Method and system of using biomarkers for xenograft damage or rejection

Abstract

Creating a single-cell methylation atlas characterizing an organ of a donor by clustering methylated single-nuclei whole genome sequences (m-snWGS) from a biopsy of the organ based at least in part on differentially methylated patterns of the m-snWGS, assigning a cell type for each cluster based at least in part on correlation of known cell type markers of the organ with the differentially methylated patterns of the m-snWGS; and providing the atlas to include a database with genomic sequences based at least in part on the m-snWGS, methylation patterns of the genomic sequences based at least in part on the m-snWGS, and cell type designation for the genomic sequences based at least in part on the cell type assigned for each cluster.

Description

Attorney Docket No. 243735.000430  METHOD AND SYSTEM OF USING BIOMARKERS FOR XENOGRAFT DAMAGE OR REJECTION CROSS-REFERENCE TO RELATED APPLICATIONS

[0001] This application claims priority to U.S. Provisional Patent Application No. 63 / 634,376, filed April 15, 2024, the disclosure of which is herein incorporated by reference in its entirety. STATEMENT REGARDING FEDERALLY SPONSORED RESEARCH

[0002] This invention was made with government support under HG009491, OD033430, P30 CA016087, and AI144522 awarded by the National Institutes of Health. The government has certain rights in the invention. FIELD OF THE INVENTION

[0003] The present invention related to indicating a type of xenograft damage or rejection response in a subject who has received a xenograft transplantation. BACKGROUND

[0004] In the United States, 37 million people have chronic kidney disease (CKD) with approximately 808,000 having End-Stage Renal Disease (ESRD), and over 25,000 individuals received a kidney transplant in 2022. Only one in four transplant waitlisted patients receive a life-saving organ with 40% of listed patients dying within 5 years while waiting for an allograft.

[0005] Over 100,000 people are currently on the transplant waiting list in the United States, with only a third of these patients eventually receiving a transplant (Organ Procurement and Transplantation Network, 2021). The transplantation of solid-organs across species has enormous potential to address this unmet need for life saving organs.

[0006] Xenotransplantation using organs derived from genetically-modified pigs into humans is a promising avenue for addressing human organ shortage. Pigs have significant advantages for pig-to- human transplant including: organs being at an appropriate size at approximately 8 months after birth; many offspring per litter; and, they are reasonably easy to clone. 1    311769024v1 Attorney Docket No. 243735.000430

[0007] Due to the similarity of organ physiology and size, the pig has been identified as the most acceptable donor species for xenotransplantation to humans, with their organs being suitable after 8-10 months of age (Hryhorowicz, et al., 2017). To support this, there has been considerable progress in the last decade in pig genome-editing which has reduced the immunologic barriers and incompatibilities with humans (Cooper, 2012).

[0008] The primary pig molecules involved in hyper-acute rejection involve the processing of the glycan Alpha-Gal onto cell surfaces. Galactose-α-1,3-galactose (α-Gal), a widespread terminal carbohydrate modification on glycoproteins and glycolipids, is synthesized by α-1,3- galactosyltransferase (GGTA1) (Galili, 1993; Galili, et al., 1988). Unlike most species, humans and non- human primates (NHPs) lack α-Gal, generating strong anti-α-Gal antibodies (Abs) that precipitate hyperacute rejection of α-Gal-expressing organ grafts (Griesemer, et al., 2014). GGTA1 pig knockouts (GT-KO) overcome this initial major immune barrier to xenotransplantation (Wolbrom, et al., 2023). In December 2020 the Food and Drug Administration (FDA) approved a specific GT-KO pig, GalSafe™ for human food consumption and for potential downstream therapeutics. There are however ~4,000 additional genes in pigs vs humans which make up a substantial reservoir of potential xenoantigens. While gene-edited pigs have been successfully transplanted into nonhuman primates (NHP) with survival rates of over a year being commonplace (Adams, et al., 2018; Butler, et al., 2016; Kim, et al., 2019; Yamamoto, et al., 2020), NHP models are unable to fully recapitulate human physiology and immunology.

[0009] Addition glycans including CMAH and B4GALNT2 have also been knocked out, along with the growth hormone receptor gene (GHR) to prevent continued growth of pig organs once transplanted into study subjects. Knock-in genes have also been shown to have value, including 6 human transgene insertions (CD46, CD55, CD47, THBD, PROCR, HMOX1).

