GEO series

Single-cell analyses reveal increased gene expression variability in human neurodevelopmental conditions

GSE279894 Homo sapiens (human) Single-cell RNA-seq 2 samples Submitted 2025/02/04 Platform GPL24676

At a glance

Single-cell RNA-seq · human · 2 samples

What it is: Single-cell RNA-seqGene activity measured in thousands of individual cells, so cell types can be told apart.
Organism: Homo sapiens (human)
Samples: 2 samples
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: Single-cell analyses reveal increased gene expression variability in human neurodevelopmental conditionsAmerican journal of human genetics · 2025

Summary (written by the submitters)

Interindividual variation in phenotypic penetrance and severity is found in many neurodevelopmental conditions, including the autosomal aneuploidy Trisomy 21 (T21). However, mechanisms underlying such variation remain incompletely resolved. Here, we investigate the hypothesis that T21 can drive differential gene variance, not just differential gene expression.

Read the full summary ↓

Leveraging multiple, independent single-cell and single-nucleus RNA sequencing datasets from human brain-relevant cell and tissue types, we identify a significant increase in transcriptional variance driven by T21 as well as CHD8 haploinsufficiency. Our analyses are consistent with a global and in part stochastic increase in transcriptional variance which is uncoupled from changes in transcript abundance. Highly variable genes tend to be lineage-restricted with modest enrichment for H3K27me3, while least variable genes are more likely to be constrained and associate with active histone marks. Our results show that neurodevelopmental conditions can drive increased transcriptional variance in brain cell types, and provide a scaffold for understanding variance in disease.

Published in

Single-cell analyses reveal increased gene expression variability in human neurodevelopmental conditions

Upadhya S, Klein JA, Nathanson A et al. · American journal of human genetics 2025 · PMID 40056913 · doi:10.1016/j.ajhg.2025.02.011

This dataset

Open GSE279894 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 GSE279894_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 PRJNA1175268 and SRA study SRP539653. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 40056913) ↗

Samples in this study

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+ 2 more — browse all 2 samples with per-sample file links →

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