TitleSingle-cell RNA-seq highlights intratumoral heterogeneity in primary glioblastoma
Data descriptionSingle-cell RNA-seq of 430 single glioblastoma cells isolated from 5 individual tumors and 102 single cells from gliomasphere cells lines.
Doi10.1126/science.1254257
Web-link of the paperhttp://science.sciencemag.org/content/344/6190/1396
Data typescRNA-seq
DatabaseGene Expression Omnibus (GEO)
Accession numberGSE57872
URL of the datahttps://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE57872
SpeciesHuman
TissueBrain
Cell typePrimary glioblastomas cells
GBM6 and GBM8 cell lines
Number of cells430
102
Cell capture platformFlow cytometry
Library preparation protocolSMART-Seq
Unique molecular identifier (Y/N)N
Spike-in (Y/N)N
Full-length (Y/N)Y
Brief summary of the scientific questionHuman 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. Use single-cell RNA sequencing to profile cells from primary glioblastomas. Reveal previously unappreciated heterogeneity in diverse regulatory programs central to glioblastoma biology, prognosis, and therapy.
Brief summary of the bioinformatics processingClustering of CNV profiles inferred from RNA-seq data. Heatmap of CNV signal normalized against the “normal” cluster. Multidimensional scaling. RNA-seq read densities over surface receptor genes are depicted for individual cells. Gene sets that vary coherently between cells in specific tumors or across the global data set were identified by principal component analysis or clustering. Hierarchical clustering of gene sets. Heatmap shows expression of the cell cycle meta-signature.
CitationPatel, A., Tirosh, I., Trombetta, J., Shalek, A., Gillespie, S., & Wakimoto, H. et al. (2014). Single-cell RNA-seq highlights intratumoral heterogeneity in primary glioblastoma. Science, 344(6190), 1396-1401.
Web-link of the paperhttp://science.sciencemag.org/content/344/6190/1396