Simultaneous measurement of chromatin accessibility, DNA methylation, and nucleosome phasing in single cells

Tracking single-cell evolution using clock-like chromatin accessibility loci

Single-cell chromatin accessibility sequencing (scATAC-seq) reconstructs developmental trajectory by phenotypic similarity. However, inferring the exact developmental trajectory is challenging. Previous studies showed age-associated DNA methylation (DNAm) changes in specific genomic regions, termed clock-like differential methylation loci (ClockDML). Age-associated DNAm could either result from or result in chromatin accessibility changes at ClockDML. As cells undergo mitosis, the heterogeneity of chromatin accessibility on clock-like loci is reduced, providing a measure of mitotic age. In this study, we developed a method, called EpiTrace, that counts the fraction of opened clock-like loci from scATAC-seq data to determine cell age and perform lineage tracing in various cell lineages and animal species. It shows concordance with known developmental hierarchies, correlates well with DNAm-based clocks and is complementary with mutation-based lineage tracing, RNA velocity and stemness predictions. Applying EpiTrace to scATAC-seq data reveals biological insights with clinically relevant implications, ranging from hematopoiesis, organ development, tumor biology and immunity to cortical gyrification.

Advances and applications in single-cell and spatial genomics

The applications of single-cell and spatial technologies in recent times have revolutionized the present understanding of cellular states and the cellular heterogeneity inherent in complex biological systems. These advancements offer unprecedented resolution in the examination of the functional genomics of individual cells and their spatial context within tissues. In this review, we have comprehensively discussed the historical development and recent progress in the field of single-cell and spatial genomics. We have reviewed the breakthroughs in single-cell multi-omics technologies, spatial genomics methods, and the computational strategies employed toward the analyses of single-cell atlas data. Furthermore, we have highlighted the advances made in constructing cellular atlases and their clinical applications, particularly in the context of disease. Finally, we have discussed the emerging trends, challenges, and opportunities in this rapidly evolving field.

Single-cell genome and transcriptome sequencing without upfront whole-genome amplification reveals cell state plasticity of melanoma subclones

Single-cell multi-omics methods enable the study of cell state diversity, which is largely determined by the interplay of the genome, epigenome, and transcriptome. Here, we describe Gtag&T-seq, a genome-and-transcriptome sequencing (G&T-seq) protocol of the same single cells that omits whole-genome amplification (WGA) by using direct genomic tagmentation (Gtag). Gtag drastically decreases the cost and improves coverage uniformity at single-cell and pseudo-bulk levels compared to WGA-based G&T-seq. We also show that transcriptome-based DNA copy number inference has limited resolution and accuracy, underlining the importance of affordable multi-omic approaches. Applying Gtag&T-seq to a melanoma xenograft model before treatment and at minimal residual disease revealed differential cell state plasticity and treatment response between cancer subclones. In summary, Gtag&T-seq is a low-cost and accurate single-cell multi-omics method that explores genetic alterations and their functional consequences in single cells at scale.

Epigenetic Impacts of Non-Coding Mutations Deciphered Through Pre-Trained DNA Language Model at Single-Cell Resolution

DNA methylation plays a critical role in gene regulation, affecting cellular differentiation and disease progression, particularly in non-coding regions. However, predicting the epigenetic consequences of non-coding mutations at single-cell resolution remains a challenge. Existing tools have limited prediction capacity and struggle to capture dynamic, cell-type-specific regulatory changes that are crucial for understanding disease mechanisms. Here, Methven, a deep learning framework designed is presented to predict the effects of non-coding mutations on DNA methylation at single-cell resolution. Methven integrates DNA sequence with single-cell ATAC-seq data and models SNP-CpG interactions over 100 kbp genomic distances. By using a divide-and-conquer approach, Methven accurately predicts both short- and long-range regulatory interactions and leverages the pre-trained DNA language model for enhanced precision in classification and regression tasks. Methven outperforms existing methods and demonstrates robust generalizability to monocyte datasets. Importantly, it identifies CpG sites associated with rheumatoid arthritis, revealing key pathways involved in immune regulation and disease progression. Methven's ability to detect progressive epigenetic changes provides crucial insights into gene regulation in complex diseases. These findings demonstrate Methven's potential as a powerful tool for basic research and clinical applications, advancing this understanding of non-coding mutations and their role in disease, while offering new opportunities for personalized medicine.

Atlas-scale single-cell DNA methylation profiling with sciMETv3

Single-cell methods to assess DNA methylation have not achieved the same level of cell throughput per experiment compared to other modalities, with large-scale datasets requiring extensive automation, time, and other resources. Here, we describe sciMETv3, a combinatorial indexing-based technique that enables atlas-scale libraries to be produced in a single experiment. To reduce the sequencing burden, we demonstrate the compatibility of sciMETv3 with capture techniques to enrich regulatory regions, as well as the ability to leverage enzymatic conversion, which can yield higher library diversity. We showcase the throughput of sciMETv3 by producing a >140,000 cell library from human middle frontal gyrus split across four multiplexed individuals using both Illumina and Ultima sequencing instrumentation. Finally, we introduce sciMET+ATAC to enable high-throughput exploration of the interplay between chromatin accessibility and DNA methylation within the same cell.