[0010] Understanding the human immune responses against these pig xenografts, and developing robust prognostic and diagnostic markers of xenograft rejection is crucial for future success in compassionate use and first-in-human trials (Hryhorowicz, et al., 2017; Cooper, 2012). SUMMARY OF THE INVENTION

[0011] One embodiment includes a method of creating a single-cell methylation atlas characterizing an organ of a donor. The method includes clustering methylated single-nuclei whole genome sequences (m-snWGS) from a biopsy of the organ based at least in part on differentially methylated patterns of the 2    311769024v1 Attorney Docket No. 243735.000430  m-snWGS; assigning a cell type for each cluster based at least in part on correlation of known cell type markers of the organ with the differentially methylated patterns of the m-snWGS; and providing the atlas to include a database including: genomic sequences, methylation patterns, and cell type designation. The genomic sequences are based at least in part on the m-snWGS. The methylation patterns of the genomic sequences are based at least in part on the m-snWGS. The cell type designation for the genomic sequences is based at least in part on the cell type assigned for each cluster.

[0012] Steps of the method embodiments can be executed in various orders to achieve the desired output of the method as understood by a person skilled in the pertinent art. Methods can include additional and / or alternative steps as understood by a person skilled in the pertinent art.

[0013] One embodiment includes a system of one or more computers configured to perform particular operations or actions by virtue of having software, firmware, hardware, or a combination of them installed on the system that in operation causes or cause the system to perform the actions. One or more computer programs can be configured to perform particular operations or actions by virtue of including instructions that, when executed by data processing apparatus, cause the apparatus to perform the actions. The system of one or more computers can be configured to computationally carry out at least a portion of the steps of some or all of the method embodiments. For instance, the system can include non- transitory computer-readable medium in communication with one or more processors, and including instructions thereon, that when executed by the processor(s) causes the system to execute steps of the method embodiments. Software methods may be carried out substantially or entirely by the system.

[0014] One embodiment of a software method for indicating a xenograft damage or rejection response in a subject having received a xenograft transplantation includes the steps of: receiving methylation patterns of xenograft-specific cell-free DNA (cfDNA) determined from a sample obtained from the subject; determining cell type from which the xenograft-specific cfDNA is derived based at least in part on the methylation patterns; determining the xenograft damage or rejection response in the subject based at least in part on the cell type of the cfDNA; and providing, as an output, an indication of the xenograft damage or rejection response.

[0015] One embodiment of a software method for indicating a xenograft damage or rejection response in a subject or predicting the risk of the subject developing a xenograft damage or rejection response includes the steps of: receiving methylation patterns of xenograft-specific cell-free DNA (cfDNA) determined from a sample obtained from the subject; determining a level of xenograft-specific cfDNA or the proportion of xenograft-specific cfDNA to a total cfDNA in the sample based at least in part on 3    311769024v1 Attorney Docket No. 243735.000430  the methylation patterns of xenograft-specific cfDNA; comparing the level or the proportion of the xenograft-specific cfDNA determined in step (b) to a corresponding control; determining that the subject is developing or is at a risk of developing the xenograft damage or rejection response if the level or the proportion of the xenograft-specific cfDNA is increased by 20% or more as compared to the control; and providing, as an output, an indication that the subject is developing or is at a risk of developing the xenograft damage or rejection response.

[0016] In another aspect, the present disclosure provides a method of diagnosing a xenograft damage or rejection response in a subject or predicting the risk of the subject developing a xenograft damage or rejection response, wherein the subject has received a xenograft transplantation, said method comprising: a) determining a proportion of xenograft-specific cell-free DNA (cfDNA) to total cfDNA in a sample obtained from the subject; and b) determining that the subject is developing or is at a risk of developing the xenograft damage or rejection response if the proportion of the xenograft-specific cfDNA determined in step (a) is more than 1%.

[0017] In another aspect, the present disclosure provides a method of diagnosing a xenograft damage or rejection response in a subject or predicting the risk of the subject developing a xenograft damage or rejection response, wherein the subject has received a xenograft transplantation, said method comprising: a) determining a level of xenograft-specific cell-free DNA (cfDNA) or a proportion of xenograft-specific cfDNA to total cfDNA in a sample obtained from the subject; b) comparing the level or the proportion of the xenograft-specific cfDNA determined in step (a) to a corresponding control; and c) determining that the subject is developing or is at a risk of developing the xenograft damage or rejection response if the level or the proportion of the xenograft-specific cfDNA determined in step (a) is increased by 20% or more as compared to the control. In some embodiments, the control is a predetermined standard or a level or proportion of the xenograft-specific cfDNA determined in a sample obtained from the subject at an earlier time point.

[0018] In various embodiments, the methods described herein comprise sequencing the cfDNA.

[0019] In various embodiments, the methods described herein further comprise determining methylation patterns of the xenograft-specific cfDNA.

