Single cell cancer epigenetics
2022; Elsevier BV; Volume: 8; Issue: 10 Linguagem: Inglês
10.1016/j.trecan.2022.06.005
ISSN2405-8033
AutoresMarta Casado-Peláez, Alberto Bueno-Costa, Manel Esteller,
Tópico(s)Cancer Genomics and Diagnostics
ResumoThe epigenome encompasses several mechanisms controlling gene expression that can be aberrantly regulated during cancer development and progression. Tumors are highly complex and heterogeneous biological systems that require the study of epigenetic alterations at a single cell resolution.Several single cell technologies developed to study different layers of the epigenome, such as chromatin accessibility or histone modifications, have been developed and applied in cancer research over the past few years, improving our understanding of the mechanisms driving tumorigenesis.Although these techniques are promising, most are still nascent and present limitations, such as low throughput and limited coverage. In addition, the analysis and integration of the various single cell epigenomic data modalities have challenges and require the development of new computational tools. 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. 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. The importance of epigenetics in both basic and clinical research is indisputable. In the field of cancer, epigenetic alterations have important implications for many aspects of this disease. Indeed, non-mutational epigenetic reprogramming was recently designated as a mechanistic determinant that enables the acquisition of cancer hallmark capabilities [1.Hanahan D. Hallmarks of cancer: new dimensions.Cancer Discov. 2022; 12: 31-46Crossref PubMed Scopus (326) Google Scholar]. Although it is well established that cancer cells may arise from genetic mutations that drive carcinogenesis, many types of tumor lack strong genetic drivers that could explain important malignant processes, such as tumor progression [2.Turajilic S. et al.Resolving genetic heterogeneity in cancer.Nat. Rev. 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These epigenetic mechanisms, which regulate the expression of genes without altering the DNA sequence, fall into five main categories: (i) DNA methylation; (ii) chromatin accessibility; (iii) histone modifications; (iv) DNA–protein interactions; and (v) chromatin tridimensional architecture [6.Allis C.D. Jenuwein T. The molecular hallmarks of epigenetic control.Nat. Rev. Genet. 2016; 17: 487-500Crossref PubMed Google Scholar,7.Darwiche N. Epigenetic mechanisms and the hallmarks of cancer: an intimate affair.Am. J. Cancer Res. 2020; 10: 1954-1978PubMed Google Scholar]. Each type of mechanism can be experimentally studied using several bulk methodologies (Box 1). Unfortunately, due to the complex cellular heterogeneity of many types of tumor, valuable information is lost when using these techniques, since all the possible data that could be retrieved from a single cell point of view are masked by the bulk cell averaging. Nonetheless, with the emergence of single cell-sequencing technologies [5.Nam A.S. et al.Integrating genetic and non-genetic determinants of cancer evolution by single-cell multi-omics.Nat. Rev. Genet. 2021; 22: 3-18Crossref PubMed Scopus (83) Google Scholar,8.Yalan L. et al.Applications of single-cell sequencing in cancer research: progress and perspectives.J. Hematol. Oncol. 2021; 14: 91Crossref PubMed Scopus (15) Google Scholar], many aspects of this tumoral heterogeneity that were otherwise impossible to assess are now open for exploration.Box 1Bulk methodologies to analyze epigenetic mechanismsVarious bulk methodologies have been used to understand epigenetic mechanisms: (i) DNA methylation, taking advantage of bisulfite chemistry, can be analyzed by whole-genome bisulfite sequencing (WGBS), reduced representation bisulfite sequencing (RRBS), or 450k/850k Illumina methylation arrays [128.Ortiz-Barahona V. et al.Use of DNA methylation profiling in translational oncology.Semin. Cancer Biol. 2020; 83: 523-535Crossref PubMed Scopus (7) Google Scholar]; (ii) DNA accessibility is mainly studied using the assay for transposase-accessible chromatin sequencing (ATAC-seq) [129.Marinov G.K. Shipony Z. Interrogating the accessible chromatin landscape of eukaryote genomes using ATAC-seq.Methods Mol. Biol. 2021; 2243: 183-226Crossref PubMed Scopus (0) Google Scholar]; and (iii) histone modifications and (iv) DNA–protein interactions can be studied by chromatin immunoprecipitation sequencing (ChIP-seq) [130.Nakato R. Sakata T. Methods for ChIP-seq analysis: a practical workflow and advanced applications.Methods. 