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Mapping the glial transcriptome in Huntington’s disease using snRNAseq: Selective disruption of glial signatures across brain regions

GSE281069 Homo sapiens (human) Single-nucleus RNA-seq 44 samples Submitted 2024/11/09 Platform GPL24676
At a glance

Single-nucleus RNA-seq · human · 44 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).
Organism
Homo sapiens (human)
Samples
44 samples: 44 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
Mapping the glial transcriptome in Huntington's disease using snRNAseq: selective disruption of glial signatures across brain regionsActa neuropathologica communications · 2024
Summary (written by the submitters)
Huntington’s disease (HD) is an autosomal dominant neurodegenerative disease with a fatal outcome. There is accumulating evidence of a prominent role of glia in the pathology of HD, and we investigated this by conducting single nuclear RNA sequencing (snRNAseq) of human post mortem brain in four differentially affected regions; caudate nucleus, frontal cortex, hippocampus and cerebellum.
Read the full summary ↓
Across 127,205 nuclei from donors with HD and age/sex matched controls, wefound heterogeneity of glia which is altered in HD. We describe prominent changes in the abundance of certain subtypes of astrocytes, microglia, oligodendrocyte precursor cells and oligodendrocytes between HD and control samples, and these differences are widespread across brain regions. Furthermore, we highlight possible mechanisms that characterise the glial contribution to HD pathology including depletion of myelinating oligodendrocytes, an oligodendrocyte-specific upregulation of the calmodulin-dependent 3’,5’-cyclic nucleotide phosphodiesterase 1A (PDE1A) and an upregulation of molecular chaperones as a cross-glial signature and a potential adaptive response to the accumulation of mutant huntingtin (mHTT). Our results support the hypothesis that glia have an important role in the pathology of HD, and show that all types of glia are affected in the disease.
Published in
Mapping the glial transcriptome in Huntington's disease using snRNAseq: selective disruption of glial signatures across brain regions
Bøstrand SMK, Seeker LA, Bestard-Cuche N et al. · Acta neuropathologica communications 2024 · PMID 39428482 · doi:10.1186/s40478-024-01871-3
This dataset
Open GSE281069 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 GSE281069_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: 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 PRJNA1181917 and SRA study SRP543156. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 39428482) ↗
Study design
44 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 44 sample titles: 44 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

+ 4 more — browse all 44 samples with per-sample file links →

Datasets on a similar topic

Search all human RNA-seq datasets in GEO →

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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.