Cancer-associated fibroblast classification in single-cell and spatial proteomics data.

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

书目信息

引用：Cords L, Tietscher S, Anzeneder T, Langwieder C, Rees M, de Souza N, Bodenmiller B. (2023). Cancer-associated fibroblast classification in single-cell and spatial proteomics data. Nature communications. PMID 37463917 · PMC10354071 · DOI 10.1038/s41467-023-39762-1

证据类型：PRIMARY_RESEARCH

主题：rna-seq、single-cell、proteomics

被引次数（采集时）：312

原始记录：[Europe PMC](https://europepmc.org/article/MED/37463917)

来源许可：[CC-BY](https://creativecommons.org/licenses/by/)

作者摘要（按来源许可复用）

Cancer-associated fibroblasts (CAFs) are a diverse cell population within the tumour microenvironment, where they have critical effects on tumour evolution and patient prognosis. To define CAF phenotypes, we analyse a single-cell RNA sequencing (scRNA-seq) dataset of over 16,000 stromal cells from tumours of 14 breast cancer patients, based on which we define and functionally annotate nine CAF phenotypes and one class of pericytes. We validate this classification system in four additional cancer types and use highly multiplexed imaging mass cytometry on matched breast cancer samples to confirm our defined CAF phenotypes at the protein level and to analyse their spatial distribution within tumours. This general CAF classification scheme will allow comparison of CAF phenotypes across studies, facilitate analysis of their functional roles, and potentially guide development of new treatment strategies in the future.

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