Methylation-based deconvolution of cell-free DNA
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ABSTRACT:
PROVIDER: EGAS00001007493 | EGA |
REPOSITORIES: EGA
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Project description:Current methods for mapping the tissue-of-origin of circulating cell-free DNA (cfDNA) are still insufficient. Here, we have extended a previously developed methylated CpG tandems amplification and sequencing (MCTA-Seq) method for quantitative analysis of tissue-of-origin of plasma cfDNA. By comparing paired plasma cfDNA and white blood cell genomic DNA, we have demonstrated that the liver is the major non-hematopoietic tissue contributing to plasma cfDNA in healthy adults, accounting for approximately 2%. Furthermore, we have detected changes in liver-derived DNA in patients with benign liver diseases and increases in pancreas-derived DNA in acute pancreatitis patients. Interestingly, our results suggest that DNA derived from pathological tissues makes a minor contribution to the increased cfDNA in many clinical cases. Finally, we have identified a tissue-specific hypermethylated cfDNA marker located in the intragenic regions of tissue-specifichighlyexpressed genes. This study represents valuable progress in the field of cfDNA and offers promise for clinical research and medical diagnostics using the described method.
2019-07-01 | GSE118690 | GEO
Project description:Cell-free DNA (cfDNA) contains a composite map of the epigenomes of its cells-of-origin. Tissue-specific TF binding inferred from cfDNA could enable us to track disease states in humans in a non-invasive manner. Here, we present a method to identify the subset of genome-wide transcription factor binding sites that are protected in plasma. We map binding at tissue-specific sites of constitutive factor CTCF and tissue-specific factors PU.1, LYL1, ER, and FOXA1 in plasma cfDNA. Our method also captures the chromatin structure around the factor-bound sites in their cells-of-origin. We define pure tumor TF signatures in an in vivo model by applying our method to estrogen receptor-positive (ER+) breast cancer xenografts. The tumor-specific cfDNA protections of ER-α and FOXA1 reflect TF-specific accessibility across human breast tumors, demonstrating our ability to capture tumor TF binding in plasma. By modeling cfDNA from cancer donors as a mixture of healthy plasma and pure tumor signatures, we can identify tumor TF binding in humans. Thus, our method enables non-invasive mapping of the regulatory phenotype of cancer in humans.
2021-05-24 | GSE171434 | GEO
Project description:Plasma cell-free DNA (cfDNA) is a noninvasive biomarker for cell death of all organs. Deciphering the tissue origin of cfDNA can reveal abnormal cell death because of diseases, which has great clinical potential in disease detection and monitoring. Despite the great promise, the sensitive and accurate quantification of tissue-derived cfDNA remains challenging to existing methods due to the limited characterization of tissue methylation and the reliance on unsupervised methods. To fully exploit the clinical potential of tissue-derived cfDNA, here we present one of the largest comprehensive and high-resolution methylation atlas based on 521 noncancer tissue samples spanning 29 major types of human tissues. We systematically identified fragment-level tissue-specific methylation patterns and extensively validated them in orthogonal datasets. Based on the rich tissue methylation atlas, we develop the first supervised tissue deconvolution approach, a deep-learning-powered model, cfSort, for sensitive and accurate tissue deconvolution in cfDNA. On the benchmarking data, cfSort showed superior sensitivity and accuracy compared to the existing methods. We further demonstrated the clinical utilities of cfSort with two potential applications: aiding disease diagnosis and monitoring treatment side effects. The tissue-derived cfDNA fraction estimated from cfSort reflected the clinical outcomes of the patients. In summary, the tissue methylation atlas and cfSort enhanced the performance of tissue deconvolution in cfDNA, thus facilitating cfDNA-based disease detection and longitudinal treatment monitoring.
| EGAS00001007213 | EGA
Project description:We performed all stimulatory experiments using THP1 cell line as a representative of primary human monocytes to show fundamental role of the cfDNA in healthy organisms. The experiments were conducted in duplicates using plasma containing cfDNA (NP) and the reference one with cfDNA removed by DNase (TP) to recognize unequivocally the effect of plasma cfDNA on transcriptome and proteome of monocytes. We used native human plasma samples obtained from healthy volunteers with no animal serum addition to cultivation medium in order to avoid the presence of uncharacterized animal cfDNA in the experiments.
