preferentially interacts with ATXN1, and CIC-S with ATXN1L. Our data underscore the importance of isoform–paralog interplay in studying regional vulnerability in neurodegenerative diseases.
Published in
Functional divergence of Capicua isoforms explains differential tissue vulnerability in neurological disease
Lee H, Gonzalez EV, Rivera EM et al. · Genes & development 2026 · PMID 42476817 · doi:10.1101/gad.353596.125
This dataset
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.

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 GSE310453_RAW.tar (it can run to tens of gigabytes).

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.

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 PRJNA1366358 and SRA study SRP646822. Searching any of these in the dataset finder brings you back here.

Study design
9 conditions, mostly with about 8 replicates each
Lung, P6, WT, ×8 Lung, P6, Cic-S-KO, ×8 Lung, P6, Cic-L-KO, ×8 Cortex, 12-week-old, WT, ×8 Cortex, 12-week-old, Cic-L-KO, ×8 Cerebellum, 12-week-old, WT, ×8 Cerebellum, 12-week-old, Cic-L-KO, ×8 Cortex, 12-week-old, Cic-S-KO, ×7 +1 more

Supports a between-group comparison across 70 samples.

How we read this: 9 replicated groups read from 70 sample titles; they account for 70 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

+ 30 more — browse all 70 samples with per-sample file links →

Datasets on a similar topic

Search all mouse RNA-seq datasets in GEO →

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