转录组
计算生物学
计算机科学
单元格排序
分类
核糖核酸
生物
数据科学
细胞
基因
基因表达
遗传学
程序设计语言
作者
Sai Guna Ranjan Gurazada,Kevin L. Cox,Kirk J. Czymmek,Blake C. Meyers
摘要
Single-cell RNA-seq is a tool that generates a high resolution of transcriptional data that can be used to understand regulatory networks in biological systems. In plants, several methods have been established for transcriptional analysis in tissue sections, cell types, and/or single cells. These methods typically require cell sorting, transgenic plants, protoplasting, or other damaging or laborious processes. Additionally, the majority of these technologies lose most or all spatial resolution during implementation. Those that offer a high spatial resolution for RNA lack breadth in the number of transcripts characterized. Here, we briefly review the evolution of spatial transcriptomics methods and we highlight recent advances and current challenges in sequencing, imaging, and computational aspects toward achieving 3D spatial transcriptomics of plant tissues with a resolution approaching single cells. We also provide a perspective on the potential opportunities to advance this novel methodology in plants.
科研通智能强力驱动
Strongly Powered by AbleSci AI