Methods and systems for monitoring organ health and disease

Abstract

To provide methods, compositions, and systems for monitoring tissue and organ health. [Solution] The methods, compositions, and systems provided herein allow for the extraction of locus-specific copy number signals from cell-free DNA (cfDNA) samples to identify tissue-specific cfDNA copy number profiles and to quantify tissue fractions in cfDNA samples. This disclosure relates to systems, methods, and compositions for analyzing cfDNA in a sample to extract cfDNA locus-specific copy number signals in order to quantify tissue and / or cell-specific fractions of cfDNA in the sample.

Description

[Technical Field]

[0001] field The systems, methods, and compositions provided herein relate to methods for extracting locus-specific cfDNA copy number signals from samples for health monitoring, diagnostics, or cell profiling and analysis. Specifically, the systems, methods, and compositions relate to methods for analyzing cell-free DNA (cfDNA) in a sample to determine the relative contribution of tissue or cell type to total cfDNA in the sample. The methods provided herein utilize sequence-specific cfDNA coverage, intensity, or copy number signals and do not involve direct determination of methylation status in cfDNA. [Background technology]

[0002] background In recent years, cell-free DNA (cfDNA) has emerged as a promising source for discovering biomarkers for disease diagnostics. In particular, fetal cfDNA and intact fetal cells can enter the maternal blood circulation. Consequently, analysis of such fetal genetic material can enable early non-invasive prenatal testing (NIPT). A key challenge in performing NIPT on fetal cfDNA is that it is typically mixed with maternal cfDNA, and therefore, cfDNA analysis is hindered by the need to explain maternal genotype signals. Furthermore, cfDNA analysis is useful as a diagnostic tool for cancer detection and diagnosis. [Overview of the Initiative] [Means for solving the problem]

[0003] Current protocols for preparing sequencing libraries from cell-free nucleic acid samples (e.g., plasma samples) typically involve the isolation of cfDNA for the preparation of sequencing libraries for analysis. However, existing cfDNA analysis methods, whether for NIPT applications or oncological applications, rely on the extraction of signals of genetic alterations from cfDNA sequencing and are therefore limited to NIPT and oncology.

[0004] Abstract This disclosure relates to a system, method, and composition for analyzing cfDNA in a sample, for extracting a cfDNA locus-specific copy number signal in order to quantify the tissue and / or cell-specific fraction of cfDNA in the sample.

[0005] Some embodiments provided herein relate to methods for analyzing cell-free DNA (cfDNA) in biological samples. In some embodiments, the sample is derived from a human subject with potential cell death or tissue or disease damage. In some embodiments, cell death or tissue / organ damage includes diseases involving organ damage such as blunt trauma such as head injury, drug toxicity in the liver or kidney, cardiac injury in cardiomyopathy, kidney injury in kidney disease, liver injury in liver disease, or beta cell death in diabetes. In some embodiments, cell death or tissue / organ damage includes cancer or pregnancy, where an excess of cell death or cell turnover occurs.

[0006] In some embodiments, the method includes the steps of: obtaining a biological sample containing cfDNA, wherein the cfDNA comprises a plurality of cfDNA fragments, each fragment corresponding to one or more tissues or cell types; quantifying each cfDNA fragment to generate a genome-wide or targeted (locus-specific) cfDNA profile, wherein the genome-wide cfDNA profile comprises a plurality of copy number signals, each copy number (including coverage or intensity) signal corresponding to a cfDNA fragment; and comparing the genome-wide cfDNA copy number signal profile with a collection of reference copy number signal profiles to determine or quantify the source of cell damage, tissue damage, or organ damage. In some embodiments, the method optionally includes the step of enriching the cfDNA from the sample by pull-down or PCR to provide enriched cfDNA.

[0007] Some embodiments provided herein relate to methods for monitoring the progression of tissue or organ damage in a subject. In some embodiments, the method includes the steps of: obtaining a biological sample from a subject, wherein the biological sample contains cell-free DNA (cfDNA); quantifying the cfDNA in the sample to obtain a genome-wide cfDNA copy number signal profile containing multiple copy number signals, wherein each copy number signal corresponds to a cfDNA fragment of a specific cell or tissue type; and comparing the genome-wide cfDNA copy number signal profile to a collection of known copy number signal profiles of a healthy subject or pure tissue type. In some embodiments, the quantification step is performed without using PCR or enrichment. In some embodiments, the difference in copy number signals in the sample compared to known copy number signals correlates with the state in the subject related to tissue or organ damage. [Brief explanation of the drawing]

[0008] [Figure 1]Figure 1 illustrates plots depicting kidney tissue and blood signal profiles of cfDNA along targeted chromosomal locations. Tissue / cell type-specific signals are extracted from plasma cfDNA copy number signals obtained from cfDNA sequencing of kidney disease patients using a non-negative matrix factorization method. Target regions are assayed by multiplex PCR in cfDNA samples.

[0009] [Figure 2] Figure 2 depicts a plot showing results for predicting renal failure in patients based on the quantification of the renal cfDNA fraction in blood plasma.

[0010] [Figure 3] Figures 3A and 3B depict plots of the time-course pattern of the proportion of kidney tissue-derived DNA as a function of time in a set of kidney transplant recipients. Figure 3A shows the estimated kidney fraction of donor kidney cfDNA, and Figure 3B shows the estimated kidney fraction of the patient's own kidney cfDNA. Both Figures 3A and 3B show statistically significant changes over time, and the patterns of change over time are consistent with biomedical procedures known to these patients.

[0011] [Figure 4] Figure 4 depicts the component fractions of colonic cfDNA across various diseases, revealing that the Crohn's disease fraction was significantly larger than those in the other diseases analyzed.

[0012] [Figure 5] Figure 5 depicts a block diagram illustrating the process for evaluating cfDNA samples for tissue cfDNA quantification. [Modes for carrying out the invention]

[0013] Detailed explanation In the following detailed description, references are made to the accompanying drawings which form part of this specification. In the drawings, unless the context indicates otherwise, similar symbols typically identify similar components. The embodiments described in the detailed description, drawings and claims are not intended to be limiting. Other embodiments may be utilized and other variations may be made without departing from the spirit or scope of the subject matter presented herein. It will be readily apparent that all aspects of this disclosure described in general herein and illustrated in the drawings can be arranged, substituted, combined, separated and designed in a wide variety of different configurations as expressly intended herein.

[0014] Embodiments of the systems, methods, and compositions provided herein relate to the step of analyzing nucleic acid fragments in a sample to determine how many nucleic acid fragments originate from different parts of the genome of different parts of the body in question. More specifically, the systems, methods, and compositions provided herein relate to the step of analyzing cfDNA populations in a sample to determine the relative amounts of cfDNA originating from different parts of the genome of different parts of the body in question. Thus, the systems, methods, and compositions relating to the quantification of tissue origin of cfDNA can be used in a wide range of applications involving elevated cell death or elevated genetic modification, including, for example, applications for monitoring disease progression, monitoring organ or tissue health, diagnosing or detecting disease, determining drug efficacy or toxicity, or monitoring neonatal health.

