Cross-tissue analysis of methylomic variation in Alzheimer’s disease

Methylomic profiling implicates cortical deregulation of ANK1 in Alzheimer's disease.

Lunnon, K. et al.Nature Neuroscience 10.1038/nn.3782

For the first (discovery) stage of our analysis, we used multiple tissues from donors (n = 122) archived in the MRC London Brainbank for Neurodegenerative Disease. From each donor, we isolated genomic DNA from four brain regions (entorhinal cortex (EC), n = 104; superior temporal gyrus (STC), n = 113; prefrontal cortex (PFC), n = 110; CER, n = 108) and, where available, from whole blood obtained pre-mortem (n = 57) (Supplementary Tables 1 and 2). DNA methylation was quantified using the Illumina 450K Human Methylation array, with pre-processing, normalization and stringent quality control undertaken as previously described11 (Online Methods). Our analyses focused on identifying differentially methylated positions (DMPs) associated with Braak staging, a standardized measure of neurofibrillary tangle burden determined at autopsy12, with all analyses controlling for age and sex. We first assessed DNA methylation differences identified in the EC, given that it is a primary and early site of neuropathology in AD5. Two of the top-ranked EC DMPs (cg11823178, the top-ranked EC DMP, and cg05066959, the fourth-ranked EC DMP) were located 91 bp away from each other in the ankyrin 1 (ANK1) gene on chromosome 8, which encodes a brain-expressed protein13 involved in compartmentalization of the neuronal plasma membrane14 (Fig. 1a). These DMPs are also located proximal to the NKX6-3 gene, encoding a homeodomain transcription factor involved in the development of the brain15, 16. Increased EC DNA methylation at both CpG sites was associated with Braak stage (cg11823178: r = 0.47, t102= 5.39, nominal P = 4.59 × 10−7; cg05066959: r = 0.41, t102 = 5.37, nominal P = 1.34 × 10−5;Fig. 1b). Hypermethylation at both DMPs was significantly associated with Braak score in the STG (cg11823178: r = 0.37, t111 = 4.15, nominal P = 6.51 × 10−5; cg05066959: r = 0.33, t111 = 3.67, nominal P = 3.78 × 10−4) and the PFC (cg11823178: r = 0.29, t108 = 3.12, nominal P = 2.33 × 10−3; cg05066959: r = 0.32, t108= 3.52, nominal P = 6.48 × 10−4) (Fig. 1c). In contrast, no significant neuropathology-associated hypermethylation was detected at either CpG site in the CER (cg11823178: r = 0.01, t106 = 0.082, nominal P = 0.935; cg05066959: r = −0.08, t106 = 0.085, nominal P = 0.395) (Fig. 1d), a region largely protected from neurodegeneration in AD, nor was elevated DNA methylation at either site associated with AD diagnosis in whole blood collected pre-mortem (data not shown).

Notably, we observe a significant overlap in Braak-associated DMPs across the three cortical regions profiled in the London discovery cohort: 38 (permuted P < 0.005) and 30 (permuted P<0.005) of the 100 top-ranked EC probes were significantly differentially methylated in the same direction in the STG and PFC, respectively (Supplementary Table 8), with a highly significant correlation of top-ranked Braak-associated DNA methylation scores across these sites (EC versus STG: r = 0.88, P = 6.73 × 10−14; EC versus PFC: r = 0.83, P = 8.77 × 10−13). There was, however, a clear distinction between cortical regions and CER, with the top-ranked CER DMPs appearing to be more tissue specific and not differentially methylated in cortical regions (permutedP values for enrichment all > 0.05), although ~15% of the top-ranked cortical DMPs were differentially methylated in CER (permuted P values ≤ 0.01), indicating that these represent relatively pervasive AD-associated changes that are observed across multiple tissues. We subsequently used a meta-analysis method to highlight consistent Braak-associated DNA methylation differences across all three cortical regions in the discovery cohort. The top-ranked cross-cortex DMPs are shown in Table 2 and Supplementary Table 9, and DMRs identified using comb-p are listed in Supplementary Table 10. Of note, cg11823178 was the most significant cross-cortex DMP (Δ = 3.20, Fisher's P = 3.42 × 10−11, Brown's P = 1.00 × 10−6), with cg05066959 again being ranked fourth (Δ = 4.26, Fisher's P = 1.24 × 10−9, Brown's P = 6.24 × 10−6; Fig. 1e) and a DMR spanning these probes being associated with neuropathology (Sidak-corrected P = 3.39 × 10−4) (Supplementary Table 10). Together, these data suggest that cortical DNA hypermethylation at the ANK1 locus is robustly associated with AD-related neuropathology.

