High-Resolution Single-Cell DNA Methylation Measurements Reveal Epigenetically Distinct Hematopoietic Stem Cell Subpopulations

New frameworks for hematopoiesis derived from single-cell genomics

ABSTRACT

Recent advancements in single-cell genomics have enriched our understanding of hematopoiesis, providing intricate details about hematopoietic stem cell biology, differentiation, and lineage commitment. Technological advancements have highlighted extensive heterogeneity of cell populations and continuity of differentiation routes. Nevertheless, intermediate "attractor" states signify structure in stem and progenitor populations that link state transition dynamics to fate potential. We discuss how innovative model systems quantify lineage bias and how stress accelerates differentiation, thereby reducing fate plasticity compared with native hematopoiesis. We conclude by offering our perspective on the current model of hematopoiesis and discuss how a more precise understanding can translate to strategies that extend healthy hematopoiesis and prevent disease.

sciMET-cap: high-throughput single-cell methylation analysis with a reduced sequencing burden

DNA methylation is a key component of the mammalian epigenome, playing a regulatory role in development, disease, and other processes. Robust, high-throughput single-cell DNA methylation assays are now possible (sciMET); however, the genome-wide nature of DNA methylation results in a high sequencing burden per cell. Here, we leverage target enrichment with sciMET to capture sufficient information per cell for cell type assignment using substantially fewer sequence reads (sciMET-cap). Accumulated off-target coverage enables genome-wide differentially methylated region (DMR) calling for clusters with as few as 115 cells. We characterize sciMET-cap on human PBMCs and brain (middle frontal gyrus).

Synovial Sarcoma Chromatin Dynamics Reveal a Continuum in SS18:SSX Reprograming

Synovial sarcoma (SyS) is an aggressive soft-tissue malignancy characterized by a pathognomonic chromosomal translocation leading to the formation of the SS18::SSX fusion oncoprotein. SS18::SSX associates with mammalian BAF complexes suggesting deregulation of chromatin architecture as the oncogenic driver in this tumour type. To examine the epigenomic state of SyS we performed comprehensive multi-omics analysis on 52 primary pre-treatment human SyS tumours. Our analysis revealed a continuum of epigenomic states across the cohort at fusion target genes independent of rare somatic genetic lesions. We identify cell-of-origin signatures defined by enhancer states and reveal unexpected relationships between H2AK119Ub1 and active marks. The number of bivalent promoters, dually marked by the repressive H3K27me3 and activating H3K4me3 marks, has strong prognostic value and outperforms tumor grade in predicting patient outcome. Finally, we identify SyS defining epigenomic features including H3K4me3 expansion associated with striking promoter DNA hypomethylation in which SyS displays the lowest mean methylation level of any sarcoma subtype. We explore these distinctive features as potential vulnerabilities in SyS and identify H3K4me3 inhibition as a promising therapeutic strategy.

Unveiling aging dynamics in the hematopoietic system insights from single-cell technologies

As the demographic structure shifts towards an aging society, strategies aimed at slowing down or reversing the aging process become increasingly essential. Aging is a major predisposing factor for many chronic diseases in humans. The hematopoietic system, comprising blood cells and their associated bone marrow microenvironment, intricately participates in hematopoiesis, coagulation, immune regulation and other physiological phenomena. The aging process triggers various alterations within the hematopoietic system, serving as a spectrum of risk factors for hematopoietic disorders, including clonal hematopoiesis, immune senescence, myeloproliferative neoplasms and leukemia. The emerging single-cell technologies provide novel insights into age-related changes in the hematopoietic system. In this review, we summarize recent studies dissecting hematopoietic system aging using single-cell technologies. We discuss cellular changes occurring during aging in the hematopoietic system at the levels of the genomics, transcriptomics, epigenomics, proteomics, metabolomics and spatial multi-omics. Finally, we contemplate the future prospects of single-cell technologies, emphasizing the impact they may bring to the field of hematopoietic system aging research.

Low-input and single-cell methods for Infinium DNA methylation BeadChips

The Infinium BeadChip is the most widely used DNA methylome assay technology for population-scale epigenome profiling. However, the standard workflow requires over 200 ng of input DNA, hindering its application to small cell-number samples, such as primordial germ cells. We developed experimental and analysis workflows to extend this technology to suboptimal input DNA conditions, including ultra-low input down to single cells. DNA preamplification significantly enhanced detection rates to over 50% in five-cell samples and ∼25% in single cells. Enzymatic conversion also substantially improved data quality. Computationally, we developed a method to model the background signal's influence on the DNA methylation level readings. The modified detection P-value calculation achieved higher sensitivities for low-input datasets and was validated in over 100 000 public diverse methylome profiles. We employed the optimized workflow to query the demethylation dynamics in mouse primordial germ cells available at low cell numbers. Our data revealed nuanced chromatin states, sex disparities, and the role of DNA methylation in transposable element regulation during germ cell development. Collectively, we present comprehensive experimental and computational solutions to extend this widely used methylation assay technology to applications with limited DNA.

Somatic epimutations enable single-cell lineage tracing in native hematopoiesis across the murine and human lifespan

Current approaches to lineage tracing of stem cell clones require genetic engineering or rely on sparse somatic DNA variants, which are difficult to capture at single-cell resolution. Here, we show that targeted single-cell measurements of DNA methylation at single-CpG resolution deliver joint information about cellular differentiation state and clonal identities. We develop EPI-clone, a droplet-based method for transgene-free lineage tracing, and apply it to study hematopoiesis, capturing hundreds of clonal trajectories across almost 100,000 single-cells. Using ground-truth genetic barcodes, we demonstrate that EPI-clone accurately identifies clonal lineages throughout hematopoietic differentiation. Applied to unperturbed hematopoiesis, we describe an overall decline of clonal complexity during murine ageing and the expansion of rare low-output stem cell clones. In aged human donors, we identified expanded hematopoietic clones with and without genetic lesions, and various degrees of clonal complexity. Taken together, EPI-clone enables accurate and transgene-free single-cell lineage tracing at scale.

Chromosomal single-strand break repair and neurological disease: Implications on transcription and emerging genomic tools

Cells are constantly exposed to various sources of DNA damage that pose a threat to their genomic integrity. One of the most common types of DNA breaks are single-strand breaks (SSBs). Mutations in the repair proteins that are important for repairing SSBs have been reported in several neurological disorders. While several tools have been utilised to investigate SSBs in cells, it was only through recent advances in genomics that we are now beginning to understand the architecture of the non-random distribution of SSBs and their impact on key cellular processes such as transcription and epigenetic remodelling. Here, we discuss our current understanding of the genome-wide distribution of SSBs, their link to neurological disorders and summarise recent technologies to investigate SSBs at the genomic level.

