Advanced sequencing-based high-throughput and long-read single-cell transcriptome analysis

被引:2
|
作者
Huang, Shanqing [2 ]
Shi, Weixiong [2 ]
Li, Shiyu [2 ]
Fan, Qian [1 ]
Yang, Chaoyong [1 ,2 ]
Cao, Jiao [1 ]
Wu, Lingling [1 ]
机构
[1] Shanghai Jiao Tong Univ, Renji Hosp, Inst Mol Med, Shanghai Key Lab Nucl Acid Chem & Nanomed,Sch Med, Shanghai 200127, Peoples R China
[2] Xiamen Univ, Coll Chem & Chem Engn, Discipline Intelligent Instrument & Equipment, Xiamen 361005, Peoples R China
基金
中国国家自然科学基金;
关键词
NUCLEUS RNA-SEQ; GENE-EXPRESSION; MESSENGER-RNA; TISSUE; REVEALS; OMICS; TIME;
D O I
10.1039/d4lc00105b
中图分类号
Q5 [生物化学];
学科分类号
071010 ; 081704 ;
摘要
Cells are the fundamental building blocks of living systems, exhibiting significant heterogeneity. The transcriptome connects the cellular genotype and phenotype, and profiling single-cell transcriptomes is critical for uncovering distinct cell types, states, and the interplay between cells in development, health, and disease. Nevertheless, single-cell transcriptome analysis faces daunting challenges due to the low abundance and diverse nature of RNAs in individual cells, as well as their heterogeneous expression. The advent and continuous advancements of next-generation sequencing (NGS) and third-generation sequencing (TGS) technologies have solved these problems and facilitated the high-throughput, sensitive, full-length, and rapid profiling of single-cell RNAs. In this review, we provide a broad introduction to current methodologies for single-cell transcriptome sequencing. First, state-of-the-art advancements in high-throughput and full-length single-cell RNA sequencing (scRNA-seq) platforms using NGS are reviewed. Next, TGS-based long-read scRNA-seq methods are summarized. Finally, a brief conclusion and perspectives for comprehensive single-cell transcriptome analysis are discussed. In this review, we provide a broad introduction to advanced high-throughput and long-read single-cell transcriptome analysis using next-generation sequencing and third-generation sequencing platforms.
引用
收藏
页码:2601 / 2621
页数:21
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