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Advances in single-cell omics and multiomics for high-resolution molecular profiling | Experimental & Molecular Medicine
<!DOCTYPE html> <html lang="en" class="grade-c"> <head> <title>Advances in single-cell omics and multiomics for high-resolution molecular profiling | Experimental & Molecular Medicine</title> <link rel="alternate" type="application/rss+xml" href="https://www.nature.com/emm.rss"/> <link rel="preconnect" href="https://cmp.nature.com" crossorigin> <meta http-equiv="X-UA-Compatible" content="IE=edge"> <meta name="applicable-device" content="pc,mobile"> <meta name="viewport" content="width=device-width,initial-scale=1.0,maximum-scale=5,user-scalable=yes"> <meta name="360-site-verification" content="5a2dc4ab3fcb9b0393241ffbbb490480" /> <script data-test="dataLayer"> window.dataLayer = [{"content":{"category":{"contentType":"review article","legacy":{"webtrendsPrimaryArticleType":"reviews","webtrendsSubjectTerms":"next-generation-sequencing;pcr-based-techniques","webtrendsContentCategory":null,"webtrendsContentCollection":null,"webtrendsContentGroup":"Experimental & Molecular 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Traditional bulk omics approaches average signals from heterogeneous cell populations, thereby obscuring important cellular nuances. Single-cell omics studies enable the analysis of individual cells and reveal diverse cell types, dynamic cellular states, and rare cell populations. These techniques offer unprecedented resolution and sensitivity, enabling researchers to unravel the molecular landscape of individual cells. Furthermore, the integration of multimodal omics data within a single cell provides a comprehensive and holistic view of cellular processes. By combining multiple omics dimensions, multimodal omics approaches can facilitate the elucidation of complex cellular interactions, regulatory networks, and molecular mechanisms. This integrative approach enhances our understanding of cellular systems, from development to disease. This review provides an overview of the recent advances in single-cell and multimodal omics for high-resolution molecular profiling. We discuss the principles and methodologies for representatives of each omics method, highlighting the strengths and limitations of the different techniques. In addition, we present case studies demonstrating the applications of single-cell and multimodal omics in various fields, including developmental biology, neurobiology, cancer research, immunology, and precision medicine. Each cell is a bustling city of genetic material and proteins, but traditional methods often blur the individuality of each cell by averaging the data. Single-cell omics techniques, however, allow us to focus on individual cells, revealing the rich variety of cell types, states, and rare populations that make up our tissues and organs. In this review, researchers delve into the details of single-cell omics and multi-modal omics. They explore on how to isolate cells, add unique barcodes to keep track of them, and then analyze their genetic and molecular content. The integration of data across different molecular dimensions is a leap forward in the field, and it paves the way for future discoveries that could transform medicine. The potential implications of this research are vast, offering hope for more precise and personalized medical treatments in the future. 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Traditional bulk omics approaches average signals from heterogeneous cell populations, thereby obscuring important cellular nuances. Single-cell omics studies enable the analysis of individual cells and reveal diverse cell types, dynamic cellular states, and rare cell populations. These techniques offer unprecedented resolution and sensitivity, enabling researchers to unravel the molecular landscape of individual cells. Furthermore, the integration of multimodal omics data within a single cell provides a comprehensive and holistic view of cellular processes. By combining multiple omics dimensions, multimodal omics approaches can facilitate the elucidation of complex cellular interactions, regulatory networks, and molecular mechanisms. This integrative approach enhances our understanding of cellular systems, from development to disease. This review provides an overview of the recent advances in single-cell and multimodal omics for high-resolution molecular profiling. We discuss the principles and methodologies for representatives of each omics method, highlighting the strengths and limitations of the different techniques. In addition, we present case studies demonstrating the applications of single-cell and multimodal omics in various fields, including developmental biology, neurobiology, cancer research, immunology, and precision medicine. Each cell is a bustling city of genetic material and proteins, but traditional methods often blur the individuality of each cell by averaging the data. Single-cell omics techniques, however, allow us to focus on individual cells, revealing the rich variety of cell types, states, and rare populations that make up our tissues and organs. In this review, researchers delve into the details of single-cell omics and multi-modal omics. They explore on how to isolate cells, add unique barcodes to keep track of them, and then analyze their genetic and molecular content. The integration of data across different molecular dimensions is a leap forward in the field, and it paves the way for future discoveries that could transform medicine. The potential implications of this research are vast, offering hope for more precise and personalized medical treatments in the future. 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Traditional bulk omics approaches average signals from heterogeneous cell populations, thereby obscuring important cellular nuances. Single-cell omics studies enable the analysis of individual cells and reveal diverse cell types, dynamic cellular states, and rare cell populations. These techniques offer unprecedented resolution and sensitivity, enabling researchers to unravel the molecular landscape of individual cells. Furthermore, the integration of multimodal omics data within a single cell provides a comprehensive and holistic view of cellular processes. By combining multiple omics dimensions, multimodal omics approaches can facilitate the elucidation of complex cellular interactions, regulatory networks, and molecular mechanisms. This integrative approach enhances our understanding of cellular systems, from development to disease. This review provides an overview of the recent advances in single-cell and multimodal omics for high-resolution molecular profiling. We discuss the principles and methodologies for representatives of each omics method, highlighting the strengths and limitations of the different techniques. In addition, we present case studies demonstrating the applications of single-cell and multimodal omics in various fields, including developmental biology, neurobiology, cancer research, immunology, and precision medicine. Each cell is a bustling city of genetic material and proteins, but traditional methods often blur the individuality of each cell by averaging the data. Single-cell omics techniques, however, allow us to focus on individual cells, revealing the rich variety of cell types, states, and rare populations that make up our tissues and organs. In this review, researchers delve into the details of single-cell omics and multi-modal omics. They explore on how to isolate cells, add unique barcodes to keep track of them, and then analyze their genetic and molecular content. The integration of data across different molecular dimensions is a leap forward in the field, and it paves the way for future discoveries that could transform medicine. The potential implications of this research are vast, offering hope for more precise and personalized medical treatments in the future. 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data-test="article-title" data-article-title="">Advances in single-cell omics and multiomics for high-resolution molecular profiling</h1> <ul class="c-article-author-list c-article-author-list--short" data-test="authors-list" data-component-authors-activator="authors-list"><li class="c-article-author-list__item"><a data-test="author-name" data-track="click" data-track-action="open author" data-track-label="link" href="#auth-Jongsu-Lim-Aff1" data-author-popup="auth-Jongsu-Lim-Aff1" data-author-search="Lim, Jongsu">Jongsu Lim</a><sup class="u-js-hide"><a href="#Aff1">1</a></sup><sup class="u-js-hide"> <a href="#na1">na1</a></sup>, </li><li class="c-article-author-list__item"><a data-test="author-name" data-track="click" data-track-action="open author" data-track-label="link" href="#auth-Chanho-Park-Aff1" data-author-popup="auth-Chanho-Park-Aff1" data-author-search="Park, Chanho">Chanho Park</a><sup class="u-js-hide"><a href="#Aff1">1</a></sup><sup class="u-js-hide"> <a 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data-author-popup="auth-Junil-Kim-Aff2" data-author-search="Kim, Junil">Junil Kim</a><sup class="u-js-hide"><a href="#Aff2">2</a></sup> & </li><li class="c-article-author-list__show-more" aria-label="Show all 6 authors for this article" title="Show all 6 authors for this article">…</li><li class="c-article-author-list__item"><a data-test="author-name" data-track="click" data-track-action="open author" data-track-label="link" href="#auth-Dong_Sung-Lee-Aff1" data-author-popup="auth-Dong_Sung-Lee-Aff1" data-author-search="Lee, Dong-Sung" data-corresp-id="c1">Dong-Sung Lee<svg width="16" height="16" focusable="false" role="img" aria-hidden="true" class="u-icon"><use xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="#icon-eds-i-mail-medium"></use></svg></a><span class="u-js-hide"> <a class="js-orcid" href="http://orcid.org/0000-0003-3815-7057"><span class="u-visually-hidden">ORCID: </span>orcid.org/0000-0003-3815-7057</a></span><sup class="u-js-hide"><a href="#Aff1">1</a></sup> </li></ul><button aria-expanded="false" class="c-article-author-list__button"><svg width="16" height="16" focusable="false" role="img" aria-hidden="true" class="u-icon"><use xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="#icon-eds-i-chevron-down-medium"></use></svg><span>Show authors</span></button> <p class="c-article-info-details" data-container-section="info"> <a data-test="journal-link" href="/emm" data-track="click" data-track-action="journal homepage" data-track-category="article body" data-track-label="link"><i data-test="journal-title">Experimental & Molecular Medicine</i></a> <b data-test="journal-volume"><span class="u-visually-hidden">volume</span> 56</b>, <span class="u-visually-hidden">pages </span>515–526 (<span data-test="article-publication-year">2024</span>)<a href="#citeas" class="c-article-info-details__cite-as u-hide-print" data-track="click" data-track-action="cite this article" data-track-label="link">Cite this article</a> </p> <div 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href="/subjects/next-generation-sequencing" data-track="click" data-track-action="view subject" data-track-label="link">Next-generation sequencing</a></li><li class="c-article-subject-list__subject"><a href="/subjects/pcr-based-techniques" data-track="click" data-track-action="view subject" data-track-label="link">PCR-based techniques</a></li> </ul> </div> </div> <div class="c-article-body"> <section aria-labelledby="Abs1" data-title="Abstract" lang="en"><div class="c-article-section" id="Abs1-section"><h2 class="c-article-section__title js-section-title js-c-reading-companion-sections-item" id="Abs1">Abstract</h2><div class="c-article-section__content" id="Abs1-content"><p>Single-cell omics technologies have revolutionized molecular profiling by providing high-resolution insights into cellular heterogeneity and complexity. Traditional bulk omics approaches average signals from heterogeneous cell populations, thereby obscuring important cellular nuances. Single-cell omics studies enable the analysis of individual cells and reveal diverse cell types, dynamic cellular states, and rare cell populations. These techniques offer unprecedented resolution and sensitivity, enabling researchers to unravel the molecular landscape of individual cells. Furthermore, the integration of multimodal omics data within a single cell provides a comprehensive and holistic view of cellular processes. By combining multiple omics dimensions, multimodal omics approaches can facilitate the elucidation of complex cellular interactions, regulatory networks, and molecular mechanisms. This integrative approach enhances our understanding of cellular systems, from development to disease. This review provides an overview of the recent advances in single-cell and multimodal omics for high-resolution molecular profiling. We discuss the principles and methodologies for representatives of each omics method, highlighting the strengths and limitations of the different techniques. In addition, we present case studies demonstrating the applications of single-cell and multimodal omics in various fields, including developmental biology, neurobiology, cancer research, immunology, and precision medicine.</p></div></div></section> <noscript> </noscript> <section aria-labelledby="inline-recommendations" data-title="Inline Recommendations" class="c-article-recommendations" data-track-component="inline-recommendations"> <h3 class="c-article-recommendations-title" id="inline-recommendations">Similar content being viewed by others</h3> <div class="c-article-recommendations-list"> <div class="c-article-recommendations-list__item"> <article class="c-article-recommendations-card" itemscope itemtype="http://schema.org/ScholarlyArticle"> <div class="c-article-recommendations-card__img"><img src="https://media.springernature.com/w215h120/springer-static/image/art%3A10.1038%2Fs41580-023-00615-w/MediaObjects/41580_2023_615_Fig1_HTML.png" loading="lazy" alt=""></div> <div class="c-article-recommendations-card__main"> <h3 class="c-article-recommendations-card__heading" itemprop="name headline"> <a class="c-article-recommendations-card__link" itemprop="url" href="https://www.nature.com/articles/s41580-023-00615-w?fromPaywallRec=false" data-track="select_recommendations_1" data-track-context="inline recommendations" data-track-action="click recommendations inline - 1" data-track-label="10.1038/s41580-023-00615-w">The technological landscape and applications of single-cell multi-omics </a> </h3> <div class="c-article-meta-recommendations" data-test="recommendation-info"> <span class="c-article-meta-recommendations__item-type">Article</span> <span class="c-article-meta-recommendations__date">06 June 2023</span> </div> </div> </article> </div> <div class="c-article-recommendations-list__item"> <article class="c-article-recommendations-card" itemscope itemtype="http://schema.org/ScholarlyArticle"> <div class="c-article-recommendations-card__img"><img src="https://media.springernature.com/w215h120/springer-static/image/art%3A10.1038%2Fs41576-023-00580-2/MediaObjects/41576_2023_580_Fig1_HTML.png" loading="lazy" alt=""></div> <div class="c-article-recommendations-card__main"> <h3 class="c-article-recommendations-card__heading" itemprop="name headline"> <a class="c-article-recommendations-card__link" itemprop="url" href="https://www.nature.com/articles/s41576-023-00580-2?fromPaywallRec=false" data-track="select_recommendations_2" data-track-context="inline recommendations" data-track-action="click recommendations inline - 2" data-track-label="10.1038/s41576-023-00580-2">Methods and applications for single-cell and spatial multi-omics </a> </h3> <div class="c-article-meta-recommendations" data-test="recommendation-info"> <span class="c-article-meta-recommendations__item-type">Article</span> <span class="c-article-meta-recommendations__date">02 March 2023</span> </div> </div> </article> </div> <div class="c-article-recommendations-list__item"> <article class="c-article-recommendations-card" itemscope itemtype="http://schema.org/ScholarlyArticle"> <div class="c-article-recommendations-card__img"><img src="https://media.springernature.com/w215h120/springer-static/image/art%3A10.1038%2Fs41581-021-00463-x/MediaObjects/41581_2021_463_Fig1_HTML.png" loading="lazy" alt=""></div> <div class="c-article-recommendations-card__main"> <h3 class="c-article-recommendations-card__heading" itemprop="name headline"> <a class="c-article-recommendations-card__link" itemprop="url" href="https://www.nature.com/articles/s41581-021-00463-x?fromPaywallRec=false" data-track="select_recommendations_3" data-track-context="inline recommendations" data-track-action="click recommendations inline - 3" data-track-label="10.1038/s41581-021-00463-x">Multi-omics integration in the age of million single-cell data </a> </h3> <div class="c-article-meta-recommendations" data-test="recommendation-info"> <span class="c-article-meta-recommendations__item-type">Article</span> <span class="c-article-meta-recommendations__date">20 August 2021</span> </div> </div> </article> </div> </div> </section> <script> window.dataLayer = window.dataLayer || []; window.dataLayer.push({ recommendations: { recommender: 'semantic', model: 'specter', policy_id: 'NA', timestamp: 1732674450, embedded_user: 'null' } }); </script> <div class="main-content"> <section data-title="Introduction"><div class="c-article-section" id="Sec1-section"><h2 class="c-article-section__title js-section-title js-c-reading-companion-sections-item" id="Sec1">Introduction</h2><div class="c-article-section__content" id="Sec1-content"><p>Single-cell omics techniques have transformed biological research, offering unprecedented insights into cellular intricacies. Conventional bulk sequencing methods have a limited ability to uncover heterogeneity and complexity within a population of cells, as they provide averaged data. In contrast, single-cell sequencing enables the characterization of individual cells, revealing diverse cell types, dynamic cellular states, and rare cell populations that were concealed within the ensemble of bulk measurements.