2021-05-18 | PXD017285 | Pride
Project description:Non-invasive detection of aberrant DNA methylation could provide invaluable biomarkers for earlier detection of triple negative breast cancer (TNBC) which could help clinicians for easier and more efficient treatment options. We evaluated genome-wide DNA methylation data derived from TNBC and normal breast tissues, peripheral blood of TNBC cases and controls, and reference samples of sorted blood and mammary cells. Differentially methylated regions (DMRs) between TNBC and normal breast tissues were stringently selected, verified, and externally validated. A machine learning algorithm was applied to select the top DMRs, which then were evaluated on plasma-derived circulating cell-free DNA (cfDNA) samples of TNBC patients and healthy controls. We identified 23 DMRs accounting for the methylation profile of blood cells and reference mammary cells and then selected six top DMRs for cfDNA analysis. We quantified un-/methylated copies of these DMRs by droplet digital PCR analysis in a plasma test set from TNBC patients and healthy controls and confirmed our findings obtained on tissues. Differential cfDNA methylation was confirmed in an independent validation set of plasma samples. A methylation score combining signatures of the top three DMRs overlapping with the SPAG6, LINC10606, and TBCD/ZNF750 genes had the best capability to discriminate TNBC patients from controls (AUC=0.74 in test set and AUC=0.77 in validation set). Our findings demonstrate the usefulness of cfDNA-based methylation signatures as non-invasive liquid biopsy markers for diagnosis of TNBC.
2022-11-23 | GSE207998 | GEO
Project description:The cell type composition of many biological tissues varies widely across samples. Such sample heterogeneity hampers efforts to probe the role of each cell type in the tissue microenvironment. Current approaches that address this issue have drawbacks. Cell sorting or single-cell based experimental techniques disrupt in situ interactions and alter physiological status of cells in tissues. Computational methods are flexible and promising; but they often estimate either sample-specific proportions of each cell type or cell-type-specific gene expression profiles, not both, by requiring the other as input. We introduce a computational Complete Deconvolution method that can estimate both sample-specific proportions of each cell type and cell-type-specific gene expression profiles simultaneously using bulk RNA-Seq data only (CDSeq). We assessed our method’s performance using several synthetic and experimental mixtures of varied but known cell-type composition and compared its performance to the performance of two state-of-the-art deconvolution methods on the same mixtures. The results showed CDSeq can estimate both sample-specific proportions of each component cell type and cell-type-specific gene expression profiles with high accuracy. CDSeq holds promise for computationally deciphering complex mixtures of cell types, each with differing expression profiles, using RNA-seq data measured in bulk tissue .
2018-12-12 | GSE123604 | GEO
Project description:Circulating nucleic acids are present in plasma and are derived from a range of cell types, mainly cells of hematopoietic origin, enabling their use as biomarkers for diseases involving these cell types. Using cell type-specific methylation markers, we determined that megakaryocytes are major contributors to cfDNA (~26%), while erythroblasts contribute 1-4% of cfDNA in healthy individuals. Additionally, we identified megakaryocyte-derived DNA within platelets, providing non-invasive access to the megakaryocyte genome and epigenome. Analysis of cfDNA in women who have received male platelet transfusions indicates that megakaryocyte-derived cfDNA does not originate from platelets but is rather released directly from megakaryocytes. The concentration of megakaryocyte-derived cfDNA is elevated in individuals with pathologies involving increased platelet production (Essential Thrombocythemia, Idiopathic Thrombocytopenic Purpura) and decreased by bone marrow suppression (due to chemotherapy), while erythrocyte progenitor cfDNA is elevated in patients with Thalassemia. Megakaryocyte- and erythroblast-specific DNA methylation patterns may serve as novel biomarkers for pathologies involving increased or decreased thrombopoiesis and erythropoiesis.
