Diverse genetic, epigenetic, and developmental programs drive glioblastoma, an incurable and poorly understood tumor, but their precise characterization remains challenging. Here, we use an integrative approach spanning single-cell RNA-sequencing of 28 tumors, bulk genetic and expression analysis of 401 specimens from the The Cancer Genome Atlas (TCGA), functional approaches, and single-cell lineage tracing to derive a unified model of cellular states and genetic diversity in glioblastoma. We find that malignant cells in glioblastoma exist in four main cellular states that recapitulate distinct neural cell types, are influenced by the tumor microenvironment, and exhibit plasticity. The relative frequency of cells in each state varies between glioblastoma samples and is influenced by copy number amplifications of the CDK4, EGFR, and PDGFRA loci and by mutations in the NF1 locus, which each favor a defined state. Our work provides a blueprint for glioblastoma, integrating the malignant cell programs, their plasticity, and their modulation by genetic drivers.

doi: medRxiv preprint NOTE: This preprint reports new research that has not been certified by peer review and should not be used to guide clinical practice.

The last decade has seen a rapid expansion of interest in extracellular vesicles (EVs) released by cells and proposed to mediate intercellular communication in physiological and pathological conditions. Considering that the genetic content of EVs reflects that of their respective parent cell, many researchers have proposed EVs as a source of biomarkers in various diseases. So far, the question of heterogeneity in given EV samples is rarely addressed at the experimental level. Because of their relatively small size, EVs are difficult to reliably isolate and detect within a given sample. Consequently, standardized protocols that have been optimized for accurate characterization of EVs are lacking despite recent advancements in the field. Continuous improvements in pre-analytical parameters permit more efficient assessment of EVs, however, methods to more objectively distinguish EVs from background, and to interpret multiple single-EV parameters are lacking. Here, we review EV heterogeneity according to their origin, mode of release, membrane composition, organelle and biochemical content, and other factors. In doing so, we also provide an overview of currently available and potentially applicable methods for single EV analysis. Finally, we examine the latest findings from experiments that have analyzed the issue at the single EV level and discuss potential implications.