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Genome-wide epigenomic profiling for biomarker discovery


A myriad of diseases is caused or characterized by alteration of epigenetic patterns, including changes in DNA methylation, post-translational histone modifications, or chromatin structure. These changes of the epigenome represent a highly interesting layer of information for disease stratification and for personalized medicine. Traditionally, epigenomic profiling required large amounts of cells, which are rarely available with clinical samples. Also, the cellular heterogeneity complicates analysis when profiling clinical samples for unbiased genome-wide biomarker discovery. Recent years saw great progress in miniaturization of genome-wide epigenomic profiling, enabling large-scale epigenetic biomarker screens for disease diagnosis, prognosis, and stratification on patient-derived samples. All main genome-wide profiling technologies have now been scaled down and/or are compatible with single-cell readout, including: (i) Bisulfite sequencing to determine DNA methylation at base-pair resolution, (ii) ChIP-Seq to identify protein binding sites on the genome, (iii) DNaseI-Seq/ATAC-Seq to profile open chromatin, and (iv) 4C-Seq and HiC-Seq to determine the spatial organization of chromosomes. In this review we provide an overview of current genome-wide epigenomic profiling technologies and main technological advances that allowed miniaturization of these assays down to single-cell level. For each of these technologies we evaluate their application for future biomarker discovery. We will focus on (i) compatibility of these technologies with methods used for clinical sample preservation, including methods used by biobanks that store large numbers of patient samples, and (ii) automation of these technologies for robust sample preparation and increased throughput.


Within fundamental and clinical research and in clinical practice, biomarkers play an important role to facilitate disease diagnosis, prognosis, and selection of targeted therapies in patients. As such, biomarkers are critical for personalized medicine to improve disease stratification: the identification of groups of patients with shared (biological) characteristics, such as a favorable response to a particular drug [1, 2]. Biomarkers need to fulfill a number of requirements, the most important of which is to show high predictive value. From a practical perspective, the detection method for a biomarker must be accurate, relatively easy to carry out, and show high reproducibility [3]. Over the last decade, there has been an increasing interest in biomarkers at the hand of rapid developments within high-throughput molecular biology technologies, capable of identifying “molecular biomarkers” [4, 5]. Molecular biomarkers possess a critical advantage over more traditional biomarkers during the exploratory phase of biomarker discovery, as many candidate molecular biomarkers can be assayed in parallel. This particularly involves screening of (epi)genomic features at a genome-wide scale, often making use of powerful next-generation sequencing (NGS)-based technologies. These screens can assess very large numbers of loci for the presence or absence of a certain (epi)genomic feature. Subsequently, these loci can be evaluated as a potential biomarker by determining their correlation between samples with different characteristics, for example, by comparing healthy versus diseased tissue.

To be suitable for biomarker discovery, (epi)genomic profiling assays need to fulfill a number of important requirements. To accommodate sample collection for batch processing, clinical samples are often preserved by freezing or by formaldehyde crosslinking. Therefore, an important requirement for (epi)genomic biomarker screening technologies is that these are compatible with processed samples. Additionally, this allows inclusion of clinical samples that have been processed for biobanking, or to use such samples for replication or validation. Biobanks collect large numbers of samples such as tissues or DNA (deoxyribonucleic acid) and the associated patient information, which is highly valuable for retrospective biomarker studies [69]. Exploratory screens for candidate biomarkers mainly rely on the use of patient specimens, which are obtained in small quantities, while also biobanks often contain limited quantities of patient material. Therefore, a second requirement is that assays used for biomarker discovery are compatible with miniaturization to allow processing of low-input samples. Furthermore, robust biomarker discovery is dependent on the screening of large numbers of samples due to the inherent clinical and biological variability between patient samples [10]. Assays used for biomarker discovery therefore benefit from automation and digitalization, facilitating upscaling while reducing the chance of errors due to human handling.

Genomic features that are utilized for molecular biomarker discovery can be separated in two categories: (i) changes in the DNA sequence itself, such as mutations and rearrangements, and (ii) changes in the epigenome, represented by molecules and structures associated with the DNA such as DNA methylation and post-translational histone modifications. This review will focus on the latter category, as recent developments in epigenetic profiling technologies have not only greatly increased our knowledge on epigenetic regulation, but also allow for large-scale discovery of molecular epigenetic biomarkers. The first section of this review provides an overview of epigenetic features and how these can be assayed. We discuss how misregulation of epigenetic processes may lead to disease, providing mechanistic rationale for the use of epigenetic features as biomarkers. The feasibility of applying epigenetic biomarkers in the clinic is demonstrated by examples of DNA methylation biomarkers that have reached clinical stages. In the second part of this review, we will focus on current genome-wide epigenomic profiling technologies, and whether these are already or will likely become compatible with biomarker discovery in the near future. We will evaluate these approaches with three criteria in mind: (i) the possibility to use frozen or chemically fixed material in these assays, (ii) compatibility with miniaturization and single-cell profiling, and (iii) the current level of automation.

Main text

The epigenome

Within a eukaryotic cell, the DNA is packaged to fit into the small volume of the nucleus in a highly organized fashion. The basic unit of chromatin involves the DNA wrapped around nucleosomes consisting of two copies of each of the core histones H2A, H2B, H3, and H4: the so-called beads-on-string structure [11]. Subsequent compaction leads to higher order structures including the formation of very dense arrays of nucleosomes observed in heterochromatin [12, 13]. Despite being tightly packed, the chromatin appears to be highly plastic to allow processes such as transcription, DNA damage repair, DNA remodeling, and DNA replication. This plasticity is facilitated by several factors that influence both local and global chromatin architectures. The most prominent features affecting chromatin structure are reversible covalent modifications of the DNA, e.g., cytosine methylation and hydroxymethylation mainly occurring within the genomic CG context (CpGs), and reversible post-translational modifications of histones, e.g., lysine acetylation, lysine and arginine methylation, serine and threonine phosphorylation, and lysine ubiquitination and sumoylation. These modifications are set by specific classes of enzymes: DNA methyltransferases (DNMTs) in case of cytosine methylation [14] or histone-modifying enzymes [15]. Besides facilitating chromatin compaction, modifications of the DNA and histones are read by adaptor molecules, chromatin-modifying enzymes, and transcription factors (TFs) that contribute to the regulation of transcription and other chromatin-related processes [15, 16]. Next to modifications of DNA and histones, the three-dimensional (3D) conformation of the DNA within the nucleus imposes an additional regulatory layer of gene expression [17].

The chromatin state of a cell, including the genomic localization of modifications of DNA and histones, TF binding sites, and 3D DNA structure, is generally referred to as the epigenome. The epigenome is an important layer that regulates which parts of the genome are accessible and thereby active and which parts are condensed and hence inactive. As such, epigenetic changes are a major driver of development and important to gain and maintain cellular identity. Each of the approximately 200 distinct cell types in the human body has essentially the same genome but has a unique epigenome that serves to instruct specific gene expression programs present within the cells. To gain insight in this variation, the epigenetic features of these cell types (Fig. 1) are comprehensively studied at a genome-wide scale using high-resolution technologies as summarized in Table 1. Most of the approaches are based on NGS, which generally yield higher sensitivity and resolution as compared to alternative readouts such as microarrays, and provide additional information such as allele specificity [18, 19]. The International Human Epigenome Consortium (IHEC; and associated consortia such as BLUEPRINT and National Institutes of Health (NIH) Roadmap use these technologies to generate human reference data sets for a range of epigenetic features [2023]. The aim of IHEC is to generate approximately 1000 reference epigenomes which are made publicly available. This data contains a wealth of information on the epigenetic mechanisms acting in healthy cells and serves as a valuable reference for comparisons with malignant cells and tissues [24, 25].

Fig. 1

Main epigenetic features (indicated by orange arrows) that can be assayed on a genome-wide scale using sequencing-based technologies

Table 1 Summary of the main epigenetic features and the principles, caveats, and requirements of the main technologies used for their profiling

Comparative analyses of epigenomes are complicated by the epigenetic variability that is present between individuals within a population. Genetic variation such as SNPs (single-nucleotide polymorphisms) or indels in regulatory sequences or mutations in epigenetic enzymes will have a direct effect on the epigenome [2629]. Furthermore, environmental factors such as lifestyle, stress, and nutrition influence epigenetic patterns [3033]. Also, epigenetic patterns change during aging. In fact, DNA methylation markers in saliva and blood can be used for accurate estimation of age [3437]. Thus, epigenetic patterns are plastic and change during development and over time. The variability between individuals has to be accounted for in epigenetic studies including biomarker discovery and hence large cohorts need to be studied to overcome the intra-individual variation. In this respect, it is important to note that the extent of the intra-individual variation is much less as compared to the variation observed between tissues within individuals, at least for DNA methylation [3840].

It has become increasingly clear that misregulation or mutations of epigenetic enzymes are at the basis of a broad range of syndromes and diseases [41]. Mutations in epigenetic enzymes are frequently observed in cancer [42], intellectual disability [43], neurological disorders such as Alzheimer’s, Parkinson’s, and Huntington’s disease [44], and autoimmune diseases such as rheumatoid arthritis [4547] and type 1 diabetes [48]. Most studies have been performed in cancer: ~30% of all driver genes characterized in cancer are related to chromatin structure and function [42]. Well-known examples of genes in which mutations can promote or drive tumorigenesis include DNMT3A and TET2, involved in DNA methylation and DNA demethylation, respectively, and EZH2, which is part of the polycomb repressive complex 2 (PRC2) complex that trimethylates lysine 27 on histone 3 (H3K27me3) [4951]. Apart from mutations in epigenetic enzymes, mistargeting of epigenetic enzymes, such as the silencing of CDKN2A and MLH1 by aberrant promoter DNA methylation, is considered to drive tumor formation [52]. Given their prominent roles in cancer and various other diseases, epigenetics enzymes represent promising targets for therapeutic intervention. For example, small molecules targeting enzymes involved in the post-translational modifications of histones, such as SAHA (suberanilohydroxamic acid; Vorinostat) inhibiting histone deacetylases (HDACs), are effective as therapeutic drugs for a range of tumor types including T cell lymphomas in case of SAHA [5355]. See Rodriguez and Miller [56], Qureshi and Mehler [57], and various papers within this special issue for excellent recent reviews on the use of small molecules to target epigenetic enzymes and their current status in clinical applications.

Epigenetic biomarkers

Molecular diagnosis and prognosis is traditionally often based on (immuno)histochemistry or immunoassays, for example by assaying prostate-specific antigen (PSA) in case of testing for prostate cancer [58]. Also , changes in RNA (ribonucleic acid) expression, genetic alterations, and chromosomal abnormalities represent powerful biomarkers in various diseases including cancer [59]. Notable examples are mutations in the BRCA1 and BRCA2 genes in breast and ovarian cancer or the presence of the Philadelphia chromosome in leukemia [6062]. With the growing understanding that changes in the epigenome and chromatin are related with or causative in disease [41], it became clear that epigenetic alterations represent promising features to be used as biomarkers. An important characteristic for their use as biomarker is that epigenetic marks, in particular DNA methylation, are known to survive sample storage conditions reasonably well [63, 64]. Another convenient characteristic is that almost every biological tissue sample or body fluid such as blood or saliva can be used for analysis of DNA methylation and other epigenetic marks [22, 65, 66]. This robustness makes the application of epigenetic biomarkers in a clinical environment attractive.

Over the recent years, it has become clear that epigenetic features contain a high predictive value during various stages of disease. These analyses thus far mainly focused on DNA methylation. DNA methylation has been shown to be informative for disease diagnosis, prognosis, and stratification. Some of the DNA methylation-based epigenetic biomarkers, such as the methylation status of VIM and SEPT9 for colorectal cancer, SHOX2 for lung cancer, and GSTP1 for prostate cancer, are in clinical use and diagnostic kits are commercially available [6771]. In case of one of the best characterized biomarkers, GSTP1, a meta study (mainly using prostatectomy tissue or prostate sextant biopsies) showed that hypermethylation of the promoter allows to diagnose prostate cancer with a sensitivity of 82% and a specificity of 95% [72]. Importantly, the use of multiple DNA methylation biomarkers (combining hypermethylation of GSTP1, APC, RASSF1, PTGS2, and MDR1) resulted in a sensitivity and specificity of up to 100% [73]. See Heyn and Esteller [74] for a recent comprehensive overview of DNA methylation biomarkers and its potential use in the clinic. In addition to its diagnostic potential, it has been well established that DNA methylation is informative for patient prognosis in terms of tumor recurrence and overall survival. For example, the hypermethylation of four genes, CDKN2A, cadherin 13 (CDH13), RASSF1, and APC, can be used to predict tumor progression of stage 1 non-small cell lung cancer (NSCLC) [75]. In addition to disease prognosis, DNA methylation has been shown to be valuable for patient stratification to predict response to chemotherapeutic treatment. A well-known example is hypermethylation of MGMT in glioblastoma, which render the tumors sensitive to alkylating agents [76, 77] such as carmustine and temozolomide.

