Extended-representation bisulfite sequencing of gene regulatory elements in multiplexed samples and single cells

Epigenomic heterogeneity as a source of tumour evolution

In the past decade, remarkable progress in cancer medicine has been achieved by the development of treatments that target DNA sequence variants. However, a purely genetic approach to treatment selection is hampered by the fact that diverse cell states can emerge from the same genotype. In multicellular organisms, cell-state heterogeneity is driven by epigenetic processes that regulate DNA-based functions such as transcription; disruption of these processes is a hallmark of cancer that enables the emergence of defective cell states. Advances in single-cell technologies have unlocked our ability to quantify the epigenomic heterogeneity of tumours and understand its mechanisms, thereby transforming our appreciation of how epigenomic changes drive cancer evolution. This Review explores the idea that epigenomic heterogeneity and plasticity act as a reservoir of cell states and therefore as a source of tumour evolution. Best practices to quantify epigenomic heterogeneity and explore its various causes and consequences are discussed, including epigenomic reprogramming, stochastic changes and lasting memory. The design of new therapeutic approaches to restrict epigenomic heterogeneity, with the long-term objective of limiting cancer development and progression, is also addressed.

Enriched Methylomes of Low-input and Fragmented DNA Using Fragment Ligation EXclusive Methylation Sequencing (FLEXseq)

Methylome profiling is an emerging clinical tool for tumor classification and liquid biopsies. Here, we developed FLEXseq, a genome-wide methylation profiler that enriches and sequences the fragments of DNA flanking the CCGG motif. FLEXseq strongly correlates (Pearson's r = 0.97) with whole genome bisulfite sequencing (WGBS) while enriching 18-fold. To demonstrate the broad applicability of FLEXseq, we verified its usage across cells, body fluids, and formalin-fixed paraffin-embedded (FFPE) tissues. DNA dilutions down to 250 pg decreased CpG coverage, but bias in methylation remained low (Pearson's r ≥ 0.90) compared to a 10 ng input. FLEXseq offers a cost-efficient, base-pair resolution methylome with potential as a diagnostic tool for tissue and liquid biopsies.

A multiomics approach reveals RNA dynamics promote cellular sensitivity to DNA hypomethylation

The search for new approaches in cancer therapy requires a mechanistic understanding of cancer vulnerabilities and anti-cancer drug mechanisms of action. Problematically, some effective therapeutics target cancer vulnerabilities that have poorly defined mechanisms of anti-cancer activity. One such drug is decitabine, a frontline therapeutic approved for the treatment of high-risk acute myeloid leukemia (AML). Decitabine is thought to kill cancer cells selectively via inhibition of DNA methyltransferase enzymes, but the genes and mechanisms involved remain unclear. Here, we apply an integrated multiomics and CRISPR functional genomics approach to identify genes and processes associated with response to decitabine in AML cells. Our integrated multiomics approach reveals RNA dynamics are key regulators of DNA hypomethylation induced cell death. Specifically, regulation of RNA decapping, splicing and RNA methylation emerge as important regulators of cellular response to decitabine.

Deciphering breast cancer dynamics: insights from single-cell and spatial profiling in the multi-omics era

As one of the most common tumors in women, the pathogenesis and tumor heterogeneity of breast cancer have long been the focal point of research, with the emergence of tumor metastasis and drug resistance posing persistent clinical challenges. The emergence of single-cell sequencing (SCS) technology has introduced novel approaches for gaining comprehensive insights into the biological behavior of malignant tumors. SCS is a high-throughput technology that has rapidly developed in the past decade, providing high-throughput molecular insights at the individual cell level. Furthermore, the advent of multitemporal point sampling and spatial omics also greatly enhances our understanding of cellular dynamics at both temporal and spatial levels. The paper provides a comprehensive overview of the historical development of SCS, and highlights the most recent advancements in utilizing SCS and spatial omics for breast cancer research. The findings from these studies will serve as valuable references for future advancements in basic research, clinical diagnosis, and treatment of breast cancer.

