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Single-Cell Multiomics: Technologies and Data Analysis Methods Jeongwoo Lee1,Doyounghyeon1 and Daehee Hwang1

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Single-Cell Multiomics: Technologies and Data Analysis Methods Jeongwoo Lee1,Doyounghyeon1 and Daehee Hwang1 Lee et al. Experimental & Molecular Medicine (2020) 52:1428–1442 https://doi.org/10.1038/s12276-020-0420-2 Experimental & Molecular Medicine REVIEW ARTICLE Open Access Single-cell multiomics: technologies and data analysis methods Jeongwoo Lee1,DoYoungHyeon1 and Daehee Hwang1 Abstract Advances in single-cell isolation and barcoding technologies offer unprecedented opportunities to profile DNA, mRNA, and proteins at a single-cell resolution. Recently, bulk multiomics analyses, such as multidimensional genomic and proteogenomic analyses, have proven beneficial for obtaining a comprehensive understanding of cellular events. This benefit has facilitated the development of single-cell multiomics analysis, which enables cell type-specific gene regulation to be examined. The cardinal features of single-cell multiomics analysis include (1) technologies for single- cell isolation, barcoding, and sequencing to measure multiple types of molecules from individual cells and (2) the integrative analysis of molecules to characterize cell types and their functions regarding pathophysiological processes based on molecular signatures. Here, we summarize the technologies for single-cell multiomics analyses (mRNA- genome, mRNA-DNA methylation, mRNA-chromatin accessibility, and mRNA-protein) as well as the methods for the integrative analysis of single-cell multiomics data. Introduction undetectable through bulk analysis and vice versa. 6 1234567890():,; 1234567890():,; 1234567890():,; 1234567890():,; Recent advances in single-cell isolation and barcoding Moreover, Villani et al. clustered human blood dendritic technologies have enabled DNA, mRNA, and protein cells (DCs) and monocytes using scRNA-seq and identi- profiles to be measured at a single-cell resolution. Various fied a subpopulation of DCs with a potent T cell activation experimental protocols have been developed and applied ability. These studies demonstrate that single-cell analyses to diverse cellular systems to demonstrate the power of provide unique insights into cell subpopulations and their – single-cell level analyses1 4. For example, Tirosh et al.5 functions associated with pathophysiological processes. applied single-cell RNA sequencing (scRNA-seq) to Multiomics analyses at the bulk tumor level have been human melanoma and identified two groups of malignant reported to provide a comprehensive understanding of cells with high expression of the microphthalmia- cellular processes through the integration of different associated transcription factor (MITF) gene: a master types of molecular data (e.g., data on mutations, mRNAs, melanocyte transcriptional regulator group (MITF-high proteins, and metabolites). For example, proteogenomic cells) and a group expressing the AXL gene conferring analyses have been applied to colorectal7,8, ovarian9,10, resistance to targeted therapies (AXL-high cells). breast11,12, and gastric cancers13. Mun et al.13 identified Although bulk analysis showed that each tumor could be correlations between somatic mutations (e.g., nonsynon- classified as MITF-high or AXL-high, the single-cell ymous somatic mutations in the ARID1A gene, a com- analysis further revealed that every tumor contained ponent of SWI/SNF chromatin remodeling complexes) both groups of malignant cells, but the MITF-high tumors and altered signaling pathways (e.g., PI3K-AKT and harbored a subpopulation of AXL-high cells that were MAPK signaling), which facilitate the interpretation of the functional associations of somatic mutations and signal- ing pathways in gastric cancers. Moreover, they found Correspondence: Daehee Hwang ([email protected]) that patient subtypes identified on the basis of mRNA 1School of Biological Sciences, Seoul National University, Seoul 08826, Republic of Korea expression patterns could be further divided according to These authors contributed equally: Jeongwoo Lee, Do Young Hyeon © The Author(s) 2020 Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a linktotheCreativeCommons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/. Official journal of the Korean Society for Biochemistry and Molecular Biology Lee et al. Experimental & Molecular Medicine (2020) 52:1428–1442 1429 ibrutinib showed upregulation of genes involved in cell 19 DNA cycle and Toll-like receptor signaling. Jia