A second (replication) cortical data set was generated using DNA isolated from two regions (STG and PFC) obtained from a cohort of brains archived in the Mount Sinai Alzheimer's Disease and Schizophrenia Brain Bank (n = 144), with detailed neuropathology data including Braak staging and amyloid burden (Online Methods)19. Notably, Braak-associated DNA methylation scores for the 100 top-ranked cross-cortex DMPs identified in the London discovery cohort (Supplementary Table 9) were strongly correlated with neuropathology-associated differences at the same probes in both cortical regions profiled in the Mount Sinai replication cohort (STG Braak score: r = 0.63, P= 2.66 × 10−12; PFC Braak score: r = 0.64, P = 6.03 × 10−13; STG amyloid burden: r = 0.46, P = 1.09 × 10−6; PFC amyloid burden: r = 0.65, P = 2.87 × 10−13; Fig. 2a). Furthermore, increased DNA methylation at each of the two ANK1 CpG sites was significantly associated with elevated Braak score (Table 1 and Fig. 2b) in the STG (cg11823178: r = 0.28, t142 = 3.62, nominal P = 1.63 × 10−4; cg05066959: r = 0.25, t142 = 3.29, nominal P = 5.78 × 10−4) and PFC (cg11823178:r = 0.24, t140 = 3.14, nominal P = 1.07 × 10−3; cg05066959: r = 0.21, t140 = 2.75, nominal P = 4.00 × 10−3), and also amyloid pathology (Fig. 2c) in the STG (cg11823178: r = 0.21, t142 = 2.81, nominal P = 4.99 × 10−4; cg05066959: r = 0.27, t142 = 3.47, nominal P =5.65 × 10−4) and PFC (cg11823178: r = 0.29, t140 = 3.69, nominal P = 2.35 × 10−4; cg05066959: r = 0.19, t140 = 2.56, nominal P = 9.93 × 10−3).

To further confirm the association between cortical ANK1 hypermethylation and neuropathology, we used bisulfite-pyrosequencing to quantify DNA methylation across an extended region spanning eight CpG sites, including cg11823178 and cg05066959, in DNA extracted from a third independent collection of matched EC, STG and PFC tissue (n = 62) obtained from the Thomas Willis Oxford Brain Collection20 (Online Methods and Supplementary Table 11a). Average DNA methylation across this region was significantly elevated in all three cortical regions tested (EC, P= 0.0004; STG, P = 0.0008; PFC, P = 0.014) in affected individuals (Supplementary Fig. 1), most notably in the EC, where six of the eight CpG sites assessed were characterized by significant AD-associated hypermethylation (Fig. 2d). A meta-analysis of cg11823178 and cg05066959 across all three independent cohorts confirmed consistent neuropathology-associated hypermethylation in each of the cortical regions assessed (Fig. 2e,f). Further evidence to support our conclusions came from an independent EWAS of AD pathology in 708 cortical samples (De Jager et al.)21. There was a significant correlation (r = 0.57, P = 1.55 × 10−9) between the 100 top-ranked DNA methylation changes identified in our cross-cortex analyses and neuropathology-associated differences at the same probes in the study by De Jager et al. (Fig. 2g)21. Conversely, neuropathology-associated DNA methylation scores for top-ranked DMPs in De Jager et al.21 were strongly correlated (r = 0.49, P = 7.8 × 10−10) with those that we observed using the cross-cortex model for the same probes in our discovery cohort (Supplementary Fig. 2). In particular, De Jager et al.21 also identified a highly significant association between elevated DNA methylation at cg11823178 and cg05066959 and AD-related neuropathology. Together, these data provide compelling evidence for an association between ANK1 hypermethylation and the neuropathological features of AD, specifically in the cortical regions associated with disease manifestation. Although not previously implicated in dementia, genetic variation in ANK1 is associated with diabetic phenotypes22, 23, 24, an interesting observation given the established links between type 2 diabetes and AD25.

