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

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

The elephant in the room: impact factor.

Development (Cambridge, England)·2026
Same author

Boundary constraints can determine pattern emergence.

Development (Cambridge, England)·2026
Same author

The notochord: development, disease and stem cell-based modelling.

Development (Cambridge, England)·2026
Same author

Development's 2025 Outstanding Paper Prize.

Development (Cambridge, England)·2026
Same author

'Why is publishing so expensive?'

Development (Cambridge, England)·2026
Same author

Gene regulatory networks: from correlative models to causal explanations.

Nature reviews. Genetics·2026
Same journal

High-throughput DNA engineering by mating bacteria.

Cell systems·2026
Same journal

Living bacterial reservoir computers for information processing and sensing.

Cell systems·2026
Same journal

A data-driven modeling framework for mapping genotypes to synthetic microbial community functions.

Cell systems·2026
Same journal

BulkFormer: A large-scale foundation model for bulk transcriptomes.

Cell systems·2026
Same journal

Glycoform engineering of a mammalian platform to sculpt a humanized recombinant bioscavenger.

Cell systems·2026
Same journal

Targeted genomic editing of human gut Bacteroides species based on CRISPR-associated transposases.

Cell systems·2026
查看所有相关文章

相关实验视频

Updated: Jun 26, 2025

Time-lapse Live Imaging and Quantification of Fast Dendritic Branch Dynamics in Developing Drosophila Neurons
08:23

Time-lapse Live Imaging and Quantification of Fast Dendritic Branch Dynamics in Developing Drosophila Neurons

Published on: September 25, 2019

6.2K

使用潜伏动态系统和时间解析的转录学来重建发育轨迹.

Rory J Maizels1, Daniel M Snell2, James Briscoe2

  • 1The Francis Crick Institute, 1 Midland Road, London NW1 1AT, UK; University College, London, UK.

Cell systems
|May 16, 2024
PubMed
概括
此摘要是机器生成的。

我们开发了sci-FATE2用于代谢标记和计算工具 (VelvetVAE,VelvetSDE),以建模细胞命运动态. 该框架增强了使用单细胞转录组学数据的发育过程的理解.

关键词:
RNA的速度RNA的速度深度学习是一种深度学习.基因监管网络 基因监管网络单细胞转录组学 单细胞转录组学变量自动编码器变量自动编码器

更多相关视频

Comprehensive Analysis of Transcription Dynamics from Brain Samples Following Behavioral Experience
08:14

Comprehensive Analysis of Transcription Dynamics from Brain Samples Following Behavioral Experience

Published on: August 26, 2014

11.6K
Real-time Bioluminescence Imaging of Notch Signaling Dynamics during Murine Neurogenesis
10:25

Real-time Bioluminescence Imaging of Notch Signaling Dynamics during Murine Neurogenesis

Published on: December 12, 2019

7.6K

相关实验视频

Last Updated: Jun 26, 2025

Time-lapse Live Imaging and Quantification of Fast Dendritic Branch Dynamics in Developing Drosophila Neurons
08:23

Time-lapse Live Imaging and Quantification of Fast Dendritic Branch Dynamics in Developing Drosophila Neurons

Published on: September 25, 2019

6.2K
Comprehensive Analysis of Transcription Dynamics from Brain Samples Following Behavioral Experience
08:14

Comprehensive Analysis of Transcription Dynamics from Brain Samples Following Behavioral Experience

Published on: August 26, 2014

11.6K
Real-time Bioluminescence Imaging of Notch Signaling Dynamics during Murine Neurogenesis
10:25

Real-time Bioluminescence Imaging of Notch Signaling Dynamics during Murine Neurogenesis

Published on: December 12, 2019

7.6K

科学领域:

  • 发展生物学 发展生物学
  • 计算生物学 计算生物学
  • 基因组学就是基因组学.

背景情况:

  • 单细胞转录学提供了细胞状态的快照,限制了像细胞命运决定这样的动态过程的研究.
  • 现有的时间推断方法,如代谢标记和拼接分析,具有固有的局限性.
  • 了解细胞命运动态对于发育生物学和再生医学至关重要.

研究的目的:

  • 开发一个集成的实验和计算框架,用于高分辨率的动态建模细胞命运决策.
  • 提高单细胞数据的质量和时间分辨率,以研究分化.
  • 创建强大的计算工具来推断和模拟细胞轨迹.

主要方法:

  • 开发了sci-FATE2,一种优化的代谢标记技术,以提高单细胞RNA测序数据质量.
  • 应用VelvetVAE,一个变异性自编码器,用于准确的基因表达速度推断.
  • 利用VelvetSDE,一个神经随机微分方程模型,模拟细胞轨迹分布.

主要成果:

  • 通过使用sci-FATE2.2,对45,000个胚胎干细胞进行了分析,使其分化为神经管身份.
  • 与现有工具相比,VelvetVAE在速度推断方面表现出优异的性能.
  • VelvetSDE成功地回顾了数据集分布,捕捉了命运决策边界和基因表达动态.

结论:

  • 开发的框架将单细胞分析从静态描述转变为生物过程的动态模型.
  • 这种方法为研究发展细胞命运决定背后的复杂机制提供了强大的工具.
  • 改进的实验方法和先进的计算建模的整合为研究细胞动态开辟了新的途径.