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Multi-OMIC analysis of Huntington disease reveals a neuroprotective astrocyte state (snRNA-Seq)

GSE242197 Homo sapiens (human) Single-nucleus RNA-seq 65 samples Submitted 2024/06/13 Platform GPL24676
At a glance

Single-nucleus RNA-seq · human · 65 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
65 samples: 34 × OFGroups 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
Multi-omic analysis of Huntington's disease reveals a compensatory astrocyte stateNature communications · 2024
Summary (written by the submitters)
Huntington disease (HD) is an incurable neurodegenerative disease that is characterized by neuronal loss and astrogliosis. One hallmark of HD is the selective neuronal vulnerability of striatal medium spiny neurons and to date, the underlying mechanisms of this selective vulnerability have not been fully defined. Here, we employed a multi-omic approach including single nucleus RNAseq (snRNAseq), bulk RNAseq, lipidomics, CAG repeat length measurements, and multiplexed immunofluorescence on post-mortem brain tissue from multiple brain regions of HD and control donors.
Read the full summary ↓
We defined a signature of genes that is driven by CAG repeat length and found it enriched in astrocytic and microglial genes. Moreover, weighted gene correlation network analysis showed loss of connectivity of astrocytic and microglial modules in HD and identified modules that correlated with CAG-repeat length which further implicated inflammatory pathways and metabolism. We performed lipidomic analysis of HD and control brains and identified several lipid species that correlate with HD grade, including ceramides and very long chain fatty acids. Integration of lipidomics and bulk transcriptomics identified a consensus gene signature that correlates with HD grade and HD lipidomic abnormalities and implicated the unfolded protein response pathway. Because astrocytes are critical for brain lipid metabolism and play important roles in regulating inflammation, we analyzed our snRNAseq dataset with an emphasis on astrocyte pathology. We found two main astrocyte types that spanned multiple brain regions; these types correspond to protoplasmic astrocytes, and fibrous-like, CD44-positive astrocytes. HD pathology was differentially associated with these cell types in a region-specific manner. One protoplasmic astrocyte cluster showed high expression of metallothionein genes, the depletion of which positively correlated with the depletion of vulnerable medium spiny neuronal clusters in the caudate nucleus. We confirmed that metallothioneins were increased in cingulate HD astrocytes but were unchanged or even decreased in caudate astrocytes. We combined existing genome-wide association studies (GWAS) with a GWA study conducted on HD patients from the original Venezuelan cohort and identified a single-nucleotide polymorphism in the metallothionein gene locus associated with delayed age of onset. Functional studies found that metallothionein overexpressing astrocytes are better able to buffer glutamate and were neuroprotective of induced pluripotent stem cells-derived HD MSNs as well as against rotenone-induced neuronal death in vitro. Finally, we found that metallothionein- overexpressing astrocytes increased the phagocytic activity of microglia in vitro and increased the expression of genes involved in fatty acid binding. Together, we identified an astrocytic phenotype that is regionally-enriched in less vulnerable brain regions that can be leveraged to protect neurons in HD.
Published in
Multi-omic analysis of Huntington's disease reveals a compensatory astrocyte state
Paryani F, Kwon JS, Ng CW et al. · Nature communications 2024 · PMID 39112488 · doi:10.1038/s41467-024-50626-0
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 GSE242197_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 PRJNA1011955 and SRA study SRP458079. Searching any of these in the dataset finder brings you back here.

Study design
34 × OF

Supports a between-group comparison across 34 samples.

How we read this: 1 replicated groups read from 65 sample titles; they account for 34 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

+ 25 more — browse all 65 samples with per-sample file links →

Datasets on a similar topic

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