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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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Integrated bioinformatics analyses identifying key transcriptomes correlated with prognosis and immune infiltrations in lung squamous cell carcinoma
R. Alghamdi,Maryam H. Al-Zahrani +1 more
TL;DR: In this paper , a series of bioinformatics methods were utilized for conducting the whole study and the identification of key transcriptomic signatures can be elucidated by the crucial mechanism of lung carcinogenesis.
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ACAP1 Deficiency Predicts Inferior Immunotherapy Response in Solid Tumors
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Impact of BRAF kinase inhibitors on the miRNomes and transcriptomes of melanoma cells General subjects
TL;DR: Heinemann et al. as mentioned in this paper investigated the impact of BRAF inhibitors on miRNomes and transcriptomes, using in vitro melanoma models consisting of BRAf inhibitor-sensitive and -resistant cell lines generated in their laboratory.
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Learning transcriptional and regulatory dynamics driving cancer cell plasticity using neural ODE-based optimal transport
Alexander Tong,Manik Kuchroo,Shabarni Gupta,Aarthi Venkat,Beatriz P. San Juan,Laura Rangel,Brandon Zhu,John G. Lock,Christine L. Chaffer,Smita Krishnaswamy +9 more
TL;DR: In this paper , a neural ordinary differential equation network is proposed to learn continuous dynamics via interpolation of population flows between sampled timepoints, by running causality analysis on the output of TrajectoryNet, they compute rich and complex gene-gene networks that drive pathogenic trajectories forward.
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Mouse fetal growth restriction through parental and fetal immune gene variation and intercellular communications cascade
Gurman Kaur,Caroline B. M. Porter,Orr Ashenberg,Jack Lee,Samantha J. Riesenfeld,Matan Hofree,Maria Aggelakopoulou,Ayshwarya Subramanian,Subita Balaram Kuttikkatte,Kathrine E. Attfield,Christiane Desel,Jessica L. Davies,Hayley G. Evans,Inbal Avraham-Davidi,Lan Nguyen,Danielle Dionne,Anna Neumann,Lise T. Jensen,Thomas R Barber,Elizabeth J. Soilleux,Mary Carrington,Gil McVean,Orit Rozenblatt-Rosen,Aviv Regev,Lars Fugger +24 more
TL;DR: In this article , the interaction between the maternal KIR2DL1 and paternally inherited HLA-C*0501, expressed on fetal trophoblast cells, leads to fetal growth restriction in a humanized mouse model.
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