AMAR-seq: Automated Multimodal Sequencing of DNA Methylation, Chromatin Accessibility, and RNA Expression with Single-Cell Resolution

Parallel single-cell multimodal sequencing is the most intuitive and precise tool for cellular status research. In this study, we propose AMAR-seq to automate methylation, chromatin accessibility, and RNA expression coanalysis with single-cell precision. We validated the accuracy and robustness of AMAR-seq in comparison with standard single-omics methods. The high gene detection rate and genome coverage of AMAR-seq enabled us to establish a genome-wide gene expression regulatory atlas and triple-omics landscape with single base resolution and implement single-cell copy number variation analysis. Applying AMAR-seq to investigate the process of mouse embryonic stem cell differentiation, we revealed the dynamic coupling of the epigenome and transcriptome, which may contribute to unraveling the molecular mechanisms of early embryonic development. Collectively, we propose AMAR-seq for the in-depth and accurate establishment of single-cell multiomics regulatory patterns in a cost-effective, efficient, and automated manner, paving the way for insightful dissection of complex life processes.

Single-cell omics: experimental workflow, data analyses and applications

Cells are the fundamental units of biological systems and exhibit unique development trajectories and molecular features. Our exploration of how the genomes orchestrate the formation and maintenance of each cell, and control the cellular phenotypes of various organismsis, is both captivating and intricate. Since the inception of the first single-cell RNA technology, technologies related to single-cell sequencing have experienced rapid advancements in recent years. These technologies have expanded horizontally to include single-cell genome, epigenome, proteome, and metabolome, while vertically, they have progressed to integrate multiple omics data and incorporate additional information such as spatial scRNA-seq and CRISPR screening. Single-cell omics represent a groundbreaking advancement in the biomedical field, offering profound insights into the understanding of complex diseases, including cancers. Here, we comprehensively summarize recent advances in single-cell omics technologies, with a specific focus on the methodology section. This overview aims to guide researchers in selecting appropriate methods for single-cell sequencing and related data analysis.

Joint single-cell profiling resolves 5mC and 5hmC and reveals their distinct gene regulatory effects

Oxidative modification of 5-methylcytosine (5mC) by ten-eleven translocation (TET) DNA dioxygenases generates 5-hydroxymethylcytosine (5hmC), the most abundant form of oxidized 5mC. Existing single-cell bisulfite sequencing methods cannot resolve 5mC and 5hmC, leaving the cell-type-specific regulatory mechanisms of TET and 5hmC largely unknown. Here, we present joint single-nucleus (hydroxy)methylcytosine sequencing (Joint-snhmC-seq), a scalable and quantitative approach that simultaneously profiles 5hmC and true 5mC in single cells by harnessing differential deaminase activity of APOBEC3A toward 5mC and chemically protected 5hmC. Joint-snhmC-seq profiling of single nuclei from mouse brains reveals an unprecedented level of epigenetic heterogeneity of both 5hmC and true 5mC at single-cell resolution. We show that cell-type-specific profiles of 5hmC or true 5mC improve multimodal single-cell data integration, enable accurate identification of neuronal subtypes and uncover context-specific regulatory effects on cell-type-specific genes by TET enzymes.

Long-read sequencing unveils novel somatic variants and methylation patterns in the genetic information system of early lung cancer

Lung cancer poses a global health challenge, necessitating advanced diagnostics for improved outcomes. Intensive efforts are ongoing to pinpoint early detection biomarkers, such as genomic variations and DNA methylation, to elevate diagnostic precision. We conducted long-read sequencing on cancerous and adjacent non-cancerous tissues from a patient with lung adenocarcinoma. We identified somatic structural variations (SVs) specific to lung cancer by integrating data from various SV calling methods and differentially methylated regions (DMRs) that were distinct between these two tissue samples, revealing a unique methylation pattern associated with lung cancer. This study discovered over 40,000 somatic SVs and over 180,000 DMRs linked to lung cancer. We identified approximately 700 genes of significant relevance through comprehensive analysis, including genes intricately associated with many lung cancers, such as NOTCH1, SMOC2, CSMD2, and others. Furthermore, we observed that somatic SVs and DMRs were substantially enriched in several pathways, such as axon guidance signaling pathways, which suggests a comprehensive multi-omics impact on lung cancer progression across various biological investigation levels. These datasets can potentially serve as biomarkers for early lung cancer detection and may hold significant value in clinical diagnosis and treatment applications.

Reduced representative methylome profiling of cell-free DNA for breast cancer detection

BACKGROUND

Whole-genome methylation sequencing of cfDNA is not cost-effective for tumor detection. Here, we introduce reduced representative methylome profiling (RRMP), which employs restriction enzyme for depletion of AT-rich sequence to achieve enrichment and deep sequencing of CG-rich sequences.

METHODS

We first verified the ability of RRMP to enrich CG-rich sequences using tumor cell genomic DNA and analyzed differential methylation regions between tumor cells and normal whole blood cells. We then analyzed cfDNA from 29 breast cancer patients and 27 non-breast cancer individuals to detect breast cancer by building machine learning models.

