Detection of liver damage using cell-free methylated DNA

Abstract

Methods of detecting cell-type-specific liver damage in a subject who received a liver transplant, the method comprising sequencing cell-free DNA (cfDNA) in a sample from the subject; determining cell-type from which the cfDNA originated by identifying methylation patterns in nucleotide sequence of one or more portions of the cfDNA that contains methylation sites, in which the cell-type is determined when the methylation pattern in the one or more portions is the same as a cell-type specific methylation pattern; and measuring the quantity of the cfDNA of the determined cell-type. The cell-type-specific liver damage is detected when the measured quantity of the cfDNA of the determined cell-type is greater as compared to a quantity of cfDNA of the determined cell-type in subjects without liver transplant damage. Methods also relating to treatment of subjects who received a liver transplant.

Description

TITLEDETECTION OF LIVER DAMAGE USING CELL-FREE METHYLATED DNACROSS-REFERENCE TO RELATED APPLICATIONS

[0001] This application claims benefit of U. S. Provisional Application No. 63 / 714,126, filed on October 30, 2024, which is incorporated herein by reference in its entirety.STATEMENT REGARDING FEDERALLY SPONSORED RESEARCH OR DEVELOPMENT

[0002] This invention was made with government support under CA250307 awarded by the National Institutes of Health. The government has certain rights in the invention,SEQUENCE LISTING

[0003] The instant application contains a Sequence Listing, which has been submitted electronically in XML format and is hereby incorporated by reference in its entirety. Said XML copy, created on October 29, 2025, is named Georgtown_049_WOl-Sequence_Listing, and is 2,014 bytes in size.BACKGROUND OF THE INVENTION

[0004] Liver transplant is a primary option for patients with end-stage liver disease and acute liver failure. The procedure is associated with high success and survival rates — the National Institute of Diabetes and Digestive and Kidney Diseases estimates that the average survival rates for first-time liver transplant recipients is nearly 90% at one year after surgery, about 81% at three years after surgery', and about 73% at five years after surgery [1], However, complications, mostly occurring during the first month, are still a primary cause of postoperative death [2], Treatments of complications are available, although their success is improved by an early and accurate diagnosis of the complications.

[0005] For example, biliary complications after liver transplant, such as ascending cholangitis, strictures (both anastomotic and nonanastomotic), leaks, and recurrence of primary sclerosing cholangitis, contribute significantly to post-transplant morbidity andmortality in both living and deceased donor transplant recipients. Enhanced detection of biliary cell-type-specific damage allows for differentiation from hepatocellular forms of allograft injury and associated tissue damage. This enables an earlier and more accurate diagnosis of biliary complications and improved non-invasive monitoring post-treatment.

[0006] Yet, conventional diagnostic and monitoring methods for these conditions often necessitate cross-sectional imaging techniques, such as magnetic resonance cholangiopancreatography (MRCP), or invasive procedures like endoscopic retrograde cholangiopancreatography (ERCP) or liver biopsy, which pose additional risks to patients [3, 4], Liver biopsy in particular, while remaining as the gold-standard to confirm diagnosis and monitor response to treatment [5], may be accompanied by complications such as pain, hemoperitoneum, and hemobilia [6], And while research is being conducted to identify noninvasive biomarkers that are indicative of liver damage, most current biomarkers have a limited scope and fail to identify cellular causes of allograft injury [5, 7], As the treatment of the various complications can differ greatly, a definitive diagnosis is crucial.

[0007] In addition, the liver allograft is susceptible to a broad range of insult and injury from the time that it is removed from the donor [5], Ex situ organ preservation procedures such as normothermic machine perfusion or hypothermic oxygenated machine perfusion have been used to minimize damage to liver allograft pre-transplantation [2], However, while parameters such as vascular flow, bile production, glucose, and lactate metabolization are generated as a high-level assessment of the viability of the pre-transplant liver [8], detection of cellular damage would be even more informative to help achieve successful liver transplantation.

[0008] Thus, there remains a need for a noninvasive, effective means of diagnosing liver allograft damage at the cellular level, both pre- and post-transplantation.SUMMARY OF INVENTION

[0009] The present invention is based in part on the discovery of liver cell-type-specific DNA methylation patterns in circulating, cell-free methylated DNA, and the use of these DNA methylation patterns to monitor cellular damage in liver tissue both before and after transplantation.

[0010] Thus, in one aspect, the present invention provides a method of detecting cell-type-specific damage in ex vivo liver tissue. In some embodiments, the method comprises (a) sequencing cell-free DNA (cfDNA) in a sample from the ex vivo liver tissue; (b) determining cell-type from which the cfDNA originated by identifying methylation patterns in nucleotide sequence of one or more portions of the cfDNA that contains methylation sites, in which the cell-type is determined when the methylation pattern in the one or more portions is the same as a cell-type specific methylation pattern; and (c) measuring the quantity of the cfDN A of the determined cell-type; the cell-type-specific damage in the ex vivo liver tissue is detected when the measured quantity of the cfDNA of the determined cell-type is greater as compared to a normal quantity of cfDNA of the determined cell-type. In another aspect, the present invention provides a method of treating cell-type specific liver damage in ex vivo liver tissue, the method comprising administering a treatment for the cell-type specific liver damage to the ex vivo liver tissue, in which the ex vivo liver tissue is determined to have cell-type specific liver damage by the method of detecting cell-type-specific damage in ex vivo liver tissue described herein.

[0011] In some embodiments, the method of detecting cell-type-specific damage in ex vivo liver tissue, the method comprises, at two or more time points, (a) sequencing cfDNA in a sample from the ex vivo liver tissue; (b) determining cell-type from which the cfDNA originated by identifying methylation patterns in nucleotide sequence of one or more portions of the cfDNA that contains methylation sites, in which the cell-type is determined when the methylation pattern in the one or more portions is the same as a cell-type specific methylation pattern; and (c) measuring the quantity of the cfDNA of the determined cell-type; the cell-type-specific damage in the ex vivo liver tissue is detected when the measured quantity of the cfDNA of the determined cell-type is increased between a later time point and an earlier time point. In one aspect, the present invention provides a method of treating cell-type specific liver damage in ex vivo liver tissue, the method comprising administering a treatment for the cell-type specific liver damage to the ex vivo liver tissue, in which the ex vivo liver tissue is determined to have cell-type specific liver damage by the method of detecting cell-type-specific damage in ex vivo liver tissue described herein.

[0012] In another aspect, the present invention provides a method of treating cell-type specific liver damage in ex vivo liver tissue. In some embodiments, the method comprisesadministering a treatment for the cell-type specific liver damage to the ex vivo liver tissue and monitoring the cell -type specific liver damage, in which the monitoring comprises (a) sequencing cfDNA in a sample from the ex vivo liver tissue; (b) determining cell-type from which the cfDNA originated by identifying methylation patterns in nucleotide sequence of one or more portions of the cfDNA that contains methylation sites, in which the cell-type is determined when the methylation pattern in the one or more portions is the same as a celltype specific methylation pattern; and (c) measuring the quantity of the cfDNA of the determined cell-type; the cell-type-specific damage in the ex vivo liver tissue is determined to be present when the measured quantity of the cfDNA of the determined cell-type is greater as compared to a normal quantity of cfDNA of the determined cell-type. In other embodiments, the method of treating cell-type specific liver damage in ex vivo liver tissue comprises administering a treatment for the cell-type specific liver damage to the ex vivo liver tissue and monitoring the cell-type specific liver damage, in which the monitoring comprises, at two or more time points, (a) sequencing cfDNA in a sample from the ex vivo liver tissue; (b) determining cell-type from which the cfDNA originated by identifying methylation patterns in nucleotide sequence of one or more portions of the cfDNA that contains methylation sites, in which the cell-type is determined when the methylation pattern in the one or more portions is the same as a cell-type specific methylation pattern; and (c) measuring the quantity of the cfDNA of the determined cell-type; the cell-type-specific damage in the ex vivo liver tissue is determined to have increased when the measured quantity of the cfDNA of the determined cell-type is increased between a later time point and an earlier time point, and the cell-type-specific damage in the ex vivo liver tissue is determined to have decreased when the measured quantity of the cfDNA of the determined cell-type is decreased between a later time point and an earlier time point.

[0013] In one aspect, the present invention provides a method of detecting cell-type-specific liver damage in a subject who received a liver transplant or in a subject who is a candidate to receive a liver transplant. In some embodiments, the method comprises (a) sequencing cfDNA in a sample from the subject; (b) determining cell-type from which the cfDNA originated by identifying methylation patterns in nucleotide sequence of one or more portions of the cfDNA that contains methylation sites, in which the cell-type is determined when the methylation pattern in the one or more portions is the same as a cell-type specific methylation pattern; and (c) measuring the quantity of the cfDNA of the determined cell-type. Inembodiments in which the subject received a liver transplant, the cell-type-specific liver damage is detected when the measured quantity of the cfDNA of the determined cell-type is greater as compared to a quantity of cfDNA of the determined cell-type in subjects without liver transplant damage. In embodiments in which the subject is a candidate to receive a liver transplant, the cell-type-specific liver damage is detected when the measured quantity of the cfDNA of the determined cell-type is greater as compared to a normal quantity of cfDNA of the determined cell-type. In one aspect, the present invention provides a method of treating cell-type specific liver damage in a subject who received a liver transplant, the method comprising administering a treatment for the cell-type specific liver damage to the subject, in which the subject is determined to have cell-type specific liver damage by a method comprising the method of detecting cell-type-specific liver damage in a subject who received a liver transplant described herein.

[0014] In other embodiments, the method of detecting cell-type-specific liver damage in a subject who received a liver transplant or in a subject who is a candidate to receive a liver transplant, comprises, at two or more time points, (a) sequencing cfDNA in a sample from the subject; (b) determining cell-type from which the cfDNA originated by identifying methylation patterns in nucleotide sequence of one or more portions of the cfDNA that contains methylation sites, in which the cell-type is determined when the methylation pattern in the one or more portions is the same as a cell-type specific methylation pattern; and (c) measuring the quantity of the cfDNA of the determined cell-type; the cell-type-specific damage is detected when the measured quantity of the cfDNA of the determined cell-type is increased between a later time point and an earlier time point. In one aspect, the present invention provides a method of treating cell-type specific liver damage in a subject who received a liver transplant, the method comprising administering a treatment for the cell-type specific liver damage to the subject, in which the subject is determined to have cell-type specific liver damage by a method comprising the method of detecting cell-type-specific liver damage in a subject who received a liver transplant described above.

[0015] In a further aspect, the present invention provides a method of treating cell-type specific liver damage in a subject who received a liver transplant. In some embodiments, the method comprises administering a treatment for the cell-type specific liver damage to the subject and monitoring the cell-type specific liver damage, in which the monitoringcomprises (a) sequencing cfDNA in a sample from the subject; (b) determining cell-type from which the cfDNA originated by identifying methylation patterns in nucleotide sequence of one or more portions of the cfDNA that contains methylation sites, in which the cell-type is determined when the methylation pattern in the one or more portions is the same as a celltype specific methylation pattern; and (c) measuring the quantity of the cfDNA of the determined cell-type; the cell-type-specific liver damage is detected when the measured quantity of the cfDNA of the determined cell -type is greater as compared to a quantity of cfDNA of the determined cell-type in subjects without liver transplant damage.

[0016] In other embodiments, the method of treating cell-type specific liver damage in a subject who received a liver transplant comprises administering a treatment for the cell-type specific liver damage to the subject and monitoring the cell-type specific liver damage, in which the monitoring comprises, at two or more time points, (a) sequencing cfDNA in a sample from the subject; (b) determining cell-type from which the cfDNA originated by identifying methylation patterns in nucleotide sequence of one or more portions of the cfDNA that contains methylation sites, in which the cell-type is determined when the methylation pattern in the one or more portions is the same as a cell-type specific methylation pattern; and (c) measuring the quantity of the cfDNA of the determined cell-type; the cell-type-specific damage is determined to have increased when the measured quantity of the cfDNA of the determined cell-type is increased between a later time point and an earlier time point, and the cell-type-specific damage is determined to have decreased when the measured quantity of the cfDNA of the determined cell-type is decreased between a later time point and an earlier time point.

[0017] In another aspect, the present invention provides a method of detecting cell-type-specific liver damage in a liver tissue donor prior to liver tissue removal. In some embodiments, the method comprises (a) sequencing cfDNA in a sample from the liver tissue donor; (b) determining cell-type from which the cfDNA originated by identifying methylation patterns in nucleotide sequence of one or more portions of the cfDNA that contains methylation sites, in which the cell-type is determined when the methylation pattern in the one or more portions is the same as a cell-type specific methylation pattern; and (c) measuring the quantity of the cfDNA of the determined cell-type; the cell-type-specific liver damage is detected when the measured quantity of the cfDNA of the determined cell-type isgreater as compared to a normal quantity of cfDNA of the determined cell-type. In one aspect, the present invention provides a method of treating cell-type specific liver damage in a liver tissue donor prior to liver tissue removal, the method comprising administering a treatment for the cell-type specific liver damage to the liver tissue donor, in which the liver tissue donor is determined to have cell-type specific liver damage by a method comprising the method of detecting cell-type-specific liver damage in a liver tissue donor prior to liver tissue removal described herein.

[0018] In other embodiments, the method of detecting cell-type-specific liver damage in a liver tissue donor prior to liver tissue removal comprises, at two or more time points, (a) sequencing cfDNA in a sample from the liver tissue donor; (b) determining cell-type from which the cfDNA originated by identifying methylation patterns in nucleotide sequence of one or more portions of the cfDNA that contains methylation sites, in which the cell-type is determined when the methylation pattern in the one or more portions is the same as a celltype specific methylation pattern; and (c) measuring the quantity of the cfDNA of the determined cell-type; the cell-type-specific damage is detected when the measured quantity of the cfDNA of the determined cell-type is increased between a later time point and an earlier time point. In one aspect, the present invention provides a method of treating cell-type specific liver damage in a liver tissue donor prior to liver tissue removal, the method comprising administering a treatment for the cell-type specific liver damage to the liver tissue donor, in which the liver tissue donor is determined to have cell-type specific liver damage by a method comprising the method of detecting cell-type-specific liver damage in a liver tissue donor prior to liver tissue removal described above.

[0019] In another aspect, the present invention provides a method of determining etiology of liver damage in a subject who received a liver transplant, the method comprising (a) sequencing cfDNA in a sample from the subject; (b) determining cell-type from which the cfDNA originated by identifying methylation patterns in nucleotide sequence of one or more portions of the cfDNA that contains methylation sites, in which the cell-type is determined when the methylation pattern in the one or more portions is the same as a cell-type specific methylation pattern; and (c) measuring the quantity of the cfDNA of the identified methylation patterns; the etiology of the liver damage is determined when the identified methylation patterns and their quantity form a methylation profile that is the same as a knownmethylation profile associated with an etiology of liver damage. In yet another aspect, the present invention provides a method of treating liver damage in a subject who received a liver transplant, the method comprising administering a treatment for the liver damage to the subject, in which the etiology of the liver damage is determined by a method comprising the method of determining etiology of liver damage in a subject who received a liver transplant described herein.

[0020] In some embodiments, he cell -type is selected from biliary-small-ductal -epithelial cells, biliary-large-ductal-epithelial cells, liver-endothelial cells, hepatocytes, liver-resident-immune cells, and hepatic-stellate cells. In certain embodiments, the cell -type is further selected from memory B-cells, naive B-cells, CD4+ mature T-cells, CD8+ mature T-cells, natural killer cells, monocyte / macrophages / and neutrophils.

[0021] In some embodiments, the methylation pattern comprises a nucleotide sequence containing at least three CpG dinucleotides.

[0022] In some embodiments, the cell-type specific methylation pattern comprises a hy pom ethylated nucleotide sequence. In certain embodiments, the cell-type specific methylation pattern is selected from Table 1.

[0023] In some embodiments, the cell-type specific methylation pattern comprises a hypermethyl ted nucleotide sequence. In certain embodiments, the cell-type specific methylation pattern is selected from Table 2.

[0024] In some embodiments, one or more aspects of the methods of the present invention may be performed with a computer program. For example, in certain embodiments, the identification of methylation patterns in the nucleotide sequence of the one or more portions of the cfDNA that contains methylation sites may be performed with a first computer program.

[0025] In some embodiments, the method s of the present invention may further comprise comparing the methylation pattern in the one or more portions of the cfDNA that contains methylation sites to cell-type specific methylation patterns. In certain embodiments, the comparison may be with a second computer program.

[0026] In some embodiments, the methods of the present invention also may further comprise transferring to a user interface on an electronic display one or more of thefollowing: (i) the result of the identification of methylation patterns in the nucleotide sequence of the one or m ore portions of the cfDNA that contain s methylation sites; (ii) the result of the comparison of the methylation pattern in the one or more portions of the cfDNA that contains methylation sites to cell-type specific methylation patterns; and / or (iii) the determination of the cell-type from which the cfDNA originated.

[0027] In a further aspect, the present invention provides a kit comprising one or more pairs of a forward primer and a reverse primer that are complementary to known genomic regions containing a cell-type specific methylation pattern selected from Table 1 or Table 2. In yet another aspect, the present invention provides a kit comprising one or more panels of hybridization probes designed to capture nucleotides comprising a sequence of a cell-type specific methylation pattern selected from Table 1 or Table 2.

[0028] These and other embodiments of the invention are further described in the “Brief Description of the Figures,” “Detailed Description,” “Examples,” “Figures,” and “Claims” sections of this patent disclosure, each of which sections is intended to be read in conjunction with, and in the context of, all other sections of the present patent disclosure. Furthermore, one of skill in the art will recognize that the various embodiments of the present invention described herein can be combined in various different ways, and that such combinations are within the scope of the present invention.BRIEF DESCRIPTION OF THE DRAWING FIGURES

[0029] FIG. 1 shows a study overview using cell-free methylated DNA in blood to monitor cellular damage after liver transplant, as described in Example 1. Serial serum samples were collected from 28 patients before and after liver transplant at predetermined time points (n=100 samples) during the first month with additional time points in patients experiencing complications as they arose. Phenotype-matched samples were also collected from an additional 16 patients with allograft injury at the time of liver-biopsy proven diagnosis (n=30 samples). cfDNA methylome profiling of serum samples was performed using hybridization capture-sequencing of bisulfite-treated cfDNA. Then, cell-type-specific DNA methylation blocks identified from reference data of healthy tissues were used to trace the origins of patient cfDNA fragments. Cellular damages of the transplanted organ as well as other recipient organs were quantified to monitor systemic impact.

[0030] FIG. 2 shows characterization of human healthy cell-type-specific reference methylation data, as described in Example 1. Panel A shows significant pathways related to the biological function of genes annotated to liver cell-type-specific hypomethylated blocks.Panel B shows a schematic illustrating digestion of biliary tissues and enrichment of EpCAM (+) epithelial populations from the small-ductal versus large-ductal epithelial layers. Panel C shows heatmap of biliary epithelial differentially methylated blocks (DMBs). Each cell shows the average methylation across all CpGs in the block comparing biliary-large-ductal, biliary-small-ductal, average across other liver cell-types, and average across all non-liver cell-types in the atlas. Panel D shows Uniform Manifold Approximation and Projection (UMAP) depicting relationship between different cell types with whole genome bisulfite sequencing (WGBS) reference datasets included for analysis. Average methylation was calculated for each sample within blocks of at least three CpG sites and the top 10% of captured blocks were selected showing the highest variability across all samples.

[0031] FIG. 3 shows liver cell-type DNA methylation atlas relative to other healthy tissues, as described in Example 1. Panel A shows heatmap of cell-type-specific DMBs identified from reference WGBS data of healthy human cell-types. Each cell in the plot marks the methylation score of one genomic region (rows) at each of 20 cell-types (columns), with up to 50 blocks shown per cell type. The methylation score represents the number of fully unmethylated or methylated read-pairs divided by total coverage for hypo- and hypermethylated blocks, respectively. Panel B shows heatmap highlighting the top 25 hepatocyte-specific DMBs. Panel C shows an example of one hepatocyte-specific hypomethylated block (highlighted in blue), upstream of Enoyl-CoA Hydratase, Short Chain 1 (ECHS ) highly expressed in hepatocytes. The alignment from the University of California, Santa Cruz (UCSC) genome browser depicts the average DNA methylation (DNAm) across WGBS samples from five different liver cell-types as well as PBMC samples. Chromatin organization marks in hepatocytes are displayed (blue tracks) to show accessibility (DNAse I hypersensitivity, DHS) and regulatory function (H3K27ac binding).Panel D shows fragment-level visualization of methylation sequencing reads at hepatocytespecific hypomethylated block in reference WGBS samples from five different liver celltypes.

