研究单细胞的时间动态：表达、谱系和调控网络。
Studying temporal dynamics of single cells: expression, lineage and regulatory networks.
作者信息
Pan Xinhai, Zhang Xiuwei
机构信息
School of Computational Science and Engineering, Georgia Institute of Technology, Atlanta, GA 30332 USA.
出版信息
Biophys Rev. 2023 Aug 4;16(1):57-67. doi: 10.1007/s12551-023-01090-5. eCollection 2024 Feb.
UNLABELLED
Learning how multicellular organs are developed from single cells to different cell types is a fundamental problem in biology. With the high-throughput scRNA-seq technology, computational methods have been developed to reveal the temporal dynamics of single cells from transcriptomic data, from phenomena on cell trajectories to the underlying mechanism that formed the trajectory. There are several distinct families of computational methods including Trajectory Inference (TI), Lineage Tracing (LT), and Gene Regulatory Network (GRN) Inference which are involved in such studies. This review summarizes these computational approaches which use scRNA-seq data to study cell differentiation and cell fate specification as well as the advantages and limitations of different methods. We further discuss how GRNs can potentially affect cell fate decisions and trajectory structures.
SUPPLEMENTARY INFORMATION
The online version contains supplementary material available at 10.1007/s12551-023-01090-5.
未标注
了解多细胞器官如何从单细胞发育成不同细胞类型是生物学中的一个基本问题。借助高通量单细胞RNA测序（scRNA-seq）技术，已开发出计算方法来从转录组数据中揭示单细胞的时间动态，从细胞轨迹上的现象到形成轨迹的潜在机制。有几个不同的计算方法家族，包括轨迹推断（TI）、谱系追踪（LT）和基因调控网络（GRN）推断，它们都参与了此类研究。本综述总结了这些利用scRNA-seq数据研究细胞分化和细胞命运决定的计算方法，以及不同方法的优缺点。我们进一步讨论了基因调控网络如何可能影响细胞命运决定和轨迹结构。
补充信息
在线版本包含可在10.1007/s12551-023-01090-5获取的补充材料。
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