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Illumina Inc genome wide dna methylation microarray data

In this study, genome-wide <t>DNA</t> <t>methylation</t> of 100 samples from 11 patients was assessed. Each patient had multiple primary small intestinal neuroendocrine tumours (ranging from 2-16 per patient), and a subset of 9 patients had a matched normal small intestinal epithelial sample assessed. A subset of 8 patients also had metastases originating from their SI-NETs (ranging from 1-2 per patient). DNA methylation data was used to assess differential methylation relating to the multifocal tumours, the epigenetic clock was used to assess the ‘timing’ of tumour development and metabolic traits predicted by DNA methylation were compared between samples.

Genome Wide Dna Methylation Microarray Data, supplied by Illumina Inc, used in various techniques. Bioz Stars score: 97/100, based on 1618 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
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Average 97 stars, based on 1618 article reviews

genome wide dna methylation microarray data - by Bioz Stars, 2026-07

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1) Product Images from "Epigenetic investigation of multifocal small intestinal neuroendocrine tumours reveals accelerated ageing of tumours and epigenetic alteration of metabolic genes"

Article Title: Epigenetic investigation of multifocal small intestinal neuroendocrine tumours reveals accelerated ageing of tumours and epigenetic alteration of metabolic genes

Journal: bioRxiv

doi: 10.1101/2024.12.02.626017

In this study, genome-wide DNA methylation of 100 samples from 11 patients was assessed. Each patient had multiple primary small intestinal neuroendocrine tumours (ranging from 2-16 per patient), and a subset of 9 patients had a matched normal small intestinal epithelial sample assessed. A subset of 8 patients also had metastases originating from their SI-NETs (ranging from 1-2 per patient). DNA methylation data was used to assess differential methylation relating to the multifocal tumours, the epigenetic clock was used to assess the ‘timing’ of tumour development and metabolic traits predicted by DNA methylation were compared between samples.

Figure Legend Snippet: In this study, genome-wide DNA methylation of 100 samples from 11 patients was assessed. Each patient had multiple primary small intestinal neuroendocrine tumours (ranging from 2-16 per patient), and a subset of 9 patients had a matched normal small intestinal epithelial sample assessed. A subset of 8 patients also had metastases originating from their SI-NETs (ranging from 1-2 per patient). DNA methylation data was used to assess differential methylation relating to the multifocal tumours, the epigenetic clock was used to assess the ‘timing’ of tumour development and metabolic traits predicted by DNA methylation were compared between samples.

Techniques Used: Genome Wide, DNA Methylation Assay, Methylation

(A) Plot of age predictions for normal epithelia samples (green), primary tumours (purple) and metastatic tumours (blue). Chronological age is indicated with the orange points and patients are indicated in order of chronological age. (B) Boxplot of age acceleration difference for the skin and blood clock predictions. (C) Somatic mutation count from tumours correlates with DNA methylation age in the skin and blood clock (p=0.0007).

Figure Legend Snippet: (A) Plot of age predictions for normal epithelia samples (green), primary tumours (purple) and metastatic tumours (blue). Chronological age is indicated with the orange points and patients are indicated in order of chronological age. (B) Boxplot of age acceleration difference for the skin and blood clock predictions. (C) Somatic mutation count from tumours correlates with DNA methylation age in the skin and blood clock (p=0.0007).

Techniques Used: Mutagenesis, DNA Methylation Assay

Image Search Results

H1‐0 is consistently upregulated in preleukemia and BCP‐ALL expressing ETV6::RUNX1 . (A) Principal component analysis (PCA) plot of ETV6::RUNX1 + (E::R) and wild‐type (WT) hiPSC transcriptome profiles based on all detected genes ( n = 16,328). (B) Hierarchical clustering analysis of differentially expressed genes (absolute fold change > 2 and p < 0.05) between ETV6::RUNX1 + and WT hiPSCs detected by RNA‐seq. (C) H1‐0 expression levels determined by RT‐qPCR in ETV6::RUNX1 + and WT hiPSCs subjected to RNA‐seq. Values were normalized to HW8 WT expression levels as well as to GAPDH expression. (D) Representative Western blot analysis of ETV6::RUNX1, H1‐0, ETV6, and β‐actin levels in ETV6::RUNX1 + and WT hiPSCs. (E) H1‐0 levels in HSCs (CD19‐CD34+CD45RA‐), IL7R+ (CD19‐CD34+CD45RA+IL7R+), and pro‐B (CD19+CD34+) cells differentiated from ETV6::RUNX1 + or reverted MIFF3 hiPSCs, and fetal liver cells. Data are derived from an RNA‐seq dataset by Böiers et al. (accession number E‐MTAB‐6382 <xref ref-type=

Journal: HemaSphere

Article Title: H1‐0 is a specific mediator of the repressive ETV6::RUNX1 transcriptional landscape in preleukemia and B cell acute lymphoblastic leukemia

