美国威尔康奈尔医学院Olivier Elemento和André F. Rendeiro共同合作近期取得重要工作进展，他们研究开发出多色成像中组织结构的自动化识别方法。相关研究成果2022年10月31日在线发表于《自然—方法学》杂志上。
研究小组开发了一种称为UTAG的方法，在没有人为干预的情况下识别和量化多色图像中的显微解剖组织结构。他们的方法结合了细胞表型信息和细胞物理邻近性，以准确识别健康和病变组织中器官特异性微观解剖结构域。研究团队将他们的方法应用于健康和疾病状态下的各种类型图像，表明它可以持续检测人体组织中更高层次的架构，量化健康和疾病组织之间的结构差异，并揭示器官尺度上的组织模式。
据介绍，多色成像和空间转录组学能够高度解析细胞表型的空间特征，但在很大程度上仍依赖于费力的人工注释来理解组织结构的高阶模式。因此，人们对组织结构的高阶模式知之甚少，也没有系统地与疾病的病理或临床结果联系起来。
附：英文原文
Title: Unsupervised discovery of tissue architecture in multiplexed imaging
Author: Kim, Junbum, Rustam, Samir, Mosquera, Juan Miguel, Randell, Scott H., Shaykhiev, Renat, Rendeiro, Andr F., Elemento, Olivier
Issue&Volume: 2022-10-31
Abstract: Multiplexed imaging and spatial transcriptomics enable highly resolved spatial characterization of cellular phenotypes, but still largely depend on laborious manual annotation to understand higher-order patterns of tissue organization. As a result, higher-order patterns of tissue organization are poorly understood and not systematically connected to disease pathology or clinical outcomes. To address this gap, we developed an approach called UTAG to identify and quantify microanatomical tissue structures in multiplexed images without human intervention. Our method combines information on cellular phenotypes with the physical proximity of cells to accurately identify organ-specific microanatomical domains in healthy and diseased tissue. We apply our method to various types of images across healthy and disease states to show that it can consistently detect higher-level architectures in human tissues, quantify structural differences between healthy and diseased tissue, and reveal tissue organization patterns at the organ scale.
DOI: 10.1038/s41592-022-01657-2
Source: https://www.nature.com/articles/s41592-022-01657-2
来源：科学网 小柯机器人