Single-cell sequencing to multi-omics: technologies and applications

doi:10.1186/s40364-024-00643-4

Review

. 2024 Sep 27;12(1):110.

doi: 10.1186/s40364-024-00643-4.

Single-cell sequencing to multi-omics: technologies and applications

Xiangyu Wu^#¹, Xin Yang^#¹, Yunhan Dai², Zihan Zhao¹, Junmeng Zhu³, Hongqian Guo⁴, Rong Yang⁵

Affiliations

¹ Department of Urology, Nanjing Drum Tower Hospital, Affiliated Hospital of Medical School, Nanjing University, 321 Zhongshan Road, Nanjing, 210008, Jiangsu, China.
² Medical School, Nanjing University, Nanjing, China.
³ Department of Oncology, Nanjing Drum Tower Hospital, Affiliated Hospital of Medical School, Nanjing University, Nanjing, China.
⁴ Department of Urology, Nanjing Drum Tower Hospital, Affiliated Hospital of Medical School, Nanjing University, 321 Zhongshan Road, Nanjing, 210008, Jiangsu, China. dr.ghq@nju.edu.cn.
⁵ Department of Urology, Nanjing Drum Tower Hospital, Affiliated Hospital of Medical School, Nanjing University, 321 Zhongshan Road, Nanjing, 210008, Jiangsu, China. doctoryr@gmail.com.

^# Contributed equally.

PMID: 39334490
PMCID: PMC11438019
DOI: 10.1186/s40364-024-00643-4

Review

Single-cell sequencing to multi-omics: technologies and applications

Xiangyu Wu et al. Biomark Res. 2024.

. 2024 Sep 27;12(1):110.

doi: 10.1186/s40364-024-00643-4.

Authors

Xiangyu Wu^#¹, Xin Yang^#¹, Yunhan Dai², Zihan Zhao¹, Junmeng Zhu³, Hongqian Guo⁴, Rong Yang⁵

Affiliations

¹ Department of Urology, Nanjing Drum Tower Hospital, Affiliated Hospital of Medical School, Nanjing University, 321 Zhongshan Road, Nanjing, 210008, Jiangsu, China.
² Medical School, Nanjing University, Nanjing, China.
³ Department of Oncology, Nanjing Drum Tower Hospital, Affiliated Hospital of Medical School, Nanjing University, Nanjing, China.
⁴ Department of Urology, Nanjing Drum Tower Hospital, Affiliated Hospital of Medical School, Nanjing University, 321 Zhongshan Road, Nanjing, 210008, Jiangsu, China. dr.ghq@nju.edu.cn.
⁵ Department of Urology, Nanjing Drum Tower Hospital, Affiliated Hospital of Medical School, Nanjing University, 321 Zhongshan Road, Nanjing, 210008, Jiangsu, China. doctoryr@gmail.com.

^# Contributed equally.

PMID: 39334490
PMCID: PMC11438019
DOI: 10.1186/s40364-024-00643-4

Abstract

Cells, as the fundamental units of life, contain multidimensional spatiotemporal information. Single-cell RNA sequencing (scRNA-seq) is revolutionizing biomedical science by analyzing cellular state and intercellular heterogeneity. Undoubtedly, single-cell transcriptomics has emerged as one of the most vibrant research fields today. With the optimization and innovation of single-cell sequencing technologies, the intricate multidimensional details concealed within cells are gradually unveiled. The combination of scRNA-seq and other multi-omics is at the forefront of the single-cell field. This involves simultaneously measuring various omics data within individual cells, expanding our understanding across a broader spectrum of dimensions. Single-cell multi-omics precisely captures the multidimensional aspects of single-cell transcriptomes, immune repertoire, spatial information, temporal information, epitopes, and other omics in diverse spatiotemporal contexts. In addition to depicting the cell atlas of normal or diseased tissues, it also provides a cornerstone for studying cell differentiation and development patterns, disease heterogeneity, drug resistance mechanisms, and treatment strategies. Herein, we review traditional single-cell sequencing technologies and outline the latest advancements in single-cell multi-omics. We summarize the current status and challenges of applying single-cell multi-omics technologies to biological research and clinical applications. Finally, we discuss the limitations and challenges of single-cell multi-omics and potential strategies to address them.

Keywords: Computational biology; Metabolome; Microbiome; Proteomics; Single-cell multi-omics; Spatial transcriptomics; scBCR-seq; scRNA-seq; scTCR-seq.

