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剖析多层细胞-细胞通信与信号反循环从空间转录组学数据的信号反.

Lulu Yan1, Jinyu Cheng2, Qing Nie3

  • 1School of Mathematics, Sun Yat-sen University, Guangzhou 510275, China.

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概括
此摘要是机器生成的。

空间转录学 (ST) 揭示了复杂的细胞-细胞通信 (CCC). 我们的新方法stMLnet集成空间数据以识别多层信号反循环,改善细胞间和细胞内通信通路的推断.

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科学领域:

  • 计算生物学 计算生物学
  • 基因组学就是基因组学.
  • 系统生物学 系统生物学

背景情况:

  • 空间转录学 (ST) 为细胞相互作用提供了新的见解.
  • 从ST数据中推断功能性细胞-细胞通信 (CCC),考虑到空间上下文和信号,仍然具有挑战性.

研究的目的:

  • 从ST数据引入stMLnet,一种用于从ST数据中识别CCC中的多层信号反循环的计算方法.
  • 为了利用空间信息和多层信号调节来增强CCC推断.

主要方法:

  • stMLnet使用扩散和质量行动模型量化空间依赖的联结体受体信号活动.
  • 该方法将细胞间信号映射到细胞内目标.
  • 与七种方法进行基准测试,评估了细胞间联体受体推断和细胞内向基因预测.

主要成果:

  • 与现有方法相比,stMLnet在细胞间联体受体推断和细胞内向基因预测方面都表现出卓越的性能.
  • 该方法证明在各种ST数据集 (seqFISH+,Slide-seq v2,MERFISH,Stereo-seq) 中具有稳定性和可扩展性,分辨率和基因覆盖范围各不相同.
  • 在COVID-19肺组织ST数据中,stMLnet确定了炎症反应中的多层信号反循环.

结论:

  • stMLnet提供了一种有效的工具,用于从ST数据中剖析复杂的配体/受体-标调节和多细胞信号回路.
  • 这一进步有助于理解空间CCC的机械和功能作用.
  • 该方法支持从空间转录学研究中获得更深入的生物学见解.