RESULTS

RRMP captured 81.9% CpG islands and 75.2% gene promoters when sequenced to 10 billion base pairs, with an enrichment efficiency being comparable to RRBS. RRMP allowed us to assess DNA methylation changes between tumor cells and whole blood cells. Applying our approach to cfDNA from 29 breast cancer patients and 27 non-breast cancer individuals, we developed machine learning models that could discriminate between breast cancer and non-breast cancer controls (AUC = 0.85), suggesting possibilities for truly non-invasive cancer detection.

CONCLUSIONS

We developed a new method to achieve reduced representative methylome profiling of cell-free DNA for tumor detection.

DIRECT: Digital Microfluidics for Isolation-Free Shared Library Construction of Single-Cell DNA Methylome and Transcriptome

Simultaneous profiling of DNA methylation and gene expression within single cells is a powerful technology to dissect complex gene regulatory network of cells. However, existing methods are based on picking a single-cell in a tube and split single-cell lysate into two parts for transcriptome and methylome library construction, respectively, which is costly and cumbersome. Here, DIRECT is proposed, a digital microfluidics-based method for high-efficiency single-cell isolation and simultaneous analysis of the methylome and transcriptome in a single library construction. The accuracy of DIRECT is demonstrated in comparison with bulk and single-omics data, and the high CpG site coverage of DIRECT allows for precise analysis of copy number variation information, enabling expansion of single cell analysis from two- to three-omics. By applying DIRECT to monitor the dynamics of mouse embryonic stem cell differentiation, the relationship between DNA methylation and changes in gene expression during differentiation is revealed. DIRECT enables accurate, robust, and reproducible single-cell DNA methylation and gene expression co-analysis in a more cost-effective, simpler library preparation and automated manner, broadening the application scenarios of single-cell multi-omics analysis and revealing a more comprehensive and fine-grained map of cellular regulatory landscapes.

Dupsifter: a lightweight duplicate marking tool for whole genome bisulfite sequencing

SUMMARY

In whole genome sequencing data, polymerase chain reaction amplification results in duplicate DNA fragments coming from the same location in the genome. The process of preparing a whole genome bisulfite sequencing (WGBS) library, on the other hand, can create two DNA fragments from the same location that should not be considered duplicates. Currently, only one WGBS-aware duplicate marking tool exists. However, it only works with the output from a single tool, does not accept streaming input or output, and requires a substantial amount of memory relative to the input size. Dupsifter provides an aligner-agnostic duplicate marking tool that is lightweight, has streaming capabilities, and is memory efficient.

AVAILABILITY AND IMPLEMENTATION

Source code and binaries are freely available at https://github.com/huishenlab/dupsifter under the MIT license. Dupsifter is implemented in C and is supported on macOS and Linux.

Shortcut barcoding and early pooling for scalable multiplex single-cell reduced-representation CpG methylation sequencing at single nucleotide resolution

DNA methylation is essential for a wide variety of biological processes, yet the development of a highly efficient and robust technology remains a challenge for routine single-cell analysis. We developed a multiplex scalable single-cell reduced representation bisulfite sequencing (msRRBS) technology. It allows cell-specific barcoded DNA fragments of individual cells to be pooled before bisulfite conversion, free of enzymatic modification or physical capture of the DNA ends, and achieves read mapping rates of 62.5 ± 3.9%, covering 60.0 ± 1.4% of CpG islands and 71.6 ± 1.6% of promoters in K562 cells. Its reproducibility is shown in duplicates of bulk cells with close to perfect correlation (R = 0.97-0.99). At a low 1 Mb of clean reads, msRRBS provides highly consistent coverage of CpG islands and promoters, outperforming the conventional methods with orders of magnitude reduction in cost. Here, we use this method to characterize the distinct methylation patterns and cellular heterogeneity of six cell lines, plus leukemia and hepatocellular carcinoma models. Taking 4 h of hands-on time, msRRBS offers a unique, highly efficient approach for dissecting methylation heterogeneity in a variety of multicellular systems.