2021; 187: 44-53Crossref PubMed Scopus (6) Google Scholar]; (v) chromatin 3D architecture can be explored with multiple types of chromosome conformation capture technology, such as 3C, 4C, 5C, Hi-C, promoter-capture Hi-C, and ChIA-PET [131.Sati S. Cavalli G. Chromosome conformation capture technologies and their impact in understanding genome function.Chromosoma. 2016; 126: 33-44Crossref PubMed Scopus (97) Google Scholar,132.Javierre B.M. et al.Lineage-specific genome architecture links enhancers and non-coding disease variants to target gene promoters.Cell. 2016; 167: 1369-1384Abstract Full Text Full Text PDF PubMed Scopus (486) Google Scholar]. One important common drawback among these techniques is the need for a considerable sample size, demanding thousands to millions of cells as the minimal input. Thus, these techniques are considered 'bulk methodologies', by which we obtain an average value from the whole-cell bulk [133.Carter B. Zhao K. The epigenetic basis of cellular heterogeneity.Nat. Rev. Genet. 2021; 22: 235-250Crossref PubMed Scopus (3) Google Scholar]. Various epigenetic deconvolution strategies can be applied to bulk data, but with a substantial risk of retrieving artifacts or losing difficult-to-detect minor subclones [134.Chakravarthy A. et al.Pan-cancer deconvolution of tumour composition using DNA methylation.Nat. Commun. 2018; 9: 3220Crossref PubMed Scopus (114) Google Scholar]. Nevertheless, bulk methodologies have been indispensable tools for our current understanding of epigenetics and its relationship with cancer. For example, they allowed for the methylation-based classification of diffuse gliomas (LGm1-LGm6) [135.Ceccarelli M. et al.Molecular profiling reveals biologically discrete subsets and pathways of progression in diffuse glioma.Cell. 2016; 164: 550-563Abstract Full Text Full Text PDF PubMed Google Scholar], the potential classification of cancers of unknown primary [123.Moran S. et al.Epigenetic profiling to classify cancer of unknown primary: a multicentre, retrospective analysis.Lancet Oncol. 2016; 17: 1386-1395Abstract Full Text Full Text PDF PubMed Scopus (251) Google Scholar], and the histone modification-based tracking of cell differentiation states [136.Völker-Albert M. et al.Histone modifications in stem cell development and their clinical implications.Stem Cell Rep. 2020; 15: 1196-1205Abstract Full Text Full Text PDF PubMed Scopus (0) Google Scholar]. Various bulk methodologies have been used to understand epigenetic mechanisms: (i) DNA methylation, taking advantage of bisulfite chemistry, can be analyzed by whole-genome bisulfite sequencing (WGBS), reduced representation bisulfite sequencing (RRBS), or 450k/850k Illumina methylation arrays [128.Ortiz-Barahona V. et al.Use of DNA methylation profiling in translational oncology.Semin. Cancer Biol. 2020; 83: 523-535Crossref PubMed Scopus (7) Google Scholar]; (ii) DNA accessibility is mainly studied using the assay for transposase-accessible chromatin sequencing (ATAC-seq) [129.Marinov G.K. Shipony Z. Interrogating the accessible chromatin landscape of eukaryote genomes using ATAC-seq.Methods Mol. Biol. 2021; 2243: 183-226Crossref PubMed Scopus (0) Google Scholar]; and (iii) histone modifications and (iv) DNA–protein interactions can be studied by chromatin immunoprecipitation sequencing (ChIP-seq) [130.Nakato R. Sakata T. Methods for ChIP-seq analysis: a practical workflow and advanced applications.Methods. 2021; 187: 44-53Crossref PubMed Scopus (6) Google Scholar]; (v) chromatin 3D architecture can be explored with multiple types of chromosome conformation capture technology, such as 3C, 4C, 5C, Hi-C, promoter-capture Hi-C, and ChIA-PET [131.Sati S. Cavalli G. Chromosome conformation capture technologies and their impact in understanding genome function.Chromosoma. 2016; 126: 33-44Crossref PubMed Scopus (97) Google Scholar,132.Javierre B.M. et al.Lineage-specific genome architecture links enhancers and non-coding disease variants to target gene promoters.Cell. 2016; 167: 1369-1384Abstract Full Text Full Text PDF PubMed Scopus (486) Google Scholar]. One important common drawback among these techniques is the need for a considerable sample size, demanding thousands to millions of cells as the minimal input. Thus, these techniques are considered 'bulk methodologies', by which we obtain an average value from the whole-cell bulk [133.Carter B. Zhao K. The epigenetic basis of cellular heterogeneity.Nat. Rev. Genet. 