Together, these examples show the power and feasibility of using epigenetic features, and in particular DNA methylation, as biomarkers. Epigenetic biomarkers are complementary to genetic biomarkers. Whereas genetic mutations can (among others) disrupt protein function due to amino acid changes, epigenetic alterations can de-regulate mechanisms such as transcriptional control, leading to the inappropriate silencing or activation of genes. Notably, epigenetic changes occur early and at high frequencies in a wide range of diseases including cancer [78]. It has been suggested that epigenetic alterations occur at higher percentages of tumors than genetic variations, resulting in a higher sensitivity in the detection of tumors [79].

Genome-wide epigenetic profiling for DNA methylation biomarkers

Thus far, the discovery of the epigenetic biomarkers mostly relied on targeted approaches using individual gene loci known or suspected to be involved in the etiology or progression of the disease or other phenotype under study. Despite the challenges in the identification of biomarkers using such approaches, this yielded a number of important epigenetic biomarkers. However, these approaches require a priori knowledge for the selection of candidate biomarkers.

In order to perform unbiased screens in the exploratory phase of biomarker discovery, genome-wide profiling technologies have spurred molecular biomarker discovery (detailed information on epigenomic profiling assays is presented in Table 1). Using these technologies, the entire (epi)genome can be interrogated for potential biomarkers by comparing healthy versus diseased cells/tissue, malignant versus non-malignant tumors, or drug-sensitive versus drug-resistant tumors. This enables selection of candidate biomarkers that are most informative for disease detection, prognosis, or stratification. The use of genome-wide screens furthermore enables to detect and evaluate combinations of (many) candidate loci, which often results in increased sensitivity and specificity of the biomarker. Importantly, the identification of individual genomic loci or genes as biomarkers from large datasets requires robust statistical testing such as multiple-testing correction (although traditional tests like the Bonferroni correction are over-conservative since there is often correlation between loci, i.e., they are not independent) or stringent false discovery rate (FDR) control (for example, by the Benjamini–Hochberg procedure) [8082]. To define sets of biomarkers from large dataset, alternative statistical methods (such as sparse principle component analysis (PCA) or sparse canonical correlation analysis (CCA) [83, 84]) are available as well. In light of (i) challenges with the experimental setup when using patient material, (ii) costs, and (iii) the extensive computational analysis associated with the exploratory phase of biomarker discovery, genome-wide screens are often performed on relatively small cohorts. Independent of the (statistical) methods used, it is essential to validate (sets of) candidate biomarkers in follow-up studies on large cohorts using targeted epigenetic approaches before potential application in the clinic [85].

Recent years have seen an increasing number of studies using genome-wide epigenetic profiling to predict disease outcome. For a range of tumors, including childhood acute lymphoblastic leukemia [86], kidney cancer [87], NSCLC [88], rectal cancer [89], cervical cancer [90, 91], breast cancer [92, 93], and glioblastoma [94], DNA methylome analysis has been shown to be of prognostic value. Most of these studies define changes in DNA methylation at single sites or at small subsets of sites that represent potential disease signatures. Although these studies are often restricted to a subset of CpGs within the genome and mostly rely on relatively small sample sizes, they show the power of performing genome-wide biomarker screens.

Currently, the most popular platform used in the exploratory phase of DNA methylation biomarker discovery represents the Infinium HumanMethylation450 BeadChip array (further referred to as “450K array”; see a short explanation of the 450K array within Table 1). The probes on the 450K array mainly represent functional CpG islands and functional elements such as promoters, enhancers, and TF binding sites. Main advantages of the 450K array for the detection of DNA methylation as compared to other DNA methylation platforms include (i) its high reproducibility, (ii) the straightforward analysis methods, (iii) the large number of samples that have been profiled using the 450K array thus far (which can be used for comparative purposes), and (iv) the relatively low costs. A disadvantage, like with all bisulfite-based methods (unless combined with additional chemical procedures), is that the 450K array is unable to distinguish between DNA methylation and DNA hydroxymethylation. Hydroxymethylated cytosines represent an intermediate step during demethylation of methylated cytosines but is relatively stable and is therefore likely to have specific biological functions as well [95]. It should be noted that levels of DNA hydroxymethylation are generally much lower as compared to levels of DNA methylation (for example, DNA hydroxymethylation levels are >95% lower in case of peripheral blood mononuclear cell (PBMC) [96]). A further disadvantage of the 450K array is that  genetic differences between samples might result in false positives, in particular since a subset of probes on the 450K array target polymorphic CpGs that overlap SNPs [97, 98]. For association studies using large cohorts, computational methods (based on principle components) have been developed to account for population stratification resulting from differences in allele frequencies [98100].

To enable robust screening for a (set of) potential biomarker(s), most current studies apply the 450K array on up to several hundred samples. To narrow down and validate candidate biomarkers, more targeted DNA methylation assays are used on the same or a very similar-sized cohort [101]. Subsequently, the remaining candidate biomarkers are further validated on larger cohorts using targeted DNA methylation assays that are compatible with routine clinical use, for example, by amplicon bisulfite sequencing [85]. Using this powerful workflow, tumors for which prognostic biomarkers have been identified include rectal cancer [102], breast cancer [103], hepatocellular carcinoma [104], and chronic lymphocytic leukemia (CLL) [105, 106]. Interestingly, using a similar workflow, sets of DNA methylation biomarkers have recently been identified that are prognostic for the aggressiveness of tumors in prostate cancer [107, 108]. Such studies are very important for improving treatment of prostate cancer by avoiding (radical) prostatectomy in cases where careful monitoring of the tumor over time is preferred.

Biomarkers other than DNA methylation

The majority of epigenetic biomarkers identified thus far involve changes in DNA methylation. However, in light of the various types of epigenetic misregulation associated with diseases, changes in epigenetic features other than DNA methylation are likely to become powerful molecular biomarkers as well. ChIP-Seq profiling has revealed prominent differences in binding sites of post-translational histone modifications and other proteins between healthy and cancer tissue, both in leukemia as well as in solid tumors. For example, localized changes in H3 acetylation have been reported in leukemia (see, for example, Martens et al. [109] and Saeed et al. [110]). For solid tumors, differential estrogen receptor (ER) binding and H3K27me3 as determined by ChIP-Seq has been shown to be associated with clinical outcome in breast cancer [111, 112]. Also, androgen receptor (AR) profiling predicts prostate cancer outcome [113]. A recent study identified tumor-specific enhancer profiles in colorectal, breast, and bladder carcinomas using H3K4me2 ChIP-Seq [114]. Next to ChIP-Seq, DNAseI hypersensitivity assays have identified tumor-specific open chromatin sites for several types of cancer (see, for example, Jin et al. [115]). In terms of chromatin conformation, it has recently been shown that disruption of the 3D conformation of the genome can result in inappropriate enhancer activity causing mis-expression of genes including proto-oncogenes [116, 117]. These examples show that, besides DNA methylation, changes in (i) protein binding sites (including post-translational histone modifications), (ii) accessible (open) chromatin, and (iii) the 3D conformation of the genome represent epigenetic features that are potential effective biomarkers (Fig. 1). The near absence of biomarkers based on these epigenetic features is mainly due to practical reasons. ChIP-Seq as well as other comprehensive epigenetic profiling technologies traditionally require (much) more input material, up to 1 × 106 cells or more, to obtain robust results as compared to DNA methylation profiling (Table 1). This is particularly challenging for (banked) patient samples, which are often available in small quantities that might not be compatible with epigenetic profiling other than DNA methylation profiling. Also, profiling of such epigenetic features often require elaborate and delicate workflows (Table 1). Hence, quantitation and reproducibility of ChIP-Seq and other epigenetic profiling assays besides DNA methylation profiling are challenging. Furthermore, DNA methylation profiling is better compatible with (archived) frozen or fixed samples.

However, the last 2 years have seen a spectacular progress in miniaturization of epigenetic profiling assays. In various instances, this included automation of (part of) the workflow, improving the robustness of the assays and its output. Also, improved workflows for epigenetic profiling of frozen or fixed samples have been reported. Although this involved proof-of-concept studies in basic research settings, these efforts are likely to have significant impetus on genome-wide epigenetic screens for candidate biomarkers. The remainder of this review will provide an overview of the current status of genome-wide epigenetic profiling and the technological advances that facilitate miniaturization, automation, and compatibility with preserved samples.

New developments in epigenetic profiling: compatibility with preservation methods

Most epigenetic profiling assays have been developed using fresh material in order to preserve the native chromatin architecture. However, epigenetic biomarker screens require the use of patient-derived clinical samples that are generally processed to preserve the samples as well as to allow convenient sample handling, for example, for sectioning of biopsies. Also, samples present in biobanks are fixed to allow long-time storage. In particular for retrospective studies, epigenetic profiling technologies that are applied for biomarker screens should therefore be compatible with methodologies that are routinely used for sample preservation: freezing and chemical fixation (in particular FFPE fixation) [118].


Freezing of tissue specimens is typically performed by snap-freezing with subsequent storage at −80 °C or in liquid nitrogen [119]. Freezing seems to maintain nuclear integrity and chromatin structure very well (Fig. 2). WGBS [120], ChIP-Seq [121123], ATAC-Seq [124, 125], and DNAseI-Seq [126, 127] all have been shown to be compatible with frozen cells or tissues. 

Chemical Fixation (FFPE)

Chemical fixation generally includes overnight crosslinking with formaldehyde at high concentrations (up to 10%), followed by dehydration and paraffin embedding (so-called “FFPE”: formalin-fixed, paraffin-embedded) [128]. Although procedures for FFPE fixation are time-consuming, FFPE fixation has the advantage that samples can be stored at room temperature and that samples can be evaluated by morphology or immunohistochemistry (prior to possible further processing such as epigenetic profiling).

Fig. 2

Compatibility of commonly used sample preservation methods with current epigenome profiling assays. A dotted line indicates that these assays would benefit from further optimization

FFPE conditions do not affect DNA methylation, and also formaldehyde and paraffin do not interfere with the WGBS profiling procedure [129]. However, epigenetic assays other than bisulfite-based DNA methylation profiling are cumbersome with FFPE samples (Fig. 2). In case of ChIP-Seq, crosslinking generally occurs in much milder conditions (1% formaldehyde for 10 min) as compared to the harsh conditions used for FFPE fixation [120], which can complicate shearing and epitope accessibility. Pathology tissue (PAT)-ChIP has been reported to prepare FFPE samples for ChIP-Seq by the use of deparaffinization, rehydration, and MNase treatment followed by sonication at high power [130, 131]. However, PAT-ChIP comes with various limitations including the long running time of the protocol (up to 4 days) and the fact that it is not compatible with all ChIP-grade antibodies. Interestingly, some of these issues have been resolved in the very recently developed fixed-tissue (FiT)-Seq procedure, which might open up new avenues for ChIP-Seq profiling of FFPE samples [114]. DNaseI-Seq on FFPE samples has been reported at the expense of a drop in signal-to-noise ratios of around 50% as compared to the use of fresh material [115].

Despite new developments for ChIP-Seq and DNaseI-Seq, this overview shows that DNA methylation is still the most robust of all epigenetic marks for profiling of samples that are processed by freezing or chemical fixation. Although most other epigenetic profiling assays are compatible with frozen samples (at the expense of signal-to-noise ratios for some of the assays), they are generally not or poorly compatible with FFPE specimens (Fig. 2). This also implies that for these assays, it is much more challenging to make use of laser microdissection to select specific regions of interest from specimens for epigenetic analysis, for example, to separate tumor cells from stromal cells [132, 133]. An additional advantage of using DNA methylation for biomarker screening is that, in contrast to the other epigenetic profiling assays discussed, the profiling can be performed on isolated genomic DNA. This enables the use of genomic DNA from clinical DNA banks to be included in DNA methylation biomarker screens.

It should be noted that in contrast to retrospective studies, it might be feasible to use fresh or fresh-frozen patient material for screening in prospective biomarker studies. However, the use of fresh(-frozen) material in these studies could interfere with further development of potential biomarkers if it turns out that these biomarkers are incompatible with (FFPE-)fixed patient material present in the clinic. In all cases, when collecting patient samples for profiling of epigenetic marks, it is important to keep the time between surgical removal and fixation or freezing as short as possible to avoid epitope destruction and/or breakdown of the chromatin. It would therefore be helpful if the procedure time up to fixation would be documented for banked samples, so as to evaluate whether such banked samples are suitable for the epigenetic profiling technology of choice.

