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相关概念视频

Ribosome Profiling02:24

Ribosome Profiling

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Ribosome profiling or ribo-sequencing is a deep sequencing technique that produces a snapshot of active translation in a cell. It selectively sequences the mRNAs protected by ribosomes to get an insight into a cell’s translation landscape at any given point in time.
Applications of ribosome profiling
Ribosome profiling has many applications, including in vivo monitoring of translation inside a particular organ or tissue type and quantifying new protein synthesis levels.
The technique...
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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: Jul 10, 2025

Spatial Profiling of Protein and RNA Expression in Tissue: An Approach to Fine-Tune Virtual Microdissection
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Spatial Profiling of Protein and RNA Expression in Tissue: An Approach to Fine-Tune Virtual Microdissection

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SPACEL:基于深度学习的空间转录组架构的表征.

Hao Xu1, Shuyan Wang2,3, Minghao Fang2

  • 1Department of Oncology, The First Affiliated Hospital of USTC, School of Basic Medical Sciences, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, 230027, China.

Nature communications
|November 22, 2023
PubMed
概括
此摘要是机器生成的。

空间转录学 (ST) 分析是通过深度学习工具包 SPACEL 进行的. SPACEL能够从多个ST切片中准确地进行细胞类型解,空间域识别和3D组织重建.

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科学领域:

  • 基因组学就是基因组学.
  • 计算生物学 计算生物学
  • 生物信息学是一种生物信息学.

背景情况:

  • 空间转录学 (ST) 技术在组织环境中映射mRNA表达.
  • 分析多个ST切片和重建3D组织架构是相当大的挑战.

研究的目的:

  • 引入SPACEL,这是一个用于全面的ST数据分析的深度学习框架.
  • 解决ST数据联合分析和3D重建方面的局限性.

主要方法:

  • 斯波因特模块:使用多层感知子和概率模型用于ST斑点中的细胞类型解卷.
  • 平面模块:使用图形卷积网络和对抗式学习来进行交叉切片空间域识别.
  • 斯库布模块:自动化了ST切片的坐标转换和堆叠,用于3D组织重建.

主要成果:

  • 与19种现有方法相比,SPACEL在细胞类型解方面表现优越.
  • 该框架准确地识别了跨多个ST切片的空间连贯域.
  • 从各种ST数据集中,SPACEL实现了精确的3D对齐和组织架构的重建.

结论:

  • SPACEL为空间转录学数据处理提供了一个集成和高性能工具包.
  • 深度学习方法显著改善了多切片ST数据和3D组织重建的分析.
  • SPACEL代表了研究3D转录空间组织的有价值的进步.