[0020] In some embodiments, the methods comprise determining cell type(s) from which the xenograft-specific cfDNA is derived based at least in part on the methylation patterns.

[0021] In some embodiments, the methods comprise comparing the methylation patterns of the xenograft-specific cfDNA to a single-cell methylation atlas to determine the cell type(s). 4    311769024v1 Attorney Docket No. 243735.000430

[0022] In some embodiments, the methods further comprise determining a type of xenograft damage or rejection response based at least in part on the cell type(s) of the xenograft-specific cfDNA.

[0023] In another aspect, provided herein is a method of determining a type of xenograft damage or rejection response in a subject, wherein the subject has received a xenograft transplantation, said method comprising: a) determining methylation patterns of xenograft-specific cfDNA identified in a sample obtained from the subject; b) determining cell type(s) from which the xenograft-specific cfDNA is derived based at least in part on the methylation patterns; and c) determining the type of xenograft damage or rejection response in the subject based at least in part on the cell type(s) of the xenograft- specific cfDNA determined at step (b). In some embodiments, step (b) comprises comparing the methylation patterns of the xenograft-specific cfDNA to a single-cell methylation atlas to determine the cell type(s).

[0024] In various embodiments, the methods further comprise determining methylation patterns of recipient-specific cfDNA. In some embodiments, the method further comprises determining cell type(s) from which the recipient-specific cfDNA is derived based at least in part on the methylation patterns. In some embodiments, the method further comprises determining a type of xenograft rejection response in the subject based at least in part on the cell type(s) of the recipient-specific cfDNA.

[0025] In various embodiments, the type of xenograft damage or rejection response is selected from an antibody-mediated rejection, acute cellular rejection (e.g., T-cell mediated rejection), podocyte damage, ischemia damage, and any other cellular damage. In one embodiment, the method comprises determining the type of xenograft damage or rejection response is an antibody-mediated rejection when at least a portion of the xenograft-specific cfDNA is determined to be derived primarily from endothelial cells.

[0026] In some embodiments, the methods further comprise administering to the subject a treatment for the xenograft damage or rejection response based on the type of xenograft damage or rejection response.

[0027] In various embodiments, the method further comprises monitoring the subject for xenograft damage or rejection response.

[0028] In various embodiments, the subject has received the xenograft transplantation from a porcine donor and the xenograft-specific cfDNA is porcine cfDNA.

[0029] In some embodiments, the subject has received a kidney, heart, lung, liver, bone marrow, or pancreas transplantation from a porcine donor. 5    311769024v1 Attorney Docket No. 243735.000430

[0030] In some embodiments, the cell type(s) of the xenograft-specific cfDNA are determined based on the methylation patterns of one or more marker genes selected from Table 2.2.

[0031] In various embodiments, the sample is a bodily fluid. In some embodiments, the sample is whole blood, plasma, serum, or urine.

[0032] In various embodiments, the method comprises obtaining two or more samples from the subject at different time points after the xenograft transplantation and repeating the method for each sample. In one embodiment, the sample is obtained from the subject at least three times a week.

[0033] In various embodiments, the method comprises isolating cfDNA prior to step (a).

[0034] In various embodiments, the isolated cfDNA is about 20-800 bp long. In some embodiments, the isolated cfDNA is 120–220 base pairs (bp) long, with a peak at approximately 167 bp long. BRIEF DESCRIPTION OF THE DRAWINGS

[0035] The above and further aspects of this invention should be read with reference to the drawings, in which like elements in different drawings are identically numbered. The drawings, which are not necessarily to scale, depict selected embodiments and are not intended to limit the scope of the invention. The detailed description illustrates by way of example, not by way of limitation, the principles of the invention. This description will clearly enable one skilled in the art to make and use the invention, and describes several embodiments, adaptations, variations, alternatives, and uses of the invention, including what is presently believed to be the best mode of carrying out the invention.

[0036] Figures 1A-1B show an overview of the study described herein. Figure 1A shows a graphical description of the experiment and major omics types profiled. Figure 1B shows a table indicating the time points that were assessed by each individual assay. A timeline of immunosuppressive treatments and clinical microbiological testing is also included.