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Conflict of interest statement

The authors declare no competing interests.

The authors declare no conflicts of interest.

Figures

**Fig. 1**
(A) Conventional scRNA-seq technologies. (B) Overview of the single-cell multi-omics scheme

**Fig. 2**
A brief method and principle of single-cell multi-omics technologies for (A) scTCR/BCR-seq, (B) ClickTag, (C) proteome, (D) microbiome, (E) metabolome, (F) epigenome

**Fig. 3**
The principles of key technologies in the dimensions of (A) time and (B) space at the single-cell level

**Fig. 4**
Applications of single-cell multi-omics in biological research and clinical practice

See this image and copyright information in PMC

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References

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1. Ramskold D, Luo S, Wang YC, Li R, Deng Q, Faridani OR, Daniels GA, Khrebtukova I, Loring JF, Laurent LC, et al. Full-length mRNA-Seq from single-cell levels of RNA and individual circulating tumor cells. Nat Biotechnol. 2012;30:777–82. - PMC - PubMed
1. Jaitin DA, Kenigsberg E, Keren-Shaul H, Elefant N, Paul F, Zaretsky I, Mildner A, Cohen N, Jung S, Tanay A, Amit I. Massively parallel single-cell RNA-seq for marker-free decomposition of tissues into cell types. Science. 2014;343:776–9. - PMC - PubMed
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[1] Tang F, Barbacioru C, Wang Y, Nordman E, Lee C, Xu N, Wang X, Bodeau J, Tuch BB, Siddiqui A, et al. mRNA-Seq whole-transcriptome analysis of a single cell. Nat Methods. 2009;6:377–82. - PubMed

[2] Tang F, Barbacioru C, Wang Y, Nordman E, Lee C, Xu N, Wang X, Bodeau J, Tuch BB, Siddiqui A, et al. mRNA-Seq whole-transcriptome analysis of a single cell. Nat Methods. 2009;6:377–82. - PubMed

[3] Ramskold D, Luo S, Wang YC, Li R, Deng Q, Faridani OR, Daniels GA, Khrebtukova I, Loring JF, Laurent LC, et al. Full-length mRNA-Seq from single-cell levels of RNA and individual circulating tumor cells. Nat Biotechnol. 2012;30:777–82. - PMC - PubMed

[4] Ramskold D, Luo S, Wang YC, Li R, Deng Q, Faridani OR, Daniels GA, Khrebtukova I, Loring JF, Laurent LC, et al. Full-length mRNA-Seq from single-cell levels of RNA and individual circulating tumor cells. Nat Biotechnol. 2012;30:777–82. - PMC - PubMed

[5] Jaitin DA, Kenigsberg E, Keren-Shaul H, Elefant N, Paul F, Zaretsky I, Mildner A, Cohen N, Jung S, Tanay A, Amit I. Massively parallel single-cell RNA-seq for marker-free decomposition of tissues into cell types. Science. 2014;343:776–9. - PMC - PubMed

[6] Jaitin DA, Kenigsberg E, Keren-Shaul H, Elefant N, Paul F, Zaretsky I, Mildner A, Cohen N, Jung S, Tanay A, Amit I. Massively parallel single-cell RNA-seq for marker-free decomposition of tissues into cell types. Science. 2014;343:776–9. - PMC - PubMed

[7] Hashimshony T, Wagner F, Sher N, Yanai I. CEL-Seq: single-cell RNA-Seq by multiplexed linear amplification. Cell Rep. 2012;2:666–73. - PubMed

[8] Hashimshony T, Wagner F, Sher N, Yanai I. CEL-Seq: single-cell RNA-Seq by multiplexed linear amplification. Cell Rep. 2012;2:666–73. - PubMed

[9] Zhang SQ, Ma KY, Schonnesen AA, Zhang M, He C, Sun E, Williams CM, Jia W, Jiang N. High-throughput determination of the antigen specificities of T cell receptors in single cells. Nat Biotechnol 2018. - PMC - PubMed

[10] Zhang SQ, Ma KY, Schonnesen AA, Zhang M, He C, Sun E, Williams CM, Jia W, Jiang N. High-throughput determination of the antigen specificities of T cell receptors in single cells. Nat Biotechnol 2018. - PMC - PubMed

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Single-cell sequencing to multi-omics: technologies and applications

Affiliations

Single-cell sequencing to multi-omics: technologies and applications

Authors

Affiliations

Abstract

Conflict of interest statement

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