Single-cell spatial landscapes of the lung tumour immune microenvironment.
Single-cell spatial landscapes of the lung tumour immune microenvironment.
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肺肿瘤免疫微环境的单细胞空间景观。
DOI：
10.1038/s41586-022-05672-3
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发表时间：
2023-03
期刊：
影响因子：
64.8
通讯作者：
Walsh, Logan A. A.
中科院分区：
文献类型：
--
作者：
Sorin, Mark;Rezanejad, Morteza;Karimi, Elham;Fiset, Benoit;Desharnais, Lysanne;Perus, Lucas J. M.;Milette, Simon;Yu, Miranda W. W.;Maritan, Sarah M. M.;Dore, Samuel;Pichette, Emilie;Enlow, William;Gagne, Andreanne;Wei, Yuhong;Orain, Michele;Manem, Venkata S. K.;Rayes, Roni;Siegel, Peter M. M.;Camilleri-Broet, Sophie;Fiset, Pierre Olivier;Desmeules, Patrice;Spicer, Jonathan D. D.;Quail, Daniela F. F.;Joubert, Philippe;Walsh, Logan A. A.
单细胞技术以无与伦比的分辨率揭示了肿瘤免疫微环境的复杂性。大多数临床策略依赖于肿瘤亚型的组织病理学分层，但这些分层亚型中单细胞表型的空间背景知之甚少。在这里，我们应用成像质量细胞术来表征来自五种组织学模式的416例肺腺癌患者的肿瘤和免疫状况。我们解析了160多万个细胞，能够对免疫谱系和激活状态进行空间分析，并与包括生存在内的不同临床相关性。使用深度学习，我们可以使用单个1mm2的肿瘤核心来高精度地预测那些手术后病情恶化的患者，这可能为手术切除后的临床处理提供信息。我们的数据集代表了非小细胞肺癌研究社区的宝贵资源，并举例说明了空间分辨率在单细胞分析中的应用。这项研究还强调了人工智能如何提高我们对癌症进展背后的微环境特征的理解，这些微环境特征可能会影响未来的临床实践。利用成像质量细胞术，对416例肺腺癌的肿瘤和免疫空间分布进行了表征，结合深度学习，可以高精度地预测临床结果。
Single-cell technologies have revealed the complexity of the tumour immune microenvironment with unparalleled resolution. 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-mm2 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. Using imaging mass cytometry, the tumour and immunological spatial landscapes of 416 lung adenocarcinomas are characterized, which, when combined with deep learning, can predict clinical outcomes with high accuracy.
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DOI：
10.4049/jimmunol.1103015
发表时间：
2012-07-15
期刊：
Journal of immunology (Baltimore, Md. : 1950)
影响因子：
--
作者：
Chang CF;D'Souza WN;Ch'en IL;Pages G;Pouyssegur J;Hedrick SM
通讯作者：
Hedrick SM
影响因子：
22.7
作者：
Ali, H. Raza;Jackson, Hartland W.;Bodenmiller, Bernd
通讯作者：
Bodenmiller, Bernd
肺癌演化过程中高塑性细胞状态的出现。
影响因子：
50.3
作者：
Marjanovic ND;Hofree M;Chan JE;Canner D;Wu K;Trakala M;Hartmann GG;Smith OC;Kim JY;Evans KV;Hudson A;Ashenberg O;Porter CBM;Bejnood A;Subramanian A;Pitter K;Yan Y;Delorey T;Phillips DR;Shah N;Chaudhary O;Tsankov A;Hollmann T;Rekhtman N;Massion PP;Poirier JT;Mazutis L;Li R;Lee JH;Amon A;Rudin CM;Jacks T;Regev A;Tammela T
通讯作者：
Tammela T
影响因子：
50.3
作者：
Leader AM;Grout JA;Maier BB;Nabet BY;Park MD;Tabachnikova A;Chang C;Walker L;Lansky A;Le Berichel J;Troncoso L;Malissen N;Davila M;Martin JC;Magri G;Tuballes K;Zhao Z;Petralia F;Samstein R;D'Amore NR;Thurston G;Kamphorst AO;Wolf A;Flores R;Wang P;Müller S;Mellman I;Beasley MB;Salmon H;Rahman AH;Marron TU;Kenigsberg E;Merad M
通讯作者：
Merad M
影响因子：
10.9
作者：
Enfield, Katey S. S.;Martin, Spencer D.;Guillaud, Martial
通讯作者：
Guillaud, Martial