</p><p>Single-cell analysis driven by high-throughput sequencing and mass spectrometry provides high-resolution insights into genomes, transcriptomes, proteomes, and epigenetics. This approach uncovers hidden complexities in cellular landscapes, providing novel insights into cellular development, diseases, and cellular mechanisms.</p><p>However, biological systems are complex and driven by interactions between omics layers. In recognition of this, the emerging field of single-cell multimodal omics has emerged to integrate information across diverse molecular dimensions within a single cell. This approach provides a holistic view of processes, illuminating the interconnected networks that shape cell behavior.</p><p>Multimodal omics data enable the identification of causal relationships between layers, revealing how genetics affect gene expression, epigenetics, proteins, and metabolites. This integrative approach is particularly useful for dissecting complex diseases. Understanding the molecular mechanisms underlying disease pathogenesis requires a multidimensional perspective.</p><p>In this review, we discuss the key principles and technical considerations underlying single-cell omics and multimodal omics. We explore the technical principles, experimental workflows, and biological insights gained from these approaches. Additionally, we highlight the challenges and opportunities in the field, discuss emerging technologies, and provide insights into the future directions of single-cell and multimodal omics research.</p></div></div></section><section data-title="Single-cell isolation and barcoding"><div class="c-article-section" id="Sec2-section"><h2 class="c-article-section__title js-section-title js-c-reading-companion-sections-item" id="Sec2">Single-cell isolation and barcoding</h2><div class="c-article-section__content" id="Sec2-content"><p>Because single-cell sequencing technologies aim to understand and profile each cell according to its characteristics, cells must be separated and isolated from cell samples or whole tissues before analysis. Magnetic-activated cell sorting (MACS)<sup><a data-track="click" data-track-action="reference anchor" data-track-label="link" data-test="citation-ref" aria-label="Reference 1" title="Miltenyi, S., Muller, W., Weichel, W. & Radbruch, A. High gradient magnetic cell separation with MACS. Cytometry 11, 231–238 (1990)." href="/articles/s12276-024-01186-2#ref-CR1" id="ref-link-section-d69075120e492">1</a></sup>, fluorescence-activated cell sorting (FACS)<sup><a data-track="click" data-track-action="reference anchor" data-track-label="link" data-test="citation-ref" aria-label="Reference 2" title="Basu, S., Campbell, H. M., Dittel, B. N., Ray, A. Purification of specific cell population by fluorescence activated cell sorting (FACS). J.Vis.Exp. 1546 
 https://doi.org/10.3791/1546
 
 (2010)." href="/articles/s12276-024-01186-2#ref-CR2" id="ref-link-section-d69075120e496">2</a></sup>, and various microfluidic technologies are often employed for high-throughput studies. These technologies allow the isolation and analysis of large populations of cells in a more efficient and automated manner, enabling high-throughput experiments. FACS can simultaneously analyze cells according to size, granularity, and fluorescence, allowing multiparameter analysis<sup><a data-track="click" data-track-action="reference anchor" data-track-label="link" data-test="citation-ref" aria-label="Reference 2" title="Basu, S., Campbell, H. M., Dittel, B. N., Ray, A. Purification of specific cell population by fluorescence activated cell sorting (FACS). J.Vis.Exp. 1546 
 https://doi.org/10.3791/1546
 
 (2010)." href="/articles/s12276-024-01186-2#ref-CR2" id="ref-link-section-d69075120e500">2</a></sup>. Although FACS has become a prominent single-cell isolation method because of its ability to analyze multiple parameters simultaneously and its high specificity, it has certain technical limitations. For example, FACS requires sufficient cell density for effective analysis and may face challenges in isolating single cells from low-density populations. Rapid flow and fluorescence exposure during FACS can affect cell viability, potentially resulting in separation failure.</p><p>Microfluidic devices have revolutionized single-cell analysis by enabling the high-throughput processing of tens of thousands of single cells. These devices consist of microfluidic channels and reaction chambers controlled by valves, allowing for precise manipulation and isolation of droplets within microfluidic chips, where each droplet encapsulates a single cell, enabling the parallel processing of numerous cells. Another technology utilizes devices with nanowells that provide individual compartments for single cells. Microfluidic devices offer significant advantages, including increased throughput, reduced cost per cell, and the ability to scale down reaction volumes to the nanoliter or picoliter scale. This reduction in reaction volume minimizes reagent consumption and can shift the main cost barrier from library preparation to sequencing. Consequently, researchers can generate larger datasets while optimizing costs<sup><a data-track="click" data-track-action="reference anchor" data-track-label="link" data-test="citation-ref" aria-label="Reference 3" title="Autebert, J. et al. Microfluidic: an innovative tool for efficient cell sorting. Methods 57, 297–307 (2012)." href="/articles/s12276-024-01186-2#ref-CR3" id="ref-link-section-d69075120e507">3</a></sup>.</p><p>Cell barcoding is a crucial step in a single-cell sequencing workflow, allowing libraries from multiple individual cells to be sequenced together in a single pool. This enables the efficient sequencing of many cells while preserving their identity for downstream analysis. In plate-based techniques, the cell barcode is typically added to the final PCR step before sequencing, in which sequencing libraries from different cells are combined. Microfluidics-based barcoding methods offer the advantage of incorporating cell barcodes into the protocol earlier, often allowing the entire pool of libraries to be processed in a single tube. This early incorporation of barcodes reduces the number of handling steps and the potential sample loss<sup><a data-track="click" data-track-action="reference anchor" data-track-label="link" data-test="citation-ref" aria-label="Reference 4" title="Kim, I. S. Single-cell molecular barcoding to decode multimodal information defining cell states. Mol. Cells 46, 74–85 (2023)." href="/articles/s12276-024-01186-2#ref-CR4" id="ref-link-section-d69075120e514">4</a></sup>.</p><p>Cell barcoding techniques offer a powerful means to barcode and sequence individual cells, enabling the deconvolution of sequence data into files that represent each cell. These methods facilitate high-throughput single-cell sequencing while preserving cell identity and enabling downstream analysis.</p></div></div></section><section data-title="Objectives of single-cell mono-omics"><div class="c-article-section" id="Sec3-section"><h2 class="c-article-section__title js-section-title js-c-reading-companion-sections-item" id="Sec3">Objectives of single-cell mono-omics</h2><div class="c-article-section__content" id="Sec3-content"><h3 class="c-article__sub-heading" id="Sec4">Genome</h3><p>Single-cell genomics provides a new perspective on biological functions through the study of genetic variants at the individual cell level. However, the small amount of DNA obtained from a single cell (typically at the picogram level) poses challenges for amplification and analysis. Genomic research has progressed rapidly in recent decades with the development of amplification methods. Whole-genome amplification (WGA) technologies have been developed to amplify fragments of the entire genome of a single cell while minimizing amplification errors and avoiding contamination.</p><p>One commonly used technique is degenerate oligonucleotide-primed (DOP)-PCR, which is a specialized PCR method for WGA. DOP-PCR utilizes primers with random sequences to bind to various sites in the genome, followed by a second amplification stage in which fragments tagged by a specific sequence are selectively amplified<sup><a data-track="click" data-track-action="reference anchor" data-track-label="link" data-test="citation-ref" aria-label="Reference 5" title="Cheung, V. G. & Nelson, S. F. Whole genome amplification using a degenerate oligonucleotide primer allows hundreds of genotypes to be performed on less than one nanogram of genomic DNA. Proc. Natl. Acad. Sci. USA 93, 14676–14679 (1996)." href="/articles/s12276-024-01186-2#ref-CR5" id="ref-link-section-d69075120e536">5</a></sup>. Although DOP-PCR allows amplification from low amounts of starting materials, it can result in low genome coverage owing to site-specific preferential amplification. Multiple displacement amplification (MDA) amplifies DNA isothermally using φ29 DNA polymerase, resulting in high coverage but exhibiting amplification bias<sup><a data-track="click" data-track-action="reference anchor" data-track-label="link" data-test="citation-ref" aria-label="Reference 6" title="Dean, F. B., Nelson, J. R., Giesler, T. L. & Lasken, R. S. Rapid amplification of plasmid and phage DNA using Phi 29 DNA polymerase and multiply-primed rolling circle amplification. Genome Res. 11, 1095–1099 (2001)." href="/articles/s12276-024-01186-2#ref-CR6" id="ref-link-section-d69075120e540">6</a></sup>. However, methods that rely on WGA produce artifacts that complicate the discovery of variants. These artifacts include locus and allelic dropouts, uneven amplification, chimeric DNA molecules, and base-copy errors.</p><p>Primary template-directed amplification (PTA) is a recently reported method that builds on MDA to achieve quasilinear amplification. By incorporating exonuclease-resistant terminators, PTA suppresses additional amplification, resulting in higher accuracy, uniformity, and reproducibility than other methods for single-cell genome analysis<sup><a data-track="click" data-track-action="reference anchor" data-track-label="link" data-test="citation-ref" aria-label="Reference 7" title="Gonzalez-Pena, V. et al. Accurate genomic variant detection in single cells with primary template-directed amplification. Proc. Natl. Acad. Sci. USA 118, e2024176118 (2021)." href="/articles/s12276-024-01186-2#ref-CR7" id="ref-link-section-d69075120e547">7</a></sup>. Multiplexed end-tagging amplification of complementary strands (META-CS) allows the accurate identification of de novo single-nucleotide variants (SNVs) in a single cell. This enables amplification in a one-tube reaction while labeling the two DNA strands differently, facilitating the comparison of complementary positions and filtering out false positives<sup><a data-track="click" data-track-action="reference anchor" data-track-label="link" data-test="citation-ref" aria-label="Reference 8" title="Xing, D., Tan, L., Chang, C., Li, H. & Xie, X. S. Accurate SNV detection in single cells by transposon-based whole-genome amplification of complementary strands. Proc. Natl. Acad. Sci. USA 118, e2013106118 (2021)." href="/articles/s12276-024-01186-2#ref-CR8" id="ref-link-section-d69075120e551">8</a></sup>.</p><p>Microfluidic-based WGA methods are promising and are actively being developed. These methods offer numerous advantages over traditional WGA techniques, making them highly attractive for single-cell genomic research. Microfluidic platforms provide a high degree of automation and integration, allowing multiple steps in the WGA process to be performed within a single device. This integration simplifies workflow, saves time, and minimizes the risk of sample contamination or loss<sup><a data-track="click" data-track-action="reference anchor" data-track-label="link" data-test="citation-ref" aria-label="Reference 3" title="Autebert, J. et al. Microfluidic: an innovative tool for efficient cell sorting. Methods 57, 297–307 (2012)." href="/articles/s12276-024-01186-2#ref-CR3" id="ref-link-section-d69075120e558">3</a></sup>.</p><p>Single-stranded sequencing using microfluidic reactors (SISSOR)<sup><a data-track="click" data-track-action="reference anchor" data-track-label="link" data-test="citation-ref" aria-label="Reference 9" title="Chu, W. K. et al. Ultraaccurate genome sequencing and haplotyping of single human cells. Proc. Natl. Acad. Sci. USA 114, 12512–12517 (2017)." href="/articles/s12276-024-01186-2#ref-CR9" id="ref-link-section-d69075120e566">9</a></sup> utilizes a microfluidic device to isolate DNA in a single cell. The DNA is then separated into Watson and Crick strands, which are randomly partitioned into nanoliter-scale compartments within the device. This partitioning allows for the amplification and barcoded library preparation of genomic DNA. SISSOR offers high sequencing accuracy with low error rates, although it may exhibit reduced genomic coverage owing to the potential loss of DNA fragments during the strand separation and partitioning processes.</p><h3 class="c-article__sub-heading" id="Sec5">Transcriptome</h3><p>The transcriptome directly influences protein translation. Although the genetic information is nearly identical across all human cells, the transcriptomic data of individual cells reveals distinct gene activity patterns. Recent advances in single-cell RNA-seq have enabled the characterization of cells at the single-cell and spatiotemporal levels and the establishment of diverse projects, such as the Human Cell Atlas project<sup><a data-track="click" data-track-action="reference anchor" data-track-label="link" data-test="citation-ref" aria-label="Reference 10" title="Panina, Y., Karagiannis, P., Kurtz, A., Stacey, G. N. & Fujibuchi, W. Human Cell Atlas and cell-type authentication for regenerative medicine. Exp. Mol. Med. 52, 1443–1451 (2020)." href="/articles/s12276-024-01186-2#ref-CR10" id="ref-link-section-d69075120e578">10</a></sup>. In addition to characterization, scRNA-seq has been used to study cytidine deaminase (CDA) as a potential druggable target in <i>ALK</i> fusion-positive non-small cell lung cancer (NSCLC)<sup><a data-track="click" data-track-action="reference anchor" data-track-label="link" data-test="citation-ref" aria-label="Reference 11" title="Heo, H. et al. DNA methylome and single-cell transcriptome analyses reveal CDA as a potential druggable target for ALK inhibitor–resistant lung cancer therapy. Exp. Mol. Med. 54, 1236–1249 (2022)." href="/articles/s12276-024-01186-2#ref-CR11" id="ref-link-section-d69075120e585">11</a></sup>.</p><p>Cell expression by linear amplification and sequencing2 (CEL-seq2) enhances CEL-seq using barcodes that incorporate UMIs and random priming, leading to improved read mapping percentages and sensitivity while reducing bias<sup><a data-track="click" data-track-action="reference anchor" data-track-label="link" data-test="citation-ref" aria-label="Reference 12" title="Hashimshony, T. et al. CEL-Seq2: sensitive highly-multiplexed single-cell RNA-Seq. Genome Biol. 17, 1–7 (2016)." href="/articles/s12276-024-01186-2#ref-CR12" id="ref-link-section-d69075120e592">12</a></sup>. In massive parallel single-cell RNA sequencing (MARS-seq2.0), the reaction volume is reduced during reverse transcription, resulting in decreased noise and increased sensitivity<sup><a data-track="click" data-track-action="reference anchor" data-track-label="link" data-test="citation-ref" aria-label="Reference 13" title="Keren-Shaul, H. et al. MARS-seq2. 0: an experimental and analytical pipeline for indexed sorting combined with single-cell RNA sequencing. Nat. Protoc. 14, 1841–1862 (2019)." href="/articles/s12276-024-01186-2#ref-CR13" id="ref-link-section-d69075120e596">13</a></sup>. However, these methods primarily capture 3′ end transcripts, making it difficult to determine sequences or isoforms at the 5′ end<sup><a data-track="click" data-track-action="reference anchor" data-track-label="link" data-test="citation-ref" aria-label="Reference 13" title="Keren-Shaul, H. et al. MARS-seq2. 0: an experimental and analytical pipeline for indexed sorting combined with single-cell RNA sequencing. Nat. Protoc. 14, 1841–1862 (2019)." href="/articles/s12276-024-01186-2#ref-CR13" id="ref-link-section-d69075120e600">13</a></sup>.</p><p>Droplet-based technologies such as 10X Genomics Chromium<sup><a data-track="click" data-track-action="reference anchor" data-track-label="link" data-test="citation-ref" aria-label="Reference 14" title="Zheng, G. X. et al. Massively parallel digital transcriptional profiling of single cells. Nat. Commun. 