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Optimized culturing yields high success rates and preserves molecular heterogeneity, enabling personalized screening for high-grade gliomas [scRNA-seq]

GSE248543 Homo sapiens Expression profiling by high throughput sequencing 10 samples 2025/05/27 GPL18573
Summary
Robust and reliable and in vitro models are essential in the discovery of new treatment options for high-grade glioma (HGG), however, establishing successful patient-derived cell cultures has posed significant challenges. We established glioma stem-like cell (GSC) cultures from 114 consecutive HGG specimens, obtained via traditional surgical resection and/or ultrasonic aspiration, using fully-dissociated single cells (single cell-derived, SCD) and partially dissociated tissue fragments (3D-derived, 3DD). Copy number profiling assessed genetic similarities, while single-cell RNA sequencing evaluated transcriptomic heterogeneity. Proof-of-concept personalized drug screening was performed using a panel of approved anti-cancer agents. Higher success rates in establishing GSC cultures were obtained from ultrasonic aspirates (SCD and 3DD) and 3DD surgical samples compared to SCD cultures from surgical samples. Combining these approaches in parallel yielded a 96% success rate. In rare cases, copy number variations in original tumor tissue were lost in culture, leading to discernible changes in cell morphology. Single-cell sequencing revealed greater heterogeneity of transcriptomic clusters in ultrasonic aspiration-derived cultures compared to resection-derived cultures. Our study's protocol supported the screening of 20 anti-cancer agents within a clinically-relevant timeframe in 16 of 18 consecutive HGG samples. Our optimized protocol therefore provides a reliable tool for generating HGG cell cultures which capture the tumors’ molecular characteristics and supports precision medicine applications.
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NCBI GEO page ↗ Paper (PMID 40425813) ↗ {# Names what the click gives you. "Open in finder" meant nothing to a visitor who arrived from a search engine and has never seen the tool. #} Find more human RNA-seq datasets →
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