[0032] FIG. 4 shows characterization of liver cell-type-specific hypermethylated DNA blocks, as described in Example 1. Panel A shows the percentage of liver cell -type-specific blocks that overlap with CpG islands based on UCSC hg!9 annotations (77% hypermethylated; 16% hy pom ethylated blocks) (Fisher’s exact test, p < 0.05). Panel B shows the percentage of motifs (p < 0.05) enriched in liver cell-type-specific blocks containing a CpG dinucleotide (61% hypermethylated; 15% hypomethylated) (Fisher’s exact test, p < 0.05). Pane! C shows top transcription factor (TF) binding sites enriched within liver cell type-specific hypermethylated blocks, from HOMER known motif analysis.Captured blocks without liver cell type-specific methylation were used as background. Panel D shows fraction of cell type-specific hypermethylated blocks labeled as different chromatin states in chromHMM annotations from the same cell-type. Panel E shows top 10 pathways related to the biological function of genes annotated to liver cell-type-specific hypermethylated blocks. Panel F shows the percentage of hyper- and hypo-methylated DMBs identified for each liver cell type.

[0033] FIG. 5 shows characterization of liver-resident immune cell-type-specific DNA methylation, as described in Example 1. Panel A shows significant pathways related to the biological function of genes annotated to liver-immune cell-type-specific hypomethylated blocks. Panel B shows an example of one liver-immune-specific hypomethylated block (highlighted in blue), within the CD247 gene locus and upstream of CREG1 that are both highly expressed in liver-resident immune cells. The alignment from the UCSC genome browser depicts the average DNA methylation (DNAm) across WGBS samples from five different liver cell-types as well as PBMC samples. Chromatin organization marks in liverresident immune cells are displayed to show accessibility (ATAC-seq) and regulatory function (H3K27ac binding). Panel C shows enriched expression of liver-resident-immune genes relative to bulk liver tissue to validate purity and identity of starting cell populations.Panel D shows distance heatmap showing similarity of reference methylomes. Liver-resident immune cell methylomes cluster distinctly and are most similar to colon tissue-resident macrophage methylomes in between peripheral blood macrophages and peripheral blood lymphocyte methylomes. Average methylation was calculated for each sample within blocks of at least three CpG sites and the top 10% of captured blocks were selected showing the highest variability across all samples.

[0034] FIG. 6 shows data demonstrating that liver cell-specific hypomethylated DNA blocks coincide with other cell-specific epigenetic marks, as described in Example 1. Panels A and B show relationship of liver cell-type-specific hypomethylated blocks to chromatin accessibility (Panel A) and H3K27ac binding (Pane! B). Summary plots show the intensity of DHS / ATAC-seq or H3K27ac marks in a ±5-kb regions surrounding each hepatocyte-, biliary epithelial- or hepatic stellate-, liver endothelial-, and liver-immune cell-specific hypomethylated block, respectively. Solid lines represent plot summary with standard error depicted by semi-transparent colored region. Panel C shows fraction of cell-type specific hypomethylated blocks labeled as enhancers, associated with H3K4mel mark, in chromHMM annotations for the same cell-types. Panel D shows UCSC genome browser alignment at one example hepatocyte-specific hypomethylated block containing the FOXA1 binding sequence. Average methylation across WGBS samples shown in purple tracks. Panel E shows pioneer and developmental TF binding sites enriched within liver cell-type specific hypomethylated blocks, from HOMER motif analysis. Captured blocks without liver cell-type-specific methylation were used as background. Hepatocyte DHS, H3K27ac, and H3K4mel data were obtained from the German Epigenome programme (DEEP). Hepatocyte FOXA1 Chip-seq data was obtained from the ENCODE project. Biliary epithelial ATAC-seq data and Hepatic stellate H3K27ac histone modification data were obtained from the ENCODE project.

[0035] FIG. 7 shows origins of cellular damage after liver transplant derived from methylated cfDNA fragments, as described in Example 1. Panel A shows a schematic illustrating collection of serial serum samples from 28 liver transplant patients pre-transplant and post-reperfusion on post-operative day 0 (PODO). Panel B shows average cellular origins of cfDNA estimates. Panel C shows correlation of aspartate transaminase (AST) and alanine transaminase (ALT) enzyme activity with hepatocyte-derived cfDNA (p < 0.05, Spearman r = 0.81 AST; r = 0.82 ALT). Panels D, E, and G-I show pre-transplant (PRE) and post-reperfusion (POST) fraction of cfDNAs from myeloid (Panel D), hepatocyte (Panel E), endothelial (Panel G), hepatic stellate (Panel H), and neuronal cells (Panel I) of individual patients. The mean ± SEM of the cohort is shown in bold. Right axes: Fold change relative to pre-transplant. Panel F shows concentration of cfDNA isolated from patient serum. Individual patient and median values are shown. Wilcoxon matched-pairs signed rank test was used for comparison amongst groups. For Panels D-I, n=28. *p < 0.05;myeloid p=0.0001, hepatocyte p=0.0001, endothelial p=0.0025, hepatic stellate p=0.0001, neuron p=0.029, concentration p=0.001.

[0036] FIG. 8 shows expanded liver cell-type-specific DNA methylation atlases inform origins of cellular damage after liver transplant, as described in Example 1 Data for cardiomyocyte (Panel A), gastric-epithelial (Panel B), biliary-epithelial (Panel C), endothelial (Panel D), hepatocyte (Panel E), hepatic stellate (Panel F), myeloid (Panel G), and neuron (Panel H) cfDNA (in Geq / mL) in serum samples collected pre-transplant (PRE) or post-reperfusion (POST) on PODO (n=28 patients) are shown. Mean ± SEM fold change relative to pre-transplant levels is shown in bold. Wilcoxon matched-pairs signed rank test was used for comparison amongst groups. *p<0.05; cardiomyocyte p=0.0027, gastric-epithelial p=0.015, biliary p=0.0250, endothelial p=0.0001, hepatocyte p=0.0001, hepatic stellate p=0.0001, myeloid p=0.0187, neuron p=0.0017.

[0037] FIG. 9 shows time course of cell-type-specific damage after liver transplant, as described in Example 1. Panel A shows a schematic illustrating collection of serial serum samples from 20 liver transplant patients pre-transplant, post-reperfusion (PODO), postoperative day 7 and 30 (POD7, POD30). Panel B shows the etiologies of allograft injury. By one year after transplant, 11 of 20 patients were diagnosed with different etiologies of allograft injury by for-cause biopsy. Panel C shows hepatocyte cfDNA time course in patients with no allograft injury (left) or with allograft injury (right). Mean ± SEM of each cohort is shown in bold. Panel D shows an average hepatocyte cfDNA on POD7 and POD30, grouped by outcome (Mann-Whitney test, p<0.05). Panel E shows biliary cfDNA time course in patients with no allograft injury (left) or allograft injury (right). Mean ± SEM of each cohort is shown in bold. Panel F shows average biliary cfDNA on POD7 and POD30, grouped by outcome (Mann-Whitney test, p<0.05). Panel G shows the time course of five liver cell type cfDNAs in patients with no allograft injury (left) or with allograft injury (right). Mean + SD. For Panels D and F, NS p > 0,05, *p<0.05; biliary p:::0.009; hepatocyte p=0.002.

[0038] FIG. 10 shows cell-free DNA composition changes after transplant in patients with graft acceptance or injury, as described in Example 1. Panels A-E show results in which serum samples from 20 liver transplant patients were collected pre-transplant (PRE), postreperfusion (PODO), post-operative day 7 and 30 (POD7, POD30). By 6 months post-transplant 9 patients showed graft acceptance, and 11 patients graft injury. Panel A shows average cfDNA composition estimated from fragment-level deconvolution of serum samples. Panel B shows association of Banff Rejection Activity Index (RAI) lesion grading of liver biopsies at time of clinical diagnosis of allograft injury with liver- and immune-derived cfDNA in the circulation. Panel C shows average of endothelial cfDNA on P0D7 and POD30 (Mann-Whitney test, ns p>0.05). Panel D shows average of hepatic stellate cfDNA on POD7 and POD30 (Mann-Whitney test, ns p>0.05). Panel E shows average of total liver cfDNA (hepatocyte, biliary-epithelial, liver-endothelial and hepatic stellate) on P0D7 and POD30 (Mann-Whitney test, p<0.05). Panels F and G show correlation of biliary cfDNA (biliary-small-ductal + biliary-large-ductal epithelial) with alkaline phosphatase (ALP) serum levels (Panel F) (Spearman r = 0.29; ns p>0.05) or with serum bilirubin levels (Panel G) (Spearman r = 0.25; ns p>0.05). Panel H shows concentration of cfDNA isolated from patient serum. Individual values and mean + SD at each timepoint, grouped by outcome. (Mann- Whitney test, ns p>0.05).

[0039] FIG. 11 shows data demonstrating that cell-free methylated DNA indicates cellular sources of allograft injury, as described in Example 1. Panel A shows schematic illustrating collection of serum samples at the time of for-cause liver biopsies (FC-bx) to diagnose allograft injury. All biopsies were taken within one year of liver transplant and samples are representative of 24 patients (n=30 samples). Panel B shows cellular origins of cfDNAs classified by injury patterns observed in biopsies. Top: average fractions of different cellular sources detected for each type of allograft injury. Bottom: contingency stacked bar graph depicts the proportion of solid-organ cellular Geq within each individual sample where each stack represents a different sample and the height of each segment within the stack represents the relative proportion of cfDNA within that cell-type group across samples. Panel C shows hepatocyte cfDNA in serum samples with hepatocellular or mixed hepatobiliary injury compared to biliary injury alone (Mann-Whitney test, p<0.05). Panel D shows biliary cfDNA in serum samples with biliary or mixed hepatobiliary' injury compared to hepatocellular injury' alone (Mann-Whitney test, p<0.05). In Panels B-D, serum samples are classified as n=14 hepatocellular, n=10 mixed hepatobiliary, and n=6 biliary etiologies of allograft injury. Panels E-H show time courses of cellular damage during the peri -transplant time period in patients with hepatocellular, biliary, and mixed hepatobiliary forms of allograftinjury. Timepoints corresponding to complications and liver-biopsy proven diagnoses are marked by an asterisk. Panel E shows patient with COVID- 19 infection at P0D15 and FC-bx diagnosis of acute cellular rejection (ACR) with hyperbilirubinemia at POD120 (mixed injury classification). Elevated kidney epithelial cfDNA detected on PODO, P0D15, and POD30 match with the hepato-renal syndrome (HRS) diagnosis pre-transplant and acute kidney injury (AKI) after transplant. Panel F shows a patient with FC-bx diagnosis of hepatic ischemia with hyperbilirubinemia at POD1 (mixed injury classification). AKI was indicated by elevated creatinine levels P0D9 and elevated kidney epithelial cfDNA on POD30 Panel G shows a patient with FC-bx diagnosis of ACR at POD9 and P0D15 (hepatocellular injury classification). AKI was indicated by elevated creatinine levels on POD8 and elevated kidney epithelial cfDNA POD7, P0D9, and POD30. Panel H shows a patient with diagnosis of biloma at POD43 (biliary' injury classification).

[0040] FIG. 12 shows immune cell subset and cell-free DN A composition changes after transplant, as described in Example 1. Panel A shows average cellular origins of immune cell subsets in cfDNA from serial serum samples from liver transplant patients collected pretransplant and post-reperfusion on post-operative day 0 (PODO), POD7, and POD30. Panel B shows average cellular origins of immune cell subsets in cfDNA from serum samples collected at the time of for-cause liver biopsies (FC-bx) to diagnose allograft injury (classified by injury patterns observed in biopsies). All biopsies were taken within 1 year of liver transplant and samples are representative of 24 patients (n=30 samples). In Panels A and B, the top graphs show average proportion of lymphoid immune cell subsets; and the bottom graphs show average proportion of all immune cell subsets. Panel C shows fraction of neutrophil cfDNA in serum samples with hepatocellular or mixed hepatobiliary injury compared to biliary injury' alone. Panel D shows fraction of Monocyte / Macrophage cfDNA in serum samples with hepatocellular or mixed hepatobiliary injury compared to biliary injury alone. Panel E shows fraction of cfDNA from all solid-organ cell-types in serum samples with hepatocellular or mixed hepatobiliary' injury' compared to biliary' injury' alone. In Panels C-E, serum samples are classified as n=14 hepatocellular, n=10 mixed hepatobiliary, and n=6 biliary' etiologies of allograft injury (Mann-Whitney test, *p<0.05).

[0041] FIG. 13 shows characterization of neuron-specific DNA methylation, as described in Example 1 Panel A shows significant pathways related to the biological function of genesannotated to neuron cell-type-specific hypomethylated blocks. Panel B shows fraction of neuron-specific DNA methylation blocks labeled as different chromatin states in chromHMM annotations from the same cell-type (downloaded from the ENCODE project ENCSR539JGB).

[0042] FIG. 14 shows pairwise comparison of methylation status and cellular origins of cfDNA isolated from serum and plasma of healthy human controls, as described in Example 1, Panels A-D show density heatmap comparing methylation status across blocks in cfDNA isolated from paired human serum and plasma samples from healthy controls (n=4). Panel E shows immune and solid organ Geq from cfDNA isolated from serum versus plasma. Panel F shows predicted %immune versus %solid-organ derived cfDNA extracted from either serum or plasma. For Panels A-F, methylation data from paired serum and plasma samples from healthy controls were reanalyzed from GSE200187. For Panels E and F, data is presented as mean ± SD; n=4 samples per group. Individual serum-plasma pairs are represented by differently colored dots. Wilcoxon matched-pairs signed rank test was used for comparisons amongst groups. NS, p > 0.05; *p < 0.05. Panel G shows predicted %hepatocyte versus %biliary-epithelial derived cfDNA extracted from healthy controls (n=40). Data was obtained and reanalyzed from GSE200187, GSE186458, and phs000846.

[0043] FIG. 15 shows comparison of methylation status and cellular origins of cfDNA isolated from serum and plasma of liver transplant patients, as described in Example 1. Panel A shows cellular origins of cfDNA fragments in paired serum and plasma samples (n=3 patients). Panel B shows density heatmap comparing methylation status across blocks in cfDNA isolated from paired human serum and plasma (n=9 paired samples). Methylation status is represented by M-values (Logit transformation of (3-values) that have normal distribution. Methylation levels are highly correlated at the block level (Pearson’s r::::0.874, p < 0.05). Panel C shows correlation of predicted liver cell-type Geq in paired serum and plasma samples (Spearman r = 0.82, p < 0.05). Panel D shows predicted %Immune versus %Solid Organ derived cfDNA extracted from either serum or plasma. Panel E shows immune and solid organ Geq from cfDNA isolated from serum versus plasma. For Panels D and E, data is presented as mean ± SD; n=9 samples per group. Individual serum-plasma pairs are represented by differently colored dots. Wilcoxon matched-pairs signed rank test was used for comparisons amongst groups. NS, p > 0.05; *p < 0.05.

[0044] FIG. 16 shows extended pairwise comparison of methylation status and concentration of cfDNA isolated from serum and plasma of liver transplant patients, as described in Example 1. Panel A shows density heatmap comparing methylation status across blocks in cfDNA isolated from paired human serum and plasma (n=3 individuals each with paired serum and plasma at PODO, POD7, and POD30). Methylation levels are highly correlated at the block level (average Spearman’s rho =0.795, p < 0.05). Panel B shows density heatmap comparing methylation status across blocks in cfDNA isolated from paired human serum and plasma on POD7 only. Methylation levels are highly correlated at the block level (average Spearman’s rho =0.763, p < 0.05). Panel C shows correlation of methylation status across blocks in cfDNA isolated from paired human serum and plasma at each timepoint. Panel D shows concentration of cfDNA isolated from paired serum and plasma (ng / mL) at each timepoint.

[0045] FIG. 17 shows enrichment and purity estimates of sorted cell populations for DNA methylation and RNA-sequencing analysis, as described in Example 1. Panel A shows liver-endothelial enriched gene expression relative to bulk liver tissue. Panel B shows hepatic-stellate enriched gene expression relative to bulk liver tissue. Panel C shows validation of biliary epithelial cells was done using DNA methylation at previously published regions with specificity to biliary tissues. Proportion of fully methylated or unmethylated fragments was assessed at the following regions (chr2:232262210-232263058 (B3GNT7); chr2:232263147-232263382 (B3GNT7); chrl 1:1252410-1252491 (MUC5B); chrl9:55741530-55741921 (TMEM86B); chrl6:58077527-58077828 (MMP15) (DOI:10.1038 / s41586-022-05580-6). Panel D shows fold-expression of ACTA2 (smooth muscle alpha-2 actin) in smooth muscle relative to purified hepatic stellate cell populations. For Panels A-D, expression data were generated from paired RNA-sequencing of the same liver-endothelial and hepatic stellate cell populations used to generate methylation reference data. Bulk liver RNA expression was averaged from 226 bulk liver tissues re-analyzed from GTEx (https: / / gtexportal.org / home / downloads / adult-gtex / bulk tissue expression). Smooth muscle RNA expression data was re-analyzed from the Human Protein Atlas (https: / / www.proteinatlas.org / download / ma single cell type. tsv).

[0046] FIG. 18 shows the approach for the study described in Example 2 (Panel A), and a heatmap depicting DNA the top 50 differentially methylated blocks with the highest score foreach cell-type and the top hepatocyte-specific differentially methylated blocks, indicating methylation paterns to detect the cell type of origin of methylated cfDNA (Panel B).

[0047] FIG. 19 shows a shows a computer control system that is programmed or otherwise configured to implement methods of the present invention.DETAILED DESCRIPTION OF THE INVENTION

[0048] Some of the main aspects of the present invention are summarized in the Summary of Invention. Additional aspects are described in this Detailed Description of the Invention, and in the Examples, Drawings, and Claims sections of this disclosure. The description in each section of this disclosure is intended to be read in conjunction with the other sections.Furthermore, the various embodiments described in each section of this disclosure can be combined in various different ways, and all such combinations are intended to fall within the scope of the present invention.

[0049] The practice of the present invention can employ, unless otherwise indicated, conventional techniques of molecular biology, computational biology, genomics, epigenomics, and bioinformatics, which are within the skill of the art.

[0050] In order that the present invention can be more readily understood, certain terms are first defined. Additional definitions are set forth throughout the disclosure. Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention is related.

[0051] Any headings provided herein are not limitations of the various aspects or embodiments of the present invention, which can be had by reference to the specification as a whole. Accordingly, the terms defined immediately below are more fully defined by reference to the specification in its entirety,

[0052] All references cited in this disclosure are hereby incorporated by reference in their entireties. In addition, any manufacturers’ instructions or catalogues for any products cited or mentioned herein are incorporated by reference. Documents incorporated by reference into this text, or any teachings therein, can be used in the practice of the present invention.Documents incorporated by reference into this text are not admitted to be prior art.Definitions

[0053] The phraseology or terminology in this disclosure is for the purpose of description and not of limitation, such that the terminology or phraseology of the present specification is to be interpreted by the skilled artisan in light of the teachings and guidance.

[0054] As used in this specification and the appended claims, the singular forms “a,” “an,” and “the” include plural referents, unless the context clearly dictates otherwise. The terms “a” (or “an”) as well as the terms “one or more” and “at least one” can be used interchangeably.

[0055] Furthermore, “and / or” is to be taken as specific disclosure of each of the two specified features or components with or without the other. Thus, the term “and / or” as used in a phrase such as “A and / or B” is intended to include A and B, A or B, A (alone), and B (alone).Likewise, the term “and / or” as used in a phrase such as “A, B, and / or C” is intended to include A, B, and C; A, B, or C; A or B; A or C; B or C; A and B; A and C; B and C; A (alone); B (alone); and C (alone).

[0056] Wherever embodiments are described with the language “comprising,” otherwise analogous embodiments described in terms of “consisting of’ and / or “consisting essentially of’ are included.

[0057] Units, prefixes, and symbols are denoted in their Systeme International d’Unites (SI) accepted form. Numeric ranges are inclusive of the numbers defining the range, and any individual value provided herein can serve as an endpoint for a range that includes other individual values provided herein. For example, a set of values such as 1, 2, 3, 8, 9, and 10 is also a disclosure of a range of numbers from 1-10, from 1-8, from 3-9, and so forth.Likewise, a disclosed range is a disclosure of each individual value (i.e., intermediate) encompassed by the range, including integers and fractions. For example, a stated range of 5-10 is also a disclosure of 5, 6, 7, 8, 9, and 10 individually, and of 5.2, 7.5, 8.7, and so forth.

[0058] Unless otherwise indicated, the terms “at least” or “about” preceding a series of elements is to be understood to refer to every' element in the series. The term “about” preceding a numerical value includes ± 10% of the recited value. For example, a concentration of about 1 mg / mL includes 0.9 mg / mL to 1.1 mg / mL. Likewise, a concentration range of about 1% to 10% (w / v) includes 0.9% (w / v) to 11% (w / vj.

