Journal: Nature Communications

Article Title: Atlas of imprinted and allele-specific DNA methylation in the human body

doi: 10.1038/s41467-025-57433-1

Figure Lengend Snippet: Using fragment-level analysis, we identified 324,759 genomic loci showing a mixture of fully methylated and fully unmethylated DNA fragments. Genetic variation at neighboring SNPs was used to split the fragments by genotype, and to identify 34,426 loci that show allele-specific methylation. These were analyzed across multiple donors and classified as sequence-dependent allele-specific methylation (SD-ASMs, bottom-left) where methylation consistently segregates with one allele, or parental allele-specific methylation (bottom-right) if both methylated and unmethylated epialleles exist, regardless of the genotype, suggesting that methylation is associated with parental, rather than genetic, origin. Overall, we identified 460 parental-ASM loci, including most known imprinted DMRs, as well as multiple novel regions. 78 of these regions are associated with imprinted genes. One such novel region within CHD7 , a gene associated with CHARGE syndrome, was validated as maternally methylated. Remarkably, some of these regions also show cell-type-specific effects, including escape of allele-specific methylation.

Article Snippet: To a large extent, this is due to the fact that most genome-wide methylome datasets are based on DNA methylation arrays (Illumina BeadChip 450 K and EPIC), and are limited to a predefined set of CpGs, capturing only 1.5%-3% of the 28 million methylation sites in the human genome.

Techniques: Methylation, Sequencing

Journal: Nature Communications

Article Title: Atlas of imprinted and allele-specific DNA methylation in the human body

doi: 10.1038/s41467-025-57433-1

Figure Lengend Snippet: A A computational algorithm identifies bimodal regions ( n = 324,759), by analyzing deeply sequenced methylomes from 39 cell types. Shown is a bimodal region (chr19:54039871-54043130, hg19, highlighted) where 51% of DNA fragments are methylated (black), and 46% are unmethylated, in a colon macrophage sample purified from a single donor. B Similarly, DNA fragments from Adipocytes were split by a common T/G SNP (rs2071094, chr11:2021164) to show allele-specific methylation. Fragments carrying the T allele are unmethylated (white), whereas G allele fragments are methylated. ( C ) Contingency table of alleles by methylation, as shown in (B). All 29 unmethylated fragments are from the G genotype, whereas all 22 methylated ones carry the T genotype (adj. p 7.1E-19 ≤ 7.1E-19, Fisher’s exact). D Genetic/Epigenetic table across multiple samples/cell types (rs9330298, chr1:153590254, hg19). Here, for all samples (homozygous or heterozygous), unmethylated fragments (U) have the G genotype, whereas the methylated fragments (M) are associated with the alternative T genotype, consistent with sequence-dependent allele-specific methylation (SD-ASM). E A similar table for rs80269905 (chr11:2720873), is consistent with parental allele-specific methylation. All samples are bimodal (showing both U and M fragments), and heterozygous samples are associated with allele-specific bimodal patterns but switch across different donors.

Article Snippet: To a large extent, this is due to the fact that most genome-wide methylome datasets are based on DNA methylation arrays (Illumina BeadChip 450 K and EPIC), and are limited to a predefined set of CpGs, capturing only 1.5%-3% of the 28 million methylation sites in the human genome.

Techniques: Methylation, Purification, Sequencing

Journal: Nature Communications

Article Title: Atlas of imprinted and allele-specific DNA methylation in the human body

doi: 10.1038/s41467-025-57433-1

Figure Lengend Snippet: A DNA fragments from adipocytes purified from a single donor, at the known imprinted differentially methylated region (iDMR) of the IGF2 gene (chr11:2018812-2024740, hg19). 52% of fragments are fully methylated (black circles) whereas 45% are fully unmethylated (white circles). B Stacked bars showing the percent of methylated (red), unmethylated (green) or mixed (yellow) DNA fragments, across 202 purified samples, spanning from 39 cell types, where a nearly balanced pattern of 1:1 ratio between unmethylated and methylated fragments is shown for this known imprinted region. C Same for the known iDMR of IGF2R (chr6:160426558-160427561, hg19), showing a bimodal (imprinted) pattern across all samples, except for small intestine and colon epithelial cells ( n = 13 samples), where both alleles are fully methylated (right, bottom). Grey asterisks mark samples from a single donor who exhibits bi-allelic methylation.

