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Single-cell spatial landscapes of the lung tumour immune microenvironment.

Single-cell technologies have revealed the complexity of the tumour immune microenvironment with unparalleled resolution 1-9 . Most clinical strategies rely on histopathological stratification of tumour subtypes, yet the spatial context of single-cell phenotypes within these stratified subgroups is poorly understood. Here we apply imaging mass cytometry to characterize the tumour and immunological landscape of samples from 416 patients with lung adenocarcinoma across five histological patterns. We resolve more than 1.6 million cells, enabling spatial analysis of immune lineages and activation states with distinct clinical correlates, including survival. Using deep learning, we can predict with high accuracy those patients who will progress after surgery using a single 1-mm 2 tumour core, which could be informative for clinical management following surgical resection. Our dataset represents a valuable resource for the non-small cell lung cancer research community and exemplifies the uti

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Single-cell spatial landscapes of the lung tumour immune microenvironment.

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

书目信息

  • 引用：Sorin M, Rezanejad M, Karimi E, Fiset B, Desharnais L, Perus LJM, Milette S, Yu MW, Maritan SM, Doré S, Pichette É, Enlow W, Gagné A, Wei Y, Orain M, Manem VSK, Rayes R, Siegel PM, Camilleri-Broët S, Fiset PO, Desmeules P, Spicer JD, Quail DF, Joubert P, Walsh LA. (2023). Single-cell spatial landscapes of the lung tumour immune microenvironment. Nature. PMID 36725934 · PMC9931585 · DOI 10.1038/s41586-022-05672-3
  • 证据类型：PRIMARY_RESEARCH
  • 主题：single-cell
  • 被引次数（采集时）：303
  • 原始记录：[Europe PMC](https://europepmc.org/article/MED/36725934)
  • 来源许可：[CC-BY](https://creativecommons.org/licenses/by/)

    • 作者摘要（按来源许可复用）

    Single-cell technologies have revealed the complexity of the tumour immune microenvironment with unparalleled resolution 1-9 . Most clinical strategies rely on histopathological stratification of tumour subtypes, yet the spatial context of single-cell phenotypes within these stratified subgroups is poorly understood. Here we apply imaging mass cytometry to characterize the tumour and immunological landscape of samples from 416 patients with lung adenocarcinoma across five histological patterns. We resolve more than 1.6 million cells, enabling spatial analysis of immune lineages and activation states with distinct clinical correlates, including survival. Using deep learning, we can predict with high accuracy those patients who will progress after surgery using a single 1-mm 2 tumour core, which could be informative for clinical management following surgical resection. Our dataset represents a valuable resource for the non-small cell lung cancer research community and exemplifies the utility of spatial resolution within single-cell analyses. This study also highlights how artificial intelligence can improve our understanding of microenvironmental features that underlie cancer progression and may influence future clinical practice.

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    Single-cell spatial landscapes of the lung tumour immune microenvironment. · GeniOmics