Profiling the epigenome using long-read sequencing

The advent of single-molecule, long-read sequencing (LRS) technologies by Oxford Nanopore Technologies and Pacific Biosciences has revolutionized genomics, transcriptomics and, more recently, epigenomics research. These technologies offer distinct advantages, including the direct detection of methylated DNA and simultaneous assessment of DNA sequences spanning multiple kilobases along with their modifications at the single-molecule level. This has enabled the development of new assays for analyzing chromatin states and made it possible to integrate data for DNA methylation, chromatin accessibility, transcription factor binding and histone modifications, thereby facilitating comprehensive epigenomic profiling. Owing to recent advancements, alternative, nascent and translating transcripts can be detected using LRS approaches. This Review discusses LRS-based experimental and computational strategies for characterizing chromatin states and highlights their advantages over short-read sequencing methods. Furthermore, we demonstrate how various long-read methods can be integrated to design multi-omics studies to investigate the relationship between chromatin states and transcriptional dynamics.

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.

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.

Progress in multifactorial single-cell chromatin profiling methods

Chromatin states play a key role in shaping overall cellular states and fates. Building a complete picture of the functional state of chromatin in cells requires the co-detection of several distinct biochemical aspects. These span DNA methylation, chromatin accessibility, chromosomal conformation, histone posttranslational modifications, and more. While this certainly presents a challenging task, over the past few years many new and creative methods have been developed that now enable co-assay of these different aspects of chromatin at single cell resolution. This field is entering an exciting phase, where a confluence of technological improvements, decreased sequencing costs, and computational innovation are presenting new opportunities to dissect the diversity of chromatin states present in tissues, and how these states may influence gene regulation. In this review, I discuss the spectrum of current experimental approaches for multifactorial chromatin profiling, highlight some of the experimental and analytical challenges, as well as some areas for further innovation.

guidedNOMe-seq quantifies chromatin states at single allele resolution for hundreds of custom regions in parallel

Since the introduction of next generation sequencing technologies, the field of epigenomics has evolved rapidly. However, most commonly used assays are enrichment-based methods and thus only semi-quantitative. Nucleosome occupancy and methylome sequencing (NOMe-seq) allows for quantitative inference of chromatin states with single locus resolution, but this requires high sequencing depth and is therefore prohibitively expensive to routinely apply to organisms with large genomes. To overcome this limitation, we introduce guidedNOMe-seq, where we combine NOMe profiling with large scale sgRNA synthesis and Cas9-mediated region-of-interest (ROI) liberation. To facilitate quantitative comparisons between multiple samples, we additionally develop an R package to standardize differential analysis of any type of NOMe-seq data. We extensively benchmark guidedNOMe-seq in a proof-of-concept study, dissecting the interplay of ChAHP and CTCF on chromatin. In summary we present a cost-effective, scalable, and customizable target enrichment extension to the existing NOMe-seq protocol allowing genome-scale quantification of nucleosome occupancy and transcription factor binding at single allele resolution.

Deep learning approaches for non-coding genetic variant effect prediction: current progress and future prospects

Recent advancements in high-throughput sequencing technologies have significantly enhanced our ability to unravel the intricacies of gene regulatory processes. A critical challenge in this endeavor is the identification of variant effects, a key factor in comprehending the mechanisms underlying gene regulation. Non-coding variants, constituting over 90% of all variants, have garnered increasing attention in recent years. The exploration of gene variant impacts and regulatory mechanisms has spurred the development of various deep learning approaches, providing new insights into the global regulatory landscape through the analysis of extensive genetic data. Here, we provide a comprehensive overview of the development of the non-coding variants models based on bulk and single-cell sequencing data and their model-based interpretation and downstream tasks. This review delineates the popular sequencing technologies for epigenetic profiling and deep learning approaches for discerning the effects of non-coding variants. Additionally, we summarize the limitations of current approaches in variant effect prediction research and outline opportunities for improvement. We anticipate that our study will offer a practical and useful guide for the bioinformatic community to further advance the unraveling of genetic variant effects.

Opportunities and Challenges in Advancing Plant Research with Single-cell Omics

Plants possess diverse cell types and intricate regulatory mechanisms to adapt to the ever-changing environment of nature. Various strategies have been employed to study cell types and their developmental progressions, including single-cell sequencing methods which provide high-dimensional catalogs to address biological concerns. In recent years, single-cell sequencing technologies in transcriptomics, epigenomics, proteomics, metabolomics, and spatial transcriptomics have been increasingly used in plant science to reveal intricate biological relationships at the single-cell level. However, the application of single-cell technologies to plants is more limited due to the challenges posed by cell structure. This review outlines the advancements in single-cell omics technologies, their implications in plant systems, future research applications, and the challenges of single-cell omics in plant systems.

scDM: A deep generative method for cell surface protein prediction with diffusion model