[0059] As used herein, the terms “cell-free DNA” or “cfDNA” or “circulating cell-free DNA” refers to DNA that is circulating in the peripheral blood of a subject. The DNA molecules in cfDNA may have a median size that is no greater than 1 kb (for example, about 50 bp to 500 bp, or about 80 bp to 400 bp, or about 100 bp to 1 kb), although fragments having a median size outside of this range may be present. This term is intended to encompass free DNA molecules that are circulating in the bloodstream as well as DNA molecules that are present in extra-cellul r vesicles (such as exosomes) that are circulating in the bloodstream.

[0060] “Methylation site” refers to a CpG dinucleotide to which a methyl group can potentially be added.

[0061] “Methylation pattern” refers to the pattern generated by the nucleotide sequence and the presence of methylated CpGs or non-methylated CpGs of a segment of DNA. For example, if a nucleotide sequence contains three CpGs, one methylation pattern is the nucleotide sequence in which all three CpGs are methylated; a different methylation pattern is the nucleotide sequence in which none of the three CpGs are methylated.

[0062] “Methylation status” refers to whether a methylation site is methylated or not methylated.

[0063] As used herein, “hypermethylated” refers to the presence of methylated methylation sites. For example, a hypermethylated nucleotide sequence means that each methylation site in the sequence is methylated.

[0064] As used herein, “hypomethylated” refers to the presence of non-methylated methylation sites. For example, a hypomethylated nucleotide sequence means that each methylation site in the nucleotide sequence is not methylated.

[0065] The term “sequencing” as used herein refers to a method by which the identity of at least 10 consecutive nucleotides for example, the identity of at least 20, at least 50, at least 100 or at least 200 or more consecutive nucleotides) of a polynucleotide is obtained.

[0066] A “subject” or “individual” or “patient” is any subject, particularly a mammalian subject, for whom diagnosis, prognosis, or therapy is desired. Mammalian subjects include humans, domestic animals, farm animals, sports animals, and laboratory animals including,e.g., humans, non-human primates, canines, felines, porcines, bovines, equines, rodents, including rats and mice, rabbits, etc.

[0067] As used herein, a “candidate” to receive a liver transplant refers to a subject that meet established criteria for liver transplantation. Preferably, the established criteria is based the guidelines from the American / Association for the Study of Liver Disease [9], The candidate typically has a life-limiting liver disease, which may present in various forms, such as acute liver failure, decompensated cirrhosis, hepatic malignancy or other tumor disease, hepatic vascular disease, or inborn metabolic disorders.

[0068] As used herein, a “liver tissue donor” refers to a source from which liver tissue will be removed for transplantation into a subject. The source may be a healthy subject, or a subject who is injured or near-death, such as on life-sustaining support. The source also may be deceased, brain dead, or circulatory dead.

[0069] As used herein, “etiology” refers to the cause or origin of disease or condition.

[0070] An “effective amount” of an active agent is an amount sufficient to carry out a specifically stated purpose.

[0071] Terms such as “treating” or “treatment” or “to treat” or “alleviating” or “to alleviate” refer to therapeutic measures that cure, slow down, lessen symptoms of, and / or halt progression of a diagnosed pathologic condition or disorder. In certain embodiments, a subject is successfully “treated” for a disease or disorder if the patient shows total, partial, or transient alleviation or elimination of at least one symptom or measurable physical parameter associated with the disease or disorder.Methods of the Present Invention

[0072] The present invention relates to methods that utilize circulating cfDNA to determine cell-type specific damage. The majority of cfDNA fragments peak around 167 bp, corresponding to the length of DNA wrapped around a nucleosome (147 bp) plus a linker fragment (20 bp). This nucleosomal footprint in cfDNA reflects degradation by nucleases as a by-product of cell death

[0010] , cfDNA can be released from cells due to such events as necrosis or apoptosis.

[0073] DNA methylation typically involves covalent addition of a methyl group to the 5-carbon of cytosine (5mc) with the human and mouse genomes containing 28 and 13 million CpG sites respectively [11, 12], Stable, cell-type specific patterns of DNA methylation are conserved during DNA replication and thus provide the predominant mechanism for inherited cellular memory during cell growth [13, 14], DNA methylation changes associated with disease and physiological aging occur at locations throughout the epigenome that are distinct from regions critical to cell-type identity, making methylated cfDNA a robust cell-type specific readout across diverse patient populations [12, 14].

[0074] While recent studies have demonstrated the feasibility of Tissue-Of-Origin (TOO) analysis using cfDNA methylation, such studies traditionally averaged the methylation status across a population of fragments present at single CpG sites [15, 16], The present invention involves sequencing portions of cfDNA to identify patterns of differential methylation, and using these patterns of differential methylation to determine specific types of liver cells from which the cfDNA originated.

[0075] Therefore, in one aspect, the present invention provides a method of detecting cell-type-specific damage in ex vivo liver tissue. In some embodiments, the method may comprise (a) sequencing cfDNA in a sample from the ex vivo liver tissue; (b) determining cell-type from which the cfDNA originated by identifying methylation patterns in nucleotide sequence of one or more portions of the cfDN A that contains methylation sites, in which the cell-type is determined when the methylation pattern in the one or more portions is the same as a cell-type specific methylation pattern; and (c) measuring the quantity of the cfDN A of the determined cell-type; cell-type-specific damage in the ex vivo liver tissue is detected when the measured quantity of the cfDNA of the determined cell-type is greater as compared to a normal quantity of cfDNA of the determined cell-type. In other embodiments, the method may comprise, at two or more time points, (a) sequencing cfDNA in a sample from the ex vivo liver tissue; (b) determining cell-type from which the cfDNA originated by identifying methylation patterns in nucleotide sequence of one or more portions of the cfDNA that contains methylation sites, in which the cell-type is determined when the methylation pattern in the one or more portions is the same as a cell-type specific methylation pattern; and (c) measuring the quantity of the cfDNA of the determined cell-type; cell-type-specific damage in the ex vivo liver tissue is detected when the measured quantity of thecfDNA of the determined cell-type is increased between a later time point and an earlier time point.

[0076] In another aspect, the present invention provides a method of treating cell-type specific liver damage in ex vivo liver tissue comprising administering a treatment for the celltype specific liver damage to the ex vivo liver tissue. The ex vivo liver tissue may be determined to have cell-type specific liver damage using a method of detecting cell-type-specific damage in ex vivo liver tissue according to the present invention,

[0077] In yet another aspect, the present invention provides a method of treating cell-type specific liver damage in ex vivo liver tissue comprising administering a treatment for the celltype specific liver damage to the ex vivo liver tissue and monitoring the cell-type specific liver damage. In some embodiments, monitoring may comprise (a) sequencing cfDNA in a sample from the ex vivo liver tissue; (b) determining cell-type from which the cfDNA originated by identifying methylation patterns in nucleotide sequence of one or more portions of the cfDNA that contains methylation sites, in which the cell-type is determined when the methylation pattern in the one or more portions is the same as a cell-type specific methylation pattern; and (c) measuring the quantity of the cfDNA of the determined cell-type; cell-type-specific damage in the ex vivo liver tissue is determined to be present when the measured quantity of the cfDNA of the determined cell-type is greater as compared to a normal quantity of cfDNA of the determined cell-type. In other embodiments, the monitoring may comprise, at two or more time points, (a) sequencing cfDNA in a sample from the ex vivo liver tissue; (b) determining cell-type from which the cfDNA originated by identifying methylation patterns in nucleotide sequence of one or more portions of the cfDNA that contains methylation sites, in which the cell-type is determined when the methylation pattern in the one or more portions is the same as a cell-type specific methylation pattern; and (c) measuring the quantity of the cfDNA of the determined cell-type; the cell-type-specific damage in the ex vivo liver tissue is determined to have increased when the measured quantity of the cfDNA of the determined cell-type is increased between a later time point and an earlier time point, and the cell-type-specific damage in the ex vivo liver tissue is determined to have decreased when the measured quantity of the cfDNA of the determined cell-type is decreased between a later time point and an earlier time point.

[0078] The ex vivo liver tissue may be liver tissue that was removed from a donor. In some embodiments, the ex vivo liver tissue is preserved. Examples of preservation techniques include, but are not limited to, normothermic preservation, hypothermic preservation, slow freezing, and vitrification. In certain embodiments, the ex vivo liver tissue is preserved by machine perfusion, such as normothermic machine perfusion.

[0079] In one aspect, the present invention provides a method of detecting cell-type-specific liver damage in a subject. In some embodiments, the subject received a liver transplant. In other embodiments, the subject is a candidate to receive a liver transplant. In some embodiments, the method may comprise (a) sequencing cfDNA in a sample from the subject; (b) determining cell-type from which the cfDNA originated by identifying methylation patterns in nucleotide sequence of one or more portions of the cfDNA that contains methylation sites, in which the cell-type is determined when the methylation pattern in the one or more portions is the same as a cell-type specific methylation pattern; and (c) measuring the quantity of the cfDNA of the determined cell-type. In embodiments in which the subject received a liver transplant, cell-type-specific damage is detected when the measured quantity of the cfDN A of the determined cell-type is greater as compared to a quantity of cfDNA of the determined cell-type in subjects without liver transplant damage, such as without allograft injury. In embodiments in which the subject is a candidate to receive a liver transplant, cell-type-specific liver damage is detected when the measured quantity of the cfDNA of the determined cell-type is greater as compared to a normal quantity of cfDNA of the determined cell-type.

[0080] In other embodiments, the method of detecting cell-type-specific liver damage in a subject may comprise, at two or more time points, (a) sequencing cfDNA in a sample from the subject; (b) determining cell-type from which the cfDNA originated by identifying methylation patterns in nucleotide sequence of one or more portions of the cfDNA that contains methylation sites, in which the cell -type is determined when the methylation pattern in the one or more portions is the same as a cell-type specific methylation pattern; and (c) measuring the quantity of the cfDNA of the determined cell-type; cell-type-specific damage is detected when the measured quantity of the cfDNA of the determined cell-type is increased between a later time point and an earlier time point.

[0081] In another aspect, the present invention provides a method of treating cell-type specific liver damage in a subject, in which the method comprises administering a treatment for the cell-type specific liver damage to the subject. In some embodiments, the subject received a liver transplant. In other embodiments, the subject is a candidate to receive a liver transplant. The subject may be determined to have cell-type specific liver damage by a method of detecting cell-type-specific damage in a subject according to the present invention.

[0082] In yet another aspect, the present invention provides a method of treating cell-type specific liver damage in a subject, in which the method comprises administering a treatment for the cell-type specific liver damage to the subject and monitoring the cell-type specific liver damage. In some embodiments, the subject received a liver transplant. In other embodiments, the subject is a candidate to receive a liver transplant. In some embodiments, the monitoring may comprise (a) sequencing cfDNA in a sample from the subject; (b) determining cell-type from which the cfDNA originated by identifying methylation patterns in nucleotide sequence of one or more portions of the cfDNA that contains methylation sites, in which the cell-type is determined when the methylation pattern in the one or more portions is the same as a cell-type specific methylation pattern; and (c) measuring the quantity of the cfDNA of the determined cell-type. In embodiments in which the subject received a liver transplant, cell-type-specific damage is detected when the measured quantity of the cfDNA of the determined cell-type is greater as compared to a quantity of cfDNA of the determined cell-type in subjects without liver transplant damage, such as without allograft injury. In embodiments in which the subject is a candidate to receive a liver transplant, cell-type-specific liver damage is detected when the measured quantity of the cfDNA of the determined cell-type is greater as compared to a normal quantity of cfDNA of the determined cell-type.

[0083] In other embodiments, the monitoring may comprise, at two or more time points, (a) sequencing cfDNA in a sample from the subject; (b) determining cell-type from which the cfDNA originated by identifying methylation patterns in nucleotide sequence of one or more portions of the cfDNA that contains methylation sites, in which the cell-type is determined when the methylation pattern in the one or more portions is the same as a cell-type specific methylation pattern; and (c) measuring the quantity of the cfDNA of the determined celltype; the cell-type-specific damage is determined to have increased when the measured quantity of the cfDNA of the determined cell-type is increased between a later time point andan earlier time point, and the cell-type-specific damage is determined to have decreased when the measured quantity of the cfDNA of the determined cell-type is decreased between a later time point and an earlier time point.

[0084] If the monitoring determines that cell-type-specific damage is present or that cell-type-specific damage is increased, it may be an indication that the treatment is not effective. Therefore, the methods may further comprise adjusting the treatment administered to the subject, such as by changing the type of treatment (e.g., changing the agent that is administered) or the treatment regimen (e.g, changing the treatment dosing or frequency).

[0085] In a further aspect, the present invention provides a method of detecting cell-type-specific liver damage in a liver tissue donor prior to liver tissue removal. In some embodiments, the method may comprise (a) sequencing cfDNA in a sample from the liver tissue donor; (b) determining cell-type from which the cfDNA originated by identifying methylation patterns in nucleotide sequence of one or more portions of the cfDNA that contains methylation sites, in which the cell-type is determined when the methylation pattern in the one or more portions is the same as a cell-type specific methylation pattern; and (c) measuring the quantity of the cfDNA of the determined cell-type; cell-type-specific damage is detected when the measured quantity of the cfDNA of the determined cell-type is greater as compared to a normal quantity of cfDNA of the determined cell-type. In other embodiments, the method may comprise, at two or more time points, (a) sequencing cfDNA in a sample from the liver tissue donor; (b) determining cell-type from which the cfDNA originated by identifying methylation patterns in nucleotide sequence of one or more portions of the cfDNA that contains methylation sites, in which the cell-type is determined when the methylation pattern in the one or more portions is the same as a cell-type specific methylation pattern; and (c) measuring the quantity of the cfDNA of the determined cell-type; cell-type-specific damage is detected when the measured quantity of the cfDNA of the determined celltype is increased between a later time point and an earlier time point.

[0086] In an additional aspect, the present invention provides a method of treating cell-type specific liver damage in a liver tissue donor prior to liver tissue removal, in which the method comprises administering a treatment for the cell-type specific liver damage to the liver tissue donor. The liver tissue donor may be determined to have cell-type specific liver damage by amethod of detecting cell-type-specific damage in a liver tissue donor according to the present invention.

[0087] In yet another aspect, the present invention provides a method of treating cell-type specific liver damage in a liver tissue donor, in which the method comprises administering a treatment for the cell-type specific liver damage to the liver tissue donor and monitoring the cell-type specific liver damage. In some embodiments, the monitoring may comprise (a) sequencing cfDNA in a sample from the liver tissue donor; (b) determining cell-type from which the cfDNA originated by identifying methylation patterns in nucleotide sequence of one or more portions of the cfDNA that contains methylation sites, in which the cell-type is determined when the methylation pattern in the one or more portions is the same as a celltype specific methylation pattern; and (c) measuring the quantity of the cfDNA of the determined cell-type; cell-type-specific damage is determined to be present when the measured quantity of the cfDNA of the determined cell-type is greater as compared to a normal quantity of cfDN A of the determined cell-type. In other embodiments, the method may comprise, at two or more time points, (a) sequencing cfDNA in a sample from the liver tissue donor; (b) determining cell-type from which the cfDNA originated by identifying methylation patterns in nucleotide sequence of one or more portions of the cfDNA that contains methylation sites, in which the cell-type is determined when the methylation pattern in the one or more portions is the same as a cell-type specific methylation pattern; and (c) measuring the quantity of the cfDNA of the determined cell-type; the cell-type-specific damage is determined to have increased when the measured quantity of the cfDNA of the determined cell-type is increased between a later time point and an earlier time point, and the cell -type-specific damage is determined to have decreased when the measured quantity of the cfDNA of the determined cell-type is decreased between a later time point and an earlier time point.

[0088] The liver tissue donor may be a living tissue donor or a deceased donor. In some embodiments, the liver tissue donor may be a donor after brain death or a donor after circulatory death. In certain embodiments, the liver tissue donor may be subject to normothermic regional perfusion.

[0089] In another aspect, the present invention provides a method of determining etiology of liver damage in a subject who received a liver transplant. In some embodiments, the methodmay comprise (a) sequencing cfDNA in a sample from the subject; (b) determining cell-type from which the cfDNA originated by identifying methylation patterns in nucleotide sequence of one or more portions of the cfDNA that contains methylation sites, in which the cell-type is determined when the methylation pattern in the one or more portions is the same as a celltype specific methylation pattern; and (c) measuring the quantity of the cfDNA of the identified methylation patterns; the etiology of the liver damage is determined when the identified methylation patterns form a methylation profile that is the same as a methylation profile associated with an etiology of liver damage.

[0090] In another aspect, the present invention provides a method of treating liver damage in a subject who received a liver transplant, in which the method comprises administering a treatment for the liver damage to the subject. The etiology of the liver damage may be determined by a method of determining etiology of liver damage in a subject who received a liver transplant according to the present invention.

[0091] As used herein, a “methylation profile” comprises one or more methylation patterns and their quantity. The methylation profile formed from identified methylation patterns and their quantity can be used to determine an etiology of liver damage if the methylation profile is the same as a methylation profile associated with an etiology of liver damage. Such methylation profiles associated with an etiology of liver damage are described in Example 1.

[0092] In methods of the present invention, examples of an etiology of the liver damage may include, but are not limited to, acute antibody mediated rejection; chronic rejection; ischemiareperfusion injury; recurrence of underlying liver diseases such as autoimmune hepatitis, metabolic dysfunction-associated steatohepatitis (MASH), fatty liver disease, primary sclerosing cholangitis (PSC), primary biliary cirrhosis (PBC), or alcoholic liver disease; biliary complications such as biliary' strictures (anastomotic or non-anastomotic); ischemic cholangiopathy; cholangitis; choledocholithiasis; gallstones; biliary' leaks; biloma; viral, bacterial, or parasitic infection, such as viral hepatitis; drug-, toxin- or chemical-induced liver damage; vascular disorder such as ischemic hepatitis, thrombosis, stenosis, blockage, or compression of the hepatic artery, portal vein, or hepatic vein, or inferior vena cava; Budd-Chiari-Syndrome; ischemic hepatitis due to shock or heart failure; metabolic diseases such as diabetes mellitus; liver disease associated with chronic heart failure; benign or malignant liver tumor; trauma; amyloidosis; sarcoidosis; genetic disorder such as hemochromatosis;glycogen storage disease; progressive familial intrahepatic cholestasis; and any combination thereof.

[0093] In some embodiments of the methods of the present invention, the liver tissue may be a portion of a liver. In other embodiments, the liver tissue may be an entire liver organ.

[0094] In methods of the present invention involving administration of a treatment, the treatment may comprise an effective amount of an agent or therapy known in the art, and may depend on the disease or ailment for which a remedy is being sought. In embodiments in which treatment is administered to a subject who received a liver transplant, examples of treatment include, but are not limited to, immunosuppressants such as tacrolimus and cyclosporine for graft rejection; corticosteroids for acute cell rejection; and endoscopic dilation, stenting, and / or surgical revision for biliary anastomosis. In embodiments in which treatment is administered to a candidate to receive a liver transplant or to a liver tissue donor prior to liver tissue removal, examples of treatment include, but are not limited to, N-acetyl cysteine for acute liver failure induced by acetaminophen or other causes; acyclovir for herpes hepatitis or varicella zoster-related acute liver failure; ganciclovir for hepatitis due to cytomegalovirus; steroids and / or immunosuppressant agents for autoimmune hepatitis; ursodeoxycholic acid and / or obeticholic acid for primary biliary cholangitis; copper chelation for Wilson disease; and iron chelation and / or phlebotomy for hemochromatosis.

[0095] In methods of the present invention, the normal quantity of cfDNA comprises a quantity of cfDNA for the determined cell-type that is generated in a population of individuals who have a healthy liver or who were not administered the treatment. As used herein, an individual may be determined to have a “healthy” liver if the liver does not demonstrate any signs of impairment of dysfunction. In some embodiments, an individual may be determined to have a healthy liver after one or more of the following determinations: (a) blood test measures a clinically normal level of one or more, or all, of alanine transaminase (ALT), aspartate transaminase (AST), alkaline phosphatase (ALP), albumin, total bilirubin, gamma-glutamyltransferase (GGT), L-lactate dehydrogenase (LDH), and prothrombin time (PT); (b) Child-Pugh score of five or six; (c) normal functional capacity including, but not limited to, protein synthesis, metabolism, and detoxification; (d) normal liver structure and morphology, including normal size and appearance without tumors orstructural abnormalities on imaging; and (e) absence of liver disease as determined by clinical evaluation and imaging.

[0096] In methods of the present invention, a quantity of cfDNA of the determined cell-type in subjects without liver transplant damage is a quantity of cfDNA for the determined celltype that is generated in a population of individuals who have received a liver transplant and do not exhibit any indication of liver transplant damage. Liver transplant damage may be indicated by signs known in the art, and may be based on, for example, serum chemistry, functional capacity, and liver structure and morphology.

