Literature DB >> 25567865

[Recent progress in single-cell RNA-Seq analysis].

Wen Lu1, Tang Fuchou1.   

Abstract

Cell heterogeneity is a general feature of biological tissues. Standard transcriptome analysis approaches require tens of thousands of cells to provide an average view of gene expression and ignore the information of gene expression heterogeneity. The single-cell RNA-Seq technologies profile gene expression at the single-cell level and serve as powerful tools to identify distinct phenotypic cell types within a heterogeneous population. The single-cell RNA-Seq technologies have been developed rapidly in recent years. The methodological progress includes a variety of cDNA amplification methods, the quantitative analysis of the sensitivity and noise of the technologies, and the development of the low-coverage high-throughput single-cell RNA-Seq and the in situ RNA-Seq technologies. Furthermore, the scope of application is extended from early embryonic development to tissue and organ development, immunology and oncology. In this review, we discuss recent progress in methodology and applications of the single-cell RNA-Seq technologies.

Mesh:

Year:  2014        PMID: 25567865     DOI: 10.3724/SP.J.1005.2014.1069

Source DB:  PubMed          Journal:  Yi Chuan        ISSN: 0253-9772


  3 in total

1.  Heterogeneous circRNA expression profiles and regulatory functions among HEK293T single cells.

Authors:  Chaofang Zhong; Shaojun Yu; Maozhen Han; Jiahuan Chen; Kang Ning
Journal:  Sci Rep       Date:  2017-10-31       Impact factor: 4.379

2.  Dosage compensation in the process of inactivation/reactivation during both germ cell development and early embryogenesis in mouse.

Authors:  Xiaoyong Li; Zhiqiang Hu; Xuelin Yu; Chen Zhang; Binbin Ma; Lin He; Chaochun Wei; Ji Wu
Journal:  Sci Rep       Date:  2017-06-16       Impact factor: 4.379

3.  Single-Cell Ribonucleic Acid Sequencing Clarifies Cold Tolerance Mechanisms in the Pacific White Shrimp (Litopenaeus Vannamei).

Authors:  Weilin Zhu; Chunling Yang; Xiuli Chen; Qingyun Liu; Qiangyong Li; Min Peng; Huanling Wang; Xiaohan Chen; Qiong Yang; Zhenping Liao; Min Li; Chuanyan Pan; Pengfei Feng; Digang Zeng; Yongzhen Zhao
Journal:  Front Genet       Date:  2022-01-12       Impact factor: 4.599

  3 in total

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