The executors of organismal functions are proteins, and the transition from RNA to protein is subject to post-transcriptional regulation; therefore, considering both RNA and surface protein expression simultaneously can provide additional evidence of biological processes. Cellular indexing of transcriptomes and epitopes by sequencing (CITE-Seq) technology can measure both RNA and protein expression in single cells, but these experiments are expensive and time-consuming. Due to the lack of computational tools for predicting surface proteins, we used datasets obtained with CITE-seq technology to design a deep generative prediction method based on diffusion models and to find biological discoveries through the prediction results. In our method, the scDM, which predicts protein expression values from RNA expression values of individual cells, uses a novel way of encoding the data into a model and generates predicted samples by introducing Gaussian noise to gradually remove the noise to learn the data distribution during the modelling process. Comprehensive evaluation across different datasets demonstrated that our predictions yielded satisfactory results and further demonstrated the effectiveness of incorporating information from single-cell multiomics data into diffusion models for biological studies. We also found that new directions for discovering therapeutic drug targets could be provided by jointly analysing the predictive value of surface protein expression and cancer cell drug scores.

Single-Cell Chromatin Accessibility Analysis Reveals the Epigenetic Basis and Signature Transcription Factors for the Molecular Subtypes of Colorectal Cancers

Colorectal cancer is a highly heterogeneous disease, with well-characterized subtypes based on genome, DNA methylome, and transcriptome signatures. To chart the epigenetic landscape of colorectal cancers, we generated a high-quality single-cell chromatin accessibility atlas of epithelial cells for 29 patients. Abnormal chromatin states acquired in adenomas were largely retained in colorectal cancers, which were tightly accompanied by opposite changes of DNA methylation. Unsupervised analysis on malignant cells revealed two epigenetic subtypes, exactly matching the iCMS classification, and key iCMS-specific transcription factors (TFs) were identified, including HNF4A and PPARA for iCMS2 tumors and FOXA3 and MAFK for iCMS3 tumors. Notably, subtype-specific TFs bind to distinct target gene sets and contribute to both interpatient similarities and diversities for both chromatin accessibilities and RNA expressions. Moreover, we identified CpG-island methylator phenotypes and pinpointed chromatin state signatures and TF regulators for the CIMP-high subtype. Our work systematically revealed the epigenetic basis of the well-known iCMS and CIMP classifications of colorectal cancers.

SIGNIFICANCE

Our work revealed the epigenetic basis of the well-known iCMS and CIMP classifications of colorectal cancers. Moreover, interpatient minor similarities and major diversities of chromatin accessibility signatures of TF target genes can faithfully explain the corresponding interpatient minor similarities and major diversities of RNA expression signatures of colorectal cancers, respectively. This article is featured in Selected Articles from This Issue, p. 897.

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.

Single-Cell Sequencing: High-Resolution Analysis of Cellular Heterogeneity in Autoimmune Diseases

Autoimmune diseases (AIDs) are complex in etiology and diverse in classification but clinically show similar symptoms such as joint pain and skin problems. As a result, the diagnosis is challenging, and usually, only broad treatments can be available. Consequently, the clinical responses in patients with different types of AIDs are unsatisfactory. Therefore, it is necessary to conduct more research to figure out the pathogenesis and therapeutic targets of AIDs. This requires research technologies with strong extraction and prediction capabilities. Single-cell sequencing technology analyses the genomic, epigenomic, or transcriptomic information at the single-cell level. It can define different cell types and states in greater detail, further revealing the molecular mechanisms that drive disease progression. These advantages enable cell biology research to achieve an unprecedented resolution and scale, bringing a whole new vision to life science research. In recent years, single-cell technology especially single-cell RNA sequencing (scRNA-seq) has been widely used in various disease research. In this paper, we present the innovations and applications of single-cell sequencing in the medical field and focus on the application contributing to the differential diagnosis and precise treatment of AIDs. Despite some limitations, single-cell sequencing has a wide range of applications in AIDs. We finally present a prospect for the development of single-cell sequencing. These ideas may provide some inspiration for subsequent research.

A review of the role of epigenetic studies for intramuscular fat deposition in beef cattle

Intramuscular fat (IMF) deposition profoundly influences meat quality and economic value in beef cattle production. Meanwhile, contemporary developments in epigenetics have opened new outlooks for understanding the molecular basics of IMF regulation, and it has become a key area of research for world scholars. Therefore, the aim of this paper was to provide insight and synthesis into the intricate relationship between epigenetic mechanisms and IMF deposition in beef cattle. The methodology involves a thorough analysis of existing literature, including pertinent books, academic journals, and online resources, to provide a comprehensive overview of the role of epigenetic studies in IMF deposition in beef cattle. This review summarizes the contemporary studies in epigenetic mechanisms in IMF regulation, high-resolution epigenomic mapping, single-cell epigenomics, multi-omics integration, epigenome editing approaches, longitudinal studies in cattle growth, environmental epigenetics, machine learning in epigenetics, ethical and regulatory considerations, and translation to industry practices from perspectives of IMF deposition in beef cattle. Moreover, this paper highlights DNA methylation, histone modifications, acetylation, phosphorylation, ubiquitylation, non-coding RNAs, DNA hydroxymethylation, epigenetic readers, writers, and erasers, chromatin immunoprecipitation followed by sequencing, whole genome bisulfite sequencing, epigenome-wide association studies, and their profound impact on the expression of crucial genes governing adipogenesis and lipid metabolism. Nutrition and stress also have significant influences on epigenetic modifications and IMF deposition. The key findings underscore the pivotal role of epigenetic studies in understanding and enhancing IMF deposition in beef cattle, with implications for precision livestock farming and ethical livestock management. In conclusion, this review highlights the crucial significance of epigenetic pathways and environmental factors in affecting IMF deposition in beef cattle, providing insightful information for improving the economics and meat quality of cattle production.