[0097] In methods of the present invention involving two or more time points, the number of time points may range from two to 20 time points, or two to 15 time points, or two to ten time points, or two to five time points, including any number of time points therebetween, e.g., two time points, three time points, four time points, five time points, six time points, seven time points, eight time points, nine time points, ten time points, 11 time points, 12 time points, 13 time points, 14 time points, 15 time points, 16 time points, 17 time points, 18 time points, 19 time points, or 20 time points.

[0098] The “earlier time point” and the “later time point” in the comparison between the measured quantity of the cfDN A of the determined cell-type at later time point and the measured quantity of the cfDN A of the determined cell-type at later time point an earlier time point may be any two time points in which the measured quantity of the cfDNA of the determined cell-type has been obtained. In some embodiments, the “earlier time point” and the “later time point” may be consecutive time points in which the measured quantity of the cfDNA of the determined cell -type has been obtained. In some embodiments, the “earlier time point” and the “later time point” may be the first time point and the last time point, respectively, in which the measured quantity of the cfDNA of the determined cell-type has been obtained.

[0099] The duration between time points may range from one hour to several months. For instance, the duration may be one hour to two days, or one hour to 24 hours, or two hours to 24 hours, or four hours to 24 hours, or five hours to 24 hours, or eight hours to 24 hours, or any duration therebetween, e.g., one hour, two hours, three hours, four hours, five hours, six hours, seven hours, eight hours, nine hours, ten hours, 11 hour, 12 hours, 13 hours, 14 hours, 15 hours, 16 hours, 17 hours, 18 hours, 19 hours, 20 hours, 21 hours, 22 hours, 23 hours, or24 hours. Alternatively, each time point may be one or more days apart. For instance, a time point may be taken daily, every two days, every three days, every four days, every five days, every' six days, every week every two weeks, every three weeks, every four weeks, every month, every two months, every three months, every four months, every five months, every six months, every seven months, every eight months, every nine months, every' ten months, every 11 months, every year, or any time therebetween.

[0100] In embodiments of methods of the present invention in which a treatment is administered, the “earlier time point” may be a baseline time point, e.g., prior to administration of the treatment. A baseline time point is preferably performed within 24, 48, or 72 hours, or within one, two, three, or four weeks prior to the first administration. In certain embodiments, a baseline time point is performed within 24 hours prior to the first administration. In such embodiments, the “later time point” may be after the treatment is administered; or if the treatment comprises multiple doses, the “later time point” may be after one or more of the doses, or after all of the doses.

[0101] In embodiments of methods of the present invention in which a subject received a liver transplant, the “earlier time point” may a time point immediately after the transplanted liver is received, for example, within 24 hours, or within 12 hours, or within six hours, or within three hours, of receiving the transplanted liver.

[0102] The increase or decrease in the measured quantity of the cfDNA of the determined cell-type as compared to the normal quantity of cfDNA of the cfDNA of the determined celltype, or the increase or decrease in the measured quantity of the cfDNA of the determined cell-type between a later time point and an earlier timepoint, may be, for example, a percent increase or decrease of about 0.1% to 100%, such as about 0.1%, 0.5%, 1%, 2%, 3%, 4%, 5%, 6%, 7%, 8%, 9%, 10%, 20%, 30%, 40%, 50%, 60%, 70%, 80%, 90%, or 100%; or may be a fold increase of at least about 2-fold, such as about 2-fold, or 3-fold, or 4-fold, or 5-fold, or 6-fold, or 7-fold, or 8-fold, or 9-fold, or 10-fold. In some embodiments, the increase or decrease may be any increase or decrease that is determined to be statistically significant (e.g., p < 0.05, p < 0.01, etc.) as calculated by statistical methods known in the art.

[0103] Methods for quantifying the cfDNA are known in the art and include, but are not limited to, quantitative polymerase chain reaction (PCR); droplet digital PCR; fluorescencebased quantification methods e.g., Qubit); chromatography techniques such as gaschromatography, supercritical fluid chromatography, and liquid chromatography, such as partition chromatography, adsorption chromatography, ion exchange chromatography, size exclusion chromatography, thin-layer chromatography, and affinity chromatography; electrophoresis techniques, such as capillary electrophoresis, capillary zone electrophoresis, capillary isoelectric focusing, capillary electrochromatography, micellar electrokinetic capillary chromatography, isotachophoresis, transient isotachophoresis, and capillary gel electrophoresis; comparative genomic hybridization; microarrays; and bead arrays.

[0104] In methods of the present invention, the sample in which cfDNA is sequenced may be a biological fluid such as blood, serum, plasma, bile, saliva, urine, bile, or sputum; biopsy tissue; or organ perfusion solution. In certain embodiments, the sample from the ex vivo liver tissue may be selected from organ perfusion solution, bile, and biopsy tissue. In certain embodiments, the sample from the subject or the liver tissue donor may be selected from peripheral blood, serum, plasma, bile, and biopsy tissue.

[0105] The cfDNA can be extracted from the sample by methods known in the art. For example, cfDNA may be extracted using magnetic bead-based methods, column-based methods, ethanol precipitation-based methods, anion-exchange resin-based methods, phenolchloroform-based methods, or filtration-based methods. Kits for extracting cfDNA are also commercially available, for example, QIAamp Circulating Nucleic Acid Kit (Qiagen®), QIAamp MinElute ccfDNA Midi Kit (Qiagen®), QIAsymphony DSP Circulating DNA Kit (Qiagen®), Maxwell RSC ccfDNA Plasma Kit (Promega®), Zymo Quick ccfDNA Serum & Plasma Kit (Zymo Research®), and MagMax Cell-Free DNA Isolation Kit (Thermo Fisher®). Each of these kits can be used in accordance with the manufacturer’s instructions.

[0106] Different DNA methylation detection technologies may be used in the present invention. Examples include, but are not limited to, a restriction enzyme digestion approach, which involves cleaving DNA at enzyme-specific CpG sites; an affinity-enrichment method, for instance, methylated DNA immunoprecipitation sequencing (MeDIP-seq) or methyl-CpG-binding domain sequencing (MBD-seq); bisulfite conversion methods such as whole genome bisulfite sequencing (WGBS), reduced representation bisulfite sequencing (RRBS), methylated CpG tandem amplification and sequencing (MCTA-seq), and methylation arrays; enzymatic approaches, such as enzymatic methyl-sequencing (EM-seq) or ten-eleven translocation (TET)-assisted pyridine borane sequencing (TAPS); and other methods that donot require treatment of DNA, for instance, by nanopore-sequencing from Oxford Nanopore Technologies (ONT) and single molecule real-time (SMRT) sequencing from Pacific Biosciences (PacBio).

[0107] Comparison of the methylation pattern in nucleotide sequence of the cfDNA with a cell-type specific methylation pattern may comprise identifying the presence of a methylation pattern in the nucleotide sequence of the cfDNA, or a portion thereof, that are attributed to specific cell types. The specific cell-types may be selected from bi liary-sm all-ductal -epithelial cells, biliary-large-ductal-epithelial cells, liver-endothelial cells, hepatocytes, liverresident-immune cells, and hepatic-stellate cells. In some embodiments, the specific celltypes may also include memory B-cells, naive B-cells, CD4+ mature T-cells, CD8+ mature T-cells, natural killer cells, monocyte / macrophages, and neutrophils.

[0108] In some embodiments, the identification of the presence of a methylation pattern can be performed by hybridization capture sequencing of cfDNA. In other embodiments, the identification of the presence of a methylation pattern can be performed using bisulfite amplicon sequencing.

[0109] One or more aspects of the methods of the present invention may be performed with a computer program. For example, in some embodiments, analysis of the sequence of the cfDNA may be performed with a computer program (for instance, a first computer program). In some embodiments, the identification of methylation patterns in the nucleotide sequence of the one or more portions of the cfDNA that contains methylation sites may be performed with a computer program (for instance, a first or second computer program).

[0110] In some embodiments, the methods of the present invention may further comprise comparing the methylation pattern in the one or more portions of the cfDNA that contains methylation sites to cell-type specific methylation patterns. In certain embodiments, the comparison may be with a computer program (for instance, a first, second, or third computer program).

[0111] In some embodiments, the methods of the present invention also may further comprise transferring to a user interface on an electronic display one or more of the following: (i) the result of sequencing the cfDNA; (ii) the result of the identification of methylation patterns in the nucleotide sequence of the one or more portions of the cfDNAthat contains methylation sites; (iii) the result of the comparison of the methylation pattern in the one or more portions of the cfDNA that contains methylation sites to cell-type specific methylation patterns; and / or (iv) the determination of the cell-type from which the cfDNA originated.

[0112] The methylation pattern may comprise a nucleotide sequence containing at least 1 CpG dinucleotide, or at least about 2 CpG dinucleotides, or at least about 3 CpG dinucleotides. In some embodiments, the methylation pattern may comprise a nucleotide sequence containing at least about 4 CpG dinucleotides, or at least about 5 CpG dinucleotides, or at least about 6 CpG dinucleotides, or at least about 7 CpG dinucleotides, or at least about 8 CpG dinucleotides, or at least about 9 CpG dinucleotides, or at least about 10 CpG dinucleotides.

[0113] In some embodiments, the cell-type specific methylation pattern associated with celltypes biliary-small-ductal-epithelial cells, biliary-large-ductal-epithelial cells, liver-endothelial cells, hepatocytes, liver-resident-immune cells, and hepatic-stellate cells comprises a hypom ethylated nucleotide sequence. In certain embodiments, the cell-type specific methylation pattern is selected from Table 1, which were identified by the inventors in the study described in Example 1.

[0114] In some embodiments, the cell-type specific methylation pattern associated with celltypes biliary-small-ductal-epithelial cells, biliary-large-ductal-epithelial cells, liver-endothelial cells, hepatocytes, liver-resident-immune cells, and hepatic-stellate cells comprises a hypermethylated nucleotide sequence. In certain embodiments, the cell-type specific methylation pattern is selected from Table 2, which were identified by the inventors in the study described in Example 1.

[0115] The cell-type specific methylation patterns are provided in Table 1 and Table 2 as genomic locations, z.c., the start and end points of human chromosomes, which is with reference to the Homo sapiens full genome as provided by University of California Santa Cruz, version hg!9 (Genome Reference Consortium GRCh37, February 2009). Based on this information, one of ordinary skill in the art would understand the nucleotide sequences represented by each of the methylation patterns.

[0116] In some embodiments, the cell-type specific methylation pattern associated with celltypes memory B-cells, naive B-cells, CD4+ mature T-cells, natural killer cells, monocyte / macrophages / and neutrophils are disclosed in International Application Nos. PCT / US2022 / 038242 and PCT / US2022 / 038244, which are incorporated herein by reference.Kits

[0117] In another aspect, the present invention provides a kit and / or panel for use in the methods of the present invention.

[0118] In some embodiments, the kit may comprise one or more pairs of a forward primer and a reverse primer that are complementary to known genomic regions comprising a methylation pattern selected from Table 1 or Table 2. In some embodiments, the kit may comprise intercalating primers,

[0119] In some embodiments, the kit may comprise a panel of hybridization probes designed to capture nucleotides comprising a sequence of a methylation pattern selected from Table 1 or Table 2.

[0120] In certain embodiments, the kit may further comprise one or more of the following: instructions for use; at least one additional reagent, and / or one or more additional active agent; and a label indicating the intended use of the contents of the kit. In this context, the term “label” includes any writing or recorded material supplied on or with the kit, or that otherwise accompanies the kit.Computer Systems and Devices

[0121] In another aspect, the present invention provides computer control systems that are programmed to implement methods of the present invention. For example, computer systems may be used in sequencing the cfDNA, and / or in identifying methylation patterns in the nucleotide sequence of the one or more portions of the cfDNA that contains methylation sites, and / or in comparing the methylation pattern in the one or more portions of the cfDNA that contains methylation sites to cell-type specific methylation patterns.

[0122] The computer system may comprise one or more of the following components: (a) a device configured to receive sequencing data or sequencing reads; (b) a device configured to identify methylation patterns in nucleotide sequence that contains methylation sites; (c) adevice configured to compare the methylation patterns to cell-type specific methylation patterns; and (d) a device configured to measuring the quantity of cfDNA. In some embodiments, the computer system contains cell-type specific methylation patterns selected from Table 1, from Table 2, or from both Table 1 and Table 2,

[0123] A device may be used to receive an array and the data associated with that array. In some embodiments, the device may be configured to compare the array data with a control. In certain embodiments, the array data may be a methylation pattern. In certain embodiments, the device may be a computer system.

[0124] FIG. 19 depicts a computer system 101 that is programmed or otherwise configured for use with methods of the present invention. The computer system 101 may regulate various aspects of the present invention, including, for example, detecting cell-type-specific damage in ex vivo liver tissue, detecting cell-type-specific liver damage, monitoring cell-type specific liver damage, and / or determining etiology of liver damage. In another example, the computer system may be used to determine hypomethylation, hypermethylation, or both hypomethylation and hypermethylation of a region of a chromosome or of a CpG site. The computer system 101 may be an electronic device of a user or a computer system that is remotely located with respect to the electronic device. In certain embodiments, the electronic device may be a mobile electronic device.

[0125] The computer system 101 may comprise a central processing unit (CPU) (also referred to herein as “processor” or “computer processor”) 105, which may comprise a single-core, a multi-core processor, or multiple processors operating in parallel. In some embodiments, the computer system 101 may further comprise one or more of the following components: a memory unit 110, such as random-access memory, read-only memory, or flash memory; an electronic storage unit 115, such as a hard disk, a communication interface 120, such as a network adapter for enabling communication with one or more other systems; and one or more peripheral devices 125, such as cache memory, additional data storage, and / or electronic display adapters.

[0126] The memory' unit 110, electronic storage unit 115, communication interface 120, and one or more peripheral devices 125 may be operatively connected to the CPU 105 via a communication bus (solid lines), such as a motherboard. In some embodiments, theelectronic storage unit 115 functions as a data repository for storing data associated with the operation of the computer system 101.

[0127] The computer system 101 may be operatively coupled to a computer network (“network”) 130 through the communication interface 120. In some embodiments, the network 130 may be the Internet, an intranet, or an extranet in communication with the Internet. In certain embodiments, the network 130 may include a telecommunications and / or data network. In some cases, the network 130 may comprise one or more servers to facilitate distributed computing, such as cloud computing. The network 130, optionally in combination with the computer system 101, may further support a peer-to-peer configuration, allowing connected devices to operate as either clients or servers.

[0128] The electronic storage unit 115 may store files, such as drivers, libraries, and application programs. In some embodiments, the electronic storage unit 115 may also retain user data, including user preferences and custom software. The computer system 101 may further include additional data storage units external to the computer system 101, such as remote server connected via an intranet or the Internet.

[0129] The CPU 105 may execute a sequence of machine-readable instructions embodied in a program or software. In some embodiments, the instructions may be stored in the memory unit 110 and may direct the CPU 105 to perform methods in accordance with the present invention. The CPU 105 may perform typical operations such as fetch, decode, execute, and writeback during program execution.

[0130] The CPU 105 may be implemented as part of an integrated circuit. In some embodiments, additional components of the computer system 101 may also be integrated within the same circuit. In certain embodiments, the circuit may comprise an application specific integrated circuit (ASIC).

[0131] The computer system 101 may communicate with one or more remote computer systems via the network 130. For instance, the computer system 101 may exchange data with a remote system operated by a user, such as a laboratory technician or healthcare professional. Exemplary remote computer systems may include, but are not limited to, personal computers (including portable computers), tablet devices (e.g, Apple® iPad, Samsung® Galaxy Tab), telephones, smartphones (e.g, Apple® iPhone or Android®-enableddevices), and personal digital assistants (PDAs). The user may access the computer system 101 through the network 130.

[0132] The methods of the present invention may be implemented using machine-executable code stored in an electronic storage location of the computer system 101, such as the memory unit 110 or electronic storage unit 115. The machine-readable code may be provided as software, which, when executed by the CPU 105, performs operations described in the present disclosure. In some embodiments, the code may be transferred from the electronic storage unit 115 to the memory unit 110 for rapid access by the CPU 105. In certain embodiments, the electronic storage unit 115 may be omitted, and the executable instructions stored directly in the memory unit 110.

[0133] The code may be supplied as pre-compiled instructions configured for use with a processer capable of executing the code or may be compiled at runtime. In some embodiments, the programming language used to supply the code may be selected to support pre-compilation or just-in-time (JIT) compilation.

[0134] Aspects of the systems and methods provided herein, including the computer system 101, may be embodied in programming. Various features of the technology may thus be considered as “products” or “articles of manufacture,” typically in the form of machineexecutable code and / or associated data embodied in a machine-readable medium. In some embodiments, such code is stored on an electronic storage medium, including, but not limited to, read-only memory (ROM), random-access memory (RAM), flash memory, or a hard disk. Suitable storage media include tangible, non-transitory memory components such as semiconductor memories, magnetic tapes, and disk drives that provide persistent storage of software programming.

[0135] Portions or all of the software may, at times, be transmitted via the Internet or other telecommunications networks. Such transmission may, for example, enable transfer of software components from a host or management server to an application service or client computer platform. Accordingly, other forms of media capable of carrying the software include optical, electrical, and electromagnetic signals, transmitted through wired, optical, or wireless channels. These physical carriers — such as fiber optics, cables, or air interfaces — may therefore also be considered media bearing the software. Unless otherwise specified asnon-transitory, terms such as computer or machine “readable medium” encompasses any medium capable of providing instructions to a processor for execution.

[0136] Accordingly, a machine-readable medium, such as computer-executable code, may take various forms, including, but not limited to, tangible storage media, a carrier wave media, or transmission media. Non-volatile storage media include, for example, optical or magnetic disks such as hard drives and CD-ROMS, while volatile storage media include dynamic memory such as main memory. Transmission media may include coaxial cables, copper wire, and fiber optic lines, and electromagnetic or acoustic signals (e.g., radio frequency or infrared communications). Exemplary computer-readable media include, without limitation: floppy disks, magnetic tapes, CD-ROMs, DVDs, R / XM, ROM, PROMs, EPROMs, flash memory, carrier waves, and other forms of data transmission media. Many of these media types may be employed to convey one or more sequences of instructions for processor execution.

[0137] The computer system 101 may comprise or be in communication with an electronic display 135 that comprises a user interface (UI) 140. The UI 140 may, for example, display a methylation pattern of the subject. Examples of UIs include, but are not limited to, graphical user interfaces (GUIs) and web-based user interfaces.

[0138] Methods and systems of the present invention may be implemented through one or more algorithms executed by the CPU 105. In some embodiments, an algorithm may identify methylation patterns in a nucleotide sequence. In some embodiments, an algorithm may compare and determine whether a first methylation pattern (for example, a methylation pattern in nucleotide sequence of one or more portions of cfDNA) is the same as a second methylation pattern (for example, a cell-type specific methylation pattern, such as cell-type specific methylation pattern shown in Table 1 and / or Table 2).EXAMPLESExample 1

[0139] A study (McNamara etal., 2024

[0017] ; incorporated herein by reference) was conducted that mapped methylation patterns in cfDNA fragments from blood samples ofpatients after liver transplant, and that determined methylation patterns that can identify cell types in different organs.Results

[0140] C ellular damages after liver transplant indicated by cell-free methylated DNA in the circulation. Serial serum samples were collected from 28 adult liver transplant patients during the peri-transplant time period and cfDNA methylation from samples at predetermined timepoints up to one month after transplant (n = 100 samples) was profiled. Samples were also collected from patients experiencing complications and phenotype-matched samples from an additional 16 patients at the time of for-cause liver biopsy (FC-bx) used to identify allograft injury (n:::30 samples) were added. Cell-free DNA fragments isolated from these 130 serum samples were bisulfite treated, enriched for sequences of interest by methylome-wide hybridization capture and subjected to sequence analysis (FIG. 1). The tissue and cell type origins of cfDNA fragments in the circulation were mapped to an expanded atlas of cell-type-specific DNA methylation to infer tissue damage and differentiate amongst causes of allograft injury.

[0141] Characterization of liver cell-type-specific epigenomes to expand sequencing-based DNA methylation atlas of healthy tissues. To identify cellular origins of cfDNA fragments in the circulation, the existing cell-type-specific DNA methylation atlas was expanded to liver cell-types relevant for injury and repair, and methylome-sequencing data for hepatic stellate, liver endothelial, biliary epithelial, and liver-resident immune cell populations was generated. In addition, published whole genome bisulfite sequencing (WGBS) data from purified healthy human cell-types was also included [18, 19], This resulted in curation of over 450 WGBS datasets encompassing over 40 cell-types from diverse populations of donors generating a reference methylome atlas as previously described [18,19], Briefly, the data was first segmented into homogenously methylated blocks where DNA methylation levels at adjacent CpG sites were highly correlated across different cell types. Then, the analysis was restricted to the 364,268 blocks covered by the hybridization capture panel used in the analysis of cfDNA in human serum (captures 80Mb, -20% of CpGs). Average methylation was calculated within blocks of at least three CpG sites and unsupervised clustering analysis was performed for the top 10% variable blocks across all samples. With the additional data incorporated, samples still clustered strongly by cell type and developmental lineage (FIG. 2,Panel D). Notably, parenchymal and nonparenchymal liver cell methylomes did not cluster together. Instead, samples clustered with other cell-types of the same lineage, independent of the germ layer origin of their tissues of residence. Interestingly, biliary epithelial samples isolated from intrahepatic ducts and the gallbladder (biliary-small-ductal) demonstrated distinct methylation patterns compared to biliary epithelial samples isolated from the larger main hepatic, common bile, and pancreatic ducts (biliary-large-ductal) (FIG. 2, Panels B and C).

