Human cancers are complex ecosystems composed of cells with distinct phenotypes, genotypes and epigenetic states, but current models do not adequately reflect tumor composition in patients. We used single cell RNA-seq to profile 430 cells from five primary glioblastomas, which we found to be inherently variable in their expression of diverse transcriptional programs related to oncogenic signaling, proliferation, complement/immune response and hypoxia. We also observed a continuum of stemness-related expression states that enabled us to identify putative regulators of stemness in vivo. Finally, we show that established glioblastoma subtype classifiers are variably expressed across individual cells within a tumor and demonstrate the potential prognostic implications of such intratumoral heterogeneity. Thus, we reveal previously unappreciated heterogeneity in diverse regulatory programs central to glioblastoma biology, prognosis, and therapy.Tumor heterogeneity poses a major challenge to cancer diagnosis and treatment. It can manifest as variability between tumors, wherein different stages, genetic lesions or * Corresponding authors: Bernstein.bradley@mgh.harvard.edu (BEB), aregev@broadinstitute.org (AR), Suva.Mario@mgh.harvard.edu (MLS). † These authors contributed equally to this work. ‡ These authors contributed equally to this work. Glioblastoma is an archetypal example of a heterogeneous cancer and one of the most lethal human malignancies (9, 10). Intratumoral heterogeneity and redundant signaling routes likely underlie the inability of conventional and targeted therapies to achieve long-term remissions (11-13). These tumors contain cellular niches enriched for distinct phenotypic properties, including transient quiescence and self-renewal (14-16), adaptation to hypoxia (17), and resistance to radiation induced DNA damage (18,19). DNA and RNA profiles of bulk tumors have enabled genetic and transcriptional classification of glioblastomas (20,21). However, the relationships between different sources of intratumoral heterogeneitygenetic, transcriptional and functional -remain obscure.Single cell transcriptome analysis by 23) should in principle enable functional characterization from landmark genes and annotated gene sets, relate in vivo states to in vitro models, inform transcriptional classifications based on bulk tumors, and even capture genetic information for expressed transcripts. To interrogate intratumoral heterogeneity systematically, we isolated individual cells from five freshly resected and dissociated human glioblastomas and generated single cell full-length transcriptomes using SMART-seq (96-192 cells/tumor, total 672 cells; Fig. 1A). Prior to sorting, the suspension was depleted for CD45 + cells to remove inflammatory infiltrate. As a control, we also generated population (bulk) RNA-seq profiles from the CD45-depleted tumor samples. All tumors were IDH1/2 wild type primary glioblastomas ( Fig. S1) and three were EGFR amplified as determined by routine clinical tests (Table S1). We exclude...

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.