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相关概念视频

Imaging Biological Samples with Optical Microscopy01:18

Imaging Biological Samples with Optical Microscopy

Optical microscopy uses optic principles to provide detailed images of samples. Antonie van Leeuwenhoek designed the first compound optical microscope in the 17th century to visualize blood cells, bacteria, and yeast cells. In 1830, Joseph Jackson Lister created an essentially modern light microscope. The 20th century saw the development of microscopes with enhanced magnification and resolution.
In optical microscopy, the specimen to be viewed is placed on a glass slide and clipped on the stage...

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相关实验视频

Updated: Jun 8, 2026

Visualization, Quantification, and Mapping of Immune Cell Populations in the Tumor Microenvironment
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来自串行部分的多omics数据的空间集成使用新的多omics成像集成工具集.

Maximilian Wess1,2, Maria K Andersen1,3, Elise Midtbust1,3

  • 1Department of Circulation and Medical Imaging, NTNU-Norwegian University of Science and Technology, Trondheim, 7491, Norway.

GigaScience
|May 14, 2025
PubMed
概括

一个新的Python框架,MIIT,集成了来自不同组织部分的空间奥米克数据. 该工具集通过将质谱成像和空间转录学结合起来,为精准医学提供了全面的癌症生物学理解.

关键词:
图像注册 图像注册 图像注册质谱仪成像成像 质谱仪成像成像空间转录学 空间转录学

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科学领域:

  • 生物医学研究的研究.
  • 计算生物学是一种计算生物学.
  • 基因组学和蛋白质组学

背景情况:

  • 了解异质瘤中的癌症生物学需要多omics数据和空间分辨率.
  • 空间转录组学 (ST) 和质谱成像 (MSI) 捕获空间组学数据,但通常应用于串行段.
  • 从串行部分整合数据对于利用多omics见解至关重要.

研究的目的:

  • 开发一个用于整合空间解析的多omics数据的计算框架.
  • 为了使在连续组织切片上执行的不同奥米克技术的数据结合起来.

主要方法:

  • 开发一个Python框架的多omics成像集成工具集 (MIIT).
  • 实现一个非刚性注册算法 (GreedyFHist) 来对准串行段.
  • 从新鲜冷的序列截面中对244张图像进行了GreedyFHist的验证.

主要成果:

  • MIIT成功地整合了空间分辨的多omics数据.
  • GreedyFHist在串行截面注册方面取得了最先进的性能.
  • 从前列腺癌中整合ST和MSI数据的概念验证,将基因特征与代谢测量相关联.

结论:

  • MIIT是一个准确,可定制和开源的框架,用于整合空间omics数据.
  • 该框架有助于将来自不同空间空间技术的数据结合起来.
  • 通过实现多模式空间数据集成,MIIT提高了对癌症生物学的理解.