[0037] Figures 2A-2L show high resolution spatio-transcriptional profiling reveals infiltrating human cells. Figure 2A shows an overview of the principle of Xenium spatial transcriptomics (10X Genomics, CA). Figure 2B shows a separation of pig and human nuclei by the amount of identified transcripts originating from both genomes (here shown for post-operative day (POD)33). Figure 2C shows a dimension reduction map of human cells identified in the spatial transcriptomics assay, sorted by identified cell type (all 9 time points integrated). Figure 2D shows a dimension reduction map of the pig cells identified in the spatial transcriptomics assay, sorted by identified cell type (all 9 time points integrated). Figure 2E shows cell type specific gene markers for human cells, identified from spatial 6    311769024v1 Attorney Docket No. 243735.000430  transcriptomics, used for their cell type label (all 9 time points integrated). Figure 2F shows cell type specific gene markers for pig cells, identified from spatial transcriptomics, used for their cell type label (all 9 time points integrated). Figure 2G shows the result of spatial transcriptomics cell type labeling. Transplanted tissue H&E slide (top), with the corresponding spatial transcriptomics slide (bottom), showing segmentation based cells, sorted by cell type annotation on the DAPI fluorescence image. Figure 2H shows glomerulus and infiltrating human cells, with cell type annotation and associated markers. Figure 2I shows human cells infiltrating in the renal cortex, with cell type annotation and associated markers. Figure 2J shows a dimension reduction map of cells identified in recipient PBMC scRNA-seq, sorted by cell type (left). Subtypes of T cells, NK cells, B cells and Dendritic cells are shown on the right. Figures 2K-L show cell type specific marker genes of all PBMCs (Figure 2K) and of B cells and DCs (Figure 2L), identified from scRNA-seq, and used for their cell type label.

[0038] Figures 3A-3M show early host immune cell response involving NK cells, pDCs and plasma / B cells. Figure 3A shows the percentage of human cells identified on the spatial transcriptomics slide, across all identified cells. Figures 3B-D show the percentage of NK cells observed on flow cytometry (Figure 3B), PBMC scRNA-seq (out of all cells) (Figure 3C), and spatial transcriptomics (Figure 3D). Figures 3E-F show the percentage of pDCs observed on PBMC scRNA-seq (out of all cells) (Figure 3E) and spatial transcriptomics (Figure 3F). Figures 3G-H show the percentage of B cells (Figure 3G) and plasmablasts (Figure 3H) observed in PBMC scRNA-seq. Figure 3I shows clonotype diversity from PBMC BCRseq. Figure 3J shows shared clonotypes across timepoints for PBMC BCRseq. Figure 3K shows the BCR class switching pattern across the course of the study Figure 3L shows expression of human CXCL9, CXCL10, and CXCL11 observed in the tissue bulk RNAseq. Figure 3M shows expression of human CXCL9, CXCL10, and CXCL11 observed in the tissue spatial transcriptomics.

[0039] Figures 4A-4H shows human T cell response to transplantation. Figure 4A shows the percentage of T cells observed in PBMC scRNA-seq. Figures 4B-C show the percentage of CD8T (Figure 4B) and CD4T (Figure 4C) cells observed in spatial transcriptomics data. Figure 4D shows the percentages of T cell subtypes observed in PBMC scRNA-seq data. Figures 4E-F show the expression levels of CD38 seen across time points in human CD8T and NK cells in tissue spatial transcriptomics (Figure 4E) and in CD8 TEM in PBMC scRNA-seq data (Figure 4F). Figure 4G show shared clonotypes across time points for PBMC TCRseq. Figure 4H shows V gene and J gene usage of Top 20% Clonotypes from TCRseq data. 7    311769024v1 Attorney Docket No. 243735.000430

[0040] Figures 5A-5O show transplanted tissue response highlights damage signal response at POD21. Figure 5A shows the inflammatory gene-cluster found from bulk RNA-seq longitudinal analysis. Trend of expression (left) and top pathway enrichments (right) are shown. Figure 5B shows time point markers from spatial transcriptomics. Figures 5C-D show the proportion of SPP1+ cells (SPP1 encodes secreted phosphoprotein 1, a protein involved in immune responses and is indicative of cellular damage from resident immune cells) (Figure 5C) and fibroblasts (Figure 5D) across time points in spatial transcriptomics data. Figure 5E shows SPP1+ cells subcluster markers. Figure 5F shows the proportion of SPP1+ subclusters in spatial transcriptomics. Figure 5G shows colocalization analysis of SPP1+ cells. Figure 5H shows the proportion of pig immune cells across time points in spatial transcriptomics data. Figure 5I shows T cell marker expression across time points, in pig immune cells (tissue spatial transcriptomics data). Figure 5J shows macrophage marker gene expression across cell types (tissue spatial transcriptomics data). Figure 5K shows pig immune cells subcluster 5 marker gene expression. Figure 5L shows the proportion of pig immune cells subcluster 5 in spatial transcriptomics. Figure 5M shows colocalization analysis of pig immune cells. Figure 5N shows the region of POD21 biopsy corresponding to high level of SPP1+ cells. The H&E stains show IFAT. Figure 5O shows POD33 biopsy with high levels of human immune cells.