Article Snippet: To a large extent, this is due to the fact that most genome-wide methylome datasets are based on DNA methylation arrays (Illumina BeadChip 450 K and EPIC), and are limited to a predefined set of CpGs, capturing only 1.5%-3% of the 28 million methylation sites in the human genome.

Techniques: Purification, Methylation

Journal: Nature Communications

Article Title: Atlas of imprinted and allele-specific DNA methylation in the human body

doi: 10.1038/s41467-025-57433-1

Figure Lengend Snippet: A Average CpG methylation plot (chr11:2018807-2021899) within the known iDMR for H19 / IGF2 . The highlighted 692 bp patch (chr11:2020097-2020789) shows monoallelic methylation in purified granulocytes and T cells samples (as expected) but is fully methylated in hepatocytes, and pancreas ductal epithelial samples. An average of 50% methylation is expected for known iDMRs (dotted line). B Sequenced DNA fragments from within the highlighted region, revealing intra-iDMR biallelic methylation. Indeed, almost all fragments from the hepatocytes and pancreas are fully methylated, compared to half of granulocyte and T cell DNA fragments.

Article Snippet: To a large extent, this is due to the fact that most genome-wide methylome datasets are based on DNA methylation arrays (Illumina BeadChip 450 K and EPIC), and are limited to a predefined set of CpGs, capturing only 1.5%-3% of the 28 million methylation sites in the human genome.

Techniques: CpG Methylation Assay, Methylation, Purification

Journal: Nature Communications

Article Title: Atlas of imprinted and allele-specific DNA methylation in the human body

doi: 10.1038/s41467-025-57433-1

Figure Lengend Snippet: A Fragment-level analysis of a novel parent-of-origin cell-type-specific ASM region we identified (chr8:61627212-61627412, hg19) shows biallelic methylation (red) in endothelial cells, neurons, fibroblasts, and blood cells, but bimodal methylation patterns in hepatocytes and epithelial cells (1:1 ratio of fully methylated and fully unmethylated sequenced fragments of ≥3 CpGs). B – F Genetic/epigenetic analysis of parental allele-specific methylation in tongue-epithelial cells, validated across 15 families (a total of 33 children and their parents, Data ). For each trio, we used targeted-PCR next-generation sequencing (after bisulfite conversion) to measure the genotype (rs7826035, C/T, chr8:61627312 hg19) and the methylation status of six CpG sites (chr8:61627190-61627349 hg19, measured on the bottom strand). B A trio (family ID IMP017) showing homozygous C/C for the mother, with C/T heterozygosity for the father. The child T allele is therefore of paternal origin. Blue bars correspond to relative allelic read count. Unmethylated DNA fragments (green bars) are limited to the parental T allele, suggesting maternal-specific methylation. C same as ( B ) for a family with three heterozygous children (family ID IMP012). D A family where the T allele of heterozygous child 1 is maternal, whereas unmethylated fragments (green) are all from the paternal C allele. Two additional siblings are C/C homozygous and not shown (family ID IMP005). E – F Examples of a C/C homozygous family and a C/T heterozygous family, where the parent-of-origin of unmethylated fragments cannot be associated with a parent-of-origin (family IDs IMP014, IMP011). All remaining families were homozygous (C/C, not shown). G Bimodal tissue count track (orange) and -log10 of Fisher’s method for combining Fisher’s exact p values for allele-specific methylation at individual samples (blue track), along with a gene annotation track showing the region surrounding the identified DMR.