[0142] Based on the unsupervised clustering analysis, the reference WGBS data was grouped into 20 groups for downstream analysis. Cell-type specific differentially methylated blocks (DMBs) were identified within these groups taking a one-vs-all approach as previously described

[0018] , The co-methylation status of neighboring CpG sites in each block distinguished amongst all cell types included in the final groups (Table 1 and Table 2). The heatmap in FIG. 3, Panel A depicts the top 50 blocks with the highest score for each celltype and the top hepatocyte-specific blocks are emphasized in FIG. 3, Panels B and C. The methylation sequencing approach taken here allowed for assessment of fragment-level methylation patterns rather than the limited single-site resolution of methylation arrays (FIG.3, Panel D). Whereas bulk tissue analyses average the methylation status amongst all celltypes, purified cell-specific methylome analysis allowed for discovery of features critical to the identity of non-parenchymal cell-types that contribute only few cells to the overall population and therefore would otherwise be missed.

[0143] Liver cell-type-specific hypomethylated blocks coincide with cell-type specific chromatin accessibility and H3K27ac binding. The cell-type-specific DMBs identified using the expanded WGBS reference data resembled those of previously published methylation atlases, being largely hypomethylated, intragenic, and annotated to genes relevant for cell function and identity (FIG. 2, Panel A; FIG. 4, Panel E; and FIG. 5, Panel A) [18-20], Notably, cell-type-specific hypermethylated DMBs were much less frequent (17% on average) and enriched for CpG islands compared to cell-type-specific hypomethylated DMBs that were located in relatively CpG-depleted, GC-low regions characteristic of programmed demethylation occurring at enhancers (FIG. 4, Panel A) [14, 19, 21], Indeed, the majority of liver cell-type-specific hypomethylated DMBs were enhancers by chromHMM annotations (FIG. 6, Panel C). In contrast, the majority of liver cell-type-specific hypermethylatedDMBs were annotated to bivalent TSS / enhancers and repressive Polycomb targets (FIG. 4, Panel D). This matches with the function of cell-type-specific hypermethylation in repressing genes associated with embryonic stem cell pluripotency to stabilize cellular differentiation during development (FIG. 4, Panel E) [22-24], To further explore the liver cell-type-specific DMBs identified, additional chromatin accessibility and histone modification data was generated and compiled to characterize the integrated epigenomes of hepatocyte, biliary epithelial, hepatic stellate, liver endothelial, and liver-resident immune cell-types. Liver cell-type-specific hypomethylated blocks were found to also be regions with cell-type specific chromatin accessibility and H3K27ac binding, emphasizing the regulatory importance of these regions in maintaining cell-type-specific features reflected in the multi-omic datasets (FIG. 6, Panels A and B).

[0144] Pioneer transcription factor binding sites (TFBS) enriched within liver cell-type-specific DNA methylation blocks. Motif analysis was performed to explore association of the identified liver cell-type-specific DNA methylation with transcription factor binding.Enriched motifs were found for several pioneer transcription factors within hypomethylated DMBs, including FOXA1 / 2, PAX7, CUX1, HNR5A2, DUX4, OTX2, GATA, SOX17, ATF4 and PU.1 (FIG. 6, Panels D and E). Enriched motifs of binding sites were also found for several liver developmental TFs known to cooperate with pioneer TFs, including HNF4a, HNF6, PDX1, RARa, COUP-TFII, and RUNX1 [25-27], Pioneer factors are a subclass of TFs that can bind to closed chromatin and elicit an extended functional capacity of the domain, often through local chromatin opening and demethylation

[0025] , As such, they act as master regulators of development and are known to drive cell fate transitions

[0025] , However, it was surprising to find binding sites for pioneer TFs also enriched within liver-specific hypermethylated DMBs (FIG. 4, Panel C). Interestingly, CpG dinucleotides were also enriched within the motifs found in hyperm ethylated DMBs and several methylationsensitive TFs were amongst the top hits, including NRF1 where methylation is known to directly repress TF binding (FIG. 4, Panel B)

[0028] , Although less common, pioneer TFs have been shown to recruit transcriptional repressors and establish a closed and further silenced chromatin architecture [22, 29, 30]. Several TFBS of transcriptional repressors known to interact with pioneer TFs were also enriched, including TBET, TRPS1, ZNF669, and E2F7. Annotation of the majority of liver-specific hypermethylated DMBs to bivalent TSS / enhancer regions coincides with the ability of some pioneer TFs to simulate PRC2complex-inducing H3K27me3 -marked heterochromatin, often deposited on lineage-specific enhancers (FIG. 4, Panel D) [31-33], In composite, these results match with the role of the liver cell-type-specific methylation blocks identified here as being critical for cell identity.

[0145] Origins of cellular damage immediately after liver transplant. To identify the origins of cfDNA fragments in the circulation of liver transplant patients, the top 100 methylation blocks for each cell-type group were used and an expanded liver cell-type-specific DNA methylation atlas was generated (see Methods below). A fragment-level deconvolution algorithm was applied, previously validated to estimate relative contributions from cfDNA methylation sequencing data (see Table 3, which shows liver transplant serial cfDNA sample concentrations and predicted liver cell-type proportions from fragment-level deconvolution analysis; Table 5, which shows liver transplant serial cfDNA sample concentrations and predicted liver cell-type Geq from fragment-level deconvolution analysis; and Table 7, which shows liver transplant serial cfDNA sample concentrations and predicted immune subset celltype proportions from fragment-level deconvolution analysis)

[0019] , First, to explore the changing cfDNA makeup after liver transplant, changes across all patients was assessed, comparing pre-transplant cfDNA origins to post-reperfusion changes in serially collected blood samples from 28 liver transplant patients on the day of surgery (PODO; FIG. 7, Panel A). There was a significant ~5-fold increase in cfDNA concentration after transplant across all patients in the cohort, reflecting increased cell turnover from the surgical procedure itself (p<0.05, Wilcoxon matched-pairs signed rank test) (FIG. 7, Panel F). From the deconvolution analysis, it was found that liver cell types mainly contributed to this increase (FIG. 7, Panel B) with a significant increase in hepatocyte, hepatic stellate, and endothelial cfDNA fraction and a corresponding relative decrease in myeloid cfDNA that constitutes most of the hematopoietic signal at baseline (p<0.05, Wilcoxon matched-pairs signed rank test) (FIG. 7, Panels D, E, G, and H). The concentration of hepatocyte cfDNA in genome equivalents / mL (Geq / mL) correlated with patient AST and ALT liver enzyme values (Spearman r = 0.81 AST and r = 0.82 ALT, p<0.05) (FIG. 7, Panel C). The homogenous cfDNA changes across all patients show applicability of this approach across a diverse patient cohort. Beyond damages to the allografted liver cell-types, it was found that the transplant procedure resulted in multiple tissue cellular damages of the recipient as well. It was surprising that there was a significant increase in neuron-derived cfDNA after transplant (FIG. 7, Panel I), While only representing a small overall fraction of the total cfDNA, therewas an average 4-fold increase in this signal indicating neuronal cell death during the procedure. In addition, there were also significant increases in cardiomyocyte, biliary-ductal, and gastric-epithelial cfDNAs in Geq / mL (FIG. 8).

[0146] Sustained elevation of hepatocyte and biliary epithelial cfDNA indicate allograft injury. Additional serum samples were collected in a subset of 20 liver transplant patients to explore cfDNA changes over time during the first month after transplant, the highest risk period for post-transplant complications (FIG. 9, Panel A). Of these patients, 11 (55%) had liver biopsies showing allograft injury within the first year. There were no differences in cfDN A concentration after transplant comparing these two outcome groups (FIG. 10, Panel H), though there were changes in cfDNA composition when comparing across the entire cohort (FIG. 10, Panel A). Liver-epithelial cellular damage mostly recovered in patients without allograft injury during the first post-operative week. In contrast, patients diagnosed with allograft injury during the first year after transplant had sustained elevation of hepatocyte and biliary epithelial cfDNA from POD7-POD30 (p<0.05, Mann-Whitney test) (FIG. 9, Panels C to F). Despite these differences in liver epithelial signals, there was no significant difference in hepatic stellate or endothelial cfDNA associated with different outcomes (FIG. 10, Panels C and D). These findings were irrespective of the type of allograft injury diagnosed at the eventual time of for-cause liver biopsy (FC-bx). Of the patients with allograft injury, 7 (of 20) were diagnosed with hepatocellular, 3 mixed hepatobiliary, and 1 biliary forms of allograft injury (FIG. 9, Panel B). The majority of patients were diagnosed with allograft injury beyond the first month, but five patients were diagnosed within the first month (all within the first-year post-transplant). Despite variation in timing, elevated liver epithelial cfDNA was detected during the first post-operative month in all 11 patients with allograft injury’, with an elevated signal detected a median of 63 days (range 2-203 days) ahead of the time of tissue-biopsy based diagnosis. Comparing the trajectory of liver cell-type damages over time, patients without allograft injury had higher levels of lymphoid and endothelial cfDNA relative to hepatocyte and biliary epithelial cfDNA at POD30 (FIG. 9, Panel G). This suggests that the cell-specific composition of liver-derived cfDNA provides added context and that the ratio may be useful to monitor and predict injury patterns during the peri -transpl ant time period relative to the total liver-derived cfDNA signal (FIG. 10, Panel E).

[0147] Cell-free DNA methylation indicates the source of allograft injury. Phenotype-matched samples from additional patients at the time of for-cause liver biopsy (FC-bx) used to diagnose allograft injury were added, evaluating 30 serum samples from 24 individuals (FIG. 11, Panel A). Samples were classified as having hepatocellular (n:::14), biliary (n = 6), or mixed hepatobiliary (n=10) forms of allograft injury from histopathological analysis of the paired biopsy tissues. Notably, the composition of cfDNA was significantly different at the time of biopsy-proven phenotypes, comparing hepatocellular and biliary etiologies of allograft injury (FIG. 11, Panel B; Table 4, which shows liver transplant phenotype-matched cfDNA sample concentrations and predicted liver cell-type proportions at FC-bx from fragment-level deconvolution analysis; Table 6, which shows liver transplant phenotype-matched cfDNA sample concentrations and predicted liver cell-type Geq at FC-bx from fragment-level deconvolution analysis; and Table 8, which shows liver transplant phenotype-matched cfDNA sample concentrations and predicted immune subset cell-type proportions at FC-bx from fragment-level deconvolution analysis). Hepatocyte cfDNA was increased in samples with hepatocellular or mixed hepatobiliary injury' compared to pure biliary injury' (p<0.05, Mann-Whitney test) (FIG. 11, Panel C). Likewise, biliary cfDNA was increased in samples with biliary or mixed hepatobiliary' injury compared to hepatocellular injury (p<0.05, Mann-Whitney test) (FIG. 11, Panel D). There was also an increase in myeloid-derived cfDNA in patients with biliary’ etiologies of allograft injury relative to those with hepatocellular etiologies (p<0.05, Mann-Whitney test) (FIG. 12, Panels B-E). Further, there was an association between standardized lesion grading (Banff Rejection Activity Index, RAI) of liver biopsy tissues and total liver cfDNA in serum samples of patients with allograft injury (FIG. 10, Panel B) The cfDNA composition changes over time reflected the trajectory of cellular damages in patients with different injury' types (FIG. 11, Panels E to H; patient details in the legend). At the time of FC-bx only hepatocyte cfDNA was detected in a patient with hepatocellular injury (FIG. 11, Panel G), only biliary epithelial cfDNA in a patient with pure biliary injury (FIG. 11, Panel H), and both hepatocyte and biliary epithelial cfDNA in two patients with different etiologies of mixed hepatobiliary injury’ (FIG. 11, Panels E and F). Taken as a whole, distinct cellular damages after liver transplant are detectable by the analysis of blood samples.Discussion

[0148] The liver cell-type-specific DNA methylation atlas sheds light onto the epigenomic characteristics established early during development by identifying genomic regions of cell identity that are stably maintained in differentiated cells. The results showed that DNA methylation coincides with other liver cell -type-specific epigenetic marks, validating the biological relevance of these regions and enhancing their utility in detecting altered turnover of cells from DNA fragments shed in the circulation. The majority of liver-cell-specific DMBs were found to be hypomethylated. However, enriched TFBS of pioneer factors were found within both hypo- and hyper-methylated liver-cell-specific DMBs. Surprisingly, it was also found that CpG dinucleotides enriched within TFBS motifs associated with methylationsensitive TFs in hypermethylated DMBs. This suggests that cell-type-specific hypo- and hyper- methylated regions may play a similar function in different contexts to repress precursor or stem cell transcriptional programs and control terminal differentiation into distinct cell types.

[0149] Sufficient numbers of DNA methylation blocks were identified to discriminate cells of origin of liver-derived DNA fragments in the circulation, including hepatocyte, biliary-epithelial, hepatic stellate, and endothelial cells. Also, extended liver-resident immune cell markers were identified with relaxed specificity thresholds to use for characterization of liver cell-specific epigenetic data (FIG. 5).

[0150] The expanded liver cell -type-specific methylation atlas allowed for detection of tissue-derived fragments in the circulation to reveal cell types in the recipient impacted by the transplant procedure, comparing post-reperfusion signals to the pre-transplant baseline. The donor liver can be damaged in several ways including, cold and warm ischemia, surgical anastomoses, and reperfusion injuries [2, 34-37], These tissue effects were reflected by a relative increase in hepatocyte, hepatic stellate, and endothelial cfDNA compared to the myeloid-derived baseline signal. However, the increase in neuron and cardiomyocyte cfDNA after the transplant was surprising. The increase in neuron cfDNA could be caused by neurotoxicity from the general anesthesia; although, neurological complications are more common after liver (30%) than after heart. (4%) or kidney transplants (0.5%)

[0038] , The neuron-specific methylation patterns used for analysis were identified using purified CNS neuron methyl omes (not PNS neurons). This is also reflected by the functionalcharacterization of these regions acting in programs relevant to CNS neurons (FIG. 13). Increased susceptibility or comorbidity due to the pathophysiology of the underlying hepatic disease may play a role and contribute to increased neuronal cell death in liver transplant patients. Interestingly, the concentration of the pre-transplant cfDNA was ~ 100-fold higher compared to cfDNA from healthy individuals. Inflammation and activation of coagulation in liver failure has been found to increase cfDNA concentration, but also the increased concentration could result from impaired clearance mechanisms since these individuals all have end-stage liver disease and many also have kidney failure as a co-morbidity. Other studies have also found elevated cfDNA concentrations in individuals with impaired liver tissue function as well as demonstrated similar concentrations after solid organ transplants [39-42],

[0151] The large increase in liver-cell-derived cfDNA after transplant can serve as a proof-of-concept to validate the prediction accuracy of deconvolution results. A complete fragment-level deconvolution model was used to estimate relative abundance and changes in the cfDNA composition, shown to accurately detect cfDNA from a source at 0.1% resolution

[0019] , Also, a significant correlation was found between hepatocyte cfDNA and AST / ALT liver enzyme activity (FIG. 7, Panel C). However, biliary cfDNA was not found to correlate significantly with alkaline phosphatase (ALP) or bilirubin levels (FIG. 10, Panels F and G).The short half-life of cfDNA (15 mins - 2 hours) relative to commonly monitored liver function parameters also contributes to some discrepancy between these parameters [43. 44], Changes in cell-type-specific cfDNAs reflect changes in cell turnover and thus measure different facets of tissue dysfunction. It is noteworthy that hepatocyte cfDNA is typically detectable in healthy individuals whereas biliary-epithelial cfDNA is mostly below detection (FIG. 14, Panel G). While the cfDNA used in this study was extracted from serial serum samples, cfDNA was size-selected to remove contaminating HMW-DNA from hematopoietic cell lysis during the process of serum preparation to allow for generalizability to other analyses of banked serum samples. The analysis of paired plasma and serum samples described in Methods shows close correlation for the solid organ-derived cfDNAs from either source (FIGS. 14-16). Further studies can benchmark these results against cfDNA derived from plasma collected with and without WBC-stabilizing tubes to further generalizability for clinical application.

[0152] Sustained elevation of liver epithelial cfDNA during the first month after transplant was found to be associated with allograft injury, while patients without allograft injury had significantly reduced levels of liver epithelial cfDNA as early as the first week posttransplant. Importantly, liver cell-specific methylation patterns appear to be stably maintained during the ongoing processes of tissue damage, repair, and remodeling after transplant. Elevated liver epithelial signals were detected in all patients with allograft injury, despite being a diverse cohort with several different types of allograft injury represented. The results suggest that cell-free methylated DNA has predictive and diagnostic value to detect allograft injury earlier than clinical diagnosis by liver biopsy. Notably, a significant difference in hepatic stellate or endothelial cfDNA was not found comparing patients with allograft injury to those without allograft injury during the first month post-transplant. This observation is in contrast with liver damage after radiation treatment of patients with rightsided breast cancers where liver endothelial cfDNA showed a >10-fold increase after radiation and delayed recovery to baseline one month after treatment in comparison to hepatocyte cfDNA

[0018] , Thus, cfDNAs reflect distinct cellular responses to different types of injury and repair in the same organ.

[0153] Beyond damage to the allograft, altered cfDNA methylation patterns are also able to reveal cellular damages of other patient organs to indicate extra-hepatic toxicity and immune cell turnover [45-47], This is a useful application of cfDNA methylation patterns that can simultaneously allow for monitoring of common pulmonary', renal, cardiac, and neurological complications after liver transplants. Acute kidney injury (AKI) is one of the most common post-operative complications, occurring in up to 78% of liver transplant patients [2], Elevated kidney epithelial cfDNA was able to be detected in several patients in our cohort experiencing hepato-renal syndrome (HRS) pre-transplant as well as those experiencing AKI post-transplant (FIG. 11, Panels E-H). In addition, divergent trajectories of lymphoid versus myeloid cfDNA were noticed, with lymphoid cfDNA demonstrating more dynamic changes compared to myeloid cfDNA that remains a constant background signal (FIG. 11, Panels E-H; and FIG. 12). Elevated lymphoid cfDNA corresponding to infection in several patients was found, including one patient with a COVID-19 infection (FIG. 11, Panel E)

[0154] Many studies have demonstrated the utility of donor-derived (dd) cfDNA to detect allograft injury [48-52], However, dd-cfDNA is unable to discriminate amongst differentcauses of allograft injury. Likewise, it remains a challenge to distinguish causes relying on clinical presentation alone. Therefore, liver biopsy is still the gold standard to confirm a diagnosis and evaluate for response to treatment [5], Here, it was found that cfDNA methylation is able to detect and differentiate hepatocellular versus biliary causes of allograft injury at the time of biopsy-proven diagnosis (FC-bx). Biliary complications after liver transplant, such as ascending cholangitis, strictures (both anastomotic and non-anastomotic), leaks, and recurrence of primary sclerosing cholangitis, contribute significantly to posttransplant morbidity and mortality in both living and deceased donor transplant recipients. Conventional diagnostic and monitoring methods for these conditions often necessitate cross-sectional imaging techniques, such as MRCP, or invasive procedures like ERCP or liver biopsy, which pose additional risks to patients [53, 54], Enhanced detection of biliary cell-type-specific damage allows for differentiation from hepatocellular forms of allograft injury and associated tissue damage. This enables an earli er and more accurate diagnosi s of biliary complications and improved non-invasive monitoring post-treatment. Incorporating cfDNA as a diagnostic tool into clinical practice could potentially reduce the need for invasive procedures and facilitate early intervention with targeted treatment.