8, 14049 (2017)." href="/articles/s12276-024-01186-2#ref-CR14" id="ref-link-section-d69075120e607">14</a></sup> and Drop-seq<sup><a data-track="click" data-track-action="reference anchor" data-track-label="link" data-test="citation-ref" aria-label="Reference 15" title="Macosko, E. Z. et al. Highly parallel genome-wide expression profiling of individual cells using nanoliter droplets. Cell 161, 1202–1214 (2015)." href="/articles/s12276-024-01186-2#ref-CR15" id="ref-link-section-d69075120e611">15</a></sup> have become popular owing to their cost-effectiveness and high throughput. Beads are used to capture RNA from the oil and create reaction droplets. Chromium uses soft hydrogel beads, whereas Drop-seq uses small hard resin beads, resulting in different cell capture rates.</p><p>Split pool ligation-based transcriptome sequencing (SPLiT-seq) involves iterative splitting and pooling of cells, allowing for diverse cell barcode combinations<sup><a data-track="click" data-track-action="reference anchor" data-track-label="link" data-test="citation-ref" aria-label="Reference 16" title="Rosenberg, A. B. et al. Single-cell profiling of the developing mouse brain and spinal cord with split-pool barcoding. Science 360, 176–182 (2018)." href="/articles/s12276-024-01186-2#ref-CR16" id="ref-link-section-d69075120e618">16</a></sup>. This method accommodates fixed cells or nuclei and offers flexibility in experimental design<sup><a data-track="click" data-track-action="reference anchor" data-track-label="link" data-test="citation-ref" aria-label="Reference 16" title="Rosenberg, A. B. et al. Single-cell profiling of the developing mouse brain and spinal cord with split-pool barcoding. Science 360, 176–182 (2018)." href="/articles/s12276-024-01186-2#ref-CR16" id="ref-link-section-d69075120e622">16</a></sup>.</p><p>Several methods, including molecular crowding single-cell RNA barcoding and sequencing (mcSCRB-seq)<sup><a data-track="click" data-track-action="reference anchor" data-track-label="link" data-test="citation-ref" aria-label="Reference 17" title="Bagnoli, J. W. et al. Sensitive and powerful single-cell RNA sequencing using mcSCRB-seq. Nat. Commun. 9, 2937 (2018)." href="/articles/s12276-024-01186-2#ref-CR17" id="ref-link-section-d69075120e630">17</a></sup>, switching mechanism at 5′ end of RNA template sequencing3 (SMART-seq3)<sup><a data-track="click" data-track-action="reference anchor" data-track-label="link" data-test="citation-ref" aria-label="Reference 18" title="Hagemann-Jensen, M. et al. Single-cell RNA counting at allele and isoform resolution using Smart-seq3. Nat. Biotechnol. 38, 708–714 (2020)." href="/articles/s12276-024-01186-2#ref-CR18" id="ref-link-section-d69075120e634">18</a></sup>, and FLASH-seq<sup><a data-track="click" data-track-action="reference anchor" data-track-label="link" data-test="citation-ref" aria-label="Reference 19" title="Hahaut, V. et al. Fast and highly sensitive full-length single-cell RNA sequencing using FLASH-seq. Nat. Biotechnol. 40, 1447–1451 (2022)." href="/articles/s12276-024-01186-2#ref-CR19" id="ref-link-section-d69075120e638">19</a></sup>, employ full-length cDNA library construction and sequencing. These methods utilize template-switching oligos (TSOs) to create full-length cDNA libraries and identify the 5′ ends of transcripts, and they incorporate UMIs to mitigate PCR bias. FLASH-seq combines split reverse transcription and PCR processes with an improved reverse transcriptase, enhancing cDNA yield and reducing amplification noise and hands-on time<sup><a data-track="click" data-track-action="reference anchor" data-track-label="link" data-test="citation-ref" aria-label="Reference 19" title="Hahaut, V. et al. Fast and highly sensitive full-length single-cell RNA sequencing using FLASH-seq. Nat. Biotechnol. 40, 1447–1451 (2022)." href="/articles/s12276-024-01186-2#ref-CR19" id="ref-link-section-d69075120e642">19</a></sup>.</p><p>Vast transcriptome analyses of single cells using dA-tailing (VASA-seq)<sup><a data-track="click" data-track-action="reference anchor" data-track-label="link" data-test="citation-ref" aria-label="Reference 20" title="Salmen, F. et al. High-throughput total RNA sequencing in single cells using VASA-seq. Nat. Biotechnol. 40, 1780–1793 (2022)." href="/articles/s12276-024-01186-2#ref-CR20" id="ref-link-section-d69075120e649">20</a></sup> are available in both plate-based and droplet-based formats, providing versatility based on experimental objectives. This approach enables the formation of full-length cDNA libraries and the capture of nonpolyadenylated transcripts. VASA-seq can detect coding RNAs, long noncoding RNAs, transcription factors, and small noncoding RNAs<sup><a data-track="click" data-track-action="reference anchor" data-track-label="link" data-test="citation-ref" aria-label="Reference 20" title="Salmen, F. et al. High-throughput total RNA sequencing in single cells using VASA-seq. Nat. Biotechnol. 40, 1780–1793 (2022)." href="/articles/s12276-024-01186-2#ref-CR20" id="ref-link-section-d69075120e653">20</a></sup>.</p><p>All of the abovementioned scRNA-seq methods have constraints in capturing longer transcripts, identifying splicing events, and distinguishing between transcript isoforms because they are based on short-read sequencing. Specialized methods have been developed to address these limitations. Multiplexed array isoform sequencing (MAS-ISO-seq) employs long-read sequencing by attaching a dU-containing adapter to cDNA ends, enabling the sequencing of long reads in a single pass<sup><a data-track="click" data-track-action="reference anchor" data-track-label="link" data-test="citation-ref" aria-label="Reference 21" title="Al’Khafaji, A. M. et al. High-throughput RNA isoform sequencing using programmable cDNA concatenation. Biorxiv: 2021.10. 01.462818 (2021)." href="/articles/s12276-024-01186-2#ref-CR21" id="ref-link-section-d69075120e660">21</a></sup>. Single-nucleus isoform RNA sequencing (SnISOr-seq) distinguishes intronic reads by splitting transcripts into intronic and exonic cDNAs and analyzing only exonic cDNA through long-read sequencing, thereby enhancing the analysis of the desired exons<sup><a data-track="click" data-track-action="reference anchor" data-track-label="link" data-test="citation-ref" aria-label="Reference 22" title="Hardwick, S. A. et al. Single-nuclei isoform RNA sequencing unlocks barcoded exon connectivity in frozen brain tissue. Nat. Biotechnol. 40, 1082–1092 (2022)." href="/articles/s12276-024-01186-2#ref-CR22" id="ref-link-section-d69075120e664">22</a></sup>. Long-read sequencing-based methods such as MAS-ISO-seq and SnISOr-seq address the limitations of short-read sequencing-based methods and offer improved capabilities for characterizing longer transcripts and transcript isoforms<sup><a data-track="click" data-track-action="reference anchor" data-track-label="link" data-test="citation-ref" aria-label="Reference 23" title="Zhang, Y., Lu, L. & Li, X. Detection technologies for RNA modifications. Exp. Mol. Med. 54, 1601–1616 (2022)." href="/articles/s12276-024-01186-2#ref-CR23" id="ref-link-section-d69075120e668">23</a></sup>. The properties of these methods are listed in Table <a data-track="click" data-track-label="link" data-track-action="table anchor" href="/articles/s12276-024-01186-2#Tab1">1</a>.</p><div class="c-article-table" data-test="inline-table" data-container-section="table" id="table-1"><figure><figcaption class="c-article-table__figcaption"><b id="Tab1" data-test="table-caption">Table 1 Comparison of the properties of transcriptome sequencing methods.</b></figcaption><div class="u-text-right u-hide-print"><a class="c-article__pill-button" data-test="table-link" data-track="click" data-track-action="view table" data-track-label="button" rel="nofollow" href="/articles/s12276-024-01186-2/tables/1" aria-label="Full size table 1"><span>Full size table</span><svg width="16" height="16" focusable="false" role="img" aria-hidden="true" class="u-icon"><use xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="#icon-eds-i-chevron-right-small"></use></svg></a></div></figure></div><h3 class="c-article__sub-heading" id="Sec6">Proteome</h3><p>Proteome research at the single-cell level provides a wealth of information about the diversity of proteins within a cell population and important insights into cellular functions, disease mechanisms, and developmental processes because proteins can be considered the end material of biological processes in cells.</p><p>Mass spectrometry (MS) allows researchers to identify and quantify proteins based on their mass-to-charge ratios (m/z). MS-based methods can be used for single-cell protein sequencing by coupling with other techniques to capture and isolate individual cells.</p><p>Matrix-assisted laser desorption/ionization (MALDI)<sup><a data-track="click" data-track-action="reference anchor" data-track-label="link" data-test="citation-ref" aria-label="Reference 24" title="Nadler, W. M. et al. MALDI versus ESI: the impact of the ion source on peptide identification. J. Proteome Res. 16, 1207–1215 (2017)." href="/articles/s12276-024-01186-2#ref-CR24" id="ref-link-section-d69075120e1242">24</a></sup> and laser ablation electrospray ionization (LAESI) are MS-based techniques commonly used for single-cell protein sequencing. In MALDI, a laser is used to ionize proteins on a matrix-coated surface, which are then detected using a mass spectrometer. In LAESI, a laser is used to ablate proteins directly from the cell surface, after which the proteins are ionized and detected using a mass spectrometer. These techniques offer high sensitivity and are well suited for analyzing small numbers of cells<sup><a data-track="click" data-track-action="reference anchor" data-track-label="link" data-test="citation-ref" aria-label="Reference 24" title="Nadler, W. M. et al. MALDI versus ESI: the impact of the ion source on peptide identification. J. Proteome Res. 16, 1207–1215 (2017)." href="/articles/s12276-024-01186-2#ref-CR24" id="ref-link-section-d69075120e1246">24</a></sup>.</p><p>Fluorescence-based methods are another approach to single-cell protein sequencing. These methods rely on fluorescent probes or antibodies that specifically bind to the proteins of interest. The fluorescence signal is detected and quantified using microscopy or flow cytometry. Single-cell western blotting (scWB)<sup><a data-track="click" data-track-action="reference anchor" data-track-label="link" data-test="citation-ref" aria-label="Reference 25" title="Hughes, A. J. et al. Single-cell western blotting. Nat. Methods 11, 749–755 (2014)." href="/articles/s12276-024-01186-2#ref-CR25" id="ref-link-section-d69075120e1253">25</a></sup> is a fluorescence-based technique that allows the detection of specific proteins within individual cells. In scWB, a single cell is lysed, and the proteins within the cell are subsequently separated via gel electrophoresis. The separated proteins are then transferred onto a membrane as in traditional western blotting.</p><h3 class="c-article__sub-heading" id="Sec7">Methylome</h3><p>DNA methylation is an epigenetic modification involving the addition of CH<sub>3</sub> to deoxyribonucleosides, predominantly 5-methylcytosine (5mC), that is particularly common in vertebrates. DNA methyltransferases (DNMTs) deposit and maintain methyl groups, whereas ten-eleven translocation (TET) dioxygenase removes them. CpG islands (CGIs), often found in promoters and gene bodies, are primary sites of methylation. CpG promoter methylation typically reduces gene expression, whereas gene body methylation enhances gene expression. Methylation rarely occurs at non-CpG (CpH) sites, which are frequently found in neurons.</p><p>Methylation plays a role in X-chromosome inactivation, genomic imprinting, and transposon suppression. It is also an epigenetic feature of cancers and other diseases. Therefore, many methylation assays (bisulfite sequencing, chromatography, mass spectrometry, ELISA, and restriction digestion) have been developed. Here, we describe single-cell-based methylome methods (Table <a data-track="click" data-track-label="link" data-track-action="table anchor" href="/articles/s12276-024-01186-2#Tab2">2</a>).</p><div class="c-article-table" data-test="inline-table" data-container-section="table" id="table-2"><figure><figcaption class="c-article-table__figcaption"><b id="Tab2" data-test="table-caption">Table 2 Methylome sequencing methods with brief explanations and differences in workflows.</b></figcaption><div class="u-text-right u-hide-print"><a class="c-article__pill-button" data-test="table-link" data-track="click" data-track-action="view table" data-track-label="button" rel="nofollow" href="/articles/s12276-024-01186-2/tables/2" aria-label="Full size table 2"><span>Full size table</span><svg width="16" height="16" focusable="false" role="img" aria-hidden="true" class="u-icon"><use xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="#icon-eds-i-chevron-right-small"></use></svg></a></div></figure></div><p>Single-cell bisulfite sequencing (scBS-Seq) was adapted from traditional bisulfite sequencing for single-cell DNA methylation analysis at a single-base resolution<sup><a data-track="click" data-track-action="reference anchor" data-track-label="link" data-test="citation-ref" aria-label="Reference 26" title="Smallwood, S. A. et al. Single-cell genome-wide bisulfite sequencing for assessing epigenetic heterogeneity. Nat. Methods 11, 817–820 (2014)." href="/articles/s12276-024-01186-2#ref-CR26" id="ref-link-section-d69075120e1771">26</a></sup>. DNA bisulfite treatment converts unmethylated cytosines to uracil while preserving methylated cytosines<sup><a data-track="click" data-track-action="reference anchor" data-track-label="link" data-test="citation-ref" aria-label="Reference 26" title="Smallwood, S. A. et al. Single-cell genome-wide bisulfite sequencing for assessing epigenetic heterogeneity. Nat. Methods 11, 817–820 (2014)." href="/articles/s12276-024-01186-2#ref-CR26" id="ref-link-section-d69075120e1775">26</a></sup>. To mitigate the high costs of whole-genome sequencing in scBS-Seq, technologies such as scRRBS have focused on CpG-enriched genomic regions<sup><a data-track="click" data-track-action="reference anchor" data-track-label="link" data-test="citation-ref" aria-label="Reference 27" title="Wang, K. et al. Q-RRBS: a quantitative reduced representation bisulfite sequencing method for single-cell methylome analyses. Epigenetics 10, 775–783 (2015)." href="/articles/s12276-024-01186-2#ref-CR27" id="ref-link-section-d69075120e1779">27</a></sup>. In scRRBS, the process has been streamlined in a single tube<sup><a data-track="click" data-track-action="reference anchor" data-track-label="link" data-test="citation-ref" aria-label="Reference 27" title="Wang, K. et al. Q-RRBS: a quantitative reduced representation bisulfite sequencing method for single-cell methylome analyses. Epigenetics 10, 775–783 (2015)." href="/articles/s12276-024-01186-2#ref-CR27" id="ref-link-section-d69075120e1783">27</a></sup>.</p><p>However, because adapters are ligated before bisulfite treatment, these methods suffer from high DNA loss. In single-cell whole-genome bisulfite sequencing (scWGBS) and scBS, this problem is solved via postbisulfite adapter tagging (PBAT)<sup><a data-track="click" data-track-action="reference anchor" data-track-label="link" data-test="citation-ref" aria-label="Reference 26" title="Smallwood, S. A. et al. Single-cell genome-wide bisulfite sequencing for assessing epigenetic heterogeneity. Nat. Methods 11, 817–820 (2014)." href="/articles/s12276-024-01186-2#ref-CR26" id="ref-link-section-d69075120e1790">26</a>,<a data-track="click" data-track-action="reference anchor" data-track-label="link" data-test="citation-ref" aria-label="Reference 28" title="Farlik, M. et al. Single-cell DNA methylome sequencing and bioinformatic inference of epigenomic cell-state dynamics. Cell. Rep. 10, 1386–1397 (2015)." href="/articles/s12276-024-01186-2#ref-CR28" id="ref-link-section-d69075120e1793">28</a></sup>. Unlike scWGBS or scBS, Msc-RRBS does not utilize postbisulfite adapter tagging (PBAT) and is not affected by bisulfite treatment because of the use of a methylated adapter<sup><a data-track="click" data-track-action="reference anchor" data-track-label="link" data-test="citation-ref" aria-label="Reference 29" title="Charlton, J. et al. Global delay in nascent strand DNA methylation. Nat. Struct. Mol. Biol. 25, 327–332 (2018)." href="/articles/s12276-024-01186-2#ref-CR29" id="ref-link-section-d69075120e1797">29</a></sup>.</p><p>Single-nucleus methylome sequencing 2 (SnmC-seq2), an improved version of snmC-seq<sup><a data-track="click" data-track-action="reference anchor" data-track-label="link" data-test="citation-ref" aria-label="Reference 30" title="Luo, C. et al. Single-cell methylomes identify neuronal subtypes and regulatory elements in mammalian cortex. Science 357, 600–604 (2017)." href="/articles/s12276-024-01186-2#ref-CR30" id="ref-link-section-d69075120e1805">30</a></sup>, reduced the frequency of hybridization of random primers by using random primer H (RP-H), which lacks the nucleotide ‘G’. This reduces the dNTP contamination rate through additional quenching steps<sup><a data-track="click" data-track-action="reference anchor" data-track-label="link" data-test="citation-ref" aria-label="Reference 31" title="Luo, C. et al. Robust single-cell DNA methylome profiling with snmC-seq2. Nat. Commun. 