[0015] In one embodiment, a biological sample known to contain cfDNA, such as blood plasma, is collected from a subject suspected of having a specific type of organ injury or elevated cell turnover. Whole-genome sequencing (WGS) analysis is performed on the cfDNA in the biological sample to identify genomic regions that may show more or less cfDNA than in a typical subject. For example, if a subject has liver injury or kidney failure, it can be expected that more cfDNA will be obtained from the liver or kidney compared to a baseline control population. Once the sequencing analysis is complete, it is compared using various different machine learning, artificial intelligence, or other protocols to identify the difference in cfDNA from the subject to the baseline control. In one embodiment, part of the analysis may include a step of quantifying the relative fractions of cfDNA from different tissues from the subject and a normal baseline control. In some embodiments, quantification may include one or both of the steps of determining a set of reference tissue profiles and quantifying the fraction of tissue cfDNA in the cfDNA sample based on genome-wide cfDNA coverage data.

[0016] For example, for genome-wide or targeted cfDNA copy number profiles for a set of normal and / or affected samples, a set of reference cfDNA coverage profiles is derived, and the resulting linear combinations reconstruct the cfDNA copy number signals from the normal and / or affected samples. Each reference profile corresponds to a specific cell or tissue type. Unsupervised machine learning methods, such as non-negative matrix factorization, can be used to degrade the individual-derived cfDNA signals and extract reference tissue or cell-specific profiles, thereby generating a baseline reference profile. Depending on the type of body fluid, the dominant cell or tissue type may differ. For example, with respect to plasma, the leukocyte signal profile would be the primary contributor. Exemplary analyses of extracted kidney tissue and blood signal profiles of cfDNA along targeted chromosomal locations are depicted in Figure 1.

[0017] Traditional methods for analyzing cfDNA require sequence-specific detection, which limits the sensitivity of the assay and does not provide an accurate, reliable, or reproducible determination of the relative contribution of each tissue type in the subject to the total cfDNA in a biological sample. For example, traditional approaches cannot determine how much of the cfDNA in a sample originates from the lungs, spleen, liver, kidneys, etc., compared to a normal sample. Previous methods of cfDNA sequencing were for applications related to monitoring the status of transplanted tissues or cancer. However, such methods require allelic-based analysis, which necessitates sequencing and detection of single nucleotide variations between donor and host or tumor and normal tissue. There are no existing methods that can quantify the organ health status of a subject from cfDNA sequencing, array hybridization, or similar methods.

[0018] Furthermore, traditional methods for monitoring organ or tissue health are performed by tissue biopsy. Tissue biopsy can be used to test and determine the presence or extent of disease based on specific tissue, which can be done by extracting cells or tissue from a biopsy sample taken from the subject. However, these methods are invasive, time-consuming, expensive, and generally carry an increased risk of unintended health consequences.

[0019] In contrast, the systems, methods, and compositions described herein relate to the step of determining the amount of cfDNA fragments originating from various tissues. Furthermore, these systems, methods, and compositions are non-invasive and can provide immediate determination of the dynamics of cell death or tissue damage. The systems, methods, and compositions provided herein can enable the early detection of various signs before clinical symptoms or functional deterioration of the subject's body are found. Moreover, these methods do not require the selection of a specifically targeted organ, but instead allow caregivers to discover which organs are deteriorating, which is not possible when using tissue biopsy as a screening method. In this regard, these methods, systems, and compositions can enable multi-organ quantification and monitoring at once, in a single analysis, with less sampling bias than tissue biopsy methods.

[0020] Unless otherwise stated, the implementation of the methods and systems disclosed herein involves conventional techniques and apparatus commonly used in the fields of molecular biology, microbiology, protein purification, protein engineering, protein and DNA sequencing, and recombinant DNA, which are within the scope of the skill of those skilled in the art. Such techniques and apparatus are known to those skilled in the art and are described in numerous textbooks and references (e.g., Sambrooket al., “Molecular Cloning: A Laboratory Manual,” Third Edition (Cold Spring Harbor),

[2001] ); and Ausubel et al. See al., “Current Protocols in Molecular Biology”

[1987] .

[0021] A numerical range encompasses the numbers that define that range. Any maximum numerical limit defined herein is intended to include all such lower numerical limits, as if lower numerical limits were explicitly written herein. Any minimum numerical limit defined herein will include all such higher numerical limits, as if higher numerical limits were explicitly written herein. Any numerical range defined herein will include all such narrow numerical ranges that fall within such broader numerical ranges, as if all narrow numerical ranges were explicitly written herein.

[0022] Unless otherwise specified herein, all technical and scientific terms used herein have the same meaning as those commonly understood by those skilled in the art. Various scientific dictionaries containing the terms included herein are well known and available to those skilled in the art. Any methods and materials similar to or equivalent to those described herein may be found useful in carrying out or examining the embodiments disclosed herein, although only a few methods and materials are described.

[0023] The terms defined immediately below are better described by referring to this specification as a whole. Please understand that this disclosure is not limited to the specific methodologies, protocols, and reagents described herein, as they may vary depending on the context in which they are used by those skilled in the art. Where used herein, the singular terms “a,” “an,” and “the” include plural references unless the context otherwise clearly indicates otherwise.

[0024] Unless otherwise specified, nucleic acids are written from left to right with a 5' to 3' orientation, while amino acid sequences are written from left to right with an amino to carboxyl orientation.

[0025] As used herein, “polynucleotide” and “nucleic acid” may be used interchangeably and may refer to polymeric forms of nucleotides of either ribonucleotide or deoxyribonucleotide length. Thus, these terms include single-stranded, double-stranded, or multi-stranded DNA or RNA. Examples of polynucleotides include genes or gene fragments, cell-free DNA (cfDNA), whole-genome DNA, genomic DNA, epigenome, genomic DNA fragments, exons, introns, messenger RNA (mRNA), regulatory RNA, transfer RNA, ribosomal RNA, non-coding RNA (ncRNA), such as PlWI-interacting RNA (piRNA), small interfering RNA (siRNA) and long non-coding RNA (lncRNA), small hairpin (shRNA), nuclear small RNA (snRNA), microRNA (miRNA), nucleolar small RNA (snoRNA) and viral RNA, ribozymes, cDNA, recombinant polynucleotides, branched polynucleotides, plasmids, vectors, isolated DNA of any sequence, isolated RNA of any sequence, nucleic acid probes, primers, or amplified copies of any of the above. Polynucleotides may include nucleotides with non-native bases, nucleotides with modified native bases such as aza or deazapurine, methylated nucleotides and modified nucleotide analogs. Polynucleotides can consist of specific sequences of four nucleotide bases: adenine (A), cytosine (C), guanine (G), and thymine (T). For example, when a polynucleotide is RNA, uracil (U) may be present as a natural substitute for thymine. Uracil may also be used in DNA. The term "nucleic acid sequence" can refer to the alphabetical representation of a polynucleotide, or to any nucleic acid molecule containing natural and non-natural bases.

[0026] The term donor DNA (dDNA) refers to DNA molecules that originate from the cells of a transplant donor. In various implementations, dDNA is found in samples obtained from donees who have received transplanted tissue or organs from a donor.