2021; 22: 235-250Crossref PubMed Scopus (3) Google Scholar]. Various epigenetic deconvolution strategies can be applied to bulk data, but with a substantial risk of retrieving artifacts or losing difficult-to-detect minor subclones [134.Chakravarthy A. et al.Pan-cancer deconvolution of tumour composition using DNA methylation.Nat. Commun. 2018; 9: 3220Crossref PubMed Scopus (114) Google Scholar]. Nevertheless, bulk methodologies have been indispensable tools for our current understanding of epigenetics and its relationship with cancer. For example, they allowed for the methylation-based classification of diffuse gliomas (LGm1-LGm6) [135.Ceccarelli M. et al.Molecular profiling reveals biologically discrete subsets and pathways of progression in diffuse glioma.Cell. 2016; 164: 550-563Abstract Full Text Full Text PDF PubMed Google Scholar], the potential classification of cancers of unknown primary [123.Moran S. et al.Epigenetic profiling to classify cancer of unknown primary: a multicentre, retrospective analysis.Lancet Oncol. 2016; 17: 1386-1395Abstract Full Text Full Text PDF PubMed Scopus (251) Google Scholar], and the histone modification-based tracking of cell differentiation states [136.Völker-Albert M. et al.Histone modifications in stem cell development and their clinical implications.Stem Cell Rep. 2020; 15: 1196-1205Abstract Full Text Full Text PDF PubMed Scopus (0) Google Scholar]. A tumor is a highly heterogeneous entity comprising malignant and non-malignant cells, each of which has crucial roles in cancer progression [9.Dagogo-Jack I. Shaw A.T. Tumour heterogeneity and resistance to cancer therapies.Nat. Rev. Clin. Oncol. 2018; 15: 81-94Crossref PubMed Scopus (1187) Google Scholar]. The development of single cell epigenomic sequencing technologies can help to properly dissect non-genetic dependencies of malignant progression and unravel this tumor complexity. There are six important aspects of cancer biology related to tumor heterogeneity in which epigenetic alterations have a key role (Figure 1): (i) clonal heterogeneity; (ii) TME; (iii) spatial organization and intercellular crosstalk; (iv) differentiation and developmental programs (phenotypic plasticity); (v) metastasis; and (vi) the appearance of new resistance mechanisms to therapy. Thus, it is necessary to develop single cell resolution technologies that allow us to understand the epigenetic cues that are otherwise undetectable using bulk methodologies. In this review, we catalog current technologies that facilitate the study of different epigenetic characteristics at the single cell level. We classify each technology based on the epigenetic mechanism under study (DNA methylation, chromatin accessibility, histone modifications and DNA–protein interactions, and chromatin 3D architecture), focusing first on mono-omic methodologies (techniques that allow the study of only one epigenetic mechanism on a single cell) and then on multi-omic methodologies (which allow the study of multiple layers of information simultaneously on a single cell). In addition, we summarize currently available single cell spatial epigenomic methodologies and their potential in cancer research. We also highlight recent discoveries and insights gained from these single cell epigenetic technologies, how they can contribute to solve many current challenges in cancer research (mostly derived from tumor heterogeneity), their current limitations, and their potential in translational/clinical scenarios. A tumor can comprise multiple malignant subclones, each with unique genetic and epigenetic properties [10.McGranahan N. Swanton C. Clonal heterogeneity and tumor evolution: past, present, and the future.Cell. 2017; 168: 613-628Abstract Full Text Full Text PDF PubMed Scopus (1220) Google Scholar,11.Oakes C.C. et al.Evolution of DNA methylation is linked to genetic aberrations in chronic lymphocytic leukemia.Cancer Discov. 2014; 4: 348-361Crossref PubMed Scopus (113) Google Scholar]. As a cancer population evolves, cells accumulate genetic and epigenetic alterations that contribute to the appearance of new clones that may harbor novel, selective advantages (e.g., enhanced proliferation, resistance to therapy, invasiveness, etc.) [5.Nam A.S. et al.Integrating genetic and non-genetic determinants of cancer evolution by single-cell multi-omics.Nat. Rev. Genet. 