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.
Project description:Nucleosomes are the basic unit of packaging of eukaryotic chromatin, and nucleosome positioning can differ substantially between cell types. Here, we sequence 14.5 billion plasma-borne cell-free DNA (cfDNA) fragments (700-fold coverage) to generate genome-wide maps of in vivo nucleosome occupancy. We identify 13 million local maxima of nucleosome protection, spanning 2.53 gigabases (Gb) of the human genome, whose positions and spacings correlate with nuclear architecture, gene structure and gene expression. We further show that short cfDNA fragments - poorly recovered by standard protocols - directly footprint the in vivo occupancy of DNA-bound transcription factors such as CTCF. The sequence composition of cfDNA has previously been used to noninvasively monitor cancer, pregnancy and organ transplantation, but a key limitation of this paradigm is its dependence on genotypic differences to distinguish between contributing tissues. We show that nucleosome spacing in gene bodies and cis-regulatory elements, inferred from cfDNA in healthy individuals, correlates most strongly with transcriptional and epigenetic features of lymphoid and myeloid cells, consistent with hematopoietic cell death as the normal source of cfDNA. We build on this observation to show how in vivo nucleosome footprints can be used to infer the cell types that contribute to circulating cfDNA in pathological states such as cancer. Because it does not rely on genotypic differences, this strategy may enable the noninvasive cfDNA-based monitoring of a much broader set of clinical conditions than is currently possible. Sequencing of cfDNA libraries from healthy individuals, pooled healthy individuals and individuals with disease for the identification of nucleosomes and protection from other DNA binding proteins.
Project description:The early detection of tissue and organ damage associated with autoimmune diseases (AID) has been identified as key to improve long-term survival, but non-invasive biomarkers are lacking. Elevated cell-free DNA (cfDNA) levels have been observed in AID and inflammatory bowel disease (IBD), prompting interest to use cfDNA as a potential non-invasive diagnostic and prognostic biomarker. Despite these known disease-related changes in concentration, it remains impossible to identify AID and IBD patients through cfDNA analysis alone. By using unsupervised clustering on large sets of shallow whole-genome sequencing (sWGS) cfDNA data, we uncover AID- and IBD-specific genome-wide patterns in plasma cfDNA in both the obstetric and general AID and IBD populations. Supervised learning of the genome-wide patterns allows AID prediction with 50% sensitivity at 95% specificity. Importantly, the method can identify pregnant women with AID during routine non-invasive prenatal screening. Since AID pregnancies have an increased risk of severe complications, early recognition or detection of new onset AID can redirect pregnancy management and limit potential adverse events. This method opens up new avenues for screening, diagnosis and monitoring of AID and IBD.
Project description:Increased levels of donor-derived cell-free DNA (dd-cfDNA) in recipient plasma have been associated with acute cellular rejection (ACR) after heart transplantation. DNA sequence differences have been used to distinguish between donor and recipient cfDNA but epigenetic differences could also potentially identify dd-cfDNA. This study aimed to assess the feasibility of using ventricle-specific methylation patterns in human cfDNA as an alternative biomarker for ACR in cardiac transplantation.
Project description:Nucleosomes are the basic unit of packaging of eukaryotic chromatin, and nucleosome positioning can differ substantially between cell types. Here, we sequence 14.5 billion plasma-borne cell-free DNA (cfDNA) fragments (700-fold coverage) to generate genome-wide maps of in vivo nucleosome occupancy. We identify 13 million local maxima of nucleosome protection, spanning 2.53 gigabases (Gb) of the human genome, whose positions and spacings correlate with nuclear architecture, gene structure and gene expression. We further show that short cfDNA fragments - poorly recovered by standard protocols - directly footprint the in vivo occupancy of DNA-bound transcription factors such as CTCF. The sequence composition of cfDNA has previously been used to noninvasively monitor cancer, pregnancy and organ transplantation, but a key limitation of this paradigm is its dependence on genotypic differences to distinguish between contributing tissues. We show that nucleosome spacing in gene bodies and cis-regulatory elements, inferred from cfDNA in healthy individuals, correlates most strongly with transcriptional and epigenetic features of lymphoid and myeloid cells, consistent with hematopoietic cell death as the normal source of cfDNA. We build on this observation to show how in vivo nucleosome footprints can be used to infer the cell types that contribute to circulating cfDNA in pathological states such as cancer. Because it does not rely on genotypic differences, this strategy may enable the noninvasive cfDNA-based monitoring of a much broader set of clinical conditions than is currently possible.
Project description:Blood cell counts often fail to report on immune processes occurring in remote tissues. Here we use 25 immune cell type-specific methylation patterns in circulating cell-free DNA (cfDNA) for studying 26 human immune cell dynamics. We characterized cfDNA released from specific immune cell types in 27 healthy individuals (N=242), cross sectionally and longitudinally. Immune cfDNA levels had no 28 individual steady state as opposed to blood cell counts, suggesting that cfDNA concentration reflects 29 adjustment of cell survival to maintain homeostatic cell numbers. We also observed selective elevation 30 of immune-derived cfDNA upon perturbations of immune homeostasis. Following influenza 31 vaccination (N=92), B-cell-derived cfDNA levels increased prior to elevated B-cell counts and 32 predicted efficacy of antibody production. Patients with Eosinophilic Esophagitis (N=21) and B-cell 33 lymphoma (N=27) showed selective elevation of eosinophil and B-cell cfDNA respectively, which 34 were undetectable by cell counts in blood. Immune-derived cfDNA provides a novel biomarker for 35 monitoring immune responses to physiological and pathological processes that are not accessible using 36 conventional methods.
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.