9, 3824–2 (2018)." href="/articles/s12276-024-01186-2#ref-CR31" id="ref-link-section-d69075120e1809">31</a></sup>. Single-cell splinted ligation adapter tagging (scSPLAT) improves mappability using a splinted adapter as the second adapter. No artificial low-complexity sequence is added, and the process is free from the risk of artificial sequences due to the carryover of free nucleotides<sup><a data-track="click" data-track-action="reference anchor" data-track-label="link" data-test="citation-ref" aria-label="Reference 32" title="Raine, A. et al. scSPLAT, a scalable plate-based protocol for single cell WGBS library preparation. Sci. Rep. 12, 5772–5772 (2022)." href="/articles/s12276-024-01186-2#ref-CR32" id="ref-link-section-d69075120e1813">32</a></sup>.</p><p>Single-cell combinatorial indexing for methylation analysis (sci-MET) is a scWGBS method that incorporates a combinatorial indexing strategy<sup><a data-track="click" data-track-action="reference anchor" data-track-label="link" data-test="citation-ref" aria-label="Reference 33" title="Mulqueen, R. M. et al. Highly scalable generation of DNA methylation profiles in single cells. Nat. Biotechnol. 36, 428–431 (2018)." href="/articles/s12276-024-01186-2#ref-CR33" id="ref-link-section-d69075120e1820">33</a></sup>. Furthermore, an enhanced version of sci-MET, sci-METv2<sup><a data-track="click" data-track-action="reference anchor" data-track-label="link" data-test="citation-ref" aria-label="Reference 34" title="Nichols, R. V. et al. High-throughput robust single-cell DNA methylation profiling with sciMETv2. Nat. Commun. 13, 7627–3 (2022)." href="/articles/s12276-024-01186-2#ref-CR34" id="ref-link-section-d69075120e1824">34</a></sup>, exhibits high methylome coverage for sci-METv2.LA (linear amplification) and reduced costs and preparation time for sci-METv2.SL (splint ligation). However, it has a low read insert size due to damage during bisulfite conversion, which can be addressed using enzymatic conversion methods<sup><a data-track="click" data-track-action="reference anchor" data-track-label="link" data-test="citation-ref" aria-label="Reference 34" title="Nichols, R. V. et al. High-throughput robust single-cell DNA methylation profiling with sciMETv2. Nat. Commun. 13, 7627–3 (2022)." href="/articles/s12276-024-01186-2#ref-CR34" id="ref-link-section-d69075120e1828">34</a></sup>. A recently developed method, single-cell combinatorial indexing with enzymatic conversion (sciEM), addresses the challenges associated with bisulfite conversion by utilizing <i>APOBEC, TET2</i> for enzymatic conversion, and a G-depleted random linear primer to improve CpH mapping<sup><a data-track="click" data-track-action="reference anchor" data-track-label="link" data-test="citation-ref" aria-label="Reference 35" title="Chatterton, Z. et al. Single-cell DNA methylation sequencing by combinatorial indexing and enzymatic DNA methylation conversion. Cell. Biosci. 13, 2–9 (2023)." href="/articles/s12276-024-01186-2#ref-CR35" id="ref-link-section-d69075120e1835">35</a></sup>. Compared with previous methods, sciEM provides increased genomic coverage<sup><a data-track="click" data-track-action="reference anchor" data-track-label="link" data-test="citation-ref" aria-label="Reference 35" title="Chatterton, Z. et al. Single-cell DNA methylation sequencing by combinatorial indexing and enzymatic DNA methylation conversion. Cell. Biosci. 13, 2–9 (2023)." href="/articles/s12276-024-01186-2#ref-CR35" id="ref-link-section-d69075120e1840">35</a></sup>. Enzymatic conversion significantly improves DNA methylation<sup><a data-track="click" data-track-action="reference anchor" data-track-label="link" data-test="citation-ref" aria-label="Reference 35" title="Chatterton, Z. et al. Single-cell DNA methylation sequencing by combinatorial indexing and enzymatic DNA methylation conversion. Cell. Biosci. 13, 2–9 (2023)." href="/articles/s12276-024-01186-2#ref-CR35" id="ref-link-section-d69075120e1844">35</a></sup>.</p><p>Microfluidic diffusion-based RRBS (MID-RRBS) is a methylation sequencing method that utilizes a reagent-swapping approach to reduce the loss of DNA molecules during the purification step between bisulfite treatment and desulfonation<sup><a data-track="click" data-track-action="reference anchor" data-track-label="link" data-test="citation-ref" aria-label="Reference 36" title="Ma, S. et al. Cell-type-specific brain methylomes profiled via ultralow-input microfluidics. Nat. Biomed. Eng. 2, 183–194 (2018)." href="/articles/s12276-024-01186-2#ref-CR36" id="ref-link-section-d69075120e1851">36</a></sup>.</p><h3 class="c-article__sub-heading" id="Sec8">Chromatin accessibility</h3><p>Nucleosomes, which consist of DNA wrapped around histone proteins, hinder transcription, replication, repair, and recombination by obstructing relevant factors. Therefore, chromatin accessibility sequencing techniques rely primarily on enzymatic methylation or cleavage. Single-cell adaptations of these methods enable examination of chromatin accessibility at the individual cell level, providing insights into the dynamics and heterogeneity of chromatin accessibility.</p><p>The discovery of periodic DNase hypersensitivity sites (DHSs) has played a crucial role in the development of genome-wide chromatin accessibility sequencing methods. scDNase-seq detects a greater number of DHSs per cell than scATAC-seq, thereby providing increased resolution. However, this method requires a longer hands-on time<sup><a data-track="click" data-track-action="reference anchor" data-track-label="link" data-test="citation-ref" aria-label="Reference 37" title="Jin, W. et al. Genome-wide detection of DNase I hypersensitive sites in single cells and FFPE tissue samples. Nature 528, 142–146 (2015)." href="/articles/s12276-024-01186-2#ref-CR37" id="ref-link-section-d69075120e1867">37</a></sup>. scMNase-seq is a single-cell adaptation of MNase-seq that utilizes MNase as both an endonuclease and an exonuclease<sup><a data-track="click" data-track-action="reference anchor" data-track-label="link" data-test="citation-ref" aria-label="Reference 38" title="Gao, W., Lai, B., Ni, B. & Zhao, K. Genome-wide profiling of nucleosome position and chromatin accessibility in single cells using scMNase-seq. Nat. Protoc. 15, 68–85 (2020)." href="/articles/s12276-024-01186-2#ref-CR38" id="ref-link-section-d69075120e1871">38</a></sup>. Unlike other methods, scMNase-seq has the advantage of cutting linker DNA, allowing the precise determination of nucleosome boundaries<sup><a data-track="click" data-track-action="reference anchor" data-track-label="link" data-test="citation-ref" aria-label="Reference 38" title="Gao, W., Lai, B., Ni, B. & Zhao, K. Genome-wide profiling of nucleosome position and chromatin accessibility in single cells using scMNase-seq. Nat. Protoc. 15, 68–85 (2020)." href="/articles/s12276-024-01186-2#ref-CR38" id="ref-link-section-d69075120e1875">38</a></sup>. However, this method provides limited information, capturing only 3%–10% of the nucleosome and subnucleosomal fragments<sup><a data-track="click" data-track-action="reference anchor" data-track-label="link" data-test="citation-ref" aria-label="Reference 38" title="Gao, W., Lai, B., Ni, B. & Zhao, K. Genome-wide profiling of nucleosome position and chromatin accessibility in single cells using scMNase-seq. Nat. Protoc. 15, 68–85 (2020)." href="/articles/s12276-024-01186-2#ref-CR38" id="ref-link-section-d69075120e1879">38</a></sup>.</p><p>Single-cell nucleosome occupancy and methylome sequencing (scNOMe-seq) utilizes CpG methyltransferases to analyze accessibility by detecting methylation levels<sup><a data-track="click" data-track-action="reference anchor" data-track-label="link" data-test="citation-ref" aria-label="Reference 39" title="Pott, S. Simultaneous measurement of chromatin accessibility, DNA methylation, and nucleosome phasing in single cells. Elife 6, e23203 (2017)." href="/articles/s12276-024-01186-2#ref-CR39" id="ref-link-section-d69075120e1886">39</a></sup>. Unlike other methods that rely on read counting, scNOMe-seq provides detailed accessibility information by analyzing the methylation status of individual sequenced reads, including CpG sites that independently report accessibility<sup><a data-track="click" data-track-action="reference anchor" data-track-label="link" data-test="citation-ref" aria-label="Reference 39" title="Pott, S. Simultaneous measurement of chromatin accessibility, DNA methylation, and nucleosome phasing in single cells. Elife 6, e23203 (2017)." href="/articles/s12276-024-01186-2#ref-CR39" id="ref-link-section-d69075120e1890">39</a></sup>. In single-cell variants, the ability to simultaneously detect chromatin accessibility and methylation levels makes this approach a valuable tool for studying the heterogeneity of single cells within complex mixtures<sup><a data-track="click" data-track-action="reference anchor" data-track-label="link" data-test="citation-ref" aria-label="Reference 39" title="Pott, S. Simultaneous measurement of chromatin accessibility, DNA methylation, and nucleosome phasing in single cells. Elife 6, e23203 (2017)." href="/articles/s12276-024-01186-2#ref-CR39" id="ref-link-section-d69075120e1894">39</a></sup>.</p><p>Tn5 transposase-based methods such as single-cell assays for transposase-accessible chromatin using sequencing (scATAC-seq) have revolutionized single-cell epigenomic mapping<sup><a data-track="click" data-track-action="reference anchor" data-track-label="link" data-test="citation-ref" aria-label="Reference 40" title="Buenrostro, J. D. et al. Single-cell chromatin accessibility reveals principles of regulatory variation. Nature 523, 486–490 (2015)." href="/articles/s12276-024-01186-2#ref-CR40" id="ref-link-section-d69075120e1901">40</a></sup>. High-throughput scATAC-seq utilizes fluorescence imaging and addressable reagent deposition to achieve high throughput and cost effectiveness<sup><a data-track="click" data-track-action="reference anchor" data-track-label="link" data-test="citation-ref" aria-label="Reference 41" title="Mezger, A. et al. High-throughput chromatin accessibility profiling at single-cell resolution. Nat. Commun. 9, 3647-x (2018)." href="/articles/s12276-024-01186-2#ref-CR41" id="ref-link-section-d69075120e1905">41</a></sup>. Plate-based scATAC-seq minimizes material loss and labor while generating highly complex data<sup><a data-track="click" data-track-action="reference anchor" data-track-label="link" data-test="citation-ref" aria-label="Reference 42" title="Xu, W. et al. A plate-based single-cell ATAC-seq workflow for fast and robust profiling of chromatin accessibility. Nat. Protoc. 16, 4084–4107 (2021)." href="/articles/s12276-024-01186-2#ref-CR42" id="ref-link-section-d69075120e1909">42</a></sup>. Droplet microfluidics-based approaches such as dscATAC-seq and dsciATAC-seq further enhance cell throughput and barcoding capabilities by utilizing combinatorial indexing<sup><a data-track="click" data-track-action="reference anchor" data-track-label="link" data-test="citation-ref" aria-label="Reference 43" title="Lareau, C. A. et al. Droplet-based combinatorial indexing for massive-scale single-cell chromatin accessibility. Nat. Biotechnol. 37, 916–924 (2019)." href="/articles/s12276-024-01186-2#ref-CR43" id="ref-link-section-d69075120e1913">43</a></sup>.</p><h3 class="c-article__sub-heading" id="Sec9">Chromatin conformation capture</h3><p>In humans, the nucleus contains chromosomes that are organized into territories. These territories comprise different compartments, including topologically associated domains (TADs) and DNA loops. The CCCTC-binding factor (CTCF) shapes these features, affecting gene expression and genome organization in 3D. Chromosome structure and enhancer–promoter contacts affect gene expression. An altered chromatin conformation can lead to disease. Recent studies have shown that structural variants (SVs) that affect 3D genome organization contribute to cancer and other disorders<sup><a data-track="click" data-track-action="reference anchor" data-track-label="link" data-test="citation-ref" aria-label="Reference 44" title="Xu, Z. et al. Structural variants drive context-dependent oncogene activation in cancer. Nature 612, 564–572 (2022)." href="/articles/s12276-024-01186-2#ref-CR44" id="ref-link-section-d69075120e1925">44</a></sup>.</p><p>To investigate chromatin conformation and understand its heterogeneity and dynamics, several techniques based on Hi-C have been developed, which reduce the sequencing library size by utilizing a pull-down step<sup><a data-track="click" data-track-action="reference anchor" data-track-label="link" data-test="citation-ref" aria-label="Reference 45" title="Lieberman-Aiden, E. et al. Comprehensive mapping of long-range interactions reveals folding principles of the human genome. Science 326, 289–293 (2009)." href="/articles/s12276-024-01186-2#ref-CR45" id="ref-link-section-d69075120e1932">45</a></sup>. Hi-C and its derivatives can be classified into two groups depending on their cyclization system, dilute ligation systems and in situ ligation systems, for all single-cell Hi-C protocols, such as Dip-C<sup><a data-track="click" data-track-action="reference anchor" data-track-label="link" data-test="citation-ref" aria-label="Reference 46" title="Tan, L., Xing, D., Chang, C., Li, H. & Xie, X. S. Three-dimensional genome structures of single diploid human cells. Science 361, 924–928 (2018)." href="/articles/s12276-024-01186-2#ref-CR46" id="ref-link-section-d69075120e1936">46</a></sup> and single-cell Hi-C<sup><a data-track="click" data-track-action="reference anchor" data-track-label="link" data-test="citation-ref" aria-label="Reference 47" title="Nagano, T. et al. Single-cell Hi-C reveals cell-to-cell variability in chromosome structure. Nature 502, 59–64 (2013)." href="/articles/s12276-024-01186-2#ref-CR47" id="ref-link-section-d69075120e1940">47</a></sup>.</p><p>Single-cell high-throughput chromosome conformation capture (scHi-C) is a powerful method for investigating the 3D structure of an entire genome in individual cells. This method provides insight into the folding structure of the genome in a single cell at specific time points. The first single-cell Hi-C (scHi-C) technique introduced in-nucleus ligation, in which proximity ligation was performed within intact nuclei rather than after nuclear lysis, as in dilute ligation-based Hi-C<sup><a data-track="click" data-track-action="reference anchor" data-track-label="link" data-test="citation-ref" aria-label="Reference 47" title="Nagano, T. et al. Single-cell Hi-C reveals cell-to-cell variability in chromosome structure. Nature 502, 59–64 (2013)." href="/articles/s12276-024-01186-2#ref-CR47" id="ref-link-section-d69075120e1947">47</a></sup>. This in-nucleus ligation approach improved the quality of the Hi-C data by preserving the chromatin conformation within individual cells. However, this method still requires mechanical isolation of single nuclei within individual cells. The introduction of combinatorial indexing-based methods has solved this problem. One such method is sciHi-C, which utilizes combinatorial indexing to process thousands of cells without physical isolation or microfluidic manipulation<sup><a data-track="click" data-track-action="reference anchor" data-track-label="link" data-test="citation-ref" aria-label="Reference 48" title="Ramani, V. et al. Massively multiplex single-cell Hi-C. Nat. Methods 14, 263–266 (2017)." href="/articles/s12276-024-01186-2#ref-CR48" id="ref-link-section-d69075120e1951">48</a></sup>. Another combinatorial indexing-based method, called single cell-indexed DLO Hi-C (sciDLO Hi-C), which does not require biotin labeling or pulldown, has also been developed<sup><a data-track="click" data-track-action="reference anchor" data-track-label="link" data-test="citation-ref" aria-label="Reference 49" title="Lin, D. et al. Decoding the spatial chromatin organization and dynamic epigenetic landscapes of macrophage cells during differentiation and immune activation. Nat. Commun. 13, 5857–5 (2022)." href="/articles/s12276-024-01186-2#ref-CR49" id="ref-link-section-d69075120e1955">49</a></sup>.</p><h3 class="c-article__sub-heading" id="Sec10">Histone modification</h3><p>Histone modifications play crucial roles in regulating gene expression. Various chemical groups can be added to and removed from the N-terminal tails of histones. Histone modifications result in different chromatin states that can activate or repress gene expression. Understanding the landscape of histone modifications at the single-cell level is essential for studying epigenetic programs and predicting transcription states.</p><p>Chromatin immunoprecipitation (ChIP) is used to identify the DNA-binding sites of a specific protein. After fragmentation with micrococcal nuclease (MNase), histone–DNA complexes containing specific modifications are immunoprecipitated using specific antibodies. ChIP-seq requires a large number of samples because of its low signal-to-noise ratio (SNR). Droplet-based chromatin immunoprecipitation (Drop-ChIP) overcomes the limitation of labeling the target loci of chromatin to capture histone modifications at the single-cell level<sup><a data-track="click" data-track-action="reference anchor" data-track-label="link" data-test="citation-ref" aria-label="Reference 50" title="Rotem, A. et al. Single-cell ChIP-seq reveals cell subpopulations defined by chromatin state. Nat. Biotechnol. 33, 1165–1172 (2015)." href="/articles/s12276-024-01186-2#ref-CR50" id="ref-link-section-d69075120e1970">50</a></sup>.