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We identified evidence for cortex-specific hypermethylation at CpG sites in the ANK1 gene associated with AD neuropathology. Definitively distinguishing cause from effect in epigenetic epidemiology is difficult, especially for disorders such as AD that manifest in inaccessible tissues such as the brain and are not amenable to longitudinal study9, 10. However, our observation of highly consistent changes across multiple regions of the cortex in several independent sample cohorts suggests that the identified loci are directly relevant to the pathogenesis of AD. In this regard, the ANK1 DMR reported here, and subsequently confirmed by De Jager et al.21, represents one of the most robust molecular associations with AD yet identified.

Alzheimer’s disease: early alterations in brain DNA methylation at ANK1, BIN1, RHBDF2 and other loci.

De Jager, P. L. et al.Nature Neuroscience 10.1038/nn.3786

Our data set consisted of methylation measures at 415,848 discrete CpG dinucleotides in 708 subjects. These methylation profiles were generated using the Illumina HumanMethylation450 beadset and a sample of dorsolateral prefrontal cortex obtained from each individual. Since we dissected out the gray matter from each sample, we have profiled a piece of tissue composed primarily of different neuronal populations and other parenchymal cells such as glia. These subjects are part of the Religious Order Study (ROS) or the Memory and Aging Project (MAP), two prospective cohort studies of aging that include brain donation at the time of death. Since the subjects are cognitively non-impaired at study entry, we have studied a random selection of the older population. Over the course of the study, some subjects decline cognitively and display a range of amyloid pathology burden at the time of death, with 60.8% of subjects meeting a pathologic diagnosis of AD14 (Supplementary Table 1a). To technically validate the nature of our data, we compared our Illumina-derived data to genome-wide DNA methylation sequence data generated from the same brain DNA samples in four of the subjects (two non-impaired and two AD subjects): in these four subjects, we see a very strong correlation (mean r= 0.97) between the estimated levels of methylation generated by the two technologies, consistent with prior reports15.

Notably, when examining the nature of human cortical methylation profiles across our subject population, we note that the mean Pearson correlation of methylation levels for all possible subject pairs is 0.98 (Supplementary Figure 1), suggesting that that majority of CpG sites do not show significant interindividual variation in methylation levels despite the very different life course of each of these older subjects. As expected, we see many more differences in DNA methylation profiles between our cortical samples and lymphoblastic cell lines from HapMap individuals that were profiled for assessments of data quality in our experiment (Supplementary Figure 2).

Distribution of associated CpGs among different chromatin states

Alzheimer’s disease: early alterations in brain DNA methylation at ANK1, BIN1, RHBDF2 and other loci.

De Jager, P. L. et al.Nature Neuroscience 10.1038/nn.3786

To better understand the functional consequences of the associated CpGs, we interpreted our results in relation to a chromatin map of the dorsolateral prefrontal cortex, generated in collaboration with the Epigenomics Roadmap team (http://www.roadmapepigenomics.org). It is derived from two MAP subjects who were cognitively non-impaired at the time of death and had minimal AD-associated pathology on post-mortem examination. Using histone modification profiles and established methods27, each 200 bp segment of the genome is annotated as being in one of 11 chromatin states (Figure 3a) that capture the transcriptional states and putative regulatory elements found in this tissue. Using this reference map, we see that at least some of the 71 associated CpGs are found in every chromatin state but that there is an enrichment of associated CpGs in regions predicted to be weak enhancers (p=0.0098) or to be in a weakly transcribed chromatin state (p=0.028), (Figure 3b, Supplementary Table 4). Further, we see a strong under-representation in regions displaying a strong promoter profile in the reference chromatin map (p=8x10-4). These data suggest that the chromatin architecture of strong promoters that drive fundamental cellular processes of neurons and glia in the healthy brain may not be strongly altered by AD. Rather, methylation changes appear to primarily affect genomic regions that are weakly transcribed or inactive in the healthy older brain. There are no enrichments noted in different genic features or in different structures defined in relation to CpG islands (Supplementary Figure 5a and 5b).

Epigenomic profiling in Alzheimer’s disease

Conserved epigenomic signals in mice and humans reveal immune basis of Alzheimer’s disease.