Understanding glioblastoma at the single-cell level: Recent advances and future challenges

Glioblastoma, the most aggressive and prevalent form of primary brain tumor, is characterized by rapid growth, diffuse infiltration, and resistance to therapies. Intrinsic heterogeneity and cellular plasticity contribute to its rapid progression under therapy; therefore, there is a need to fully understand these tumors at a single-cell level. Over the past decade, single-cell transcriptomics has enabled the molecular characterization of individual cells within glioblastomas, providing previously unattainable insights into the genetic and molecular features that drive tumorigenesis, disease progression, and therapy resistance. However, despite advances in single-cell technologies, challenges such as high costs, complex data analysis and interpretation, and difficulties in translating findings into clinical practice persist. As single-cell technologies are developed further, more insights into the cellular and molecular heterogeneity of glioblastomas are expected, which will help guide the development of personalized and effective therapies, thereby improving prognosis and quality of life for patients.

Single-cell transcriptomics: background, technologies, applications, and challenges

Single-cell sequencing was developed as a high-throughput tool to elucidate unusual and transient cell states that are barely visible in the bulk. This technology reveals the evolutionary status of cells and differences between populations, helps to identify unique cell subtypes and states, reveals regulatory relationships between genes, targets and molecular mechanisms in disease processes, tumor heterogeneity, the state of the immune environment, etc. However, the high cost and technical limitations of single-cell sequencing initially prevented its widespread application, but with advances in research, numerous new single-cell sequencing techniques have been discovered, lowering the cost barrier. Many single-cell sequencing platforms and bioinformatics methods have recently become commercially available, allowing researchers to make fascinating observations. They are now increasingly being used in various industries. Several protocols have been discovered in this context and each technique has unique characteristics, capabilities and challenges. This review presents the latest advancements in single-cell transcriptomics technologies. This includes single-cell transcriptomics approaches, workflows and statistical approaches to data processing, as well as the potential advances, applications, opportunities and challenges of single-cell transcriptomics technology. You will also get an overview of the entry points for spatial transcriptomics and multi-omics.

Integration of single-cell transcriptome and chromatin accessibility and its application on tumor investigation

The advent of single-cell sequencing techniques has not only revolutionized the investigation of biological processes but also significantly contributed to unraveling cellular heterogeneity at unprecedented levels. Among the various methods, single-cell transcriptome sequencing stands out as the best established, and has been employed in exploring many physiological and pathological activities. The recently developed single-cell epigenetic sequencing techniques, especially chromatin accessibility sequencing, have further deepened our understanding of gene regulatory networks. In this review, we summarize the recent breakthroughs in single-cell transcriptome and chromatin accessibility sequencing methodologies. Additionally, we describe current bioinformatic strategies to integrate data obtained through these single-cell sequencing methods and highlight the application of this analysis strategy on a deeper understanding of tumorigenesis and tumor progression. Finally, we also discuss the challenges and anticipated developments in this field.

Advances in single-cell omics and multiomics for high-resolution molecular profiling

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.

Single-cell analysis of the epitranscriptome: RNA modifications under the microscope

The identification of mechanisms capable of modifying genetic information by the addition of covalent RNA modifications distinguishes a level of complexity in gene expression which challenges key long-standing concepts of RNA biology. One of the current challenges of molecular biology is to properly understand the molecular functions of these RNA modifications, with more than 170 different ones having been identified so far. However, it has not been possible to map specific RNA modifications at a single-cell resolution until very recently. This review will highlight the technological advances in single-cell methodologies aimed at assessing and testing the biological function of certain RNA modifications, focusing on m6A. These advances have allowed for the development of novel strategies that enable the study of the 'epitranscriptome'. Nevertheless, despite all these improvements, many challenges and difficulties still need fixing for these techniques to work efficiently.

