Search

Glioblastomas harbor diverse cell populations, including rare glioblastoma stem cells (GSCs) that drive tumorigenesis. To characterize functional diversity within this population, we performed single-cell RNA sequencing on >69,000 GSCs cultured from the tumors of 26 patients. We observed a high degree of inter- and intra-GSC transcriptional heterogeneity that could not be fully explained by DNA somatic alterations. Instead, we found that GSCs mapped along a transcriptional gradient spanning two cellular states reminiscent of normal neural development and inflammatory wound response. Genome-wide CRISPR–Cas9 dropout screens independently recapitulated this observation, with each state characterized by unique essential genes. Further single-cell RNA sequencing of >56,000 malignant cells from primary tumors found that the majority organize along an orthogonal astrocyte maturation gradient yet retain expression of founder GSC transcriptional programs. We propose that glioblastomas grow out of a fundamental GSC-based neural wound response transcriptional program, which is a promising target for new therapy development. Pugh and colleagues use single-cell RNA sequencing, CRISPR screens and functional assays to define a gradient of developmental and wound-response cell states in glioblastoma stem cells, revealing insights into glioblastoma origins and potential therapeutic targets.

Tumours are routinely profiled with single-cell RNA sequencing (scRNA-seq) to characterize their diverse cellular ecosystems of malignant, immune, and stromal cell types. When combining data from multiple samples or studies, batch-specific technical variation can confound biological signals. However, scRNA-seq batch integration methods are often not designed for, or benchmarked, on datasets containing cancer cells. Here, we compare 5 data integration tools applied to 171,206 cells from 5 tumour scRNA-seq datasets. Based on our results, STACAS and fastMNN are the most suitable methods for integrating tumour datasets, demonstrating robust batch effect correction while preserving relevant biological variability in the malignant compartment. This comparison provides a framework for evaluating how well single-cell integration methods correct for technical variability while preserving biological heterogeneity of malignant and non-malignant cell populations.