Gjoneska, E. et al.Nature 10.1038/nature14252

For epigenome analysis, we used chromatin immunoprecipitation sequencing (ChIP-seq) to profile seven chromatin marks9: H3K4me3 (associated primarily with active promoters); H3K4me1 (enhancers); H3K27ac (enhancer/promoter activation); H3K27me3 (Polycomb repression); H3K36me3 and H4K20me1 (transcription); and H3K9me3 (heterochromatin) (Extended Data Fig. 1a). We used ChromHMM to learn a chromatin state model (see Methods, Extended Data Fig. 3a) defined by recurrent combinations of histone modifications marks, consisting of promoters, enhancers, transcribed, bivalent, repressed, heterochromatic, and low-signal states (Extended Data Fig. 3a). We defined 57,840 active promoters using H3K4me3 peaks within promoter chromatin states, and 151,447 active enhancer regions using H3K27ac peaks within enhancer chromatin states (Extended Fig. 1a; Supplementary Table S3; see Methods).

Transcriptional profiling in Alzheimer’s disease

Conserved epigenomic signals in mice and humans reveal immune basis of Alzheimer’s disease.

Gjoneska, E. et al.Nature 10.1038/nature14252

For transcriptome analysis, we used RNA sequencing to quantify gene expression changes for 13,836 ENSEMBL genes (see Methods; Extended Data Fig. 1a; Supplementary Table S1). We found 2,815 up-regulated genes and 2,310 down-regulated genes in the CK-p25 AD-mouse model as compared to CK littermate controls (at q<0.01; Supplementary Table S1), which we classified into transient (2 weeks only), late-onset (6 weeks only), and consistent (both) (Fig. 1a; Extended Data Fig. 4a, Supplementary Table S1). These showed distinct functional enrichments (Fig. 1a; Supplementary Table S2), with transient-increase genes enriched in cell cycle functions (p<10-92), consistent-increase genes enriched in immune (p<10-10) and stimulus response functions (p<10-4), and consistent- and late-decrease genes enriched in synaptic and learning functions (p<10-12).

Epigenetic footprinting in human embryonic stem-cell-derived neural progenitor cells

Dissecting neural differentiation regulatory networks through epigenetic footprinting.

Ziller, M. J. et al.Nature 10.1038/nature13990

Human pluripotent stem cell derived models that accurately recapitulate neural development in vitro and allow for the generation of specific neuronal subtypes are of major interest to the stem cell and biomedical community. Notch signalling, particularly through the Notch effector HES5, is a major pathway critical for the onset and maintenance of neural progenitor cells in the embryonic and adult nervous system 1-3. This can be exploited to isolate distinct populations of human embryonic stem-cell-derived neural progenitor cells 4. Here we report the transcriptional and epigenomic analysis of six consecutive stages derived from a HES5::e–GFP HES5–GFP reporter human embryonic stem cell line 5 differentiated along the neural trajectory. In order to dissect the regulatory mechanisms that orchestrate the stage-specific differentiation process, we developed a computational framework to infer key regulators of each cell-state transition based on the progressive remodelling of the epigenetic landscape and then validated these through a pooled short hairpin RNA screen. We were also able to refine our previous observations on epigenetic priming at transcription factor binding sites and show here that they are mediated by combinations of core and stage-specific factors. Taken together, we demonstrate the utility of our system and outline a general framework, not limited to the context of the neural lineage, to dissect regulatory circuits of differentiation.

Figure 1: Cortex-specific hypermethylation of ANK1 is correlated with AD-associated neuropathology in the brain.
figure 1

(a) Linear regression models demonstrated that cg11823178 in ANK1 was the top-ranked neuropathology-associated DMP in the EC in the London discovery cohort (n = 104). The adjacent probe, cg05066959, was also significantly associated with neuropathology. Green bars (bottom row) denote the location of annotated CpG islands. (b) EC DNA methylation at both CpG sites was strongly associated with Braak score (cg11823178: r = 0.47, t102 = 5.39, P = 4.59 × 10−7; cg05066959: r = 0.41, t102 = 5.37, P = 1.34 × 10−5). (c) Both probes were also associated with neuropathology in the other cortical regions assessed in the same individuals, being significantly correlated with Braak score in the STG (n = 113) (cg11823178: r= 0.37, t111 = 4.15, P = 6.51 × 10−5; cg05066959: r = 0.33, t111 = 3.67, P = 3.78 × 10−4) and the PFC (n= 110) (cg11823178: r = 0.29, t108 = 3.12, P = 2.33 × 10−3; cg05066959: r = 0.32, t108 = 3.52, P = 6.48 × 10−4). (d) There was no association between DNA methylation and Braak score at either ANK1 probe in the CER (n = 108) (cg11823178: r = 0.01, t106 = 0.082, P = 0.935; cg05066959: r = −0.08, t106 = 0.085, P = 0.395), a region largely protected against AD-related neuropathology. (e) cg11823178 was the top-ranked cross-cortex DMP (Fisher's χ2(6) = 60.6, P = 3.42 × 10−11), with cg05066959 also being strongly associated with Braak score (Fisher's χ2(6) = 52.9, P = 1.24 × 10−9).