Methylomics and cancer: the current state of methylation profiling and marker development for clinical care

Epigenetic modifications have long been recognized as an essential level in transcriptional regulation linking behavior and environmental conditions or stimuli with biological processes and disease development. Among them, methylation is the most abundant of these reversible epigenetic marks, predominantly occurring on DNA, RNA, and histones. Methylation modification is intimately involved in regulating gene transcription and cell differentiation, while aberrant methylation status has been linked with cancer development in several malignancies. Early detection and precise restoration of dysregulated methylation form the basis for several epigenetics-based therapeutic strategies. In this review, we summarize the current basic understanding of the regulation and mechanisms responsible for methylation modification and cover several cutting-edge research techniques for detecting methylation across the genome and transcriptome. We then explore recent advances in clinical diagnostic applications of methylation markers of various cancers and address the current state and future prospects of methylation modifications in therapies for different diseases, especially comparing pharmacological methylase/demethylase inhibitors with the CRISPRoff/on methylation editing systems. This review thus provides a resource for understanding the emerging role of epigenetic methylation in cancer, the use of methylation-based biomarkers in cancer detection, and novel methylation-targeted drugs.

Decoding Human Biology and Disease Using Single-cell Omics Technologies

Over the past decade, advances in single-cell omics (SCO) technologies have enabled the investigation of cellular heterogeneity at an unprecedented resolution and scale, opening a new avenue for understanding human biology and disease. In this review, we summarize the developments of sequencing-based SCO technologies and computational methods, and focus on considerable insights acquired from SCO sequencing studies to understand normal and diseased properties, with a particular emphasis on cancer research. We also discuss the technological improvements of SCO and its possible contribution to fundamental research of the human, as well as its great potential in clinical diagnoses and personalized therapies of human disease.

Digital-scRRBS: A Cost-Effective, Highly Sensitive, and Automated Single-Cell Methylome Analysis Platform via Digital Microfluidics

Single-cell DNA methylation sequencing is highly effective for identifying cell subpopulations and constructing epigenetic regulatory networks. Existing methylome analyses require extensive starting materials and are costly, complex, and susceptible to contamination, thereby impeding the development of single-cell methylome technology. In this work, we report digital microfluidics-based single-cell reduced representation bisulfite sequencing (digital-scRRBS), the first microfluidics-based single-cell methylome library construction platform, which is an automatic, effective, reproducible, and reagent-efficient technique to dissect the single-cell methylome. Using our digital microfluidic chip, we isolated single cells in 15 s and successfully constructed single-cell methylation sequencing libraries with a unique genome mapping rate of up to 53.6%, covering up to 2.26 million CpG sites. Digital-scRRBS demonstrates a high capacity for distinguishing cell identity and tracking DNA methylation during drug administration. Digital-scRRBS expands the applicability of single-cell methylation methods as a versatile tool for epigenetic analysis of rare cells and populations with high levels of heterogeneity.

Biomarkers of aging

Aging biomarkers are a combination of biological parameters to (i) assess age-related changes, (ii) track the physiological aging process, and (iii) predict the transition into a pathological status. Although a broad spectrum of aging biomarkers has been developed, their potential uses and limitations remain poorly characterized. An immediate goal of biomarkers is to help us answer the following three fundamental questions in aging research: How old are we? Why do we get old? And how can we age slower? This review aims to address this need. Here, we summarize our current knowledge of biomarkers developed for cellular, organ, and organismal levels of aging, comprising six pillars: physiological characteristics, medical imaging, histological features, cellular alterations, molecular changes, and secretory factors. To fulfill all these requisites, we propose that aging biomarkers should qualify for being specific, systemic, and clinically relevant.

Technologies and applications of single-cell DNA methylation sequencing

DNA methylation is the most stable epigenetic modification. In mammals, it usually occurs at the cytosine of CpG dinucleotides. DNA methylation is essential for many physiological and pathological processes. Aberrant DNA methylation has been observed in human diseases, particularly cancer. Notably, conventional DNA methylation profiling technologies require a large amount of DNA, often from a heterogeneous cell population, and provide an average methylation level of many cells. It is often not realistic to collect sufficient numbers of cells, such as rare cells and circulating tumor cells in peripheral blood, for bulk sequencing assays. It is therefore essential to develop sequencing technologies that can accurately profile DNA methylation using small numbers of cells or even single cells. Excitingly, many single-cell DNA methylation sequencing and single-cell omics sequencing technologies have been developed, and applications of these methods have greatly expanded our understanding of the molecular mechanism of DNA methylation. Here, we summaries single-cell DNA methylation and multi-omics sequencing methods, delineate their applications in biomedical sciences, discuss technical challenges, and present our perspective on future research directions.

