基于卵巢癌的空间分辨率的转录组数据,学习针对连接体和受体的定向非循环图
Shrabanti Chowdhury1, Sammy Ferri-Borgogno2, Peng Yang3
1Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, 1399 Park Ave, New York, NY 10029, United States.
Briefings in bioinformatics
|March 10, 2025
概括
我们开发了LRnetST,一种使用空间转录学分析瘤细胞通信的新方法. 它确定了对免疫反应和瘤进展至关重要的联结体受体相互作用,为卵巢癌提供了洞察力.
科学领域:
- 计算生物学是一种计算生物学.
- 癌症研究 癌症研究
- 基因组学就是基因组学.
背景情况:
- 瘤微环境通信是免疫激活和抑制的关键.
- 体受体相互作用形成复杂的细胞信号网络.
- 空间转录学 (ST) 能够在现场描述这些网络.
研究的目的:
- 引入LRnetST,一种用于分析ST数据中的联体受体相互作用网络的新型统计方法.
- 描述瘤,免疫和树皮细胞之间的细胞-细胞通信网络.
- 为LRnetST提供一个开源的R包.
主要方法:
- LRnetST使用针对零膨胀ST数据量身定制的定向非循环图模型.
- 结合了从配体受体数据库中获得的先前知识.
- 采用引导聚合来进行可靠的网络估计.
主要成果:
- 将LRnetST应用于高等级的卵巢血清瘤ST数据,揭示了保存和独特的连接体受体规则.
- 确定了影响瘤-免疫/瘤细胞通信的关键相互作用.
- 使用独立的MERFISH和CosMx SMI数据集验证的发现.
结论:
- LRnetST有效地表征瘤微环境中的联体受体网络.
- 鉴定的相互作用为卵巢瘤生物学和免疫反应提供了洞察力.
- 这种方法有助于利用空间奥米克学研究癌症中细胞间的细胞通信.
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