Maternal upbringing and selective breeding for voluntary exercise behavior modify patterns of DNA methylation and expression of genes in the mouse brain

Selective breeding has been utilized to study the genetic basis of exercise behavior, but research suggests that epigenetic mechanisms, such as DNA methylation, also contribute to this behavior. In a previous study, we demonstrated that the brains of mice from a genetically selected high runner (HR) line have sex-specific changes in DNA methylation patterns in genes known to be genomically imprinted compared to those from a non-selected control (C) line. Through cross-fostering, we also found that maternal upbringing can modify the DNA methylation patterns of additional genes. Here, we identify an additional set of genes in which DNA methylation patterns and gene expression may be altered by selection for increased wheel-running activity and maternal upbringing. We performed bisulfite sequencing and gene expression assays of 14 genes in the brain and found alterations in DNA methylation and gene expression for Bdnf, Pde4d and Grin2b. Decreases in Bdnf methylation correlated with significant increases in Bdnf gene expression in the hippocampus of HR compared to C mice. Cross-fostering also influenced the DNA methylation patterns for Pde4d in the cortex and Grin2b in the hippocampus, with associated changes in gene expression. We also found that the DNA methylation patterns for Atrx and Oxtr in the cortex and Atrx and Bdnf in the hippocampus were further modified by sex. Together with our previous study, these results suggest that DNA methylation and the resulting change in gene expression may interact with early-life influences to shape adult exercise behavior.

Low-input and single-cell methods for Infinium DNA methylation BeadChips

The Infinium BeadChip is the most widely used DNA methylome assay technology for population-scale epigenome profiling. However, the standard workflow requires over 200 ng of input DNA, hindering its application to small cell-number samples, such as primordial germ cells. We developed experimental and analysis workflows to extend this technology to suboptimal input DNA conditions, including ultra-low input down to single cells. DNA preamplification significantly enhanced detection rates to over 50% in five-cell samples and ∼25% in single cells. Enzymatic conversion also substantially improved data quality. Computationally, we developed a method to model the background signal's influence on the DNA methylation level readings. The modified detection p -values calculation achieved higher sensitivities for low-input datasets and was validated in over 100,000 public datasets with diverse methylation profiles. We employed the optimized workflow to query the demethylation dynamics in mouse primordial germ cells available at low cell numbers. Our data revealed nuanced chromatin states, sex disparities, and the role of DNA methylation in transposable element regulation during germ cell development. Collectively, we present comprehensive experimental and computational solutions to extend this widely used methylation assay technology to applications with limited DNA.

sciMET-cap: High-throughput single-cell methylation analysis with a reduced sequencing burden

DNA methylation is a key component of the mammalian epigenome, playing a regulatory role in development, disease, and other processes. Robust, high-throughput single-cell DNA methylation assays are now possible (sciMET); however, the genome-wide nature of DNA methylation results in a high sequencing burden per cell. Here, we leverage target enrichment with sciMET to capture sufficient information per cell for cell type assignment using substantially fewer sequence reads (sciMET-cap). Sufficient off-target coverage further enables the production of near-complete methylomes for individual cell types. We characterize sciMET-cap on human PBMCs and brain (middle frontal gyrus).

Single-cell methylation sequencing data reveal succinct metastatic migration histories and tumor progression models

Recent studies exploring the impact of methylation in tumor evolution suggest that although the methylation status of many of the CpG sites are preserved across distinct lineages, others are altered as the cancer progresses. Because changes in methylation status of a CpG site may be retained in mitosis, they could be used to infer the progression history of a tumor via single-cell lineage tree reconstruction. In this work, we introduce the first principled distance-based computational method, Sgootr, for inferring a tumor's single-cell methylation lineage tree and for jointly identifying lineage-informative CpG sites that harbor changes in methylation status that are retained along the lineage. We apply Sgootr on single-cell bisulfite-treated whole-genome sequencing data of multiregionally sampled tumor cells from nine metastatic colorectal cancer patients, as well as multiregionally sampled single-cell reduced-representation bisulfite sequencing data from a glioblastoma patient. We show that the tumor lineages constructed reveal a simple model underlying tumor progression and metastatic seeding. A comparison of Sgootr against alternative approaches shows that Sgootr can construct lineage trees with fewer migration events and with more in concordance with the sequential-progression model of tumor evolution, with a running time a fraction of that used in prior studies. Lineage-informative CpG sites identified by Sgootr are in inter-CpG island (CGI) regions, as opposed to intra-CGIs, which have been the main regions of interest in genomic methylation-related analyses.

scHiMe: predicting single-cell DNA methylation levels based on single-cell Hi-C data

Recently a biochemistry experiment named methyl-3C was developed to simultaneously capture the chromosomal conformations and DNA methylation levels on individual single cells. However, the number of data sets generated from this experiment is still small in the scientific community compared with the greater amount of single-cell Hi-C data generated from separate single cells. Therefore, a computational tool to predict single-cell methylation levels based on single-cell Hi-C data on the same individual cells is needed. We developed a graph transformer named scHiMe to accurately predict the base-pair-specific (bp-specific) methylation levels based on both single-cell Hi-C data and DNA nucleotide sequences. We benchmarked scHiMe for predicting the bp-specific methylation levels on all of the promoters of the human genome, all of the promoter regions together with the corresponding first exon and intron regions, and random regions on the whole genome. Our evaluation showed a high consistency between the predicted and methyl-3C-detected methylation levels. Moreover, the predicted DNA methylation levels resulted in accurate classifications of cells into different cell types, which indicated that our algorithm successfully captured the cell-to-cell variability in the single-cell Hi-C data. scHiMe is freely available at http://dna.cs.miami.edu/scHiMe/.

Unraveling Heterogeneity in the Aging Hematopoietic Stem Cell Compartment: An Insight From Single-cell Approaches

Specific cell types and, therefore, organs respond differently during aging. This is also true for the hematopoietic system, where it has been demonstrated that hematopoietic stem cells alter a variety of features, such as their metabolism, and accumulate DNA damage, which can lead to clonal outgrowth over time. In addition, profound changes in the bone marrow microenvironment upon aging lead to senescence in certain cell types such as mesenchymal stem cells and result in increased inflammation. This heterogeneity makes it difficult to pinpoint the molecular drivers of organismal aging gained from bulk approaches, such as RNA sequencing. A better understanding of the heterogeneity underlying the aging process in the hematopoietic compartment is, therefore, needed. With the advances of single-cell technologies in recent years, it is now possible to address fundamental questions of aging. In this review, we discuss how single-cell approaches can and indeed are already being used to understand changes observed during aging in the hematopoietic compartment. We will touch on established and novel methods for flow cytometric detection, single-cell culture approaches, and single-cell omics.

