FauxFlow - Single Cell Melanoma

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Patient/Sample data available for selections: Sample; Age; Sex; Mut; Pretreat; Site; Posttreat; Alive
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Important: There are a lot of zero values in these data. Some noise has been added to spread out the data and make the low values look 'flow-like'. However, these zero values are really an undefined mixture of low readings and missing data (due to low-depth rna-seq coverage and/or challenges of single cell rna library preparation). Keep this in mind, and treat low values (and their population statistics) with some skepticism.

Table of correlations and anti-correlations

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Table of populations for selected genes

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Contributors: Jim Holloway, Nathan Siemers (Bristol-Myers Squibb), and Broad Single Cell RNA-seq database (Tirosh et al.)
Tirosh I, Izar B, Prakadan SM, Wadsworth MH 2nd, Treacy D, Trombetta JJ, Rotem A, Rodman C, Lian C, Murphy G, Fallahi-Sichani M, Dutton-Regester K, Lin JR, Cohen O, Shah P, Lu D, Genshaft AS, Hughes TK, Ziegler CG, Kazer SW, Gaillard A, Kolb KE, Villani AC, Johannessen CM, Andreev AY, Van Allen EM, Bertagnolli M, Sorger PK, Sullivan RJ, Flaherty KT, Frederick DT, Jané-Valbuena J, Yoon CH, Rozenblatt-Rosen O, Shalek AK, Regev A, Garraway LA Dissecting the multicellular ecosystem of metastatic melanoma by single-cell RNA-seq. Science. 2016 Apr 8;352(6282):189-96. doi: 10.1126/science.aad0501
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.
2017-08-24



































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