2021; 22: 3-18Crossref PubMed Scopus (83) Google Scholar]. The detection of these clones is crucial to understand tumor progression and its impact on clinical outcome. Single cell methodologies are able to detect each one of these clones (especially minor, difficult-to-detect, subclones), thus revealing valuable prognostic information. A tumor does not comprise solely malignant cells but harbors myriad types of non-malignant cell with distinct roles in cancer progression. The T cell content is directly associated with tumor progression in many cancer types, with cytotoxic T cells (Tc) and helper T cells (Th1, Th2, and Th17) correlating with good prognosis [12.Tay R.E. et al.Revisiting the role of CD4 + T cells in cancer immunotherapy-new insights into old paradigms.Cancer Gene Ther. 2021; 28: 5-17Crossref PubMed Scopus (0) Google Scholar]. Tumor-associated macrophages have crucial roles in cancer progression, depending on their M1/M2 differentiation state [13.Baghban R. et al.Tumor microenvironment complexity and therapeutic implications at a glance.Cell Commun. Signal. 2020; 18: 59Crossref PubMed Scopus (321) Google Scholar]. Additionally, natural killer (NK) cells, B cells, endothelial cells, fibroblasts, and other cell types participate in this complex interactome [14.Anderson N.M. Simon M.C. The tumor microenvironment.Curr. Biol. 2020; 30: R921-R925Abstract Full Text Full Text PDF PubMed Scopus (186) Google Scholar]. These microenvironmental interactions profoundly modulate the epigenome of both tumoral and nontumoral cells, generating an epigenetic crosstalk that directly determines cancer progression [15.Marks D.L. Epigenetic control of the tumor microenvironment.Epigenomics. 2016; 8: 1671-1687Crossref PubMed Scopus (32) Google Scholar]. Thus, studying these epigenetic signals at the single cell level is mandatory to decipher this complex tumoral interactome. Malignant and non-malignant cells are not randomly distributed inside a tumor, but instead occupy specific positions in the tumoral space, generating discrete cell–cell interactions that impact disease progression [16.Noble R. et al.Spatial structure governs the mode of tumour evolution.Nat. Ecol. Evol. 2022; 6: 207-217Crossref PubMed Scopus (3) Google Scholar]. Knowing the spatial distribution of each cell has been crucial for assessing the 'heat' of certain types of tumor (e.g., melanoma), in which the relative quantity and position of cytotoxic T cells are key determinants of cancer progression [17.Trujillo J.A. et al.T cell-inflamed versus non-T cell-inflamed tumors: a conceptual framework for cancer immunotherapy drug development and combination therapy selection.Cancer Immunol. Res. 2018; 6: 990-1000Crossref PubMed Scopus (202) Google Scholar]. In addition, DNA methylation heterogeneity is dependent on the spatial organization of colorectal cancer (CRC) cells in patients with locoregional cancer, correlating with relapse-free and overall survival [18.Martínez-Cardús A. et al.Epigenetic homogeneity within colorectal tumors predicts shorter relapse-free and overall survival times for patients with locoregional cancer.Gastroenterology. 2016; 151: 961-972Abstract Full Text Full Text PDF PubMed Scopus (32) Google Scholar]. Microscopy techniques, such as immunohistochemistry. have enabled great advances in this aspect. Nevertheless, these techniques lack the resolution and specificity to unveil the different epigenetic characteristics for each cell. Thus, single cell-sequencing technologies (including cutting-edge spatial epigenomics) will enable researchers to infer how spatial cues correlate with epigenetic changes inside a tumor, which is of strong clinical value. The cancer stem cell hypothesis states that tumor growth depends, at least in part, on the asymmetrical divisions of malignant stem cells that differentiate to specific types of committed cancer cell [19.Lim J.R. et al.Cancer stem cell characteristics and their potential as therapeutic targets.Med. Oncol. 2021; 38: 76Crossref PubMed Scopus (2) Google Scholar]. In addition, depending on their epigenetic background, each malignant cell can follow a specific developmental program that will impact the progression of cancer. For example, in glioblastoma, there are at least four types of malignant cell state program, some related to higher stemness (neural progenitor-like and oligodendrocyte progenitor-like cells), and others related to a more differentiated state (astrocyte-like and mesenchymal-like cells) [20.Neftel C. et al.An integrative model of cellular states, plasticity, and genetics for glioblastoma.Cell. 2019; 178: 835-849Abstract Full Text Full Text PDF PubMed Scopus (598) Google Scholar]. The identity of each cell is maintained by epigenetic memory mechanisms (e.g., DNA methylation) that ensure full commitment to specific transcriptional programs [21.Lee H.J. Reprogramming the methylome: erasing memory and creating diversity.Cell Stem Cell. 2014; 