GEO series

Multimodal single-cell profiling reveals neuronal vulnerability and pathological cell states in focal cortical dysplasia

GSE268807 Homo sapiens (human) Single-cell RNA-seq 22 samples Submitted 2024/10/15 Platform GPL20795Platform GPL24676

At a glance

Single-cell RNA-seq · human · 22 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: 22 samples: 22 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: Multimodal single-cell profiling reveals neuronal vulnerability and pathological cell states in focal cortical dysplasiaiScience · 2024

Summary (written by the submitters)

Focal cortical dysplasia (FCD) is a neurodevelopmental condition characterized by malformations of the cerebral cortex that often cause drug-resistant epilepsy. In this study, we performed multi-omics single-nuclei profiling to map the chromatin accessibility and transcriptome landscapes of FCD type II, generating a comprehensive multimodal single-nuclei dataset comprising 61,525 cells from 11 clinical samples of lesions and controls.

Read the full summary ↓

Our findings revealed profound chromatin, transcriptomic, and cellular alterations affecting neuronal and glial cells in FCD lesions, including the selective loss of upper-layer excitatory neurons, significant expansion of oligodendrocytes and immature astrocytic populations, and a distinct neuronal subpopulation harboring dysmorphic neurons. Furthermore, we uncovered activated microglia subsets, particularly in FCD IIb cases. This comprehensive study unveils neuronal and glial cell states driving FCD development and epileptogenicity, enhancing our understanding of FCD and offering directions for targeted therapy development.

Published in

Multimodal single-cell profiling reveals neuronal vulnerability and pathological cell states in focal cortical dysplasia

Galvão IC, Lemoine M, Messias LA et al. · iScience 2024 · PMID 39640563 · doi:10.1016/j.isci.2024.111337

This dataset

Open GSE268807 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 GSE268807_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 PRJNA1118775 and SRA study SRP511103. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 39640563) ↗

Study design

22 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 22 sample titles: 22 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

GSM8299712 G120_N.1, GEX
GSM8299713 G120_N.1, ATAC
GSM8299714 G120_D.2, GEX
GSM8299715 G120_D.2, ATAC
GSM8299716 G120_D.3, GEX
GSM8299717 G120_D.3_ATAC
GSM8299718 G133_N.2, GEX
GSM8299719 G133_N.2, ATAC
GSM8299720 G133_D.2, GEX
GSM8299721 G133_D.2, ATAC
GSM8299722 G187_D, GEX
GSM8299723 G187_D, ATAC
GSM8299724 G150_D, GEX
GSM8299725 G150_D, ATAC
GSM8299726 G171_D, GEX
GSM8299727 G171_D, ATAC
GSM8299728 G159_D, GEX
GSM8299729 G159_D, ATAC
GSM8299730 G210_D, GEX
GSM8299731 G210_D, ATAC
GSM8299732 G129_D, GEX
GSM8299733 G129_D, ATAC

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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.