Modeling glioblastoma invasion using human brain organoids and single-cell transcriptomics. Issue 8 (16th April 2020)

• Record Type: Journal Article
• Authors: Krieger, Teresa G; Tirier, Stephan M; Park, Jeongbin; Jechow, Katharina; Eisemann, Tanja; Peterziel, Heike; Angel, Peter; Eils, Roland; Conrad, Christian
• Is Part Of: Neuro-oncology. Volume 22:Issue 8(2020)
• Page Start: 1138
• Page End: 1149
• Publication Date: 2020-04-16
• Subjects: glioblastoma -- organoids -- single-cell RNA sequencing -- tumor cell heterogeneity -- tumor invasion; Brain Neoplasms -- Periodicals; Brain -- Tumors -- Periodicals; Brain -- Cancer -- Periodicals; Nervous system -- Cancer -- Periodicals; 616.99481
• DOI: 10.1093/neuonc/noaa091
• Languages: English
• ISSNs: 1522-8517

Abstract

Abstract: Background: Glioblastoma (GBM) consists of devastating neoplasms with high invasive capacity, which have been difficult to study in vitro in a human-derived model system. Therapeutic progress is also limited by cellular heterogeneity within and between tumors, among other factors such as therapy resistance. To address these challenges, we present an experimental model using human cerebral organoids as a scaffold for patient-derived GBM cell invasion. Methods: This study combined tissue clearing and confocal microscopy with single-cell RNA sequencing of GBM cells before and after co-culture with organoid cells. Results: We show that tumor cells within organoids extend a network of long microtubes, recapitulating the in vivo behavior of GBM. Transcriptional changes implicated in the invasion process are coherent across patient samples, indicating that GBM cells reactively upregulate genes required for their dispersion. Potential interactions between GBM and organoid cells identified by an in silico receptor–ligand pairing screen suggest functional therapeutic targets. Conclusions: Taken together, our model has proven useful for studying GBM invasion and transcriptional heterogeneity in vitro, with applications for both pharmacological screens and patient-specific treatment selection on a time scale amenable to clinical practice.