14: 710-719Abstract Full Text Full Text PDF PubMed Scopus (223) Google Scholar]. Thus, detecting alterations in this epigenetic machinery at the single cell level may provide valuable information on potential malignant differentiation trajectories, predicting how the tumor may progress and deciding which type of treatment should be applied. Some cancer cells acquire the ability to leave their primary site and colonize distant tissues, which is the cause of most cancer-related deaths [22.Fares J. et al.Molecular principles of metastasis: a hallmark of cancer revisited.Signal Transduct. Target. Ther. 2020; 5: 28Crossref PubMed Scopus (390) Google Scholar]. From its transformation until its settlement on a new tissue, the metastatic cancer cell experiences drastic changes, such as acquiring a higher motility program (epithelial–to-mesenchymal transition), avoiding immune cell surveillance, and adapting to the new secondary site [22.Fares J. et al.Molecular principles of metastasis: a hallmark of cancer revisited.Signal Transduct. Target. Ther. 2020; 5: 28Crossref PubMed Scopus (390) Google Scholar]. No genetic driver mutations specific to metastasis have yet been identified, suggesting that dynamic epigenetic mechanisms are involved in key steps of metastasis [4.Chen J.F. Yan Q. The roles of epigenetics in cancer progression and metastasis.Biochem. J. 2021; 478: 3373-3393Crossref PubMed Scopus (2) Google Scholar,23.Patel S.A. Vanharanta S. Epigenetic determinants of metastasis.Mol. Oncol. 2017; 11: 79-96Crossref PubMed Scopus (37) Google Scholar]. Single cell technologies will be useful to confidently detect in primary and secondary sites those cancer cells that have a metastatic-prone epigenetic background. Certain malignant subclones that are undetectable by bulk methodologies due to their low abundance may harbor key mutations and epimutations that render them resistant to treatments that otherwise affect other more abundant subclones [24.Wang X. et al.Drug resistance and combating drug resistance in cancer.Cancer Drug Resist. 2019; 2: 141-160PubMed Google Scholar]. These resistant subclones are most likely to become the predominant ones after the first line of treatment, representing the most common cause of relapse. Alterations in epigenetic mechanisms have been strongly linked with antitumoral drug resistance [25.Hayashi T. Konishi I. Correlation of anti-tumour drug resistance with epigenetic regulation.Br. J. Cancer. 2021; 124: 681-682Crossref PubMed Scopus (4) Google Scholar]. For example, during bortezomib treatment in multiple myeloma, certain cancer subclones enter a slow-cycling, drug-tolerant reversible state, as a consequence of epigenetic plasticity rather than of genetic determinants. Another case of non-genetically determined resistance to therapy are alterations in histone H3 lysine 4 demethylases, such as KDM5, which contribute to transcriptomic heterogeneity in breast cancer, leading to a decreased sensitivity to antiestrogens [26.Hinohara K. et al.KDM5 histone demethylase activity links cellular transcriptomic heterogeneity to therapeutic resistance.Cancer Cell. 2018; 34: 939-953Abstract Full Text Full Text PDF PubMed Scopus (96) Google Scholar]. In taxane-resistant triple-negative breast cancer (TNBC), global DNA hypomethylation and relocation of histone H3K27 trimethylation enable an epigenetic state that enables cancer cells to become resistant to paclitaxel, thus creating a new therapeutic vulnerability by using epigenetic inhibitors [27.Deblois G. et al.Epigenetic switch-induced viral mimicry evasion in chemotherapy-resistant breast cancer.Cancer Discov. 2020; 10: 1312-1329Crossref PubMed Google Scholar]. There are many more well-documented cases in which non-genetic determinants are the main drivers of the appearance of new resistant subclones [28.Marine J.-C. et al.Non-genetic mechanisms of therapeutic resistance in cancer.Nat. Rev. Cancer. 2020; 20: 743-756Crossref PubMed Scopus (114) Google Scholar]. Thus, detecting these resistant subclones early during diagnosis, using single cell epigenetic technologies, would significantly help clinicians to select the best treatment combinations. Additionally, the ability to detect minimal residual disease after treatment is fundamental, because it constitutes a prognostic biomarker that can predict relapse in some cancers [29.Schuurhuis G.J. et al.Minimal/measurable residual disease in AML: a consensus document from the European Leukemia Net MRD Working Party.Blood. 