[0027] Circulating cell-free DNA, or simply cell-free DNA (cfDNA), is a DNA fragment that is not confined to cells but circulates freely in the bloodstream or other bodily fluids. It is known that cfDNA can have different origins, in some cases originating from donor tissue DNA circulating in the recipient's blood, in some cases from tumor cells or tumor-affected cells, and in others from fetal DNA circulating in the mother's blood. Other non-limiting examples include cfDNA originating from tissues or organs native to the same organism, such as the kidneys, lungs, brain, and heart. Levels of tissue-specific cfDNA may increase or decrease when cell death, tissue damage, or organ damage occurs, including, for example, blunt trauma such as head trauma, drug toxicity in the liver or kidneys, cardiac injury in cardiomyopathy, kidney injury in kidney disease, liver injury in liver disease, and beta cell death in diabetes. Examples include cancer and pregnancy, where excessive cell death or cell turnover occurs.

[0028] Generally, cfDNA is fragmented and contains only a small portion of the genome, which may differ from the genome of the individual from which the cfDNA is obtained. The exact mechanism of cfDNA biogenesis is unknown. Generally, cfDNA is thought to originate from apoptotic or necrotic cell death, but there is also evidence suggesting active cfDNA release from living cells. Generally, cfDNA has a diverse range of cell types, and the genome-wide cfDNA profile of a subject can vary depending on cell origin and health status.

[0029] The terms non-circulating genomic DNA (gDNA) or cellular DNA are used to refer to DNA molecules localized to a cell, often including the complete genome.

[0030] The binomial distribution is a discrete probability distribution of the number of successes in a series of n independent experiments, each asking a yes / no question and each having its own Boolean result: a random variable containing a single bit of information: positive (by probability p) or negative (by probability q=1-p). For a single trial, i.e., n=1, the binomial distribution is a Bernoulli distribution. The binomial distribution is frequently used to model the number of successes in a sample of size n drawn from a population of size N. If a random variable X follows a binomial distribution with parameters n∈N and p∈[0,1], then the random variable X is written as X~B(n,p).

[0031] When the events occur at a known average rate and independently of the time elapsed since the last event, the Poisson distribution, denoted herein as Pois(), is a discrete probability distribution that represents the probability of a given number of events occurring at a fixed interval in time and / or space. The Poisson distribution can also be used for the number of events at other specified intervals, such as distance, area, or volume. The probability of observing k events at an interval following a Poisson distribution is given by the following equation:

number

[0032] The gamma distribution is a two-parameter family of continuous probability distributions. There are three commonly used parameterizations: shape parameter k and scale parameter θ; shape parameter α=k and inverse scale parameter β=1 / θ called ratio parameter; or shape parameter k and mean parameter μ=k / β. In each of these three forms, both parameters are positive real numbers. The gamma distribution is the maximum entropy probability distribution of a random variable X, with respect to it E[X]=kθ=α / β fixed and greater than zero,

number

number

[0033] In this specification, the term “sample” refers to a sample typically derived from a biological body, such as a body fluid, cell, tissue, organ, or organism, and may be referred to herein as a biological sample. Such samples include, but are not limited to, sputum / oral fluid, amniotic fluid, blood, blood fractions or fine-needle biopsy samples (e.g., surgical biopsy, fine-needle biopsy, etc.), urine, ascites, pleural fluid, and others. Samples are often taken from human subjects (e.g., patients), but the assay can be used with samples derived from any mammal, including but not limited to dogs, cats, horses, goats, sheep, cattle, pigs, etc. Samples can be used directly as obtained from the biological source or after pretreatment to modify the properties of the sample. For example, such pretreatment may include the preparation of plasma from blood, the dilution of viscous liquids, and so on. Pretreatment methods may include, but are not limited to, filtration, precipitation, dilution, distillation, mixing, centrifugation, freezing, freeze-drying, concentration, amplification, nucleic acid fragmentation, inactivation of interfering components, addition of reagents, and dissolution. When such pretreatment methods are used with respect to a sample, such pretreatment methods typically result in the presence of the target nucleic acid(s) in the test sample at a concentration sometimes proportional to that in the untreated test sample (e.g., a sample not subjected to any such pretreatment method(s)). Such “treated” or “processed” samples are still considered biological “test” samples with respect to the methods described herein.

[0034] In this specification, the term “biological fluid” refers to a liquid taken from a biological source, including, for example, blood, serum, plasma, sputum, lavage fluid, cerebrospinal fluid, urine, semen, sweat, tears, saliva, and others. As used herein, the terms “blood,” “plasma,” and “serum” expressly include fractions or processed portions thereof. Similarly, if a sample is taken from a biopsy, swab, smear, etc., “sample” expressly includes processed fractions or portions derived from the biopsy, swab, smear, etc.

[0035] Samples may be obtained from subjects and may be used to monitor tissue or organ health, diagnose or detect disease, or otherwise analyze samples from subjects. As used herein, “subject” refers to an animal that is the target of a procedure, observation, or experiment. “Animals” includes cold-blooded and warm-blooded vertebrates and invertebrates, such as fish, crustaceans, reptiles, and mammals in particular. “Mammals” includes, without limitation, mice, rats, rabbits, guinea pigs, dogs, cats, sheep, goats, cattle, horses, primates, such as monkeys, chimpanzees, and apes, and especially humans. A subject may be a subject that has or is suspected of having cancer, a genetic disorder, organ or tissue injury, or other disease or disorder that can be monitored. In some embodiments, a subject is an organ recipient, such as a subject that is a recipient of an organ transplant. In some embodiments, a subject has potential organ injury due to a chronic disease or blunt trauma.

[0036] Embodiments of this system, method, and composition relate to the steps of obtaining a sample from a subject, monitoring, detecting, evaluating, predicting or diagnosing a disease or disorder in the subject, monitoring tissue or organ damage in the subject, or evaluating or quantifying the tissue origin of nucleic acids. The disease may include, for example, cancer, genetic disorders, organ-specific disorders, or other diseases or disorders characterized by cfDNA increases in different genomic regions based on tissue origin and / or disease type.

[0037] As used herein, the term "reference genome" refers to any specific known genome sequence, whether partial or complete, of any organism that can be used as a reference for an identified sequence of origin. "Genome" refers to the complete genetic information of an organism or virus as expressed in a nucleic acid sequence.

[0038] Some embodiments of the methods, systems, and compositions provided herein relate to the step of simultaneously quantifying the relative contributions of multiple tissues or cell types in a cfDNA sample based on genome-wide cfDNA copy number (CN) signals. Depending on the intended application, the cfDNA sample may be derived from a biological sample, such as blood, plasma, urine, cerebrospinal fluid, or any other type of human bodily fluid. Genome-wide cfDNA coverage, copy number, or intensity signal can be obtained by sequencing-based DNA molecular counting, such as by any sequencing technique or a hybridization-based DNA copy number quantification technique. In some embodiments, the cfDNA may be subjected to targeted PCR or enrichment assays or genome-wide amplification before copy number signal measurement. In any embodiment, various amplification methods may be used, such as non-specific amplification of the entire genome, such as whole-genome amplification (WGA) methods, such as MDA, or highly targeted PCR amplification of several or a single selected region of a few kb.

[0039] By considering cfDNA coverage from a biological sample or set of biological samples derived from any of the systems or methods described herein, relative fractions of different tissues can be quantified. In some embodiments, quantification may include one or both of the steps of determining a set of reference tissue profiles and quantifying the fraction of tissue cfDNA in a cfDNA sample based on genome-wide or targeted cfDNA coverage data.