</p><p>Another strategy for reducing noise is cleavage of targets and release using nucleases (CUT and RUN)<sup><a data-track="click" data-track-action="reference anchor" data-track-label="link" data-test="citation-ref" aria-label="Reference 51" title="Skene, P. J. & Henikoff, S. An efficient targeted nuclease strategy for high-resolution mapping of DNA binding sites. Elife 6, e21856 (2017)." href="/articles/s12276-024-01186-2#ref-CR51" id="ref-link-section-d69075120e1977">51</a></sup>. After binding to target histones, MNase binds to protein A and specific antibodies to cleave and release chromatin fragments. CUT and RUN requires additional steps, such as DNA end polishing and adapter ligation, for sequencing library preparation, which increases time, cost, and labor. A strategy using Tn5 transposase was developed to address these problems. Single-cell cleavage under targets and tagmentation (scCUT and tag) uses the fusion protein pA-Tn5, which binds to antibodies<sup><a data-track="click" data-track-action="reference anchor" data-track-label="link" data-test="citation-ref" aria-label="Reference 52" title="Bartosovic, M., Kabbe, M. & Castelo-Branco, G. Single-cell CUT&Tag profiles histone modifications and transcription factors in complex tissues. Nat. Biotechnol. 39, 825–835 (2021)." href="/articles/s12276-024-01186-2#ref-CR52" id="ref-link-section-d69075120e1981">52</a></sup>. Preloaded DNA adapters in Tn5 are integrated into chromatin, and the indexed DNA fragments are released at the same time.</p><p>Most existing methods for mapping histone marks are limited to profiling one histone modification at a time. Single-cell chromatin immunocleavage and unmixed sequencing (scChIX-seq) allows for the analysis of multiple histone markers in a single cell. We analyzed two histone markers, both together and separately. Two histone signals from double-incubated cells were separated and interpreted using single-incubated datasets as training data to profile each histone marker individually via a computer algorithm<sup><a data-track="click" data-track-action="reference anchor" data-track-label="link" data-test="citation-ref" aria-label="Reference 53" title="Yeung, J. et al. scChIX-seq infers dynamic relationships between histone modifications in single cells. Nat. Biotechnol. 41, 813–823 (2023)." href="/articles/s12276-024-01186-2#ref-CR53" id="ref-link-section-d69075120e1988">53</a></sup>.</p><h3 class="c-article__sub-heading" id="Sec11">Single-cell multimodal omics</h3><p>Single-cell multimodal omics techniques have emerged as powerful tools for studying complex biological processes occurring within single cells. These techniques enable the simultaneous analysis of multiple omics layers, such as genomics, transcriptomics, proteomics, and epigenomics, within individual cells. By integrating information from different molecular levels, researchers can gain a more comprehensive understanding of cellular behavior and regulation. There are several variations of single-cell multimodal omics techniques, each focusing on different molecular layers (Fig. <a data-track="click" data-track-label="link" data-track-action="figure anchor" href="/articles/s12276-024-01186-2#Fig1">1</a>).</p><div class="c-article-section__figure js-c-reading-companion-figures-item" data-test="figure" data-container-section="figure" id="figure-1" data-title="Sequencing methods for single-cell multimodal omics analysis."><figure><figcaption><b id="Fig1" class="c-article-section__figure-caption" data-test="figure-caption-text">Fig. 1: Sequencing methods for single-cell multimodal omics analysis.</b></figcaption><div class="c-article-section__figure-content"><div class="c-article-section__figure-item"><a class="c-article-section__figure-link" data-test="img-link" data-track="click" data-track-label="image" data-track-action="view figure" href="/articles/s12276-024-01186-2/figures/1" rel="nofollow"><picture><img aria-describedby="Fig1" src="//media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs12276-024-01186-2/MediaObjects/12276_2024_1186_Fig1_HTML.png" alt="figure 1" loading="lazy" width="685" height="650"></picture></a></div><div class="c-article-section__figure-description" data-test="bottom-caption" id="figure-1-desc"><p>Numerous single-cell multiomics sequences have been developed and can be classified into seven types. The blue box represents dual omics technology, and the magenta box represents technology that handles three or more omics simultaneously.</p></div></div><div class="u-text-right u-hide-print"><a class="c-article__pill-button" data-test="article-link" data-track="click" data-track-label="button" data-track-action="view figure" href="/articles/s12276-024-01186-2/figures/1" data-track-dest="link:Figure1 Full size image" aria-label="Full size image figure 1" rel="nofollow"><span>Full size image</span><svg width="16" height="16" focusable="false" role="img" aria-hidden="true" class="u-icon"><use xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="#icon-eds-i-chevron-right-small"></use></svg></a></div></figure></div><h3 class="c-article__sub-heading" id="Sec12">Single-cell multimodal omics methods that simultaneously address the genome and transcriptome</h3><p>Simultaneous examination of the genome and transcriptome is an important experimental method for directly identifying changes in phenotypic material because the transcriptome is generated from the genome through transcription. The observation of mutations and specificity in the reconstitution of DNA sequences into RNA sequences is an important indicator of phenotypic changes in a cell.</p><p>The earliest methods of this type were gDNA-mRNA sequencing (DR-seq)<sup><a data-track="click" data-track-action="reference anchor" data-track-label="link" data-test="citation-ref" aria-label="Reference 54" title="Dey, S. S., Kester, L., Spanjaard, B., Bienko, M. & Van Oudenaarden, A. Integrated genome and transcriptome sequencing of the same cell. Nat. Biotechnol. 33, 285–289 (2015)." href="/articles/s12276-024-01186-2#ref-CR54" id="ref-link-section-d69075120e2033">54</a></sup> and genome and transcriptome sequencing (G&T-seq)<sup><a data-track="click" data-track-action="reference anchor" data-track-label="link" data-test="citation-ref" aria-label="Reference 55" title="Macaulay, I. C. et al. G&T-seq: parallel sequencing of single-cell genomes and transcriptomes. Nat. Methods 12, 519–522 (2015)." href="/articles/s12276-024-01186-2#ref-CR55" id="ref-link-section-d69075120e2037">55</a></sup>, which require dividing RNA and DNA extracted from a single cell. In DR-seq, preamplified nucleic acids are divided into RNA and DNA and sequenced (Fig. <a data-track="click" data-track-label="link" data-track-action="figure anchor" href="/articles/s12276-024-01186-2#Fig2">2a</a>). DR-seq is a plate-based and low-throughput method that minimizes the risk of nucleic acid loss. In G&T-seq, oligo-dT-coated magnetic beads are used to separate poly-A mRNA from DNA, and the fractionated DNA and RNA are analyzed (Fig. <a data-track="click" data-track-label="link" data-track-action="figure anchor" href="/articles/s12276-024-01186-2#Fig2">2b</a>). However, DR-seq is limited by the fact that only the 3′ end of RNA can be sequenced, while G&T-seq can sequence the full length of RNA.</p><div class="c-article-section__figure js-c-reading-companion-figures-item" data-test="figure" data-container-section="figure" id="figure-2" data-title="Protocols for multimodal omics methods for simultaneous analysis of the genome and transcriptome."><figure><figcaption><b id="Fig2" class="c-article-section__figure-caption" data-test="figure-caption-text">Fig. 2: Protocols for multimodal omics methods for simultaneous analysis of the genome and transcriptome.</b></figcaption><div class="c-article-section__figure-content"><div class="c-article-section__figure-item"><a class="c-article-section__figure-link" data-test="img-link" data-track="click" data-track-label="image" data-track-action="view figure" href="/articles/s12276-024-01186-2/figures/2" rel="nofollow"><picture><img aria-describedby="Fig2" src="//media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs12276-024-01186-2/MediaObjects/12276_2024_1186_Fig2_HTML.png" alt="figure 2" loading="lazy" width="685" height="959"></picture></a></div><div class="c-article-section__figure-description" data-test="bottom-caption" id="figure-2-desc"><p>Here, we present an overview of single-cell multimodal omics methods that coprofile the genome and transcriptome. <b>a</b> gDNA-mRNA sequencing (DR-seq) involves preamplification of DNA and RNA from a single cell before splitting the material for separate genome and transcriptome sequencing. <b>b</b> In genome and transcriptome sequencing (G&T-seq), the genetic material is amplified after physical separation using beads. <b>c</b> Simultaneous isolation of genomic DNA and total RNA (SIDR-seq) involves the use of magnetic microbeads for hypotonic lysis to isolate the nucleus from the cytoplasmic RNA. <b>d</b> Direct nuclear tagmentation and RNA sequencing (DNTR-seq) gently lyses the cell membrane, enabling the precipitation of the nucleus through centrifugation and controlled aspiration, which separates the intact nucleus from the RNA. <b>e</b> For scONE-seq, all processes, including amplification and sequencing, are performed in single tubes. This is possible because genomic DNA and RNA are barcoded differentially. <b>f</b> Sample preparation for TARGET-seq relies on mild protease digestion, which enhances the release of genomic DNA and mRNA. Separate amplification processes are used for DNA and RNA analyses. For each process, red lines represent DNA- and DNA-related processes, whereas blue lines represent RNA- and RNA-related processes.</p></div></div><div class="u-text-right u-hide-print"><a class="c-article__pill-button" data-test="article-link" data-track="click" data-track-label="button" data-track-action="view figure" href="/articles/s12276-024-01186-2/figures/2" data-track-dest="link:Figure2 Full size image" aria-label="Full size image figure 2" rel="nofollow"><span>Full size image</span><svg width="16" height="16" focusable="false" role="img" aria-hidden="true" class="u-icon"><use xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="#icon-eds-i-chevron-right-small"></use></svg></a></div></figure></div><p>While simultaneous isolation of genomic DNA and total RNA sequencing (SIDR-seq)<sup><a data-track="click" data-track-action="reference anchor" data-track-label="link" data-test="citation-ref" aria-label="Reference 56" title="Han, K. Y. et al. SIDR: simultaneous isolation and parallel sequencing of genomic DNA and total RNA from single cells. Genome Res. 28, 75–87 (2018)." href="/articles/s12276-024-01186-2#ref-CR56" id="ref-link-section-d69075120e2089">56</a></sup> and direct nuclear tagmentation and RNA-sequencing (DNTR-seq)<sup><a data-track="click" data-track-action="reference anchor" data-track-label="link" data-test="citation-ref" aria-label="Reference 57" title="Zachariadis, V., Cheng, H., Andrews, N. & Enge, M. A highly scalable method for joint whole-genome sequencing and gene-expression profiling of single cells. Mol. Cell 80, 541–553.e5 (2020)." href="/articles/s12276-024-01186-2#ref-CR57" id="ref-link-section-d69075120e2093">57</a></sup> share similarities with G&T-seq in terms of amplifying genetic materials after separation, they differ from G&T-seq in that they separate intact nuclei. For SIDR-seq, cells are first cultured with antibody-conjugated magnetic microbeads (Fig. <a data-track="click" data-track-label="link" data-track-action="figure anchor" href="/articles/s12276-024-01186-2#Fig2">2c</a>). This step allows selective labeling of target cells. Subsequent separation is achieved through hypotonic lysis, which causes cells to swell and rupture, resulting in the release of cytoplasmic RNA while preserving an intact nucleus. This approach is advantageous for studying nonpoly(A) RNA and long RNAs and for accurate detection of copy number variations (CNVs) and single-nucleotide polymorphisms (SNPs). In contrast, the separation process in DNTR-seq involves partial lysis of the cell membrane, followed by centrifugation and aspiration to separate intact nuclei from other cellular components (Fig. <a data-track="click" data-track-label="link" data-track-action="figure anchor" href="/articles/s12276-024-01186-2#Fig2">2d</a>).</p><p>TARGET-seq focuses on improving coverage of key mutations<sup><a data-track="click" data-track-action="reference anchor" data-track-label="link" data-test="citation-ref" aria-label="Reference 58" title="Rodriguez-Meira, A. et al. Unravelling intratumoral heterogeneity through high-sensitivity single-cell mutational analysis and parallel RNA sequencing. Mol. Cell 73, 1292–1305.e8 (2019)." href="/articles/s12276-024-01186-2#ref-CR58" id="ref-link-section-d69075120e2106">58</a></sup>. Sample preparation involves mild protease digestion to increase the release of gDNA and mRNAs, followed by heat inactivation of the protease to prevent interference in subsequent steps (Fig. <a data-track="click" data-track-label="link" data-track-action="figure anchor" href="/articles/s12276-024-01186-2#Fig2">2f</a>). RT and PCR amplification are performed separately to generate cDNA from mRNA and amplify the gDNA, respectively. In contrast, scONE-seq<sup><a data-track="click" data-track-action="reference anchor" data-track-label="link" data-test="citation-ref" aria-label="Reference 59" title="Yu, L. et al. scONE-seq: a single-cell multi-omics method enables simultaneous dissection of phenotype and genotype heterogeneity from frozen tumors. Sci. Adv. 9, eabp8901 (2023)." href="/articles/s12276-024-01186-2#ref-CR59" id="ref-link-section-d69075120e2113">59</a></sup> simplifies the measurement of single cells in a one-tube reaction. During sample preparation, gDNA and RNA are barcoded differentially. Differentially labeled gDNA and cDNA are simultaneously amplified and converted into a sequencing library in a single-tube reaction<sup><a data-track="click" data-track-action="reference anchor" data-track-label="link" data-test="citation-ref" aria-label="Reference 59" title="Yu, L. et al. scONE-seq: a single-cell multi-omics method enables simultaneous dissection of phenotype and genotype heterogeneity from frozen tumors. Sci. Adv. 9, eabp8901 (2023)." href="/articles/s12276-024-01186-2#ref-CR59" id="ref-link-section-d69075120e2117">59</a></sup> (Fig. <a data-track="click" data-track-label="link" data-track-action="figure anchor" href="/articles/s12276-024-01186-2#Fig2">2e</a>).</p><h3 class="c-article__sub-heading" id="Sec13">DNA methylation-related methods for single-cell multimodal omics analysis</h3><p>Simultaneous examination of the transcriptome and methylome can provide valuable insights into the interplay between DNA methylation and transcription in cell populations with inherent heterogeneity. Single-cell genome-wide methylome and transcriptome sequencing (scM&T-seq) (the application of scBS-seq to G&T-seq), which physically separates RNA and DNA using oligo-dT-attached beads, has demonstrated a negative association between non-CGI promoter methylation and transcription in single cells<sup><a data-track="click" data-track-action="reference anchor" data-track-label="link" data-test="citation-ref" aria-label="Reference 60" title="Angermueller, C. et al. Parallel single-cell sequencing links transcriptional and epigenetic heterogeneity. Nat. Methods 13, 229–232 (2016)." href="/articles/s12276-024-01186-2#ref-CR60" id="ref-link-section-d69075120e2132">60</a></sup>. Another method, scMT-seq (the combination of scRRBS with Smart-seq2), which separates the cytosolic fraction by micropipetting, revealed that methylation of CpG promoters has no effect on gene expression levels. scMT-seq offers higher transcriptome coverage than single-cell triple omics sequencing (scTrio-seq) but has low CpG coverage and a high rate of allele drop-out<sup><a data-track="click" data-track-action="reference anchor" data-track-label="link" data-test="citation-ref" aria-label="Reference 61" title="Hu, Y. et al. Simultaneous profiling of transcriptome and DNA methylome from a single cell. Genome Biol. 17, 1–11 (2016)." href="/articles/s12276-024-01186-2#ref-CR61" id="ref-link-section-d69075120e2136">61</a></sup>. SMART-RRBS<sup><a data-track="click" data-track-action="reference anchor" data-track-label="link" data-test="citation-ref" aria-label="Reference 62" title="Gu, H. et al. Smart-RRBS for single-cell methylome and transcriptome analysis. Nat. Protoc. 16, 4004–4030 (2021)." href="/articles/s12276-024-01186-2#ref-CR62" id="ref-link-section-d69075120e2140">62</a></sup> (the combination of MSC-RRBS with Smart-seq2), which divides DNA and mRNA using oligo-dT primers, can be used to identify rare tumor cells, study drug mechanisms, and detect CNVs<sup><a data-track="click" data-track-action="reference anchor" data-track-label="link" data-test="citation-ref" aria-label="Reference 62" title="Gu, H. et al. Smart-RRBS for single-cell methylome and transcriptome analysis. Nat. Protoc. 