Dataset Information

Dissecting the multicellular ecosystem of metastatic melanoma by single-cell RNA-seq.

ABSTRACT: To explore the distinct genotypic and phenotypic states of melanoma tumors, we applied single-cell RNA sequencing (RNA-seq) to 4645 single cells isolated from 19 patients, profiling malignant, immune, stromal, and endothelial cells. Malignant cells within the same tumor displayed transcriptional heterogeneity associated with the cell cycle, spatial context, and a drug-resistance program. In particular, all tumors harbored malignant cells from two distinct transcriptional cell states, such that tumors characterized by high levels of the MITF transcription factor also contained cells with low MITF and elevated levels of the AXL kinase. Single-cell analyses suggested distinct tumor microenvironmental patterns, including cell-to-cell interactions. Analysis of tumor-infiltrating T cells revealed exhaustion programs, their connection to T cell activation and clonal expansion, and their variability across patients. Overall, we begin to unravel the cellular ecosystem of tumors and how single-cell genomics offers insights with implications for both targeted and immune therapies.

SUBMITTER: Tirosh I

PROVIDER: S-EPMC4944528 | biostudies-literature | 2016 Apr

REPOSITORIES: biostudies-literature

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Publications

Dissecting the multicellular ecosystem of metastatic melanoma by single-cell RNA-seq.

Tirosh Itay I Izar Benjamin B Prakadan Sanjay M SM Wadsworth Marc H MH Treacy Daniel D Trombetta John J JJ Rotem Asaf A Rodman Christopher C Lian Christine C Murphy George G Fallahi-Sichani Mohammad M Dutton-Regester Ken K Lin Jia-Ren JR Cohen Ofir O Shah Parin P Lu Diana D Genshaft Alex S AS Hughes Travis K TK Ziegler Carly G K CG Kazer Samuel W SW Gaillard Aleth A Kolb Kellie E KE Villani Alexandra-Chloé AC Johannessen Cory M CM Andreev Aleksandr Y AY Van Allen Eliezer M EM Bertagnolli Monica M Sorger Peter K PK Sullivan Ryan J RJ Flaherty Keith T KT Frederick Dennie T DT Jané-Valbuena Judit J Yoon Charles H CH Rozenblatt-Rosen Orit O Shalek Alex K AK Regev Aviv A Garraway Levi A LA

Science (New York, N.Y.) 20160401 6282

To explore the distinct genotypic and phenotypic states of melanoma tumors, we applied single-cell RNA sequencing (RNA-seq) to 4645 single cells isolated from 19 patients, profiling malignant, immune, stromal, and endothelial cells. Malignant cells within the same tumor displayed transcriptional heterogeneity associated with the cell cycle, spatial context, and a drug-resistance program. In particular, all tumors harbored malignant cells from two distinct transcriptional cell states, such that t ...[more]

PMID: 27124452

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Project description:Single-cell RNA sequencing (scRNA-seq) technologies have precipitated the development of bioinformatic tools to reconstruct cell lineage specification and differentiation processes with single-cell precision. However, current start-up costs and recommended data volumes for statistical analysis remain prohibitively expensive, preventing scRNA-seq technologies from becoming mainstream. Here, we introduce single-cell amalgamation by latent semantic analysis (SALSA), a versatile workflow that combines measurement reliability metrics with latent variable extraction to infer robust expression profiles from ultra-sparse sc-RNAseq data. SALSA uses a matrix focusing approach that starts by identifying facultative genes with expression levels greater than experimental measurement precision and ends with cell clustering based on a minimal set of Profiler genes, each one a putative biomarker of cluster-specific expression profiles. To benchmark how SALSA performs in experimental settings, we used the publicly available 10X Genomics PBMC 3K dataset, a pre-curated silver standard from human frozen peripheral blood comprising 2,700 single-cell barcodes, and identified 7 major cell groups matching transcriptional profiles of peripheral blood cell types and driven agnostically by < 500 Profiler genes. Finally, we demonstrate successful implementation of SALSA in a replicative scRNA-seq scenario by using previously published DropSeq data from a multi-batch mouse retina experimental design, thereby identifying 10 transcriptionally distinct cell types from > 64,000 single cells across 7 independent biological replicates based on < 630 Profiler genes. With these results, SALSA demonstrates that robust pattern detection from scRNA-seq expression matrices only requires a fraction of the accrued data, suggesting that single-cell sequencing technologies can become affordable and widespread if meant as hypothesis-generation tools to extract large-scale differential expression effects.

Dataset Information

Dissecting the multicellular ecosystem of metastatic melanoma by single-cell RNA-seq.

Publications

Dissecting the multicellular ecosystem of metastatic melanoma by single-cell RNA-seq.

Similar Datasets

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