GEO series

In vitro butyrate modifies epigenetic and immune pathways in peripheral mononuclear cells from children with neurodevelopmental disorders associated with chromatin dysregulation

GSE296038 Homo sapiens (human) Single-cell RNA-seq 12 samples Submitted 2026/03/18 Platform GPL34281

At a glance

Single-cell RNA-seq · human · 12 samples

What it is: Single-cell RNA-seqGene activity measured in thousands of individual cells, so cell types can be told apart. Read from the summary: GEO's own type field only says 'expression profiling by sequencing', so confirm it in the paper.
Organism: Homo sapiens (human)
Samples: 12 samples: 12 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: Butyrate modifies epigenetic and immune pathways in peripheral mononuclear cells from children with neurodevelopmental disorders associated with chromatin dysregulationNeurotherapeutics : the journal of the American Society for Experimental NeuroTherapeutics · 2026

Summary (written by the submitters)

Pathogenic DNA variants in chromatin-related genes cause an important minority of neurodevelopmental disorders (NDDs). Epigenetic mechanisms, including chromatin regulation, are associated with the etiopathogenesis of NDDs. Therapeutic strategies targeting chromatin dysregulation, such as histone deacetylase inhibition with butyrate, show promise; however, its effects remain poorly understood.

Read the full summary ↓

We performed single-cell RNA sequencing (scRNA seq) on 101,539 peripheral immune cells from four children with functionally impairing NDDs (median age 11.2 years, IQR = 3.5 years; 3 females): three with de novo pathogenic variants in chromatin-related genes (KMT2D, CHD7, and MECP2), and one without a monogenic diagnosis (non-monogenic), compared with two sex-matched healthy controls (median age 12.5 years, IQR = 0.94 years; 1 female). Patient cells also underwent scRNA seq sequencing after in vitro butyrate treatment. Untreated patient cells showed dysregulation of ribosomal and immune pathways compared to controls. Specifically, KMT2D, CHD7, and the non-monogenic patients exhibited downregulation of ribosomal pathways, and upregulation of immune pathways. In contrast, the MECP2 patient displayed upregulation of ribosomal pathways and mixed regulation of immune pathways. Butyrate treatment largely reversed these pathways, normalizing ribosomal and immune pathways in KMT2D, CHD7, and non-monogenic patient cells, with partial effects in MECP2. Overall, butyrate induced up-regulation of ribosome, GTPase, cytoskeletal, mitochondrial pathways, and down-regulation of epigenetic (histone, transcription factors) and immune pathways. We propose that chromatin dysregulation is associated with a common RNA signature involving ribosomal pathways and immune dysregulation. Butyrate is a potential modulator of epigenetic and immune dysregulation in NDDs associated with chromatin dysregulation.

Published in

Butyrate modifies epigenetic and immune pathways in peripheral mononuclear cells from children with neurodevelopmental disorders associated with chromatin dysregulation

Hayes JP, Han VX, Keating BA et al. · Neurotherapeutics : the journal of the American Society for Experimental NeuroTherapeutics 2026 · PMID 41260988 · doi:10.1016/j.neurot.2025.e00792

This dataset

Open GSE296038 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 GSE296038_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 PRJNA1257368 and SRA study SRP582492. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 41260988) ↗

Study design

12 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 12 sample titles: 12 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

GSM8963832 control_monogenic_butyrate
GSM8963833 control_monogenic_media
GSM8963834 nonmonogenic_butyrate
GSM8963835 nonmonogenic_media
GSM8963836 KMT2D_butyrate
GSM8963837 KMT2D_media
GSM8963838 CHD7_butyrate
GSM8963839 CHD7_media
GSM8963840 control_nonmonogenic_butyrate
GSM8963841 control_nonmonogenic_media
GSM8963842 MECP2_butyrate
GSM8963843 MECP2_media

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