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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 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.

The promising application of cell-cell interaction analysis in cancer from single-cell and spatial transcriptomics.

> 商业许可源文 · EUROPE_PMC · [CC-BY](https://creativecommons.org/licenses/by/)

书目信息

  • 引用:Wang X, Almet AA, Nie Q. (2023). The promising application of cell-cell interaction analysis in cancer from single-cell and spatial transcriptomics. Seminars in cancer biology. PMID 37454878 · PMC10627116 · DOI 10.1016/j.semcancer.2023.07.001
  • 证据类型:REVIEW
  • 主题:rna-seq、single-cell、spatial-omics
  • 被引次数(采集时):57
  • 原始记录:[Europe PMC](https://europepmc.org/article/MED/37454878)
  • 来源许可:[CC-BY](https://creativecommons.org/licenses/by/)
  • 作者摘要(按来源许可复用)

    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.

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    The promising application of cell-cell interaction analysis in cancer from single-cell and spatial transcriptomics. · GeniOmics