Jove
Visualize
联系我们
JoVE
x logofacebook logolinkedin logoyoutube logo
关于 JoVE
概览领导团队博客JoVE 帮助中心
作者
出版流程编辑委员会范围与政策同行评审常见问题投稿
图书馆员
用户评价订阅访问资源图书馆顾问委员会常见问题
研究
JoVE JournalMethods CollectionsJoVE Encyclopedia of Experiments存档
教育
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab Manual教师资源中心教师网站
使用条款与条件
隐私政策
政策

相关概念视频

Cell Specific Gene Expression01:58

Cell Specific Gene Expression

4.8K
4.8K
Overview of Cell-Matrix Interactions01:24

Overview of Cell-Matrix Interactions

7.5K
The extracellular matrix or ECM holds cells together to form a tissue and allows the cells within the tissue to communicate. ECM comprises proteins such as fibronectin, collagen, laminin, etc. The most abundant protein in this space is collagen. Collagen fibers are interwoven with carbohydrate-containing protein molecules called proteoglycans. ECM allows cell migration and provides a structural scaffold at cell adhesion that anchors the cell when the extracellular matrix proteins interact with...
7.5K
Cell Signaling Feedback Loops01:07

Cell Signaling Feedback Loops

6.6K
Positive and negative feedback loops are crucial for regulating biological signaling systems. These feedback loops are processes that connect output signals to their inputs.
Negative feedback loops
Most signaling systems have negative feedback loops that can perform different functions such as output limiter, and adaptation.
Output limiter
Upon receiving an input signal, the cellular response rapidly increases until a threshold is reached. Beyond this threshold, a negative feedback loop...
6.6K
Diversity in Cell Signaling Responses01:22

Diversity in Cell Signaling Responses

6.8K
The physiological function of a cell and cellular communication are outcomes of a range of extrinsic signals, intracellular signaling pathways, and cellular responses. No two cell types express the same repertoire of signaling components. Receptors are highly selective for their cognate ligands, but once activated, they can alter multiple cellular processes such as DNA transcription, protein synthesis, and metabolic activity. 
Graded and Abrupt Responses
Some signaling systems generate...
6.8K

您也可能阅读

相关文章

通过共同作者、期刊和引用图与本文相关的文章。

排序
Same author

Integrative cross-sample alignment and spatially differential gene analysis for spatial transcriptomics.

Nature communications·2026
Same author

Inferring stochastic dynamics by biophysical Neural ODE using single-cell transcriptomics.

Nature communications·2026
Same author

Reconstructing single-cell resolution from spatial transcriptomics with CellRefiner.

Nature communications·2026
Same author

Rete ridges form via evolutionarily distinct mechanisms in mammalian skin.

Nature·2026
Same author

Optimal Transport based Cross-Domain Integration for Heterogeneous Data.

Journal of the American Statistical Association·2025
Same author

Retinal polyunsaturated fatty acid supplementation reverses aging-related vision decline in mice.

Science translational medicine·2025
Same journal

PCSK5 promotes angiogenesis and cardiac repair after myocardial infarction.

Nature communications·2026
Same journal

PfApiAT2 is a proline transporter essential for the transmission of Plasmodium falciparum by the mosquito vector.

Nature communications·2026
Same journal

Transient distortions of the South Atlantic Anomaly radiation environments driven by electric fields.

Nature communications·2026
Same journal

Structural basis of the regulation by CDK11 kinase of early spliceosome activation and evidence for its proofreading by DHX15 helicase.

Nature communications·2026
Same journal

Structural and mechanistic insights into primer synthesis initiation by DNA primase.

Nature communications·2026
Same journal

Changes in heritability and shared environmentality of educational attainment across twentieth-century Norway.

Nature communications·2026
查看所有相关文章

相关实验视频

Updated: Sep 17, 2025

Using R, Seurat, and CellChat to Analyze a Single-Cell Transcriptomics Dataset of Mouse Skin Wound Healing
08:58

Using R, Seurat, and CellChat to Analyze a Single-Cell Transcriptomics Dataset of Mouse Skin Wound Healing

Published on: August 1, 2025

569

剖析由单细胞转录组数据使用细胞间通信诱导的交叉通话.

Jiawen Hou1,2, Wei Zhao3,4, Qing Nie5,6,7

  • 1Department of Mathematics, University of California Irvine, Irvine, CA, USA.

Nature communications
|July 2, 2025
PubMed
概括
此摘要是机器生成的。

本研究介绍了SigXTalk,这是一种机器学习方法,用于使用单细胞RNA测序 (scRNA-seq) 数据分析细胞细胞通信 (CCC) 中的途径交叉通话. SigXTalk量化了信号忠诚度和特异性,以揭示复杂的监管网络中的关键分子参与者.

更多相关视频

Transcriptome Analysis of Single Cells
07:27

Transcriptome Analysis of Single Cells

Published on: April 25, 2011

30.1K
Single-cell Microinjection for Cell Communication Analysis
09:59

Single-cell Microinjection for Cell Communication Analysis

Published on: February 26, 2017

11.4K

相关实验视频

Last Updated: Sep 17, 2025

Using R, Seurat, and CellChat to Analyze a Single-Cell Transcriptomics Dataset of Mouse Skin Wound Healing
08:58

Using R, Seurat, and CellChat to Analyze a Single-Cell Transcriptomics Dataset of Mouse Skin Wound Healing

Published on: August 1, 2025

569
Transcriptome Analysis of Single Cells
07:27

Transcriptome Analysis of Single Cells

Published on: April 25, 2011

30.1K
Single-cell Microinjection for Cell Communication Analysis
09:59

Single-cell Microinjection for Cell Communication Analysis

Published on: February 26, 2017

11.4K

科学领域:

  • 计算生物学 计算生物学
  • 系统生物学 系统生物学
  • 基因组学就是基因组学.

背景情况:

  • 细胞-细胞通信 (CCC) 涉及复杂的信号通路.
  • 使用单细胞RNA测序 (scRNA-seq) 数据分析CCC网络的现有方法往往忽略了路径交叉通话.
  • 了解交叉通话对于破译细胞反应和疾病机制至关重要.

研究的目的:

  • 开发一种基于机器学习的新方法,SigXTalk,用于分析CCC中的路径交叉通话.
  • 量化信号保真度和特异性,测量交叉通话的影响.
  • 提供CCC诱导的监管网络的系统分析,考虑交叉通话.

主要方法:

  • 开发了SigXTalk,一种利用超图形学习的机器学习方法.
  • 在受体,转录因子和基因之间编码了更高阶的关系.
  • 量化信号忠实度和特异性,以评估交叉声效应.

主要成果:

  • SigXTalk有效地识别了交叉通话路径内的关键共享分子.
  • 该方法准确地确定了共享分子在传输CCC信息中的作用.
  • 基准测试证明了SigXTalk在模拟和现实数据上的有效性,稳定性和准确性.
  • SigXTalk成功地识别了特定疾病的信号,目标,网络和CCC模式.
  • 该方法可以追踪交叉通话途径的时间演变.

结论:

  • SigXTalk提供了一个强大的工具,用于剖析CCC中的路径交叉声.
  • 该方法通过结合交叉通话来提高对监管网络的理解.
  • 在疾病分析和理解动态生物过程方面,SigXTalk具有重要的应用.