Grassin, E. | bioRxiv Science

bioRxiv2026

Glioblastoma Invasion Remodels Neural Circuits and Drives Persistent GABAergic Dysfunction in Human Brain Organoids

BackgroundGlioblastoma (GBM) is characterized by neurological dysfunction caused by tumor cells that interact with and alter neuronal circuits. However, the specific neuronal populations and molecular mechanisms most susceptible to GBM invasion remain poorly understood. MethodsWe created a human tumor-brain organoid model by combining U87 glioblastoma cells with iPSC-derived neural organoids. This system enabled us to study tumor-neural interactions over an extended period under standard temozolomide (TMZ) treatment. We used single-cell transcriptomics to monitor cell-type-specific responses. ResultsOur model recapitulated the diffuse infiltration observed in patients, leading to extensive structural remodeling and a profound loss of neuronal and glial populations. Single-cell analysis revealed that TMZ suppressed proliferative and biosynthetic programs but enriched for stress-responsive, mesenchymal-like, and therapy-adapted tumor states. Notably, GABAergic neurons exhibited the greatest transcriptional vulnerability, with [~]36% (7,499 of 20,659) of genes differentially expressed. Invasion triggered endoplasmic reticulum stress and shut down metabolic, respiratory, synaptic, and ion-homeostatic pathways. Crucially, SLC12A5-expressing GABAergic neurons plummeted from 31% to 12%, accompanied by a sharp decline in KCC2 protein expression. While TMZ partially rescued neuronal metabolic and electron transport chain function, it failed to restore SLC12A5/KCC2 expression or inhibitory signaling. ConclusionsGBM invasion leads to a continued imbalance of chloride in GABAergic networks, and this disruption remains even after undergoing tumor-targeted chemotherapy. This human iPSC-derived tumor-brain organoid platform provides a reliable and scalable system for studying complex tumor-neural interactions and exploring therapeutic approaches that aim to eliminate the tumor while preserving neural function.

cancer biology↗

bioRxiv2025

EVscope: A Comprehensive Bioinformatics Pipeline for Accurate and Robust Analysis of Total RNA Sequencing from Extracellular Vesicles

MotivationExtracellular vesicle (EV) RNA sequencing has emerged as a powerful approach for studying RNA biomarkers and intercellular communication. Nevertheless, the extremely low abundance, fragmented nature and ubiquitous tissue origin of EV RNAs, alongside potential contamination from co-isolated materials, such as free DNA and bacterial RNA, pose substantial analytical challenges. These complexities highlight a pressing need for a standardized, computational workflow that ensures robust quality control and EV RNA characterization. ResultsHere, we present EVscope, an open-source bioinformatics pipeline designed specifically for processing EV RNA-seq datasets. EVscope employs an optimized genome-wide expectation-maximization (EM) algorithm that significantly improves multi-mapping read assignment at single-base resolution by effectively leveraging alignment scores (AS) and local read coverage, specifically tailored for fragmented and low-abundance EV RNAs. Notably, EVscope uniquely generates EM-based BigWig files for downstream analysis, a capability currently unavailable in existing EM-based BigWig quantification tools. The pipeline systematically integrates 27 major steps, including quality control, analysis of library structure, contamination assessment, read alignment, read strandedness detection, UMI-based deduplication, RNA quantification, genomic DNA (gDNA) contamination correction, cellular and tissue source inference and visualization with a comprehensive HTML report. EVscope incorporates a comprehensive, updated annotation covering 19 distinct RNA biotypes, encompassing protein-coding genes, lncRNAs, miRNAs, piRNAs, retrotransposons (LINEs, SINEs, ERVs), and additional non-coding RNAs (tRNAs, rRNAs, snoRNAs). Furthermore, it leverages two highly balanced circRNA detection algorithms for robust circular RNA identification. Notably, a downstream module enables the inference of the tissue/cellular origins of EV RNAs using bulk and single-cell RNA-seq reference datasets. EVscope is implemented as a convenient, single-command Bash pipeline leveraging Conda-managed standard software packages and custom scripts, ensuring reproducibility and straightforward deployment. Availability and implementationCode, documentation, and tutorials are available at GitHub (https://github.com/TheDongLab/EVscope) and archived on Zenodo (https://zenodo.org/records/15577789).

bioinformatics↗