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Microglia contribute to polyG-dependent neurodegeneration in neuronal intranuclear inclusion disease II

GSE269183 Mus musculus (mouse) Single-nucleus RNA-seq 16 samples Submitted 2024/07/04 Platform GPL24247
At a glance

Single-nucleus RNA-seq · mouse · 16 samples

What it is
Single-nucleus RNA-seqGene activity measured in individual nuclei (used when whole cells are hard to isolate, for example from frozen tissue). Read from the summary: GEO's own type field only says 'expression profiling by sequencing', so confirm it in the paper.
Organism
Mus musculus (mouse)
Samples
16 samples: 4 × Cx-3A6 vs 4 × Cx-3A7 vs 4 × Cx-3A8 vs 4 × Cx-3A9Groups 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
Microglia contribute to polyG-dependent neurodegeneration in neuronal intranuclear inclusion diseaseActa neuropathologica · 2024
Summary (written by the submitters)
Neuronal intranuclear inclusion disease (NIID) is a neurodegenerative disease caused by GGC repeat expansion of NOTCH2NLC. Despite the identification of uN2CpolyG, a toxic polyglycine (polyG) protein, the pathogenic mechanisms of NIID remain unclear. Herein, we investigated the role of polyG by expressing wild-type NOTCH2NLC, expanded NOTCH2NLC with 100 GGC repeats (translating or not translating into uN2CpolyG), or mutated NOTCH2NLC that encodes a pure polyG without GGC-repeat RNA and C-terminal stretch (uN2CpolyG-dCT) in mice.
Read the full summary ↓
Both uN2CpolyG and uN2CpolyG-dCT induced the formation of inclusions composed by filamentous materials and caused neurodegenerative phenotypes in mice, including impaired motor and cognitive performance, shortened lifespan, and pathological lesions such as white matter lesions, microgliosis, and astrogliosis. By contrast, the sole expression of GGC-repeat RNA was non-pathogenic. Combining with bulk and single-nuclei RNA-seq, we determined the common molecular signatures associated with expressing uN2CpolyG and uN2CpolyG-dCT in the brain, especially the up-regulation of inflammation and microglia markers whereas down-regulation of immediate early genes and splicing factors. Importantly, microglia-mediated inflammation was visualized in NIID patients by positron emission tomography, which correlated with white matter atrophy levels. Moreover, microglia ablation ameliorated neurodegeneration in uN2CpolyG-expressing mice but did not alter polyG inclusions. Together, these results demonstrate that polyG is critical for the pathogenesis of NIID, and microglia contribute to polyG-induced neurodegeneration.
Published in
Microglia contribute to polyG-dependent neurodegeneration in neuronal intranuclear inclusion disease
Zhong S, Lian Y, Zhou B et al. · Acta neuropathologica 2024 · PMID 39150562 · doi:10.1007/s00401-024-02776-0
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 GSE269183_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: Single-cell studies usually ship their counts as 10x files (matrix.mtx, barcodes.tsv, features.tsv), an .h5 file, or an .rds / .h5ad object that Seurat or Scanpy can open. Look for those among the supplementary files.

Also filed as BioProject PRJNA1120470 and SRA study SRP512045. Searching any of these in the dataset finder brings you back here.

Study design
4 × Cx-3A6 vs 4 × Cx-3A7 vs 4 × Cx-3A8 vs 4 × Cx-3A9

Supports a between-group comparison across 16 samples.

How we read this: 4 replicated groups read from 16 sample titles; they account for 16 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
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