doi: 10.1002/hem3.70116

Figure Lengend Snippet: H1‐0 is consistently upregulated in preleukemia and BCP‐ALL expressing ETV6::RUNX1 . (A) Principal component analysis (PCA) plot of ETV6::RUNX1 + (E::R) and wild‐type (WT) hiPSC transcriptome profiles based on all detected genes ( n = 16,328). (B) Hierarchical clustering analysis of differentially expressed genes (absolute fold change > 2 and p < 0.05) between ETV6::RUNX1 + and WT hiPSCs detected by RNA‐seq. (C) H1‐0 expression levels determined by RT‐qPCR in ETV6::RUNX1 + and WT hiPSCs subjected to RNA‐seq. Values were normalized to HW8 WT expression levels as well as to GAPDH expression. (D) Representative Western blot analysis of ETV6::RUNX1, H1‐0, ETV6, and β‐actin levels in ETV6::RUNX1 + and WT hiPSCs. (E) H1‐0 levels in HSCs (CD19‐CD34+CD45RA‐), IL7R+ (CD19‐CD34+CD45RA+IL7R+), and pro‐B (CD19+CD34+) cells differentiated from ETV6::RUNX1 + or reverted MIFF3 hiPSCs, and fetal liver cells. Data are derived from an RNA‐seq dataset by Böiers et al. (accession number E‐MTAB‐6382 ¹⁰ ). Data were analyzed for statistical significance using an ordinary one‐way ANOVA (* p < 0.05, ** p < 0.01). H1‐0 levels across two leukemia patient cohorts derived from the (F) PeCan St. Jude database ³⁰ , ³¹ and (G) an expression microarray dataset (accession number GSE87070 ³² ). The number of patients per leukemia entity and mean expression is indicated. Data were analyzed for statistical significance using an ordinary one‐way ANOVA (*** p < 0.001). (H) H1‐0 expression was quantified by RT‐qPCR in PDX samples ( n = 9). Mean expression ± standard deviation is shown. (I) RNA‐seq expression levels of H1‐0 in control and ETV6 shRNA‐transduced REH cells. Data are derived from E‐MTAB‐10308 ¹¹ and are normalized to control shRNA. Mean expression ± standard deviation is indicated. Statistical significance was determined by performing a one‐way ANOWA (*** p < 0.001). (J) Pearson correlation of H1‐0 and RUNX1 expression in healthy bone marrow cells ( n = 71) derived from the MILE study (R2 platform, accession number GSE13159 ³³ ).

Article Snippet: Hence, we analyzed previously published 450K Infinium microarray DNA methylation data comprising patient samples of T‐ALL and six B‐ALL subtypes ( n = 546).

Techniques: Expressing, RNA Sequencing, Quantitative RT-PCR, Western Blot, Derivative Assay, Microarray, Standard Deviation, Control, shRNA

ETV6::RUNX1 induces H1‐0 promoter activation . (A) Schematic representation of the H1‐0 locus, including the 512‐bp region (nucleotides −351 to +161 from TSS) encompassing promoter‐like signature EH38E2163184 (ENCODE). The H1‐0 CpG island (CGI) shore and 450K Infinium array probes are indicated. (B) 293T cells were transfected with a vector encoding the H1‐0 promoter‐like signature indicated in (A) , together with the empty pcDNA3.1 vector or pcDNA3.1 expressing either ETV6::RUNX1 or RUNX1, and a vector expressing Renilla luciferase. Luciferase activities were normalized to Renilla luciferase activity and the empty vector control. Data represent mean values of three independent replicates ± standard deviation. Significance was calculated using an ordinary one‐way ANOVA (*** p < 0.001). Representative protein levels of ETV6::RUNX1, RUNX1, and β‐actin determined by Western blot are shown. (C) Pearson correlation of H1‐0 expression and mean DNA methylation of the H1‐0 CGI shore probes cg07141002 and cg01883777 in leukemia patients (accession number GSE49032 <xref ref-type=

⁴¹ ). Expression is shown for microarray probe 208886_at. Each dot represents a single patient. (D) H1‐0 DNA methylation in different leukemia entities is visualized as a heatmap with each column corresponding to a single patient (accession number GSE49032 ⁴¹ ). Within each entity, patients are sorted according to mean DNA methylation of CGI shore probes cg07141002 and cg01883777. The total number of patients per entity is indicated. " width="100%" height="100%">

Journal: HemaSphere

Article Title: H1‐0 is a specific mediator of the repressive ETV6::RUNX1 transcriptional landscape in preleukemia and B cell acute lymphoblastic leukemia

doi: 10.1002/hem3.70116

Figure Lengend Snippet: ETV6::RUNX1 induces H1‐0 promoter activation . (A) Schematic representation of the H1‐0 locus, including the 512‐bp region (nucleotides −351 to +161 from TSS) encompassing promoter‐like signature EH38E2163184 (ENCODE). The H1‐0 CpG island (CGI) shore and 450K Infinium array probes are indicated. (B) 293T cells were transfected with a vector encoding the H1‐0 promoter‐like signature indicated in (A) , together with the empty pcDNA3.1 vector or pcDNA3.1 expressing either ETV6::RUNX1 or RUNX1, and a vector expressing Renilla luciferase. Luciferase activities were normalized to Renilla luciferase activity and the empty vector control. Data represent mean values of three independent replicates ± standard deviation. Significance was calculated using an ordinary one‐way ANOVA (*** p < 0.001). Representative protein levels of ETV6::RUNX1, RUNX1, and β‐actin determined by Western blot are shown. (C) Pearson correlation of H1‐0 expression and mean DNA methylation of the H1‐0 CGI shore probes cg07141002 and cg01883777 in leukemia patients (accession number GSE49032 ⁴¹ ). Expression is shown for microarray probe 208886_at. Each dot represents a single patient. (D) H1‐0 DNA methylation in different leukemia entities is visualized as a heatmap with each column corresponding to a single patient (accession number GSE49032 ⁴¹ ). Within each entity, patients are sorted according to mean DNA methylation of CGI shore probes cg07141002 and cg01883777. The total number of patients per entity is indicated.

Article Snippet: Hence, we analyzed previously published 450K Infinium microarray DNA methylation data comprising patient samples of T‐ALL and six B‐ALL subtypes ( n = 546).

