时间空间和网络模型的融合,以优先考虑单细胞扰动中的多尺度效应
Osafu Augustine Egbon1,2, John W Hickey3, Benedict Anchang1,2
1Biostatistics and Computational Biology Branch, National Institute of Environmental Health Sciences, 111 T W Alexander Dr Rall Building, Research Triangle Park, 27709, Durham, NC, United States.
Briefings in bioinformatics
|June 22, 2025
概括
Perturb-STNet识别了驱动细胞变化的关键调节者,随着时间和空间的推移. 这种网络分析工具有助于了解疾病并制定个性化医疗策略.
科学领域:
- 单细胞生物学 单细胞生物学
- 系统生物学 系统生物学
- 计算生物学是一种计算生物学.
背景情况:
- 了解细胞对干扰的反应对于个性化医学至关重要.
- 目前的方法缺乏使用具有空间和时间分辨率的单细胞数据来量化复杂系统中的动态影响的能力.
研究的目的:
- 介绍Perturb-STNet,这是一个用于分析单细胞扰动数据中的时空调节网络的新框架.
- 识别由于干扰 (pSTDERs) 驱动疾病过程的空间和时间差异表达调节器.
主要方法:
- 利用基于网络的时空模型.
- 排名pSTDER和估计动态监管网络.
- 利用合成数据,肺癌数据和来自小鼠黑色素瘤和大肠炎模型的时间单细胞成像数据.
主要成果:
- 在验证研究中,Perturb-STNet与标准方法相比,表现优越.
- 确定了黑色素瘤的关键调节剂和调解相互作用,建议使用检查点抑制和Treg耗尽等治疗策略.
- 在结肠炎中发现了关键基因和调解器对,与免疫调节和组织修复有关,提供了潜在的治疗点.
结论:
- Perturb-STNet能够在单细胞扰动数据中对时空调节网络进行可靠的识别.
- 该框架提供了对疾病进展和治疗反应的见解,在不同的背景下进行治疗.
- 通过动态细胞相互作用来发现新的治疗策略.
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