Rare diseases of epigenetic origin: Challenges and opportunities

Rare diseases (RDs), more than 80% of which have a genetic origin, collectively affect approximately 350 million people worldwide. Progress in next-generation sequencing technology has both greatly accelerated the pace of discovery of novel RDs and provided more accurate means for their diagnosis. RDs that are driven by altered epigenetic regulation with an underlying genetic basis are referred to as rare diseases of epigenetic origin (RDEOs). These diseases pose unique challenges in research, as they often show complex genetic and clinical heterogeneity arising from unknown gene-disease mechanisms. Furthermore, multiple other factors, including cell type and developmental time point, can confound attempts to deconvolute the pathophysiology of these disorders. These challenges are further exacerbated by factors that contribute to epigenetic variability and the difficulty of collecting sufficient participant numbers in human studies. However, new molecular and bioinformatics techniques will provide insight into how these disorders manifest over time. This review highlights recent studies addressing these challenges with innovative solutions. Further research will elucidate the mechanisms of action underlying unique RDEOs and facilitate the discovery of treatments and diagnostic biomarkers for screening, thereby improving health trajectories and clinical outcomes of affected patients.

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.

Post-bisulfite Adaptor Tagging Based on an ssDNA Ligation Technique (tPBAT)

Post-bisulfite adaptor tagging (PBAT) is a concept that enables the preparation of an efficient sequencing library from bisulfite-treated DNA, and it also means the protocol implemented the concept. Although the previous PBAT or rPBAT was sensitive enough for single-cell methylome analysis, the protocol had several drawbacks owing to the repeated random priming reactions. To resolve these problems, we developed a unique single-strand DNA ligation technique, termed TACS ligation, and established a new protocol called tPBAT. With tPBAT, the data quality improved, with a longer insert and higher mapping rate than that obtained with rPBAT. In addition, paired-end sequencing and indexing were supported by the default. In this chapter, the tPBAT protocol is introduced, and a thorough description of its application to small samples is provided.

High-throughput robust single-cell DNA methylation profiling with sciMETv2

DNA methylation is a key epigenetic property that drives gene regulatory programs in development and disease. Current single-cell methods that produce high quality methylomes are expensive and low throughput without the aid of extensive automation. We previously described a proof-of-principle technique that enabled high cell throughput; however, it produced only low-coverage profiles and was a difficult protocol that required custom sequencing primers and recipes and frequently produced libraries with excessive adapter contamination. Here, we describe a greatly improved version that generates high-coverage profiles (~15-fold increase) using a robust protocol that does not require custom sequencing capabilities, includes multiple stopping points, and exhibits minimal adapter contamination. We demonstrate two versions of sciMETv2 on primary human cortex, a high coverage and rapid version, identifying distinct cell types using CH methylation patterns. These datasets are able to be directly integrated with one another as well as with existing snmC-seq2 datasets with little discernible bias. Finally, we demonstrate the ability to determine cell types using CG methylation alone, which is the dominant context for DNA methylation in most cell types other than neurons and the most applicable analysis outside of brain tissue.

MC profiling: a novel approach to analyze DNA methylation heterogeneity in genome-wide bisulfite sequencing data

DNA methylation is an epigenetic mark implicated in crucial biological processes. Most of the knowledge about DNA methylation is based on bulk experiments, in which DNA methylation of genomic regions is reported as average methylation. However, average methylation does not inform on how methylated cytosines are distributed in each single DNA molecule. Here, we propose Methylation Class (MC) profiling as a genome-wide approach to the study of DNA methylation heterogeneity from bulk bisulfite sequencing experiments. The proposed approach is built on the concept of MCs, groups of DNA molecules sharing the same number of methylated cytosines. The relative abundances of MCs from sequencing reads incorporates the information on the average methylation, and directly informs on the methylation level of each molecule. By applying our approach to publicly available bisulfite-sequencing datasets, we individuated cell-to-cell differences as the prevalent contributor to methylation heterogeneity. Moreover, we individuated signatures of loci undergoing imprinting and X-inactivation, and highlighted differences between the two processes. When applying MC profiling to compare different conditions, we identified methylation changes occurring in regions with almost constant average methylation. Altogether, our results indicate that MC profiling can provide useful insights on the epigenetic status and its evolution at multiple genomic regions.

scTAM-seq enables targeted high-confidence analysis of DNA methylation in single cells

Single-cell DNA methylation profiling currently suffers from excessive noise and/or limited cellular throughput. We developed scTAM-seq, a targeted bisulfite-free method for profiling up to 650 CpGs in up to 10,000 cells per experiment, with a dropout rate as low as 7%. We demonstrate that scTAM-seq can resolve DNA methylation dynamics across B-cell differentiation in blood and bone marrow, identifying intermediate differentiation states that were previously masked. scTAM-seq additionally queries surface-protein expression, thus enabling integration of single-cell DNA methylation information with cell atlas data. In summary, scTAM-seq is a high-throughput, high-confidence method for analyzing DNA methylation at single-CpG resolution across thousands of single cells.

DNA methylome of primary spermatocyte reveals epigenetic dysregulation associated with male sterility of cattleyak

DNA cytosine methylation modification in the germline is of particular importance since it is a highly heritable epigenetic mark. Although cytosine methylation has been analyzed at the genome-scale for several mammalian species, our knowledge of DNA methylation patterns and the mechanisms underlying male hybrid sterility is still limited in domestic animals such as cattleyak. Here we for the first time show the genome-wide and single-base resolution landscape of methylcytosines (mC) in the primary spermatocyte (PSC) genome of yak with normal spermatogenesis and the inter-specific hybrid cattleyak with male infertility. A comparative investigation revealed that widespread differences are observed in the composition and patterning of DNA cytosine methylation between the two methylomes. Global CG or non-CG DNA methylation levels, as well as the number of mC sites, are increased in cattleyak compared to yak. Notably, the DNA methylome in cattleyak PSC exhibits promoter hypermethylation of meiosis-specific genes and piRNA pathway genes with respect to yak. Furthermore, major retrotransposonson classes are predominantly hypermethylated in cattleyak while those are fully hypomethylated in yak. KEGG pathway enrichment indicates Rap1 signaling and MAPK pathways may play potential roles in the spermatogenic arrest of cattleyak. Our present study not only provides valuable insights into distinct features of the cattleyak PSC methylome but also paves the way toward elucidating the complex, yet highly coordinated epigenetic modification during male germline development for inter-specific hybrid animals.

A novel methylated cell-free DNA marker panel to monitor treatment response in metastatic prostate cancer

Aim: This study examined circulating cell-free DNA (cfDNA) biomarkers associated with androgen treatment resistance in metastatic castration resistance prostate cancer (mCRPC). Materials & methods: We designed a panel of nine candidate cfDNA methylation markers using droplet digital PCR (Methyl-ddPCR) and assessed methylation levels in sequentially collected cfDNA samples from patients with mCRPC. Results: Increased cfDNA methylation in eight out of nine markers during androgen-targeted treatment correlated with a faster time to clinical progression. Cox proportional hazards modeling and logistic regression analysis further confirmed that higher cfDNA methylation during treatment was significantly associated with clinical progression. Conclusion: Overall, our findings have revealed a novel methylated cfDNA marker panel that could aid in the clinical management of metastatic prostate cancer.