Techniques: Activation Assay, Transfection, Plasmid Preparation, Expressing, Luciferase, Activity Assay, Control, Standard Deviation, Western Blot, DNA Methylation Assay, Microarray

H1‐0 expression decreases during hematopoiesis . (A) H1‐0 expression in ETV6::RUNX1 + BCP‐ALL ( n = 6) and healthy B cell precursor stages derived from a published RNA‐seq dataset (accession number GSE115656 <xref ref-type=

⁴⁵ ). B cell precursor fractions are HSCs (CD34+CD19‐IgM‐), pro‐B cells (CD34+CD19+IgM‐), pre‐B cells (CD34‐CD19+IgM‐) and immature B cells (CD34‐CD19+IgM+). (B) H1‐0 expression in healthy B cell precursor stages derived from a published expression microarray dataset (accession number GSE24759 ⁴⁶ ). B cell precursor fractions are HSCs (CD34+CD38‐), pro‐B cells (CD34+CD10+CD19+), pre‐B cells (CD34‐CD10+CD19+), naïve B cells (CD19+IgD+CD27‐), and mature B cells (CD19+IgD+CD27+). (B, C) Mean expression ± standard deviation is indicated and data was analyzed for statistical significance using an ordinary one‐way ANOVA (* p < 0.05, *** p < 0.001). (C) Min–max‐normalized mean expression per cell type derived from a fetal liver scRNA‐seq dataset (accession number E‐MTAB‐7407 ⁴⁷ ). (D) H1‐0 expression levels across normal B‐lymphoid differentiation distinguishing cell cycle status is depicted in a scRNA‐seq UMAP visualization of B cell precursor cells from bone marrow of eight healthy donors. ⁴⁸ " width="100%" height="100%">

Journal: HemaSphere

Article Title: H1‐0 is a specific mediator of the repressive ETV6::RUNX1 transcriptional landscape in preleukemia and B cell acute lymphoblastic leukemia

doi: 10.1002/hem3.70116

Figure Lengend Snippet: H1‐0 expression decreases during hematopoiesis . (A) H1‐0 expression in ETV6::RUNX1 + BCP‐ALL ( n = 6) and healthy B cell precursor stages derived from a published RNA‐seq dataset (accession number GSE115656 ⁴⁵ ). B cell precursor fractions are HSCs (CD34+CD19‐IgM‐), pro‐B cells (CD34+CD19+IgM‐), pre‐B cells (CD34‐CD19+IgM‐) and immature B cells (CD34‐CD19+IgM+). (B) H1‐0 expression in healthy B cell precursor stages derived from a published expression microarray dataset (accession number GSE24759 ⁴⁶ ). B cell precursor fractions are HSCs (CD34+CD38‐), pro‐B cells (CD34+CD10+CD19+), pre‐B cells (CD34‐CD10+CD19+), naïve B cells (CD19+IgD+CD27‐), and mature B cells (CD19+IgD+CD27+). (B, C) Mean expression ± standard deviation is indicated and data was analyzed for statistical significance using an ordinary one‐way ANOVA (* p < 0.05, *** p < 0.001). (C) Min–max‐normalized mean expression per cell type derived from a fetal liver scRNA‐seq dataset (accession number E‐MTAB‐7407 ⁴⁷ ). (D) H1‐0 expression levels across normal B‐lymphoid differentiation distinguishing cell cycle status is depicted in a scRNA‐seq UMAP visualization of B cell precursor cells from bone marrow of eight healthy donors. ⁴⁸

Article Snippet: Hence, we analyzed previously published 450K Infinium microarray DNA methylation data comprising patient samples of T‐ALL and six B‐ALL subtypes ( n = 546).

Techniques: Expressing, Derivative Assay, RNA Sequencing, Microarray, Standard Deviation

Journal: bioRxiv

Article Title: Epigenetic investigation of multifocal small intestinal neuroendocrine tumours reveals accelerated ageing of tumours and epigenetic alteration of metabolic genes

doi: 10.1101/2024.12.02.626017

Figure Lengend Snippet: In this study, genome-wide DNA methylation of 100 samples from 11 patients was assessed. Each patient had multiple primary small intestinal neuroendocrine tumours (ranging from 2-16 per patient), and a subset of 9 patients had a matched normal small intestinal epithelial sample assessed. A subset of 8 patients also had metastases originating from their SI-NETs (ranging from 1-2 per patient). DNA methylation data was used to assess differential methylation relating to the multifocal tumours, the epigenetic clock was used to assess the ‘timing’ of tumour development and metabolic traits predicted by DNA methylation were compared between samples.

Article Snippet: Genome-wide DNA methylation microarray data was generated on the Illumina iScan System using the Illumina Infinium MethylationEPIC BeadChip according to the manufacturer’s protocol.

Techniques: Genome Wide, DNA Methylation Assay, Methylation

Journal: bioRxiv

Article Title: Epigenetic investigation of multifocal small intestinal neuroendocrine tumours reveals accelerated ageing of tumours and epigenetic alteration of metabolic genes

doi: 10.1101/2024.12.02.626017

Figure Lengend Snippet: (A) Plot of age predictions for normal epithelia samples (green), primary tumours (purple) and metastatic tumours (blue). Chronological age is indicated with the orange points and patients are indicated in order of chronological age. (B) Boxplot of age acceleration difference for the skin and blood clock predictions. (C) Somatic mutation count from tumours correlates with DNA methylation age in the skin and blood clock (p=0.0007).