Factors and Methods for the Detection of Gene Expression Regulation

Gene-expression regulation involves multiple processes and a range of regulatory factors. In this review, we describe the key factors that regulate gene expression, including transcription factors (TFs), chromatin accessibility, histone modifications, DNA methylation, and RNA modifications. In addition, we also describe methods that can be used to detect these regulatory factors.

Computational Methods for Single-cell DNA Methylome Analysis

Dissecting intercellular epigenetic differences is key to understanding tissue heterogeneity. Recent advances in single-cell DNA methylome profiling have presented opportunities to resolve this heterogeneity at the maximum resolution. While these advances enable us to explore frontiers of chromatin biology and better understand cell lineage relationships, they pose new challenges in data processing and interpretation. This review surveys the current state of computational tools developed for single-cell DNA methylome data analysis. We discuss critical components of single-cell DNA methylome data analysis, including data preprocessing, quality control, imputation, dimensionality reduction, cell clustering, supervised cell annotation, cell lineage reconstruction, gene activity scoring, and integration with transcriptome data. We also highlight unique aspects of single-cell DNA methylome data analysis and discuss how techniques common to other single-cell omics data analyses can be adapted to analyze DNA methylomes. Finally, we discuss existing challenges and opportunities for future development.

Characterizing cis-regulatory elements using single-cell epigenomics

Cell type-specific gene expression patterns and dynamics during development or in disease are controlled by cis-regulatory elements (CREs), such as promoters and enhancers. Distinct classes of CREs can be characterized by their epigenomic features, including DNA methylation, chromatin accessibility, combinations of histone modifications and conformation of local chromatin. Tremendous progress has been made in cataloguing CREs in the human genome using bulk transcriptomic and epigenomic methods. However, single-cell epigenomic and multi-omic technologies have the potential to provide deeper insight into cell type-specific gene regulatory programmes as well as into how they change during development, in response to environmental cues and through disease pathogenesis. Here, we highlight recent advances in single-cell epigenomic methods and analytical tools and discuss their readiness for human tissue profiling.

Mammalian DNA methylome dynamics: mechanisms, functions and new frontiers

DNA methylation is a highly conserved epigenetic modification that plays essential roles in mammalian gene regulation, genome stability and development. Despite being primarily considered a stable and heritable epigenetic silencing mechanism at heterochromatic and repetitive regions, whole genome methylome analysis reveals that DNA methylation can be highly cell-type specific and dynamic within proximal and distal gene regulatory elements during early embryonic development, stem cell differentiation and reprogramming, and tissue maturation. In this Review, we focus on the mechanisms and functions of regulated DNA methylation and demethylation, highlighting how these dynamics, together with crosstalk between DNA methylation and histone modifications at distinct regulatory regions, contribute to mammalian development and tissue maturation. We also discuss how recent technological advances in single-cell and long-read methylome sequencing, along with targeted epigenome-editing, are enabling unprecedented high-resolution and mechanistic dissection of DNA methylome dynamics.

DNA methylation dynamics and dysregulation delineated by high-throughput profiling in the mouse

We have developed a mouse DNA methylation array that contains 296,070 probes representing the diversity of mouse DNA methylation biology. We present a mouse methylation atlas as a rich reference resource of 1,239 DNA samples encompassing distinct tissues, strains, ages, sexes, and pathologies. We describe applications for comparative epigenomics, genomic imprinting, epigenetic inhibitors, patient-derived xenograft assessment, backcross tracing, and epigenetic clocks. We dissect DNA methylation processes associated with differentiation, aging, and tumorigenesis. Notably, we find that tissue-specific methylation signatures localize to binding sites for transcription factors controlling the corresponding tissue development. Age-associated hypermethylation is enriched at regions of Polycomb repression, while hypomethylation is enhanced at regions bound by cohesin complex members. Apc ^Min/+ polyp-associated hypermethylation affects enhancers regulating intestinal differentiation, while hypomethylation targets AP-1 binding sites. This Infinium Mouse Methylation BeadChip (version MM285) is widely accessible to the research community and will accelerate high-sample-throughput studies in this important model organism.

Single cell cancer epigenetics

Bulk sequencing methodologies have allowed us to make great progress in cancer research. Unfortunately, these techniques lack the resolution to fully unravel the epigenetic mechanisms that govern tumor heterogeneity. Consequently, many novel single cell-sequencing methodologies have been developed over the past decade, allowing us to explore the epigenetic components that regulate different aspects of cancer heterogeneity, namely: clonal heterogeneity, tumor microenvironment (TME), spatial organization, intratumoral differentiation programs, metastasis, and resistance mechanisms. In this review, we explore the different sequencing techniques that enable researchers to study different aspects of epigenetics (DNA methylation, chromatin accessibility, histone modifications, DNA-protein interactions, and chromatin 3D architecture) at the single cell level, their potential applications in cancer, and their current technical limitations.