[0040] For example, for a genome-wide cfDNA copy number profile for a set of normal samples, a set of reference cfDNA coverage profiles is derived such that the resulting linear combination corresponds to the cfDNA copy number profile derived from the normal sample. While blood cfDNA copy number profiles correspond to a mixture of signals from multiple cell or tissue types, reference profiles correspond to specific cell or tissue types. Using unsupervised machine learning methods such as non-negative matrix factorization, the set of plasma cfDNA signals can be decomposed to extract the reference profile, thereby generating a set of baseline reference profiles. Depending on the type of body fluid, the dominant cell or tissue type may differ. For example, with respect to plasma, the leukocyte signal profile may be the primary contributor.

[0041] Similarly, using semi-supervised machine learning, tissue or disease-specific cfDNA profiles can be extracted from genome-wide cfDNA copy number profiles of a set of patient samples with known organ damage or specific diseases associated with organ damage, in addition to baseline reference profiles. The resulting baseline reference profiles can be used to explain the baseline portion of the cfDNA signals derived from the patient samples, and then additional tissue reference profiles can be derived from the cfDNA coverage signals that cannot be explained.

[0042] Unsupervised and semi-supervised approaches can be further coupled with supervised machine learning methods based on deep neural networks to predict cfDNA coverage profiles for tissues or cell types with limited access to relevant cfDNA samples. For example, considering epigenetic signals for a given cell type as input features, including DNase reachability signals, histone mark signals, and genomic DNA methylation signals, deep learning methods can be used to predict cfDNA coverage profiles for that cell type.

[0043] Therefore, in some embodiments, a set of reference tissue profiles is used for tissue quantification in the sample of interest. For cfDNA coverage profiles, the tissue fraction can be quantified by linearly projecting the observed cfDNA coverage profile onto a known reference profile.

[0044] Embodiments of the systems, methods, and compositions provided herein may include a wide range of applications, such as organ health monitoring, drug toxicity monitoring, sports medicine, disease diagnosis and detection, oncology, non-invasive prenatal testing (NIPT), neonatal health monitoring, or disease pathology research.

[0045] In the field of organ health monitoring, embodiments of this system, method, and composition can be used, for example, for monitoring multiple organs such as the kidneys, lungs, or heart, as well as for pre- and post-disease monitoring and diagnosis using a single blood test. Embodiments described herein include low-cost, universal blood tests targeting major organs that enable early detection and prevention of severe organ failure, including those for monitoring strategies for high-risk populations. For example, kidney health monitoring in patients with lupus or diabetes; cardiac health monitoring in individuals with a family history of cardiomyopathy; or multi-organ health monitoring in patients with sepsis. Furthermore, for example, the severity of trauma (blunt force trauma) in the head or chest / lung region is not easily accessible unless severe functional consequences are observed. Embodiments of the system, method, and composition provided herein enable quantitative monitoring of trauma severity and signal early medical intervention.

[0046] In the field of drug toxicity monitoring, embodiments of this system, method, and composition can be used, for example, to monitor the hepatic or nephrotoxicity of prescription drugs in a given patient, or to measure the hepatic or nephrotoxicity of novel drugs in clinical trials, thereby enabling personalized medicine and real-time adjustments to individual patient drug therapy regimens.

[0047] In the field of sports medicine, embodiments of this system, method, and composition can be used, for example, to monitor the extent of physical injury caused by intense training, thereby enabling rational adjustment of athlete training schedules and preventing overtraining syndrome. Cell-free DNA has been found to increase with exercise. In athletes, overtraining syndrome (OTS) is a condition that frequently occurs when they constantly and intensely push their limits. When OTS occurs, recovery can take several days to several weeks, or in some cases, the athlete may not recover at all. Muscle cfDNA quantification, and therefore an approach for the early detection and prevention of OTS, would be of great value to athletes in achieving optimal training performance.

[0048] In the field of disease diagnosis and detection, embodiments of the present system, method, and composition can be used, for example, to monitor or analyze diseases that are difficult to diagnose or frequently misdiagnosed, such as irritable bowel syndrome, inflammatory bowel disease, celiac disease, fibromyalgia, rheumatoid arthritis, multiple sclerosis, lupus, polycystic ovary syndrome, appendicitis, Crohn's disease, ulcerative colitis, or idiopathic myopathy. Some of these diseases are generally reliably diagnosed only by tissue biopsy. Many diseases, such as celiac disease, are currently diagnosed using tissue biopsy. Many diseases exist for which there are no existing diagnostic markers or for which there are no good diagnostic markers, such as chronic fatigue syndrome. Embodiments of the system, method, and composition provided herein enable the monitoring, detection, evaluation, prediction, or diagnosis of the above-mentioned and other diseases and disorders. For example, embodiments of the system and method can be used to determine a specific fraction of a particular tissue component in order to identify a particular disease. As shown in Figure 4, for example, the component fraction of colonic cfDNA is shown across various diseases, and the fraction for Crohn's disease is significantly larger than that for the other diseases analyzed.

[0049] In the field of oncology, embodiments of this system, method, and composition can be used, for example, for the quantification of tissue origin of cfDNA and the determination of cancer tissue origin, as well as for mutations from a single cfDNA whole genome sequence (WGS) assay. A WGS comprises the entire germline genome sequence of an individual (including all chromosomes).

[0050] In the field of NIPT and neonatal health monitoring, embodiments of this system, method, and composition can be used, for example, to determine and monitor maternal health status, and to measure maternal immune responses to the fetus. Some embodiments relate to the prediction of miscarriage and premature delivery. Some embodiments relate to the determination of neonatal health status, such as organ prematurity, jaundice, genetic defects, or other neonatal health conditions, by neonatal plasma cfDNA sequencing. Related to monitoring, investigating, diagnosing, or predicting.

[0051] In the field of disease pathology research, embodiments of this system, method, and composition can be used for simple and low-cost tissue origin quantification, enabling longitudinal studies, for example, by profiling the dynamics and interactions between multiple human organs, and allowing researchers to understand the pathogenesis of many diseases.

[0052] Accordingly, some embodiments provided herein relate to methods and systems for quantifying cfDNA in a subject. In some embodiments, the method includes the step of obtaining a biological sample known to contain cfDNA, such as blood plasma, from a subject having or suspected to have a specific type of cancer. As used herein, “cancer” means any type of cancer or neoplasm or malignant tumor found in mammals, particularly humans, including leukemia, sarcoma, carcinoma and melanoma. Examples of cancers include brain, breast, cervical, colon, head and neck, kidney, lung, non-small cell lung, melanoma, mesothelioma, ovarian, sarcoma, stomach, uterine and medulloblastoma cancers. Additional cancers may include, for example, Hodgkin's disease, non-Hodgkin lymphoma, multiple myeloma, neuroblastoma, breast cancer, ovarian cancer, lung cancer, rhabdomyosarcoma, primary thrombocytosis, primary macroglobulinemia, small cell lung tumor, primary brain tumor, gastric cancer, colon cancer, malignant pancreatic insulinoma, malignant carcinoid, bladder cancer, pre-malignant skin lesions, testicular cancer, lymphoma, thyroid cancer, neuroblastoma, esophageal cancer, genitourinary cancer, malignant hypercalcemia, cervical cancer, endometrial cancer, adrenocortical cancer, and prostate cancer.