New developments in epigenetic profiling: miniaturization and automation

The recent years saw great progress in low-input epigenetic profiling without significantly affecting signal-to-noise ratios (Fig. 3). Also, all main genome-wide epigenetic profiling assays are now compatible with single-cell readouts. An overview of the main technological advances that allowed miniaturization and single-cell readout is described in Table 2. Besides miniaturization, various epigenetic profiling assays, in particular ChIP-Seq, have been (partly) automated to improve reproducibility and to allow higher throughput. In this section, we briefly evaluate these new technological developments with respect to biomarker discovery.

Fig. 3

Level of comprehensiveness of epigenetic data from global epigenetic profiling assays using an increasing number of cells as input

Table 2 Overview of the main technological advances that allowed miniaturization and single-cell readout of genome-wide epigenetic profiling assays

Miniaturization of epigenetic profiling

As summarized in Table 2, Fig. 3, and Table 3, the amount of cells required for three of the main epigenetic profiling assays is currently well compatible with amounts present in patient-derived specimens or amounts present in banked patient samples. For bisulfite-based DNA methylation profiling, a starting amount of 7.5 × 104 cells for the 450K array or 3 × 103 cells for WGBS/RRBS is sufficient to obtain high-quality genome-wide profiles. For ChIP-Seq, the minimum amount of starting material is highly dependent on the protein to be profiled and the antibody that is used [134]. Although both histone modification and TF binding sites (such as ER [111, 112]) are potentially powerful as biomarker, the minimum number of cells required for histone modification profiling (~1–5 × 104 cells) is much more compatible with patient samples than the number of cells required for TF profiling (generally 1 × 105 cells or more; Tables 1 and 2). ATAC-Seq and DNAseI-Seq are compatible with as low as 200 cells and 1 × 103 cells, respectively (Table 2) [115, 135]. Together, this shows that the input requirements for bisulfite-based DNA methylation profiling, ChIP-Seq (in particular for histone modifications), and ATAC-Seq/DNAseI-Seq are well compatible with most clinical samples. The minimum number of cells currently required for 4C-Seq and HiC-Seq, at least 1 × 107 cells, is currently too high for clinical use.

Table 3 Overview of the number of cells required for the various epigenetic profiling assays

Interestingly, all main epigenetic profiling assays can now provide single-cell readouts (Table 2, Table 3). The possibility to assay individual cells within populations allows interrogation of heterogeneity which in “bulk” profiling would be averaged. This is very informative for clinical samples which can be highly heterogeneous [136]. Single-cell profiling has been shown to be powerful in obtaining molecular signatures of heterogeneous populations that shift in cell type composition [137]. As such, an important clinical application of single-cell profiling is to screen for resistant versus non-resistant cells after drug treatment [138] or to monitor disease progression [139]. In terms of biomarker discovery, the use of single-cell assays will allow to screen for cell types that are most informative for disease stratification. Also, the level of heterogeneity as measured by single-cell studies might possibly by itself be informative for disease stratification. From a practical perspective, epigenetic profiling of single cells is challenging. Since one cell only contains two copies for each genomic locus to be assayed, any loss of material during washing or enrichments steps such as immunoprecipitations will significantly impact the outcome of the assay. Similarly, background signals are hard to distinguish from true signal. One of the main strategies to account for false negative signals as well as for aspecific background is to include a large number of cells in single-cell epigenetic assays to enable proper statistics. However, this results in (very) large datasets, for which computational and statistical analysis are generally challenging. For single-cell epigenetic profiling of clinical samples, there are two additional issues to consider: (i) generation of single-cell suspensions from patient samples might be challenging, and (ii) the number of cells required as input for single-cell epigenetic profiling is generally higher than for miniaturized epigenetic profiling in order to enable capturing of single cells (Fig. 4), which might affect compatibility with patient samples. Since single-cell technologies emerged very recently, further developments in technology (to increase sensitivity and specificity) and in computational analysis (for more robust statistical testing and model development) are to be expected. Once single-cell epigenetic profiling has fully matured, it will be very powerful for biomarker discovery in heterogeneous cell populations such as human blood samples and biopsies.

Fig. 4

State-of-the-art microfluidic systems capable of performing single-cell epigenomic profiling. Simplified representation of a Fluidigm C1 integrated fluidic circuit design capable of capturing 96 single cell for ATAC-Seq [151] (a). Droplet microfluidic workflow applying barcoding of single-cell chromatin to enable pooling for subsequent ChIP experiments [152] (b). Alternatively, single cells can be captured by FACS (not shown)

Automation of epigenetic profiling

The use of genome-wide epigenetic profiling for biomarker discovery strongly benefits from automated procedures that are compatible with upscaling to facilitate large-scale screens. Main advantages of automation include (i) a reduction in variability and batch effects, both of which are frequently observed in epigenetic profiling, (ii) increased throughput, (iii) reduced procedure and/or hands-on time, and (iv) lower error rates. In light of the limited number of cells within clinical samples, a combination of automation and miniaturization is likely to be beneficial in most cases. This comes with the additional advantage of reduced reagent cost, which can be substantial considering the high costs associated with epigenetic profiling. It should be noted that epigenetic profiling thus far is mainly being performed within basic research settings on relatively small sample sizes, which are well compatible with manual handling. Therefore, most automated platforms have been developed recently to cope with the increasing sample sizes and the profiling of more challenging (clinical) samples. In this section, we focus on automation of bulk and miniaturized epigenetic profiling; information on automation of single-cell technologies is included in Table 2.

Efforts to design automated workflows for epigenetic profiling have mainly been focused on ChIP-Seq and to a lesser extent on DNA methylation profiling. This can be explained by the fact that DNA methylation profiling, and chromatin profiling (ATAC-Seq/DNAseI-Seq) as well, is relatively straightforward and therefore well compatible with manual handling. Considering 4C-Seq and HiC-Seq, these are relatively new technologies for which automated workflows have not been reported yet. For DNA methylation profiling, (parts of) the workflow for MBD-Seq, MethylCap-Seq, and MeDIP-Seq have been designed on custom-programmed robotic liquid handling systems [140142]. For ChIP-Seq, immunoprecipitations and subsequent sample preparation for sequencing have been designed on the same or similar robotic systems [143146]. However, these robotic workflows require large amounts of starting material in the range of 1 × 106 cells or more. Clearly, with such input requirements, these platforms are not readily compatible with biomarker discovery.

More recently, miniaturized automated platforms have been described for ChIP-Seq using PDMS (polydimethylsiloxane)-based microfluidic devices that have been designed to perform automated immunoprecipitations. These platforms allow to perform ChIP-Seq using as low as 1 × 103 cells [147] or 100 cells [148] due to very small reaction volumes, providing proof-of-principle that automated low-input ChIP-Seq profiling is feasible. However, to facilitate high-throughput profiling, it would be important to increase the number of parallel samples to be profiled, as currently these platforms contain a maximum of assaying four samples in parallel [147, 148]. Furthermore, integration with the labor-intensive DNA library preparation procedure would be desirable; stand-alone library preparation platforms on microfluidic devices have been reported [149, 150]. For DNA methylation profiling, various commercial low-input bisulfite conversion kits have been shown to be compatible with automation. However, a fully automated miniaturized DNA methylation profiling platform has not been reported yet.


Biomarkers are highly valuable and desirable in a wide range of clinical settings, ranging from pharmacodynamics to monitoring treatment. Here, we have provided an overview of recent developments within genome-wide profiling technologies that may enable future large-scale screens for candidate epigenetic biomarkers. When comparing compatibility with miniaturization, automation and tissue preservation methods, bisulfite-based DNA methylation profiling is currently by far superior to other epigenetic profiling technologies for large-scale biomarker discovery. DNA methylation assays are technically less challenging than most other profiling assays, as it is not dependent on delicate enzymatic reactions or on immunoprecipitation, but on chemical conversion. A critical advantage of DNA methylation profiling over other assays is that is not affected by freezing or chemical fixation, and therefore very well compatible with (archived) clinical samples. DNA methylation profiling has the additional advantage that it requires a relatively low number of cells as input. In line with these advantages, most of the epigenetic biomarkers that have been identified thus far involve changes in DNA methylation.

Despite the advantages of DNA methylation, various other epigenetic marks are promising biomarkers. Histone-modifying enzymes are frequently mutated in a range of diseases, often directly affecting epigenetic patterns of post-translational histone modifications. The main methodology to profile these post-translational histone modifications is ChIP-Seq. ChIP-Seq is challenging on samples containing low numbers of cells as well as on archived samples, often resulting in variability in signal-to-noise ratios. However, in view of the continuous improvements in ChIP-Seq procedures for (ultra-)low input samples and for fixed samples, large scale ChIP-Seq-based screens for candidate biomarkers is likely to become feasible in the near future. These screens might benefit from the automated ChIP(-Seq) platforms that are currently being developed. The development of such automated platforms will also facilitate robust integration of ChIP assays as a diagnostic tool in clinical practice.

Of the remaining technologies discussed in this paper, ATAC-Seq and DNAseI-Seq seem most compatible with profiling of clinical samples, requiring as low as several hundred cells as input. Both ATAC-Seq and DNAseI-Seq are compatible with frozen patient samples [125128], while DNAseI-Seq was recently successfully applied on FFPE samples [115]. However, as compared to DNAseI-Seq, the workflow of ATAC-Seq is much more straightforward as the adaptors for sequencing are inserted as part of the transposition. Also, at least for single-cell ATAC-Seq, a fully automated platform has been developed [151]. For biomarker discovery, compatibility of ATAC-Seq with FFPE samples would be highly desirable, as this would enable to include clinical samples from biobanks in large-scale ATAC-Seq profiling studies. This might be achieved by incorporating critical steps from the FFPE-compatible DNAseI-Seq. Although the use of open chromatin as an epigenetic biomarker has been rare thus far, the flexibility and ease of the recently developed ATAC-Seq (and possibly DNAseI-Seq) will undoubtedly boost the use of open chromatin in clinical research and clinical practice.

Together, this review shows that genome-wide epigenetic profiling technologies have very rapidly matured over the past decade. While originally these technologies were only compatible with large numbers of (in vitro cultured) cells, most of these can now be applied on samples containing very low numbers of primary cells down to single cells. Combined with an increasing number of sophisticated workflows and (automated) platforms, this will pave the way for large-scale epigenetic screens on clinical patient material. Such screens are essential to fill the need for new biomarkers for disease diagnosis, prognosis, and selection of targeted therapies, necessary for personalized medicine.




450K array:

Infinium HumanMethylation450 BeadChip array


Circular chromosome conformation capture


Androgen receptor


Assay for transposase-accessible chromatin


Canonical correlation analysis


Chromatin immunoprecipitation


Chronic lymphocytic leukemia


CG dinucleotide


DNAseI hypersensitive site


Deoxyribonucleic acid


Desoxyribonuclease 1


Estrogen receptor


Fluorescence-activated cell sorting


False discovery rate


Formalin-fixed paraffin-embedded sample


Fixed-tissue ChIP-Seq


Histone deacetylase


International Human Epigenome Consortium


Methyl-CpG binding domain protein-enriched


Methylation DNA immunoprecipitation


Methylated DNA capture


Micrococcal nuclease


Next-generation sequencing


National Institutes of Health


Non-small cell lung cancer


Pathology tissue chromatin immunoprecipitation


Peripheral blood mononuclear cell


Principle component analysis




Polycomb repressive complex


Prostate-specific antigen


Ribonucleic acid


Reduced representation bisulfite sequencing


Suberanilohydroxamic acid (Vorinostat)


single-cell bisulfite sequencing


followed by sequencing


Single-nucleotide polymorphism


Topologically associating domain


Transcription factor


Whole-genome bisulfite sequencing


  1. 1.

    Brower V. Biomarkers: portents of malignancy. Nature. 2011;471(7339):S19–21. doi:10.1038/471S19a.

    CAS  PubMed  Article  Google Scholar 

  2. 2.

    Sawyers CL. The cancer biomarker problem. Nature. 2008;452(7187):548–52. doi:10.1038/nature06913.

    CAS  PubMed  Article  Google Scholar 

  3. 3.

    Biomarkers Definitions Working Group. Biomarkers and surrogate endpoints: preferred definitions and conceptual framework. Clin Pharmacol Ther. 2001;69(3):89–95. doi:10.1067/mcp.2001.113989.

    Article  Google Scholar 

  4. 4.

    Hu ZZ, Huang H, Wu CH, Jung M, Dritschilo A, Riegel AT, et al. Omics-based molecular target and biomarker identification. Methods Mol Biol (Clifton, NJ). 2011;719:547–71. doi:10.1007/978-1-61779-027-0_26.

