Glioblastoma intra-tumor heterogeneity
Anoop P. Patel, Itay Tirosh, John J. Trombetta, Alex K. Shalek, Shawn M. Gillespie, Hiroaki Wakimoto, Daniel P. Cahill, Brian V. Nahed, William T. Curry, Robert L. Martuza, David N. Louis, Orit Rozenblatt-Rosen, Mario L. Suvà, Aviv Regev, and Bradley E. Bernstein Single-cell RNA-seq highlights intratumoral heterogeneity in primary glioblastoma Science. 2014 Jun 20; 344(6190): 1396–1401. doi: 10.1126/science.1254257
Contact person: Itay Tirosh, tirosh.itay@gmail.com
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.
(A) Workflow depicts rapid dissociation and isolation of glioblastoma cells from primary tumors for generating single cell and bulk RNA-seq profiles and deriving glioblastoma culture models. (B) Clustering of copy number variation (CNV) profiles inferred from RNA-seq data for all single cells and a normal brain sample. Clusters (dendrogram) primarily reflect tumor-specific CNV (colored bar coded as in panel D). Topmost cluster (red, arrow) contains the normal brain sample and 10 single cells, nine of which correlate with normal oligodendrocyte expression profiles and one with normal monocytes (‘Oligo’ and ‘Mono’, black and white heatmap). (C) Heatmap of CNV signal normalized against the ‘normal’ cluster defined in (B) shows CNV changes by chromosome (columns) for individual cells (rows). All cells outside the normal cluster exhibit chromosome 7 gain (red) and chromosome 10 loss (blue), which are characteristic of glioblastoma. (D)Multidimensional scaling illustrates the relative similarity between all 430 single cells and population controls. The distance between any two cells reflects the similarity of their expression profiles. Cells group by tumor (color code), but each tumor also contains outliers that are more similar to cells in other tumors. (E) RNA-seq read densities (vertical scale of 10) over surface receptor genes are depicted for individual cells (rows) from MGH30. Cell-to-cell variability suggests a mosaic pattern of receptor expression, in contrast to constitutively expressed GAPDH.