Techniques: Mutagenesis, DNA Methylation Assay

(A) Illustration of our data analysis and integration approach. The number of samples in each cohort is shown on the left. Data were used to call differentially methylated regions (DMRs) and differentially expressed (DE) genes. Data from the Gene Transcription Regulation Database provided transcription factor DNA binding information. DNA methylation data from pluripotent stem cells and normal samples were used as references and to study normal neural cell differentiation. (B) Tumor types are separated into tumor subgroups (omitted from ) based on DNA methylation, when the 10,000 most variable regions measured in both i450k and RRBS data (see the Materials and Methods section) were used for the tSNE visualization. RRBS samples are positioned adjacent to i450k samples representing the tumor subgroups that matched their clinical diagnosis. (C) Venn diagrams showing the number of DMRs in each comparison. Tumor type–specific DMRs are marked into the intersecting areas. A higher number of DMRs were detected in the RRBS than in the i450k data. For i450k results, DMRs were filtered using DNA methylation data from normal brain samples. (D) For AT/RTs, larger numbers of hypermethylated than hypomethylated regions were detected in all the comparisons in both i450k and RRBS data. The numbers of DMRs and direction of DNA methylation change for each comparison in both datasets. (E) AT/RT subgroups showed the highest DNA methylation among the pooled DMRs when compared to other tumor types and normal control samples (CONTR). Average DNA methylation of the probes hitting each i450k DMR is visualized as tumor subgroup-wise violin plots. (F) k-Means clustering analysis revealed DMR clusters that are specifically hypermethylated in AT/RTs. The median DNA methylation of the DMRs in each cluster was used to summarize the DNA methylation patterns. None of the DMR clusters showed AT/RT subtype–specific DNA methylation patterns, but there were DNA methylation differences between tumor subtypes within both MBs and PLEXs. (G) Several topologically associating domains were influenced by large-scale DNA methylation differences, especially in the AT/RT-MB comparison. Karyoplot visualizes the topologically associating domains that harbor large-scale DNA methylation differences, that is, several DMRs that were predominantly either hyper- or hypomethylated in the comparison (see the Materials and Methods section). Color indicates a comparison in which a difference was observed.

Journal: Life Science Alliance

Article Title: Aberrant DNA methylation distorts developmental trajectories in atypical teratoid/rhabdoid tumors

doi: 10.26508/lsa.202302088

Figure Lengend Snippet: (A) Illustration of our data analysis and integration approach. The number of samples in each cohort is shown on the left. Data were used to call differentially methylated regions (DMRs) and differentially expressed (DE) genes. Data from the Gene Transcription Regulation Database provided transcription factor DNA binding information. DNA methylation data from pluripotent stem cells and normal samples were used as references and to study normal neural cell differentiation. (B) Tumor types are separated into tumor subgroups (omitted from ) based on DNA methylation, when the 10,000 most variable regions measured in both i450k and RRBS data (see the Materials and Methods section) were used for the tSNE visualization. RRBS samples are positioned adjacent to i450k samples representing the tumor subgroups that matched their clinical diagnosis. (C) Venn diagrams showing the number of DMRs in each comparison. Tumor type–specific DMRs are marked into the intersecting areas. A higher number of DMRs were detected in the RRBS than in the i450k data. For i450k results, DMRs were filtered using DNA methylation data from normal brain samples. (D) For AT/RTs, larger numbers of hypermethylated than hypomethylated regions were detected in all the comparisons in both i450k and RRBS data. The numbers of DMRs and direction of DNA methylation change for each comparison in both datasets. (E) AT/RT subgroups showed the highest DNA methylation among the pooled DMRs when compared to other tumor types and normal control samples (CONTR). Average DNA methylation of the probes hitting each i450k DMR is visualized as tumor subgroup-wise violin plots. (F) k-Means clustering analysis revealed DMR clusters that are specifically hypermethylated in AT/RTs. The median DNA methylation of the DMRs in each cluster was used to summarize the DNA methylation patterns. None of the DMR clusters showed AT/RT subtype–specific DNA methylation patterns, but there were DNA methylation differences between tumor subtypes within both MBs and PLEXs. (G) Several topologically associating domains were influenced by large-scale DNA methylation differences, especially in the AT/RT-MB comparison. Karyoplot visualizes the topologically associating domains that harbor large-scale DNA methylation differences, that is, several DMRs that were predominantly either hyper- or hypomethylated in the comparison (see the Materials and Methods section). Color indicates a comparison in which a difference was observed.

Article Snippet: To study oncogenic epigenetic regulation in AT/RTs, we collected genome-wide DNA methylation Illumina microarray data (i450K) from 497 tumors and unmatched microarray expression data from 110 tumors; 89 normal brain DNA methylation samples were used as controls in i450k-based DNA methylation analysis.

Techniques: Methylation, Binding Assay, DNA Methylation Assay, Cell Differentiation, Biomarker Discovery, Comparison, Control

$(A) Most of the AT/RT-specific DMRs were hypermethylated (99% and 79% in i450K and RRBS data, respectively), whereas hypomethylated DMRs were more commonly observed in MBs (85% and 88% in i450K and RRBS data, respectively) and PLEXs (98% and 71% in i450K and RRBS data, respectively). Four-field plots for each tumor type show the number of hypermethylated and hypomethylated regions with respect to two other tumor types. Tumor-specific DMRs have the DNA methylation change in the same direction when compared to two other tumor types (e.g., hypermethylated in AT/RTs when compared to MBs and to PLEXs). (B) Most of the transcription factors and other transcriptional regulators (jointly referred to as TFs) are specifically enriched in AT/RT-hyper, MB-hyper, or PLEX-hypo DMRs and largely linked to neural differentiation, SWI/SNF, and PRC2. Upper part: upset plot showing the number of enriched TFs for regions that are hypermethylated or hypomethylated in an AT/RT-, MB-, and PLEX-specific manner. Some transcriptional regulators were enriched in several tumor-specific DMR groups. Lower part: the number of TFs in manually annotated function-related theme groups is shown for each upset plot column. The color of the heatmap shows the fraction of TFs in each theme (row). (C) Binding sites of neural TFs measured in brain tumors and other neural samples were enriched in AT/RT-hyper DMRs, whereas those measured in pluripotent stem cells were enriched in MB-hyper DMRs. GTRD TF binding data were categorized based on the measured sample type into the listed subsets (at the bottom of the plot), and the enrichment of TF binding sites in all the DMRs with AT/RT- or MB-specific DNA methylation was calculated for each of the GTRD subsets separately. Category “All*” means all the reported TF binding sites, so the full GTRD data. Results for the most relevant TFs are shown after organizing them into the theme groups listed in . The dot is not marked when a given TF or other regulator is not measured in a given GTRD subset. (D) PRC2 subunits rarely co-localize with neural TFs and other regulators in AT/RT-hypermethylated sites. Heatmap visualization of the enrichment P -value (one-sided Fisher’s exact test) for co-localization. All the adjusted P -values of 0.01 or higher are marked in white. Themes for each TF are annotated on the right-hand side of the heatmap. (E, F) In our CUT&RUN sequencing analysis, the DNA binding sites of NEUROD1 in the MB cell line overlapped regions that are hypermethylated in AT/RTs and hypomethylated in MBs (AT/RT versus MB-hyper), whereas no NEUROD1 binding sites in AT/RT samples overlapped with DMRs. Heatmaps showing the NEUROD1 binding sites located in different types of DMRs in i450K (E) and RRBS (F) data across the analyzed cell lines.$