16, 4004–4030 (2021)." href="/articles/s12276-024-01186-2#ref-CR62" id="ref-link-section-d69075120e2144">62</a></sup> (Fig. <a data-track="click" data-track-label="link" data-track-action="figure anchor" href="/articles/s12276-024-01186-2#Fig3">3a</a>). This method covers three times as many CpGs as scM&T-seq and generates fewer unwanted adapter-only sites than random-primer-based methods. However, this approach is more expensive than droplet or combinatorial indexing-based methods and has a lower throughput per cell, a low copy number of each genomic locus in a single diploid cell, and sparse methylome coverage<sup><a data-track="click" data-track-action="reference anchor" data-track-label="link" data-test="citation-ref" aria-label="Reference 62" title="Gu, H. et al. Smart-RRBS for single-cell methylome and transcriptome analysis. Nat. Protoc. 16, 4004–4030 (2021)." href="/articles/s12276-024-01186-2#ref-CR62" id="ref-link-section-d69075120e2152">62</a></sup>.</p><div class="c-article-section__figure js-c-reading-companion-figures-item" data-test="figure" data-container-section="figure" id="figure-3" data-title="Representative multimodal omics protocols related to the methylome."><figure><figcaption><b id="Fig3" class="c-article-section__figure-caption" data-test="figure-caption-text">Fig. 3: Representative multimodal omics protocols related to the methylome.</b></figcaption><div class="c-article-section__figure-content"><div class="c-article-section__figure-item"><a class="c-article-section__figure-link" data-test="img-link" data-track="click" data-track-label="image" data-track-action="view figure" href="/articles/s12276-024-01186-2/figures/3" rel="nofollow"><picture><img aria-describedby="Fig3" src="//media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs12276-024-01186-2/MediaObjects/12276_2024_1186_Fig3_HTML.png" alt="figure 3" loading="lazy" width="685" height="443"></picture></a></div><div class="c-article-section__figure-description" data-test="bottom-caption" id="figure-3-desc"><p>Overview of five types of single-cell multimodal omics methods for analyzing diverse combinations of omics-containing methylomes. <b>a</b> Protocol of switching mechanism at 5′ end of RNA template-reduced representative bisulfite sequencing (SMART-RRBS) for the analysis of the transcriptome and methylome. DNA was isolated from RNA using AMPure beads. <b>b</b> Protocol for single-nucleus methyl chromosome conformation capture sequencing (sn-m3C-seq) for the analysis of the methylome and chromosome conformation. The biotin ligation step for selecting the ligated DNA is omitted to minimize the loss of DNA fragments. <b>c</b> Protocol for single-nucleus methylcytosine, chromatin accessibility, and transcriptome sequencing (snmCAT-seq) for the analysis of the methylome, chromatin accessibility, and transcriptome. This method does not mechanically separate DNA from RNA. Instead, DNA and RNA are separated during the data processing step by utilizing methylated cytosines in the cDNA synthesis step. <b>d</b> Protocol of single-cell triple omics sequencing 2 (scTrio-seq2) for the analysis of the genome, transcriptome, and methylome. The DNA and RNA are separated by centrifugation, and reverse transcription is performed using oligo-dT primers with barcodes and UMIs. <b>e</b> Protocol for improved single-cell multiomics sequencing (iscCOOL-seq) for the analysis of the genome, methylome, and chromatin accessibility. TAILS is used to construct the libraries. In (<b>a</b>, <b>b</b>, <b>d</b>), the red lines indicate DNA, and the blue lines indicate RNA. In (<b>c</b>, <b>e</b>), the gray lines indicate DNA.</p></div></div><div class="u-text-right u-hide-print"><a class="c-article__pill-button" data-test="article-link" data-track="click" data-track-label="button" data-track-action="view figure" href="/articles/s12276-024-01186-2/figures/3" data-track-dest="link:Figure3 Full size image" aria-label="Full size image figure 3" rel="nofollow"><span>Full size image</span><svg width="16" height="16" focusable="false" role="img" aria-hidden="true" class="u-icon"><use xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="#icon-eds-i-chevron-right-small"></use></svg></a></div></figure></div><p>To fully understand the complex interactions and relationships between DNA methylation patterns and chromatin conformation, researchers have developed protocols that simultaneously analyze both DNA methylation and chromatin conformation.</p><p>sn-m3C-seq combines scHi-C with snmC-seq2<sup><a data-track="click" data-track-action="reference anchor" data-track-label="link" data-test="citation-ref" aria-label="Reference 31" title="Luo, C. et al. Robust single-cell DNA methylome profiling with snmC-seq2. Nat. Commun. 9, 3824–2 (2018)." href="/articles/s12276-024-01186-2#ref-CR31" id="ref-link-section-d69075120e2213">31</a></sup>. The sn-m3C-seq protocol modifies the standard 3 C or Hi-C protocols by omitting certain steps (such as biotin fill-in and pull-down) to minimize data loss, particularly for methylation and ligation data (Fig. <a data-track="click" data-track-label="link" data-track-action="figure anchor" href="/articles/s12276-024-01186-2#Fig3">3b</a>). This modification results in a greater percentage of captured reads than other single-cell Hi-C methods<sup><a data-track="click" data-track-action="reference anchor" data-track-label="link" data-test="citation-ref" aria-label="Reference 63" title="Galitsyna, A. A. & Gelfand, M. S. Single-cell Hi-C data analysis: safety in numbers. Brief. Bioinforma. 22, bbab316 (2021)." href="/articles/s12276-024-01186-2#ref-CR63" id="ref-link-section-d69075120e2220">63</a></sup>. Using sn-m3C-seq, researchers have defined different cell types within the human prefrontal cortex (PFC), demonstrating that contact maps can be used to distinguish between nonneuronal cells and neurons<sup><a data-track="click" data-track-action="reference anchor" data-track-label="link" data-test="citation-ref" aria-label="Reference 64" title="Lee, D. et al. Simultaneous profiling of 3D genome structure and DNA methylation in single human cells. Nat. Methods 16, 999–1006 (2019)." href="/articles/s12276-024-01186-2#ref-CR64" id="ref-link-section-d69075120e2224">64</a></sup>. Moreover, sn-m3C-seq has been employed to investigate the variability between the methylome and chromosome conformation during brain development and to construct single-cell DNA methylomes and a 3D genome atlas of the mouse brain<sup><a data-track="click" data-track-action="reference anchor" data-track-label="link" data-test="citation-ref" aria-label="Reference 65" title="Heffel, M. G. et al. Epigenomic and chromosomal architectural reconfiguration in developing human frontal cortex and hippocampus. bioRxiv: 2022.10. 07.511350 (2022)." href="/articles/s12276-024-01186-2#ref-CR65" id="ref-link-section-d69075120e2228">65</a>,<a data-track="click" data-track-action="reference anchor" data-track-label="link" data-test="citation-ref" aria-label="Reference 66" title="Liu, H. et al. Single-cell DNA methylome and 3D multi-omic atlas of the adult mouse brain. bioRxiv (2022)." href="/articles/s12276-024-01186-2#ref-CR66" id="ref-link-section-d69075120e2231">66</a></sup>. Given the significance of chromatin conformation and DNA methylation in various diseases, advancements of this methodology to include transcriptome analysis could offer a comprehensive understanding of holistic cellular responses at the individual level, particularly in diseases such as cancer.</p><p>Methyl-HiC is another method that can be used to simultaneously analyze the methylome and chromosome conformations<sup><a data-track="click" data-track-action="reference anchor" data-track-label="link" data-test="citation-ref" aria-label="Reference 67" title="Li, G. et al. Joint profiling of DNA methylation and chromatin architecture in single cells. Nat. Methods 16, 991–993 (2019)." href="/articles/s12276-024-01186-2#ref-CR67" id="ref-link-section-d69075120e2238">67</a></sup>. Unlike sn-m3C-seq, methyl-HiC includes biotin ligation and pull-down steps<sup><a data-track="click" data-track-action="reference anchor" data-track-label="link" data-test="citation-ref" aria-label="Reference 67" title="Li, G. et al. Joint profiling of DNA methylation and chromatin architecture in single cells. Nat. Methods 16, 991–993 (2019)." href="/articles/s12276-024-01186-2#ref-CR67" id="ref-link-section-d69075120e2242">67</a></sup>. Methyl-HiC is reported to detect 38,827 short-range contacts (<1 kb) and 77,811 long-range contacts (≥1 kb), whereas sn-m3C-seq can detect a significantly greater number of contacts (646,971 short-range and 195,160 long-range on average per cell)<sup><a data-track="click" data-track-action="reference anchor" data-track-label="link" data-test="citation-ref" aria-label="Reference 68" title="Kim, K., Kim, M., Kim, Y., Lee, D. & Jung, I. Hi-C as a molecular rangefinder to examine genomic rearrangements. Semin. Cell Dev. Biol. 121, 161–170 (2022)." href="/articles/s12276-024-01186-2#ref-CR68" id="ref-link-section-d69075120e2246">68</a></sup>. The observed discrepancy might be attributed to differences in the methodologies and protocols used for the two methods.</p><p>Simultaneous analysis of the genome, transcriptome, and DNA methylome is highly desirable in cancer research because of the substantial heterogeneity observed across these three omics layers. This demand has led to the development of a method called scTrio-seq that has been used to identify subpopulations of human hepatocellular carcinoma cells<sup><a data-track="click" data-track-action="reference anchor" data-track-label="link" data-test="citation-ref" aria-label="Reference 69" title="Hou, Y. et al. Single-cell triple omics sequencing reveals genetic, epigenetic, and transcriptomic heterogeneity in hepatocellular carcinomas. Cell Res. 26, 304–319 (2016)." href="/articles/s12276-024-01186-2#ref-CR69" id="ref-link-section-d69075120e2254">69</a></sup> (Table <a data-track="click" data-track-label="link" data-track-action="table anchor" href="/articles/s12276-024-01186-2#Tab3">3</a>). This method combines scRRBS, which detects DNA methylation and CNVs, with scRNA-Seq<sup><a data-track="click" data-track-action="reference anchor" data-track-label="link" data-test="citation-ref" aria-label="Reference 69" title="Hou, Y. et al. Single-cell triple omics sequencing reveals genetic, epigenetic, and transcriptomic heterogeneity in hepatocellular carcinomas. Cell Res. 26, 304–319 (2016)." href="/articles/s12276-024-01186-2#ref-CR69" id="ref-link-section-d69075120e2261">69</a></sup>. By integrating these techniques, researchers can investigate the positive correlation between DNA copy number and gene expression within relevant genomic regions and explore subpopulations with distinct CNVs, methylation levels, and RNA expression in hepatocellular carcinoma (HCC)<sup><a data-track="click" data-track-action="reference anchor" data-track-label="link" data-test="citation-ref" aria-label="Reference 69" title="Hou, Y. et al. Single-cell triple omics sequencing reveals genetic, epigenetic, and transcriptomic heterogeneity in hepatocellular carcinomas. Cell Res. 26, 304–319 (2016)." href="/articles/s12276-024-01186-2#ref-CR69" id="ref-link-section-d69075120e2265">69</a></sup>. scTrio-seq2, an improved version of scTrio-seq, incorporates multiplexed single-cell RNA-seq (scRNA-seq) using unique molecular identifiers (UMIs) inserted into oligo-dT primers for transcriptome profiling<sup><a data-track="click" data-track-action="reference anchor" data-track-label="link" data-test="citation-ref" aria-label="Reference 70" title="Bian, S. et al. Single-cell multiomics sequencing and analyses of human colorectal cancer. Science 362, 1060–1063 (2018)." href="/articles/s12276-024-01186-2#ref-CR70" id="ref-link-section-d69075120e2269">70</a></sup> (Fig. <a data-track="click" data-track-label="link" data-track-action="figure anchor" href="/articles/s12276-024-01186-2#Fig3">3d</a>). This method also incorporates scBS-seq to profile DNA methylation levels across the entire genome. scTrio-seq2 has been used to analyze demethylation aspects and differences in methylation levels between normal and cancer cells<sup><a data-track="click" data-track-action="reference anchor" data-track-label="link" data-test="citation-ref" aria-label="Reference 70" title="Bian, S. et al. Single-cell multiomics sequencing and analyses of human colorectal cancer. Science 362, 1060–1063 (2018)." href="/articles/s12276-024-01186-2#ref-CR70" id="ref-link-section-d69075120e2277">70</a>,<a data-track="click" data-track-action="reference anchor" data-track-label="link" data-test="citation-ref" aria-label="Reference 71" title="Zhou, F. et al. Reconstituting the transcriptome and DNA methylome landscapes of human implantation. Nature 572, 660–664 (2019)." href="/articles/s12276-024-01186-2#ref-CR71" id="ref-link-section-d69075120e2280">71</a></sup> (Table <a data-track="click" data-track-label="link" data-track-action="table anchor" href="/articles/s12276-024-01186-2#Tab3">3</a>).</p><div class="c-article-table" data-test="inline-table" data-container-section="table" id="table-3"><figure><figcaption class="c-article-table__figcaption"><b id="Tab3" data-test="table-caption">Table 3 Results obtained by using methylome sequencing methods (multimodal omics sequencing methods) and categories of those findings.</b></figcaption><div class="u-text-right u-hide-print"><a class="c-article__pill-button" data-test="table-link" data-track="click" data-track-action="view table" data-track-label="button" rel="nofollow" href="/articles/s12276-024-01186-2/tables/3" aria-label="Full size table 3"><span>Full size table</span><svg width="16" height="16" focusable="false" role="img" aria-hidden="true" class="u-icon"><use xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="#icon-eds-i-chevron-right-small"></use></svg></a></div></figure></div><p>The development of single-cell multiomics sequencing (scCOOL-seq) has enabled the simultaneous measurement of multiple epigenomic features in single cells<sup><a data-track="click" data-track-action="reference anchor" data-track-label="link" data-test="citation-ref" aria-label="Reference 72" title="Guo, F. et al. Single-cell multi-omics sequencing of mouse early embryos and embryonic stem cells. Cell Res. 27, 967–988 (2017)." href="/articles/s12276-024-01186-2#ref-CR72" id="ref-link-section-d69075120e2638">72</a></sup>. This technique allows the analysis of CNVs, ploidy, DNA methylation, nucleosome positioning, and chromatin state within individual cells by combining PBAT-seq and NOMe-seq data<sup><a data-track="click" data-track-action="reference anchor" data-track-label="link" data-test="citation-ref" aria-label="Reference 72" title="Guo, F. et al. Single-cell multi-omics sequencing of mouse early embryos and embryonic stem cells. Cell Res. 27, 967–988 (2017)." href="/articles/s12276-024-01186-2#ref-CR72" id="ref-link-section-d69075120e2642">72</a></sup>. scCOOL-seq can be used to study embryonic development and pathological conditions such as tumorigenesis, and it has been used to perform single-cell and parental allele-specific analyses in early mouse embryos<sup><a data-track="click" data-track-action="reference anchor" data-track-label="link" data-test="citation-ref" aria-label="Reference 72" title="Guo, F. et al. Single-cell multi-omics sequencing of mouse early embryos and embryonic stem cells. Cell Res. 27, 967–988 (2017)." href="/articles/s12276-024-01186-2#ref-CR72" id="ref-link-section-d69075120e2646">72</a></sup>. An improved version of scCOOL-seq, iscCOOL-seq, was developed to increase the mapping rate<sup><a data-track="click" data-track-action="reference anchor" data-track-label="link" data-test="citation-ref" aria-label="Reference 73" title="Gu, C., Liu, S., Wu, Q., Zhang, L. & Guo, F. Integrative single-cell analysis of transcriptome, DNA methylome and chromatin accessibility in mouse oocytes. Cell Res. 29, 110–123 (2019)." href="/articles/s12276-024-01186-2#ref-CR73" id="ref-link-section-d69075120e2650">73</a></sup>. By replacing PBAT with TAILS (a tailing- and ligation-free method for single cells), iscCOOL-seq achieved a higher mapping rate<sup><a data-track="click" data-track-action="reference anchor" data-track-label="link" data-test="citation-ref" aria-label="Reference 73" title="Gu, C., Liu, S., Wu, Q., Zhang, L. & Guo, F. Integrative single-cell analysis of transcriptome, DNA methylome and chromatin accessibility in mouse oocytes. Cell Res. 29, 110–123 (2019)." href="/articles/s12276-024-01186-2#ref-CR73" id="ref-link-section-d69075120e2654">73</a></sup> (Fig. <a data-track="click" data-track-label="link" data-track-action="figure anchor" href="/articles/s12276-024-01186-2#Fig3">3e</a>). This breakthrough has facilitated the examination of DNA methylation, chromatin accessibility, and gene expression<sup><a data-track="click" data-track-action="reference anchor" data-track-label="link" data-test="citation-ref" aria-label="Reference 73" title="Gu, C., Liu, S., Wu, Q., Zhang, L. & Guo, F. Integrative single-cell analysis of transcriptome, DNA methylome and chromatin accessibility in mouse oocytes. Cell Res. 29, 110–123 (2019)." href="/articles/s12276-024-01186-2#ref-CR73" id="ref-link-section-d69075120e2662">73</a>,<a data-track="click" data-track-action="reference anchor" data-track-label="link" data-test="citation-ref" aria-label="Reference 74" title="Li, L. et al. Single-cell multi-omics sequencing of human early embryos. Nat. Cell Biol. 20, 847–858 (2018)." href="/articles/s12276-024-01186-2#ref-CR74" id="ref-link-section-d69075120e2665">74</a></sup> (Table <a data-track="click" data-track-label="link" data-track-action="table anchor" href="/articles/s12276-024-01186-2#Tab3">3</a>).