Single-Cell Sequencing Methodologies: From Transcriptome to Multi-Dimensional Measurement,Small Methods

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Single-Cell Sequencing Methodologies: From Transcriptome to Multi-Dimensional Measurement
Small Methods ( IF 10.7 ) Pub Date : 2021-04-17 , DOI: 10.1002/smtd.202100111
Yingwen Chen ₁ , Jia Song ₂ , Qingyu Ruan ₁ , Xi Zeng ₁ , Lingling Wu ₂ , Linfeng Cai ₁ , Xuanqun Wang ₁ , Chaoyong Yang _{1,

2}

Affiliation

Cells are the basic building blocks of biological systems, with inherent unique molecular features and development trajectories. The study of single cells facilitates in-depth understanding of cellular diversity, disease processes, and organization of multicellular organisms. Single-cell RNA sequencing (scRNA-seq) technologies have become essential tools for the interrogation of gene expression patterns and the dynamics of single cells, allowing cellular heterogeneity to be dissected at unprecedented resolution. Nevertheless, measuring at only transcriptome level or 1D is incomplete; the cellular heterogeneity reflects in multiple dimensions, including the genome, epigenome, transcriptome, spatial, and even temporal dimensions. Hence, integrative single cell analysis is highly desired. In addition, the way to interpret sequencing data by virtue of bioinformatic tools also exerts critical roles in revealing differential gene expression. Here, a comprehensive review that summarizes the cutting-edge single-cell transcriptome sequencing methodologies, including scRNA-seq, spatial and temporal transcriptome profiling, multi-omics sequencing and computational methods developed for scRNA-seq data analysis is provided. Finally, the challenges and perspectives of this field are discussed.

中文翻译：

单细胞测序方法：从转录组到多维测量

细胞是生物系统的基本组成部分，具有固有的独特分子特征和发展轨迹。对单细胞的研究有助于深入了解细胞多样性、疾病过程和多细胞生物的组织。单细胞 RNA 测序 (scRNA-seq) 技术已成为研究基因表达模式和单细胞动力学的重要工具，允许以前所未有的分辨率剖析细胞异质性。然而，仅在转录组水平或 1D 水平进行测量是不完整的。细胞异质性体现在多个维度，包括基因组、表观基因组、转录组、空间甚至时间维度。因此，高度需要综合单细胞分析。此外，利用生物信息学工具解释测序数据的方式在揭示差异基因表达方面也发挥着关键作用。在这里，提供了一个全面的综述，总结了尖端的单细胞转录组测序方法，包括 scRNA-seq、空间和时间转录组分析、多组学测序和为 scRNA-seq 数据分析开发的计算方法。最后，讨论了该领域的挑战和前景。提供了为 scRNA-seq 数据分析开发的多组学测序和计算方法。最后，讨论了该领域的挑战和前景。提供了为 scRNA-seq 数据分析开发的多组学测序和计算方法。最后，讨论了该领域的挑战和前景。

更新日期：2021-06-10

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