[0053] In some embodiments, whole-genome sequencing (WGS) analysis is performed on cfDNA in a biological sample to determine whether the amount of cfDNA is elevated or decreased compared to the amount of cfDNA in a healthy patient, or compared to the cfDNA levels across a healthy patient specimen (crosssection). Regions that may exhibit a certain amount of A are identified. For example, if a patient has liver injury or liver cancer, it can be expected that elevated cfDNA levels will be obtained that are identified as originating from the liver, compared to the levels of liver-derived cfDNA from a baseline control population. The level of a particular type of cfDNA can be determined from the total cfDNA level by various algorithms provided herein, including analysis by various machine learning, artificial intelligence, or other algorithms, to identify the level and difference of specific cfDNA from the subject compared to a baseline control, or to identify and compare the levels and differences of multiple types of cfDNA from multiple tissue types. In some embodiments, the cfDNA analysis includes the step of quantifying the relative fractions of different tissue-derived cfDNA from the subject and normal baseline controls. In some embodiments, the quantification may include one or both of the steps of determining a set of reference tissue profiles and quantifying the fraction of tissue cfDNA in the cfDNA sample based on genome-wide cfDNA coverage data. The baseline control may include healthy control samples from the sample population, including samples from various geographical regions, ages, ethnicities, races, or sexes, to establish an appropriate baseline.

[0054] Some embodiments provided herein relate to methods for analyzing cell-free DNA (cfDNA) in biological samples. In some embodiments, the method includes the steps of: obtaining a biological sample containing cfDNA; enriching the cfDNA from the sample to provide enriched cfDNA, wherein the enriched cfDNA comprises a plurality of cfDNA fragments, each fragment corresponding to a specific tissue or cell type; quantifying each cfDNA fragment to generate a genome-wide cfDNA profile, wherein the genome-wide cfDNA profile comprises a plurality of copy number signals, each copy number signal corresponding to a cfDNA fragment; and comparing the genome-wide cfDNA profile to a profile of known cfDNA copy number signatures to determine cell damage, tissue damage, or organ damage.

[0055] In some embodiments, the biological sample may be any biological sample that has or is suspected to have a cfDNA profile. Therefore, the biological sample may be any sample that originates from or is obtained from the subject, such as biological fluids obtained from the subject. For example, the biological sample may be blood, plasma, serum, urine, cerebrospinal fluid, saliva, lymph, aqueous humor, vitreous fluid, cochlear fluid, tears, milk, sputum, vaginal secretions, or any combination thereof, or may originate from or be obtained from these.

[0056] In some embodiments, the enrichment of the target nucleic acid or fragment, such as the enrichment of cfDNA in a sample, may include any suitable enrichment technique. In some embodiments, cfDNA enrichment may include molecular inversion probes, in-solution capture, pull-down probes, This can include enrichment by bait sets, standard PCR, multiplex PCR, hybrid capture, endonuclease digestion, and DNase I-sensitized and selective cyclization. Enrichment can be achieved by negative selection of nucleic acids by excluding unwanted material. This type of enrichment includes "footprinting" techniques or "subtractive" hybrid capture. In the former, the target sample is safe from nuclease activity by protein protection or by single-stranded and double-stranded arrangement. In the latter, nucleic acids that bind to the "bait" probe are excluded. In some embodiments, enrichment includes amplification of cfDNA. In some embodiments, amplification includes PCR amplification or genome-wide amplification.

[0057] In some embodiments, nucleic acid quantification, such as cfDNA quantification, may include any technique suitable for determining the amount of nucleic acid or nucleic acid fragments in a sample. For example, quantification may include sequencing of cfDNA using DNA molecular counting based on sequencing, or performing DNA quantification based on hybridization.

[0058] In some embodiments, each copy number signal indicates the relative contribution of cfDNA derived from a specific tissue or cell type. As used herein, copy number refers to genome-wide cfDNA coverage in a sample, based on signals obtained by DNA molecular counting, such as by sequencing or hybridization-based DNA copy number quantification techniques.

[0059] In some embodiments, the tissue type is any tissue type in which damage occurs or may occur that is desired, analyzed, or measured. In some embodiments, the tissue type is kidney, muscle, heart, blood vessel, liver, brain, eye, lung, fat, gland, bone, bone marrow, cartilage, intestine, stomach, skin, or bladder. In some embodiments, the cell type is hematopoietic cells, neuronal cells, renal cells, epithelial cells, extracellular matrix cells, or immune cells, or any combination of cells. For example, the method may include a step of measuring or monitoring one or more tissue or organ types in a subject. Thus, in some embodiments, the genome-wide cfDNA profile quantifies the amount of cfDNA from multiple organs to provide an assessment of organ health. In some embodiments, each cfDNA fragment is quantified simultaneously. As used herein, simultaneous refers to actions performed simultaneously or substantially simultaneously. Thus, simultaneous quantification refers to the simultaneous or substantially simultaneous analysis of multiple cfDNA fragments in a single assay. Accordingly, the embodiments provided herein relate to a single-analysis universal blood test, according to which multiple organs are monitored or may be monitored in a single assay. For example, tissue cfDNA quantification can be determined in multiple tissues or in a single tissue. One example is the quantification of the renal cfDNA fraction. As shown in Figure 2, the renal fraction is higher in patients with renal failure, and the quantification described herein allows for the prediction of renal failure.

[0060] In some embodiments, samples are periodically obtained from subjects and analyzed to monitor health over time, with an initial sample analyzed at a first time point and a second sample analyzed at a second time point, and differences in cfDNA profiles are assessed to provide an indicator of changes in the cfDNA profile. Such analysis can provide information on improvement or deterioration of a particular tissue type over time. For example, such a method can be used to monitor organ grafts, monitor drug toxicity, monitor treatment regimens, monitor the health status of various organs or tissues over time, monitor maternal health at different stages of pregnancy, monitor neonatal health during pregnancy and prenatally or postnatally, or for other suitable assessments. Thus, some embodiments provided herein relate to the monitoring of organ grafts over time. In some embodiments, genome-wide cfDNA profiles indicate drug toxicity in organs. In some embodiments, the sample is a maternal sample, and the genome-wide cfDNA profile indicates fetal health. The appropriate monitoring period for a particular tissue, organ, cell, or condition can depend on the specific application and may be in increments of minutes, e.g., monitoring samples every 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 12, 15, 20, 25, 30, 35, 40, 45, 50, 55 or 60 minutes; in increments of hours, e.g., every 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 12, 15, 18, 20 or 24 hours; or in increments of days, e.g., every 1, 2, 3, 4, 5, Monitoring can occur every 6, 7, 8, 9, 10, 15, 20, 25, or 30 days; every few months, e.g., every 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, or 12 months; or every few years, e.g., every 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 15, 20, 25, 30, 35, 40, 45, 50, 55, 60, 65, 70, 75, 80 years or more; or within a time range defined by any two of the values ​​described above. For example, a kidney organ transplant can be monitored over time using the systems and methods described herein.As shown in Figures 3A and 3B, the time course of the proportion of kidney tissue-derived DNA as a function of time for donor kidney cfDNA and the patient's own kidney cfDNA can be monitored over time.

[0061] In some embodiments, the method further includes the step of subtracting a baseline reference profile from a genome-wide cfDNA profile. The baseline reference profile corresponds to a specific cell or tissue type presented in the baseline cfDNA sample, so that the baseline profile may be described in the test sample, and any change or variation from baseline can be used for diagnosis or anomaly detection.

