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Heterogeneous epigenetic variation converges on splicing dysregulation in opioid addiction

GSE341485 Homo sapiens (human) RNA-seq (gene expression) 24 samples Submitted 2026/07/27 Platform GPL24676
At a glance

RNA-seq (gene expression) · human · 24 samples

What it is
RNA-seq (gene expression)Gene activity measured by sequencing. The summary does not mention single-cell work, so this is most likely bulk: one average per sample, the usual starting point for comparing groups.
Organism
Homo sapiens (human)
Samples
24 samples: 6 × iMSN C6, Scr control, vs 6 × iMSN C6, CELF5 siRNA, vs 6 × iMSN C15, Scr control, vs 6 × iMSN C15, CELF5 siRNA,Groups 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.
Summary (written by the submitters)
Disease heterogeneity presents a major challenge for genetic and epigenetic dissection of complex traits such as opioid use disorder (OUD). Here, we leverage individual heterogeneity to identify epigenetic variation linked to opioid overdose by profiling H3K27ac ChIP-seq in nucleus accumbens (NAc) from 91 overdose cases and accidental death controls.
Read the full summary ↓
Machine learning models built on combinations of peaks distinguished cases from controls with high accuracy (AUROC = 0.95) and were enriched for OUD genetic risk. We defined individual-specific H3K27ac changes as Variant Enhancer Loci (VEL) and observed four-fold greater convergence on shared target genes in NAc than in prefrontal cortex. Convergent VEL target genes were enriched in independent OUD cohorts, and RNA splicing emerged as the top-enriched ontology. Knockdown of the splicing regulator CELF5 in iPSC-derived medium spiny neurons disrupted extracellular matrix organization genes. Collectively, this study provides new insights into opioid addiction and a conceptual framework for dissecting heterogeneous disorders.
This dataset
Open GSE341485 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 GSE341485_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: For RNA-seq the counts are usually a table among the supplementary files (look for 'counts', 'expression', 'FPKM' or 'TPM' in the file name). The series matrix of a sequencing study is sometimes nearly empty.

Also filed as BioProject PRJNA1502821 and SRA study SRP721586. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗
Study design
6 × iMSN C6, Scr control, vs 6 × iMSN C6, CELF5 siRNA, vs 6 × iMSN C15, Scr control, vs 6 × iMSN C15, CELF5 siRNA,

Supports a between-group comparison across 24 samples.

How we read this: 4 replicated groups read from 24 sample titles; they account for 24 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
Datasets on a similar topic

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