Single-Cell Multimodal Prediction via Transformers

The recent development of multimodal single-cell technology has made the possibility of acquiring multiple omics data from individual cells, thereby enabling a deeper understanding of cellular states and dynamics. Nevertheless, the proliferation of multimodal single-cell data also introduces tremendous challenges in modeling the complex interactions among different modalities. The recently advanced methods focus on constructing static interaction graphs and applying graph neural networks (GNNs) to learn from multimodal data. However, such static graphs can be suboptimal as they do not take advantage of the downstream task information; meanwhile GNNs also have some inherent limitations when deeply stacking GNN layers. To tackle these issues, in this work, we investigate how to leverage transformers for multimodal single-cell data in an end-to-end manner while exploiting downstream task information. In particular, we propose a scMoFormer framework which can readily incorporate external domain knowledge and model the interactions within each modality and cross modalities. Extensive experiments demonstrate that scMoFormer achieves superior performance on various benchmark datasets. Remarkably, scMoFormer won a Kaggle silver medal with the rank of 24/1221 (Top 2%) without ensemble in a NeurIPS 2022 competition. Our implementation is publicly available at Github.

Single-cell sequencing technology applied to epigenetics for the study of tumor heterogeneity

BACKGROUND

Previous studies have traditionally attributed the initiation of cancer cells to genetic mutations, considering them as the fundamental drivers of carcinogenesis. However, recent research has shed light on the crucial role of epigenomic alterations in various cell types present within the tumor microenvironment, suggesting their potential contribution to tumor formation and progression. Despite these significant findings, the progress in understanding the epigenetic mechanisms regulating tumor heterogeneity has been impeded over the past few years due to the lack of appropriate technical tools and methodologies.

RESULTS

The emergence of single-cell sequencing has enhanced our understanding of the epigenetic mechanisms governing tumor heterogeneity by revealing the distinct epigenetic layers of individual cells (chromatin accessibility, DNA/RNA methylation, histone modifications, nucleosome localization) and the diverse omics (transcriptomics, genomics, multi-omics) at the single-cell level. These technologies provide us with new insights into the molecular basis of intratumoral heterogeneity and help uncover key molecular events and driving mechanisms in tumor development.

CONCLUSION

This paper provides a comprehensive review of the emerging analytical and experimental approaches of single-cell sequencing in various omics, focusing specifically on epigenomics. These approaches have the potential to capture and integrate multiple dimensions of individual cancer cells, thereby revealing tumor heterogeneity and epigenetic features. Additionally, this paper outlines the future trends of these technologies and their current technical limitations.

Single-cell multi-omics sequencing of human spermatogenesis reveals a DNA demethylation event associated with male meiotic recombination

Human spermatogenesis is a highly ordered process; however, the roles of DNA methylation and chromatin accessibility in this process remain largely unknown. Here by simultaneously investigating the chromatin accessibility, DNA methylome and transcriptome landscapes using the modified single-cell chromatin overall omic-scale landscape sequencing approach, we revealed that the transcriptional changes throughout human spermatogenesis were correlated with chromatin accessibility changes. In particular, we identified a set of transcription factors and cis elements with potential functions. A round of DNA demethylation was uncovered upon meiosis initiation in human spermatogenesis, which was associated with male meiotic recombination and conserved between human and mouse. Aberrant DNA hypermethylation could be detected in leptotene spermatocytes of certain nonobstructive azoospermia patients. Functionally, the intervention of DNA demethylation affected male meiotic recombination and fertility. Our work provides multi-omics landscapes of human spermatogenesis at single-cell resolution and offers insights into the association between DNA demethylation and male meiotic recombination.

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.

The technological landscape and applications of single-cell multi-omics

Single-cell multi-omics technologies and methods characterize cell states and activities by simultaneously integrating various single-modality omics methods that profile the transcriptome, genome, epigenome, epitranscriptome, proteome, metabolome and other (emerging) omics. Collectively, these methods are revolutionizing molecular cell biology research. In this comprehensive Review, we discuss established multi-omics technologies as well as cutting-edge and state-of-the-art methods in the field. We discuss how multi-omics technologies have been adapted and improved over the past decade using a framework characterized by optimization of throughput and resolution, modality integration, uniqueness and accuracy, and we also discuss multi-omics limitations. We highlight the impact that single-cell multi-omics technologies have had in cell lineage tracing, tissue-specific and cell-specific atlas production, tumour immunology and cancer genetics, and in mapping of cellular spatial information in fundamental and translational research. Finally, we discuss bioinformatics tools that have been developed to link different omics modalities and elucidate functionality through the use of better mathematical modelling and computational methods.

A Roadmap for the Human Gut Cell Atlas

The number of studies investigating the human gastrointestinal tract using various single-cell profiling methods has increased substantially in the past few years. Although this increase provides a unique opportunity for the generation of the first comprehensive Human Gut Cell Atlas (HGCA), there remains a range of major challenges ahead. Above all, the ultimate success will largely depend on a structured and coordinated approach that aligns global efforts undertaken by a large number of research groups. In this Roadmap, we discuss a comprehensive forward-thinking direction for the generation of the HGCA on behalf of the Gut Biological Network of the Human Cell Atlas. Based on the consensus opinion of experts from across the globe, we outline the main requirements for the first complete HGCA by summarizing existing data sets and highlighting anatomical regions and/or tissues with limited coverage. We provide recommendations for future studies and discuss key methodologies and the importance of integrating the healthy gut atlas with related diseases and gut organoids. Importantly, we critically overview the computational tools available and provide recommendations to overcome key challenges.