    CAS  Article  Google Scholar 

  5. 5.

    Poste G. Bring on the biomarkers. Nature. 2011;469(7329):156–7. doi:10.1038/469156a.

    CAS  PubMed  Article  Google Scholar 

  6. 6.

    Wichmann HE, Kuhn KA, Waldenberger M, Schmelcher D, Schuffenhauer S, Meitinger T, et al. Comprehensive catalog of European biobanks. Nat Biotechnol. 2011;29(9):795–7. doi:10.1038/nbt.1958.

    CAS  PubMed  Article  Google Scholar 

  7. 7.

    Baker M. Biorepositories: building better biobanks. Nature. 2012;486(7401):141–6. doi:10.1038/486141a.

    CAS  PubMed  Article  Google Scholar 

  8. 8.

    Knoppers BM, Chisholm RL, Kaye J, Cox D, Thorogood A, Burton P, et al. A P3G generic access agreement for population genomic studies. Nat Biotechnol. 2013;31(5):384–5. doi:10.1038/nbt.2567.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  9. 9.

    Clement B, Yuille M, Zaltoukal K, Wichmann HE, Anton G, Parodi B, et al. Public biobanks: calculation and recovery of costs. Sci Transl Med. 2014;6(261):261fs45. doi:10.1126/scitranslmed.3010444.

    PubMed  Article  Google Scholar 

  10. 10.

    Rifai N, Gillette MA, Carr SA. Protein biomarker discovery and validation: the long and uncertain path to clinical utility. Nat Biotechnol. 2006;24(8):971–83. doi:10.1038/nbt1235.

    CAS  PubMed  Article  Google Scholar 

  11. 11.

    Luger K, Mader AW, Richmond RK, Sargent DF, Richmond TJ. Crystal structure of the nucleosome core particle at 2.8 A resolution. Nature. 1997;389(6648):251–60. doi:10.1038/38444.

    CAS  PubMed  Article  Google Scholar 

  12. 12.

    Song F, Chen P, Sun D, Wang M, Dong L, Liang D, et al. Cryo-EM study of the chromatin fiber reveals a double helix twisted by tetranucleosomal units. Science (New York, NY). 2014;344(6182):376–80. doi:10.1126/science.1251413.

    CAS  Article  Google Scholar 

  13. 13.

    Finch JT, Klug A. Solenoidal model for superstructure in chromatin. Proc Natl Acad Sci U S A. 1976;73(6):1897–901.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  14. 14.

    Jones PA. Functions of DNA methylation: islands, start sites, gene bodies and beyond. Nat Rev Genet. 2012;13(7):484–92. doi:10.1038/nrg3230.

    CAS  PubMed  Article  Google Scholar 

  15. 15.

    Kouzarides T. Chromatin modifications and their function. Cell. 2007;128(4):693–705. doi:10.1016/j.cell.2007.02.005.

    CAS  PubMed  Article  Google Scholar 

  16. 16.

    Jenuwein T, Allis CD. Translating the histone code. Science (New York, NY). 2001;293(5532):1074–80. doi:10.1126/science.1063127.

    CAS  Article  Google Scholar 

  17. 17.

    Dekker J. Gene regulation in the third dimension. Science (New York, NY). 2008;319(5871):1793–4. doi:10.1126/science.1152850.

    CAS  Article  Google Scholar 

  18. 18.

    Marks H, Chow JC, Denissov S, Francoijs KJ, Brockdorff N, Heard E, et al. High-resolution analysis of epigenetic changes associated with X inactivation. Genome Res. 2009;19(8):1361–73. doi:10.1101/gr.092643.109.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  19. 19.

    Marks H, Kerstens HH, Barakat TS, Splinter E, Dirks RA, van Mierlo G, et al. Dynamics of gene silencing during X inactivation using allele-specific RNA-Seq. Genome Biol. 2015;16:149. doi:10.1186/s13059-015-0698-x.

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  20. 20.

    Martens JH, Stunnenberg HG. BLUEPRINT: mapping human blood cell epigenomes. Haematologica. 2013;98(10):1487–9. doi:10.3324/haematol.2013.094243.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  21. 21.

    Beck S, Bernstein BE, Campbell RM, Costello JF, Dhanak D, Ecker JR, et al. A blueprint for an international cancer epigenome consortium. A report from the AACR Cancer Epigenome Task Force. Cancer Res. 2012;72(24):6319–24. doi:10.1158/0008-5472.can-12-3658.

    CAS  PubMed  Article  Google Scholar 

  22. 22.

    Adams D, Altucci L, Antonarakis SE, Ballesteros J, Beck S, Bird A, et al. BLUEPRINT to decode the epigenetic signature written in blood. Nat Biotechnol. 2012;30(3):224–6. doi:10.1038/nbt.2153.

    CAS  PubMed  Article  Google Scholar 

  23. 23.

    Bernstein BE, Stamatoyannopoulos JA, Costello JF, Ren B, Milosavljevic A, Meissner A, et al. The NIH Roadmap Epigenomics Mapping Consortium. Nat Biotechnol. 2010;28(10):1045–8. doi:10.1038/nbt1010-1045.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  24. 24.

    Agirre X, Castellano G, Pascual M, Heath S, Kulis M, Segura V, et al. Whole-epigenome analysis in multiple myeloma reveals DNA hypermethylation of B cell-specific enhancers. Genome Res. 2015;25(4):478–87. doi:10.1101/gr.180240.114.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  25. 25.

    Kretzmer H, Bernhart SH, Wang W, Haake A, Weniger MA, Bergmann AK, et al. DNA methylome analysis in Burkitt and follicular lymphomas identifies differentially methylated regions linked to somatic mutation and transcriptional control. Nat Genet. 2015;47(11):1316–25. doi:10.1038/ng.3413.

    CAS  PubMed  Article  Google Scholar 

  26. 26.

    Fraser HB, Lam LL, Neumann SM, Kobor MS. Population-specificity of human DNA methylation. Genome Biol. 2012;13(2):R8. doi:10.1186/gb-2012-13-2-r8.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  27. 27.

    Bell JT, Pai AA, Pickrell JK, Gaffney DJ, Pique-Regi R, Degner JF, et al. DNA methylation patterns associate with genetic and gene expression variation in HapMap cell lines. Genome Biol. 2011;12(1):R10. doi:10.1186/gb-2011-12-1-r10.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  28. 28.

    Zhang D, Cheng L, Badner JA, Chen C, Chen Q, Luo W, et al. Genetic control of individual differences in gene-specific methylation in human brain. Am J Hum Genet. 2010;86(3):411–9. doi:10.1016/j.ajhg.2010.02.005.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  29. 29.

    Gibbs JR, van der Brug MP, Hernandez DG, Traynor BJ, Nalls MA, Lai SL, et al. Abundant quantitative trait loci exist for DNA methylation and gene expression in human brain. PLoS Genet. 2010;6(5):e1000952. doi:10.1371/journal.pgen.1000952.

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  30. 30.

    Druesne N, Pagniez A, Mayeur C, Thomas M, Cherbuy C, Duee PH, et al. Diallyl disulfide (DADS) increases histone acetylation and p21(waf1/cip1) expression in human colon tumor cell lines. Carcinogenesis. 2004;25(7):1227–36. doi:10.1093/carcin/bgh123.

    CAS  PubMed  Article  Google Scholar 

  31. 31.

    Fang MZ, Wang Y, Ai N, Hou Z, Sun Y, Lu H, et al. Tea polyphenol (−)-epigallocatechin-3-gallate inhibits DNA methyltransferase and reactivates methylation-silenced genes in cancer cell lines. Cancer Res. 2003;63(22):7563–70.

    CAS  PubMed  Google Scholar 

  32. 32.

    Feil R, Fraga MF. Epigenetics and the environment: emerging patterns and implications. Nat Rev Genet. 2011;13(2):97–109. doi:10.1038/nrg3142.

    Google Scholar 

  33. 33.

    Myzak MC, Tong P, Dashwood WM, Dashwood RH, Ho E. Sulforaphane retards the growth of human PC-3 xenografts and inhibits HDAC activity in human subjects. Exp Biol Med (Maywood). 2007;232(2):227–34.

    CAS  Google Scholar 

  34. 34.

    Weidner CI, Lin Q, Koch CM, Eisele L, Beier F, Ziegler P, et al. Aging of blood can be tracked by DNA methylation changes at just three CpG sites. Genome Biol. 2014;15(2):R24. doi:10.1186/gb-2014-15-2-r24.

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  35. 35.

    Horvath S. DNA methylation age of human tissues and cell types. Genome Biol. 2013;14(10):R115. doi:10.1186/gb-2013-14-10-r115.

    PubMed  PubMed Central  Article  Google Scholar 

  36. 36.

    Bocklandt S, Lin W, Sehl ME, Sanchez FJ, Sinsheimer JS, Horvath S, et al. Epigenetic predictor of age. PLoS One. 2011;6(6):e14821. doi:10.1371/journal.pone.0014821.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  37. 37.

    Hannum G, Guinney J, Zhao L, Zhang L, Hughes G, Sadda S, et al. Genome-wide methylation profiles reveal quantitative views of human aging rates. Mol Cell. 2013;49(2):359–67. doi:10.1016/j.molcel.2012.10.016.

    CAS  PubMed  Article  Google Scholar 

  38. 38.

    Byun HM, Siegmund KD, Pan F, Weisenberger DJ, Kanel G, Laird PW, et al. Epigenetic profiling of somatic tissues from human autopsy specimens identifies tissue- and individual-specific DNA methylation patterns. Hum Mol Genet. 2009;18(24):4808–17. doi:10.1093/hmg/ddp445.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  39. 39.

    Eckhardt F, Lewin J, Cortese R, Rakyan VK, Attwood J, Burger M, et al. DNA methylation profiling of human chromosomes 6, 20 and 22. Nat Genet. 2006;38(12):1378–85. doi:10.1038/ng1909.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  40. 40.

    Davies MN, Volta M, Pidsley R, Lunnon K, Dixit A, Lovestone S, et al. Functional annotation of the human brain methylome identifies tissue-specific epigenetic variation across brain and blood. Genome Biol. 2012;13(6):R43. doi:10.1186/gb-2012-13-6-r43.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  41. 41.

    Portela A, Esteller M. Epigenetic modifications and human disease. Nat Biotechnol. 2010;28(10):1057–68. doi:10.1038/nbt.1685.

    CAS  PubMed  Article  Google Scholar 

  42. 42.

    Vogelstein B, Papadopoulos N, Velculescu VE, Zhou S, Diaz Jr LA, Kinzler KW. Cancer genome landscapes. Science (New York, NY). 2013;339(6127):1546–58. doi:10.1126/science.1235122.

    CAS  Article  Google Scholar 

  43. 43.

    Bjornsson HT. The Mendelian disorders of the epigenetic machinery. Genome Res. 2015;25(10):1473–81. doi:10.1101/gr.190629.115.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  44. 44.

    Urdinguio RG, Sanchez-Mut JV, Esteller M. Epigenetic mechanisms in neurological diseases: genes, syndromes, and therapies. Lancet Neurol. 2009;8(11):1056–72. doi:10.1016/s1474-4422(09)70262-5.

    CAS  PubMed  Article  Google Scholar 

  45. 45.

    Jin B, Tao Q, Peng J, Soo HM, Wu W, Ying J, et al. DNA methyltransferase 3B (DNMT3B) mutations in ICF syndrome lead to altered epigenetic modifications and aberrant expression of genes regulating development, neurogenesis and immune function. Hum Mol Genet. 2008;17(5):690–709. doi:10.1093/hmg/ddm341.

    CAS  PubMed  Article  Google Scholar 

  46. 46.

    Karouzakis E, Gay RE, Michel BA, Gay S, Neidhart M. DNA hypomethylation in rheumatoid arthritis synovial fibroblasts. Arthritis Rheum. 2009;60(12):3613–22. doi:10.1002/art.25018.

    CAS  PubMed  Article  Google Scholar 

  47. 47.

    Liu Y, Aryee MJ, Padyukov L, Fallin MD, Hesselberg E, Runarsson A, et al. Epigenome-wide association data implicate DNA methylation as an intermediary of genetic risk in rheumatoid arthritis. Nat Biotechnol. 2013;31(2):142–7. doi:10.1038/nbt.2487.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  48. 48.

    Miao F, Smith DD, Zhang L, Min A, Feng W, Natarajan R. Lymphocytes from patients with type 1 diabetes display a distinct profile of chromatin histone H3 lysine 9 dimethylation: an epigenetic study in diabetes. Diabetes. 2008;57(12):3189–98. doi:10.2337/db08-0645.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  49. 49.

