小柯机器人

加拿大麦吉尔大学Logan A. Walsh等研究人员合作绘制出肺肿瘤免疫微环境的单细胞空间图谱。2023年2月1日，国际知名学术期刊《自然》在线发表了这一成果。

研人员人员应用成像细胞术来描述了来自416例肺腺癌患者的5种组织学模式的肿瘤和免疫图谱。研究人员分析了超过160万个细胞，能够对具有不同临床相关性（包括生存）的免疫谱系和激活状态进行空间分析。通过使用深度学习，研究人员可以高精度地预测那些患者在手术后的进展情况，这可能为手术切除后的临床管理提供信息。这个数据集为非小细胞肺癌研究群体提供了宝贵的资源，并举例说明了单细胞分析中的空间分辨率的实用性。这项研究还强调了人工智能如何提高人们对癌症进展背后的微环境特征的理解，并可能影响未来的临床实践。

据悉，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，然而这些分层亚群中单细胞表型的空间背景尚不清楚。

附：英文原文

Title: Single-cell spatial landscapes of the lung tumour immune microenvironment

Author: Sorin, Mark, Rezanejad, Morteza, Karimi, Elham, Fiset, Benoit, Desharnais, Lysanne, Perus, Lucas J. M., Milette, Simon, Yu, Miranda W., Maritan, Sarah M., Dor, Samuel, Pichette, milie, Enlow, William, Gagn, Andranne, Wei, Yuhong, Orain, Michele, Manem, Venkata S. K., Rayes, Roni, Siegel, Peter M., Camilleri-Brot, Sophie, Fiset, Pierre Olivier, Desmeules, Patrice, Spicer, Jonathan D., Quail, Daniela F., Joubert, Philippe, Walsh, Logan A.

Issue&Volume: 2023-02-01

Abstract: Single-cell technologies have revealed the complexity of the tumour immune microenvironment with unparalleled resolution1,2,3,4,5,6,7,8,9. 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-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.

DOI: 10.1038/s41586-022-05672-3

Source: https://www.nature.com/articles/s41586-022-05672-3

Nature：《自然》，创刊于1869年。隶属于施普林格·自然出版集团，最新IF：69.504
官方网址：http://www.nature.com/
投稿链接：http://www.nature.com/authors/submit_manuscript.html