[0155] In summary, this study shows that cfDNA methylation patterns found on fragments released from dying cells can indicate the origins of cell death and tissue damage in transplant patients. Expanded atlases of DNA methylation sequencing data allow for identification of cfDNA fragments originating from a variety of cell-types in the liver, demonstrating applicability in a wide range of clinical settings. These findings from the cfDNA methylation analysis was correlated with clinical data, histopathological results, and outcomes of conventional clinical monitoring. It is concluded that cell-free methylated DNA in the circulation of liver transplant patients can indicate allograft injury and discriminate amongst causes of allograft injury matching with tissue biopsy-proven diagnosis.Methods

[0156] Study cohort. Serial serum samples were collected from 28 adult liver transplant patients at predetermined time points; pre-transplant (PRE) and post-reperfusion (POST) on post-operative day 0 (PODO), post-operative day 7 (PODO), and post-operative day 30 (POD30). Beyond this, samples were also collected in patients experiencing complications at the time of symptom presentation. Further, phenotype-matched samples were added from anadditional 16 patients at the time of for-cause liver biopsy (FC-bx) used to diagnose allograft injury. Samples were classified as having hepatocellular (n::::14), biliary (n::::6), or mixed hepatobiliary (n=10) forms of allograft injury from histopathological analysis of paired liver biopsy tissues. A schematic of the time series for sample collection can be found in FIG. 1. For serum isolation, peripheral blood (-6-12 mL) was collected in red-top venous puncture tubes and allowed to clot at room temperature for 30 minutes before centrifugation at 1200 x g for 10 min at room temperature to separate the serum fraction.

[0157] Isolation of plasma samples. Peripheral blood (-6-9 mL) was collected in lavender-top venous puncture tubes (EDTA tubes). Plasma was collected as a part of the Ficoll-Paque density gradient separation method to isolate Peripheral Blood Mononuclear Cells (PBMCs), centrifuged at 1000 x g for 20 min at room temperature without brakes. The separate plasma fraction was then centrifuged again at 3000 x g for 10 min before cfDNA isolation. CfDNA was isolated from plasma using the QIAamp Circulating Nucleic Acid kit (Qiagen) according to the manufacturer’s instructions. Cell-free DNA was quantified via Qubit fluorometer using the dsDNA BR Assay Kit (Thermo Fisher Scientific).

[0158] Isolation of circulating cfDNA. Circulating cfDNA was extracted from 2-6 mL human serum, using the QIAamp Circulating Nucleic Acid kit (Qiagen) according to the manufacturer’s instructions. cfDNA was quantified via Qubit fluorometer using the dsDNA BR Assay Kit (Thermo Fisher Scientific). Additional size selection using Beckman Coulter beads was applied to remove high-molecular weight DNA reflective of cell-lysis and leukocyte contamination as previously described. Paired serum and plasma were processed at serial timepoints for n=3 liver transplant patients and n=4 healthy controls to serve as a quality control (FIGS. 14-16). Fragment size distribution of isolated cfDNA after size selection was validated on the 2100 Bioanalyzer TapeStation (Agilent Technologies).

[0159] Cell isolation to generate reference liver cell-type epigenomes. Reference epigenomes were generated for human liver cell types to expand upon publicly available datasets. Human biliary tissues were obtained from organs not suitable for transplant that were otherwise normal according to surgical assessment. Tissues were dissected, with samples processed from lobes, common hepatic duct, gallbladder, and common bile duct. Biliary' epithelial cells (EpCAM+) were isolated from the dissected tissues according to previously-established protocols (see below; see also FIG. 2, Panel B) [55, 56],Cryopreserved passage 1 human liver sinusoidal endothelial cells (LSEC) were purchased from ScienCell research laboratories (SKU#5000). Cryopreserved passage 0 liver-resident immune cells and passage 1 human hepatic stellate cells were isolated from single donor healthy human tissues purchased from Novabiosis Lot: QGJ and JNA (liver-immune); Lot: ZMC and WAP (hepatic-stellate). Paired RNA-seq data was generated from the same cellpopulations used for DNA methylation profiling to validate the identity of cell-types obtained from commercial sources through analysis of cell type expression markers ( FIG. 17).

[0160] Dissociation protocol for biliary epithelial tissues. Human biliary tissues were obtained from organs not suitable for transplant that were otherwise normal according to surgical assessment. Tissues were dissected, with samples processed from lobes, common hepatic duct, gallbladder, and common bile duct. Biliary epithelial cells (EpCAM+) were isolated from the dissected tissues according to previously established protocols with some modifications [55,56], The inner epithelial layer was separated from the outer fibrous connective tissue and muscle layer using disposable sterile scalpels and dissecting scissors. The inner epithelial layer was then minced and enzymatically digested in a solution of Collagenase (2 mg / mL, Roche)-DNase (0.5%, ThermoFisher)-Fetal Bovine Serum (2% FBS, GIBCO) in Advanced DMEM / F-12 (Invitrogen) supplemented with 1% Penicillin / Streptomycin (Pen / Strep, GIBCO) and 1% L-Glutamine (GIBCO). Using a set 1 hour program (37C_h_TDK3) on the GentleMACS Octo Dissociator (Miltenyi Biotec), tissues were further digested by transferring solution into gentleMACS C-tubes (Miltenyi Biotec). After digestion, samples were passed through a 70 pm cell strainer (Corning) and washed with DMEM / F-12 (Invitrogen). Samples were centrifuged at 300xg for 10 mins and the supernatant removed. Cells from intrahepatic ducts (lobes) were under! ayed with an equal volume of 20% and 50% (v / v) Percoll (Sigma). Following centrifugation at 1800xg for 30 min at 4°C, the intrahepatic biliary / epithelial (IHBEC) fraction at the interface of the 20% and 50% Percoll layers was collected, washed and re-suspended in EasySep bead buffer (PBS, 0.5% BSA, 0.5M EDTA). Biliary epithelial cells (EpCAM+) were isolated from pelleted cells using EasySep magnetic beads for EpCAM positive selection (StemCell Technologies Cat #17846) according to the manufacturer’s protocol. Retained cells were considered EpCAM; biliary epithelial cells. Efficiency of isolation was determined using flow cytometry (FITC Anti-human Epithelial cell, clone 5E11.3.1; StemCell Technologies Cat #6O147F1). Validation of biliary epithelial cell purity was done using DNA methylationat previously published regions with specificity to biliary tissues (FIG. 17, Panel C). This was due to poor quality of RNA extracted from these cells that prohibited RNA-sequencing analysis due to the high levels of digestive enzymes. The biliary epithelial cell populations demonstrate purity (over 400-fold enrichment) relative to bulk liver and bulk gallbladder tissues.

[0161] Classification of serum samples at time of for -cause liver biopsy (FC-bx) to diagnose graft injury. Serum samples from 24 liver transplant patients (n = 30 serum samples) were taken at the time of for-cause liver biopsy (FC-bx) to diagnose graft injury. Patients were classified as suffering from hepatocellular (n = 14), biliary (n = 6), or mixed hepatobiliary (n=l 0 ) forms of graft injury from histopathological analysis of the liver biopsy tissues annotated by a pathologist. The following were defined as clinical etiologies of hepatocellular injury: Acute cellular rejection (ACR, rejection activity index, RAI 3+), recurrence of primary hepatic disease (including recurrence of viral hepatitis (HBV or HCV), autoimmune hepatitis, or (non)-alcoholic steatohepatitis in the transplanted organ), drug-induced hepatotoxicity, ischemia-reperfusion injury (IRI) leading to ischemic hepatitis. The following were defined as clinical etiologies of biliary injury: anastomotic and non-anastomotic biliary strictures, ascending cholangitis, ischemic cholangiopathy, recurrence of primary biliary disease (including primary sclerosing cholangitis (PSC) among others), and septic cholestasis [2, 3, 35, 37, 53], Mixed hepatobiliary forms of graft injury were characterized by diagnosis of one or more hepatocellular and one or more biliary forms of graft injury at the same timepoint.

[0162] Isolation and fragmentation of genomic DNA. Genomic DNA from tissues was extracted with the DNeasy Blood and Tissue Kit (Qiagen) following the manufacturer’s instructions and quantified via the Qubit fluorometer dsDNA BR Assay Kit (Thermo Fisher Scientific). Genomic DNA was fragmented via sonication using a Covaris M220 instrument to the recommended 150-200 base pairs before library preparation. Lambda phage DNA (Promega Corporation) was also fragmented and included as a spike-in to all DNA samples at 0.5%w / w, serving as an internal unmethylated control. Bisulfite conversion efficiency was calculated through assessing the number of unconverted C’s on unmethylated lambda phage DNA.

[0163] Segmentation and clustering analysis. The genome was segmented into blocks of homogenous methylation as previously described

[0019] , In brief, a Dynamic Programming segmentation algorithm was used to divide the genome into continuous genomic regions (blocks) showing homogenous methylation levels across multiple CpGs for each sample. The segmentation algorithm was applied to over 450 human reference WGBS methylomes and retained 364,268 blocks covered by the hybridization capture panel used in the analysis of cfDNA (probed regions span 80Mb (-20% of CpGs) on the capture panel). The top 10% most variable methylation blocks containing at least three CpG sites and coverage across 90% of samples were selected, irrespective of sample cell-type group. The average methylation was computed for each block and sample using wgbstools (—beta to table). Dimensional reduction was performed on the selected blocks using the UMAP package (V 0.2.8.2.0). Default UMAP parameters were used (15 neighbors, 2 components, Euclidean metric, and a minimum distance of 0,1),

[0164] Identification of cell-type specific methylation blocks. Tissue and cell-type specific methylation blocks were identified from reference WGBS as previously described

[0018] , A one-vs-all comparison was performed to identify differentially methylated blocks unique for each group. All cell-type-specific blocks contained a minimum of three CpG sites, with lengths of less than 2kb and at least 10 observations. In brief, the average methylation per block / sample was calculated, as the ratio of methylated CpG observations across all sequenced reads from that block. Differential blocks were sorted by the margin of separation, termed “delta beta”, defined as the minimal difference between the average methylation in any sample from the target group vs all other samples. Then, the “soft margin” between target samples and background samples was computed, allowing for some outliers using percentiles. For all hypomethylation markers the difference between the 80th percentile of the methylation status in the target group (—target. quant 0.2) and the 10th percentile of the methylation status in the background group (— bg. quant 0.1) was calculated. Conversely, for all hypermethylated markers, the difference between the 20th percentile methylation status in the target group and the 90th percentile in the background group was calculated. Blocks with a margin > 0.4 for all cell-type groups were selected. Blocks with a (-) direction are hypomethylated and (+) direction are hypermethylated, defined as a direction of methylation in the target cell-type relative to all other tissues and cell-types included in the atlas. A magnitude threshold was also used, where all hypomethylated blocks have an AverageMethylation Fraction (AMF) <0.5 and hypermethylated blocks have an AMF >0.5.However, the vast majority of cell-type specific differentially methylated blocks are much more diverged (mean AMF hypo = <10% methylation and mean AMF hyper = >80%).

[0165] Biliary epithelial samples were separated into two groups of epithelial populations, samples isolated from intrahepatic ducts and the gallbladder (biliary-small-ductal) compared to samples isolated from the larger main hepatic, common bile and pancreatic ducts (biliary-large-ductal). DMBs were identified for each biliary epithelial cell population. Estimated cell proportions from biliary-small-ductal and biliary-large-ductal epithelial cell proportions were combined to reflect the total biliary cfDNA from deconvolution analysis of serum samples from liver transplant patients. Endothelial samples were combined to identify common endothelial DMBs across all tissues. However, methylation status was conserved in liver sinusoidal endothelial methylomes for all identified common endothelial-specific methylation blocks. Liver-resident immune (CD14+, CD1 lb+, CD68+) samples were grouped with other myeloid immune samples when identifying markers for deconvolution of cfDNA fragments from liver transplant samples. Only two liver-resident immune cell-specific DMBs were identified when these samples were considered as a separate group at the same thresholds that other cell-type-specific DMBs were identified in the atlas. However, extended liver-resident immune cell markers were identified with relaxed thresholds (— margin 0.3, — target.quant 0.2, — bg. quant 0.2) to use for characterization of liver cell-specific epigenetic data. Chromatin accessibility and histone modification data in FIG. 6, Panels A-C is plotted using identified biliary-small-ductal epithelial, common endothelial, and extended liver-resident immune cell-specific DMBs (Table 1 and Table 2). There were also a reduced number of highly specific DMBs identified for peripheral immune cell subsets relative to all other cell-types. The top 25 DMBs specific to peripheral immune cell subsets (—margin 0.4, —target.quant 0.2, —bg. quant 0.1) were used and deconvolution estimates were fit into the proportions for total myeloid and total lymphoid immune cell estimates using supergroups of neutrophils, monocytes, and macrophages to form a combined myeloid group and mature B cell, naive B cell, CD4 T cell, CD8 T cell and NK cell to form a combined lymphoid group. The estimated deconvolution results of immune cell subsets are provided in FIG. 12

[0166] Methylation score and visualization of cell-type specific methylation atlas. Each DNA fragment was characterized as U (mostly unmethylated), M (mostly methylated), or X (mixed) based on the fraction of methylated CpG sites as previously described

[0019] , Thresholds of <33% methylated CpGs for U reads and >66% methylated CpGs for M were used. A methylation score for each identified cell-type specific block was then calculated based on the proportion of U / X / M reads among all reads. The U proportion was used to define hypomethylated blocks and the M proportion was used to define hypermethylated blocks. Heatmaps were generated using the pretty heatmap function in the RStudio Package for the R bioconductor (RStudioTeam, 2015).

[0167] Comparison of methylation status in cfDNA isolated from paired serum and plasma samples. Paired plasma and serum samples were collected from three liver transplant patients at serial timepoints to compare results across sample preparations. Paired plasma and serum samples from healthy controls were reanalyzed from GSE200187. The average methylation for each block and sample were computed using wgbstools (— beta to table). Correlation analysis was performed comparing the methylation status at the block level between the paired plasma and serum samples (FIGS. 14-16). Deconvolution analysis was performed to compare predicted cell-type proportions. While plasma was generated from whole blood collected in tubes treated with anticoagulant, serum was obtained after allowing blood to clot for 30 minutes at room temperature and then centrifuging the samples to remove the cellular component. Cellular components significantly increase in serum samples that sit longer than 60 minutes; however, adherence to standard operating procedures for preparation of serum and plasma have been found to greatly reduce such contamination and sources of error

[0057] , Extra steps were taken to address these concerns by ensuring timely processing of blood samples and performing an additional bead purification after cfDNA isolation to remove high-molecular weight DNA derived from blood cell lysis,

[0168] Transcription factor binding site analysis. Transcription factor binding site analysis was performed using the HOMER (V4.11.1) findMotifsGenome.pl function for known and de novo motifs with parameters “-mask -chopify -size given -cpg” and with captured blocks without liver cell-type-specific methylation used as background. All DMBs for individual liver cell-type-specific hypomethylated DMBs were assessed. Similar analysis was performed comparing all hypomethylated DMBs and all hypermethylated DMBs for livercell-types combined. Individual liver cell-type-specific hypermethylated DMBs were only assessed for hepatocytes, hepatic stellate, and biliary epithelial cell-types (due to limited hypermethylated DMBs identified for endothelial cells). For this analysis comparing all hyper- to all hypo- DMBs for liver cell types combined, enriched motifs containing CpG dinucleotides were quantified. All motifs with binomial p-value <0.05 were considered.

[0169] RNA isolation and RNA-sequencing analysis. RNA was isolated from sorted cells using the RNeasy Kit (Qiagen) according to the manufacturer’s protocol and quantified by Qubit RNA BR assay (Thermo Fisher Scientific). Total RNA was validated using an Agilent RNA 6000 nano assay on the 2100 Bioanalyzer TapeStation (Agilent Technologies). The resulting RNA Integrity number (RIN) of samples selected for RNAseq analysis was at least 7. RNA-sequencing libraries were prepared using TruSeq Total RNA library' Prep Kit (Illumina) at Novogene Corporation Inc., and 150bp paired-end sequencing was performed on an Illumina HiSeq 4000 with a depth of 50 million reads per sample. A reference index was generated using GTF annotation from GENCODE v28. Raw F ASTQ files were aligned and assembled to GRCh38 and GRCh37 with HISAT2 / Stringtie (V 2.1.0) (28). The differential expression was analyzed in R with packages EdgeR (V 3.32.1) and Rsubread (V1.6.3) (29-31). Derived counts per million and p-values were used to create a rank ordered list, which was then used for subsequent integrative analysis. Expression levels at known cell type markers from single cell expression databases were used to validate the identity of isolated cell type populations for methylome analysis (FIG. 17). The liver endothelial and hepatic stellate cell-populations demonstrate enriched expression (over 200-fold) of lineage-specific genes relative to bulk liver tissue. The stellate cell populations had over 16-fold lower expression of ACTA2 (smooth muscle alpha-2 actin) relative to smooth muscle cells. The liver-resident immune cell populations also demonstrate enriched expression (over 40-fold) of immune cell genes relative to bulk liver tissue (FIG. 5, Panel C). In addition to enriched expression of immune cell gene expression programs (TBX21, TAGAP, RUNX3, CD69, etc.), we also observed enrichment of several liver-resident lymphocyte and MAIT-Tcell genes (GNLY, KLRB1, NKG7).

[0170] Bisulfite capture-sequencing library preparation. Bisulfite capture-sequencing libraries were generated. In brief, WGBS libraries were generated using the Pico Methyl-Seq Library Prep Kit (Zymo Research) according to the manufacturer’s instructions. Libraryquality control was performed with an Agilent 2100 Bioanalyzer and quantity determined via the KAPA Library Quantification Kit (KAPA Biosystems). WGBS libraries were then pooled to meet the required 1μg DNA input necessary for targeted enrichment. However, no more than four WGBS libraries were pooled in a single hybridization reaction and the 1 pg input DNA was divided evenly between the libraries to be multiplexed. Hybridization capture was carried out according to the SeqCap Epi Enrichment System protocol (Roche NimbleGen, Inc.) using SeqCap Epi CpGiant probe pools with xGen Universal Blocker-TS Mix (Integrated DNA Technologies, USA) as the blocking reagent. Washing and recovering of the captured library, as well as PCR amplification and final purification, were carried out as recommended by the manufacturer. The capture library products were assessed by Agilent Bioanalyzer DNA 1000 assays (Agilent Technologies, Inc.). Bisulfite capture-sequencing libraries with inclusion of 15-20% spike-in PhiX Control v3 library (Illumina) were clustered on an Illumina Novaseq 6000 S4 flow cell followed by 150bp paired-end sequencing.

[0171] Bisulfite sequencing data alignment and preprocessing. Paired-end FASTQ files were trimmed using TrimGalore (V 0.6.6) (80) with parameters “—paired -q 20 — clip_Rl 10 --clip_R2 10 — three_prime_clip_R1 10 —three_prime_clip_R2 10”. Trimmed paired-end FASTQ reads were mapped to the human genome (GRCh37 / hgl9 build) using Bismark (V 0.22.3) with parameters non-directional”, then converted to BAM files using Samtools (V 1.12). BAM files were sorted and indexed using Samtools (VI.12). Reads were stripped from non-CpG nucleotides and converted to BETA and PAT files using wgbstools (V 0.1.0) (https: / / github.com / nloyfer / wgbs tools), a tool suite for working with WGBS data while preserving read-specific intrinsic dependencies. The BETA files (a wgbstools-compatible binary format) contain position and average methylation information for single CpG sites. The PAT files contain fragment-level information (including CpG starting index, methylation pattern of all covered CpGs and number of fragments with exact multiCpG pattern).

[0172] Reference DNA methylation data from healthy tissues and cells. Controlled access to reference WGBS data from normal human tissues and cell types were requested from public consortia participating in the International Human Epigenome Consortium (IHEC)

[0058] and upon approval downloaded from the European Genome-Phenome Archive (EGA), Japanese Genotype-phenotype Archive (JGA), database of Genotypes and Phenotypes (dbGAP), and ENCODE portal data repositories

[0059] , Reference WGBS data were also downloaded fromselected GEO and SRA datasets. Reference WGBS data were analyzed as previously described

[0018] ,

[0173] Generation of expanded cell-type-specific DNA methylation atlas. Previously established atlases of cell-type-specific DNA methylation were refined to include expanded data generated from liver cell-types and curated from recently published WGBS dataset of purified healthy human cell-types. Tissue and cell-type specific methylation blocks were identified from reference WGBS as previously described

[0018] , In brief, data was first segmented into blocks of homogenous methylation and then analysis was restricted to blocks covered by the hybridization capture panel used in the analysis of cfDNA (probed regions span 80Mb (-20% of CpGs) on the capture panel)

[0019] , Analysis was also restricted to blocks containing a minimum of three CpG sites, with lengths less than 2kb and at least 10 observations. Samples were divided into 20 groups by cell-type and a one-vs-all comparison was performed to identify differentially methylated blocks unique for each group. For this, the find markers Rscript (with parameters tg. quant 0.2 — bg. quant 0.1 —margin 0.4”) was used to calculate the average methylation per block / sample and rank the blocks according to the difference in average methylation between any sample from the target group and all other samples

[0018] . Blocks with a (-) direction are hypom ethylated and (+) direction are hypermethylated, defined as a as a direction of methylation in the target cell-type relative to all other tissues and cell-types included in the atlas. Identified liver cell-type-specific DMBs meeting these specified requirements are listed in Table 1 and Table 2.