    Delhommeau F, Dupont S, Della Valle V, James C, Trannoy S, Masse A, et al. Mutation in TET2 in myeloid cancers. N Engl J Med. 2009;360(22):2289–301. doi:10.1056/NEJMoa0810069.

    PubMed  Article  Google Scholar 

  50. 50.

    Ley TJ, Ding L, Walter MJ, McLellan MD, Lamprecht T, Larson DE, et al. DNMT3A mutations in acute myeloid leukemia. N Engl J Med. 2010;363(25):2424–33. doi:10.1056/NEJMoa1005143.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  51. 51.

    Morin RD, Johnson NA, Severson TM, Mungall AJ, An J, Goya R, et al. Somatic mutations altering EZH2 (Tyr641) in follicular and diffuse large B-cell lymphomas of germinal-center origin. Nat Genet. 2010;42(2):181–5. doi:10.1038/ng.518.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  52. 52.

    Beggs AD, Jones A, El-Bahrawy M, Abulafi M, Hodgson SV, Tomlinson IP. Whole-genome methylation analysis of benign and malignant colorectal tumours. J Pathol. 2013;229(5):697–704. doi:10.1002/path.4132.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  53. 53.

    Arrowsmith CH, Bountra C, Fish PV, Lee K, Schapira M. Epigenetic protein families: a new frontier for drug discovery. Nat Rev Drug Discov. 2012;11(5):384–400. doi:10.1038/nrd3674.

    CAS  PubMed  Article  Google Scholar 

  54. 54.

    Garcia-Manero G, Yang H, Bueso-Ramos C, Ferrajoli A, Cortes J, Wierda WG, et al. Phase 1 study of the histone deacetylase inhibitor vorinostat (suberoylanilide hydroxamic acid [SAHA]) in patients with advanced leukemias and myelodysplastic syndromes. Blood. 2008;111(3):1060–6. doi:10.1182/blood-2007-06-098061.

    CAS  PubMed  Article  Google Scholar 

  55. 55.

    Whittaker SJ, Demierre MF, Kim EJ, Rook AH, Lerner A, Duvic M, et al. Final results from a multicenter, international, pivotal study of romidepsin in refractory cutaneous T-cell lymphoma. J Clin Oncol. 2010;28(29):4485–91. doi:10.1200/jco.2010.28.9066.

    CAS  PubMed  Article  Google Scholar 

  56. 56.

    Rodriguez R, Miller KM. Unravelling the genomic targets of small molecules using high-throughput sequencing. Nat Rev Genet. 2014;15(12):783–96. doi:10.1038/nrg3796.

    CAS  PubMed  Article  Google Scholar 

  57. 57.

    Qureshi IA, Mehler MF. Developing epigenetic diagnostics and therapeutics for brain disorders. Trends Mol Med. 2013;19(12):732–41. doi:10.1016/j.molmed.2013.09.003.

    CAS  PubMed  Article  Google Scholar 

  58. 58.

    Pellicciari C, Malatesta M. Identifying pathological biomarkers: histochemistry still ranks high in the omics era. Eur J Histochem. 2011;55(4):e42. doi:10.4081/ejh.2011.e42.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  59. 59.

    Mishra A, Verma M. Cancer biomarkers: are we ready for the prime time? Cancers. 2010;2(1):190–208. doi:10.3390/cancers2010190.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  60. 60.

    Miki Y, Swensen J, Shattuck-Eidens D, Futreal PA, Harshman K, Tavtigian S, et al. A strong candidate for the breast and ovarian cancer susceptibility gene BRCA1. Science (New York, NY). 1994;266(5182):66–71.

    CAS  Article  Google Scholar 

  61. 61.

    Moorman AV, Chilton L, Wilkinson J, Ensor HM, Bown N, Proctor SJ. A population-based cytogenetic study of adults with acute lymphoblastic leukemia. Blood. 2010;115(2):206–14. doi:10.1182/blood-2009-07-232124.

    CAS  PubMed  Article  Google Scholar 

  62. 62.

    Wooster R, Neuhausen SL, Mangion J, Quirk Y, Ford D, Collins N, et al. Localization of a breast cancer susceptibility gene, BRCA2, to chromosome 13q12-13. Science (New York, NY). 1994;265(5181):2088–90.

    CAS  Article  Google Scholar 

  63. 63.

    Thirlwell C, Eymard M, Feber A, Teschendorff A, Pearce K, Lechner M, et al. Genome-wide DNA methylation analysis of archival formalin-fixed paraffin-embedded tissue using the Illumina Infinium HumanMethylation27 BeadChip. Methods (San Diego, Calif). 2010;52(3):248–54. doi:10.1016/j.ymeth.2010.04.012.

    CAS  Article  Google Scholar 

  64. 64.

    Wong NC, Ashley D, Chatterton Z, Parkinson-Bates M, Ng HK, Halemba MS, et al. A distinct DNA methylation signature defines pediatric pre-B cell acute lymphoblastic leukemia. Epigenetics. 2012;7(6):535–41. doi:10.4161/epi.20193.

    CAS  PubMed  Article  Google Scholar 

  65. 65.

    Souren NY, Tierling S, Fryns JP, Derom C, Walter J, Zeegers MP. DNA methylation variability at growth-related imprints does not contribute to overweight in monozygotic twins discordant for BMI. Obesity (Silver Spring, Md). 2011;19(7):1519–22. doi:10.1038/oby.2010.353.

    CAS  Article  Google Scholar 

  66. 66.

    Belinsky SA, Palmisano WA, Gilliland FD, Crooks LA, Divine KK, Winters SA, et al. Aberrant promoter methylation in bronchial epithelium and sputum from current and former smokers. Cancer Res. 2002;62(8):2370–7.

    CAS  PubMed  Google Scholar 

  67. 67.

    Li YW, Kong FM, Zhou JP, Dong M. Aberrant promoter methylation of the vimentin gene may contribute to colorectal carcinogenesis: a meta-analysis. Tumour Biol. 2014;35(7):6783–90. doi:10.1007/s13277-014-1905-1.

    CAS  PubMed  Article  Google Scholar 

  68. 68.

    Payne SR. From discovery to the clinic: the novel DNA methylation biomarker (m)SEPT9 for the detection of colorectal cancer in blood. Epigenomics. 2010;2(4):575–85. doi:10.2217/epi.10.35.

    CAS  PubMed  Article  Google Scholar 

  69. 69.

    Dietrich D, Hasinger O, Liebenberg V, Field JK, Kristiansen G, Soltermann A. DNA methylation of the homeobox genes PITX2 and SHOX2 predicts outcome in non-small-cell lung cancer patients. Diagn Mol Pathol. 2012;21(2):93–104. doi:10.1097/PDM.0b013e318240503b.

    CAS  PubMed  Article  Google Scholar 

  70. 70.

    Wu T, Giovannucci E, Welge J, Mallick P, Tang WY, Ho SM. Measurement of GSTP1 promoter methylation in body fluids may complement PSA screening: a meta-analysis. Br J Cancer. 2011;105(1):65–73. doi:10.1038/bjc.2011.143.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  71. 71.

    Mikeska T, Craig JM. DNA methylation biomarkers: cancer and beyond. Genes. 2014;5(3):821–64. doi:10.3390/genes5030821.

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  72. 72.

    Van Neste L, Herman JG, Otto G, Bigley JW, Epstein JI, Van Criekinge W. The epigenetic promise for prostate cancer diagnosis. Prostate. 2012;72(11):1248–61. doi:10.1002/pros.22459.

    PubMed  Article  CAS  Google Scholar 

  73. 73.

    Yegnasubramanian S, Kowalski J, Gonzalgo ML, Zahurak M, Piantadosi S, Walsh PC, et al. Hypermethylation of CpG islands in primary and metastatic human prostate cancer. Cancer Res. 2004;64(6):1975–86.

    CAS  PubMed  Article  Google Scholar 

  74. 74.

    Heyn H, Esteller M. DNA methylation profiling in the clinic: applications and challenges. Nat Rev Genet. 2012;13(10):679–92. doi:10.1038/nrg3270.

    CAS  PubMed  Article  Google Scholar 

  75. 75.

    Brock MV, Hooker CM, Ota-Machida E, Han Y, Guo M, Ames S, et al. DNA methylation markers and early recurrence in stage I lung cancer. N Engl J Med. 2008;358(11):1118–28. doi:10.1056/NEJMoa0706550.

    CAS  PubMed  Article  Google Scholar 

  76. 76.

    Esteller M, Garcia-Foncillas J, Andion E, Goodman SN, Hidalgo OF, Vanaclocha V, et al. Inactivation of the DNA-repair gene MGMT and the clinical response of gliomas to alkylating agents. N Engl J Med. 2000;343(19):1350–4. doi:10.1056/nejm200011093431901.

    CAS  PubMed  Article  Google Scholar 

  77. 77.

    Hegi ME, Diserens AC, Gorlia T, Hamou MF, de Tribolet N, Weller M, et al. MGMT gene silencing and benefit from temozolomide in glioblastoma. N Engl J Med. 2005;352(10):997–1003. doi:10.1056/NEJMoa043331.

    CAS  PubMed  Article  Google Scholar 

  78. 78.

    Feinberg AP, Ohlsson R, Henikoff S. The epigenetic progenitor origin of human cancer. Nat Rev Genet. 2006;7(1):21–33. doi:10.1038/nrg1748.

    CAS  PubMed  Article  Google Scholar 

  79. 79.

    Esteller M, Corn PG, Baylin SB, Herman JG. A gene hypermethylation profile of human cancer. Cancer Res. 2001;61(8):3225–9.

    CAS  PubMed  Google Scholar 

  80. 80.

    Simmer F, Brinkman AB, Assenov Y, Matarese F, Kaan A, Sabatino L, et al. Comparative genome-wide DNA methylation analysis of colorectal tumor and matched normal tissues. Epigenetics. 2012;7(12):1355–67. doi:10.4161/epi.22562.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  81. 81.

    Gosho M, Nagashima K, Sato Y. Study designs and statistical analyses for biomarker research. Sensors (Basel, Switzerland). 2012;12(7):8966–86. doi:10.3390/s120708966.

    CAS  Article  Google Scholar 

  82. 82.

    Bock C. Epigenetic biomarker development. Epigenomics. 2009;1(1):99–110. doi:10.2217/epi.09.6.

    CAS  PubMed  Article  Google Scholar 

  83. 83.

    Rousu J, Agranoff DD, Sodeinde O, Shawe-Taylor J, Fernandez-Reyes D. Biomarker discovery by sparse canonical correlation analysis of complex clinical phenotypes of tuberculosis and malaria. PLoS Comput Biol. 2013;9(4):e1003018. doi:10.1371/journal.pcbi.1003018.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  84. 84.

    Taguchi YH, Murakami Y. Principal component analysis based feature extraction approach to identify circulating microRNA biomarkers. PLoS One. 2013;8(6):e66714. doi:10.1371/journal.pone.0066714.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  85. 85.

    BLUEPRINT-consortium. Quantitative comparison of DNA methylation assays for biomarker development and clinical applications. Nat Biotechnol. 2016;34(7):726–37. doi:10.1038/nbt.3605.

    Article  CAS  Google Scholar 

  86. 86.

    Milani L, Lundmark A, Kiialainen A, Nordlund J, Flaegstad T, Forestier E, et al. DNA methylation for subtype classification and prediction of treatment outcome in patients with childhood acute lymphoblastic leukemia. Blood. 2010;115(6):1214–25. doi:10.1182/blood-2009-04-214668.

    CAS  PubMed  Article  Google Scholar 

  87. 87.

    Lasseigne BN, Burwell TC, Patil MA, Absher DM, Brooks JD, Myers RM. DNA methylation profiling reveals novel diagnostic biomarkers in renal cell carcinoma. BMC Med. 2014;12:235. doi:10.1186/s12916-014-0235-x.

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  88. 88.

    Guo S, Yan F, Xu J, Bao Y, Zhu J, Wang X, et al. Identification and validation of the methylation biomarkers of non-small cell lung cancer (NSCLC). Clin Epigenetics. 2015;7(1):3. doi:10.1186/s13148-014-0035-3.

    PubMed  PubMed Central  Article  Google Scholar 

  89. 89.

    Exner R, Pulverer W, Diem M, Spaller L, Woltering L, Schreiber M, et al. Potential of DNA methylation in rectal cancer as diagnostic and prognostic biomarkers. Br J Cancer. 2015;113(7):1035–45. doi:10.1038/bjc.2015.303.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  90. 90.

    Boers A, Wang R, van Leeuwen RW, Klip HG, de Bock GH, Hollema H, et al. Discovery of new methylation markers to improve screening for cervical intraepithelial neoplasia grade 2/3. Clin Epigenetics. 2016;8:29. doi:10.1186/s13148-016-0196-3.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  91. 91.