[0041] Figure 6 shows the dynamics of complement pathway protein levels in serum. The statistical analysis of protein regulation is based on the XT2 Proteograph XT measurements of serum samples. The middle line, the solid line bands, and the dashed line bands correspond to the median, 50% and 95% credible intervals of the protein intensity posterior distribution, respectively.

[0042] Figure 7 shows the variability in proteomic data between the two analyses of the same component (i.e., XT1 vs. XT2). The variability in proteomic data between XT1 and XT2 is likely the result of differences in nanoparticle protein corona composition in each analysis. XT2 likely shows cleaved products of complement factor, hence representing pathway activation status.

[0043] Figure 8 shows the percentage of pig cell-free DNA as a portion of all cell-free DNA in the decedents blood over the course of 7 timepoints (Day 1, 14, 25, 28, 33, 40 and 61) across the 61 day pig- kidney to human decedent xenotransplant procedure described herein.

[0044] Figure 9 shows the decedent model, a pre-clinical approach for de-risking XenoTransplant in living humans. 10-gene-edited pigs were used to transplant porcine hearts into two brain-dead human recipients. Human decedent 1 was a 72-y.o. male (BMI=25.9, weight 82 kg) with a history of CABG surgery & heart failure. Xenograft was from a 70 kg male pig at 309 days old. Total ischemia time was 8    311769024v1 Attorney Docket No. 243735.000430  4.4 hrs (3.2 hrs in cold static storage). Hemodynamics and left ventricular stroke volume deteriorated at 30-36 hrs post-reperfusion. There was an increased need for vasopressors for visceral hypoperfusion with increased lactate / ALT / AST. The explant tissue showed progressive myocyte injury and cell death was not seen in day 1 & 2. Human decedent 2 was a 64-y.o. female (BMI=22, weight 57 kg) with prior kidney and pancreas autologous transplants maintained on mycophenolate & prednisone. Xenograft was from a 69 kg male pig at 329 days old. Total ischemia time was 3.5 hrs (2.7 hrs in cold static storage).  Cardiac function was stable throughout the study. Both human decedents 1 and 2 showed no ostensible acute cellular rejection (ACR) or antibody-mediated rejection (AbMR) as assessed with histology, flow cytometry, and a cytotoxic crossmatch assay.

[0045] Figure 10 shows study timelines and cardiac and systemic hemodynamic measurements.

[0046] Figure 11 shows pig to human decedent xenograft histology and immunohistochemistry.

[0047] Figure 12 shows integrative multi-omics in two human decedents post heart xenotransplant. Through untargeted MS, metabolomics revealed greater than 1000 metabolites. Through untargeted proteomics, more than 1000 proteins were revealed. Targeted lipidomics revealed greater than 250 lipids. Unbiased RNA-seq revealed greater than 9000 RNA molecules.

[0048] Figure 13 shows integrative analyses of RNA-seq, proteomic, metabolomic, lipidomic, and cytokine data from two decedents receiving pig heart xenografts (fuzzy c-mean clustering).

[0049] Figure 14 shows single-cell RNA-seq profiling in two human decedents receiving pig heart xenografts.

[0050] Figure 15 shows single-nuclei RNA-seq & geospatial transcriptomics in pig heart xenograft explants. The left side shows snRNA-seq of pig heart left ventricle xenograft showing both pig heart cells and infiltrating human cells. The right side shows Visium geo-location RNA mapping of spatial distribution of pig and human cells (6 and 9 cell clusters on the bottom).

[0051] Figure 16 shows perioperative cardiac xenograft dysfunction (PCXD). PCXD is characterized by heart xenograft significant dysfunction 24-48 hrs post-transplantation without rejection. PCXD is observed in 40%-60% of pig to NHP cases and is due to the inflammation cascade from prolonged ischemia, leading to IRI and immune-mediated injury (Byrne, et al., 2012). The treatment regimens reduce circulating Ab, B, and plasma cells and perfusion preservation pre-transplantation reduces the risk. mRNA studies in pig heart in NHP describe PCXD (Byrne, et al., 2011). Data was mapped back to Geospatial (Visium) and snRNA-seq. CXCL14 and PHLDA2 overlapped and were strongly expressed in the decedent 1 xenograft. The strongest pathway from the Byrne, et al. PCXD study was also the ECM 9    311769024v1 Attorney Docket No. 243735.000430  receptor interaction pathway. Pig heart snRNA-seq was measured at 0, 1, and 7 hour of ischemia for ECMO and OrganEx treated groups (Andrijevic, et al., 2022). Decedent 1 had much higher IRI-related expression than the 7 hour IRI timepoint. Expression distribution across spatial Leiden clusters (top) and expression distribution within all capture areas between samples (bottom) are shown.

