Single-cell multi-omics sequencing and its application in tumor heterogeneity

Briefings in Functional Genomics, 2023, 22, 313–328 https://doi.org/10.1093/bfgp/elad009 Advance access publication date 6 April 2023 Review Paper Editor’s Choice Single-cell multi-omics sequencing and its application in tumor heterogeneity Yuqing Sun, Zhiyu Liu, Yue Fu, Yuwei Yang, Junru Lu, Min Pan, Tian Wen, Xueying Xie, Yunfei Bai and Qinyu Ge* Abstract In recent years, the emergence and development of single-cell sequencing technologies have provided unprecedented opportunities to analyze deoxyribonucleic acid, ribonucleic acid and proteins at single-cell resolution. The advancements and reduced costs of highthroughput technologies allow for parallel sequencing of multiple molecular layers from a single cell, providing a comprehensive insight into the biological state and behavioral mechanisms of cells through the integration of genomics, transcriptomics, epigenomics and proteomics information. Researchers are actively working to further improve the cost-effectiveness, stability and high-throughput capabilities of single-cell multi-omics sequencing technologies and exploring their potential in precision medicine through clinical diagnostics. This review aims to survey the cutting-edge advancements in single-cell multi-omics sequencing, summarizing the representative technologies and their applications in profiling complex diseases, with a particular focus on tumors. Keywords: single-cell sequencing; single-cell multi-omics sequencing; precision medicine; tumor heterogeneity Introduction Single-cell sequencing technology offers a comprehensive understanding of cellular heterogeneity, providing valuable biological information that is inaccessible through traditional bulk analysis [1]. However, analyzing just one molecular layer of a single cell provides only partial information. For example, single-cell RNA sequencing (scRNA-seq) may not separate molecularly similar but functionally distinct classes of immune cells [2]. As single-cell analysis techniques continue to improve, the data collected and information gained from different aspects of single cells have greatly increased [3]. By analyzing genomics, transcriptomics, epigenomics and proteomics at the singlecell level, it is possible to access comprehensive and multiple information from an individual cell. Combining scRNA-seq with other biomolecular characterizations, such as protein profiling, provides more information than studying each separately, leading to the development of single-cell multi-omics sequencing technology. Single-cell multi-omics sequencing refers to the ability to simultaneously measure multiple modalities in one experiment or to integrate different types of omics datasets from multiple experiments [4]. And single-cell multi-omics analyses performed Yuqing Sun is a master’s student in Biomedical Engineering at Southeast University. His research interests focus on building a workflow for single-cell multi-omics sequencing analysis and establishing related databases. Zhiyu Liu is currently pursuing her Ph.D. in Biomedical Engineering from Southeast University under the supervision of Dr. Yunfei Bai. Her research interests focus on the detection of RNA methylation. Yue Fu is a master’s student in Biomedical Engineering at Southeast University. Her research interests focus on radiomics and transcriptomics in lung cancer patients. Yuwei Yang is a master’s student in Biomedical Engineering at Southeast University. Her research focuses on computational methods for identifying and analyzing RNA post-transcriptional modifications. Junru Lu is a master’s student in Biomedical Engineering at Southeast University. His research interests focus on RNA-seq, single-cell RNA-seq, and machine learning for data processing. Min Pan is an associate professor at the School of Medicine, Southeast University. She has chaired projects for the National Natural Science Foundation of China and the Natural Science Foundation of Jiangsu Province. Her current research interests include the fragmentomic patterns of cfDNA in low volumes of body fluid and spent embryo culture medium, and the relevant application in NIPT and non-invasive preimplantation genetic testing in IVF. Tian Wen received her M.S. degree in biomedical engineering from Southeast University in 2008. She is currently a researcher at the Jiangsu Provincial Center for Disease Prevention and Control in China. Her research interests focus on pathogenic detection methods, including microfluidics, chemiluminescence, gene chips, etc., and the development of various automatic, integrated, and portable devices. Xueying Xie recieved her Ph.D in Biomedical Engineering from Southeast University in 2004. In her academic work she mainly focuses on bioinformatics including biological network modeling, high-throughput biological data analysis and machine learning. Yunfei Bai received his Ph.D. in Biomedical Engineering from Southeast University in 2005. In his career at Southeast University, his studies have been focused on high throughput DNA sequencing, including total transcriptome analysis and detection of circRNA with its functional analysis as well as the expression analysis of microRNA. Qinyu Ge received his Ph.D. in Biomedical Engineering from Southeast University in 2006. In his career at Southeast University, his research interests focus on sample treatment and library preparation of high throughput DNA sequencing, including spatial transcriptome and whole genome methylation level study as well as the design and fabrication of DNA microarray and its application in cell free nucleic acid, methylation and DNA polymorphism detection. Received: November 23, 2022. Revised: February 20, 2023. Accepted: March 9, 2023 © The Author(s) 2023. Published by Oxford University Press. All rights reserved. For Permissions, please email: *Corresponding author: Qinyu Ge, State Key Lab of Bioelectronics, School of Biological Science and Medical Engineering, Southeast University, Sipailou No.2, Nanjing 210096, People’s Republic of China. Tel.: (Fax): 86-025-83792396; E-mail: 314 | Briefings in Functional Genomics, 2023, Vol. 22, No. 4 in one experiment can be broadly categorized into two groups. The first category involves profiling multiple modalities within a single cell at a time, achieved through individualized test tubes or microwells of plates. This approach is designed for comprehensive profiling but is limited by low throughput and high cost. The other methodology utilizes droplet platforms or combinatorial DNA barcoding strategies to process a large number of cells collectively, resulting in high scalability and cost-effectiveness [5]. Single-cell multi-omics technology integrates information about various molecular modalities and their relationships in individual cells. From a single-cell perspective, different omics data represents various aspects of the cell, while single-cell multi-omics sequencing provides a comprehensive and in-depth understanding of (...truncated)