</p><h3 class="c-article__sub-heading" id="Sec14">Single-cell multimodal analysis for studying the interplay between epigenetic regulation and gene expression</h3><p>Concurrent examination of the epigenome and transcriptome within individual cells allows the investigation of the relationship between epigenetic regulation and gene activity at the single-cell level, providing a deeper understanding of cellular heterogeneity and regulatory dynamics.</p><p>The sci-CAR method employs combinatorial indexing to merge sci-RNA-seq and sciATAC-seq, enabling concurrent analysis of the transcriptome and chromatin accessibility<sup><a data-track="click" data-track-action="reference anchor" data-track-label="link" data-test="citation-ref" aria-label="Reference 75" title="Cao, J. et al. Joint profiling of chromatin accessibility and gene expression in thousands of single cells. Science 361, 1380–1385 (2018)." href="/articles/s12276-024-01186-2#ref-CR75" id="ref-link-section-d69075120e2684">75</a></sup>. Despite its advantages in terms of throughput, sciCAR may yield sparse data, particularly concerning chromatin accessibility<sup><a data-track="click" data-track-action="reference anchor" data-track-label="link" data-test="citation-ref" aria-label="Reference 75" title="Cao, J. et al. Joint profiling of chromatin accessibility and gene expression in thousands of single cells. Science 361, 1380–1385 (2018)." href="/articles/s12276-024-01186-2#ref-CR75" id="ref-link-section-d69075120e2688">75</a></sup>. In contrast, scCAT-seq physically separates mRNA and DNA using Smart-seq2 for transcriptome analysis and scATAC-seq for chromatin accessibility analysis<sup><a data-track="click" data-track-action="reference anchor" data-track-label="link" data-test="citation-ref" aria-label="Reference 76" title="Liu, L. et al. Deconvolution of single-cell multi-omics layers reveals regulatory heterogeneity. Nat. Commun. 10, 470 (2019)." href="/articles/s12276-024-01186-2#ref-CR76" id="ref-link-section-d69075120e2692">76</a></sup>. This approach successfully mapped chromatin accessibility and transcriptomes in early embryos<sup><a data-track="click" data-track-action="reference anchor" data-track-label="link" data-test="citation-ref" aria-label="Reference 76" title="Liu, L. et al. Deconvolution of single-cell multi-omics layers reveals regulatory heterogeneity. Nat. Commun. 10, 470 (2019)." href="/articles/s12276-024-01186-2#ref-CR76" id="ref-link-section-d69075120e2696">76</a></sup>. SNARE-seq, a droplet-based method, captures both gDNA and mRNA, providing superior chromatin accessibility data compared with that of sci-CAR<sup><a data-track="click" data-track-action="reference anchor" data-track-label="link" data-test="citation-ref" aria-label="Reference 77" title="Chen, S., Lake, B. B. & Zhang, K. High-throughput sequencing of the transcriptome and chromatin accessibility in the same cell. Nat. Biotechnol. 37, 1452–1457 (2019)." href="/articles/s12276-024-01186-2#ref-CR77" id="ref-link-section-d69075120e2700">77</a></sup>. Paired-seq utilizes ligation-based combinatorial indexing to simultaneously barcode cDNA and gDNA, thereby increasing the throughput<sup><a data-track="click" data-track-action="reference anchor" data-track-label="link" data-test="citation-ref" aria-label="Reference 78" title="Zhu, C. et al. An ultra high-throughput method for single-cell joint analysis of open chromatin and transcriptome. Nat. Struct. Mol. Biol. 26, 1063–1070 (2019)." href="/articles/s12276-024-01186-2#ref-CR78" id="ref-link-section-d69075120e2705">78</a></sup>. This method employs an amplify-and-split strategy to sequence cDNA and gDNA separately without the need for physical mRNA and gDNA separation<sup><a data-track="click" data-track-action="reference anchor" data-track-label="link" data-test="citation-ref" aria-label="Reference 78" title="Zhu, C. et al. An ultra high-throughput method for single-cell joint analysis of open chromatin and transcriptome. Nat. Struct. Mol. Biol. 26, 1063–1070 (2019)." href="/articles/s12276-024-01186-2#ref-CR78" id="ref-link-section-d69075120e2709">78</a></sup>. SHARE-seq utilizes combinatorial indexing for barcoding and employs streptavidin beads to separate DNA and cDNA<sup><a data-track="click" data-track-action="reference anchor" data-track-label="link" data-test="citation-ref" aria-label="Reference 79" title="Ma, S. et al. Chromatin potential identified by shared single-cell profiling of RNA and chromatin. Cell 183, 1103–1116.e20 (2020)." href="/articles/s12276-024-01186-2#ref-CR79" id="ref-link-section-d69075120e2713">79</a></sup>. A recently developed method, ISSAAC-seq, combines SHERRY (single-cell chromatin accessibility profiling by integrating ATAC-seq and RNA-seq) and scATAC-seq and is suitable for both FACS and droplet-based methods<sup><a data-track="click" data-track-action="reference anchor" data-track-label="link" data-test="citation-ref" aria-label="Reference 80" title="Xu, W. et al. ISSAAC-seq enables sensitive and flexible multimodal profiling of chromatin accessibility and gene expression in single cells. Nat. Methods 19, 1243–1249 (2022)." href="/articles/s12276-024-01186-2#ref-CR80" id="ref-link-section-d69075120e2717">80</a></sup>.</p><p>Advancements in DNA adenine methyltransferase identification (DamID) have facilitated simultaneous transcriptome and histone modification analyses. Single-cell DamID with mRNA sequencing (scDam and T-seq) merges single-cell DamID with CEL-seq2, probing DNA–protein interactions and transcription in individual cells<sup><a data-track="click" data-track-action="reference anchor" data-track-label="link" data-test="citation-ref" aria-label="Reference 81" title="Rooijers, K. et al. Simultaneous quantification of protein–DNA contacts and transcriptomes in single cells. Nat. Biotechnol. 37, 766–772 (2019)." href="/articles/s12276-024-01186-2#ref-CR81" id="ref-link-section-d69075120e2724">81</a></sup>. By methylating adenines near the protein of interest, the <i>E. coli</i> Dam methyltransferase tags specific proteins, facilitating DNA–protein interaction investigations<sup><a data-track="click" data-track-action="reference anchor" data-track-label="link" data-test="citation-ref" aria-label="Reference 81" title="Rooijers, K. et al. Simultaneous quantification of protein–DNA contacts and transcriptomes in single cells. Nat. Biotechnol. 37, 766–772 (2019)." href="/articles/s12276-024-01186-2#ref-CR81" id="ref-link-section-d69075120e2731">81</a></sup>. EpiDamID, an extension of DamID, overcomes the limitations of fusing dams with chromatin-binding modules specific to histone modifications<sup><a data-track="click" data-track-action="reference anchor" data-track-label="link" data-test="citation-ref" aria-label="Reference 82" title="Rang, F. J. et al. Single-cell profiling of transcriptome and histone modifications with EpiDamID. Mol. Cell 82, 1956–1970.e14 (2022)." href="/articles/s12276-024-01186-2#ref-CR82" id="ref-link-section-d69075120e2735">82</a></sup>. This innovation allows for the profiling of various histone PTMs at a single-cell resolution, unveiling the interplay between gene expression and histone modifications at the cellular level<sup><a data-track="click" data-track-action="reference anchor" data-track-label="link" data-test="citation-ref" aria-label="Reference 82" title="Rang, F. J. et al. Single-cell profiling of transcriptome and histone modifications with EpiDamID. Mol. Cell 82, 1956–1970.e14 (2022)." href="/articles/s12276-024-01186-2#ref-CR82" id="ref-link-section-d69075120e2739">82</a></sup>.</p><p>In addition, paired-tag and same-cell epigenome and transcriptome sequencing (scSET-seq) were used to coprofile histone modifications and transcriptomes<sup><a data-track="click" data-track-action="reference anchor" data-track-label="link" data-test="citation-ref" aria-label="Reference 83" title="Zhu, C. et al. Joint profiling of histone modifications and transcriptome in single cells from mouse brain. Nat. Methods 18, 283–292 (2021)." href="/articles/s12276-024-01186-2#ref-CR83" id="ref-link-section-d69075120e2746">83</a></sup>. The paired-tag method expands upon the paired-seq method, simultaneously allowing for the capture of open chromatin and gene expression information via the CUT&Tag strategy<sup><a data-track="click" data-track-action="reference anchor" data-track-label="link" data-test="citation-ref" aria-label="Reference 83" title="Zhu, C. et al. Joint profiling of histone modifications and transcriptome in single cells from mouse brain. Nat. Methods 18, 283–292 (2021)." href="/articles/s12276-024-01186-2#ref-CR83" id="ref-link-section-d69075120e2750">83</a></sup>. scSET-seq is also based on CUT&Tag and offers a similar approach<sup><a data-track="click" data-track-action="reference anchor" data-track-label="link" data-test="citation-ref" aria-label="Reference 84" title="Sun, Z. et al. Joint single-cell multiomic analysis in Wnt3a induced asymmetric stem cell division. Nat. Commun. 12, 5941 (2021)." href="/articles/s12276-024-01186-2#ref-CR84" id="ref-link-section-d69075120e2754">84</a></sup>.</p><p>Single-cell nucleosome, methylation, and transcription sequencing (scNMT-seq)<sup><a data-track="click" data-track-action="reference anchor" data-track-label="link" data-test="citation-ref" aria-label="Reference 85" title="Clark, S. J. et al. scNMT-seq enables joint profiling of chromatin accessibility DNA methylation and transcription in single cells. Nat. Commun. 9, 781–784 (2018)." href="/articles/s12276-024-01186-2#ref-CR85" id="ref-link-section-d69075120e2762">85</a></sup> and single-cell nucleosome occupancy, DNA methylation, and RNA expression sequencing (scNOMeRe-seq)<sup><a data-track="click" data-track-action="reference anchor" data-track-label="link" data-test="citation-ref" aria-label="Reference 86" title="Wang, Y. et al. Single-cell multiomics sequencing reveals the functional regulatory landscape of early embryos. Nat. Commun. 12, 1247–1248 (2021)." href="/articles/s12276-024-01186-2#ref-CR86" id="ref-link-section-d69075120e2766">86</a></sup> allow simultaneous methylation, chromatin accessibility, and transcriptome analysis. These techniques employ GpC methylase to detect the methylation of GpC (GCH) and CpG (WCG) sites. Although these methods involve physical separation of DNA and RNA, they provide valuable insights despite potential workflow complexities and costs<sup><a data-track="click" data-track-action="reference anchor" data-track-label="link" data-test="citation-ref" aria-label="Reference 85" title="Clark, S. J. et al. scNMT-seq enables joint profiling of chromatin accessibility DNA methylation and transcription in single cells. Nat. Commun. 9, 781–784 (2018)." href="/articles/s12276-024-01186-2#ref-CR85" id="ref-link-section-d69075120e2770">85</a>,<a data-track="click" data-track-action="reference anchor" data-track-label="link" data-test="citation-ref" aria-label="Reference 86" title="Wang, Y. et al. Single-cell multiomics sequencing reveals the functional regulatory landscape of early embryos. Nat. Commun. 12, 1247–1248 (2021)." href="/articles/s12276-024-01186-2#ref-CR86" id="ref-link-section-d69075120e2773">86</a></sup>. A novel method, single-nucleus methylcytosine, chromatin accessibility, and transcriptome sequencing (snmCAT-seq), addresses these challenges by synthesizing cDNA using RT and methylated cytosine (mC)<sup><a data-track="click" data-track-action="reference anchor" data-track-label="link" data-test="citation-ref" aria-label="Reference 87" title="Luo, C. et al. Single nucleus multi-omics identifies human cortical cell regulatory genome diversity. Cell. Genom. 2, 100107 (2022)." href="/articles/s12276-024-01186-2#ref-CR87" id="ref-link-section-d69075120e2777">87</a></sup> (Fig. <a data-track="click" data-track-label="link" data-track-action="figure anchor" href="/articles/s12276-024-01186-2#Fig3">3c</a>). This approach offers advantages for analyzing frozen samples and resolves mRNA poly(A) tail limitations in the nucleus<sup><a data-track="click" data-track-action="reference anchor" data-track-label="link" data-test="citation-ref" aria-label="Reference 87" title="Luo, C. et al. Single nucleus multi-omics identifies human cortical cell regulatory genome diversity. Cell. Genom. 2, 100107 (2022)." href="/articles/s12276-024-01186-2#ref-CR87" id="ref-link-section-d69075120e2785">87</a></sup>. These techniques were utilized to study developmental processes and the brain (Table <a data-track="click" data-track-label="link" data-track-action="table anchor" href="/articles/s12276-024-01186-2#Tab3">3</a>).</p><p>Recently, Hi-C and RNA-seq were combined in HiRES, a technology that enables the simultaneous analysis of the transcriptome and chromatin conformation without physically separating RNA and DNA<sup><a data-track="click" data-track-action="reference anchor" data-track-label="link" data-test="citation-ref" aria-label="Reference 88" title="Liu, Z. et al. Linking genome structures to functions by simultaneous single-cell Hi-C and RNA-seq. Science 380, 1070–1076 (2023)." href="/articles/s12276-024-01186-2#ref-CR88" id="ref-link-section-d69075120e2795">88</a></sup>. This approach was used to create a 3D genome and transcriptome atlas of postimplantation mouse embryos, uncovering genome-wide correlations between chromatin conformation and gene expression<sup><a data-track="click" data-track-action="reference anchor" data-track-label="link" data-test="citation-ref" aria-label="Reference 88" title="Liu, Z. et al. Linking genome structures to functions by simultaneous single-cell Hi-C and RNA-seq. Science 380, 1070–1076 (2023)." href="/articles/s12276-024-01186-2#ref-CR88" id="ref-link-section-d69075120e2799">88</a></sup>. This novel omics combination has the potential to unravel developmental processes and gene expression patterns. Integrating additional epigenomic analyses, such as transcriptome analysis, into sn-m3C-seq could reveal relationships within different omics datasets for both normal and diseased samples.</p><h3 class="c-article__sub-heading" id="Sec15">Proteome-related single-cell multimodal omics methods</h3><p>The transcriptome serves as a proxy for the ‘proteome’. Proteins play crucial roles in defining the appearance, behavior, and response of cells. Although transcriptomics provides valuable insights into gene expression, it may not necessarily reflect the actual abundance of proteins within cells. Simultaneous transcriptome and proteome profiling within single cells enables researchers to bridge the gap between gene expression and protein abundance.</p><p>Cellular indexing of transcriptomes and epitopes by sequencing (CITE-seq)<sup><a data-track="click" data-track-action="reference anchor" data-track-label="link" data-test="citation-ref" aria-label="Reference 89" title="Stoeckius, M. et al. Simultaneous epitope and transcriptome measurement in single cells. Nat. Methods 14, 865–868 (2017)." href="/articles/s12276-024-01186-2#ref-CR89" id="ref-link-section-d69075120e2814">89</a></sup> and RNA expression and protein sequencing (REAP-seq)<sup><a data-track="click" data-track-action="reference anchor" data-track-label="link" data-test="citation-ref" aria-label="Reference 90" title="Peterson, V. M. et al. Multiplexed quantification of proteins and transcripts in single cells. Nat. Biotechnol. 35, 936–939 (2017)." href="/articles/s12276-024-01186-2#ref-CR90" id="ref-link-section-d69075120e2818">90</a></sup> combine highly multiplexed protein marker detection with unbiased conjugated transcriptome profiling. CITE-seq uses noncovalent streptavidin-biotinylated DNA barcodes, whereas REAP-seq uses covalent bonds between aminated DNA barcodes and antibodies. However, these methods focus on cell surface epitopes because of intracellular detection challenges.</p><p>Methods such as single-cell RNA and immunodetection (single-cell RAID)<sup><a data-track="click" data-track-action="reference anchor" data-track-label="link" data-test="citation-ref" aria-label="Reference 91" title="Gerlach, J. P. et al. Combined quantification of intracellular (phospho-)proteins and transcriptomics from fixed single cells. Sci. Rep. 9, 1469–7 (2019)." href="/articles/s12276-024-01186-2#ref-CR91" id="ref-link-section-d69075120e2825">91</a></sup> and single-cell protein and RNA coprofiling (SPARC)<sup><a data-track="click" data-track-action="reference anchor" data-track-label="link" data-test="citation-ref" aria-label="Reference 92" title="Reimegård, J. et al. A combined approach for single-cell mRNA and intracellular protein expression analysis. Commun. Biol. 4, 624 (2021)." href="/articles/s12276-024-01186-2#ref-CR92" id="ref-link-section-d69075120e2829">92</a></sup> enable intracellular protein detection along with transcriptomics. Single-cell RAID leverages reversible fixation for intracellular immunostaining by using RNA barcode conjugates (ARCs)<sup><a data-track="click" data-track-action="reference anchor" data-track-label="link" data-test="citation-ref" aria-label="Reference 91" title="Gerlach, J. P. et al. Combined quantification of intracellular (phospho-)proteins and transcriptomics from fixed single cells. Sci. Rep. 9, 1469–7 (2019)." href="/articles/s12276-024-01186-2#ref-CR91" id="ref-link-section-d69075120e2833">91</a></sup>. SPARC combines RNA sequencing with proximity extension assays for mRNA and intracellular protein measurements to overcome these limitations and the need for fixation<sup><a data-track="click" data-track-action="reference anchor" data-track-label="link" data-test="citation-ref" aria-label="Reference 92" title="Reimegård, J. et al. A combined approach for single-cell mRNA and intracellular protein expression analysis. Commun. Biol. 4, 624 (2021)." href="/articles/s12276-024-01186-2#ref-CR92" id="ref-link-section-d69075120e2837">92</a></sup>. Intranuclear cellular indexing of transcriptomes and epitopes (inCITE-seq) quantifies intranuclear proteins using DNA-conjugated antibodies and RNA sequencing on a droplet-based platform<sup><a data-track="click" data-track-action="reference anchor" data-track-label="link" data-test="citation-ref" aria-label="Reference 93" title="Chung, H. et al. Joint single-cell measurements of nuclear proteins and RNA in vivo. Nat. Methods 18, 1204–1212 (2021)." href="/articles/s12276-024-01186-2#ref-CR93" id="ref-link-section-d69075120e2841">93</a></sup>.