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Single-Cell Profiling of DNA Methylation in Autism Spectrum Disorder Prefrontal Cortex Reveals Distinct Regulatory and Aging Signatures

GSE298243 Homo sapiens (human) Single-cell RNA-seq 49 samples Submitted 2025/06/17 Platform GPL24676
At a glance

Single-cell RNA-seq · human · 49 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
49 samples: 49 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
Single-cell profiling of DNA methylation in autism spectrum disorder prefrontal cortex reveals distinct regulatory and aging signaturesCell genomics · 2026
Summary (written by the submitters)
Autism spectrum disorder (ASD) is a prevalent and heterogeneous condition that profoundly impacts health and behavior. Analysis of gene expression in the cortex broadly implicates decreased synaptic functioning in excitatory neurons and increased reactivity in glia in ASD; however, whether these phenotypes are reflected in epigenetic signatures is unknown. Here, we report on the first single-cell analysis of DNA methylation (DNAm) in ASD. We profile DNAm from over 60,000 nuclei derived from 49 donors and interpret our findings with gene expression from the same nuclei using snmCT-seq.
Published in
Single-cell profiling of DNA methylation in autism spectrum disorder prefrontal cortex reveals distinct regulatory and aging signatures
Eyring KW, Liu C, Elhajjaoui N et al. · Cell genomics 2026 · PMID 42320469 · doi:10.1016/j.xgen.2026.101278
This dataset
Open GSE298243 in finder → Find more human methylation 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 GSE298243_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 PRJNA1268576. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 42320469) ↗
Study design
49 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 49 sample titles: 49 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

+ 9 more — browse all 49 samples with per-sample file links →

Datasets on a similar topic

Search all human methylation 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.