Five hundred nanograms of genomic DNA from each sample was treated with sodium BS using the Zymo EZ-96 DNA Methylation-Gold™ Kit (Cambridge Bioscience, UK) according to the manufacturer’s standard protocol. All samples were then processed using the EPIC 850 K array (Illumina Inc, CA, USA) according to the manufacturer’s instructions, with minor amendments and quantified using an Illumina HiScan System (Illumina, CA, USA). Individuals were randomised and sorted fractions from the same individual and FACs gating run were processed on the same BeadChip, where within a BeadChip the location of each fraction was randomised. In total, 42 NeuNPos, 39 NeuNNeg/SOX10Pos, 33 NeuNNeg/SOX10Neg, 12 SATB2Pos, 19 NeuNNeg/SOX10Neg/IRF8Neg, 34 NeuNNeg/SOX10Neg/IRF8Pos and 9 SATB2Neg samples were run on the DNAm arrays.
Epic 850k array
Manufactured by Illumina
Sourced in United States
The EPIC (850k) array is a high-density genotyping microarray developed by Illumina. It is designed to provide comprehensive coverage of genetic variants across the human genome, including single nucleotide polymorphisms (SNPs) and copy number variations (CNVs). The EPIC array interrogates approximately 850,000 genetic markers, offering a powerful tool for large-scale genomic studies and research applications.
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6 protocols using epic 850k array
1
Epigenomic Profiling of Diverse Neuronal Subtypes
2
Molecular Profiling of Brain Tumors
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DNA was extracted from tumors and analyzed for genome-wide DNA methylation patterns using the Illumina EPIC (850 K) array. Processing of DNA methylation data was performed with custom approaches as previously described [4 (link), 5 (link)]. The Heidelberg Brain Tumour Classifier version v11b4 was used to determine the methylation class and calibration score for each sample via www.molecularneuropathology.org [4 (link), 5 (link)]. Patients were then divided into three groups according to their calibration score: “< 0.3” (calibration score < 0.3), “0.3–0.84” (calibration score between 0.3 and 0.84), and “≥ 0.84” (calibration score ≥ 0.84). Cut-offs for cohort separation were based on the recommendations by Capper et al. which reported a maximization of the Youden index at a calibrated score of 0.84 [4 (link), 5 (link)]. All cases were analyzed using classifier version v11b4 and the latest version v12.8.
3
Calculating DNA Methylation Age Biomarkers
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DNA methylation (DNAm) age was calculated successfully for 1,529 participants based on whole blood data measured by utilizing the Illumina Infinium platform from 2011 EPIC/850 K array. The sample size of the main analyses is smaller (n = 843–1,108) because not all participants filled in the psychological questionnaires. We report results for five DNAm age indicators that were estimated with an online calculator1 . The underlying method and R function are described in Horvath (31 (link)). The produced indicators were normalized, as this improves the predictive accuracy and makes the data more comparable to the training data (32 ). The average correlation between the samples and the gold standard was good (r ~ 0.97). In 60.6% of the samples the predicted tissue was correct and sex was always correctly predicted. This information indicates a high precision of the DNAm age predictions.
These DNAm based biomarkers can be broadly categorized into (cell-)intrinsic and extrinsic measures of epigenetic aging (25 (link), 26 (link), 33 (link)).
These DNAm based biomarkers can be broadly categorized into (cell-)intrinsic and extrinsic measures of epigenetic aging (25 (link), 26 (link), 33 (link)).
4
Tumor Tissue DNA Methylation Profiling
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DNA was extracted from tumor tissue and bulk plasma and analyzed for genome-wide DNA methylation patterns using the Illumina EPIC (850 k) array. The tumor tissue of interest for performing DNA methylation was chosen by a board-certified neuropathologist of the Department of Neuropathology, University Medical Center Hamburg-Eppendorf, Germany. Processing of DNA methylation data was performed with custom approaches [7 (link)].
5
Molecular Classification of Glioblastoma
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DNA was extracted from tumors, extracellular vesicles, and bulk plasma, and analyzed for genome-wide DNA methylation patterns using the Illumina EPIC (850k) array. Processing of DNA methylation data was performed with custom approaches.50 (link) Methylation profiling results from first surgery were submitted to the molecular neuropathology (MNP) methylation classifier v12.5 hosted by the German Cancer Research Center (DKFZ).17 (link) Patients were included if the calibrated score for the specific methylation class was >0.84 at time of diagnosis in accordance with recommendations by Capper et al.50 (link) For IDH-wildtype glioblastoma, patients with a score below 0.84 but above 0.7 with a combined gain of chromosome 7 and loss of chromosome 10 or amplification of epidermal growth factor receptor (EGFR) were included in accordance with cIMPACT-NOW criteria.51 (link) Furthermore, a class member score of ≥ 0.5 for one of the glioblastoma subclasses was required. Evaluation of the MGMT promoter methylation status was made from the classifier output v12.5 using the MGMT-STP27 method.52 (link)
6
DNA Methylation Profiling in Li-Fraumeni Syndrome
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We profiled DNA methylation of PBLs procured from patients with LFS with a germline TP53 variant (n = 359) from 287 families. A subset of the patients in this cohort overlapped with the WGS cohort (n = 47). The number of individuals profiled for methylation per family ranged from 1 to 7 with a median of 1. Our discovery cohort (n = 89) consisted of samples collected from previous work (9 (link)) composed of individuals with cancer (n = 63) and individuals who did not have cancer at the time of analysis (n = 26). Methylation analysis for these samples was performed using the Illumina HumanMethylation 450k BeadChip array. Our internal validation cohort (n = 124) consisted primarily of patients with a pathogenic germline TP53 variant from the Hospital for Sick Children who developed cancer (n = 78) and those that were cancer-free (n = 46). Our external validation cohort (n = 146) consisted exclusively of individuals with pathogenic germline TP53 variants from the NCI who developed cancer (n = 88) and those that were unaffected (n = 58) (47 (link)). Both validation cohorts were profiled using the Illumina EPIC (850k) array. By limiting the samples processed on different array types exclusively to one cohort, we mitigated any biases that may have arisen due to array type.
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