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.

Systematic evaluation of cell-type deconvolution pipelines for sequencing-based bulk DNA methylomes

DNA methylation analysis by sequencing is becoming increasingly popular, yielding methylomes at single-base pair and single-molecule resolution. It has tremendous potential for cell-type heterogeneity analysis using intrinsic read-level information. Although diverse deconvolution methods were developed to infer cell-type composition based on bulk sequencing-based methylomes, systematic evaluation has not been performed yet. Here, we thoroughly benchmark six previously published methods: Bayesian epiallele detection, DXM, PRISM, csmFinder+coMethy, ClubCpG and MethylPurify, together with two array-based methods, MeDeCom and Houseman, as a comparison group. Sequencing-based deconvolution methods consist of two main steps, informative region selection and cell-type composition estimation, thus each was individually assessed. With this elaborate evaluation, we aimed to establish which method achieves the highest performance in different scenarios of synthetic bulk samples. We found that cell-type deconvolution performance is influenced by different factors depending on the number of cell types within the mixture. Finally, we propose a best-practice deconvolution strategy for sequencing data and point out limitations that need to be handled. Array-based methods—both reference-based and reference-free—generally outperformed sequencing-based methods, despite the absence of read-level information. This implies that the current sequencing-based methods still struggle with correctly identifying cell-type-specific signals and eliminating confounding methylation patterns, which needs to be handled in future studies.

Polycomb contraction differentially regulates terminal human hematopoietic differentiation programs

Background

Lifelong production of the many types of mature blood cells from less differentiated progenitors is a hierarchically ordered process that spans multiple cell divisions. The nature and timing of the molecular events required to integrate the environmental signals, transcription factor activity, epigenetic modifications, and changes in gene expression involved are thus complex and still poorly understood. To address this gap, we generated comprehensive reference epigenomes of 8 phenotypically defined subsets of normal human cord blood.

Results

We describe a striking contraction of H3K27me3 density in differentiated myelo-erythroid cells that resembles a punctate pattern previously ascribed to pluripotent embryonic stem cells. Phenotypically distinct progenitor cell types display a nearly identical repressive H3K27me3 signature characterized by large organized chromatin K27-modification domains that are retained by mature lymphoid cells but lost in terminally differentiated monocytes and erythroblasts. We demonstrate that inhibition of polycomb group members predicted to control large organized chromatin K27-modification domains influences lymphoid and myeloid fate decisions of primary neonatal hematopoietic progenitors in vitro. We further show that a majority of active enhancers appear in early progenitors, a subset of which are DNA hypermethylated and become hypomethylated and induced during terminal differentiation.

Conclusion

Primitive human hematopoietic cells display a unique repressive H3K27me3 signature that is retained by mature lymphoid cells but is lost in monocytes and erythroblasts. Intervention data implicate that control of this chromatin state change is a requisite part of the process whereby normal human hematopoietic progenitor cells make lymphoid and myeloid fate decisions.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12915-022-01315-1.

scSPLAT, a scalable plate-based protocol for single cell WGBS library preparation

DNA methylation is a central epigenetic mark that has diverse roles in gene regulation, development, and maintenance of genome integrity. 5 methyl cytosine (5mC) can be interrogated at base resolution in single cells by using bisulfite sequencing (scWGBS). Several different scWGBS strategies have been described in recent years to study DNA methylation in single cells. However, there remain limitations with respect to cost-efficiency and yield. Herein, we present a new development in the field of scWGBS library preparation; single cell Splinted Ligation Adapter Tagging (scSPLAT). scSPLAT employs a pooling strategy to facilitate sample preparation at a higher scale and throughput than previously possible. We demonstrate the accuracy and robustness of the method by generating data from 225 single K562 cells and from 309 single liver nuclei and compare scSPLAT against other scWGBS methods.

scMelody: An Enhanced Consensus-Based Clustering Model for Single-Cell Methylation Data by Reconstructing Cell-to-Cell Similarity

Single-cell DNA methylation sequencing technology has brought new perspectives to investigate epigenetic heterogeneity, supporting a need for computational methods to cluster cells based on single-cell methylation profiles. Although several methods have been developed, most of them cluster cells based on single (dis)similarity measures, failing to capture complete cell heterogeneity and resulting in locally optimal solutions. Here, we present scMelody, which utilizes an enhanced consensus-based clustering model to reconstruct cell-to-cell methylation similarity patterns and identifies cell subpopulations with the leveraged information from multiple basic similarity measures. Besides, benefitted from the reconstructed cell-to-cell similarity measure, scMelody could conveniently leverage the clustering validation criteria to determine the optimal number of clusters. Assessments on distinct real datasets showed that scMelody accurately recapitulated methylation subpopulations and outperformed existing methods in terms of both cluster partitions and the number of clusters. Moreover, when benchmarking the clustering stability of scMelody on a variety of synthetic datasets, it achieved significant clustering performance gains over existing methods and robustly maintained its clustering accuracy over a wide range of number of cells, number of clusters and CpG dropout proportions. Finally, the real case studies demonstrated the capability of scMelody to assess known cell types and uncover novel cell clusters.

Plant Gravitropism: From Mechanistic Insights into Plant Function on Earth to Plants Colonizing Other Worlds

Gravitropism, the growth of roots and shoots toward or away from the direction of gravity, has been studied for centuries. Such studies have not only led to a better understanding of the gravitropic process itself, but also paved new paths leading to deeper mechanistic insights into a wide range of research areas. These include hormone biology, cell signal transduction, regulation of gene expression, plant evolution, and plant interactions with a variety of environmental stimuli. In addition to contributions to basic knowledge about how plants function, there is accumulating evidence that gravitropism confers adaptive advantages to crops, particularly under marginal agricultural soils. Therefore, gravitropism is emerging as a breeding target for enhancing agricultural productivity. Moreover, research on gravitropism has spawned several studies on plant growth in microgravity that have enabled researchers to uncouple the effects of gravity from other tropisms. Although rapid progress on understanding gravitropism witnessed during the past decade continues to be driven by traditional molecular, physiological, and cell biological tools, these tools have been enriched by technological innovations in next-generation omics platforms and microgravity analog facilities. In this chapter, we review the field of gravitropism by highlighting recent landmark studies that have provided unique insights into this classic research topic while also discussing potential contributions to agriculture on Earth and beyond.

