Single-cell RNA sequencing reveals intratumoral heterogeneity in primary glioblastomas. This study profiles 430 cells from five primary glioblastomas using single-cell RNA-seq, revealing inherent variability in the expression of diverse transcriptional programs related to oncogenic signaling, proliferation, complement/immune response, and hypoxia. A continuum of stemness-related expression states was identified, enabling the discovery of potential regulators of stemness in vivo. The study also shows that established glioblastoma subtype classifiers are variably expressed across individual cells within a tumor, highlighting the prognostic implications of intratumoral heterogeneity. The findings 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, where different stages, genetic lesions, or expression programs are associated with distinct outcomes or therapeutic responses. Alternatively, cells from the same tumor may harbor different mutations or exhibit distinct phenotypic or epigenetic states. Such intratumoral heterogeneity is increasingly appreciated as a determinant of treatment failure and disease recurrence. Glioblastoma is an archetypal example of a heterogeneous cancer and one of the most lethal human malignancies. Intratumoral heterogeneity and redundant signaling routes likely underlie the inability of conventional and targeted therapies to achieve long-term remissions. These tumors contain cellular niches enriched for distinct phenotypic properties, including transient quiescence and self-renewal, adaptation to hypoxia, and resistance to radiation-induced DNA damage. DNA and RNA profiles of bulk tumors have enabled genetic and transcriptional classification of glioblastomas. However, the relationships between different sources of intratumoral heterogeneity—genetic, transcriptional, and functional—remain obscure. Single-cell transcriptome analysis by RNA-seq should 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 systematically interrogate intratumoral heterogeneity, individual cells from five freshly resected and dissociated human glioblastomas were isolated and single-cell full-length transcriptomes were generated using SMART-seq. Prior to sorting, the suspension was depleted for CD45+ cells to remove inflammatory infiltrate. As a control, population (bulk) RNA-seq profiles from the CD45-depleted tumor samples were also generated. All tumors were IDH1/2 wild type primary glioblastomas and three were EGFR amplified as determined by routine clinical tests. Genes and cells with low coverage were excluded, retaining ~6,000 genes quantified in 430 cells from five patient tumors and population controls. The population level controls correlated with the average of the single cells in that tumor, supporting the accuracy of the single cell data.Single-cell RNA sequencing reveals intratumoral heterogeneity in primary glioblastomas. This study profiles 430 cells from five primary glioblastomas using single-cell RNA-seq, revealing inherent variability in the expression of diverse transcriptional programs related to oncogenic signaling, proliferation, complement/immune response, and hypoxia. A continuum of stemness-related expression states was identified, enabling the discovery of potential regulators of stemness in vivo. The study also shows that established glioblastoma subtype classifiers are variably expressed across individual cells within a tumor, highlighting the prognostic implications of intratumoral heterogeneity. The findings 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, where different stages, genetic lesions, or expression programs are associated with distinct outcomes or therapeutic responses. Alternatively, cells from the same tumor may harbor different mutations or exhibit distinct phenotypic or epigenetic states. Such intratumoral heterogeneity is increasingly appreciated as a determinant of treatment failure and disease recurrence. Glioblastoma is an archetypal example of a heterogeneous cancer and one of the most lethal human malignancies. Intratumoral heterogeneity and redundant signaling routes likely underlie the inability of conventional and targeted therapies to achieve long-term remissions. These tumors contain cellular niches enriched for distinct phenotypic properties, including transient quiescence and self-renewal, adaptation to hypoxia, and resistance to radiation-induced DNA damage. DNA and RNA profiles of bulk tumors have enabled genetic and transcriptional classification of glioblastomas. However, the relationships between different sources of intratumoral heterogeneity—genetic, transcriptional, and functional—remain obscure. Single-cell transcriptome analysis by RNA-seq should 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 systematically interrogate intratumoral heterogeneity, individual cells from five freshly resected and dissociated human glioblastomas were isolated and single-cell full-length transcriptomes were generated using SMART-seq. Prior to sorting, the suspension was depleted for CD45+ cells to remove inflammatory infiltrate. As a control, population (bulk) RNA-seq profiles from the CD45-depleted tumor samples were also generated. All tumors were IDH1/2 wild type primary glioblastomas and three were EGFR amplified as determined by routine clinical tests. Genes and cells with low coverage were excluded, retaining ~6,000 genes quantified in 430 cells from five patient tumors and population controls. The population level controls correlated with the average of the single cells in that tumor, supporting the accuracy of the single cell data.