    Lendvai A, Johannes F, Grimm C, Eijsink JJ, Wardenaar R, Volders HH, et al. Genome-wide methylation profiling identifies hypermethylated biomarkers in high-grade cervical intraepithelial neoplasia. Epigenetics. 2012;7(11):1268–78. doi:10.4161/epi.22301.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  92. 92.

    Legendre C, Gooden GC, Johnson K, Martinez RA, Liang WS, Salhia B. Whole-genome bisulfite sequencing of cell-free DNA identifies signature associated with metastatic breast cancer. Clinical epigenetics. 2015;7(1):100. doi:10.1186/s13148-015-0135-8.

    PubMed  PubMed Central  Article  Google Scholar 

  93. 93.

    Stirzaker C, Zotenko E, Song JZ, Qu W, Nair SS, Locke WJ, et al. Methylome sequencing in triple-negative breast cancer reveals distinct methylation clusters with prognostic value. Nat Commun. 2015;6:5899. doi:10.1038/ncomms6899.

    CAS  PubMed  Article  Google Scholar 

  94. 94.

    Noushmehr H, Weisenberger DJ, Diefes K, Phillips HS, Pujara K, Berman BP, et al. Identification of a CpG island methylator phenotype that defines a distinct subgroup of glioma. Cancer Cell. 2010;17(5):510–22. doi:10.1016/j.ccr.2010.03.017.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  95. 95.

    Spruijt CG, Gnerlich F, Smits AH, Pfaffeneder T, Jansen PW, Bauer C, et al. Dynamic readers for 5-(hydroxy)methylcytosine and its oxidized derivatives. Cell. 2013;152(5):1146–59. doi:10.1016/j.cell.2013.02.004.

    CAS  PubMed  Article  Google Scholar 

  96. 96.

    Udali S, Guarini P, Moruzzi S, Ruzzenente A, Tammen SA, Guglielmi A, et al. Global DNA methylation and hydroxymethylation differ in hepatocellular carcinoma and cholangiocarcinoma and relate to survival rate. Hepatology. 2015;62(2):496–504. doi:10.1002/hep.27823.

    CAS  PubMed  Article  Google Scholar 

  97. 97.

    Chen YA, Lemire M, Choufani S, Butcher DT, Grafodatskaya D, Zanke BW, et al. Discovery of cross-reactive probes and polymorphic CpGs in the Illumina Infinium HumanMethylation450 microarray. Epigenetics. 2013;8(2):203–9. doi:10.4161/epi.23470.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  98. 98.

    Houtepen LC, Vinkers CH, Carrillo-Roa T, Hiemstra M, van Lier PA, Meeus W, et al. Genome-wide DNA methylation levels and altered cortisol stress reactivity following childhood trauma in humans. Nat Commun. 2016;7:10967. doi:10.1038/ncomms10967.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  99. 99.

    Barfield RT, Almli LM, Kilaru V, Smith AK, Mercer KB, Duncan R, et al. Accounting for population stratification in DNA methylation studies. Genet Epidemiol. 2014;38(3):231–41. doi:10.1002/gepi.21789.

    PubMed  PubMed Central  Article  Google Scholar 

  100. 100.

    Daca-Roszak P, Pfeifer A, Zebracka-Gala J, Rusinek D, Szybinska A, Jarzab B, et al. Impact of SNPs on methylation readouts by Illumina Infinium HumanMethylation450 BeadChip Array: implications for comparative population studies. BMC Genomics. 2015;16:1003. doi:10.1186/s12864-015-2202-0.

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  101. 101.

    Michels KB, Binder AM, Dedeurwaerder S, Epstein CB, Greally JM, Gut I, et al. Recommendations for the design and analysis of epigenome-wide association studies. Nat Methods. 2013;10(10):949–55. doi:10.1038/nmeth.2632.

    CAS  PubMed  Article  Google Scholar 

  102. 102.

    Naumov VA, Generozov EV, Zaharjevskaya NB, Matushkina DS, Larin AK, Chernyshov SV, et al. Genome-scale analysis of DNA methylation in colorectal cancer using Infinium HumanMethylation450 BeadChips. Epigenetics. 2013;8(9):921–34. doi:10.4161/epi.25577.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  103. 103.

    van Veldhoven K, Polidoro S, Baglietto L, Severi G, Sacerdote C, Panico S, et al. Epigenome-wide association study reveals decreased average methylation levels years before breast cancer diagnosis. Clinical epigenetics. 2015;7(1):67. doi:10.1186/s13148-015-0104-2.

    PubMed  PubMed Central  Article  Google Scholar 

  104. 104.

    Villanueva A, Portela A, Sayols S, Battiston C, Hoshida Y, Mendez-Gonzalez J, et al. DNA methylation-based prognosis and epidrivers in hepatocellular carcinoma. Hepatology (Baltimore, Md). 2015;61(6):1945–56. doi:10.1002/hep.27732.

    CAS  Article  Google Scholar 

  105. 105.

    Kulis M, Heath S, Bibikova M, Queiros AC, Navarro A, Clot G, et al. Epigenomic analysis detects widespread gene-body DNA hypomethylation in chronic lymphocytic leukemia. Nat Genet. 2012;44(11):1236–42. doi:10.1038/ng.2443.

    CAS  PubMed  Article  Google Scholar 

  106. 106.

    Queiros AC, Villamor N, Clot G, Martinez-Trillos A, Kulis M, Navarro A, et al. A B-cell epigenetic signature defines three biologic subgroups of chronic lymphocytic leukemia with clinical impact. Leukemia. 2015;29(3):598–605. doi:10.1038/leu.2014.252.

    CAS  PubMed  Article  Google Scholar 

  107. 107.

    Wu Y, Davison J, Qu X, Morrissey C, Storer B, Brown L, et al. Methylation profiling identified novel differentially methylated markers including OPCML and FLRT2 in prostate cancer. Epigenetics. 2016;11(4):247–58. doi:10.1080/15592294.2016.1148867.

    PubMed  Article  Google Scholar 

  108. 108.

    Zhao S, Geybels MS, Leonardson A, Rubicz R, Kolb S, Yan Q, et al. Epigenome-wide tumor DNA methylation profiling identifies novel prognostic biomarkers of metastatic-lethal progression in men with clinically localized prostate cancer. Clin Cancer Res. 2016. doi:10.1158/1078-0432.ccr-16-0549.

    Google Scholar 

  109. 109.

    Martens JH, Brinkman AB, Simmer F, Francoijs KJ, Nebbioso A, Ferrara F, et al. PML-RARalpha/RXR alters the epigenetic landscape in acute promyelocytic leukemia. Cancer Cell. 2010;17(2):173–85. doi:10.1016/j.ccr.2009.12.042.

    CAS  PubMed  Article  Google Scholar 

  110. 110.

    Saeed S, Logie C, Francoijs KJ, Frige G, Romanenghi M, Nielsen FG, et al. Chromatin accessibility, p300, and histone acetylation define PML-RARalpha and AML1-ETO binding sites in acute myeloid leukemia. Blood. 2012;120(15):3058–68. doi:10.1182/blood-2011-10-386086.

    CAS  PubMed  Article  Google Scholar 

  111. 111.

    Ross-Innes CS, Stark R, Teschendorff AE, Holmes KA, Ali HR, Dunning MJ, et al. Differential oestrogen receptor binding is associated with clinical outcome in breast cancer. Nature. 2012;481(7381):389–93. doi:10.1038/nature10730.

    CAS  PubMed  PubMed Central  Google Scholar 

  112. 112.

    Jansen MP, Knijnenburg T, Reijm EA, Simon I, Kerkhoven R, Droog M, et al. Hallmarks of aromatase inhibitor drug resistance revealed by epigenetic profiling in breast cancer. Cancer Res. 2013;73(22):6632–41. doi:10.1158/0008-5472.can-13-0704.

    CAS  PubMed  Article  Google Scholar 

  113. 113.

    Stelloo S, Nevedomskaya E, van der Poel HG, de Jong J, van Leenders GJ, Jenster G, et al. Androgen receptor profiling predicts prostate cancer outcome. EMBO Mol Med. 2015;7(11):1450–64. doi:10.15252/emmm.201505424.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  114. 114.

    Cejas P, Li L, O’Neill NK, Duarte M, Rao P, Bowden M, et al. Chromatin immunoprecipitation from fixed clinical tissues reveals tumor-specific enhancer profiles. Nat Med. 2016;22(6):685–91. doi:10.1038/nm.4085.

    PubMed  Article  CAS  Google Scholar 

  115. 115.

    Jin W, Tang Q, Wan M, Cui K, Zhang Y, Ren G, et al. Genome-wide detection of DNase I hypersensitive sites in single cells and FFPE tissue samples. Nature. 2015;528(7580):142–6. doi:10.1038/nature15740.

    CAS  PubMed  PubMed Central  Google Scholar 

  116. 116.

    Hnisz D, Weintraub AS, Day DS, Valton AL, Bak RO, Li CH, et al. Activation of proto-oncogenes by disruption of chromosome neighborhoods. Science (New York, NY). 2016;351(6280):1454–8. doi:10.1126/science.aad9024.

    CAS  Article  Google Scholar 

  117. 117.

    Lupianez DG, Kraft K, Heinrich V, Krawitz P, Brancati F, Klopocki E, et al. Disruptions of topological chromatin domains cause pathogenic rewiring of gene-enhancer interactions. Cell. 2015;161(5):1012–25. doi:10.1016/j.cell.2015.04.004.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  118. 118.

    Lou JJ, Mirsadraei L, Sanchez DE, Wilson RW, Shabihkhani M, Lucey GM, et al. A review of room temperature storage of biospecimen tissue and nucleic acids for anatomic pathology laboratories and biorepositories. Clin Biochem. 2014;47(4-5):267–73. doi:10.1016/j.clinbiochem.2013.12.011.

    CAS  PubMed  Article  Google Scholar 

  119. 119.

    Shabihkhani M, Lucey GM, Wei B, Mareninov S, Lou JJ, Vinters HV, et al. The procurement, storage, and quality assurance of frozen blood and tissue biospecimens in pathology, biorepository, and biobank settings. Clin Biochem. 2014;47(4-5):258–66. doi:10.1016/j.clinbiochem.2014.01.002.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  120. 120.

    Wang Q, Gu L, Adey A, Radlwimmer B, Wang W, Hovestadt V, et al. Tagmentation-based whole-genome bisulfite sequencing. Nat Protoc. 2013;8(10):2022–32. doi:10.1038/nprot.2013.118.

    CAS  PubMed  Article  Google Scholar 

  121. 121.

    Dahl JA, Collas P. A rapid micro chromatin immunoprecipitation assay (microChIP). Nat Protoc. 2008;3(6):1032–45. doi:10.1038/nprot.2008.68.

    CAS  PubMed  Article  Google Scholar 

  122. 122.

    Lei H, Liu J, Fukushige T, Fire A, Krause M. Caudal-like PAL-1 directly activates the bodywall muscle module regulator hlh-1 in C. elegans to initiate the embryonic muscle gene regulatory network. Development. 2009;136(8):1241–9. doi:10.1242/dev.030668.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  123. 123.

    Savic D, Gertz J, Jain P, Cooper GM, Myers RM. Mapping genome-wide transcription factor binding sites in frozen tissues. Epigenetics Chromatin. 2013;6(1):30. doi:10.1186/1756-8935-6-30.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  124. 124.

    Milani P, Escalante-Chong R, Shelley BC, Patel-Murray NL, Xin X, Adam M, et al. Cell freezing protocol suitable for ATAC-Seq on motor neurons derived from human induced pluripotent stem cells. Sci Rep. 2016;6:25474. doi:10.1038/srep25474.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  125. 125.

    Scharer CD, Blalock EL, Barwick BG, Haines RR, Wei C, Sanz I, et al. ATAC-Seq on biobanked specimens defines a unique chromatin accessibility structure in naive SLE B cells. Scientific reports. 2016;6:27030. doi:10.1038/srep27030.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  126. 126.

    Cumbie JS, Filichkin SA, Megraw M. Improved DNase-Seq protocol facilitates high resolution mapping of DNase I hypersensitive sites in roots in Arabidopsis thaliana. Plant Methods. 2015;11:42. doi:10.1186/s13007-015-0087-1.

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  127. 127.

    Ling G, Waxman DJ. Isolation of nuclei for use in genome-wide DNase hypersensitivity assays to probe chromatin structure. Methods Mol Biol. 2013;977:13–9. doi:10.1007/978-1-62703-284-1_2.

    CAS  PubMed  Article  Google Scholar 

  128. 128.