Journal: Life Science Alliance

Article Title: Aberrant DNA methylation distorts developmental trajectories in atypical teratoid/rhabdoid tumors

doi: 10.26508/lsa.202302088

Figure Lengend Snippet: (A) Most of the AT/RT-specific DMRs were hypermethylated (99% and 79% in i450K and RRBS data, respectively), whereas hypomethylated DMRs were more commonly observed in MBs (85% and 88% in i450K and RRBS data, respectively) and PLEXs (98% and 71% in i450K and RRBS data, respectively). Four-field plots for each tumor type show the number of hypermethylated and hypomethylated regions with respect to two other tumor types. Tumor-specific DMRs have the DNA methylation change in the same direction when compared to two other tumor types (e.g., hypermethylated in AT/RTs when compared to MBs and to PLEXs). (B) Most of the transcription factors and other transcriptional regulators (jointly referred to as TFs) are specifically enriched in AT/RT-hyper, MB-hyper, or PLEX-hypo DMRs and largely linked to neural differentiation, SWI/SNF, and PRC2. Upper part: upset plot showing the number of enriched TFs for regions that are hypermethylated or hypomethylated in an AT/RT-, MB-, and PLEX-specific manner. Some transcriptional regulators were enriched in several tumor-specific DMR groups. Lower part: the number of TFs in manually annotated function-related theme groups is shown for each upset plot column. The color of the heatmap shows the fraction of TFs in each theme (row). (C) Binding sites of neural TFs measured in brain tumors and other neural samples were enriched in AT/RT-hyper DMRs, whereas those measured in pluripotent stem cells were enriched in MB-hyper DMRs. GTRD TF binding data were categorized based on the measured sample type into the listed subsets (at the bottom of the plot), and the enrichment of TF binding sites in all the DMRs with AT/RT- or MB-specific DNA methylation was calculated for each of the GTRD subsets separately. Category “All*” means all the reported TF binding sites, so the full GTRD data. Results for the most relevant TFs are shown after organizing them into the theme groups listed in . The dot is not marked when a given TF or other regulator is not measured in a given GTRD subset. (D) PRC2 subunits rarely co-localize with neural TFs and other regulators in AT/RT-hypermethylated sites. Heatmap visualization of the enrichment P -value (one-sided Fisher’s exact test) for co-localization. All the adjusted P -values of 0.01 or higher are marked in white. Themes for each TF are annotated on the right-hand side of the heatmap. (E, F) In our CUT&RUN sequencing analysis, the DNA binding sites of NEUROD1 in the MB cell line overlapped regions that are hypermethylated in AT/RTs and hypomethylated in MBs (AT/RT versus MB-hyper), whereas no NEUROD1 binding sites in AT/RT samples overlapped with DMRs. Heatmaps showing the NEUROD1 binding sites located in different types of DMRs in i450K (E) and RRBS (F) data across the analyzed cell lines.

Techniques: DNA Methylation Assay, Binding Assay, Sequencing

(A) Genomic annotations for cancer specific DMRs in all tumors for RRBS and i450k data. A higher proportion of MB-hypermethylated DMRs (7.6% and 32% in RRBS and i450k data, respectively) were located in CpG islands, when compared to MB-hypomethylated, AT/RT-hypermethylated, or PLEX-hypomethylated DMRs (1.9–5.9% and 11–12% in RRBS and i450k data, respectively). (B) Binding sites of neural differentiation factors NEUROG2, ASCL1, and PAX7 are more methylated in AT/RTs irrespective of the tumor subtype. Of the TFs linked to histone lysine methylation, EZH2 (involved in histone H3 lysine 27 trimethylation) behaved similarly, but a distinct DNA methylation pattern was observed for MIER1 and EHMT2 (involved in histone H3 lysine 9 methylation) binding sites. Violin plots visualizing the distribution of DNA methylation with selected TF binding sites in all i450k DMRs. The tumor subgroups are presented separately.

Journal: Life Science Alliance

Article Title: Aberrant DNA methylation distorts developmental trajectories in atypical teratoid/rhabdoid tumors

doi: 10.26508/lsa.202302088

Figure Lengend Snippet: (A) Genomic annotations for cancer specific DMRs in all tumors for RRBS and i450k data. A higher proportion of MB-hypermethylated DMRs (7.6% and 32% in RRBS and i450k data, respectively) were located in CpG islands, when compared to MB-hypomethylated, AT/RT-hypermethylated, or PLEX-hypomethylated DMRs (1.9–5.9% and 11–12% in RRBS and i450k data, respectively). (B) Binding sites of neural differentiation factors NEUROG2, ASCL1, and PAX7 are more methylated in AT/RTs irrespective of the tumor subtype. Of the TFs linked to histone lysine methylation, EZH2 (involved in histone H3 lysine 27 trimethylation) behaved similarly, but a distinct DNA methylation pattern was observed for MIER1 and EHMT2 (involved in histone H3 lysine 9 methylation) binding sites. Violin plots visualizing the distribution of DNA methylation with selected TF binding sites in all i450k DMRs. The tumor subgroups are presented separately.