Methods and applications for single-cell and spatial multi-omics

The joint analysis of the genome, epigenome, transcriptome, proteome and/or metabolome from single cells is transforming our understanding of cell biology in health and disease. In less than a decade, the field has seen tremendous technological revolutions that enable crucial new insights into the interplay between intracellular and intercellular molecular mechanisms that govern development, physiology and pathogenesis. In this Review, we highlight advances in the fast-developing field of single-cell and spatial multi-omics technologies (also known as multimodal omics approaches), and the computational strategies needed to integrate information across these molecular layers. We demonstrate their impact on fundamental cell biology and translational research, discuss current challenges and provide an outlook to the future.

Implications of tumour heterogeneity on cancer evolution and therapy resistance: lessons from breast cancer

Tumour heterogeneity is pervasive amongst many cancers and leads to disease progression, and therapy resistance. In this review, using breast cancer as an exemplar, we focus on the recent advances in understanding the interplay between tumour cells and their microenvironment using single cell sequencing and digital spatial profiling technologies. Further, we discuss the utility of lineage tracing methodologies in pre-clinical models of breast cancer, and how these are being used to unravel new therapeutic vulnerabilities and reveal biomarkers of breast cancer progression. © 2023 The Pathological Society of Great Britain and Ireland.

Effects of DNA Methylation on Gene Expression and Phenotypic Traits in Cattle: A Review

Gene expression in cells is determined by the epigenetic state of chromatin. Therefore, the study of epigenetic changes is very important to understand the regulatory mechanism of genes at the molecular, cellular, tissue and organ levels. DNA methylation is one of the most studied epigenetic modifications, which plays an important role in maintaining genome stability and ensuring normal growth and development. Studies have shown that methylation levels in bovine primordial germ cells, the rearrangement of methylation during embryonic development and abnormal methylation during placental development are all closely related to their reproductive processes. In addition, the application of bovine male sterility and assisted reproductive technology is also related to DNA methylation. This review introduces the principle, development of detection methods and application conditions of DNA methylation, with emphasis on the relationship between DNA methylation dynamics and bovine spermatogenesis, embryonic development, disease resistance and muscle and fat development, in order to provide theoretical basis for the application of DNA methylation in cattle breeding in the future.

New discoveries in the field of metabolism by applying single-cell and spatial omics

Single-cell multi-Omics (SCM-Omics) and spatial multi-Omics (SM-Omics) technologies provide state-of-the-art methods for exploring the composition and function of cell types in tissues/organs. Since its emergence in 2009, single-cell RNA sequencing (scRNA-seq) has yielded many groundbreaking new discoveries. The combination of this method with the emergence and development of SM-Omics techniques has been a pioneering strategy in neuroscience, developmental biology, and cancer research, especially for assessing tumor heterogeneity and T-cell infiltration. In recent years, the application of these methods in the study of metabolic diseases has also increased. The emerging SCM-Omics and SM-Omics approaches allow the molecular and spatial analysis of cells to explore regulatory states and determine cell fate, and thus provide promising tools for unraveling heterogeneous metabolic processes and making them amenable to intervention. Here, we review the evolution of SCM-Omics and SM-Omics technologies, and describe the progress in the application of SCM-Omics and SM-Omics in metabolism-related diseases, including obesity, diabetes, nonalcoholic fatty liver disease (NAFLD) and cardiovascular disease (CVD). We also conclude that the application of SCM-Omics and SM-Omics approaches can help resolve the molecular mechanisms underlying the pathogenesis of metabolic diseases in the body and facilitate therapeutic measures for metabolism-related diseases. This review concludes with an overview of the current status of this emerging field and the outlook for its future.

Single-cell and spatial transcriptomics: deciphering brain complexity in health and disease

In the past decade, single-cell technologies have proliferated and improved from their technically challenging beginnings to become common laboratory methods capable of determining the expression of thousands of genes in thousands of cells simultaneously. The field has progressed by taking the CNS as a primary research subject - the cellular complexity and multiplicity of neuronal cell types provide fertile ground for the increasing power of single-cell methods. Current single-cell RNA sequencing methods can quantify gene expression with sufficient accuracy to finely resolve even subtle differences between cell types and states, thus providing a great tool for studying the molecular and cellular repertoire of the CNS and its disorders. However, single-cell RNA sequencing requires the dissociation of tissue samples, which means that the interrelationships between cells are lost. Spatial transcriptomic methods bypass tissue dissociation and retain this spatial information, thereby allowing gene expression to be assessed across thousands of cells within the context of tissue structural organization. Here, we discuss how single-cell and spatially resolved transcriptomics have been contributing to unravelling the pathomechanisms underlying brain disorders. We focus on three areas where we feel these new technologies have provided particularly useful insights: selective neuronal vulnerability, neuroimmune dysfunction and cell-type-specific treatment response. We also discuss the limitations and future directions of single-cell and spatial RNA sequencing technologies.

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.