[0062] Some embodiments provided herein relate to methods for monitoring cancer progression in a subject. In some embodiments, the method includes the steps of: obtaining a biological sample from a subject, wherein the biological sample contains cell-free DNA (cfDNA); quantifying the cfDNA in the sample to obtain a genome-wide cfDNA profile including multiple copy number signals, wherein each copy number signal corresponds to a specific cell type or tissue type of cfDNA fragment; and comparing the multiple copy number signals to a profile of known copy number signals in a healthy subject. In some embodiments, the difference in copy number signals in the sample compared to the known copy number signals correlates with a cancerous or precancerous state in the subject. In some embodiments, total cfDNA is enriched from the sample before cfDNA quantification. In some embodiments, the method further includes the step of comparing the multiple copy number signals to a profile of known copy number signals in a cancer patient sample. In some embodiments, the biological sample includes blood, plasma, serum, urine, cerebrospinal fluid, saliva, lymph, aqueous humor, vitreous humor, cochlear juice, tears, milk, sputum, vaginal secretions, or any combination thereof. In some embodiments, quantification includes sequencing of cfDNA using DNA molecular counting based on sequencing. In some embodiments, quantification includes performing DNA quantification based on hybridization. In some embodiments, the method further includes enrichment of cfDNA before quantification of cfDNA. In some embodiments, enrichment includes amplification of cfDNA by PCR amplification or genome-wide amplification. [Examples]

[0063] Additional alternatives are disclosed in more detail in the following embodiments, but this is not intended to limit the scope of the claims. General Procedures and Methods extraction

[0064] The normal blood circulation speed is about 5 liters per minute, such that the total volume of blood circulates once per minute. This speed is much higher than the cfDNA generation and degradation kinetics, and the cfDNA composition is uniform in a person's blood within a short time frame (e.g., less than 5 minutes). Under these conditions, blood collection is approximately Poisson sampling of cfDNA. Either the multinomial distribution or the multivariate hypergeometric distribution is used for modeling DNA extraction.

[0065] The extraction process follows the Poisson distribution n” l ~Pois(n”·Σ t β t ·A tl ), or integrated, follows the multinomial distribution (n” l )~Multi(Σ t β t ·A t ,n”) (wherein n” l is the copy number at locus l, n” is the total copy of cfDNA fragments, β t is the fraction of cfDNA derived from tissue type t, and A t is the reference copy number profile of tissue type t). PCR amplification

[0066] The PCR process is approximated by the Gamma distribution n’ l ~Gamma(n” l ·ρ, θ), or integrated, follows the Dirichlet distribution (n’ l )θ~Dir(α=(n” l ·ρ)) (wherein ρ=(1+r) / (1-r) / [1-(1+r) -t , θ=[(1+r) t -1]·(1-r) / (1+r), r is the PCR amplification efficiency in each cycle, n’ l is the number of DNA molecules at locus l after PCR, and n’ is the total number of DNA molecules amplified from cfDNA fragments). Sequencing

[0067] Similar to extraction, sequencing follows the Poisson distribution n l ~Pois(n·n’ l / n') or combined, multinomial distribution (n l )~Multi(n' l / n',n)(wherein n is the number of fragments observed in sequencing, n l (where is the cfDNA copy number observed at a given locus l). several numbers

[0068] In a typical individual, approximately 5,000 mL of blood contains 1.8–44 ng / mL of plasma cfDNA, which corresponds to 1,350,000–33,000,000 copies of the human genome. 1% of the tissue fraction corresponds to 13,500–330,000 copies. For example, if 3 ng of cfDNA is used as input for a cfDNA WGS assay, this corresponds to a total of 900 copies, 9 copies of the 1% tissue genome, and 0.9 copies of the 0.1% tissue genome. (Example 1) Modeling of aggregated cfDNA signal profiles

[0069] The following example demonstrates an embodiment of modeling aggregated cfDNA signal profiles.

[0070] Ignoring extraction and PCR variability, the model S of the cfDNA signal is (n l )~Multi(Σ t β t ·A t ,n) is a large number of bins distributed approximately evenly. Assuming a (or gene locus) distribution, this is a Poisson distribution: n l ~Pois(n·Σ t β t ·A t It is close to ). Assuming a known tissue profile A, the tissue fraction B = (β t Only the following remains unknown, and this can be solved by numerical optimization.

[0071] The PS model for cfDNA signaling follows a gamma-Poisson (negative binomial) distribution n l ~NB(n” l·ρ,p=n·θ / (n’+n·θ)). n’=n”·ρ·θ,n” l =n”·Σ t β t ·A tl , and ignoring extraction variability, n is obtained l ~NB(n”·ρ·Σ t β t ·A tl ,n / (n”·ρ+n)). In the case of n<<n”·ρ, this is approximately n l ~Pois(n·Σ t β t ·A tl ), which is the same as Model S.

[0072] The E and P steps are combined into a single Dirichlet distribution (n’ l ) / θ~Dir(n”·α·1 / (1+1 / ρ)) or n’ l ~Gamma(n”·α·ρ / (1+ρ),(1+ρ)θ). A Dirichlet distribution is used to estimate the unknown multinomial probability distribution. More specifically, this extends the beta distribution to multiple dimensions, provides a smooth transition between the prior distribution and the observed distribution, and allows control over how quickly such transition occurs.

[0073] When the extraction, PCR and sequencing steps are combined together, the cfDNA signal model EPS is (n l )~DM(n” / (1+1 / ρ)·α,n) or (n l )~DM(n”·α·(1+r) / 2,n) (where DM is the Dirichlet-multinomial distribution). Assuming a large number of approximately evenly distributed bins (or loci), this is a negative binomial distribution: n l ~NB(n”·α·ρ / (1+ρ),(1+ρ)θn / [(1+ρ)θn+n’] or n l ~NB(n”·α l ·(1+r) / 2,n / [n+n”·(1+r) / 2]. μ=n·α l ,δ 2 =n·α l·[n / n”·(1 / ρ+1)+1] mean and variance. In the case of n<<n”, for example, for 30×WGS with >1ng input cfDNA, n l approaches the Poisson distribution n l ~Pois(n·α l ). Table 1 presents a list of probability models involved in cfDNA quantification, where α l =Σ t β t ·A tl and α=Σ t β t ·A t . Table 1 [Table 1]

[0074] The model PS for cfDNA signals follows the Gamma-Poisson (negative binomial) distribution n l ~NB(n” l ·ρ,p=n·θ / (n’+n·θ)). n’=n”·ρ·θ, n” l =n”·Σ t β t ·A tl Assuming and ignoring extraction variability, n l ~NB(n”·ρ·Σ t β t ·A tl ,n / (n”·ρ+n)) is obtained. In the case of n<<n”·ρ, this is approximately n l ~Pois(n·Σ t β t ·A tl ), which is the same as model S. Multiplicative update

[0075] The Poisson model n l ~Pois(n·α l ) is equivalent to non-negative matrix factorization with KL divergence as the cost. The multiplicative update algorithm β based on the non-negative matrix factorization (NM F) algorithm described in Lee and Seung, 2001 st ←β st ·Σ lA tl ·r sl / (β·A) sl / Σ l A tl is applied for computer processing of β t . Iteratively reweighted linear regression

[0076] For a given sample, with the estimated tissue fraction β0, the weighted linear regression by the cost function is defined as E(β;β0,A)=1 / 2·Σ l [(r l -(β·A) l ) 2 / (β0·A) l . After this weighted linear regression (regress) is solved (β0,A), then β←r·W -1 ·A T (A·W -1 ·A T ) -1 (where W=diag(β0·A)), which provides a further iterative update algorithm. The difference between this and the regular linear regression E=1 / 2·Σ l [(r l -(β·A) l ) 2 is the weight based on W=diag(α)=β·A L . Derivation of model EPS

[0077] (n’ l ) / θ~Dir((n” l ·ρ)) and (n” l )~Multi(α,n”) are assumed, the law of total variance is shown as follows: [Formula]

[0078] This matches Dir(n”·α·1 / (1+1 / ρ)). Assume n” l ~Pois(n”·α l ) and n’ l ~Gamma(n” l ·ρ,θ), and the law of total variance is shown as:

number

[0079] This matches gamma (n''·α·ρ / (1+ρ),(1+ρ)θ).

number

[0080] The following examples demonstrate embodiments of a method for determining tissue cfDNA reference profiles.