Techniques: Binding Assay, Methylation, DNA Methylation Assay

(A) Heatmaps visualizing the expression of TFs associated with tumor type–specific hypomethylated DMRs in RNA-seq and microarray data. (B) Same as in (A) but for hypermethylated DMRs.

Journal: Life Science Alliance

Article Title: Aberrant DNA methylation distorts developmental trajectories in atypical teratoid/rhabdoid tumors

doi: 10.26508/lsa.202302088

Figure Lengend Snippet: (A) Heatmaps visualizing the expression of TFs associated with tumor type–specific hypomethylated DMRs in RNA-seq and microarray data. (B) Same as in (A) but for hypermethylated DMRs.

Techniques: Expressing, RNA Sequencing, Microarray

(A) Pluripotent stem cells (PSCs), primary adult brain, and primary fetal brain (FB) are separated from tumor samples based on DNA methylation in tSNE visualization, when the 10,000 most variable regions in i450k data were used for visualization. (B) When using the same set of pooled DMRs as in , the median DNA methylation level of PSCs is most similar to AT/RTs. (C) AT/RT-hyper DMRs were mostly AT/RT-unique or PSC-like, whereas MB DMRs were MB-unique or FB-like. Very few MB-hypermethylated DMRs were associated with large-scale differences in DNA methylation. Tumor type–specific DMRs were categorized based on DNA methylation levels in PSC and FB samples. The bar plot on the left shows the number of DMRs in different categories. Annotations show whether DMRs are PSC-like (P), FB-like (F), or unique (different from PSCs and FB) and whether DNA methylation changes during cell differentiation from PSC to FB. The proportion of DMRs in large-scale DNA methylation differences within annotated DMR categories is shown in blue on the right. The number of DMRs is marked in the figure. (D) DMR category–related DNA binding patterns revealed transcriptional regulators (TFs) involved in tumor-unique, normal cell–like, and differentiation-related regulation of DNA methylation. PRC2 subunits were enriched in the AT/RT-unique DMRs, whereas neural TFs were enriched in both AT/RT-unique and PSC-like DMRs with varying enrichment patterns. TF binding site enrichment was calculated separately for each normal cell differentiation–related DMR category (bottom). TFs were organized into the themes listed in . The dot is not marked when a given TF is not measured in a given GTRD category.

Journal: Life Science Alliance

Article Title: Aberrant DNA methylation distorts developmental trajectories in atypical teratoid/rhabdoid tumors

doi: 10.26508/lsa.202302088

Figure Lengend Snippet: (A) Pluripotent stem cells (PSCs), primary adult brain, and primary fetal brain (FB) are separated from tumor samples based on DNA methylation in tSNE visualization, when the 10,000 most variable regions in i450k data were used for visualization. (B) When using the same set of pooled DMRs as in , the median DNA methylation level of PSCs is most similar to AT/RTs. (C) AT/RT-hyper DMRs were mostly AT/RT-unique or PSC-like, whereas MB DMRs were MB-unique or FB-like. Very few MB-hypermethylated DMRs were associated with large-scale differences in DNA methylation. Tumor type–specific DMRs were categorized based on DNA methylation levels in PSC and FB samples. The bar plot on the left shows the number of DMRs in different categories. Annotations show whether DMRs are PSC-like (P), FB-like (F), or unique (different from PSCs and FB) and whether DNA methylation changes during cell differentiation from PSC to FB. The proportion of DMRs in large-scale DNA methylation differences within annotated DMR categories is shown in blue on the right. The number of DMRs is marked in the figure. (D) DMR category–related DNA binding patterns revealed transcriptional regulators (TFs) involved in tumor-unique, normal cell–like, and differentiation-related regulation of DNA methylation. PRC2 subunits were enriched in the AT/RT-unique DMRs, whereas neural TFs were enriched in both AT/RT-unique and PSC-like DMRs with varying enrichment patterns. TF binding site enrichment was calculated separately for each normal cell differentiation–related DMR category (bottom). TFs were organized into the themes listed in . The dot is not marked when a given TF is not measured in a given GTRD category.

Techniques: DNA Methylation Assay, Cell Differentiation, Binding Assay

(A) Tumor type–specific DMRs were categorized based on DNA methylation levels in PSC and FB samples. The bar plot on the left shows the number of DMRs in different categories. Annotations show whether DMRs are PSC-like (P), FB-like (F), or unique (different from PSCs and FB) and whether DNA methylation changes during cell differentiation from PSCs. The proportion of DMRs in large-scale methylation differences within annotated DMR categories is shown in blue on the right. The number of DMRs is marked in the figure. This figure has all the possible groups (compared with ). (B) TF binding site enrichment was calculated separately for each DMR category (bottom). TFs were organized into the themes listed in . The dot is not marked when a given TF is not measured in a given GTRD category. This has all the groups shown in (A).

Journal: Life Science Alliance

Article Title: Aberrant DNA methylation distorts developmental trajectories in atypical teratoid/rhabdoid tumors

doi: 10.26508/lsa.202302088

Figure Lengend Snippet: (A) Tumor type–specific DMRs were categorized based on DNA methylation levels in PSC and FB samples. The bar plot on the left shows the number of DMRs in different categories. Annotations show whether DMRs are PSC-like (P), FB-like (F), or unique (different from PSCs and FB) and whether DNA methylation changes during cell differentiation from PSCs. The proportion of DMRs in large-scale methylation differences within annotated DMR categories is shown in blue on the right. The number of DMRs is marked in the figure. This figure has all the possible groups (compared with ). (B) TF binding site enrichment was calculated separately for each DMR category (bottom). TFs were organized into the themes listed in . The dot is not marked when a given TF is not measured in a given GTRD category. This has all the groups shown in (A).

