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The promising application of cell-cell interaction analysis in cancer from single-cell and spatial transcriptomics
Cell-cell interactions instruct cell fate and function. These interactions are hijacked to promote cancer development. Single-cell transcriptomics and spatial transcriptomics have become powerful new tools for researchers to profile the transcriptional landscape of cancer at unparalleled genetic dep...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10627116/ https://www.ncbi.nlm.nih.gov/pubmed/37454878 http://dx.doi.org/10.1016/j.semcancer.2023.07.001 |
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author | Wang, Xinyi Almet, Axel A. Nie, Qing |
author_facet | Wang, Xinyi Almet, Axel A. Nie, Qing |
author_sort | Wang, Xinyi |
collection | PubMed |
description | Cell-cell interactions instruct cell fate and function. These interactions are hijacked to promote cancer development. Single-cell transcriptomics and spatial transcriptomics have become powerful new tools for researchers to profile the transcriptional landscape of cancer at unparalleled genetic depth. In this review, we discuss the rapidly growing array of computational tools to infer cell-cell interactions from non-spatial single-cell RNA-sequencing and the limited but growing number of methods for spatial transcriptomics data. Downstream analyses of these computational tools and applications to cancer studies are highlighted. We finish by suggesting several directions for further extensions that anticipate the increasing availability of multi-omics cancer data. |
format | Online Article Text |
id | pubmed-10627116 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
record_format | MEDLINE/PubMed |
spelling | pubmed-106271162023-11-05 The promising application of cell-cell interaction analysis in cancer from single-cell and spatial transcriptomics Wang, Xinyi Almet, Axel A. Nie, Qing Semin Cancer Biol Article Cell-cell interactions instruct cell fate and function. These interactions are hijacked to promote cancer development. Single-cell transcriptomics and spatial transcriptomics have become powerful new tools for researchers to profile the transcriptional landscape of cancer at unparalleled genetic depth. In this review, we discuss the rapidly growing array of computational tools to infer cell-cell interactions from non-spatial single-cell RNA-sequencing and the limited but growing number of methods for spatial transcriptomics data. Downstream analyses of these computational tools and applications to cancer studies are highlighted. We finish by suggesting several directions for further extensions that anticipate the increasing availability of multi-omics cancer data. 2023-10 2023-07-15 /pmc/articles/PMC10627116/ /pubmed/37454878 http://dx.doi.org/10.1016/j.semcancer.2023.07.001 Text en https://creativecommons.org/licenses/by/4.0/This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) ). |
spellingShingle | Article Wang, Xinyi Almet, Axel A. Nie, Qing The promising application of cell-cell interaction analysis in cancer from single-cell and spatial transcriptomics |
title | The promising application of cell-cell interaction analysis in cancer from single-cell and spatial transcriptomics |
title_full | The promising application of cell-cell interaction analysis in cancer from single-cell and spatial transcriptomics |
title_fullStr | The promising application of cell-cell interaction analysis in cancer from single-cell and spatial transcriptomics |
title_full_unstemmed | The promising application of cell-cell interaction analysis in cancer from single-cell and spatial transcriptomics |
title_short | The promising application of cell-cell interaction analysis in cancer from single-cell and spatial transcriptomics |
title_sort | promising application of cell-cell interaction analysis in cancer from single-cell and spatial transcriptomics |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10627116/ https://www.ncbi.nlm.nih.gov/pubmed/37454878 http://dx.doi.org/10.1016/j.semcancer.2023.07.001 |
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