CUT&Tag-BS for simultaneous profiling of histone modification and DNA methylation with high efficiency and low cost

It remains a challenge to decipher the complex relationship between DNA methylation, histone modification, and the underlying DNA sequence with limited input material. Here, we developed an efficient, low-input, and low-cost method for the simultaneous profiling of genomic localization of histone modification and methylation status of the underlying DNA at single-base resolution from the same cells in a single experiment by integrating cleavage under targets and tagmentation (CUT&Tag) with tagmentation-based bisulfite sequencing (CUT&Tag-BS). We demonstrated the validity of our method using representative histone modifications of euchromatin and constitutive and facultative heterochromatin (H3K4me1, H3K9me3, and H3K27me3, respectively). Similar histone modification enrichment patterns were observed in CUT&Tag-BS compared with non-bisulfite-treated control, and H3K4me1-marked regions were found to mostly be CpG poor, lack methylation concordance, and exhibit prevalent DNA methylation heterogeneity among mouse embryonic stem cells (mESCs). We anticipate that CUT&Tag-BS will be widely applied to directly address the genomic relationship between DNA methylation and histone modification, especially in low-input scenarios with precious biological samples.

Single-cell multimodal glioma analyses identify epigenetic regulators of cellular plasticity and environmental stress response

Glioma intratumoral heterogeneity enables adaptation to challenging microenvironments and contributes to therapeutic resistance. We integrated 914 single-cell DNA methylomes, 55,284 single-cell transcriptomes and bulk multi-omic profiles across 11 adult IDH mutant or IDH wild-type gliomas to delineate sources of intratumoral heterogeneity. We showed that local DNA methylation disorder is associated with cell-cell DNA methylation differences, is elevated in more aggressive tumors, links with transcriptional disruption and is altered during the environmental stress response. Glioma cells under in vitro hypoxic and irradiation stress increased local DNA methylation disorder and shifted cell states. We identified a positive association between genetic and epigenetic instability that was supported in bulk longitudinally collected DNA methylation data. Increased DNA methylation disorder associated with accelerated disease progression and recurrently selected DNA methylation changes were enriched for environmental stress response pathways. Our work identified an epigenetically facilitated adaptive stress response process and highlights the importance of epigenetic heterogeneity in shaping therapeutic outcomes.

Enhancer-associated H3K4 methylation safeguards in vitro germline competence

Germline specification in mammals occurs through an inductive process whereby competent cells in the post-implantation epiblast differentiate into primordial germ cells (PGC). The intrinsic factors that endow epiblast cells with the competence to respond to germline inductive signals remain unknown. Single-cell RNA sequencing across multiple stages of an in vitro PGC-like cells (PGCLC) differentiation system shows that PGCLC genes initially expressed in the naïve pluripotent stage become homogeneously dismantled in germline competent epiblast like-cells (EpiLC). In contrast, the decommissioning of enhancers associated with these germline genes is incomplete. Namely, a subset of these enhancers partly retain H3K4me1, accumulate less heterochromatic marks and remain accessible and responsive to transcriptional activators. Subsequently, as in vitro germline competence is lost, these enhancers get further decommissioned and lose their responsiveness to transcriptional activators. Importantly, using H3K4me1-deficient cells, we show that the loss of this histone modification reduces the germline competence of EpiLC and decreases PGCLC differentiation efficiency. Our work suggests that, although H3K4me1 might not be essential for enhancer function, it can facilitate the (re)activation of enhancers and the establishment of gene expression programs during specific developmental transitions.

Applications of Single-Cell DNA Sequencing

Over the past decade, genomic analyses of single cells-the fundamental units of life-have become possible. Single-cell DNA sequencing has shed light on biological questions that were previously inaccessible across diverse fields of research, including somatic mutagenesis, organismal development, genome function, and microbiology. Single-cell DNA sequencing also promises significant future biomedical and clinical impact, spanning oncology, fertility, and beyond. While single-cell approaches that profile RNA and protein have greatly expanded our understanding of cellular diversity, many fundamental questions in biology and important biomedical applications require analysis of the DNA of single cells. Here, we review the applications and biological questions for which single-cell DNA sequencing is uniquely suited or required. We include a discussion of the fields that will be impacted by single-cell DNA sequencing as the technology continues to advance.

Low variability in the underlying cellular landscape adversely affects the performance of interaction-based approaches for conducting cell-specific analyses of DNA methylation in bulk samples

Statistical methods that allow for cell type specific DNA methylation (DNAm) analyses based on bulk-tissue methylation data have great potential to improve our understanding of human disease and have created unprecedented opportunities for new insights using the wealth of publicly available bulk-tissue methylation data. These methodologies involve incorporating interaction terms formed between the phenotypes/exposures of interest and proportions of the cell types underlying the bulk-tissue sample used for DNAm profiling. Despite growing interest in such "interaction-based" methods, there has been no comprehensive assessment how variability in the cellular landscape across study samples affects their performance. To answer this question, we used numerous publicly available whole-blood DNAm data sets along with extensive simulation studies and evaluated the performance of interaction-based approaches in detecting cell-specific methylation effects. Our results show that low cell proportion variability results in large estimation error and low statistical power for detecting cell-specific effects of DNAm. Further, we identified that many studies targeting methylation profiling in whole-blood may be at risk to be underpowered due to low variability in the cellular landscape across study samples. Finally, we discuss guidelines for researchers seeking to conduct studies utilizing interaction-based approaches to help ensure that their studies are adequately powered.

Comprehensive analysis of partial methylation domains in colorectal cancer based on single-cell methylation profiles

Epigenetic aberrations have played a significant role in affecting the pathophysiological state of colorectal cancer, and global DNA hypomethylation mainly occurs in partial methylation domains (PMDs). However, the distribution of PMDs in individual cells and the heterogeneity between cells are still unclear. In this study, the DNA methylation profiles of colorectal cancer detected by WGBS and scBS-seq were used to depict PMDs in individual cells for the first time. We found that more than half of the entire genome is covered by PMDs. Three subclasses of PMDS have distinct characteristics, and Gain-PMDs cover a higher proportion of protein coding genes. Gain-PMDs have extensive epigenetic heterogeneity between different cells of the same tumor, and the DNA methylation in cells is affected by the tumor microenvironment. In addition, abnormally elevated promoter methylation in Gain-PMDs may further promote the growth, proliferation and metastasis of tumor cells through silent transcription. The PMDs detected in this study have the potential as epigenetic biomarkers and provide a new insight for colorectal cancer research based on single-cell methylation data.

Detecting cord blood cell type-specific epigenetic associations with gestational diabetes mellitus and early childhood growth

BACKGROUND

Epigenome-wide association studies (EWAS) have provided opportunities to understand the role of epigenetic mechanisms in development and pathophysiology of many chronic diseases. However, an important limitation of conventional EWAS is that profiles of epigenetic variability are often obtained in samples of mixed cell types. Here, we aim to assess whether changes in cord blood DNA methylation (DNAm) associated with gestational diabetes mellitus (GDM) exposure and early childhood growth markers occur in a cell type-specific manner.