[0052] Figure 17 shows the percentage of NK cells, plasma cells, T cells, Treg cells, proliferative NK cells, and B cells observed in PBMC scRNA-seq subtyping of 61-day pig kidney to human decedent.

[0053] Figure 18 shows unique pig and human peptides / proteins from timepoints across 5 xenotransplants. Samples were processed with Seer Proteograph XT assay and LC-MS analysis was performed with Thermo Orbitrap Astral MS.

[0054] Figure 19 shows diversity of individual proteins regulation of ~8,600 proteins. A UMAP of proteins by regulation similarity revealed 20 broad clusters.

[0055] Figure 20 shows diversity of individual proteins regulation of ~8,600 proteins.  Proteinregulation profiles are shown in clusters.

[0056] Figure 21 shows generation of a single-cell methylation atlas of a pig kidney xenograft using bisulfite whole genome sequencing in 18,200 pig kidney cells.

[0057] Figure 22 shows generation of a single-cell methylation atlas of a pig kidney xenograft using bisulfite whole genome sequencing in 18,000 cells (the end of study pig kidney xenograft cells) and generation of cell-specific methylated cell-free DNA (m-cf-dd-DNA).

[0058] Figures 23A-23C show pig kidney m-snWGS library metrics of sequencing and methylation coverage across the 61-day study. The metrics were conformed to acceptable outputs with a ScaleBio Methylation Data Analysis pipeline. The raw sequence data was processed into a single-cell methylation matrix. The fastq files were demultiplexed and trimmed, the trimmed reads were aligned to the Sus scrofa 11.1 genome, methylation calls were extracted from deduplicated alignments, and the CG and CH methylation rate matrices were generated using 10kb window “bins” (Figure 23A). Subsequently, the hypo-methylation scores were calculated and binarized for each bin, and the features were filtered with greater than 25% presence in the cells. PCA and UMAP algorithms were then applied (Figure 23B), and the clusters were derived from the knn graph using the Leiden algorithm. Figure 23C shows the percentage of each cell type in m-snWGS.

[0059] Figures 24A-24I show the dynamics of human cell types in the cfDNA data across the 61-day study, identified by bioinformatics tools, Celfie, Epistate, and UXM. Figure 24A shows dynamics for human immune cells. Figure 24B shows dynamics for human B cells. Figure 24C shows dynamics for 10    311769024v1 Attorney Docket No. 243735.000430  human monocytes and macrophages. Figure 24D shows dynamics for human NK cells, T cells, and endothelial cells. Figure 24E shows dynamics for human bladder epithelial cells and kidney epithelial cells. Figure 24F shows dynamics for human gastric epithelial cells and thyroid epithelial cells. Figure 24G shows dynamics for human lung cells. Figure 24H shows dynamics for human pancreas cells. Figure 24I shows dynamics for human erythrocytes, heart cells, and liver cells.

[0060] Figure 25 shows generation of a single-cell methylation atlas of a pig kidney xenograft using bisulfite whole genome sequencing in 18,400 pig kidney cells.18,400 nuclei from 3 x mm3of pig kidney xenograft (61-day procedure) were successfully processed to library preparation and sequenced using Element AVITI to generate 29 billion reads.

[0061] Figures 26A-26B show PCA and UMAP algorithms applied to hypo-methylation scores and clusters derived from the knn graph using the Leiden algorithm.

[0062] Figure 27 shows an assessment of the proportion of cfDNA reads aligning to the pig genome Sus scrofa 11 at each time point. Two distinct peaks of elevated pig-specific cfDNA were observed at Day 21 and Day 33.

[0063] Figure 28 shows the recipient human cell types in the methylated cfDNA make-up of greater than 30 timepoints in the 61 day XTx procedure.

[0064] Figure 29 shows microbial cfDNA mapping in greater than 30 timepoints in the 61 day XTx procedure.

[0065] Figures 30A-30B show the schematic for source mapping in allo and xenografts via Oxford nanopore.

[0066] Figure 31 shows the AWS architecture diagram for a pathogen detection pipeline. This can be customized for any bioinformatics pipeline by updating the docker contained within EC2 instance.

[0067] Figure 32 shows the number of hyper and hypo-methylated genes in each cell type.

[0068] Figures 33A-33S show data processing relating to m-cf-DNA. Figure 33A shows alpha- diversity data. Figure 33B shows beta-diversity data. Figures 33C-33M show time-course (raw) data of cell type frequency over time (days). Figures 33N-33S show time course (fitted) data of cell type frequency over time (days).