[0081] Two complementary strategies can be used to estimate tissue-specific or cell-type-specific cfDNA signaling profiles. The first method uses unsupervised machine learning based on a set of samples containing the target tissue / cell in a fluctuating fraction. The second method uses supervised machine learning by predicting cfDNA signaling profiles originating from a given tissue / cell based on the tissue / cell type's genomic DNA (gDNA) epigenetic profile or gene expression profile. Unsupervised machine learning

[0082] Supervised machine learning methods apply non-negative matrix factorization to decompose cfDNA mixture signals and extract tissue-specific cfDNA coverage profiles. Poisson model n l ~Pois(n·α l ) is equivalent to non-negative matrix factorization by Kullback-Leibler (KL) divergence as a cost. KL divergence is a measure of how much a given probability distribution differs from a reference probability distribution. For a given dataset of sufficient size and tissue composition of the target tissue type, the NMF algorithm by Lee and Seung 2001 is applied to estimate the tissue fraction in each sample and to verify the tissue cfDNA profile. The tissue fraction of tissue t in a sample is β st ←βst ·Σ l A tl ·r sl / (β·A) sl / Σ l A tl On the other hand, the cfDNA signal at locus l of tissue type t is estimated by A tl ←A tl ·Σ s β st ·r sl / (β·A) sl / Σ s β st It is estimated by, where · is matrix multiplication, and r sl This is the fraction of the read data that covers locus l in sample s. Supervised machine learning

[0083] Two relevant limitations exist for unsupervised algorithms. First, they require samples from individuals under specific physiological or disease conditions; for example, learning a kidney cfDNA profile requires access to multiple cfDNA samples from patients with elevated kidney damage. Second, for tissue types with small cell populations or rare cell types, the fraction of blood cfDNA signal contributed by such cells can be very small. Therefore, a larger number of cfDNA samples are required to effectively learn the cfDNA signal profile of such tissue or cell type. These limitations can be overcome with larger datasets. However, in practice, large datasets can prevent tissue quantification based on cfDNA WGS with broad applicability to all tissue types.

[0084] For these reasons, supervised machine learning can be used to predict tissue-specific cfDNA copy number profiles from epigenetic or expression data derived from specific tissue cell samples. Supervised machine learning does not require access to cfDNA samples from patients with specific organ damage, but instead uses only isolated tissue cells derived from either normal or diseased samples. This method predicts cfDNA profiles by applying a deep neural network, more specifically a recurrent neural network or convolutional neural network, to one-dimensional sequencing data. Input features to the neural network include genome-wide DNase reachability, DNA methylation, histone methylation, histone acetylation profiles, or gene expression profiles for a given tissue type. The prediction from machine learning is a genome-wide cfDNA copy number profile for the tissue of interest.

[0085] Both intra-organizational (within-tissue) and cross-organizational (cross-tissue) cross-validation are used. This involves training and evaluating machine learning models. More specifically, tissue-specific epigenetic data is prepared as input features, and estimated tissue cfDNA coverage profiles (from unsupervised algorithms) are prepared as targets. For intra-tissue cross-validation, a subset of loci in the genome is retained for validation, while other loci are used for training. For inter-tissue cross-validation, a cfDNA reference profile of a specific cell type, such as hematopoiesis, is used for training, and a cfDNA reference profile of an additional cell type, such as kidney or lung cells, is used for validation. (Example 3) cfDNA research

[0086] The following examples demonstrate a study method for analyzing cfDNA in a sample derived from the target material. Pilot study

[0087] Plasma DNA was obtained from 10 patients with end-stage renal disease (ESRD) and 10 age-, sex-, and weight-matched normal controls and studied. 30×WGS was performed on each sample. The presence of a strong cfDNA signal that reliably distinguished ESRD from normal controls was obtained. Clustering and principal component analysis (PCA) showed that ESRD and normal samples formed distinct groups. The determined kidney fraction was <0.5% compared to the normal controls. mixture research

[0088] For three case-control pairs, synthetic cfDNA mixtures were prepared by serial dilution, mixing ESRD with control cfDNA. For each case-control pair, eight mixtures with 100%, 50%, 25%, 12.5%, 6.25%, 3.125%, 1.5625%, and 0.78125% ESRD cfDNA were diluted with control cfDNA. This dataset was used to determine the performance of tissue quantification analysis. The mixture studies demonstrated that the estimated kidney fraction was linear with respect to the true kidney fraction, and that the kidney fraction could be determined accurately (CV < 20%) to a degree as low as 0.5%.

[0089] One embodiment for validation is depicted in the block diagram of Figure 5, which illustrates the process for evaluating cfDNA samples for tissue cfDNA quantification. As shown in Figure 5, the first cohort may include controls and affected subjects, which are subjected to library preparation, 30×WGS, and then analyzed. A portion of the WGS product is subjected to biomarker discovery, while the other portion is subjected to signal validation or WGS algorithms. The second cohort may be a cohort of synthetic mixtures, including, for example, a large number of samples from diabetic subjects, lupus subjects, hypertensive subjects, kidney diseases (such as chronic kidney disease (CKD) or polycystic kidney disease (PKD)), control samples, or samples from other subjects. The mixture is applied to amplicon assays, sequencing, and algorithms to determine the performance of the method for quantifying tissues (including determining the limit of quantification (LOQ) or limit of detection (LOD) and the linearity of the method) or diagnosing diseases (including determining the sensitivity and specificity of the method). complete research

[0090] Following the mixture study, samples from approximately 200 diabetic patients at various stages of chronic kidney disease (CKD) were collected and subjected to 30× cfDNA WGS. The results showed that the estimated kidney fraction could reliably distinguish between early-stage CKD patients and end-stage CKD patients, reliably distinguish between early-stage CKD patients and diabetic patients without CKD, and correlated with the severity of kidney disease. Research on diverse organs

[0091] Five blood samples were collected from patients with heart failure or lung injury (e.g., cystic fibrosis) or from normal controls and subjected to a 30× cfDNA WGS. The results demonstrate that patients with heart failure, lung injury, or kidney disease have distinct cfDNA signal profiles that differ from those of normal controls, and that cardiac and pulmonary cfDNA fractions can be quantified. Diverse transplantation research

[0092] Five blood samples from lung or heart transplant patients were collected and attached to a 30× cfDNA WGS. The results demonstrate that heart or lung transplant patients have distinct patterns, and that the estimated lung or heart fraction correlates linearly with the donor organ fraction based on genetic variants.

