Graduate School of Medical Science and Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon 34141, Korea.
These authors contributed equally to this work.
Mol Cells. 2021 Mar 31;44(3):127-135. doi: 10.14348/molcells.2021.0002.
Since the introduction of RNA sequencing (RNA-seq) as a high-throughput mRNA expression analysis tool, this procedure has been increasingly implemented to identify cell-level transcriptome changes in a myriad of model systems. However, early methods processed cell samples in bulk, and therefore the unique transcriptomic patterns of individual cells would be lost due to data averaging. Nonetheless, the recent and continuous development of new single-cell RNA sequencing (scRNA-seq) toolkits has enabled researchers to compare transcriptomes at a single-cell resolution, thus facilitating the analysis of individual cellular features and a deeper understanding of cellular functions. Nonetheless, the rapid evolution of high throughput single-cell "omics" tools has created the need for effective hypothesis verification strategies. Particularly, this issue could be addressed by coupling cell engineering techniques with single-cell sequencing. This approach has been successfully employed to gain further insights into disease pathogenesis and the dynamics of differentiation trajectories. Therefore, this review will discuss the current status of cell engineering toolkits and their contributions to single-cell and genome-wide data collection and analyses.
自 RNA 测序 (RNA-seq) 作为一种高通量 mRNA 表达分析工具引入以来,该方法已被越来越多地用于鉴定众多模型系统中的细胞水平转录组变化。然而,早期的方法批量处理细胞样本,因此由于数据平均化,单个细胞的独特转录组模式将会丢失。尽管如此,最近和不断发展的新的单细胞 RNA 测序 (scRNA-seq) 工具包使研究人员能够以单细胞分辨率比较转录组,从而促进了对单个细胞特征的分析和对细胞功能的更深入理解。尽管如此,高通量单细胞“组学”工具的快速发展已经产生了对有效假设验证策略的需求。特别是,通过将细胞工程技术与单细胞测序相结合,可以解决这个问题。这种方法已成功地用于深入了解疾病发病机制和分化轨迹的动态。因此,本文将讨论细胞工程工具包的现状及其对单细胞和全基因组数据收集和分析的贡献。