Simultaneous Measurement of DNA Methylation and Nucleosome Occupancy in Single Cells Using scNOMe-Seq

Single-cell Nucleosome Occupancy and Methylome sequencing (scNOMe-seq) is a multimodal assay that simultaneously measures endogenous DNA methylation and nucleosome occupancy (i.e., chromatin accessibility) in single cells. scNOMe-seq combines the activity of a GpC Methyltransferase, an enzyme which methylates cytosines in GpC dinucleotides, with bisulfite conversion, whereby unmethylated cytosines are converted into thymines. Because GpC Methyltransferase acts only on cytosines present in non-nucleosomal regions of the genome, the subsequent bisulfite conversion step not only detects the endogenous DNA methylation, but also reveals the genome-wide pattern of chromatin accessibility. Implementing this technology at the single-cell level helps to capture the dynamics governing methylation and accessibility vary across individual cells and cell types. Here, we provide a scalable plate-based protocol for preparing scNOMe-seq libraries from single nucleus suspensions.

Simultaneous Single-Cell Profiling of the Transcriptome and Accessible Chromatin Using SHARE-seq

The ability to analyze the transcriptomic and epigenomic states of individual single cells has in recent years transformed our ability to measure and understand biological processes. Recent advancements have focused on increasing sensitivity and throughput to provide richer and deeper biological insights at the cellular level. The next frontier is the development of multiomic methods capable of analyzing multiple features from the same cell, such as the simultaneous measurement of the transcriptome and the chromatin accessibility of candidate regulatory elements. In this chapter, we discuss and describe SHARE-seq (Simultaneous high-throughput ATAC, and RNA expression with sequencing) for carrying out simultaneous chromatin accessibility and transcriptome measurements in single cells, together with the experimental and analytical considerations for achieving optimal results.

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.

Breast cancer heterogeneity and its implication in personalized precision therapy

Breast cancer heterogeneity determines cancer progression, treatment effects, and prognosis. However, the precise mechanism for this heterogeneity remains unknown owing to its complexity. Here, we summarize the origins of breast cancer heterogeneity and its influence on disease progression, recurrence, and therapeutic resistance. We review the possible mechanisms of heterogeneity and the research methods used to analyze it. We also highlight the importance of cell interactions for the origins of breast cancer heterogeneity, which can be further categorized into cooperative and competitive interactions. Finally, we provide new insights into precise individual treatments based on heterogeneity.

Single-cell omics: A new direction for functional genetic research in human diseases and animal models

Over the past decade, with the development of high-throughput single-cell sequencing technology, single-cell omics has been emerged as a powerful tool to understand the molecular basis of cellular mechanisms and refine our knowledge of diverse cell states. They can reveal the heterogeneity at different genetic layers and elucidate their associations by multiple omics analysis, providing a more comprehensive genetic map of biological regulatory networks. In the post-GWAS era, the molecular biological mechanisms influencing human diseases will be further elucidated by single-cell omics. This review mainly summarizes the development and trend of single-cell omics. This involves single-cell omics technologies, single-cell multi-omics technologies, multiple omics data integration methods, applications in various human organs and diseases, classic laboratory cell lines, and animal disease models. The review will reveal some perspectives for elucidating human diseases and constructing animal models.

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.

Simultaneous profiling of histone modifications and DNA methylation via nanopore sequencing

The interplay between histone modifications and DNA methylation drives the establishment and maintenance of the cellular epigenomic landscape, but it remains challenging to investigate the complex relationship between these epigenetic marks across the genome. Here we describe a nanopore-sequencing-based-method, nanoHiMe-seq, for interrogating the genome-wide localization of histone modifications and DNA methylation from single DNA molecules. nanoHiMe-seq leverages a nonspecific methyltransferase to exogenously label adenine bases proximal to antibody-targeted modified nucleosomes in situ. The labelled adenines and the endogenous methylated CpG sites are simultaneously detected on individual nanopore reads using a hidden Markov model, which is implemented in the nanoHiMe software package. We demonstrate the utility, robustness and sensitivity of nanoHiMe-seq by jointly profiling DNA methylation and histone modifications at low coverage depths, concurrently determining phased patterns of DNA methylation and histone modifications, and probing the intrinsic connectivity between these epigenetic marks across the genome.

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.

Single-Cell DNA Methylation Analysis in Cancer

Morphological, transcriptomic, and genomic defects are well-explored parameters of cancer biology. In more recent years, the impact of epigenetic influences, such as DNA methylation, is becoming more appreciated. Aberrant DNA methylation has been implicated in many types of cancers, influencing cell type, state, transcriptional regulation, and genomic stability to name a few. Traditionally, large populations of cells from the tissue of interest are coalesced for analysis, producing averaged methylome data. Considering the inherent heterogeneity of cancer, analysing populations of cells as a whole denies the ability to discover novel aberrant methylation patterns, identify subpopulations, and trace cell lineages. Due to recent advancements in technology, it is now possible to obtain methylome data from single cells. This has both research and clinical implications, ranging from the identification of biomarkers to improved diagnostic tools. As with all emerging technologies, distinct experimental, bioinformatic, and practical challenges present themselves. This review begins with exploring the potential impact of single-cell sequencing on understanding cancer biology and how it could eventually benefit a clinical setting. Following this, the techniques and experimental approaches which made this technology possible are explored. Finally, the present challenges currently associated with single-cell DNA methylation sequencing are described.

