Open Access
Dissecting the multicellular ecosystem of metastatic melanoma by single-cell RNA-seq
Itay Tirosh,Benjamin Izar,Daniel J. Treacy,John J. Trombetta,Asaf Rotem,Christopher Rodman,Christine G. Lian,George F. Murphy,Mohammad Fallahi-Sichani,Ken Dutton-Regester,Jia-Ren Lin,Ofir Cohen,Parin Shah,Diana Lu,Alexandra-Chloé Villani,Aleksandr Andreev,E.M. Van Allen,Monica M. Bertagnolli,Peter K. Sorger,Ryan J. Sullivan,Keith T. Flaherty,Dennie T. Frederick,Judit Jané-Valbuena,Orit Rozenblatt-Rosen,Sanjay M. Prakadan,Marc H. Wadsworth,Alex S. Genshaft,Travis K. Hughes,Carly G. K. Ziegler,Samuel W. Kazer,Alethe Gaillard de Saint Germain,Kellie E. Kolb,Cory M. Johannessen,Clifford H. Yoon,Alex K. Shalek,Aviv Regev,Levi A. Garraway +36 more
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TLDR
Tirosh et al. as discussed by the authors applied single-cell RNA sequencing (RNA-seq) to 4645 single cells isolated from 19 patients, profiling malignant, immune, stromal, and endothelial cells.Abstract:
Single-cell expression profiles of melanoma Tumors harbor multiple cell types that are thought to play a role in the development of resistance to drug treatments. Tirosh et al. used single-cell sequencing to investigate the distribution of these differing genetic profiles within melanomas. Many cells harbored heterogeneous genetic programs that reflected two different states of genetic expression, one of which was linked to resistance development. Following drug treatment, the resistance-linked expression state was found at a much higher level. Furthermore, the environment of the melanoma cells affected their gene expression programs. Science, this issue p. 189 Melanoma cells show transcriptional heterogeneity. 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.read more
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CanSig: discovery of shared transcriptional states across cancer patients from single-cell RNA sequencing data
Josephine Yates,Florian Barkmann,Paweł Czyż,Agnieszka Kraft,Marc Glettig,Frederieke Lohmann,Elia Saquand,Richard von der Horst,Nicolas Volken,Niko Beerenwinkel,Valentina Boeva +10 more
TL;DR: CanSig as discussed by the authors automatically preprocesses, integrates, and analyzes cancer scRNA-seq data from multiple patients to provide novel signatures of shared transcriptional states and associates these states with known biological pathways.
Journal ArticleDOI
Systemic Alterations of Cancer Cells and Their Boost by Polyploidization: Unicellular Attractor (UCA) Model
TL;DR: In this article , a unicellular attractor (UCA) model integrating essential features of the atavistic reversal, cancer attractor, somatic mutation, gene chaos, and tissue organization field theories is introduced.
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Mixed model-based deconvolution of cell-state abundances (MeDuSA) along a one-dimensional trajectory
Xi-Wei Sun,Ting Qi +1 more
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A Clustering Method Unifying Cell-Type Recognition and Subtype Identification for Tumor Heterogeneity Analysis
TL;DR: A cell similarity metric that unifies cell type recognition and subtype identification (UCRSI) is proposed, with the assumption that selectively expressed genes represent differences in underlying cell type with on/off manner, while differences in expression level represent different cell subtype with more/less manner.
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Heme overdrive rewires pan-cancer cell metabolism
Swamy R. Adapa,Gregory A. Hunter,Narmin Amin,Christopher Marinescu,Andrew Borsky,Elizabeth M. Sagatys,Said M. Sebti,Gary W. Reuther,Gloria C. Ferreira,Rays H. Y. Jiang +9 more
TL;DR: Heme overdrive re-programs cellular metabolism across diverse types of cancer and is linked to oncogenic states and cellular differentiation, and a novel “bait- and-kill” strategy is devised to target this cancer metabolic vulnerability.
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