2022-10-12 | GSE206818 | GEO
Project description:Deciphering the tissue origin of cfDNA can reveal abnormal cell death because of diseases, which has great clinical potential in disease detection and monitoring. Here we present one of the largest comprehensive and high-resolution methylation atlas based on Reduced Representative Bisulfite Sequencing (RRBS) data of 521 noncancer tissue samples spanning 29 major types of human tissues. We systematically identified fragment-level tissue-specific methylation patterns and extensively alidated the methylation signature atlas in independent methylation datasets, orthogonal epigenomic markers, and transcription regulatory elements. Based on the rich tissue methylation atlas, we develop the first supervised tissue deconvolution approach, a deep-learning-powered model, cfSort, for sensitive and accurate tissue deconvolution in cfDNA.
2023-06-15 | GSE233417 | GEO
Project description:As a non-invasive blood testing, the detection of cell-free DNA (cfDNA) methylation in plasma is raising increasing interest due to its diagnostic and biology applications. Although extensively used in cfDNA methylation analysis, bisulfite sequencing is less cost-effective. Through enriching methylated cfDNA fragments with MeDIP followed by deep sequencing, we aimed to characterize cfDNA methylome in cancer patients. In this study, we investigated the cfDNA methylation patterns in lung cancer patients by MeDIP-seq. MEDIPS package was used for the identification of differentially methylated regions (DMRs) between patients and normal ones. Overall, we identified 330 differentially methylated regions (DMRs) in gene promoter regions, 33 hypermethylation and 297 hypomethylation respectively, by comparing lung cancer patients and healthy individuals as controls. The 33 hypermethylation regions represent 32 genes. Some of the genes had been previously reported to be associated with lung cancers, such as GAS7, AQP10, HLF, CHRNA9 and HOPX. Taken together, our study provided an alternative method of cfDNA methylation analysis in lung cancer patients with potential clinical applications.
2019-03-31 | E-MTAB-7163 | biostudies-arrayexpress
Project description:Whole-genome bisulfite sequencing (BS-Seq) measures cytosine methylation changes at single-base resolution and can be used to profile cell-free DNA (cfDNA). In plasma, ultrashort single-stranded cfDNA (uscfDNA, ~50 nt) has been identified together with 167 bp double-stranded mononucleosomal cell-free DNA (mncfDNA). However, the methylation profile of uscfDNA has not been described. Conventional BS-Seq workflows may not be helpful because bisulfite conversion degrades larger DNA into smaller fragments, leading to erroneous categorization as uscfDNA. We describe the “5mCAdpBS-Seq” workflow in which pre-methylated 5mC (5-methylcytosine) single-stranded adapters are ligated to heat-denatured cfDNA before bisulfite conversion. This method retains only DNA fragments that are unaltered by bisulfite treatment, resulting in less biased uscfDNA methylation analysis. Using 5mCAdpBS-Seq, uscfDNA had lower levels of DNA methylation (~15%) and was enriched in promoters and CpG islands. Hypomethylated uscfDNA fragments were enriched in upstream transcription start sites (TSSs), and the intensity of enrichment positively affected gene expression of hemopoietic cells. Using tissue-of-origin deconvolution, we inferred that uscfDNA is derived primarily from eosinophils, neutrophils, and monocytes. As proof-of-principle, we show that characteristics of the methylation profile of uscfDNA can distinguish non-small cell lung carcinoma from non-cancer samples. The 5mCAdpBS-Seq workflow is recommended for any cfDNA methylation-based investigations.
2024-03-28 | GSE252088 | GEO