Article Snippet: To a large extent, this is due to the fact that most genome-wide methylome datasets are based on DNA methylation arrays (Illumina BeadChip 450 K and EPIC), and are limited to a predefined set of CpGs, capturing only 1.5%-3% of the 28 million methylation sites in the human genome.

Techniques: Methylation, Next-Generation Sequencing

Journal: Genome Medicine

Article Title: Nanopore-based random genomic sampling for intraoperative molecular diagnosis

doi: 10.1186/s13073-025-01427-7

Figure Lengend Snippet: Prospective molecular analysis of diagnostically challenging brain tumors with iSCORED pipeline. a Shown is the incorporated iSCORED workflow applied during intraoperative morphology-based diagnosis. Additional 10–15 scrolls of tissue sections, each 5 μm thick, are prepared to extract gDNA for subsequent iSCORED library preparation. Either MinION or PromethION sequencing could be utilized (both with concurrent analysis during sequencing). The final output graphs comprise whole genome CNV, gene amplification regions, and methylation classification with quantitative confidence scores ( Z scores for gene amplification and calibrated scores for methylation classification). b Real-time intraoperative molecular diagnosis with precise timestamps recorded from tissue arrival to final reports in 15 diagnostically challenging brain tumors. The entire workflow could be completed within ~ 105 min. The morphology-based intraoperative diagnosis was compared to generated molecular results, including methylation classification and CNV results . The numbers within the brackets of methylation classification and oncogene amplification denote the calibrated scores of corresponding diagnoses and detected copy number, respectively. * Scores of different glioblastoma subtypes. PXA = pleomorphic xanthoastrocytoma c Sequencing data, including mapped fragments and identified CpG sites, obtained within the initial 18 min (PromethION flowcells). d A comparative analysis of Nanopore-based molecular assays, including genomic detection resolution, library preparation time, input genomic DNA quantity, and required sequencing duration, reveals the iSCORED-based assay as the only method to achieve genome-wide high-resolution CNV detection within the surgical window. SMURF = sampling molecules using re-ligated fragments . STORK = short-read transpore rapid karyotyping . WGS = whole genome sequencing

Article Snippet: A custom python script converted the bedfile to make it compatible with Rapid-CNS 2 which processes the methylation information using a random forest classifier trained on Illumina BeadChip 450 K methylation array from the Heidelberg reference cohort of brain tumor methylation profiles [ ].

Techniques: Biomarker Discovery, Sequencing, Amplification, Methylation, Generated, Genome Wide, Sampling

Journal: Genome Medicine

Article Title: Nanopore-based random genomic sampling for intraoperative molecular diagnosis

doi: 10.1186/s13073-025-01427-7

Figure Lengend Snippet: Concurrent methylation analysis of primary CNV tumors. a Acquired methylation classification features with iSCORED-processed MinION sequencing over time using the Sturgeon and Rapid CNS. b Comprehensive comparison of Rapid CNS and Sturgeon methylation classification for primary brain tumors across multiple time points from the initiation of sequencing. c In silico mixture of glioblastoma, medulloblastoma, and oligodendrogliomas with control brain tissue dataset at various ratios using the Sturgeon (T2T) and Rapid CNS (hg38) (total data quantity after 1 h of sequencing). d Exact amplified regions covering EGFR oncogene in glioblastoma samples. e Methylation characterization of amplified EGFR oncogene reveals promoter hypomethylation. Subep = subependymoma, oligo = oligodendroglioma, medullo = medulloblastoma, GBM = glioblastoma

Article Snippet: A custom python script converted the bedfile to make it compatible with Rapid-CNS 2 which processes the methylation information using a random forest classifier trained on Illumina BeadChip 450 K methylation array from the Heidelberg reference cohort of brain tumor methylation profiles [ ].

Techniques: Methylation, Sequencing, Comparison, In Silico, Control, Amplification