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Figure 2: Neuropathology-associated DMPs are consistent across sample cohorts, with replicated evidence for ANK1 hypermethylation.
figure 2

(a) Braak-associated DNA methylation scores for the top-ranked cross-cortex DMPs identified using linear regression models in the London discovery cohort (Supplementary Table 9) were significantly correlated with neuropathology-associated differences at the same probes in both cortical regions profiled in the Mount Sinai replication cohort using linear regression models (PFC (n = 142) Braak score: r = 0.64, P = 6.03 × 10−13; STG (n = 144) Braak score: r = 0.63, P = 2.66 × 10−12; PFC amyloid burden: r = 0.65, P = 2.87 × 10−13; STG amyloid burden: r = 0.46, P = 1.09 × 10−6). Shown is data for Mount Sinai PFC Braak score analysis, with the two ANK1 probes (cg11823178 and cg05066959) highlighted in red. (b,c) cg11823178 and cg05066959 were significantly associated with Braak score in the STG (cg11823178: r = 0.28, t142 = 3.62, P = 1.63 × 10−4; cg05066959: r = 0.25, t142 = 3.29, P = 5.78 × 10−4) and PFC (cg11823178: r = 0.24, t140 = 3.14, P = 1.07 × 10−3; cg05066959: r = 0.21, t140 = 2.75, P = 4.00 × 10−3) (b), and amyloid pathology in the STG (cg11823178: r = 0.21, t142 = 2.81, P = 4.99 × 10−4; cg05066959:r = 0.27, t142 = 3.47, P = 5.65 × 10−4) and PFC (cg11823178: r = 0.29, t140 = 3.69, P = 2.35 × 10−4; cg05066959: r = 0.19, t140 = 2.56, P = 9.93 × 10−3) (c). (d) In the Oxford replication cohort, bisulfite-pyrosequencing was used to quantify DNA methylation across eight CpG sites spanning an extended ANK1 region. Linear models, adjusting for age and gender, confirmed significant neuropathology-associated hypermethylation in all three of the cortical regions assessed (Supplementary Fig. 1), most notably in the EC (n = 51), where six of the eight CpG sites showed a significant (amplicon average P = 0.0004) neuropathology-associated increase in DNA methylation (data is represented as mean ± s.e.m.,*P < 0.05, **P < 0.01, ***P < 0.005). (e,f) Meta-analyses across the three sample cohorts (London, Mount Sinai and Oxford) confirmed Braak-associated cortex-specific hypermethylation for both cg11823178 (e) and cg05066959 (f). Finally, there was a marked consistency in neuropathology-associated DMPs identified in our discovery cohort and those identified in De Jager et al.21. (g) Braak-associated DNA methylation scores for the 100 top-ranked cross-cortex DMPs identified in the London discovery cohort were significantly correlated with neuropathology-associated differences (neuritic-plaque load) at the same probes in the dorsolateral prefrontal cortex (DLPFC) identified by De Jager et al.21 in 708 individuals (r = 0.57, P = 1.55 × 10−9). The two ANK1 probes (cg11823178 and cg05066959) are highlighted in red.

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Figure 3: Distribution of CpGs associated (P < 1.2 × 10−7) with NP among 11 chromatin states found in mid-frontal cortex
figure 3

(a) Chromatin map of the dorsolateral prefrontal cortex. Using data generated by the National Institute of Health's Epigenomic Roadmap effort, we assigned each chromosomal segment to 1 of 11 discrete chromatin states. MAP subjects were used that were cognitively non-impaired at the time of death and had minimal pathology on neuropathological examination. The heatmap (white, low; blue, high) graphically displays the relative abundance of sequences found in a segment of DNA after immunoprecipitation for a particular histone mark. Each chromatin state had a unique complement of histone marks. (b) We used the chromatin map in a to identify the chromatin state in which each of the interrogated CpG dinucleotides were found. The histogram compares the distribution of chromatin states found at those 71 associated CpG dinucleotides whose methylation level was associated with neuritic plaques (Table 1) to the overall distribution of chromatin states found in all 415,848 CpG dinucleotides that were analyzed.