[0174] UXM fragment-level deconvolution. An expanded atlas was generated using the top 100 methylation blocks per cell-type group using the ‘uxm build’ function with parameters “--rlen 3” from the UXM deconv repository (https: / / github.com / nloyfer / LTXM deconv) (expanded atlas available at GEO under accession no. GSE262275)

[0019] , From this, each fragment is annotated as U (mostly unmethylated), M (mostly methylated) or X (mixed) depending on the number of methylated and unmethylated CpG sites. For each DMB, the proportion of U / X / M fragments is calculated across all reference WGBS cell-types and the U / M proportion reported for hypomethylated and hypermethylated DMBs, respectively. Then, the cell-type origins of cell-free DNA fragments isolated from serum of liver transplant patients are estimated using the ‘uxm deconv’ function with parameters “—rlen 3”. Briefly, a non-negative least squares (NNLS) algorithm is used to fit each cell-free DNA sample andestimate its relative contributions. Predicted cell-type proportions were converted to genome equivalents and reported as Geq / mL through multiplying the relative fraction of cell-type specific cfDNA times the concentration of cfDNA (ng / mL) by the mass of the human haploid genome 3.3 x 10-12grams.

[0175] Chromatin accessibility’ and histone modification data generation and analysis. ATAC-seq libraries were generated from liver sinusoidal endothelial (LSEC) and liverresident immune cryopreserved cells using the ATAC-seq kit (Active Motif). H3K27ac histone modification data was generated from liver sinusoidal endothelial (LSEC) and liverresident immune cryopreserved cells using the Cut& Tag-IT Assay kit (Active Motif, H3K27ac antibody cat#39133). Library products were assessed by Agilent Bioanalyzer HS DNA assays (Agilent Technologies, Inc.) and clustered on an Illumina Novaseq 6000 S4 flow cell followed by 150bp paired-end sequencing with inclusion of 10-15% spike-in PhiX Control v3 library (Illumina). Controlled access to DNase-seq, H3K4mel and H3K27ac ChlP-seq data from hepatocytes was obtained from the German Epigenome Program (DEEP) (EGAD00001002527) and publicly available ATAC-seq data from biliary tissue (gallbladder) was downloaded from the ENCODE project (ENCSR695FLC) Imputed data on H3K27ac binding in hepatic stellate cells was downloaded from the ENCODE project in bigWig format (ENCSR225UAF). ChromHMM annotations (18-state version) were downloaded from the ENCODE project (hepatocyte: ENCSR075JST, hepatic stellate:ENCSR593ZNP, endothelial: ENCSR227ZSK, neuron: ENCSR539JGB) and genomic regions associated with H3K4mel enhancer mark were extracted and reformatted in bigwig format. Paired-end FASTQ files were trimmed using TrimGalore (V 0.6.6) with parameters “—paired -q 20”. Trimmed paired-end FASTQ reads were mapped to the human genome (GRCh37 / hgl9 build) using Bowtie2-align (V 2.3.5.1) with parameters “-X 1000 —local — very-sensitive -no-mixed —dovetail — phred33”. Duplicated reads were marked with Picard (V 2.18.14) and reads with low mapping quality, duplicated or not mapped in proper pair were excluded using Samtools view (V 1.12) with parameters “-F 1796 -q 30”. BAM files were sorted and indexed using Samtools. Chromatin accessibility data was normalized to the same depth and then bigWig files were created using deepTools (V 3.5.1) with the functions ‘multiBamSummary ‘ and ‘bamCoverage’ using parameters “ — normalize Using RPKM — binSize 25”. Detection q-values were calculated for histone modification data relative to Input control using the macs3 (V3.0.0a6) bdgcmp function (-m qpois). This method uses theBH process for poisson p-values to calculate the score in any bin using control (Input) as lambda and treatment (IP sample) as observation. BigWig files were then generated using bedGraphToBigWig. Summary plots were prepared using deepTools (V 3.5.1) with functions ‘computeMatrix’ and ‘plotProfile’ using default parameters, except for ‘referencePoint=center’ and 5Kb margins.

[0176] Functional annotation, transcription factor binding site and pathway analysis. Celltype specific methylation blocks were annotated and transcription factor binding site analysis was performed using HOMER (V4.11.1) and the annotatePeaks.pl and findMotifsGenome.pl functions. Pathway analysis of genes adjacent to identified tissue and cell-type specific methylation blocks was performed using Ingenuity Pathway Analysis (IP A) (Qiagen).

[0177] Genome Browser and fragment-level visualizations. Reference WGBS samples were uploaded as custom tracks for visualization on the UCSC genome browser. Methylomes were converted to bigWig format using the wgbstools beta2bw function. Fragment-level visualization of methylation sequencing reads was performed with the wgbstools vis function with parameters “---min 3 -yebl —strict”. Chromatin accessibility, histone modification, and RNA expression data were downloaded from the IHEC data portal as bigWig files (hg 19). F0XA1 ChlP-seq data in liver was downloaded from the ENCODE project (ENCSR735KEY). Samples of the same cell type were averaged using multiBigwigSummary (v.3.5.1).

[0178] Statistics. Statistical analyses for group comparisons and correlations were performed using Prism (GraphPad Software, Inc., United States) and R (V 4.1.3). A correlation analysis was performed to assess relationship between changing cell-free methylated DNAs and LFTs using Spearman’s Rank Correlation Coefficient. Statistically significant comparisons are shown, with significance defined as p<0.05. Correction for multiple hypothesis comparisons was performed using the Benjamini -Hochberg (B-H) method corrected p-value to control the false discovery rate (FDR) from multiple pathways being tested against each gene-set. A two-stage linear step-up procedure of Benjamini, Krieger and Yekutieli was performed for p-value adjustment from multiple outcome measures.Example 2

[0179] A study is conducted to establish cfDNA-derived cell type-specific injury markers as novel biomarkers for determining organ viability at the time of machine perfusion.

[0180] Samples are utilized that have already been obtained as part of the clinical protocol. Perfusate samples are collected at baseline, one hour, two hours, and then every two hours until the terminal time point of perfusion, with typical perfusion cycles lasting circa 12 hours. Additionally, liver biopsy samples are obtained pre- and post-norm othermic perfusion, and bile samples were collected every two hours. Ten sets of samples each are selected from 20 discarded livers and 71 patients who received NMP livers, categorizing them into five groups: (1) discarded livers, (2) DBD with no complication, (3) DBD with complication, (4) DCD with no complication, and (5) and DCD with complication. Aliquots collected at early (one hour), mid (four to six hours), and late (ten to 12 hours) time points are used, totaling 150 perfusate samples. The experimental design and analyses shown in FIG. 9, Panel A and FIG. 11, Panel A are built upon, applying the cell type maps (FIG. 18, Panel B) to delineate cell types of origin from methylated cfDNA in samples from machine-perfused allografts, including serial samples of perfusate. For cfDNA analyses, cfDNA is extracted from perfusate samples using standardized protocols, and concentrations are quantified using methods described in Example 1. Methylation profiling of cfDNA is performed to identify cell type-specific injury markers, utilizing bisulfite sequencing to determine methylation patterns.

[0181] Histological analysis of liver tissue biopsies are performed to identify the nature and extent of distinct cellular damage using standard H& E staining techniques and immunohistochemistry. Biochemical markers of liver function, including ALT, AST, bilirubin, alkaline phosphatase, pH, bicarbonate levels, glucose levels, and lactate levels, measured from the perfusate, are obtained for correlation analysis. Clinical data on organ viability, including histological analysis, biochemical liver function markers, blood flow and bile production parameters, and donor risk variables (e.g, age, sex, DCD status, race, cause of death, cold and w'arm ischemia times) is integrated with methylated cfDNA data to correlate cfDNA signatures with hepatocellular and biliary damages in the allograft.Example 3

[0182] A study is conducted to establish cfDNA-derived cell type-specific injury markers as novel biomarkers for determining the nature and severity of complications post-transplant.

[0183] Patients who received NMP LTx and experienced complications due to hepatocellular or biliary injury are studied and compared to control patients who did not experience complications. Existing blood and tissue samples, along with clinical and histological data already collected from these patients, are utilized and analyzed. Mimicking experimental design shown in FIG. 9, Panel A and FIG. 11, Panel A, post-transplant blood samples are collected post-reperfusion at the end of LTx on day 0, and on days 7 and 30 post-LTx, as well as at the time of complication onset when a for-cause liver biopsy was obtained. cfDNA is extracted and methylation profiling is performed on these blood samples to identify cfDNA markers associated with specific biliary complications such as ischemic cholangiopathy and hepatocellular complications such as IRI or rejection. The methylation profiling utilizes bisulfite sequencing to determine methylation patterns, as described in Example 1. cfDNA profiles from pre-transplant (during NMP) and post-transplant samples are compared to assess the predictive value of cfDNA markers identified during NMP for post-transplant complications. For correlation with clinical data, post-LTx comprehensive clinical and histological data sets are obtained as described in Example 1. These data are analyzed blinded to the patients' outcomes to identify patterns and correlations with cfDNA markers, helping to evaluate the severity and nature of complications. Statistical analysis determines the extent to which post-LTx complications can be diagnosed and anticipated based on cfDNA profiles during NMP. Techniques such as correlation analysis, regression models, and survival analysis evaluates the predictive and diagnostic power of the cfDNA markers.Table 1. Hypomethylated cell type-specific methylation patterns.Table 2. Hypermethylated cell-type specific methylation patterns.Table 3. Liver transplant serial cfDNA sample concentrations and predicted liver cell-type proportions from UXM deconvolution analysis at top 100 cell-type specific blocks for target cell types (n = 28 patients; n = 100 samples).[BSD = biliary-small-ductal-epithelial cells; BLD = biliary-large-ductal-epithelial cells; L-ENDO = liver endothelial cells; HEPT = hepatocyte cells; STELL = hepatic-stellate cells;ACR = Acute Cellular Rejection]Table 4. Liver transplant phenotype-matched cfDNA sample concentrations and predicted liver cell-type proportions from UXM deconvolution analysis at top 100 cell-type specific blocks for target cell types (additional n = 16 patients; n = 30 samples).[Allo. Compl. = allograft complications; Liver biopsy histopath. finding = liver biopsy histopathological finding; BSD = biliary-small-ductal-epithelial cells; BLD = biliary-large-ductal-epithelial cells; L-ENDO = liver endothelial cells; HEPT = hepatocyte cells; STELL = hepatic-stellate cells; FC-bx = For-cause liver biopsy; ACR = Acute Cellular Rejection; IRI = Ischemia-Reperfusion Injury (Ischemic hepatitis); AMR = Antibody -Mediated Rejection;RAI = Rejection activity score.]Table 5. Liver transplant serial cfDNA sample concentrations and predicted liver cell-type haploid Geq from UXM deconvolution analysis at top 100 cell-type specific blocks for target liver cell types (n = 28 patients; n = 100 samples).[BSD = biliary-small-ductal-epithelial cells; BLD = biliary-large-ductal-epithelial cells; L-ENDO = liver endothelial cells; HEPT = hepatocyte cells; STELL = hepatic-stellate cells]Table 6. Liver transplant phenotype-matched cfDNA sample concentrations and predicted liver cell-type haploid Geq from UXM deconvolution analysis at top 100 cell-type specific blocks for target cell types (additional n = 16 patients; n = 30 samples).[BSD = biliary-small-ductal-epithelial cells; BLD = biliary-large-ductal-epithelial cells; L-ENDO = liver endothelial cells; HEPT = hepatocyte cells; STELL = hepatic-stellate cells]Table 7. Liver transplant serial cfDNA samples extended immune cell-type proportions from UXM deconvolution analysis (n = 28 patients; n = 100 samples).[Allo. Compl. = allograft complications; BCM = memory B-cells, BCN = naive B-cells, CD4M = CD4+ mature T-cells, CD8M = CD8+ mature T-cells, NK = natural killer cells, Mono / Macro = monocyte / macrophages, NEU = neutrophils]Table 8. Liver transplant phenotype-matched cfDNA samples extended immune cell-type proportions from UXM deconvolution analysis (additional n = 16 patients; n = 30 samples).[Allo. Compl. = allograft complications; Liver biopsy histopath. finding = liver biopsy histopathological finding; BCM = memory B-cells, BCN = naive B-cells, CD4M = CD4+ mature T-cells, CD8M = CD8+ mature T-cells, NEC = natural killer cells, Mono / Macro = monocyte / macrophages, NEU = neutrophils; Hepatocell. = hepatocellular]REFERENCES[1] Organ Procurement and Transplantation Network. Health Resources and Services Administration, U. S. Department of Health and Human Services, National data website (https: / / optn.transplant.hrsa.gOv / data / view-data-reports / national-data / #); based on OPTN data as of September 6, 2024; accessed September 14, 2024.[2] Agostini C,, Complications in post-liver transplant patients, Journal of Clinical Medicine, 2023, 12(19): 6173.[3] Seehofer D, et al., Biliary complications after liver transplantation: old problems and new Challenges, American Journal of Transplantation, 2013, 13(2): 253-265.[4] Fasullo M, et al., Post-transplant biliary complications: advances in pathophysiology, diagnosis, and treatment, BMJ Open Gastroenterology, 2022, 9(1): e000778.[5] Limaye AR, et al., Liver Biopsy - Indications, Procedures, Results, 2012, ed. Tagaya N., ISBN 978-953-51-0853-5; published online (https: / / doi.org / 10.5772 / 52617).[6] West J & Card TR, Reduced mortality rates following elective percutaneous liver biopsies, Gastroenterology, 2010, 139(4): 1230-1237.[7] Fernando JJ, et al., Non-invasive molecular biomarkers for monitoring solid organ transplantation: a comprehensive overview, International Journal of Immunogenetics, 2024, 51(2): 47-62.[8] Patrono D, et al., Viability testing during liver preservation, Current Opinion in Organ Transplantation, 2022, 27(5): 454-465.[9] Martin P, et al., Evaluation for liver transplantation in adults: 2013 practice guideline by the American Association for the Study of Liver Diseases and the American Society of Transplantation, Hepatology, 2014, 59(3): 1144-1165.

[0010] Heitzer E, et al., Cell-free DNA and apoptosis: how dead cells inform about the living, Trends in Molecular Medicine, 2020, 26(5): 519-528.

[0011] Greenberg MVC & Bourc’his D, The diverse roles of DNA methylation in mammalian development and disease, Nature Review Molecular Cell Biology, 2019, 20(10): 590- 607.

[0012] Michalak EM, et al., The roles of DNA, RNA and histone methylation in ageing and cancer, Nature Review Molecular Cell Biology, 2019, 20(10): 573-589.

[0013] Kim M & Costello J, DNA methylation: an epigenetic mark of cellular memory, Experimental & Molecular Medicine, 2017, 49(4): e322.

[0014] Dor Y & Cedar H, Principles of DNA methylation and their implications for biology and medicine, Lancet, 2018, 392(10149): 777-786.

[0015] Barefoot ME, et al., Detection of cell types contributing to cancer from circulating, cell-free methylated DNA, Frontiers in Genetics, 2021, 12: 671057.

[0016] Barefoot ME, et al., Reference Module in Biomedical Sciences, 2020, 365-378;doi:10.1016 / b978-0-12-801238-3.11669-1.

[0017] McNamara ME, et al., Circulating, cell-free DNA methylation patterns indicate cellular sources of allograft injury after liver transplant, bioRxiv, 2024.

[0018] McNamara ME, et al., Circulating cell-free methylated DNA reveals tissue-specific, cellular damage from radiation treatment. JCI Insight, 2023, 8(14): e156529.

[0019] Loyfer N, et al., A DNA methylation atlas of normal human cell types, Nature, 2023, 613(7943): 355-364.

[0020] Moss J, et al., Comprehensive human cell-type methylation atlas reveals origins of circulating cell-free DNA in health and disease, Nature Communications, 2018, 9(1): 5068.

[0021] Roy R, et al., DNA methylation signatures reveal that distinct combinations of transcription factors specify human immune cell epigenetic identity, Immunity, 2022, 54(11): 2465-2480. e5.

[0022] Kaluscha S, et al., Evidence that direct inhibition of transcription factor binding is the prevailing mode of gene and repeat repression by DNA methylation, Nature Genetics, 2022, 54(12): 1895-1906.

[0023] Fazilaty H & Basler K, Reactivation of embryonic genetic programs in tissue regeneration and disease, Nature Genetics, 2023, 55(11): 1792-1806.

[0024] Buckberry S, etal., Transient naive reprogramming corrects hiPS cells functionally and epigenetically, Nature, 2023, 620(7975): 863-872.

[0025] Balsalobre A & Drouin J, Pioneer factors as master regulators of the epigenome and cell fate, Nature Reviews: Molecular Cell Biology, 2022, 23(7): 449-464.

[0026] Barral A & Zaret KS, Pioneer factors: roles and their regulation in development, Trends in Genetics, 2024, 40(2): 134-148.

[0027] Tachmatzidi EC, etal., Transcription control of liver development, Cells, 2021, 10(8):2026.

[0028] Liu S, et al., NRF1 association with AUTS2-Polycomb mediates specific gene activation in the brain, Molecular Cell, 2021, 81(22): 4757.

[0029] Heberle E & Bardet AF, Sensitivity of transcription factors to DNA methylation, Essays in Biochemistry, 2019, 63(6): 727-741.

[0030] Kreibich E, et al., Single-molecule footprinting identifies context-dependent regulation of enhancers by DNA methylation, Molecular Cell, 2023, 83(5): 787-802. e9.

[0031] Jadhav U, et al., Acquired tissue-specific promoter bivalency is a basis for PRC2 necessity in adult cells, Cell, 2016, 165(6): 1389-1400.

[0032] Kumar D, et al., Decoding the function of bivalent chromatin in development and cancer. Genome Research, 2021, 31(12): 2170-2184.

[0033] Matsui S, et al., Pioneer and PRDM transcription factors coordinate bivalent epigenetic states to safeguard cell fate, Molecular Cell, 2024, 84(3): 476-489. elO.

[0034] Lucey MR, et al., Liver transplantation, New England Journal of Medicine, 2023,89(20): 1888-1900.

[0035] Craig EV & Heller MT, Complications of liver transplant, Abdominal Radiology (New York), 2021, 46(1): 43-67.

[0036] Dar WA, et al., Ischaemia reperfusion injury in liver transplantation: cellular and molecular mechanisms, Liver International, 2019, 39(5): 788-801.

[0037] Civantos DVP, et al., Liver Pathology, [published online ahead of print: 2021], https: / / doi.org / 10.5772 / intechopen.89094.

[0038] Weiss N & Thabut D, Neurological complications occurring after liver transplantation:role of risk factors, hepatic encephalopathy, and acute (on chronic) brain injury, Liver Transplantation, 2019, 25(3): 469-487.

[0039] Jang MK, et al., Cell-free chromatin immunoprecipitation to detect molecular pathways in heart transplantation, Life Science Alliance, 2023, 6(12): e202302003.

[0040] Balasubramanian S, et al., Cell-free DNA maps tissue injury and correlates with disease severity in lung transplant candidates, American Journal of Respiratory and Critical Care Medicines, 2024, 209(6): 727-737.

[0041] Yoshino O, et al., Elevated Plasma Levels of Cell-Free DNA During Liver Transplantation Are Associated With Activation of Coagulation, Liver Transplantation, 2020, 26(4): 602-603.

[0042] Martin- Alonso C, et al., Priming agents transiently reduce the clearance of cell-free DNA to improve liquid biopsies, Science, 2024,;383(6680): eadf2341.

[0043] Kustanovich A, et al., Life and death of circulating cell-free DNA, Cancer Biology & Therapy, 2019, 20(8): 1057-1067.

[0044] Sender R, et al., What fraction of cellular DNA turnover becomes cfDNA? eLife, 2024, 12: RP89321.

[0045] Fox-Fisher I, et al., Remote immune processes revealed by immune-derived circulating cell-free DNA, elife, 2021, 10: e70520.

[0046] Cheng AP, et al., Cell-free DNA profiling informs all major complications of hematopoietic cell transplantation, Proceedings of the National Academy of Sciences of the U. S. A., 2022, 119(4): e2113476118.

[0047] Avni B, et al., Chronic graft versus host disease detected by tissue-specific cell-free DNA methylation biomarkers, Journal of Clinical Investigation, 2024, 134(2): el63541.

[0048] Agbor-Enoh S, et al., Cell-free DNA to detect heart allograft acute rejection, Circulation, 2021, 143(12): 1184-1197.

[0049] Khush KK, et al., Noninvasive detection of graft injury after heart transplant using donor-derived cell-free DNA: a prospective multicenter study, American Journal of Transplantation, 2019, 19(10): 2889-2899.

[0050] Bu L, et al., Clinical outcomes from the Assessing Donor-derived cell-free DNA Monitoring Insights of kidney Allografts with Longitudinal surveillance (ADMIRAL) study, Kidney International, 2022, 101(4): 793-803.

