Single-Cell Transcriptome Analysis in Plants: Advances and Challenges

转录组 生物 仿形(计算机编程) 鉴定(生物学) 夸张 计算生物学 基因 遗传学 计算机科学 基因表达 拟南芥 生态学 心理学 操作系统 精神科 突变体
作者
Rahul Shaw,Xin Tian,Jian Xu
出处
期刊:Molecular Plant [Elsevier]
卷期号:14 (1): 115-126 被引量:122
标识
DOI:10.1016/j.molp.2020.10.012
摘要

The rapid and enthusiastic adoption of single-cell RNA sequencing (scRNA-seq) has demonstrated that this technology is far more than just another way to perform transcriptome analysis. It is not an exaggeration to say that the advent of scRNA-seq is revolutionizing the details of whole-transcriptome snapshots from a tissue to a cell. With this disruptive technology, it is now possible to mine heterogeneity between tissue types and within cells like never before. This enables more rapid identification of rare and novel cell types, simultaneous characterization of multiple different cell types and states, more accurate and integrated understanding of their roles in life processes, and more. However, we are only at the beginning of unlocking the full potential of scRNA-seq applications. This is particularly true for plant sciences, where single-cell transcriptome profiling is in its early stage and has many exciting challenges to overcome. In this review, we compare and evaluate recent pioneering studies using the Arabidopsis root model, which has established new paradigms for scRNA-seq studies in plants. We also explore several new and promising single-cell analysis tools that are available to those wishing to study plant development and physiology at unprecedented resolution and scale. In addition, we propose some future directions on the use of scRNA-seq technology to tackle some of the critical challenges in plant research and breeding.
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