RESULTS

We analyzed 275 cord blood samples collected at delivery from a prospective pre-birth cohort with genome-wide DNAm profiled by the Illumina MethylationEPIC array. We estimated proportions of seven common cell types in each sample using a cord blood-specific DNAm reference panel. Leveraging a recently developed approach named CellDMC, we performed cell type-specific EWAS to identify CpG loci significantly associated with GDM, or 3-year-old body mass index (BMI) z-score. A total of 1410 CpG loci displayed significant cell type-specific differences in methylation level between 23 GDM cases and 252 controls with a false discovery rate < 0.05. Gene Ontology enrichment analysis indicated that LDL transportation emerged from CpG specifically identified from B-cells DNAm analyses and the mitogen-activated protein kinase pathway emerged from CpG specifically identified from natural killer cells DNAm analyses. In addition, we identified four and six loci associated with 3-year-old BMI z-score that were specific to CD8+ T-cells and monocytes, respectively. By performing genome-wide permutation tests, we validated that most of our detected signals had low false positive rates.

CONCLUSION

Compared to conventional EWAS adjusting for the effects of cell type heterogeneity, the proposed approach based on cell type-specific EWAS could provide additional biologically meaningful associations between CpG methylation, prenatal maternal GDM or 3-year-old BMI. With careful validation, these findings may provide new insights into the pathogenesis, programming, and consequences of related childhood metabolic dysregulation. Therefore, we propose that cell type-specific analyses are worth cautious explorations.

scAMACE: Model-based approach to the joint analysis of single-cell data on chromatin accessibility, gene expression and methylation

MOTIVATION

The advancement in technologies and the growth of available single-cell datasets motivate integrative analysis of multiple single-cell genomic datasets. Integrative analysis of multimodal single-cell datasets combines complementary information offered by single-omic datasets and can offer deeper insights on complex biological process. Clustering methods that identify the unknown cell types are among the first few steps in the analysis of single-cell datasets, and they are important for downstream analysis built upon the identified cell types.

RESULTS

We propose scAMACE for the integrative analysis and clustering of single-cell data on chromatin accessibility, gene expression and methylation. We demonstrate that cell types are better identified and characterized through analyzing the three data types jointly. We develop an efficient expectationmaximization (EM) algorithm to perform statistical inference, and evaluate our methods on both simulation study and real data applications. We also provide the GPU implementation of scAMACE, making it scalable to large datasets.

AVAILABILITY

The software and datasets are available at https://github.com/cuhklinlab/scAMACE_py (python implementation) and https://github.com/cuhklinlab/scAMACE (R implementation).

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Cell identity specification in plants: lessons from flower development

Multicellular organisms display a fascinating complexity of cellular identities and patterns of diversification. The concept of 'cell type' aims to describe and categorize this complexity. In this review, we discuss the traditional concept of cell types and highlight the impact of single-cell technologies and spatial omics on the understanding of cellular differentiation in plants. We summarize and compare position-based and lineage-based mechanisms of cell identity specification using flower development as a model system. More than understanding ontogenetic origins of differentiated cells, an important question in plant science is to understand their position- and developmental stage-specific heterogeneity. Combinatorial action and crosstalk of external and internal signals is the key to cellular heterogeneity, often converging on transcription factors that orchestrate gene expression programs.

scMET: Bayesian modeling of DNA methylation heterogeneity at single-cell resolution

High-throughput single-cell measurements of DNA methylomes can quantify methylation heterogeneity and uncover its role in gene regulation. However, technical limitations and sparse coverage can preclude this task. scMET is a hierarchical Bayesian model which overcomes sparsity, sharing information across cells and genomic features to robustly quantify genuine biological heterogeneity. scMET can identify highly variable features that drive epigenetic heterogeneity, and perform differential methylation and variability analyses. We illustrate how scMET facilitates the characterization of epigenetically distinct cell populations and how it enables the formulation of novel hypotheses on the epigenetic regulation of gene expression. scMET is available at https://github.com/andreaskapou/scMET .

The regulation mechanisms and the Lamarckian inheritance property of DNA methylation in animals

DNA methylation is a stable and heritable epigenetic mechanism, of which the main functions are stabilizing the transcription of genes and promoting genetic conservation. In animals, the direct molecular inducers of DNA methylation mainly include histone covalent modification and non-coding RNA, whereas the fundamental regulators of DNA methylation are genetic and environmental factors. As is well known, competition is present everywhere in life systems, and will finally strike a balance that is optimal for the animal's survival and reproduction. The same goes for the regulation of DNA methylation. Genetic and environmental factors, respectively, are responsible for the programmed and plasticity changes of DNA methylation, and keen competition exists between genetically influenced procedural remodeling and environmentally influenced plastic alteration. In this process, genetic and environmental factors collaboratively decide the methylation patterns of corresponding loci. DNA methylation alterations induced by environmental factors can be transgenerationally inherited, and exhibit the characteristic of Lamarckian inheritance. Further research on regulatory mechanisms and the environmental plasticity of DNA methylation will provide strong support for understanding the biological function and evolutionary effects of DNA methylation.

DNA methylation patterns expose variations in enhancer-chromatin modifications during embryonic stem cell differentiation

In mammals, cellular identity is defined through strict regulation of chromatin modifications and DNA methylation that control gene expression. Methylation of cytosines at CpG sites in the genome is mainly associated with suppression; however, the reason for enhancer-specific methylation is not fully understood. We used sequential ChIP-bisulfite-sequencing for H3K4me1 and H3K27ac histone marks. By collecting data from the same genomic region, we identified enhancers differentially methylated between these two marks. We observed a global gain of CpG methylation primarily in H3K4me1-marked nucleosomes during mouse embryonic stem cell differentiation. This gain occurred largely in enhancer regions that regulate genes critical for differentiation. The higher levels of DNA methylation in H3K4me1- versus H3K27ac-marked enhancers, despite it being the same genomic region, indicates cellular heterogeneity of enhancer states. Analysis of single-cell RNA-seq profiles demonstrated that this heterogeneity correlates with gene expression during differentiation. Furthermore, heterogeneity of enhancer methylation correlates with transcription start site methylation. Our results provide insights into enhancer-based functional variation in complex biological systems.

