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相关概念视频

Protein Networks02:26

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An organism can have thousands of different proteins, and these proteins must cooperate to ensure the health of an organism. Proteins bind to other proteins and form complexes to carry out their functions. Many proteins interact with multiple other proteins creating a complex network of protein interactions.
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Diversity in Cell Signaling Responses01:22

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The physiological function of a cell and cellular communication are outcomes of a range of extrinsic signals, intracellular signaling pathways, and cellular responses. No two cell types express the same repertoire of signaling components. Receptors are highly selective for their cognate ligands, but once activated, they can alter multiple cellular processes such as DNA transcription, protein synthesis, and metabolic activity. 
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In eukaryotes, transcription and translation are compartmentalized; an mRNA is first synthesized in the nucleus and then selectively transported to the cytoplasm for protein synthesis. Before transport, a pre-mRNA undergoes several steps of post-transcriptional modifications including splicing, 5' capping, and the addition of a poly-adenine tail. Various proteins bind to the pre-mRNA during these modifications. The mRNA transport takes place with the help of multiple proteins playing...
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Spatial Profiling of Protein and RNA Expression in Tissue: An Approach to Fine-Tune Virtual Microdissection
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使用网络分析对空间转录组学 (ST) 数据进行细胞信号表征.

Azka Javaid1, H Robert Frost1

  • 1Department of Biomedical Data Science, Geisel School of Medicine, Dartmouth College, Hanover, NH 03755, USA.

Complex networks & their applications XIII : proceedings of the thirteenth International Conference on Complex Networks and Their Applications: COMPLEX NETWORKS 2024. Volume 1. International Conference on Complex Networks and Their Appl...
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概括
此摘要是机器生成的。

我们开发了一种网络分析方法,在空间转录组学 (ST) 数据中绘制细胞-细胞通信的地图. 这种方法通过建模连接体-受体相互作用来量化信号活动,揭示生物学上可信的通信模式.

关键词:
中心的中心性.网络分析 网络分析空间转录学 空间转录学

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科学领域:

  • 计算生物学 计算生物学
  • 空间转录学 空间转录学
  • 系统生物学 系统生物学

背景情况:

  • 空间转录学 (ST) 能够在组织背景下进行基因表达分析.
  • 了解细胞-细胞通信对于解释组织结构和功能至关重要.
  • 现有的方法可能无法完全捕捉连接体-受体相互作用的空间动态.

研究的目的:

  • 引入一种基于网络分析的新方法,用于在ST数据中量化细胞-细胞通信.
  • 通过加权,定向网络方法来建模配体-受体相互作用.
  • 验证该方法捕捉空间信号异质性的能力.

主要方法:

  • 构建了一个网络模型,其中节点是ST位置,边缘重量反映了联体受体表达和空间距离.
  • 利用加权的内度中心性来量化特定相互作用的信号活动.
  • 在真实的ST数据集上验证了该方法,并将其与五种现有策略进行了比较.

主要成果:

  • 该方法成功地捕获了连接体和受体的同时表达异质性.
  • 为Wnt3-Fzd1,Ephb1-Efnb3和Ptprc-Cd22相互作用生成了生物可信的细胞通信概况.
  • 证明了在ST数据中使用低维嵌入用于网络建模的重要性.

结论:

  • 网络分析方法提供了一个强大的框架,可以从ST数据中推断细胞-细胞通信.
  • 该方法有效地模拟了空间信号,考虑了表达水平和物理距离.
  • 低维基因嵌入对于构建空间转录学中准确的网络模型至关重要.