Aging of Vascular System Is a Complex Process: The Cornerstone Mechanisms

Aging is one of the most intriguing processes of human ontogenesis. It is associated with the development of a wide variety of diseases affecting all organs and their systems. The victory over aging is the most desired goal of scientists; however, it is hardly achievable in the foreseeable future due to the complexity and ambiguity of the process itself. All body systems age, lose their performance, and structural disorders accumulate. The cardiovascular system is no exception. And it is cardiovascular diseases that occupy a leading position as a cause of death, especially among the elderly. The aging of the cardiovascular system is well described from a mechanical point of view. Moreover, it is known that at the cellular level, a huge number of mechanisms are involved in this process, from mitochondrial dysfunction to inflammation. It is on these mechanisms, as well as the potential for taking control of the aging of the cardiovascular system, that we focused on in this review.

Tumor Expression Quantitative Trait Methylation Screening Reveals Distinct CpG Panels for Deconvolving Cancer Immune Signatures

DNA methylation signatures in tumors could serve as reliable biomarkers that are accessible in archival tissues for tracking the epigenetic dynamics shaped by both cancer cells and the tumor microenvironment. However, given the ultrahigh dimensionality and noncollapsible nature of the data, it remains challenging to screen all CpG sites to identify the most promising marker panels. In this article, we introduce the concept of tumor-based expression quantitative trait methylation (eQTM) for the prioritization and systematic mining of predictive biomarkers. In melanoma as a disease model, eQTM CpGs and genes represent new and efficient candidate targets to be investigated for both prognostic and immune status monitoring purposes. Three cis-eQTM CpGs (cg07786657, cg12446199, and cg00027570) were strongly associated with and can serve as surrogate biomarkers for the tumor immune cytolytic activity score (CYT). In addition, multiple eQTM genes could be further exploited for predicting immunoregulatory phenotypes. A targeted gene panel analysis identified one eQTM in TCF7 (cg25947408) as a novel candidate biomarker for uncoupling overall T-cell differentiation and exhaustion status in a tumor. The prognostic significance of this eQTM as an independent signature to CYT was validated by both The Cancer Genome Atlas and Moffitt melanoma cohort data. Overall, eQTMs represent a mechanistically distinct class of potential biomarkers that can be used to predict patient prognosis and immune status.

SIGNIFICANCE

This study provides a novel and promising approach to identify targeted epigenetic biomarkers in cancer and will spur further analysis in tumor immune phenotyping.

Experimental development of the epigenomic library construction method to elucidate the epigenetic diversity and causal relationship between epigenome and transcriptome at a single-cell level

The method of single-cell RNA sequencing has been rapidly developed, and numerous experiments have been conducted over the past decade. Their results allow us to recognize various subpopulations and rare cell states in tissues, tumors, and immune systems that are previously unidentified, and guide us to understand fundamental biological processes that determine cell identity based on single-cell gene expression profiles. However, it is still challenging to understand the principle of comprehensive gene regulation that determines the cell fate only with transcriptome, a consequential output of the gene expression program. To elucidate the mechanisms related to the origin and maintenance of comprehensive single-cell transcriptome, we require a corresponding single-cell epigenome, which is a differentiated information of each cell with an identical genome. This review deals with the current development of single-cell epigenomic library construction methods, including multi-omics tools with crucial factors and additional requirements in the future focusing on DNA methylation, chromatin accessibility, and histone post-translational modifications. The study of cellular differentiation and the disease occurrence at a single-cell level has taken the first step with single-cell transcriptome and is now taking the next step with single-cell epigenome.

Recent advances on single-cell sequencing technologies

Single-cell omics sequencing was first achieved for transcriptome in 2009, which was followed by fast development of technologies for profiling genome, DNA methylome, 3D genome architecture, chromatin accessibility, histone modifications and so on, in an individual cell. In this review we mainly focus on the recent progresses in four topics in single cell omics field- single-cell epigenome sequencing, single-cell genome sequencing for lineage tracing, spatially resolved single-cell transcriptomics, and third-generation sequencing platform-based single cell omics sequencing. We also discuss the potential applications and future directions of these single cell omics sequencing technologies for different biomedical systems, especially for the human stem cell field.