GEO series

High-Resolution Molecular Profiling of Epileptic Brain Activity via Explanted Depth Electrodes [RNA-seq]

GSE268714 Homo sapiens (human) RNA-seq (gene expression) 19 samples Submitted 2025/01/15 Platform GPL24676

At a glance

RNA-seq (gene expression) · human · 19 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: 19 samples: 19 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: High-resolution multimodal profiling of human epileptic brain activity via explanted depth electrodesJCI insight · 2025

Summary (written by the submitters)

Understanding neurological disorders necessitates systems-level approaches that integrate multimodal data, but progress has been hindered by limited sample availability, and the absence of combined electrophysiological and molecular data from live patients. Here, we demonstrate that stereo electroencephalography (sEEG)—stereologically-implanted electrodes routinely used for monitoring epilepsy—enables the integration of RNA sequencing and epigenome maps with in vivo recordings and brain imaging.

Read the full summary ↓

Here we report a method MoPEDE (Molecular Profiling of Epileptic Brain Activity via Explanted Depth Electrodes) that enables this by recovering extensive protein-coding transcripts and DNA methylation profiles from explanted depth electrodes containing matched electrophysical and radiological data, hence allowing for high-resolution reconstructions of brain structure and function in patients. Our proof-of-concept study shows that epilepsies of different etiologies have distinct molecular landscapes and identifies transcripts correlating with neurophysiological signals, including immediate early genes, inflammation markers, and axon guidance molecules. Additionally, we identify DNA methylation profiles indicative of transcriptionally permissive or restrictive chromatin states. While gene expression gradients corresponded with the assigned epileptogenicity index, we found outlier molecular fingerprints in some electrodes, potentially indicating seizure spread or generation zones not detected during clinical assessments. These findings validate that RNA profiles and genome-wide epigenetic data from explanted sEEGs offer high-resolution surrogate molecular landscapes of brain activity. This revolutionary approach has the potential to enhance diagnostic decisions and deepen our understanding of epileptogenic processes.

Published in

High-resolution multimodal profiling of human epileptic brain activity via explanted depth electrodes

Dwivedi AK, Mahesh A, Sanfeliu A et al. · JCI insight 2025 · PMID 39541170 · doi:10.1172/jci.insight.184518

This dataset

Open GSE268714 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 GSE268714_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 PRJNA1118317 and SRA study SRP510893. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 39541170) ↗

Study design

19 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 19 sample titles: 19 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

GSM8297369 GFB-15548-Cold-HIPP
GSM8297370 GFB-15549-Cold-MTG
GSM8297371 GFB-15550-Cold-CING
GSM8297372 GFB-15551-Onset-SMA
GSM8297373 GFB-15552-Onset-MTG
GSM8297374 GFB-15553-Onset-HIPP
GSM8297375 GFB-15554-Spread-MTG
GSM8297376 GFB-15555-Spread-ITG
GSM8297377 GFB-15556-Cold-INS
GSM8297378 GFB-15557-Cold-SFG
GSM8297379 GFB-15558-Cold-ORB
GSM8297380 GFB-15559-Onset-TP
GSM8297381 GFB-15560-Onset-HIPP
GSM8297382 GFB-15561-Cold-MTG
GSM8297383 GFB-15562-Spread-CING
GSM8297384 GFB-15563-Cold-SFG
GSM8297385 GFB-15564-Cold-MFG
GSM8297386 GFB-15565-Cold-CING
GSM8297387 GFB-15566-Cold-OP

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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.