[0069] Figures 34A-34C show pig kidney m-snWGS library metrics. Figure 34A shows quality control metrics. Figure 34B shows a UMAP of the methylation cell types. Figure 34C shows the percentage of each cell type in the m-sn-WGS atlas. 11    311769024v1 Attorney Docket No. 243735.000430

[0070] Figure 35 shows the proportion of cfDNA reads aligning to pig genome, Sus scrofa 11, at each time point during the xenotransplant procedure.

[0071] Figure 36 shows the proportion of pig-derived cfDNA reads aligning to the pig-specific promoter at each time point during the xenotransplant procedure.

[0072] Figure 37 shows the longitudinal abundance of xenograft cell type identified in cfDNA at each time point during the xenotransplant procedure.

[0073] Figure 38 is a flow diagram illustrating an exemplary method of creating a single-cell methylation atlas characterizing an organ of a donor.

[0074] Figure 39 illustrates a block diagram of an exemplary embodiment of a database of a single- cell methylation atlas.

[0075] Figure 40 is a flow diagram illustrating an exemplary software method for indicating a xenograft damage or rejection response in a subject having received a xenograft transplantation.

[0076] Figure 41 is a flow diagram illustrating an exemplary software method for indicating a xenograft damage or rejection response in a subject or predicting the risk of the subject developing a xenograft damage or rejection response.

[0077] Figure 42 illustrates a block diagram of an embodiment of a computing device.

[0078] Figure 43 illustrates a block diagram of an embodiment of a computing network. DETAILED DESCRIPTION OF THE INVENTION Definitions

[0079] To facilitate an understanding of the principles and features of the various embodiments of the invention, various illustrative embodiments are explained below. Although exemplary embodiments of the invention are explained in detail, it is to be understood that other embodiments are contemplated. Accordingly, it is not intended that the invention is limited in its scope to the details of construction and arrangement of components set forth in the following description or examples. The invention is capable of other embodiments and of being practiced or carried out in various ways. Also, in describing the exemplary embodiments, specific terminology will be resorted to for the sake of clarity.

[0080] It is also to be understood that the mention of one or more method steps does not preclude the presence of additional method steps or intervening method steps between those steps expressly identified. Similarly, it is also to be understood that the mention of one or more components in a composition does not preclude the presence of additional components than those expressly identified. 12    311769024v1 Attorney Docket No. 243735.000430

[0081] The implementation of the present invention can utilize, unless otherwise specified, standard techniques in genetics, computational biology, molecular biology, genomics, epigenomics, bioinformatics, and mass spectrometry, which are well-known to those skilled in the field.

[0082] The term “about” or “approximately” means within a statistically meaningful range of a value. Such a range can be within an order of magnitude, preferably within 50%, more preferably within 20%, still more preferably within 10%, and even more preferably within 5% of a given value or range. The allowable variation encompassed by the term “about” or “approximately” depends on the particular system under study, and can be readily appreciated by one of ordinary skill in the art.

[0083] The terms “a,” “an,” and “the” do not denote a limitation of quantity, but rather denote the presence of “at least one” of the referenced item.

[0084] The terms “treat” or “treatment” of a state, disorder or condition include: (1) preventing, delaying, or reducing the incidence and / or likelihood of the appearance of at least one clinical or sub- clinical symptom of the state, disorder or condition developing in a subject that may be afflicted with or predisposed to the state, disorder or condition but does not yet experience or display clinical or sub- clinical symptoms of the state, disorder or condition; or (2) inhibiting the state, disorder or condition, i.e., arresting, reducing, delaying or reversing the development of the disease or a relapse thereof (in case of maintenance treatment) or at least one clinical or sub-clinical symptom thereof; or (3) relieving the disease, i.e., causing regression of the state, disorder or condition or at least one of its clinical or sub- clinical symptoms. The benefit to a subject to be treated is either statistically significant or at least perceptible to the patient or to the physician.

[0085] The terms “subject”, “patient”, “individual”, “recipient”, and “animal” are used interchangeably herein and refer to mammals, including, without limitation, human and veterinary animals (e.g., cats, dogs, cows, horses, sheep, pigs, etc.) and experimental animal models. In a preferred embodiment, the subject is a human.

