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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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Genome comparison is one of the excellent ways to interpret the evolutionary relationships between organisms. The basic principle of genome comparison is that if two species share a common feature, it is likely encoded by the DNA sequence conserved between both species. The advent of genome sequencing technologies in the late 20th century enabled scientists to understand the concept of conservation of domains between species and helped them to deduce evolutionary relationships across diverse...
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相关实验视频

Updated: Jun 22, 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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基于测序的空间转录组方法的系统比较.

Yue You1, Yuting Fu2,3,4,5, Lanxiang Li1

  • 1Guangzhou National Laboratory, Guangzhou, China.

Nature methods
|July 4, 2024
PubMed
概括
此摘要是机器生成的。

基准测试11种基于测序的空间转录组学 (sST) 方法揭示了分子扩散对有效分辨率的影响. 空间转录组学独特地捕捉了罕见的细胞状态,有助于平台选择和标准化分析.

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Real-time Analysis of Transcription Factor Binding, Transcription, Translation, and Turnover to Display Global Events During Cellular Activation
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Targeted RNA Sequencing Assay to Characterize Gene Expression and Genomic Alterations
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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:

  • 基于测序的空间转录组学 (sST) 能够实现转录组规模的空间基因表达测量.
  • 缺乏全面的基准测试阻碍了对各种sST平台和数据集的标准化评估.

研究的目的:

  • 用定义架构的参考组织对11种sST方法进行基准测试.
  • 确定影响sST数据质量和有效解析的关键因素.
  • 为未来的sST方法评估和计算工具开发建立一个框架.

主要方法:

  • 通过使用标准化参考组织,在11个平台上生成sST数据.
  • 对分子扩散对有效分辨率的影响的分析.
  • 将sST数据属性与单细胞RNA测序进行比较.

主要成果:

  • 分子扩散显著影响不同的sST方法和组织的有效分辨率.
  • sST数据独特地捕捉了有模式的罕见细胞状态和特定标记,受测序深度和分辨率的影响.
  • 跨sST技术的变化需要标准化的评估指标.

结论:

  • 该研究为选择sST平台和开发标准化评估指标提供了一个框架.
  • 确定分子扩散是影响sST有效解决的关键因素.
  • 突出了sST在捕捉复杂的空间基因表达模式和罕见细胞群的独特优势.