</p><p>Recent advances have enabled the simultaneous analysis of proteomes and transcriptomes. However, a lack of epigenomic analysis using these omics methods has left a disconnection in the flow of gene regulation. To overcome this flow disconnection, several multimodal omics technologies have been developed to analyze epigenomes.</p><p>To simultaneously analyze proteome and chromatin accessibility, several methods have been developed, including integrated cellular indexing of the chromatin landscape and epitopes (ICICLE-seq), ATAC with select antigen profiling by sequencing (ASAP-seq), and assay for transposase-accessible chromatin (PHAGE-ATAC)<sup><a data-track="click" data-track-action="reference anchor" data-track-label="link" data-test="citation-ref" title="Swanson, E. et al. Simultaneous trimodal single-cell measurement of transcripts, epitopes, and chromatin accessibility using TEA-seq. Elife 10, e63632 (2021)." href="#ref-CR94" id="ref-link-section-d69075120e2852">94</a>,<a data-track="click" data-track-action="reference anchor" data-track-label="link" data-test="citation-ref" title="Mimitou, E. P. et al. Scalable, multimodal profiling of chromatin accessibility, gene expression and protein levels in single cells. Nat. Biotechnol. 39, 1246–1258 (2021)." href="#ref-CR95" id="ref-link-section-d69075120e2852_1">95</a>,<a data-track="click" data-track-action="reference anchor" data-track-label="link" data-test="citation-ref" aria-label="Reference 96" title="Fiskin, E. et al. Single-cell profiling of proteins and chromatin accessibility using PHAGE-ATAC. Nat. Biotechnol. 40, 374–381 (2022)." href="/articles/s12276-024-01186-2#ref-CR96" id="ref-link-section-d69075120e2855">96</a></sup>. ICICLE-seq modifies permeabilized cell scATAC-seq to incorporate measurements using barcoded antibody reagents to capture epitopes<sup><a data-track="click" data-track-action="reference anchor" data-track-label="link" data-test="citation-ref" aria-label="Reference 94" title="Swanson, E. et al. Simultaneous trimodal single-cell measurement of transcripts, epitopes, and chromatin accessibility using TEA-seq. Elife 10, e63632 (2021)." href="/articles/s12276-024-01186-2#ref-CR94" id="ref-link-section-d69075120e2859">94</a></sup>. ICICLE-seq utilizes the Tn5 transposome complex with capture sequences compatible with 10x Genomics 3′ scRNA-seq gel beads for chromatin accessibility and polyadenylated antibody barcode sequences for proteomes that can be selectively amplified<sup><a data-track="click" data-track-action="reference anchor" data-track-label="link" data-test="citation-ref" aria-label="Reference 94" title="Swanson, E. et al. Simultaneous trimodal single-cell measurement of transcripts, epitopes, and chromatin accessibility using TEA-seq. Elife 10, e63632 (2021)." href="/articles/s12276-024-01186-2#ref-CR94" id="ref-link-section-d69075120e2863">94</a></sup>. ICICLE-seq has been extended to develop transcripts, epitopes, and accessibility sequencing (TEA-seq), which can simultaneously analyze the proteome, chromatin accessibility, and transcriptome using a droplet-based multimodal omics platform and incorporating scRNA-seq. Chromatin accessibility and proteome data can also be detected by ASAP-seq<sup><a data-track="click" data-track-action="reference anchor" data-track-label="link" data-test="citation-ref" aria-label="Reference 95" title="Mimitou, E. P. et al. Scalable, multimodal profiling of chromatin accessibility, gene expression and protein levels in single cells. Nat. Biotechnol. 39, 1246–1258 (2021)." href="/articles/s12276-024-01186-2#ref-CR95" id="ref-link-section-d69075120e2867">95</a></sup>. In addition, this method can detect not only surface proteins but also cellular proteins and mtDNA by extending mtscATAC-seq to incorporate antibodies conjugated with a poly(A) tail<sup><a data-track="click" data-track-action="reference anchor" data-track-label="link" data-test="citation-ref" aria-label="Reference 95" title="Mimitou, E. P. et al. Scalable, multimodal profiling of chromatin accessibility, gene expression and protein levels in single cells. Nat. Biotechnol. 39, 1246–1258 (2021)." href="/articles/s12276-024-01186-2#ref-CR95" id="ref-link-section-d69075120e2871">95</a></sup>. Similar to TEA-seq, ASAP-seq was further improved to allow for simultaneous analysis of the transcriptome by extending CITE-seq to enable compatibility with the 10x Genomics Multiome product<sup><a data-track="click" data-track-action="reference anchor" data-track-label="link" data-test="citation-ref" aria-label="Reference 95" title="Mimitou, E. P. et al. Scalable, multimodal profiling of chromatin accessibility, gene expression and protein levels in single cells. Nat. Biotechnol. 39, 1246–1258 (2021)." href="/articles/s12276-024-01186-2#ref-CR95" id="ref-link-section-d69075120e2876">95</a></sup>. The resulting method, DOGMA-seq, enabled trimodal analysis with the optional detection of mtDNA<sup><a data-track="click" data-track-action="reference anchor" data-track-label="link" data-test="citation-ref" aria-label="Reference 95" title="Mimitou, E. P. et al. Scalable, multimodal profiling of chromatin accessibility, gene expression and protein levels in single cells. Nat. Biotechnol. 39, 1246–1258 (2021)." href="/articles/s12276-024-01186-2#ref-CR95" id="ref-link-section-d69075120e2880">95</a></sup>. Another method, PHAGE-ATAC, utilizes phages for protein detection and can also analyze dual omics in a single cell by utilizing nanobody-displaying phages<sup><a data-track="click" data-track-action="reference anchor" data-track-label="link" data-test="citation-ref" aria-label="Reference 96" title="Fiskin, E. et al. Single-cell profiling of proteins and chromatin accessibility using PHAGE-ATAC. Nat. Biotechnol. 40, 374–381 (2022)." href="/articles/s12276-024-01186-2#ref-CR96" id="ref-link-section-d69075120e2884">96</a></sup>. Phages are conjugated with a PAC tag, which is amplified using droplet linear PCR for analysis<sup><a data-track="click" data-track-action="reference anchor" data-track-label="link" data-test="citation-ref" aria-label="Reference 96" title="Fiskin, E. et al. Single-cell profiling of proteins and chromatin accessibility using PHAGE-ATAC. Nat. Biotechnol. 40, 374–381 (2022)." href="/articles/s12276-024-01186-2#ref-CR96" id="ref-link-section-d69075120e2888">96</a></sup>. This method has been previously used to detect SARS-CoV-2 in human cell populations<sup><a data-track="click" data-track-action="reference anchor" data-track-label="link" data-test="citation-ref" aria-label="Reference 96" title="Fiskin, E. et al. Single-cell profiling of proteins and chromatin accessibility using PHAGE-ATAC. Nat. Biotechnol. 40, 374–381 (2022)." href="/articles/s12276-024-01186-2#ref-CR96" id="ref-link-section-d69075120e2892">96</a></sup>.</p><p>Similarly, sequencing of nuclear protein epitope abundance, chromatin accessibility, and the transcriptome in single cells (NEAT-seq)<sup><a data-track="click" data-track-action="reference anchor" data-track-label="link" data-test="citation-ref" aria-label="Reference 97" title="Chen, A. F. et al. NEAT-seq: simultaneous profiling of intra-nuclear proteins, chromatin accessibility and gene expression in single cells. Nat. Methods 19, 547–553 (2022)." href="/articles/s12276-024-01186-2#ref-CR97" id="ref-link-section-d69075120e2899">97</a></sup> has been developed to enable simultaneous quantification of nuclear protein epitope abundance, chromatin accessibility, and the transcriptome in single cells. This technique combines the principles of ATAC-Seq and RNA-Seq with nuclear protein quantification.</p></div></div></section><section data-title="Discussion"><div class="c-article-section" id="Sec16-section"><h2 class="c-article-section__title js-section-title js-c-reading-companion-sections-item" id="Sec16">Discussion</h2><div class="c-article-section__content" id="Sec16-content"><p>The development of single-cell-based methods is driven by the limitations of bulk cell-based approaches. By analyzing individual cells, researchers can reveal cellular heterogeneity and gain insights into various biological processes. However, these methods may not be sufficient to capture the complete picture of cellular reactions to diverse stimuli or conditions.</p><p>To address this limitation, single-cell multimodal omics techniques, which combine different single omics methods, have been developed and continue to improve. These approaches, such as sn-m3C-seq<sup><a data-track="click" data-track-action="reference anchor" data-track-label="link" data-test="citation-ref" aria-label="Reference 64" title="Lee, D. et al. Simultaneous profiling of 3D genome structure and DNA methylation in single human cells. Nat. Methods 16, 999–1006 (2019)." href="/articles/s12276-024-01186-2#ref-CR64" id="ref-link-section-d69075120e2915">64</a></sup>, enable the examination of the relationships between different molecular features, such as DNA methylation, chromatin conformation, and gene expression. By integrating multiple omics datasets, researchers can obtain a deeper understanding of the complex mechanisms involved in individual cells.</p><p>However, most of these methods rely on next-generation sequencing (NGS), which typically produces short reads (up to 600 bp in length). This limitation poses challenges when analyzing long genomic regions or resolving complex genomic structures. Fortunately, long-read sequencing technologies have emerged as promising solutions.</p><p>Long-read sequencing technologies such as Oxford Nanopore Technologies (ONT) and PacBio single-molecule real-time (SMRT) sequencing can generate markedly longer reads than traditional NGS. These longer reads allow for improved characterization of genomic regions, including long-range interactions, structural variations, and multiple repeat regions. The integration of this technology into multiomics approaches holds great promise for overcoming the limitations associated with short-read sequencing.</p><p>By combining the strengths of single-cell-based methods, multiomics approaches, and long-read sequencing technologies, researchers can make new discoveries and gain a more comprehensive understanding of cellular processes and mechanisms. In the coming years, significant advancements in the field of multiomics toward the concept of ‘omniomics’, which aims to capture and characterize all molecules within a cell, are expected. 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J.L., C.P., M.K. and H.K. collected the literature and drafted the manuscript. C.P. and J.L. did the literature review, drew the figures, and revised the manuscript.</p><h3 class="c-article__sub-heading" id="corresponding-author">Corresponding author</h3><p id="corresponding-author-list">Correspondence to <a id="corresp-c1" href="mailto:eaststar0@gmail.com">Dong-Sung Lee</a>.</p></div></div></section><section data-title="Ethics declarations"><div class="c-article-section" id="ethics-section"><h2 class="c-article-section__title js-section-title js-c-reading-companion-sections-item" id="ethics">Ethics declarations</h2><div class="c-article-section__content" id="ethics-content"> <h3 class="c-article__sub-heading" id="FPar1">Competing interests</h3> <p>The authors declare no competing interests.</p> </div></div></section><section data-title="Additional information"><div class="c-article-section" id="additional-information-section"><h2 class="c-article-section__title js-section-title js-c-reading-companion-sections-item" id="additional-information">Additional information</h2><div class="c-article-section__content" id="additional-information-content"><p><b>Publisher’s note</b> Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.</p></div></div></section><section data-title="Rights and permissions"><div class="c-article-section" id="rightslink-section"><h2 class="c-article-section__title js-section-title js-c-reading-companion-sections-item" id="rightslink">Rights and permissions</h2><div class="c-article-section__content" id="rightslink-content"> <p><b>Open Access</b> 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 link to the Creative Commons licence, and indicate if changes were made. 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id="citeas">Cite this article</h3><p class="c-bibliographic-information__citation">Lim, J., Park, C., Kim, M. <i>et al.</i> Advances in single-cell omics and multiomics for high-resolution molecular profiling. <i>Exp Mol Med</i> <b>56</b>, 515–526 (2024). https://doi.org/10.1038/s12276-024-01186-2</p><p class="c-bibliographic-information__download-citation u-hide-print"><a data-test="citation-link" data-track="click" data-track-action="download article citation" data-track-label="link" data-track-external="" rel="nofollow" href="https://citation-needed.springer.com/v2/references/10.1038/s12276-024-01186-2?format=refman&flavour=citation">Download citation<svg width="16" height="16" focusable="false" role="img" aria-hidden="true" class="u-icon"><use xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="#icon-eds-i-download-medium"></use></svg></a></p><ul class="c-bibliographic-information__list" data-test="publication-history"><li 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js-c-reading-companion-sections-item" id="further-reading">This article is cited by</h2> <div class="c-article-section__content js-collapsible-section" id="further-reading-content"> <ul class="c-article-further-reading__list" id="further-reading-list"> <li class="c-article-further-reading__item js-ref-item"> <h3 class="c-article-further-reading__title" data-test="article-title"> <a class="print-link" data-track="click" data-track-action="view further reading article" data-track-label="link:Biotechnological studies towards improvement of finger millet using multi-omics approaches" href="https://doi.org/10.1007/s10142-024-01438-4"> Biotechnological studies towards improvement of finger millet using multi-omics approaches </a> </h3> <ul data-test="author-list" class="c-author-list c-author-list--compact c-author-list--truncated u-sans-serif u-mb-4 u-mt-auto"> <li>Rushikesh Sanjay Mane</li><li>Bishun Deo Prasad</li><li>V. K. Sharma</li> </ul> <p class="c-article-further-reading__journal-title"><i>Functional & Integrative Genomics</i> (2024)</p> </li> </ul> </div> </div> </section> </div> </article> </main> <aside class="c-article-extras u-hide-print" aria-label="Article navigation" data-component-reading-companion data-container-type="reading-companion" data-track-component="reading companion"> <div class="js-context-bar-sticky-point-desktop" data-track-context="reading companion"> <div class="c-pdf-download u-clear-both js-pdf-download"> <a href="/articles/s12276-024-01186-2.pdf" class="u-button u-button--full-width u-button--primary u-justify-content-space-between c-pdf-download__link" data-article-pdf="true" data-readcube-pdf-url="true" data-test="download-pdf" data-draft-ignore="true" data-track="content_download" data-track-type="article pdf download" data-track-action="download pdf" data-track-label="link" data-track-external download> <span class="c-pdf-download__text">Download PDF</span> <svg aria-hidden="true" focusable="false" width="16" height="16" class="u-icon"><use xlink:href="#icon-download"/></svg> </a> </div> </div> <div class="c-article-editorial-summary__container u-mb-16" data-component-show-more="true" id="editorial-summary"><section><h2 class="c-article-editorial-summary__title u-h3 u-mb-16">Editorial Summary</h2><h3 class="c-article-editorial-summary__article-title u-mb-8">Single-cell omics: revolutionizing molecular profiling with high-resolution insights</h3><div class="c-article-editorial-summary__content c-article-editorial-summary__content--less"><p>Each cell is a bustling city of genetic material and proteins, but traditional methods often blur the individuality of each cell by averaging the data. Single-cell omics techniques, however, allow us to focus on individual cells, revealing the rich variety of cell types, states, and rare populations that make up our tissues and organs. In this review, researchers delve into the details of single-cell omics and multi-modal omics. They explore on how to isolate cells, add unique barcodes to keep track of them, and then analyze their genetic and molecular content. The integration of data across different molecular dimensions is a leap forward in the field, and it paves the way for future discoveries that could transform medicine. 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