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The Tumor Microenvironment

The Tumor Microenvironment

Every normal cell or tissue is embedded in a complex local environment called stroma, consisting of different cell types, a basal membrane, and blood vessels. As normal cells mutate and develop into cancer cells, their local environment also changes to allow cancer progression. The tumor microenvironment (TME) consists of a complex cellular matrix of stromal cells and the developing tumor. The cross-talk between cancer cells and surrounding stromal cells is critical to disrupt normal tissue...

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Updated: Jan 7, 2026

Visualization, Quantification, and Mapping of Immune Cell Populations in the Tumor Microenvironment

Visualization, Quantification, and Mapping of Immune Cell Populations in the Tumor Microenvironment

Published on: March 25, 2020

揭示组织结构和瘤微环境从空间Omics通过超图学习.

Yi Liao¹, Chong Zhang¹, Zhikang Wang^2,3

¹School of Computer Science and Engineering, South China University of Technology, Guangzhou 510006, China.

Genomics, proteomics & bioinformatics

|December 26, 2025

概括

此摘要是机器生成的。

超星 (HyperSTAR) 是一种新的超图法,可以精确地识别空间奥米克数据中的各种分辨率的空间域. 这种工具通过捕捉复杂的细胞关系来增强对组织结构和癌症生物学的理解.

关键词:

一个更高阶的关系关系.超图形学习的学习方法决议决议决议空间域是一个空间域.空间奥米克斯空间奥米克斯

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Spatial Profiling of Protein and RNA Expression in Tissue: An Approach to Fine-Tune Virtual Microdissection

Spatial Profiling of Protein and RNA Expression in Tissue: An Approach to Fine-Tune Virtual Microdissection

Published on: July 6, 2022

Quantifying the Brain Metastatic Tumor Micro-Environment using an Organ-On-A Chip 3D Model, Machine Learning, and Confocal Tomography

Quantifying the Brain Metastatic Tumor Micro-Environment using an Organ-On-A Chip 3D Model, Machine Learning, and Confocal Tomography

Published on: August 16, 2020

相关实验视频

Last Updated: Jan 7, 2026

Visualization, Quantification, and Mapping of Immune Cell Populations in the Tumor Microenvironment

Visualization, Quantification, and Mapping of Immune Cell Populations in the Tumor Microenvironment

Published on: March 25, 2020

Spatial Profiling of Protein and RNA Expression in Tissue: An Approach to Fine-Tune Virtual Microdissection

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Quantifying the Brain Metastatic Tumor Micro-Environment using an Organ-On-A Chip 3D Model, Machine Learning, and Confocal Tomography

Quantifying the Brain Metastatic Tumor Micro-Environment using an Organ-On-A Chip 3D Model, Machine Learning, and Confocal Tomography

Published on: August 16, 2020

科学领域:

生命科学生命科学
计算生物学计算生物学
基因组学就是基因组学.

背景情况:

空间奥米克技术同时提供生物分子和空间数据,对于理解器官发育和瘤微环境至关重要.
不同的空间奥米克分辨率在更细微的尺度上准确地描述空间领域时存在挑战.

研究的目的:

开发一种新的方法,HyperSTAR,用于精确识别空间域在空间奥米克数据中的不同分辨率.
为了利用空间相邻的组织程序之间的更高阶关系,以改善域划分.

主要方法:

提出了HyperSTAR,一种基于超图的方法,利用基因表达引导的超边缘分解模块.
开发了一个超图注意力卷积神经网络,以学习超边缘的重要性并捕捉空间邻近的多点/细胞中的复杂关系.

主要成果:

超星超越现有的图形神经网络模型,在识别组织亚结构,推断时空模式和消除基因表达数据方面表现出色.
成功揭示了乳腺癌部分的空间异质性,研究结果得到了临床数据的验证.
证明了对各种空间奥米克数据类型的有效处理以及对大数据集的可扩展性.

结论:

超星 (HyperSTAR) 是空间奥米克分析的重大进步,为探索各种分辨率和数据类型的复杂空间模式提供了强大的工具.
该方法捕捉复杂的高阶关系的能力使其在癌症和发育生物学研究中具有宝贵价值.