Techniques: DNA Methylation Assay, Cell Differentiation, Methylation, Binding Assay

(A, B, C, D) Differential DNA methylation (DM) was associated with differential gene expression (DE). Gene expression and DNA methylation patterns were studied in four contexts: differential gene expression alone (A) and DE coupled with DM in the genomic neighborhood (±200 kb from the transcription start site [TSS] within the same topologically associating domain) (B), DE coupled with DM in gene-linked enhancer (C), and DE coupled with DM in the gene promoter (2 kb upstream and 500 bp downstream from the TSS) (D). Venn diagrams show the numbers of genes behaving similarly in both sequencing and array data. Differentially expressed genes associated with differential DNA methylation (B, C, D) are called DM-DE genes. Only cases where the sign of DM change was opposite to DE were included in the figure. (E) DM-DE genes in AT/RT comparisons show generally high DNA methylation among AT/RTs. Sample-wise heatmaps show the levels of DNA methylation (average methylation of variable sites) and gene expression. The rightmost heatmap summarizes in which comparison the DM-DE gene was detected, what was the direction of DNA methylation change (hyper/hypo), and the genomic location of the DMR. (F) Expression patterns of selected DM-DE genes. * P < 0.05, ** P < 0.01, *** P < 0.001. (G) Hypermethylated DMRs in relevant genes, which are hypermethylated and underexpressed in AT/RTs. CXXC5 and TCEA3 are AT/RT-specifically suppressed DM-DE genes, and NEUROG1 , EBF3 , and NEUROD2 are DM-DE genes in the AT/RT-MB comparison. Distal DMRs are connected to the TSS via an arch. Oncoprint indicates which relevant TFs have binding sites in these regions in selected GTRD categories. The color of the DMR indicates whether the DMR is PSC-like and whether it is demethylated during neural cell differentiation. The number in front of the DMR indicates the k-means cluster which DMR belongs to (see ). Gray DMRs were not included in TF binding and DMR cluster analysis as they were not AT/RT-specific.

Journal: Life Science Alliance

Article Title: Aberrant DNA methylation distorts developmental trajectories in atypical teratoid/rhabdoid tumors

doi: 10.26508/lsa.202302088

Figure Lengend Snippet: (A, B, C, D) Differential DNA methylation (DM) was associated with differential gene expression (DE). Gene expression and DNA methylation patterns were studied in four contexts: differential gene expression alone (A) and DE coupled with DM in the genomic neighborhood (±200 kb from the transcription start site [TSS] within the same topologically associating domain) (B), DE coupled with DM in gene-linked enhancer (C), and DE coupled with DM in the gene promoter (2 kb upstream and 500 bp downstream from the TSS) (D). Venn diagrams show the numbers of genes behaving similarly in both sequencing and array data. Differentially expressed genes associated with differential DNA methylation (B, C, D) are called DM-DE genes. Only cases where the sign of DM change was opposite to DE were included in the figure. (E) DM-DE genes in AT/RT comparisons show generally high DNA methylation among AT/RTs. Sample-wise heatmaps show the levels of DNA methylation (average methylation of variable sites) and gene expression. The rightmost heatmap summarizes in which comparison the DM-DE gene was detected, what was the direction of DNA methylation change (hyper/hypo), and the genomic location of the DMR. (F) Expression patterns of selected DM-DE genes. * P < 0.05, ** P < 0.01, *** P < 0.001. (G) Hypermethylated DMRs in relevant genes, which are hypermethylated and underexpressed in AT/RTs. CXXC5 and TCEA3 are AT/RT-specifically suppressed DM-DE genes, and NEUROG1 , EBF3 , and NEUROD2 are DM-DE genes in the AT/RT-MB comparison. Distal DMRs are connected to the TSS via an arch. Oncoprint indicates which relevant TFs have binding sites in these regions in selected GTRD categories. The color of the DMR indicates whether the DMR is PSC-like and whether it is demethylated during neural cell differentiation. The number in front of the DMR indicates the k-means cluster which DMR belongs to (see ). Gray DMRs were not included in TF binding and DMR cluster analysis as they were not AT/RT-specific.

Techniques: DNA Methylation Assay, Gene Expression, Sequencing, Methylation, Comparison, Expressing, Binding Assay, Cell Differentiation

(A) Expression and methylation heatmaps for DM-DE genes using public microarray data. Expression on the left and methylation on the right. (B) Expression and methylation heatmaps for DM-DE genes using sequencing data (RNA-seq, RRBS). Expression on the left and methylation on the right.

Journal: Life Science Alliance

Article Title: Aberrant DNA methylation distorts developmental trajectories in atypical teratoid/rhabdoid tumors

doi: 10.26508/lsa.202302088

Figure Lengend Snippet: (A) Expression and methylation heatmaps for DM-DE genes using public microarray data. Expression on the left and methylation on the right. (B) Expression and methylation heatmaps for DM-DE genes using sequencing data (RNA-seq, RRBS). Expression on the left and methylation on the right.

Techniques: Expressing, Methylation, Microarray, Sequencing, RNA Sequencing

(A) Expression and methylation heatmaps for tumor-specific DM-DE genes using microarray data. Expression on the left and methylation on the right. (B) Same as in A but with sequencing data (RNA-seq and RRBS). Expression on the left and methylation on the right.