Cellular dynamics are underlined by numerous regulatory layers. The regulatory mechanism of interest in this work are enhancers. Enhancers are regulatory regions responsible, mainly, for increasing the possibility of transcription of a certain gene. Enhancers are marked by two distinct chemical groups-H3K4me1 and H3K27ac on the tail of histones. Histones are the proteins responsible for DNA packaging into condensed chromatin structure. In contrast, DNA methylation is a chemical modification often found on enhancers, and is traditionally associated with repression. A long-debated question revolves around the functional relevance of DNA methylation in the context of enhancers. Here, we combined the two regulatory layers, histone marks and DNA methylation, to a single measurement that can highlight DNA methylation separately on each histone mark but at the same genomic region. When isolated with H3K4me1, enhancers showed higher levels of methylation compared to H3K27ac. As we measured the same genomic locations, we show that differences of DNA methylation between these marks can only be explained by cellular heterogeneity. We also demonstrated that these enhancers tend to play roles in stem cell differentiation and expression levels of the genes they control correlate with cell-to-cell variation.

The epigenetic basis of cellular heterogeneity

Single-cell sequencing-based methods for profiling gene transcript levels have revealed substantial heterogeneity in expression levels among morphologically indistinguishable cells. This variability has important functional implications for tissue biology and disease states such as cancer. Mapping of epigenomic information such as chromatin accessibility, nucleosome positioning, histone tail modifications and enhancer-promoter interactions in both bulk-cell and single-cell samples has shown that these characteristics of chromatin state contribute to expression or repression of associated genes. Advances in single-cell epigenomic profiling methods are enabling high-resolution mapping of chromatin states in individual cells. Recent studies using these techniques provide evidence that variations in different aspects of chromatin organization collectively define gene expression heterogeneity among otherwise highly similar cells.

Single-cell mapper (scMappR): using scRNA-seq to infer the cell-type specificities of differentially expressed genes

RNA sequencing (RNA-seq) is widely used to identify differentially expressed genes (DEGs) and reveal biological mechanisms underlying complex biological processes. RNA-seq is often performed on heterogeneous samples and the resulting DEGs do not necessarily indicate the cell-types where the differential expression occurred. While single-cell RNA-seq (scRNA-seq) methods solve this problem, technical and cost constraints currently limit its widespread use. Here we present single cell Mapper (scMappR), a method that assigns cell-type specificity scores to DEGs obtained from bulk RNA-seq by leveraging cell-type expression data generated by scRNA-seq and existing deconvolution methods. After evaluating scMappR with simulated RNA-seq data and benchmarking scMappR using RNA-seq data obtained from sorted blood cells, we asked if scMappR could reveal known cell-type specific changes that occur during kidney regeneration. scMappR appropriately assigned DEGs to cell-types involved in kidney regeneration, including a relatively small population of immune cells. While scMappR can work with user-supplied scRNA-seq data, we curated scRNA-seq expression matrices for ∼100 human and mouse tissues to facilitate its stand-alone use with bulk RNA-seq data from these species. Overall, scMappR is a user-friendly R package that complements traditional differential gene expression analysis of bulk RNA-seq data.

Altered microRNA expression links IL6 and TNF-induced inflammaging with myeloid malignancy in humans and mice

Aging is associated with significant changes in the hematopoietic system, including increased inflammation, impaired hematopoietic stem cell (HSC) function, and increased incidence of myeloid malignancy. Inflammation of aging ("inflammaging") has been proposed as a driver of age-related changes in HSC function and myeloid malignancy, but mechanisms linking these phenomena remain poorly defined. We identified loss of miR-146a as driving aging-associated inflammation in AML patients. miR-146a expression declined in old wild-type mice, and loss of miR-146a promoted premature HSC aging and inflammation in young miR-146a-null mice, preceding development of aging-associated myeloid malignancy. Using single-cell assays of HSC quiescence, stemness, differentiation potential, and epigenetic state to probe HSC function and population structure, we found that loss of miR-146a depleted a subpopulation of primitive, quiescent HSCs. DNA methylation and transcriptome profiling implicated NF-κB, IL6, and TNF as potential drivers of HSC dysfunction, activating an inflammatory signaling relay promoting IL6 and TNF secretion from mature miR-146a-/- myeloid and lymphoid cells. Reducing inflammation by targeting Il6 or Tnf was sufficient to restore single-cell measures of miR-146a-/- HSC function and subpopulation structure and reduced the incidence of hematological malignancy in miR-146a-/- mice. miR-146a-/- HSCs exhibited enhanced sensitivity to IL6 stimulation, indicating that loss of miR-146a affects HSC function via both cell-extrinsic inflammatory signals and increased cell-intrinsic sensitivity to inflammation. Thus, loss of miR-146a regulates cell-extrinsic and -intrinsic mechanisms linking HSC inflammaging to the development of myeloid malignancy.

Biological and therapeutic implications of a unique subtype of NPM1 mutated AML

In acute myeloid leukemia (AML), molecular heterogeneity across patients constitutes a major challenge for prognosis and therapy. AML with NPM1 mutation is a distinct genetic entity in the revised World Health Organization classification. However, differing patterns of co-mutation and response to therapy within this group necessitate further stratification. Here we report two distinct subtypes within NPM1 mutated AML patients, which we label as primitive and committed based on the respective presence or absence of a stem cell signature. Using gene expression (RNA-seq), epigenomic (ATAC-seq) and immunophenotyping (CyToF) analysis, we associate each subtype with specific molecular characteristics, disease differentiation state and patient survival. Using ex vivo drug sensitivity profiling, we show a differential drug response of the subtypes to specific kinase inhibitors, irrespective of the FLT3-ITD status. Differential drug responses of the primitive and committed subtype are validated in an independent AML cohort. Our results highlight heterogeneity among NPM1 mutated AML patient samples based on stemness and suggest that the addition of kinase inhibitors to the treatment of cases with the primitive signature, lacking FLT3-ITD, could have therapeutic benefit.

Bisulfite-free epigenomics and genomics of single cells through methylation-sensitive restriction

Single-cell multi-omics are powerful means to study cell-to-cell heterogeneity. Here, we present a single-tube, bisulfite-free method for the simultaneous, genome-wide analysis of DNA methylation and genetic variants in single cells: epigenomics and genomics of single cells analyzed by restriction (epi-gSCAR). By applying this method, we obtained DNA methylation measurements of up to 506,063 CpGs and up to 1,244,188 single-nucleotide variants from single acute myeloid leukemia-derived cells. We demonstrate that epi-gSCAR generates accurate and reproducible measurements of DNA methylation and allows to differentiate between cell lines based on the DNA methylation and genetic profiles.

