Volume 5, Issue 6 2100111
Review

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

Yingwen Chen

Yingwen Chen

The MOE Key Laboratory of Spectrochemical Analysis and Instrumentation, The Key Laboratory of Chemical Biology of Fujian Province, State Key Laboratory of Physical Chemistry of Solid Surfaces, Collaborative Innovation Center of Chemistry for Energy Materials, Department of Chemical Biology, College of Chemistry and Chemical Engineering, Xiamen University, Xiamen, 361005 China

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Jia Song

Corresponding Author

Jia Song

Institute of Molecular Medicine, Renji Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200127 China

E-mail: [email protected], [email protected]

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Qingyu Ruan

Qingyu Ruan

The MOE Key Laboratory of Spectrochemical Analysis and Instrumentation, The Key Laboratory of Chemical Biology of Fujian Province, State Key Laboratory of Physical Chemistry of Solid Surfaces, Collaborative Innovation Center of Chemistry for Energy Materials, Department of Chemical Biology, College of Chemistry and Chemical Engineering, Xiamen University, Xiamen, 361005 China

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Xi Zeng

Xi Zeng

The MOE Key Laboratory of Spectrochemical Analysis and Instrumentation, The Key Laboratory of Chemical Biology of Fujian Province, State Key Laboratory of Physical Chemistry of Solid Surfaces, Collaborative Innovation Center of Chemistry for Energy Materials, Department of Chemical Biology, College of Chemistry and Chemical Engineering, Xiamen University, Xiamen, 361005 China

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Lingling Wu

Lingling Wu

Institute of Molecular Medicine, Renji Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200127 China

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Linfeng Cai

Linfeng Cai

The MOE Key Laboratory of Spectrochemical Analysis and Instrumentation, The Key Laboratory of Chemical Biology of Fujian Province, State Key Laboratory of Physical Chemistry of Solid Surfaces, Collaborative Innovation Center of Chemistry for Energy Materials, Department of Chemical Biology, College of Chemistry and Chemical Engineering, Xiamen University, Xiamen, 361005 China

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Xuanqun Wang

Xuanqun Wang

The MOE Key Laboratory of Spectrochemical Analysis and Instrumentation, The Key Laboratory of Chemical Biology of Fujian Province, State Key Laboratory of Physical Chemistry of Solid Surfaces, Collaborative Innovation Center of Chemistry for Energy Materials, Department of Chemical Biology, College of Chemistry and Chemical Engineering, Xiamen University, Xiamen, 361005 China

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Chaoyong Yang

Corresponding Author

Chaoyong Yang

The MOE Key Laboratory of Spectrochemical Analysis and Instrumentation, The Key Laboratory of Chemical Biology of Fujian Province, State Key Laboratory of Physical Chemistry of Solid Surfaces, Collaborative Innovation Center of Chemistry for Energy Materials, Department of Chemical Biology, College of Chemistry and Chemical Engineering, Xiamen University, Xiamen, 361005 China

Institute of Molecular Medicine, Renji Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200127 China

E-mail: [email protected], [email protected]

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First published: 17 April 2021
Citations: 16

Abstract

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.

Conflict of Interest

The authors declare no conflict of interest.

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