Journal: Life Science Alliance

Article Title: Aberrant DNA methylation distorts developmental trajectories in atypical teratoid/rhabdoid tumors

doi: 10.26508/lsa.202302088

Figure Lengend Snippet: (A) Expression and methylation heatmaps for tumor-specific DM-DE genes using microarray data. Expression on the left and methylation on the right. (B) Same as in A but with sequencing data (RNA-seq and RRBS). Expression on the left and methylation on the right.

Techniques: Expressing, Methylation, Microarray, Sequencing, RNA Sequencing

(A) Expression of genes presented in from microarray data (GEO accession GSE42658 ). (B) Expression of AT/RT-unique DMRs with EZH2 binding site target genes from RNA-seq data. (C) NEUROG/NEUROD target genes from microarray data (GEO accession GSE42658 ). (D) NEUROG/NEUROD target genes from RNA-seq data.

Journal: Life Science Alliance

Article Title: Aberrant DNA methylation distorts developmental trajectories in atypical teratoid/rhabdoid tumors

doi: 10.26508/lsa.202302088

Figure Lengend Snippet: (A) Expression of genes presented in from microarray data (GEO accession GSE42658 ). (B) Expression of AT/RT-unique DMRs with EZH2 binding site target genes from RNA-seq data. (C) NEUROG/NEUROD target genes from microarray data (GEO accession GSE42658 ). (D) NEUROG/NEUROD target genes from RNA-seq data.

Techniques: Expressing, Microarray, Binding Assay, RNA Sequencing

DNA methylation levels in the promoter region of PRLHR gene in hippocampus from Alzheimer’s disease (AD) and controls. ( A ) The figure shows the genomic position of the amplicon (black box) validated using bisulfite cloning sequencing which contains the CpG assayed by the Infinium Human Methylation 450K BeadChip array within the promoter region of PRLHR gene. An example of the 19 CpGs composing the amplicon fully methylated (black circles) is shown. Numbers below indicate each CpG position within the amplicon in base pairs. PRLHR is located on the long arm of chromosome 10 (chr10: 120, 352, 916–120, 355, and 160). The CpG island is represented by a green box as shown in the UCSC Genome Browser. ( B ) Dot-plot chart representing 450K methylation levels for PRLHR hippocampal samples. As seen in the figure, a significant increase in DNA methylation was identified between AD patients and controls. ( C ) Dot-plot chart representing 450K methylation levels for PRLHR according to ABC scale. Horizontal lines represent median methylation values and interquartile range for each group. ( D ) Representative examples of bisulfite cloning sequencing validation for the amplicon containing the CpGs are shown. Black and white circles represent methylated and unmethylated cytosines, respectively. Each column indicates every CpG site in the examined amplicon, and each row represents an individual DNA clone. CpG1 (blue) and CpG2 (orange) assessed by pyrosequencing are represented. *** p -value < 0.001, ** p -value < 0.01 (Mann–Whitney U test).

Journal: Cells

Article Title: Liquid Biopsy in Alzheimer’s Disease Patients Reveals Epigenetic Changes in the PRLHR Gene

doi: 10.3390/cells12232679

Figure Lengend Snippet: DNA methylation levels in the promoter region of PRLHR gene in hippocampus from Alzheimer’s disease (AD) and controls. ( A ) The figure shows the genomic position of the amplicon (black box) validated using bisulfite cloning sequencing which contains the CpG assayed by the Infinium Human Methylation 450K BeadChip array within the promoter region of PRLHR gene. An example of the 19 CpGs composing the amplicon fully methylated (black circles) is shown. Numbers below indicate each CpG position within the amplicon in base pairs. PRLHR is located on the long arm of chromosome 10 (chr10: 120, 352, 916–120, 355, and 160). The CpG island is represented by a green box as shown in the UCSC Genome Browser. ( B ) Dot-plot chart representing 450K methylation levels for PRLHR hippocampal samples. As seen in the figure, a significant increase in DNA methylation was identified between AD patients and controls. ( C ) Dot-plot chart representing 450K methylation levels for PRLHR according to ABC scale. Horizontal lines represent median methylation values and interquartile range for each group. ( D ) Representative examples of bisulfite cloning sequencing validation for the amplicon containing the CpGs are shown. Black and white circles represent methylated and unmethylated cytosines, respectively. Each column indicates every CpG site in the examined amplicon, and each row represents an individual DNA clone. CpG1 (blue) and CpG2 (orange) assessed by pyrosequencing are represented. *** p -value < 0.001, ** p -value < 0.01 (Mann–Whitney U test).

Article Snippet: In this study, we used our previous DNA methylation microarray data obtained by the Infinium Human Methylation 450K BeadChip (450K array) [ ].

Techniques: DNA Methylation Assay, Amplification, Cloning, Sequencing, Methylation, Biomarker Discovery, MANN-WHITNEY

Scatter plots graphs of Spearman correlation analysis between DNA methylation levels of 450K and p-tau burden ( A ) and β-amyloid deposition ( B ). A significant positive correlation was found between PRLHR methylation and p-tau (r = 0.45; p -value < 0.01) and β-amyloid (r = 0.39; p -value < 0.05) ( n = 24).

Journal: Cells

Article Title: Liquid Biopsy in Alzheimer’s Disease Patients Reveals Epigenetic Changes in the PRLHR Gene

doi: 10.3390/cells12232679

Figure Lengend Snippet: Scatter plots graphs of Spearman correlation analysis between DNA methylation levels of 450K and p-tau burden ( A ) and β-amyloid deposition ( B ). A significant positive correlation was found between PRLHR methylation and p-tau (r = 0.45; p -value < 0.01) and β-amyloid (r = 0.39; p -value < 0.05) ( n = 24).

Article Snippet: In this study, we used our previous DNA methylation microarray data obtained by the Infinium Human Methylation 450K BeadChip (450K array) [ ].

Techniques: DNA Methylation Assay, Methylation

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