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Differential Cellular Mechanisms Underlie Language and Executive Decline in Amyotrophic Lateral Sclerosis [snRNAseq/snATACseq multiomics]

GSE290359 Homo sapiens (human) Single-nucleus RNA-seq 96 samples Submitted 2025/03/01 Platform GPL34281
At a glance

Single-nucleus RNA-seq · human · 96 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
96 samples: 46 × CGND-HRA vs 2 × U54-HRAGroups 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 Cellular Phenotypes of Language and Executive Decline in Amyotrophic Lateral SclerosisbioRxiv : the preprint server for biology · 2025
Summary (written by the submitters)
Half of all amyotrophic lateral sclerosis (ALS) patients demonstrate a spectrum of cognitive and behavioral changes over the course of the disease, but the mechanisms underlying this heterogeneity remain unclear. We assemble a high-resolution cellular map of prefrontal cortex regions of the ALS brain by integrating spatial and single-nucleus transcriptomic profiles of a cognitively stratified ALS patient cohort that includes non-neuropathological controls.
Read the full summary ↓
We find cellular programs characteristic of ALS, including a frequent gliotic response. We also find that executive and language cognitive impairments differ from ALS without cognitive impairment, and from each other, in the extent and pattern of neuronal dysregulation and neuron-glial interactions across different brain regions. These findings reveal a relationship between cognitive phenotype and prefrontal cortex dysfunction in ALS.
Published in
Distinct Cellular Phenotypes of Language and Executive Decline in Amyotrophic Lateral Sclerosis
Petrescu J, Roque CG, Jackson CA et al. · bioRxiv : the preprint server for biology 2025 · PMID 40060539 · doi:10.1101/2025.02.26.640433
This dataset
Open GSE290359 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 GSE290359_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 PRJNA1227549 and SRA study SRP566028. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 40060539) ↗
Study design
46 × CGND-HRA vs 2 × U54-HRA

Supports a between-group comparison across 48 samples.

How we read this: 2 replicated groups read from 96 sample titles; they account for 48 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

+ 56 more — browse all 96 samples with per-sample file links →

Datasets on a similar topic

Search all human 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.