    Srinivasan M, Sedmak D, Jewell S. Effect of fixatives and tissue processing on the content and integrity of nucleic acids. Am J Pathol. 2002;161(6):1961–71. doi:10.1016/s0002-9440(10)64472-0.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  129. 129.

    Zhang B, Zhou Y, Lin N, Lowdon RF, Hong C, Nagarajan RP, et al. Functional DNA methylation differences between tissues, cell types, and across individuals discovered using the M&M algorithm. Genome Res. 2013;23(9):1522–40. doi:10.1101/gr.156539.113.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  130. 130.

    Fanelli M, Amatori S, Barozzi I, Minucci S. Chromatin immunoprecipitation and high-throughput sequencing from paraffin-embedded pathology tissue. Nat Protoc. 2011;6(12):1905–19. doi:10.1038/nprot.2011.406.

    CAS  PubMed  Article  Google Scholar 

  131. 131.

    Fanelli M, Amatori S, Barozzi I, Soncini M, Dal Zuffo R, Bucci G, et al. Pathology tissue-chromatin immunoprecipitation, coupled with high-throughput sequencing, allows the epigenetic profiling of patient samples. Proc Natl Acad Sci U S A. 2010;107(50):21535–40. doi:10.1073/pnas.1007647107.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  132. 132.

    Amatori S, Ballarini M, Faversani A, Belloni E, Fusar F, Bosari S, et al. PAT-ChIP coupled with laser microdissection allows the study of chromatin in selected cell populations from paraffin-embedded patient samples. Methods. 2014;7:18. doi:10.1186/1756-8935-7-18.

    Google Scholar 

  133. 133.

    Schillebeeckx M, Schrade A, Lobs AK, Pihlajoki M, Wilson DB, Mitra RD. Laser capture microdissection-reduced representation bisulfite sequencing (LCM-RRBS) maps changes in DNA methylation associated with gonadectomy-induced adrenocortical neoplasia in the mouse. Nucleic Acids Res. 2013;41(11):e116. doi:10.1093/nar/gkt230.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  134. 134.

    Furey TS. ChIP-Seq and beyond: new and improved methodologies to detect and characterize protein-DNA interactions. Nat Rev Genet. 2012;13(12):840–52. doi:10.1038/nrg3306.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  135. 135.

    Wu J, Huang B, Chen H, Yin Q, Liu Y, Xiang Y, et al. The landscape of accessible chromatin in mammalian preimplantation embryos. Nature. 2016;534(7609):652–7. doi:10.1038/nature18606.

    CAS  PubMed  Article  Google Scholar 

  136. 136.

    Marusyk A, Almendro V, Polyak K. Intra-tumour heterogeneity: a looking glass for cancer? Nat Rev Cancer. 2012;12(5):323–34. doi:10.1038/nrc3261.

    CAS  PubMed  Article  Google Scholar 

  137. 137.

    Trapnell C. Defining cell types and states with single-cell genomics. Genome Res. 2015;25(10):1491–8. doi:10.1101/gr.190595.115.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  138. 138.

    Kim KT, Lee HW, Lee HO, Kim SC, Seo YJ, Chung W, et al. Single-cell mRNA sequencing identifies subclonal heterogeneity in anti-cancer drug responses of lung adenocarcinoma cells. Genome Biol. 2015;16:127. doi:10.1186/s13059-015-0692-3.

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  139. 139.

    Lawson DA, Bhakta NR, Kessenbrock K, Prummel KD, Yu Y, Takai K, et al. Single-cell analysis reveals a stem-cell program in human metastatic breast cancer cells. Nature. 2015;526(7571):131–5. doi:10.1038/nature15260.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  140. 140.

    Aberg KA, McClay JL, Nerella S, Xie LY, Clark SL, Hudson AD, et al. MBD-Seq as a cost-effective approach for methylome-wide association studies: demonstration in 1500 case–control samples. Epigenomics. 2012;4(6):605–21. doi:10.2217/epi.12.59.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  141. 141.

    Brinkman AB, Simmer F, Ma K, Kaan A, Zhu J, Stunnenberg HG. Whole-genome DNA methylation profiling using MethylCap-Seq. Methods (San Diego, Calif). 2010;52(3):232–6. doi:10.1016/j.ymeth.2010.06.012.

    CAS  Article  Google Scholar 

  142. 142.

    Butcher LM, Beck S. AutoMeDIP-Seq: a high-throughput, whole genome, DNA methylation assay. Methods (San Diego, Calif). 2010;52(3):223–31. doi:10.1016/j.ymeth.2010.04.003.

    CAS  Article  Google Scholar 

  143. 143.

    Aldridge S, Watt S, Quail MA, Rayner T, Lukk M, Bimson MF, et al. AHT-ChIP-Seq: a completely automated robotic protocol for high-throughput chromatin immunoprecipitation. Genome Biol. 2013;14(11):R124. doi:10.1186/gb-2013-14-11-r124.

    PubMed  PubMed Central  Article  Google Scholar 

  144. 144.

    Berguet G, Hendrickx J, Sabatel C, Laczik M, Squazzo S, Mazon Pelaez I, et al. Automating ChIP-Seq experiments to generate epigenetic profiles on 10,000 HeLa cells. J Vis Exp. 2014:(94). doi:10.3791/52150.

  145. 145.

    Gasper WC, Marinov GK, Pauli-Behn F, Scott MT, Newberry K, DeSalvo G, et al. Fully automated high-throughput chromatin immunoprecipitation for ChIP-Seq: identifying ChIP-quality p300 monoclonal antibodies. Scientific reports. 2014;4:5152. doi:10.1038/srep05152.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  146. 146.

    Wallerman O, Nord H, Bysani M, Borghini L, Wadelius C. lobChIP: from cells to sequencing ready ChIP libraries in a single day. Epigenetics & chromatin. 2015;8:25. doi:10.1186/s13072-015-0017-5.

    Article  CAS  Google Scholar 

  147. 147.

    Shen J, Jiang D, Fu Y, Wu X, Guo H, Feng B, et al. H3K4me3 epigenomic landscape derived from ChIP-Seq of 1,000 mouse early embryonic cells. Cell Res. 2015;25(1):143–7. doi:10.1038/cr.2014.119.

    CAS  PubMed  Article  Google Scholar 

  148. 148.

    Cao Z, Chen C, He B, Tan K, Lu C. A microfluidic device for epigenomic profiling using 100 cells. Nat Methods. 2015;12(10):959–62. doi:10.1038/nmeth.3488.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  149. 149.

    Kim H, Jebrail MJ, Sinha A, Bent ZW, Solberg OD, Williams KP, et al. A microfluidic DNA library preparation platform for next-generation sequencing. PLoS One. 2013;8(7):e68988. doi:10.1371/journal.pone.0068988.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  150. 150.

    Tan SJ, Phan H, Gerry BM, Kuhn A, Hong LZ, Min Ong Y, et al. A microfluidic device for preparing next generation DNA sequencing libraries and for automating other laboratory protocols that require one or more column chromatography steps. PLoS One. 2013;8(7):e64084. doi:10.1371/journal.pone.0064084.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  151. 151.

    Buenrostro JD, Wu B, Litzenburger UM, Ruff D, Gonzales ML, Snyder MP, et al. Single-cell chromatin accessibility reveals principles of regulatory variation. Nature. 2015;523(7561):486–90. doi:10.1038/nature14590.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  152. 152.

    Rotem A, Ram O, Shoresh N, Sperling RA, Goren A, Weitz DA, et al. Single-cell ChIP-Seq reveals cell subpopulations defined by chromatin state. Nat Biotechnol. 2015;33(11):1165–72. doi:10.1038/nbt.3383.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  153. 153.

    Gal-Yam EN, Egger G, Iniguez L, Holster H, Einarsson S, Zhang X, et al. Frequent switching of Polycomb repressive marks and DNA hypermethylation in the PC3 prostate cancer cell line. Proc Natl Acad Sci U S A. 2008;105(35):12979–84. doi:10.1073/pnas.0806437105.

    PubMed  PubMed Central  Article  Google Scholar 

  154. 154.

    Lister R, Pelizzola M, Dowen RH, Hawkins RD, Hon G, Tonti-Filippini J, et al. Human DNA methylomes at base resolution show widespread epigenomic differences. Nature. 2009;462(7271):315–22. doi:10.1038/nature08514.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  155. 155.

    Ohm JE, McGarvey KM, Yu X, Cheng L, Schuebel KE, Cope L, et al. A stem cell-like chromatin pattern may predispose tumor suppressor genes to DNA hypermethylation and heritable silencing. Nat Genet. 2007;39(2):237–42. doi:10.1038/ng1972.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  156. 156.

    Schlesinger Y, Straussman R, Keshet I, Farkash S, Hecht M, Zimmerman J, et al. Polycomb-mediated methylation on Lys27 of histone H3 pre-marks genes for de novo methylation in cancer. Nat Genet. 2007;39(2):232–6. doi:10.1038/ng1950.

    CAS  PubMed  Article  Google Scholar 

  157. 157.

    Wolf SF, Jolly DJ, Lunnen KD, Friedmann T, Migeon BR. Methylation of the hypoxanthine phosphoribosyltransferase locus on the human X chromosome: implications for X-chromosome inactivation. Proc Natl Acad Sci U S A. 1984;81(9):2806–10.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  158. 158.

    Stadler MB, Murr R, Burger L, Ivanek R, Lienert F, Scholer A, et al. DNA-binding factors shape the mouse methylome at distal regulatory regions. Nature. 2011;480(7378):490–5. doi:10.1038/nature10716.

    CAS  PubMed  Google Scholar 

  159. 159.

    Li N, Ye M, Li Y, Yan Z, Butcher LM, Sun J, et al. Whole genome DNA methylation analysis based on high throughput sequencing technology. Methods (San Diego, Calif). 2010;52(3):203–12. doi:10.1016/j.ymeth.2010.04.009.

    Article  CAS  Google Scholar 

  160. 160.

    Taiwo O, Wilson GA, Morris T, Seisenberger S, Reik W, Pearce D, et al. Methylome analysis using MeDIP-Seq with low DNA concentrations. Nat Protoc. 2012;7(4):617–36. doi:10.1038/nprot.2012.012.

    CAS  PubMed  Article  Google Scholar 

  161. 161.

    Weber M, Davies JJ, Wittig D, Oakeley EJ, Haase M, Lam WL, et al. Chromosome-wide and promoter-specific analyses identify sites of differential DNA methylation in normal and transformed human cells. Nat Genet. 2005;37(8):853–62. doi:10.1038/ng1598.

    CAS  PubMed  Article  Google Scholar 

  162. 162.

    Bock C, Tomazou EM, Brinkman AB, Muller F, Simmer F, Gu H, et al. Quantitative comparison of genome-wide DNA methylation mapping technologies. Nat Biotechnol. 2010;28(10):1106–14. doi:10.1038/nbt.1681.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  163. 163.

    Sandoval J, Heyn H, Moran S, Serra-Musach J, Pujana MA, Bibikova M, et al. Validation of a DNA methylation microarray for 450,000 CpG sites in the human genome. Epigenetics. 2011;6(6):692–702.

    CAS  PubMed  Article  Google Scholar 

  164. 164.

    Meissner A, Gnirke A, Bell GW, Ramsahoye B, Lander ES, Jaenisch R. Reduced representation bisulfite sequencing for comparative high-resolution DNA methylation analysis. Nucleic Acids Res. 2005;33(18):5868–77. doi:10.1093/nar/gki901.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  165. 165.

    Plongthongkum N, Diep DH, Zhang K. Advances in the profiling of DNA modifications: cytosine methylation and beyond. Nat Rev Genet. 2014;15(10):647–61. doi:10.1038/nrg3772.

    CAS  PubMed  Article  Google Scholar 

  166. 166.

    Sun Z, Wu Y, Ordog T, Baheti S, Nie J, Duan X, et al. Aberrant signature methylome by DNMT1 hot spot mutation in hereditary sensory and autonomic neuropathy 1E. Epigenetics. 2014;9(8):1184–93. doi:10.4161/epi.29676.

    PubMed  PubMed Central  Article  Google Scholar 

  167. 167.

    Cuddapah S, Barski A, Cui K, Schones DE, Wang Z, Wei G, et al. Native chromatin preparation and Illumina/Solexa library construction. Cold Spring Harb Protoc. 2009;2009(6):pdb.prot5237. doi:10.1101/pdb.prot5237.

    PubMed  PubMed Central  Article  Google Scholar 

  168. 168.

    Kasinathan S, Orsi GA, Zentner GE, Ahmad K, Henikoff S. High-resolution mapping of transcription factor binding sites on native chromatin. Nat Methods. 2014;11(2):203–9. doi:10.1038/nmeth.2766.

    CAS  PubMed  Article  Google Scholar 

  169. 169.

