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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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Neuronal Communication01:28

Neuronal Communication

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Neurons, the fundamental units of the brain and nervous system, communicate through complex electrochemical signals that underpin all cognitive and bodily functions. This communication is primarily facilitated by a process involving the generation and propagation of an action potential along the axon of the neuron. When the internal electrical charge of a neuron surpasses a certain threshold, an action potential is triggered. This rapid change in voltage travels swiftly along the axon to the...
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相关实验视频

Updated: Jan 14, 2026

Using R, Seurat, and CellChat to Analyze a Single-Cell Transcriptomics Dataset of Mouse Skin Wound Healing
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在单细胞RNA测序数据中,GraphComm使用基于图形的深度学习方法预测细胞与细胞的通信.

Emily So1,2,3, Sikander Hayat4, Sisira Kadambat Nair1

  • 1Princess Margaret Cancer Centre, University Health Network, Toronto, Canada.

Scientific reports
|October 22, 2025
PubMed
概括
此摘要是机器生成的。

GraphComm是一种新的深度学习方法,通过整合细胞内信号和空间信息来增强单细胞RNA测序数据中的细胞-细胞通信 (CCC) 预测.

关键词:
细胞细胞相互作用深度学习是一种深度学习.图表注意力网络的图表.图形神经网络是一个神经网络.单细胞RNA是一种单细胞RNA.

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Single-cell RNA Sequencing of Fluorescently Labeled Mouse Neurons Using Manual Sorting and Double In Vitro Transcription with Absolute Counts Sequencing DIVA-Seq
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科学领域:

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

背景情况:

  • 细胞-细胞通信 (CCC) 对于协调健康和疾病中的细胞功能至关重要.
  • 目前的单细胞技术提供了高分辨率,但难以捕捉复杂的细胞内相互作用和影响CCC的途径效应.
  • 现有的方法往往无法推断总体信号模式并整合空间细胞维度.

研究的目的:

  • 开发一种新的基于图形的深度学习方法,GraphComm,用于预测单细胞RNA测序 (scRNAseq) 数据集中的CCC.
  • 通过结合详细的细胞内信号模式和细胞位置来改善CCC推断.
  • 为了使转录基因数据作为复杂的网络的探索,补充基因表达与配体-受体相互作用.

主要方法:

  • 开发了GraphComm,这是一个基于图形的深度学习框架,用于CCC预测.
  • 集成的基因表达数据,包括关于细胞对连接体和受体相互作用的信息.
  • 利用超过3万个蛋白质相互作用对的数据库捕获细胞内信号模式和细胞位置.

主要成果:

  • 在scRNAseq数据集中,GraphComm成功地预测了生物相关的CCC.
  • 该方法在具有化学或遗传干扰的数据集中证明了改进的CCC推断.
  • GraphComm有效地分析了包含空间单元信息的数据集.

结论:

  • GraphComm代表了从单细胞转录基因数据中预测细胞间通信的重大进步.
  • 该方法能够整合细胞内信号和空间数据,提高了对复杂细胞交叉通话的理解.
  • GraphComm提供了一种强大的工具,可以在各种生物环境中剖析细胞通信模式.