Chromatin accessibility: methods, mechanisms, and biological insights

Access to DNA is a prerequisite to the execution of essential cellular processes that include transcription, replication, chromosomal segregation, and DNA repair. How the proteins that regulate these processes function in the context of chromatin and its dynamic architectures is an intensive field of study. Over the past decade, genome-wide assays and new imaging approaches have enabled a greater understanding of how access to the genome is regulated by nucleosomes and associated proteins. Additional mechanisms that may control DNA accessibility in vivo include chromatin compaction and phase separation - processes that are beginning to be understood. Here, we review the ongoing development of accessibility measurements, we summarize the different molecular and structural mechanisms that shape the accessibility landscape, and we detail the many important biological functions that are linked to chromatin accessibility.

Increased chromatin accessibility facilitates intron retention in specific cell differentiation states

Dynamic intron retention (IR) in vertebrate cells is of widespread biological importance. Aberrant IR is associated with numerous human diseases including several cancers. Despite consistent reports demonstrating that intrinsic sequence features can help introns evade splicing, conflicting findings about cell type- or condition-specific IR regulation by trans-regulatory and epigenetic mechanisms demand an unbiased and systematic analysis of IR in a controlled experimental setting. We integrated matched mRNA sequencing (mRNA-Seq), whole-genome bisulfite sequencing (WGBS), nucleosome occupancy methylome sequencing (NOMe-Seq) and chromatin immunoprecipitation sequencing (ChIP-Seq) data from primary human myeloid and lymphoid cells. Using these multi-omics data and machine learning, we trained two complementary models to determine the role of epigenetic factors in the regulation of IR in cells of the innate immune system. We show that increased chromatin accessibility, as revealed by nucleosome-free regions, contributes substantially to the retention of introns in a cell-specific manner. We also confirm that intrinsic characteristics of introns are key for them to evade splicing. This study suggests an important role for chromatin architecture in IR regulation. With an increasing appreciation that pathogenic alterations are linked to RNA processing, our findings may provide useful insights for the development of novel therapeutic approaches that target aberrant splicing.

Integrating Micro and Nano Technologies for Cell Engineering and Analysis: Toward the Next Generation of Cell Therapy Workflows

The emerging field of cell therapy offers the potential to treat and even cure a diverse array of diseases for which existing interventions are inadequate. Recent advances in micro and nanotechnology have added a multitude of single cell analysis methods to our research repertoire. At the same time, techniques have been developed for the precise engineering and manipulation of cells. Together, these methods have aided the understanding of disease pathophysiology, helped formulate corrective interventions at the cellular level, and expanded the spectrum of available cell therapeutic options. This review discusses how micro and nanotechnology have catalyzed the development of cell sorting, cellular engineering, and single cell analysis technologies, which have become essential workflow components in developing cell-based therapeutics. The review focuses on the technologies adopted in research studies and explores the opportunities and challenges in combining the various elements of cell engineering and single cell analysis into the next generation of integrated and automated platforms that can accelerate preclinical studies and translational research.

Long-range phasing of dynamic, tissue-specific and allele-specific regulatory elements

Epigenomic maps identify gene regulatory elements by their chromatin state. However, prevailing short-read sequencing methods cannot effectively distinguish alleles, evaluate the interdependence of elements in a locus or capture single-molecule dynamics. Here, we apply targeted nanopore sequencing to profile chromatin accessibility and DNA methylation on contiguous ~100-kb DNA molecules that span loci relevant to development, immunity and imprinting. We detect promoters, enhancers, insulators and transcription factor footprints on single molecules based on exogenous GpC methylation. We infer relationships among dynamic elements within immune loci, and order successive remodeling events during T cell stimulation. Finally, we phase primary sequence and regulatory elements across the H19/IGF2 locus, uncovering primate-specific features. These include a segmental duplication that stabilizes the imprinting control region and a noncanonical enhancer that drives biallelic IGF2 expression in specific contexts. Our study advances emerging strategies for phasing gene regulatory landscapes and reveals a mechanism that overrides IGF2 imprinting in human cells.

A guide to systems-level immunomics

The immune system is highly complex and distributed throughout an organism, with hundreds to thousands of cell states existing in parallel with diverse molecular pathways interacting in a highly dynamic and coordinated fashion. Although the characterization of individual genes and molecules is of the utmost importance for understanding immune-system function, high-throughput, high-resolution omics technologies combined with sophisticated computational modeling and machine-learning approaches are creating opportunities to complement standard immunological methods with new insights into immune-system dynamics. Like systems immunology itself, immunology researchers must take advantage of these technologies and form their own diverse networks, connecting with researchers from other disciplines. This Review is an introduction and 'how-to guide' for immunologists with no particular experience in the field of omics but with the intention to learn about and apply these systems-level approaches, and for immunologists who want to make the most of interdisciplinary networks.