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Updated: Jun 3, 2025

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

同一个幻灯片的空间多态集成揭示了瘤病毒链接的瘤微环境的空间重组

Yao Yu Yeo^1,2, Yuzhou Chang^1,3,4, Huaying Qiu¹

¹Center for Virology and Vaccine Research, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, United States.

bioRxiv : the preprint server for biology

|January 7, 2025

概括

我们开发了IN-situ DEtailed Phenotyping To High-resolution transcriptomics (IN-DEPTH),这是一个高效的空间多态的方法. 这种方法整合了空间蛋白质组学和转录组学,以揭示组织中的细胞相互作用和疾病机制.

科学领域:

生物技术是生物技术.
分子生物学分子生物学
系统生物学系统生物学

背景情况:

空间转录组学和蛋白组学为组织组织和疾病机制提供了洞察力.
当前的空间多组学方法在plex,分辨率和分析方面存在局限性.
整合这些技术对于系统级理解复杂的生物系统至关重要.

研究的目的:

引入IN-situ DEtailed Phenotyping To High-resolution transcriptomics (IN-DEPTH),这是一个资源高效的方法,用于空间多态.
开发k频带有限的光谱图交叉相关性 (SGCC) 用于整合空间多学科分析.
为了证明IN-DEPTH和SGCC在剖析组织组织和疾病中的应用.

主要方法:

IN-DEPTH采用了一种代方法,将单细胞空间蛋白质组与随后的空间转录组合在同一个幻灯片上捕获.
快速蛋白质组分析指导转录组数据的获取,保存RNA信号.
k带限频谱图交叉相关性 (SGCC) 开发用于综合分析多omics数据.

主要成果:

通过IN-DEPTH和SGCC,可以在淋巴细胞组织中精确的单细胞表型和细胞类型特定的转录组捕获.
这种方法准确地解决了与生殖中心组织相关的转录组变化.
对扩散性大B细胞淋巴瘤 (DLBCL) 的分析揭示了一个关键的瘤-巨细胞-CD4T细胞轴影响免疫功能障碍.

结论:

IN-DEPTH提供了一个可扩展和资源高效的平台,用于全面的空间多omics解剖.
综合方法有助于更深入地了解组织结构和疾病病理学.
这种方法促进了临床相关的发现,通过使详细的空间多组分析.

关键词:

生物信息学是一种生物信息学.计算生物学计算生物学 DLBCL DLBCL 是一个字母.这是EBV的EBV.图形信号处理图形信号处理空间多态图的空间多态图空间蛋白质组学空间蛋白质组学空间转录学空间转录学系统免疫学系统免疫学瘤微环境瘤微环境瘤病毒瘤病毒

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