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使用多重成像驱动的深度视觉蛋白质组的桃体癌微环境的空间蛋白质组分析协议.

Xiang Zheng1, Andreas Mund2, Matthias Mann3

  • 1Novo Nordisk Foundation Center for Protein Research, University of Copenhagen, 2200 Copenhagen, Denmark; Department of Biomedicine, Aarhus University, 8000 Aarhus, Denmark.

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概括

我们为桃体癌瘤微环境开发了一个空间蛋白质基因分析协议. 这种方法使用多重成像驱动的深度视觉蛋白质组学 (mipDVP) 来分析细胞类型和相互作用.

关键词:
生物技术和生物工程癌症 癌症 癌症 癌症显微镜的使用方法

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科学领域:

  • 在瘤学瘤学.
  • 蛋白质组学是指蛋白质组学
  • 免疫学 免疫学 免疫学

背景情况:

  • 瘤微环境 (TME) 对癌症的进展和治疗反应至关重要.
  • 了解TME内的空间细胞-细胞相互作用是确定治疗点的关键.
  • 当前的蛋白质组学方法往往缺乏空间分辨率,限制了对TME异质性的洞察力.

研究的目的:

  • 提出一种用于桃腺癌中瘤微环境的空间蛋白质定型的新方案.
  • 为了能够详细分析FFPE组织段内的瘤免疫相互作用.
  • 促进癌症中新生物标志物和功能细胞网络的识别.

主要方法:

  • 开发一个自动化的22个复合体免疫光染色和成像工作流程.
  • 实现自动化单细胞激光微解剖,以精确隔离细胞.
  • 整合单细胞类型质谱仪用于蛋白质组分析.
  • 优化用于甲固化嵌 (FFPE) 组织的工作流程.

主要成果:

  • 成功地进行了桃体癌TME的空间蛋白质基因分析.
  • 区别细胞群的空间分辨隔离,用于蛋白质组分析.
  • 促进生物标志物和功能性细胞网络的系统识别.
  • 证明该协议在研究瘤与免疫相互作用方面的实用性.

结论:

  • 提出的多重成像驱动的深度视觉蛋白质组学 (mipDVP) 协议为FFPE组织的空间蛋白质组学分析提供了一种强大的方法.
  • 这种工作流使我们能够更深入地了解瘤微环境中的细胞异质性和相互作用.
  • 该协议优化用于识别桃体癌和其他恶性瘤中的生物标志物和治疗标.