    Barski A, Cuddapah S, Cui K, Roh TY, Schones DE, Wang Z, et al. High-resolution profiling of histone methylations in the human genome. Cell. 2007;129(4):823–37. doi:10.1016/j.cell.2007.05.009.

    CAS  PubMed  Article  Google Scholar 

  170. 170.

    Johnson DS, Mortazavi A, Myers RM, Wold B. Genome-wide mapping of in vivo protein-DNA interactions. Science (New York, NY). 2007;316(5830):1497–502. doi:10.1126/science.1141319.

    CAS  Article  Google Scholar 

  171. 171.

    Meyer CA, Liu XS. Identifying and mitigating bias in next-generation sequencing methods for chromatin biology. Nat Rev Genet. 2014;15(11):709–21. doi:10.1038/nrg3788.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  172. 172.

    Tsompana M, Buck MJ. Chromatin accessibility: a window into the genome. Epigenetics & chromatin. 2014;7(1):33. doi:10.1186/1756-8935-7-33.

    Article  Google Scholar 

  173. 173.

    Hesselberth JR, Chen X, Zhang Z, Sabo PJ, Sandstrom R, Reynolds AP, et al. Global mapping of protein-DNA interactions in vivo by digital genomic footprinting. Nat Methods. 2009;6(4):283–9. doi:10.1038/nmeth.1313.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  174. 174.

    Neph S, Vierstra J, Stergachis AB, Reynolds AP, Haugen E, Vernot B, et al. An expansive human regulatory lexicon encoded in transcription factor footprints. Nature. 2012;489(7414):83–90. doi:10.1038/nature11212.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  175. 175.

    Buenrostro JD, Giresi PG, Zaba LC, Chang HY, Greenleaf WJ. Transposition of native chromatin for fast and sensitive epigenomic profiling of open chromatin, DNA-binding proteins and nucleosome position. Nat Methods. 2013;10(12):1213–8. doi:10.1038/nmeth.2688.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  176. 176.

    He HH, Meyer CA, Hu SS, Chen MW, Zang C, Liu Y, et al. Refined DNase-Seq protocol and data analysis reveals intrinsic bias in transcription factor footprint identification. Nat Methods. 2014;11(1):73–8. doi:10.1038/nmeth.2762.

    CAS  PubMed  Article  Google Scholar 

  177. 177.

    Cui K, Zhao K. Genome-wide approaches to determining nucleosome occupancy in metazoans using MNase-Seq. Methods in molecular biology (Clifton, NJ). 2012;833:413–9. doi:10.1007/978-1-61779-477-3_24.

    CAS  Article  Google Scholar 

  178. 178.

    He HH, Meyer CA, Shin H, Bailey ST, Wei G, Wang Q, et al. Nucleosome dynamics define transcriptional enhancers. Nat Genet. 2010;42(4):343–7. doi:10.1038/ng.545.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  179. 179.

    Schones DE, Cui K, Cuddapah S, Roh TY, Barski A, Wang Z, et al. Dynamic regulation of nucleosome positioning in the human genome. Cell. 2008;132(5):887–98. doi:10.1016/j.cell.2008.02.022.

    CAS  PubMed  Article  Google Scholar 

  180. 180.

    Dekker J, Rippe K, Dekker M, Kleckner N. Capturing chromosome conformation. Science (New York, NY). 2002;295(5558):1306–11. doi:10.1126/science.1067799.

    CAS  Article  Google Scholar 

  181. 181.

    de Wit E, de Laat W. A decade of 3C technologies: insights into nuclear organization. Genes Dev. 2012;26(1):11–24. doi:10.1101/gad.179804.111.

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  182. 182.

    Splinter E, de Wit E, Nora EP, Klous P, van de Werken HJ, Zhu Y, et al. The inactive X chromosome adopts a unique three-dimensional conformation that is dependent on Xist RNA. Genes Dev. 2011;25(13):1371–83. doi:10.1101/gad.633311.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  183. 183.

    Dixon JR, Selvaraj S, Yue F, Kim A, Li Y, Shen Y, et al. Topological domains in mammalian genomes identified by analysis of chromatin interactions. Nature. 2012;485(7398):376–80. doi:10.1038/nature11082.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  184. 184.

    Lieberman-Aiden E, van Berkum NL, Williams L, Imakaev M, Ragoczy T, Telling A, et al. Comprehensive mapping of long-range interactions reveals folding principles of the human genome. Science (New York, NY). 2009;326(5950):289–93. doi:10.1126/science.1181369.

    CAS  Article  Google Scholar 

  185. 185.

    Nora EP, Lajoie BR, Schulz EG, Giorgetti L, Okamoto I, Servant N, et al. Spatial partitioning of the regulatory landscape of the X-inactivation centre. Nature. 2012;485(7398):381–5. doi:10.1038/nature11049.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  186. 186.

    van de Werken HJ, de Vree PJ, Splinter E, Holwerda SJ, Klous P, de Wit E, et al. 4C technology: protocols and data analysis. Methods Enzymol. 2012;513:89–112. doi:10.1016/b978-0-12-391938-0.00004-5.

    PubMed  Article  CAS  Google Scholar 

  187. 187.

    Belton JM, McCord RP, Gibcus JH, Naumova N, Zhan Y, Dekker J. Hi-C: a comprehensive technique to capture the conformation of genomes. Methods (San Diego, Calif). 2012;58(3):268–76. doi:10.1016/j.ymeth.2012.05.001.

    CAS  Article  Google Scholar 

  188. 188.

    Smallwood SA, Lee HJ, Angermueller C, Krueger F, Saadeh H, Peat J, et al. Single-cell genome-wide bisulfite sequencing for assessing epigenetic heterogeneity. Nat Methods. 2014;11(8):817–20. doi:10.1038/nmeth.3035.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  189. 189.

    Miura F, Enomoto Y, Dairiki R, Ito T. Amplification-free whole-genome bisulfite sequencing by post-bisulfite adaptor tagging. Nucleic Acids Res. 2012;40(17):e136. doi:10.1093/nar/gks454.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  190. 190.

    Farlik M, Sheffield NC, Nuzzo A, Datlinger P, Schonegger A, Klughammer J, et al. Single-cell DNA methylome sequencing and bioinformatic inference of epigenomic cell-state dynamics. Cell Rep. 2015;10(8):1386–97. doi:10.1016/j.celrep.2015.02.001.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  191. 191.

    Adli M, Zhu J, Bernstein BE. Genome-wide chromatin maps derived from limited numbers of hematopoietic progenitors. Nat Methods. 2010;7(8):615–8. doi:10.1038/nmeth.1478.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  192. 192.

    Ng JH, Kumar V, Muratani M, Kraus P, Yeo JC, Yaw LP, et al. In vivo epigenomic profiling of germ cells reveals germ cell molecular signatures. Dev Cell. 2013;24(3):324–33. doi:10.1016/j.devcel.2012.12.011.

    CAS  PubMed  Article  Google Scholar 

  193. 193.

    Sachs M, Onodera C, Blaschke K, Ebata KT, Song JS, Ramalho-Santos M. Bivalent chromatin marks developmental regulatory genes in the mouse embryonic germline in vivo. Cell Rep. 2013;3(6):1777–84. doi:10.1016/j.celrep.2013.04.032.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  194. 194.

    Shankaranarayanan P, Mendoza-Parra MA, Walia M, Wang L, Li N, Trindade LM, et al. Single-tube linear DNA amplification (LinDA) for robust ChIP-Seq. Nat Methods. 2011;8(7):565–7. doi:10.1038/nmeth.1626.

    CAS  PubMed  Article  Google Scholar 

  195. 195.

    Zhang B, Zheng H, Huang B, Li W, Xiang Y, Peng X, et al. Allelic reprogramming of the histone modification H3K4me3 in early mammalian development. Nature. 2016;537(7621):553–7. doi:10.1038/nature19361.

    CAS  PubMed  Article  Google Scholar 

  196. 196.

    Brind’Amour J, Liu S, Hudson M, Chen C, Karimi MM, Lorincz MC. An ultra-low-input native ChIP-Seq protocol for genome-wide profiling of rare cell populations. Nat Commun. 2015;6:6033. doi:10.1038/ncomms7033.

    PubMed  Article  CAS  Google Scholar 

  197. 197.

    Liu X, Wang C, Liu W, Li J, Li C, Kou X, et al. Distinct features of H3K4me3 and H3K27me3 chromatin domains in pre-implantation embryos. Nature. 2016;537(7621):558–62. doi:10.1038/nature19362.

    CAS  PubMed  Article  Google Scholar 

  198. 198.

    Zwart W, Koornstra R, Wesseling J, Rutgers E, Linn S, Carroll JS. A carrier-assisted ChIP-Seq method for estrogen receptor-chromatin interactions from breast cancer core needle biopsy samples. BMC Genomics. 2013;14:232. doi:10.1186/1471-2164-14-232.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  199. 199.

    Jakobsen JS, Bagger FO, Hasemann MS, Schuster MB, Frank AK, Waage J, et al. Amplification of pico-scale DNA mediated by bacterial carrier DNA for small-cell-number transcription factor ChIP-Seq. BMC Genomics. 2015;16:46. doi:10.1186/s12864-014-1195-4.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  200. 200.

    Schmidl C, Rendeiro AF, Sheffield NC, Bock C. ChIPmentation: fast, robust, low-input ChIP-Seq for histones and transcription factors. Nat Methods. 2015;12(10):963–5. doi:10.1038/nmeth.3542.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  201. 201.

    Dahl JA, Jung I, Aanes H, Greggains GD, Manaf A, Lerdrup M, et al. Broad histone H3K4me3 domains in mouse oocytes modulate maternal-to-zygotic transition. Nature. 2016;537(7621):548–52. doi:10.1038/nature19360.

    CAS  PubMed  Article  Google Scholar 

  202. 202.

    Lara-Astiaso D, Weiner A, Lorenzo-Vivas E, Zaretsky I, Jaitin DA, David E, et al. Immunogenetics. Chromatin state dynamics during blood formation. Science (New York, NY). 2014;345(6199):943–9. doi:10.1126/science.1256271.

    CAS  Article  Google Scholar 

  203. 203.

    van Galen P, Viny AD, Ram O, Ryan RJ, Cotton MJ, Donohue L, et al. A multiplexed system for quantitative comparisons of chromatin landscapes. Mol Cell. 2016;61(1):170–80. doi:10.1016/j.molcel.2015.11.003.

    PubMed  Article  CAS  Google Scholar 

  204. 204.

    Cusanovich DA, Daza R, Adey A, Pliner HA, Christiansen L, Gunderson KL, et al. Epigenetics. Multiplex single-cell profiling of chromatin accessibility by combinatorial cellular indexing. Science (New York, NY). 2015;348(6237):910–4. doi:10.1126/science.aab1601.

    CAS  Article  Google Scholar 

  205. 205.

    Nagano T, Lubling Y, Stevens TJ, Schoenfelder S, Yaffe E, Dean W, et al. Single-cell Hi-C reveals cell-to-cell variability in chromosome structure. Nature. 2013;502(7469):59–64. doi:10.1038/nature12593.

    CAS  PubMed  Article  Google Scholar 

  206. 206.

    Nagano T, Lubling Y, Yaffe E, Wingett SW, Dean W, Tanay A, et al. Single-cell Hi-C for genome-wide detection of chromatin interactions that occur simultaneously in a single cell. Nat Protoc. 2015;10(12):1986–2003. doi:10.1038/nprot.2015.127.

    CAS  PubMed  Article  Google Scholar 

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We thank members of the Marks’ and Stunnenberg laboratories for discussion and insight. We thank Dr. Arjen Brinkman, Dr. Richard Bartfai, and Dr. Joost Martens for their input on the manuscript. We thank COST action “CM1406- Epigenetic Chemical Biology” for their financial support regarding the publication fee.


Research in the group of HGS is supported by the European Union grant BLUEPRINT (FP7/2011: 282510) and ERC-2013-ADG-339431 “SysStemCell.” Research in the group of HM is supported by a grant from the Netherlands Organization for Scientific Research (NWO-VIDI 864.12.007).

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HM prepared the main text and tables with help of RD and HGS. RD prepared the figures. All authors contributed to the content. All authors read and approved the final manuscript.

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The authors declare that they have no competing interests.

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Correspondence to Hendrik Marks.

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Dirks, R.A.M., Stunnenberg, H.G. & Marks, H. Genome-wide epigenomic profiling for biomarker discovery. Clin Epigenet 8, 122 (2016).

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  • Genome-wide epigenetic profiling
  • Biomarker discovery
  • Miniaturization
  • Automation
  • Single cell
  • DNA methylation
  • WGBS
  • ATAC-Seq
  • Stratification
  • Precision medicine