[0051] Grskovic M, etal., Validation of a clinical-grade assay to measure donor-derived cell- free DNA in solid organ transplant recipients, Journal of Molecular Diagnostics, 2016, 18(6):890-902.

[0052] Levitsky J, et al., Donor-derived cell-free DNA levels predict graft injury in liver transplant recipients, American Journal of Transplantation, 2022, 22(2): 532-540.

[0053] Fasullo M, et al., Post-transplant biliary complications: advances in pathophysiology, diagnosis, and treatment, BMJ Open Gastroenterology, 2022, 9(1): e000778.

[0054] Maggi EC, et al., Development of a method to implement whole-genome bisulfite sequencing of cfDNA from cancer patients and a mouse tumor model, Frontiers in Genetics, 2018, 9: 6.

[0055] Hallett JM, et al., Human biliary epithelial cells from discarded donor livers rescue bile duct structure and function in a mouse model of biliary disease, Cell Stem Cell, 2022, 29(3): 355-371. elO.

[0056] Kegel V, et al., Protocol for isolation of primary human hepatocytes and corresponding major populations of non-parenchymal liver cells, Journal of Visualized Experiments, 2016, (109): e53069.

[0057] Tuck MK, et al., Standard operating procedures for serum and plasma collection: early detection research network consensus statement standard operating procedure integration working group, Journal of Proteome Research, 2009, 8(1): 113-117.

[0058] Bujold D, et al. The International Human Epigenome Consortium Data Portal. Cell Syst. 2016;3(5):496-499.e2.

[0059] Consortium TEP, et al. Expanded encyclopaedias of DNA elements in the human and mouse genomes. Nature. 2020;583(7818):699-710.

Claims

CLAIMS1. A method of detecting cell-type-specific damage in ex vivo liver tissue, the method comprising(a) sequencing cell-free DNA (cfDNA) in a sample from the ex vivo liver tissue; (b) determining cell-type from which the cfDNA originated by identifying methylation patterns in nucleotide sequence of one or more portions of the cfDNA that contains methylation sites, wherein the cell-type is determined when the methylation pattern in the one or more portions is the same as a cell-type specific methylation pattern; and (c) measuring the quantity of the cfDNA of the determined cell-type;wherein the cell-type-specific damage in the ex vivo liver tissue is detected when the measured quantity of the cfDNA of the determined cell-type is greater as compared to a normal quantity of cfDNA of the determined cell-type.

2. A method of detecting cell-type-specific damage in ex vivo liver tissue, the method comprising, at two or more time points,(a) sequencing cell-free DNA (cfDNA) in a sample from the ex vivo liver tissue; (b) determining cell-type from which the cfDNA originated by identifying methylation patterns in nucleotide sequence of one or more portions of the cfDNA that contains methylation sites, wherein the cell-type is determined when the methylation pattern in the one or more portions is the same as a cell-type specific methylation pattern; and (c) measuring the quantity of the cfDNA of the determined cell-type;wherein the cell-type-specific damage in the ex vivo liver tissue is detected when the measured quantity of the cfDNA of the determined cell-type is increased between a later time point and an earlier time point.

3. A method of treating cell-type specific liver damage in ex vivo liver tissue, the method comprising administering a treatment for the cell-type specific liver damage to the ex vivo liver tissue,wherein the ex vivo liver tissue is determined to have cell-type specific liver damage by a method comprising:(a) sequencing cell-free DNA (cfDNA) in a sample from the ex vivo liver tissue;(b) determining cell-type from which the cfDNA originated by identifying methylation patterns in nucleotide sequence of one or more portions of the cfDNA that contains methylation sites, wherein the cell-type is determined when the methylation pattern in the one or more portions is the same as a cell-type specific methylation pattern; and (c) measuring the quantity of the cfDNA of the determined cell-type;wherein the cell-type-specific damage in the ex vivo liver tissue is determined when the measured quantity of the cfDNA of the determined cell-type is greater as compared to a normal quantity of cfDNA of the determined cell-type.

4. A method of treating cell-type specific liver damage in ex vivo liver tissue, the method comprising administering a treatment for the cell-type specific liver damage to the ex vivo liver tissue,wherein the ex vivo liver tissue is determined to have cell-type specific liver damage by a method comprising, at two or more time points,(a) sequencing cell-free DNA (cfDNA) in a sample from the ex vivo liver tissue; (b) determining cell-type from which the cfDNA originated by identifying methylation patterns in nucleotide sequence of one or more portions of the cfDNA that contains methylation sites, wherein the cell-type is determined when the methylation pattern in the one or more portions is the same as a cell-type specific methylation pattern; and (c) measuring the quantity of the cfDNA of the determined cell-type;wherein the cell-type-specific damage in the ex vivo liver tissue is detected when the measured quantity of the cfDNA of the determined cell-type is increased between a later time point and an earlier time point.

5. A method of treating cell-type specific liver damage in ex vivo liver tissue, the method comprising administering a treatment for the cell-type specific liver damage to the ex vivo liver tissue and monitoring the cell-type specific liver damage,wherein the monitoring comprises:(a) sequencing cell-free DNA (cfDNA) in a sample from the ex vivo liver tissue; (b) determining cell-type from which the cfDNA originated by identifying methylation patterns in nucleotide sequence of one or more portions of the cfDNA thatcontains methylation sites, wherein the cell-type is determined when the methylation pattern in the one or more portions is the same as a cell-type specific methylation pattern; and (c) measuring the quantity of the cfDNA of the determined cell-type;wherein the cell-type-specific damage in the ex vivo liver tissue is determined to be present when the measured quantity of the cfDNA of the determined cell-type is greater as compared to a normal quantity of cfDNA of the determined cell-type.

6. A method of treating cell-type specific liver damage in ex vivo liver tissue, the method comprising administering a treatment for the cell-type specific liver damage to the ex vivo liver tissue and monitoring the cell-type specific liver damage,wherein the monitoring comprises, at two or more time points,(a) sequencing cell-free DNA (cfDNA) in a sample from the ex vivo liver tissue; (b) determining cell-type from which the cfDNA originated by identifying methylation patterns in nucleotide sequence of one or more portions of the cfDNA that contains methylation sites, wherein the cell-type is determined when the methylation pattern in the one or more portions is the same as a cell-type specific methylation pattern; and (c) measuring the quantity of the cfDNA of the determined cell-type;wherein the cell-type-specific damage in the ex vivo liver tissue is determined to have increased when the measured quantity of the cfDNA of the determined cell-type is increased between a later time point and an earlier time point, and the cell-type-specific damage in the ex vivo liver tissue is determined to have decreased when the measured quantity of the cfDNA of the determined cell-type is decreased between a later time point and an earlier time point.

7. The method of any one of claims 1-6, wherein the sample is selected from organ perfusion solution, bile, and biopsy tissue.

8. The method of any one of claims 1-7, wherein the ex vivo liver tissue is preserved by machine perfusion.

9. The method of claim 8, wherein the ex vivo liver tissue is preserved by normothermic machine perfusion.

10. A method of detecting cell-type-specific liver damage in a subject who received a liver transplant, the method comprising(a) sequencing cell-free DNA (cfDNA) in a sample from the subject;(b) determining cell-type from which the cfDNA originated by identifying methylation patterns in nucleotide sequence of one or more portions of the cfDNA that contains methylation sites, wherein the cell-type is determined when the methylation pattern in the one or more portions is the same as a cell-type specific methylation pattern; and (c) measuring the quantity of the cfDNA of the determined cell-type;wherein the cell-type-specific liver damage is detected when the measured quantity of the cfDNA of the determined cell-type is greater as compared to a quantity of cfDNA of the determined cell-type in subjects without liver transplant damage.

11. A method of detecting cell-type-specific liver damage in a subject who received a liver transplant, the method comprising, at two or more time points,(a) sequencing cell-free DNA (cfDNA) in a sample from the subject;(b) determining cell-type from which the cfDNA originated by identifying methylation patterns in nucleotide sequence of one or more portions of the cfDNA that contains methylation sites, wherein the cell-type is determined when the methylation pattern in the one or more portions is the same as a cell-type specific methylation pattern; and (c) measuring the quantity of the cfDNA of the determined cell-type;wherein the cell-type-specific damage is detected when the measured quantity of the cfDNA of the determined cell-type is increased between a later time point and an earlier time point.12 A method of detecting cell-type-specific liver damage in a subject who is a candidate to receive a liver transplant, the method comprising:(a) sequencing cell-free DNA (cfDNA) in a sample from the subject;(b) determining cell-type from which the cfDNA originated by identifying methylation patterns in nucleotide sequence of one or more portions of the cfDNA that contains methylation sites, wherein the cell-type is determined when the methylation pattern in the one or more portions is the same as a cell-type specific methylation pattern; and(c) measuring the quantity of the cfDNA of the determined cell-type;wherein the cell-type-specific liver damage is detected when the measured quantity of the cfDNA of the determined cell-type is greater as compared to a normal quantity of cfDNA of the determined cell-type.

13. A method of detecting cell-type-specific liver damage in a subject who is a candidate to receive a liver transplant, the method comprising, at two or more time points:(a) sequencing cell-free DNA (cfDNA) in a sample from the subject;(b) determining cell-type from which the cfDNA originated by identifying methylation patterns in nucleotide sequence of one or more portions of the cfDNA that contains methylation sites, wherein the cell-type is determined when the methylation pattern in the one or more portions is the same as a cell-type specific methylation pattern; and (c) measuring the quantity of the cfDNA of the determined cell-type;wherein the cell-type-specific damage is detected when the measured quantity of the cfDNA of the determined cell-type is increased between a later time point and an earlier time point.

14. A method of treating cell-type specific liver damage in a subject who received a liver transplant, the method comprising administering a treatment for the cell-type specific liver damage to the subject,wherein the subject is determined to have cell-type specific liver damage by a method comprising:(a) sequencing cell-free DNA (cfDNA) in a sample from the subject;(b) determining cell-type from which the cfDNA originated by identifying methylation patterns in nucleotide sequence of one or more portions of the cfDNA that contains methylation sites, wherein the cell-type is determined when the methylation pattern in the one or more portions is the same as a cell-type specific methylation pattern; and (c) measuring the quantity of the cfDNA of the determined cell-type;wherein the cell-type-specific liver damage is detected when the measured quantity of the cfDNA of the determined cell-type is greater as compared to a quantity of cfDNA of the determined cell-type in subjects without liver transplant damage.

15. A method of treating cell-type specific liver damage in a subject who received a liver transplant, the method comprising administering a treatment for the cell-type specific liver damage to the subject,wherein the subject is determined to have cell-type specific liver damage by a method comprising, at two or more time points:(a) sequencing cell-free DNA (cfDNA) in a sample from the subject;(b) determining cell-type from which the cfDNA originated by identifying methylation patterns in nucleotide sequence of one or more portions of the cfDNA that contains methylation sites, wherein the cell-type is determined when the methylation pattern in the one or more portions is the same as a cell-type specific methylation pattern; and (c) measuring the quantity of the cfDNA of the determined cell-type;wherein the cell-type-specific damage is detected when the measured quantity of the cfDNA of the determined cell-type is increased between a later time point and an earlier time point.

16. A method of treating cell-type specific liver damage in a subject who received a liver transplant, the method comprising administering a treatment for the cell-type specific liver damage to the subject and monitoring the cell-type specific liver damage, wherein the monitoring comprises:(a) sequencing cell-free DNA (cfDNA) in a sample from the subject;(b) determining cell-type from which the cfDNA originated by identifying methylation patterns in nucleotide sequence of one or more portions of the cfDNA that contains methylation sites, wherein the cell-type is determined when the methylation pattern in the one or more portions is the same as a cell-type specific methylation pattern; and (c) measuring the quantity of the cfDNA of the determined cell-type;wherein the cell-type-specific liver damage is detected when the measured quantity of the cfDNA of the determined cell-type is greater as compared to a quantity of cfDNA of the determined cell-type in subjects without liver transplant damage.

17. A method of treating cell-type specific liver damage in a subject who received a liver transplant, the method comprising administering a treatment for the cell-type specific liver damage to the subject and monitoring the cell-type specific liver damage,wherein the monitoring comprises, at two or more time points:(a) sequencing cell-free DNA (cfDNA) in a sample from the subject;(b) determining cell-type from which the cfDNA originated by identifying methylation patterns in nucleotide sequence of one or more portions of the cfDNA that contains methylation sites, wherein the cell-type is determined when the methylation pattern in the one or more portions is the same as a cell-type specific methylation pattern; and (c) measuring the quantity of the cfDNA of the determined cell-type;wherein the cell-type-specific damage is determined to have increased when the measured quantity of the cfDNA of the determined cell-type is increased between a later time point and an earlier time point, and the cell-type-specific damage is determined to have decreased when the measured quantity of the cfDNA of the determined cell-type is decreased between a later time point and an earlier time point.

18. The method of claim 16 or 17, further comprising adjusting the treatment administered to the subject when the cell-type-specific damage is present or when the cell-type-specific damage is increased.19 A method of detecting cell-type-specific liver damage in a liver tissue donor prior to liver tissue removal, the method comprising:(a) sequencing cell-free DNA (cfDNA) in a sample from the liver tissue donor;(b) determining cell-type from which the cfDNA originated by identifying methylation patterns in nucleotide sequence of one or more portions of the cfDNA that contains methylation sites, wherein the cell-type is determined when the methylation pattern in the one or more portions is the same as a cell-type specific methylation pattern; and (c) measuring the quantity of the cfDNA of the determined cell-type;wherein the cell-type-specific liver damage is detected when the measured quantity of the cfDNA of the determined cell-type is greater as compared to a normal quantity of cfDNA of the determined cell-type.

20. A method of detecting cell-type-specific liver damage in a liver tissue donor prior to liver tissue removal, the method comprising, at two or more time points:(a) sequencing cell-free DNA (cfDNA) in a sample from the liver tissue donor;(b) determining cell-type from which the cfDNA originated by identifying methylation patterns in nucleotide sequence of one or more portions of the cfDNA that contains methylation sites, wherein the cell-type is determined when the methylation pattern in the one or more portions is the same as a cell-type specific methylation pattern; and (c) measuring the quantity of the cfDNA of the determined cell-type;wherein the cell-type-specific damage is detected when the measured quantity of the cfDNA of the determined cell-type is increased between a later time point and an earlier time point.

21. A method of treating cell-type specific liver damage in a liver tissue donor prior to liver tissue removal, the method comprising administering a treatment for the celltype specific liver damage to the liver tissue donor,wherein the liver tissue donor is determined to have cell-type specific liver damage by a method comprising:(a) sequencing cell-free DNA (cfDNA) in a sample from the liver tissue donor;(b) determining cell-type from which the cfDNA originated by identifying methylation patterns in nucleotide sequence of one or more portions of the cfDNA that contains methylation sites, wherein the cell-type is determined when the methylation pattern in the one or more portions is the same as a cell-type specific methylation pattern; and (c) measuring the quantity of the cfDNA of the determined cell-type,wherein damage specific to the cell-type in the liver is detected when the measured quantity of the cfDNA of the determined cell-type is greater as compared to a normal quantity of cfDNA of the determined cell-type.

22. A method of treating cell-type specific liver damage in a liver tissue donor prior to liver tissue removal, the method comprising administering a treatment for the celltype specific liver damage to the liver tissue donor,wherein the liver tissue donor is determined to have cell-type specific liver damage by a method comprising, at two or more time points:(a) sequencing cell-free DNA (cfDNA) in a sample from the liver tissue donor;(b) determining cell-type from which the cfDNA originated by identifying methylation patterns in nucleotide sequence of one or more portions of the cfDNA thatcontains methylation sites, wherein the cell-type is determined when the methylation pattern in the one or more portions is the same as a cell-type specific methylation pattern; and (c) measuring the quantity of the cfDNA of the determined cell-type; and wherein an increase in the measured quantity of the cfDNA of the determined celltype at a later time point as compared to an earlier time point is indicative of damage specific to the cell-type in the liver.

23. The method of any one of claims 19-22, wherein the liver tissue donor is a living donor.

24. The method of any one of claims 19-22, wherein the liver tissue donor is a deceased donor.

25. The method of any one of claims 19-22, wherein the liver tissue donor is either a donor after brain death or a donor after circulatory death.

26. The method of any one of claims 19-22, wherein the liver tissue donor is subjected to normothermic regional perfusion.27 A method of determining etiology of liver damage in a subject who received a liver transplant, the method comprising:(a) sequencing cell-free DNA (cfDNA) in a sample from the subject;(b) determining cell-type from which the cfDNA originated by identifying methylation patterns in nucleotide sequence of one or more portions of the cfDNA that contains methylation sites, wherein the cell-type is determined when the methylation pattern in the one or more portions is the same as a cell-type specific methylation pattern; and (c) measuring the quantity of the cfDNA of the identified methylation patterns; wherein the etiology of the liver damage is determined when the identified methylation patterns and their quantity form a methylation profile that is the same as a methylation profile associated with an etiology of liver damage.

28. A method of treating liver damage in a subject who received a liver transplant, the method comprising administering a treatment for the liver damage to the subject, wherein the etiology of the liver damage is determined by a method comprising: (a) sequencing cell-free DNA (cfDNA) in a sample from the subject;(b) determining cell-type from which the cfDNA originated by identifying methylation patterns in nucleotide sequence of one or more portions of the cfDNA that contains methylation sites, wherein the cell-type is determined when the methylation pattern in the one or more portions is the same as a cell-type specific methylation pattern; and (c) measuring the quantity of the cfDNA of the identified methylation patterns; wherein the etiology of the liver damage is determined when the identified methylation patterns and their form a methylation profile that is the same as a methylation profile associated with an etiology of liver damage.

29. The method of claim 27 or 28, wherein the etiology of liver damage is selected from acute cellular rejection, acute antibody -mediated rejection, chronic rejection, ischemiareperfusion injury, recurrence of underlying liver diseases, biliary strictures, ischemic cholangiopathy, cholangitis, choledocholithiasis, gallstones, biliary leaks, biloma, pathogenic infection, induced liver damage, vascular disorders, Budd-Chiari-Syndrome, ischemic hepatitis, metabolic disease, liver disease associated with chronic heart failure, liver tumors, trauma, amyloidosis, sarcoidosis, genetic disorders, glycogen storage diseases, and progressive familial intrahepatic cholestasis.

30. The method of any one of claims 9-29, wherein the sample is selected from peripheral blood, serum, plasma, bile, and biopsy tissue.

31. The method of any one of claims 1, 3, 5, 12, 19, and 21, wherein the normal quantity of cfDNA comprises a quantity of cfDNA for the determined cell-type that is generated in a population of individuals who have a healthy liver or who were not administered the treatment.

32. The method of any one of claims 1-31, wherein the cell-type is selected from biliary-small-ductal-epithelial cells, biliary-large-ductal-epithelial cells, liver-endothelial cells, hepatocytes, liver-resident-immune cells, and hepatic-stellate cells.

33. The method of claim 32, wherein the cell-type is further selected from memory B-cells, naive B-cells, CD4+ mature T-cells, CD8+ mature T-cells, natural killer cells, monocyte / macrophages / and neutrophils.

34. The method of any one of claims 1-33, wherein the methylation pattern comprises a nucleotide sequence containing at least three CpG dinucleotides.

35. The method of any one of claims 1-34, wherein the cell-type specific methylation pattern comprises a hypomethylated nucleotide sequence.

36. The method of any one of claims 1-35, wherein the cell-type specific methylation pattern is selected from Table 1.

37. The method of any one of claims 1-36, wherein the cell-type specific methylation pattern comprises a hypermethylated nucleotide sequence.

38. The method of any one of claims 1-34 or 37, wherein the cell-type specific methylation pattern is selected from Table 2.

39. The method of any one of claims 1-38, wherein the identification of methylation patterns in the nucleotide sequence of the one or more portions of the cfDNA that contains methylation sites is with a first computer program, executed on a computer.

40. The method of any one of claims 1-39, further comprising comparing the methylation pattern in the one or more portions of the cfDNA that contains methylation sites to cell-type specific methylation patterns with a second computer program, executed on a computer.

41. The method of any one of claims 1-14, further comprising transferring to a user interface on an electronic display:(i) the result of the identification of methylation patterns in the nucleotide sequence of the one or more portions of the cfDNA that contains methylation sites;(ii) the result of the comparison of the methylation pattern in the one or more portions of the cfDNA that contains methylation sites to cell-type specific methylation patterns; and / or (iii) the determination of the cell-type from which the cfDNA originated.

42. A kit comprising one or more pairs of a forward primer and a reverse primer that are complementary to known genomic regions containing a cell-type specific methylation pattern selected from Table 1 or Table 2.

43. A kit comprising one or more panels of hybridization probes designed to capture nucleotides comprising a sequence of a cell-type specific methylation pattern selected from Table 1 or Table 2.

44. The kit of claim 42 or 43, further comprising instructions for use, one or more reagents, one or more additional active agents, a label indicating intended use of the contents of the kit, or a combination thereof.

Images