Search research articles

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 Journal Methods Collections JoVE Encyclopedia of Experiments 存档

教育

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual 教师资源中心教师网站

使用条款与条件

Search research articles

相关实验视频

Updated: Jul 6, 2025

Droplet Barcoding-Based Single Cell Transcriptomics of Adult Mammalian Tissues

Droplet Barcoding-Based Single Cell Transcriptomics of Adult Mammalian Tissues

Published on: January 10, 2019

使用单细胞时代转录组学解决细胞动态.

Yifei Liu¹, Kai Huang¹, Wanze Chen¹

¹Key Laboratory of Quantitative Synthetic Biology, Shenzhen Institute of Synthetic Biology, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China.

Current opinion in biotechnology

|January 9, 2024

概括

此摘要是机器生成的。

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

Characterization of the novel KIR3DL10150213 and KIR3DL1112 alleles using sequence-based typing.

HLA·2019

Same author

Poly(ADP-ribose) polymerase 1 accelerates vascular calcification by upregulating Runx2.

Nature communications·2019

Same author

A photo-inducible protein-inorganic nanoparticle assembly for active targeted tumour theranostics.

Nanoscale·2019

Same author

Complex wireframe DNA nanostructures from simple building blocks.

Nature communications·2019

Same author

MiR-876-3p regulates cisplatin resistance and stem cell-like properties of gastric cancer cells by targeting TMED3.

Journal of gastroenterology and hepatology·2019

Same author

Structural basis of broad ebolavirus neutralization by a human survivor antibody.

Nature structural & molecular biology·2019

了解细胞动态是发展和疾病的关键. 本综述涵盖了计算模型和活细胞转录组学,用于研究单细胞水平的细胞状态转换.

科学领域:

细胞生物学细胞生物学
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:

细胞动力学,细胞状态转换的研究,对于理解发育和疾病至关重要.
单细胞转录组学为这些动态过程提供了全基因组的洞察力.
目前的方法通常只提供快照,需要计算或补充方法来推断动态.

研究的目的:

从转录组学数据重建细胞动态的计算方法的审查.
讨论这些计算模型的原则,假设和解释.
突出新兴的活细胞转录组学技术作为一个补充的,没有假设的方法.

主要方法:

从单细胞RNA测序数据推断细胞状态轨迹的计算策略的审查.
在转录学数据集中利用时间信息的方法的分析.
对非破坏性活细胞转录组技术的讨论.

主要成果:

计算模型可以通过分析转录组数据来重建细胞动态,通过固有的时间信号或集成信息.
这些模型依赖于特定的假设,需要仔细解释.
活细胞转录学提供了直接的,没有假设的观测,可以补充计算预测.

更多相关视频

Author Spotlight: Integrating Single-Cell Transcriptomics with Organoid Cultures for Advanced Research and Therapeutic Insights

Author Spotlight: Integrating Single-Cell Transcriptomics with Organoid Cultures for Advanced Research and Therapeutic Insights

Published on: June 28, 2024

Transcriptome Analysis of Single Cells

Transcriptome Analysis of Single Cells

Published on: April 25, 2011

相关实验视频

Last Updated: Jul 6, 2025

Droplet Barcoding-Based Single Cell Transcriptomics of Adult Mammalian Tissues

Droplet Barcoding-Based Single Cell Transcriptomics of Adult Mammalian Tissues

Published on: January 10, 2019

Author Spotlight: Integrating Single-Cell Transcriptomics with Organoid Cultures for Advanced Research and Therapeutic Insights

Author Spotlight: Integrating Single-Cell Transcriptomics with Organoid Cultures for Advanced Research and Therapeutic Insights

Published on: June 28, 2024

Transcriptome Analysis of Single Cells

Transcriptome Analysis of Single Cells

Published on: April 25, 2011

结论:

计算建模和活细胞转录组学都对全面了解细胞动态至关重要.
整合这些方法为研究细胞在发育和疾病中的转变提供了一个强大的工具包.
未来的研究可以利用这些结合的方法来获得更深入的生物学见解.