Cell lineage-specific methylome and genome alterations in gout

In this study, we examined data from 69 gout patients and 1,455 non-gout controls using a MethylationEPIC BeadChip assay and Illumina HiSeq platform to identify lineage-specific epigenetic alterations and associated genetic factors that contributed to gouty inflammation. Cell lineage-specific differentially methylated sites were identified using CellDMC after adjusting for sex, age, alcohol drinking, smoking status, and smoking history (total pack-years). Different cell lineages displayed distinct differential methylation. Ingenuity Pathway Analysis and NetworkAnalyst indicated that many differential methylated sites were associated with interleukin-1β expression in monocytes. On the UCSC Genome Browser and WashU Epigenome Browser, metabolic trait, cis-methylation quantitative trait loci, genetic, and functional annotation analyses identified nine methylation loci located in interleukin-1β-regulating genes (PRKCZ, CIDEC, VDAC1, CPT1A, BIRC2, BRCA1, STK11, and NLRP12) that were associated specifically with gouty inflammation. All nine sites mapped to active regulatory elements in monocytes. MoLoTool and ReMap analyses indicated that the nine methylation loci overlapped with binding sites of several transcription factors that regulated interleukin-1β production and gouty inflammation. Decreases in PRKCZ and STK11 methylation were also associated with higher numbers of first-degree relatives who also had gout. The gouty-inflammation specific methylome and genome alterations could potentially aid in the identification of novel therapeutic targets.

Tissue and sex-specific programming of DNA methylation by perinatal lead exposure: implications for environmental epigenetics studies

Early developmental environment can influence long-term health through reprogramming of the epigenome. Human environmental epigenetics studies rely on surrogate tissues, such as blood, to assess the effects of environment on disease-relevant but inaccessible target tissues. However, the extent to which environment-induced epigenetic changes are conserved between these tissues is unclear. A better understanding of this conservation is imperative for effective design and interpretation of human environmental epigenetics studies. The Toxicant Exposures and Responses by Genomic and Epigenomic Regulators of Transcription (TaRGET II) consortium was established by the National Institute of Environmental Health Sciences to address the utility of surrogate tissues as proxies for toxicant-induced epigenetic changes in target tissues. We and others have recently reported that perinatal exposure to lead (Pb) is associated with adverse metabolic outcomes. Here, we investigated the sex-specific effects of perinatal exposure to a human environmentally relevant level of Pb on DNA methylation in paired liver and blood samples from adult mice using enhanced reduced-representation bisulphite sequencing. Although Pb exposure ceased at 3 weeks of age, we observed thousands of sex-specific differentially methylated cytosines in the blood and liver of Pb-exposed animals at 5 months of age, including 44 genomically imprinted loci. We observed significant tissue overlap in the genes mapping to differentially methylated cytosines. A small but significant subset of Pb-altered genes exhibit basal sex differences in gene expression in the mouse liver. Collectively, these data identify potential molecular targets for Pb-induced metabolic diseases, and inform the design of more robust human environmental epigenomics studies.

Interweaving Tumor Heterogeneity into the Cancer Epigenetic/Metabolic Axis

Significance: The epigenomic/metabolic landscape in cancer has been studied extensively in the past decade and forms the basis of various drug targets. Yet, cancer treatment remains a challenge, with clinical trials exhibiting limited efficacy and high relapse rates. Patients respond differently to therapy, which is fundamentally attributed to tumor heterogeneity, both across and within tumors. This review focuses on the interactions between the heterogeneous tumor microenvironment (TME) and the epigenomic/metabolic axis in cancer, as well as the emerging technologies under development to aid heterogeneity studies. Recent Advances: Interlinks between epigenetics and metabolism in cancer have been reported. Emerging studies have unveiled interactions between the TME and cancer cells that play a critical role in regulating epigenetics and reprogramming cancer metabolism, suggesting a three-way cross talk. Critical Issues: This cross talk accentuates the multiplex nature of cancer, and the importance of considering tumor heterogeneity in various epigenomic/metabolic cancer studies. Future Directions: With the advancement in single-cell profiling, it may be possible to identify cancer subclones and their unique vulnerabilities to develop a multimodal therapy. Drugs targeting the TME are currently being studied, and a better understanding of the TME in regulating cancer epigenetics and metabolism may hold the key to identifying novel therapeutic targets.

Prospects for Use of Single-Cell Sequencing to Assess DNA Methylation in Asthma

Asthma is a complex disease with an increasing prevalence rate caused by the interaction of multiple genetically inherited and environmental factors. Epigenetics link genetic susceptibility and environmental factors. DNA methylation is an epigenetic modification that plays a crucial role in the regulation of growth and development, gene expression, and disease. Relatively little is known about DNA methylation in asthma, with few studies to date using single-cell sequencing to analyze the molecular mechanism by which DNA methylation regulates asthma. Cells with similar phenotypes may be heterogeneous in function and transcription, as may their genetic information. Although multi-omics methods, such as studies of the genome, transcriptome, and epigenome, can be used to evaluate biological processes, these methods are applicable only to groups of cells or tissues and provide averages that may obscure direct correlations among multiple layers of data. Single-cell sequencing technology can clarify the methylation and expression of genes in different populations of cells, in contrast to traditional multi-omics sequencing, which can determine only average values of cell populations. Single-cell sequence can therefore better reflect the pathogenesis of asthma, as it can clarify the function and regulatory mechanism of DNA methylation in asthma, and detect new genes and molecular markers that may become therapeutic targets in this disease.

Epiclomal: Probabilistic clustering of sparse single-cell DNA methylation data

We present Epiclomal, a probabilistic clustering method arising from a hierarchical mixture model to simultaneously cluster sparse single-cell DNA methylation data and impute missing values. Using synthetic and published single-cell CpG datasets, we show that Epiclomal outperforms non-probabilistic methods and can handle the inherent missing data characteristic that dominates single-cell CpG genome sequences. Using newly generated single-cell 5mCpG sequencing data, we show that Epiclomal discovers sub-clonal methylation patterns in aneuploid tumour genomes, thus defining epiclones that can match or transcend copy number-determined clonal lineages and opening up an important form of clonal analysis in cancer. Epiclomal is written in R and Python and is available at https://github.com/shahcompbio/Epiclomal.

DNA methylation is an epigenetic mark that occurs when methyl groups are attached to the DNA molecule, thereby playing decisive roles in numerous biological processes. Advances in technology have allowed the generation of high-throughput DNA methylation sequencing data from single cells. One of the goals is to group cells according to their DNA methylation profiles; however, a major challenge arises due to a large amount of missing data per cell. To address this problem, we developed a novel statistical model and framework: Epiclomal. Our approach uses a hierarchical mixture model to borrow statistical strength across cells and neighboring loci to accurately define cell groups (clusters). We compare our approach to different methods on both synthetic and published datasets. We show that Epiclomal is more robust than other approaches, producing more accurate clusters of cells in the majority of experimental scenarios. We also apply Epiclomal to newly generated single-cell DNA methylation data from breast cancer xenografts. Our results show that methylation-based clusters can mirror or in some instances transcend the clusters defined by single-cell copy number analysis. This illustrates the importance of single-cell DNA methylation analysis in understanding cellular heterogeneity in cancer.