Studying temporal dynamics of single cells: expression, lineage and regulatory networks
Studying temporal dynamics of single cells: expression, lineage and regulatory networks
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研究单细胞的时间动态：表达、谱系和调控网络
DOI：
10.1007/s12551-023-01090-5
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发表时间：
2023
影响因子：
--
通讯作者：
Zhang, Xiuwei
中科院分区：
文献类型：
--
作者：
Pan, Xinhai;Zhang, Xiuwei
了解多细胞器官是如何从单个细胞发育成不同类型的细胞是生物学中的一个基本问题。随着高通量scRNA-seq技术的发展，已经开发出计算方法来从转录数据中揭示单个细胞的时间动力学，从细胞轨迹的现象到形成轨迹的潜在机制。这些研究涉及几种不同的计算方法，包括轨迹推断(TI)、谱系追踪(LT)和基因调控网络(GRN)推理。本文综述了利用scRNA-seq数据研究细胞分化和细胞命运的各种计算方法，以及不同方法的优点和局限性。我们进一步讨论GRN如何潜在地影响细胞命运决定和轨迹结构。
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.