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相关概念视频

RNA-seq03:21

RNA-seq

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RNA sequencing, or RNA-Seq, is a high-throughput sequencing technology used to study the transcriptome of a cell. Transcriptomics helps to interpret the functional elements of a genome and identify the molecular constituents of an organism. Additionally, it also helps in understanding the development of an organism and the occurrence of diseases. 
Before the discovery of RNA-seq, microarray-based methods and Sanger sequencing were used for transcriptome analysis. However, while...
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相关实验视频

Updated: Jan 18, 2026

Author Spotlight: Integrating Organoid Models with Single-Cell and Spatial Transcriptomics Technologies
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haCCA:基于点的空间转录组和代谢组的多模块集成.

Jing Xu1,2,3, Xiao-Tian Shen1,4, Chen Zhang1,4

  • 1Department of General Surgery, Huashan Hospital, Fudan University, Shanghai, China.

Communications biology
|January 16, 2026
PubMed
概括

我们开发了haCCA,这是一个新的工作流程,集成了空间转录组学和MALDI-MSI数据,用于同时对mRNA和代谢物进行空间分析. 这种方法提高了整合的准确性,并使我们能够对组织层面的生物过程有新的见解.

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科学领域:

  • 计算生物学 计算生物学
  • 生物信息学是一种生物信息学.
  • 空间奥米克斯 空间奥米克斯

背景情况:

  • 空间转录组学和MALDI-MSI分别为mRNA和代谢物提供高分辨率的空间数据.
  • 整合这些数据集是具有挑战性的,因为不同的坐标系和特征空间.
  • 现有的方法缺乏可靠的方法,用于准确的跨平台空间数据集成.

研究的目的:

  • 介绍haCCA,用于整合空间转录组学和代谢组学数据的工作流.
  • 为了使mRNA和来自相邻组织部分的代谢物同时进行空间分析.
  • 为了提高空间奥米克数据集成的准确性.

主要方法:

  • haCCA使用修改的空间登记技术.
  • 规范相关性分析 (CCA) 用于构建一个共享的潜空间.
  • 高相关性特征对在数据集之间转移以进行整合.

主要成果:

  • 与现有方法相比,haCCA在模拟和真实数据上的整合准确度有所提高.
  • 该工作流成功实现了mRNA和代谢物的同时空间分析.
  • 对Akt/Yap驱动的Padi4-/-ICC模型的应用揭示了中性粒细胞外细胞陷 (NET) 的空间分布和代谢效应.

结论:

  • haCCA为整合空间转录组学和代谢组学数据提供了有效的解决方案.
  • 工作流程促进了空间代谢变化的 in situ 和 in vivo 探索.
  • 有一个Python包可用于提高haCCA工作流的可访问性和可用性.