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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 18, 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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空间转录组技术 空间转录组技术

Tsai-Ying Chen1,2,3, Li You1,2,3, Jose Angelito U Hardillo2,4

  • 1Department of Molecular Genetics, Erasmus University Medical Center, 3015 GD Rotterdam, The Netherlands.

Cells
|August 26, 2023
PubMed
概括
此摘要是机器生成的。

空间转录组学测量了组织间的基因表达,进步了对细胞相互作用和癌症等疾病的理解. 本综述涵盖了当前的技术,新兴的方法和数据集成,以获得更深入的生物学见解.

关键词:
基于NGS的空间配置文件.图像引导的空间分辨率单细胞测序.基于成像的空间概况.基于探测器的空间分析.空间奥米克技术技术

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相关实验视频

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科学领域:

  • 基因组学就是基因组学.
  • 分子生物学分子生物学
  • 生物信息学是一种生物信息学.

背景情况:

  • 空间转录学允许在组织背景下测量基因表达.
  • 了解细胞组织和相互作用在生物学和疾病中至关重要.

研究的目的:

  • 审查关键的空间转录技术及其应用.
  • 讨论解决当前局限性的新兴技术.
  • 探索空间转录组学与其他组学数据的整合.

主要方法:

  • 审查现有的空间转录技术.
  • 对技术进步和局限性的分析.
  • 讨论多主题数据整合策略.

主要成果:

  • 存在各种不同的空间转录技术,具有不同的能力.
  • 新技术正在出现,以克服当前的局限性.
  • 与其他omics数据的整合增强了生物洞察力.

结论:

  • 空间转录学是研究组织组织和细胞表型的强大工具.
  • 新兴技术和数据集成有望在各种生物领域深入理解.
  • 这项技术为神经科学,发育生物学和癌症研究提供了新的见解.