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      Spatially resolved transcriptomics reveals the architecture of the tumor-microenvironment interface

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          Abstract

          During tumor progression, cancer cells come into contact with various non-tumor cell types, but it is unclear how tumors adapt to these new environments. Here, we integrate spatially resolved transcriptomics, single-cell RNA-seq, and single-nucleus RNA-seq to characterize tumor-microenvironment interactions at the tumor boundary. Using a zebrafish model of melanoma, we identify a distinct “interface” cell state where the tumor contacts neighboring tissues. This interface is composed of specialized tumor and microenvironment cells that upregulate a common set of cilia genes, and cilia proteins are enriched only where the tumor contacts the microenvironment. Cilia gene expression is regulated by ETS-family transcription factors, which normally act to suppress cilia genes outside of the interface. A cilia-enriched interface is conserved in human patient samples, suggesting it is a conserved feature of human melanoma. Our results demonstrate the power of spatially resolved transcriptomics in uncovering mechanisms that allow tumors to adapt to new environments.

          Abstract

          During tumor progression, cancer cells contact different neighboring cell types, but it is unclear how these interactions affect cancer cell behavior. Here, the authors use spatially resolved transcriptomics and single-cell RNA-seq to study the role of cilia at the tumormicroenvironment interface.

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          Most cited references76

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          Gene Ontology: tool for the unification of biology

          Genomic sequencing has made it clear that a large fraction of the genes specifying the core biological functions are shared by all eukaryotes. Knowledge of the biological role of such shared proteins in one organism can often be transferred to other organisms. The goal of the Gene Ontology Consortium is to produce a dynamic, controlled vocabulary that can be applied to all eukaryotes even as knowledge of gene and protein roles in cells is accumulating and changing. To this end, three independent ontologies accessible on the World-Wide Web (http://www.geneontology.org) are being constructed: biological process, molecular function and cellular component.
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            Gene set enrichment analysis: A knowledge-based approach for interpreting genome-wide expression profiles

            Although genomewide RNA expression analysis has become a routine tool in biomedical research, extracting biological insight from such information remains a major challenge. Here, we describe a powerful analytical method called Gene Set Enrichment Analysis (GSEA) for interpreting gene expression data. The method derives its power by focusing on gene sets, that is, groups of genes that share common biological function, chromosomal location, or regulation. We demonstrate how GSEA yields insights into several cancer-related data sets, including leukemia and lung cancer. Notably, where single-gene analysis finds little similarity between two independent studies of patient survival in lung cancer, GSEA reveals many biological pathways in common. The GSEA method is embodied in a freely available software package, together with an initial database of 1,325 biologically defined gene sets.
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              Comprehensive Integration of Single-Cell Data

              Single-cell transcriptomics has transformed our ability to characterize cell states, but deep biological understanding requires more than a taxonomic listing of clusters. As new methods arise to measure distinct cellular modalities, a key analytical challenge is to integrate these datasets to better understand cellular identity and function. Here, we develop a strategy to "anchor" diverse datasets together, enabling us to integrate single-cell measurements not only across scRNA-seq technologies, but also across different modalities. After demonstrating improvement over existing methods for integrating scRNA-seq data, we anchor scRNA-seq experiments with scATAC-seq to explore chromatin differences in closely related interneuron subsets and project protein expression measurements onto a bone marrow atlas to characterize lymphocyte populations. Lastly, we harmonize in situ gene expression and scRNA-seq datasets, allowing transcriptome-wide imputation of spatial gene expression patterns. Our work presents a strategy for the assembly of harmonized references and transfer of information across datasets.
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                Author and article information

                Contributors
                itai.yanai@nyulangone.org
                whiter@mskcc.org
                Journal
                Nat Commun
                Nat Commun
                Nature Communications
                Nature Publishing Group UK (London )
                2041-1723
                1 November 2021
                1 November 2021
                2021
                : 12
                : 6278
                Affiliations
                [1 ]GRID grid.51462.34, ISNI 0000 0001 2171 9952, Cancer Biology and Genetics, , Memorial Sloan Kettering Cancer Center, ; New York, NY USA
                [2 ]GRID grid.240324.3, ISNI 0000 0001 2109 4251, Institute for Computational Medicine, , NYU Langone Health, ; New York, NY USA
                [3 ]GRID grid.51462.34, ISNI 0000 0001 2171 9952, Weill Cornell/Rockefeller/Sloan Kettering Tri-Institutional MD-PhD Program, , Memorial Sloan Kettering Cancer Center, ; New York, NY USA
                Author information
                http://orcid.org/0000-0002-6971-1738
                http://orcid.org/0000-0001-9099-9169
                Article
                26614
                10.1038/s41467-021-26614-z
                8560802
                34725363
                ddf04e1a-0242-4783-800e-4a525f16728e
                © The Author(s) 2021

                Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.

                History
                : 7 December 2020
                : 6 October 2021
                Funding
                Funded by: FundRef https://doi.org/10.13039/100000002, U.S. Department of Health & Human Services | National Institutes of Health (NIH);
                Award ID: R01CA229215
                Award ID: R01CA238317
                Award ID: DP2CA186572
                Award Recipient :
                Funded by: U.S. Department of Health & Human Services | National Institutes of Health (NIH)
                Funded by: U.S. Department of Health & Human Services | National Institutes of Health (NIH)
                Funded by: FundRef https://doi.org/10.13039/100010304, Pershing Square Foundation;
                Funded by: FundRef https://doi.org/10.13039/100005976, Harry J. Lloyd Charitable Trust;
                Categories
                Article
                Custom metadata
                © The Author(s) 2021

                Uncategorized
                cancer microenvironment,computational biology and bioinformatics,cancer models,rna sequencing,melanoma

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