2018; 131: 1275-1291Crossref PubMed Scopus (537) Google Scholar]. Single cell techniques encompass a breakthrough methodology that has revolutionized the way in which complex biological systems can be characterized by looking at one cell at a time. Single cell approaches are essential to properly examine the underlying complexity of tumors and explore cellular heterogeneity at several levels. With the advent of single cell RNA-sequencing (scRNA-seq), the transcriptome has become the molecular level most exploited by single cell technologies. It has accelerated our understanding of cancer biology, enabling the characterization of the intratumoral heterogeneity and cellular architecture of several cancer types at unprecedented resolution [20.Neftel C. et al.An integrative model of cellular states, plasticity, and genetics for glioblastoma.Cell. 2019; 178: 835-849Abstract Full Text Full Text PDF PubMed Scopus (598) Google Scholar,30.van Galen P. et al.Single-cell RNA-seq reveals AML hierarchies relevant to disease progression and immunity.Cell. 2019; 176: 1265-1281Abstract Full Text Full Text PDF PubMed Scopus (288) Google Scholar, 31.Costa A. et al.Fibroblast heterogeneity and immunosuppressive environment in human breast cancer.Cancer Cell. 2018; 33: 463-479Abstract Full Text Full Text PDF PubMed Scopus (602) Google Scholar, 32.Aoki T. et al.Single-cell transcriptome analysis reveals disease-defining t-cell subsets in the tumor microenvironment of classic hodgkin lymphoma.Cancer Discov. 2020; 10: 406-421Crossref PubMed Scopus (82) Google Scholar, 33.Campillo-Marcos I. et al.Single-cell technologies and analyses in hematopoiesis and hematological malignancies.Exp. Hematol. 2021; 98: 1-13Abstract Full Text Full Text PDF PubMed Google Scholar]. Additionally, there are emerging single cell DNA-sequencing technologies that allow us to profile, in an amplicon-based and targeted manner, recurrently mutated genes, providing the genotype of every cell by detecting single nucleotide variants (SNPs) and copy number variants (CNVs) [34.Miles L.A. et al.Single-cell mutation analysis of clonal evolution in myeloid malignancies.Nature. 2020; 587: 477-482Crossref PubMed Scopus (117) Google Scholar]. Nevertheless, transcriptional cell state diversity among malignant cells in a tumor is often independent of genetic clonal heterogeneity, highlighting the importance of developing epigenetic single cell analysis tools to assess this heterogeneity [35.Chaligne R. et al.Epigenetic encoding, heritability and plasticity of glioma transcriptional cell states.Nat. Genet. 2021; 53: 1469-1479Crossref PubMed Scopus (20) Google Scholar]. Although single cell techniques aimed at studying the epigenome have not evolved as rapidly compared with those for the transcriptome, new approaches are being developed to explore the different epigenetic mechanisms of gene regulation. 5-Methylcytosine (5mC) is the most well-known DNA modification. In mammals, this methylation mostly occurs in cytosines that are followed by a guanine, forming a 5′-to-3′ CpG pair. Approximately 70% of all human gene promoters are enriched with multiple clustered CpG pairs, forming 'CpG islands' that are prone to 5mC methylation [36.Deaton A.M. Bird A. CpG islands and the regulation of transcription.Genes Dev. 2011; 25: 1010-1022Crossref PubMed Scopus (1966) Google Scholar]. In these regions, methylation acts as a repressive switch, restricting gene expression. Additionally, 5mC can be found in other genomic regions, such as gene bodies and distant regulatory regions (enhancers and CTCF sites), regulating their function in cis. In most types of cancer, DNA methylation is significantly deregulated. Promoter hyper/hypomethylation in tumor suppressors/oncogenes is a well-established driver of tumoral progression [37.Berdasco M. Esteller M. Clinical epigenetics: seizing opportunities for translation.Nat. Rev. Genet. 2019; 20: 109-127Crossref PubMed Scopus (227) Google Scholar]. In addition, deregulation in enhancer methylation and other distant regulatory regions may have crucial implications in cancer by fostering tumoral epigenetic heterogeneity [38.Bell R.E. et al.Enhancer methylation dynamics contribute to cancer plasticity and patient mortality.Genome Res. 2016; 26: 601-611Crossref PubMed Scopus (73) Google Scholar]. Bulk methodologies helped revolutionize our understanding in this area. Most of these methodologies are based on the conversion of unmethylated cytosines to uracil after bisulfite treatment of the DNA. This allows the detection of methylated cytosines using sequencing or array-based methods [18.Martínez-Cardús A. et al.Epi
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