[0093] The term "comprising" is used herein to mean "including This is synonymous with "contains" or "characterized by," and is comprehensive or open-ended, not excluding additional, unlisted elements or method steps.

[0094] The above description discloses some methods and materials of the present invention. The present invention allows for modifications in methods and materials, as well as changes in manufacturing methods and apparatus. Such modifications will be apparent to those skilled in the art from consideration of this disclosure or from the practice of the invention disclosed herein. Consequently, the present invention is not intended to be limited to the specific embodiments disclosed herein, but is intended to cover all modifications and substitutions that fall within the true scope and spirit of the invention.

[0095] All references cited herein, including but not limited to published and unpublished applications, patents, and literature, are incorporated herein by reference in their entirety and thus form part of this Specified. To the extent that any publications and patents or patent applications incorporated herein by reference conflict with any disclosures contained herein, this Specified is intended to take precedence and / or supersede any such conflicting material. The present invention provides, for example, the following items: (Item 1) A method for analyzing cell-free DNA (cfDNA) in biological samples, The steps include obtaining a biological sample containing cfDNA; A step of extracting cfDNA from the sample and providing purified cfDNA, wherein the purified cfDNA comprises a plurality of cfDNA fragments, each fragment corresponding to a specific tissue or cell type; A step of quantifying the cfDNA fragments to generate a genome-wide cfDNA copy number profile, wherein the genome-wide cfDNA copy number profile includes multiple copy number signals, and each copy number signal corresponds to a cfDNA fragment; The steps include comparing the genome-wide cfDNA copy number profile with a set of known cfDNA signatures to determine cell damage, tissue damage, or organ damage. A method that includes this. (Item 2) The method according to item 1, wherein the biological sample includes blood, plasma, serum, urine, cerebrospinal fluid, saliva, lymph, aqueous humor, vitreous fluid, cochlear fluid, tears, milk, sputum, vaginal secretions, or any combination thereof. (Item 3) The method according to item 1, wherein the quantification step includes a step of sequencing the cfDNA using DNA molecular counting based on sequencing. (Item 4) The method according to item 1, further comprising the step of enriching the cfDNA fragment of interest. (Item 5) The method according to item 1, wherein the quantification step includes a step of performing DNA quantification based on hybridization. (Item 6) The method according to item 1, wherein the extraction step includes size-based enrichment for excluding gDNA and enriching cfDNA fragments. (Item 7) The method according to item 1, wherein the enrichment step includes amplification of the cfDNA. (Item 8) The method described in item 7, wherein amplification includes PCR amplification or genome-wide amplification. (Item 9) The method according to item 1, wherein each copy number signal indicates the relative contribution of cfDNA derived from a specific tissue or cell type. (Item 10) The method according to item 1, wherein the tissue type is kidney, muscle, heart, blood vessel, liver, brain, eye, lung, fat, gland, bone, bone marrow, cartilage, intestine, stomach, skin, or bladder. (Item 11) The method according to item 1, wherein the cell type is a blood cell, neuron cell, kidney cell, epithelial cell, extracellular matrix cell, beta cell, or immune cell. (Item 12) The method according to item 1, wherein the genome-wide cfDNA profile is used to quantify the amount of cfDNA from multiple organs in order to provide an assessment of organ or multi-organ health. (Item 13) The method according to item 1, wherein the aforementioned sample is periodically obtained from the subject and analyzed to monitor their health over time. (Item 14) The method of item 13, further comprising the step of monitoring the organ transplant over time. (Item 15) The method according to item 1, wherein the genome-wide cfDNA profile indicates drug toxicity or efficacy in an organ. (Item 16) The method according to item 1, wherein the sample is a maternal sample and the genome-wide cfDNA profile indicates fetal health. (Item 17) The method according to item 1, wherein multiple organs are quantified simultaneously by projecting the genome-wide cfDNA profile onto a set of reference cfDNA profiles corresponding to various tissues and cell types. (Item 18) The method according to item 1, further comprising the step of subtracting a baseline reference profile from the genome-wide cfDNA profile. (Item 19) A method for monitoring disease progression in a subject, A step of obtaining a biological sample from the subject, wherein the biological sample contains cell-free DNA (cfDNA); A step of quantifying the cfDNA in the sample to obtain a genome-wide cfDNA profile containing multiple copy number signals, wherein each copy number signal corresponds to a cfDNA fragment of a specific cell type or tissue type; The steps include comparing the plurality of cfDNA copy number signals with a set of known copy number signals from a healthy subject. A method comprising the following, wherein the difference in the copy number signal in the sample compared to the known copy number signal correlates with disease progression in the subject. (Item 20) The method according to item 19, wherein total cfDNA is concentrated from the sample before the step of quantifying the cfDNA. (Item 21) The method according to item 19, further comprising the step of comparing the plurality of copy number signals with the profiles of known copy number signals from a patient sample. (Item 22) The method according to item 19, wherein the biological sample includes blood, plasma, serum, urine, cerebrospinal fluid, saliva, lymph, aqueous humor, vitreous fluid, cochlear fluid, tears, milk, sputum, vaginal secretions, or any combination thereof. (Item 23) The method according to item 19, wherein the quantification step includes a step of sequencing the cfDNA using DNA molecular counting based on sequencing. (Item 24) The method according to item 19, wherein the quantification step includes a step of performing DNA quantification based on hybridization. (Item 25) The method according to item 19, wherein the disease is selected from heart failure, lung injury, diabetes, Crohn's disease, or kidney disease. (Item 26) The method according to item 25, wherein the enrichment step includes a step of amplifying the cfDNA by targeted amplification or genome-wide amplification. (Item 27) A method for monitoring tissue and organ health in a subject, A step of obtaining a biological sample from the subject, wherein the biological sample contains cell-free DNA (cfDNA); A step of quantifying the cfDNA in the sample to obtain a genome-wide cfDNA profile containing multiple copy number signals, wherein each copy number signal corresponds to a cfDNA fragment of a specific cell type or tissue type; The steps include comparing the plurality of cfDNA copy number signals with a set of known copy number signals from a healthy subject. A method comprising the following, wherein the difference in the copy number signal in the sample compared to the known copy number signal correlates with a change in organ health status in the subject. (Item 28) The method according to item 27, wherein total cfDNA is concentrated from the sample before the step of quantifying the cfDNA. (Item 29) The method according to item 27, further comprising the step of comparing the plurality of copy number signals with the profiles of known copy number signals from a patient-derived sample having poor tissue or organ health. (Item 30) The method according to item 27, wherein the biological sample includes blood, plasma, serum, urine, cerebrospinal fluid, saliva, lymph, aqueous humor, vitreous fluid, cochlear fluid, tears, milk, sputum, vaginal secretions, or any combination thereof. (Item 31) The method according to item 27, wherein the quantification step includes a step of sequencing the cfDNA using DNA molecular counting based on sequencing. (Item 32) The method according to item 27, wherein the quantification step includes a step of performing DNA quantification based on hybridization. (Item 33) The method according to item 27, wherein the enrichment step includes a step of amplifying the cfDNA by targeted amplification or genome-wide amplification.

Claims

[Claim 1] The invention as shown in the drawings.

Images