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Figure 4: Chromatin state conservation.
figure 4

a, Chromatin state definitions and abbreviations in mouse using combinatorial patterns of the seven marks profiled, used to define promoters (states 1-3; A=active, U=upstream, D=downstream), gene bodies (4-6; Tx=transcribed, 3p=3 prime), enhancers (7-9; G=genic, 1=strong, 2=weak), bivalent (10), repressed Polycomb (11), heterochromatin (12), and low signal (13-14). Darker blue indicates a higher enrichment of the measured histone mark (x axis) to be found in a particular state (y-axis). b, Promoter, enhancer, and repressed chromatin states in mouse hippocampus (rows), as profiled in this study, align to matching chromatin states in human (columns), as profiled by the Roadmap Epigenomics Consortium10. Shading indicates enrichment relative to human chromatin state abundance (columns). The number of regions overlapping is shown in each cell of the heatmap.

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Figure 5: Conserved gene expression changes between mouse and human AD are associated with immune and neuronal functions.
figure 5

a, 6 distinct temporal classes of differentially expressed genes are denoted, transient (early) increase (pink) or decrease (light blue), consistent increase (red) or decrease (blue), and late (6 wk) increase (dark red) or decrease (navy blue). Expression is shown relative to the mean of three replicates at 2 week control (CK) mice. Shown are the most significant distinct biological process gene ontology categories in each class of differentially regulated genes (* denotes enrichment of hypergeometric p<0.01). Gray boxes indicate no overlapping genes. b, t-statistic identifying the bias of each differentially regulated class of genes in AD cases relative to controls; negative t denotes lower expression in AD, positive t denotes higher expression in AD. c, Enrichment of gene ontology categories for differentially expressed genes between AD cases and controls in human2. Enrichment of each gene ontology category examined in the gene expression analysis was calculated for d, H3K4me3 promoters (red) and e, H3K27ac enhancers (yellow). Asterix (*) denotes categories with a binomial p<0.01.

Enrichment of regulatory motifs within changing f, promoters (top) and g, enhancers (bottom) in the mouse AD model. Overlap of changing h, promoters (top) and i, enhancers (bottom) with regions shown to be bound by immune (orange) and neuronal (purple) transcriptional factors and co-factors profiled using ChIP-seq in mouse immune and neuronal tissues15-19.

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Figure 6: Consecutive stages of ES cell derived neural progenitors are characterized by distinct epigenetic states
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a. Left: Schematic of the cell system. Middle: Normalized read-count level for H3K27ac over a 1.4 mega base (mb) region around the SOX2 locus (chr3:180,854,252-182,259,543). ChIP-Seq read counts were normalized to 1 million reads and scaled to the same level (1.5) for all tracks shown. Right: Additional tracks for H3K4me3, H3K4me1 and H3K27me3 as well as DNAme (scale 0-100%), OTX2 and expression covering a 100 kilo base (kb) sub-region (chr3:181,389,523-181,490,148) of this locus. Histone and RNA-Seq data were normalized to 1 million reads and are shown on distinct scales. b. Maximum gene set activity levels shown as z-scores for genes expressed in defined brain structures (left) and developmental time points (right) based on the mouse Allen Brain Atlas. Gene set activity was defined as average expression level of all member genes followed by z-score computation across all nine cell types.

Abbreviations: Rostral secondary prosencephalone (RSP), Telencephalon (Tel), peduncular (caudal) hypothalamus (PHy), Hypothalamus (p3), pre-thalamus (p2), pre-tectum (p1), midbrain (M), prepontine hindbrain (PPH), pontine hindbrain (PH), pontomedullary hindbrain (PMH), medullary hindbrain (MH); and embryonic (E)11.5, E13.5, E15.5, E18.5 as well postnatal P4, P14 and P28. c. Distribution of DNAme levels for differentially methylated regions (delta meth≥0.2, p≤0.01) across state transitions, For instance, distributions for regions gaining methylation in the transition from ES cell to NE (top left) at all stages of differentiation. Distinct methylation level trace plots are shown for regions gaining methylation (left) during the specific transitions (indicated on the side) and loss of methylation (right). Black labeled samples are based on WGBS data and grey color samples (LRG and LNP) were profiled by RRBS. d. Barplot of the frequency and associated mark of epigenetic changes for all cell state transitions broken up into gain and loss for consecutive differentiation stages.

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