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Optimized Nuclei Isolation and snRNA-seq Reveal Oligodendrocyte Pathway Dysregulation in MOGHE Brain Tissue from Pediatric Patients.

GSE318030 Homo sapiens Expression profiling by high throughput sequencing 4 samples Submitted 2026/05/08 Platform GPL21697
Summary
Single-cell RNA sequencing (scRNA-seq) is a powerful tool for exploring cellular diversity, but isolating intact cells from complex tissues like the brain remains challenging. Single-nucleus RNA sequencing (snRNA-seq) overcomes these limitations by profiling nuclear RNA from frozen or archived tissue, reducing dissociation bias. Here, we present a simplified protocol for nuclei isolation from frozen human brain biopsies optimized for high yield and minimal debris, enabling robust snRNA-seq analysis. To validate it, we applied the protocol to biopsies from two pediatric patients with mild malformation of cortical development with oligodendroglial hyperplasia and epilepsy (MOGHE) carrying somatic SLC35A2 variants. snRNA-seq of isolated nuclei revealed a marked increase in oligodendrocytes in MOGHE samples, consistent with histopathological observations. Differential gene-expression analysis of oligodendrocyte-derived nuclei showed dysregulation of key pathways, including NOTCH, WNT/β-catenin, SLIT/ROBO, and Rho-GTPases signaling, as well as pathways associated with oxidative stress and neuroinflammation, and impaired neuron-glia communication. Despite advances in transcriptomic chemistries allowing fixed-cell analyses, reliable and debris-free nuclei isolation remains essential for generating high-quality single-nucleus data. Our streamlined protocol offers a reproducible, adaptable approach compatible with current and emerging snRNA-seq technologies.
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Direct links to NCBI, no account and no request form: the whole study as GSE318030_RAW.tar, processed values as the series matrix, the supplementary file directory, and per-sample supplementary files for any of the 4 samples. Raw sequencing reads are also available from ENA.

Also filed as BioProject PRJNA1416350. Searching any of these in the dataset finder brings you back here.

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