et al. also methylation Epigenome integrated single-cell transcriptome and chromatin DR-seq scM&T-seq G&T-seq scMT-seq accessibility data to study the developmental trajectories SIDR scTrio-seq TARGET-seq scNMT-seq of mouse embryonic cardiac progenitor cells and identi- scTrio-seq fied marker genes linking transcriptional and epigenetic Chromatin Genome accessibility regulation during development. Therefore, single-cell sci-CAR multiomics analysis can provide more comprehensive SNARE-seq Association of genomic scNMT-seq insights into cell type-specific gene regulation than single- alterations and gene expression Transcriptome cell mono-omics analysis. Regulatory relationship between epigenetic The core components of single-cell multiomics analysis changes and gene expression are (1) technologies for single-cell isolation, barcoding, and sequencing, to measure multiple types of molecules PEA/STA from the same cells, and (2) integrative analysis of the PLAYR CITE-seq Correlation of gene molecules measured at the single-cell level, to identify cell REAP-seq expression and protein RAID expression levels types and their functions related to pathophysiological ECCITE-seq processes based on the molecular signatures. Here, we first review the technologies used in single-cell multio- Proteome mics analyses, mainly focusing on mRNA-genome, mRNA-DNA methylation, mRNA-chromatin accessi- Fig. 1 An overview of single-cell multiomics sequencing bility, and mRNA-protein data (Fig. 1 and Table 1). By technologies. Single-cell multiomics sequencing technologies and presenting representative applications of these technolo- the expected outcomes are illustrated. Technologies that measure more than two types of data are included in multiple categories (e.g., gies, we illustrate the expected outcomes from the inte- scTrio-seq in transcriptome-genome and transcriptome-DNA grative analysis of multiple types of data, including methylation categories). associations of genomic alterations and gene expression, regulatory relationships between epigenetic changes and gene expression, and correlations between mRNA and protein abundance and/or phosphorylation data, provid- protein expression (Fig. 1). Finally, we summarize the ing detailed molecular signatures for immunogenic and methods for the integrative analysis of single-cell mul- invasive diffuse gastric cancers. Other integrative analyses tiomics data. of mRNA data with DNA methylation, histone mod- ification, microRNA and/or mutation data have also been Cell isolation and barcoding – reported14 17. These multiomics studies demonstrate that For single-cell multiomics analysis, it is essential to integrative analyses of different types of omics data can isolate multiple types of molecules from the same cells, provide more comprehensive insights into tumor biology which involves (1) the isolation of single cells and (2) the than a single type of omics data alone due to their com- subsequent barcoding of multiple types of molecules. The plementary nature. isolation of single cells begins with the mechanical or The advantage of this approach has prompted the enzymatic dissociation of viable cells followed by cap- development of single-cell multiomics technologies. Var- turing single cells from the dissociated cell suspension. ious experimental protocols for single-cell multiomics Several capture methods used for single-cell mono-omics analysis (e.g., mRNA-DNA methylation and mRNA-pro- analysis are commonly employed in single-cell multiomics tein) have been developed and applied to examine cell analysis, including (1) low-throughput methods to capture type-specific gene regulation. Gaiti et al.18 integrated tens or hundreds of cells, including laser capture micro- single-cell transcriptome and DNA methylome data and dissection20 and robotic micromanipulation21, and (2) identified a lineage tree of human chronic lymphocytic high-throughput methods to capture tens of thousands of leukemia (CLL) after drug (ibrutinib) treatment and its cells, including fluorescence-activated cell sorting (FACS) link to the transcriptional transition after therapy. They followed by plate-based isolation and the use of micro- first used epigenome data to construct a lineage tree for fluidic platforms with microfluidic channels and reaction CLL cells based on stochastic DNA methylation changes, chambers or nanowells4. Low-throughput methods retain referred to as epimutations, and found that different CLL spatial information on the isolated cells, while this infor- lineages were preferentially affected
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