[0086] The terms “sample”, “subject sample” and “test sample” are used herein to refer to any biological specimen obtained from a subject or patient. The fluid, cell, or tissue sample from a subject can be assayed for determining DNA (e.g., cell-free DNA) levels, proportions, methylation levels, or other properties of the DNA. In some embodiments, the sample may include whole blood, plasma, serum, lymph, peripheral blood mononuclear cells, urine, lung lavage, buccal swabs, saliva, or tissue from a biopsy. 13    311769024v1 Attorney Docket No. 243735.000430

[0087] In this context, the terms “cell-free DNA” or “cfDNA” refer to DNA that circulates in the bodily fluids (e.g., peripheral blood) of a subject. The DNA molecules in cfDNA typically have a median size not exceeding 1000 base pairs (bp) (for example, ranging from about 20 bp to 800 bp, or about 80 bp to 400 bp, or about 120 bp to 220 bp), although fragments with median sizes outside this range may also be present. This term includes both free DNA molecules circulating in the bloodstream and DNA molecules found in extracellular vesicles (e.g., exosomes) that are also circulating in the bloodstream. In the context of the present disclosure, cfDNA molecules in the subject may originate from the xenograft and / or the subject.

[0088] A “methylation site” generally refers to a CpG dinucleotide, but can also encompass non-CpG dinucleotides, such as a CH dinucleotide. In mammals, methylation is the most prevalent at a CpG dinucleotide, where a cytosine nucleotide is followed by a guanine nucleotide. Methylation can also occur in other dinucleotide contexts, such as CH sites, where a cytosine nucleotide followed by an adenine, thymine, or cytosine nucleotide.

[0089] A “methylation pattern” describes the genomic locations and / or frequencies of methylated and non-methylated CpG and / or non-CpG dinucleotides within a DNA segment. For example, in a DNA segment containing three CpGs, one methylation pattern could be all three CpGs being methylated; another pattern could be all three CpGs not being methylated; another could be only the first CpG being methylated; another could be only the second CpG being methylated; and yet another pattern could be the first and second CpGs being methylated, among other combinations.

[0090] “Methylation status” of a CpG or non-CpG dinucleotide indicates whether a CpG or non-CpG dinucleotide is methylated or not. “Methylation status” of a DNA segment indicates whether it is hyper or hypomethylated.

[0091] The term “hypermethylated” refers to a higher frequency of methylated CpG and / or non-CpG dinucleotides in a DNA segment as compared to the same DNA segment in a reference genome. A reference genome can be, for example, the genome of a pig, such as Sus scrofa 9.2, Sus scrofa 11 (e.g., Sus scrofa 11.1), Sus scrofa 10 (e.g., Sus scrofa 10.3) (The Swine Genome Sequencing Consortium (SGSC)), or JH-T2T (Cao, et al., 2024).

[0092] Conversely, “hypomethylated” refers to a lower frequency of methylated CpG and / or non-CpG dinucleotides in a DNA segment as compared to the same DNA segment in a reference genome.

[0093] In the context of the present disclosure, the term “genomic region” is used interchangeably with “DNA segment” or “DNA fragment”. 14    311769024v1 Attorney Docket No. 243735.000430

[0094] The terms “component,” “engine,” “module,” “system,” “server,” “processor,” “memory,” and the like are intended to include one or more computer-related units, such as but not limited to hardware, firmware, a combination of hardware and software, software, or software in execution. For example, a component may be, but is not limited to being, a process running on a processor, an object, an executable, a thread of execution, a program, and / or a computer. By way of illustration, both an application running on a computing device and the computing device can be a component. One or more components can reside within a process and / or thread of execution and a component may be localized on one computer and / or distributed between two or more computers. In addition, these components can execute from various computer readable media having various data structures stored thereon. The components may communicate by way of local and / or remote processes such as in accordance with a signal having one or more data packets, such as data from one component interacting with another component in a local system, distributed system, and / or across a network such as the Internet with other systems by way of the signal.

[0095] The terms “comprising” or “containing” or “including” are meant that at least the named element, or method step is present in article or method, but does not exclude the presence of other elements or method steps, even if the other such elements or method steps have the same function as what is named.

[0096] As used herein, unless otherwise specified, the use of the ordinal adjectives “first,” “second,” “third,” etc., to describe a common object, merely indicate that different instances of like objects are being referred to, and are not intended to imply that the objects so described must be in a given sequence, either temporally, spatially, in ranking, or in any other manner.

[0097] In this description, numerous specific details are set forth. It is to be understood, however, that implementations of the disclosed technology may be practiced without these specific details. In other instances, well-known methods, structures, and techniques have not been shown in detail in order not to obscure an understanding of this description. References to “one embodiment,” “an embodiment,” “some embodiments,” “example embodiment,” “various embodiments,” “one implementation,” “an implementation,” “example implementation,” “various implementations,” “some implementations,” etc., indicate that the implementation(s) of the disclosed technology so described may include a particular feature, structure, or characteristic, but not every implementation necessarily includes the particular feature, structure, or characteristic. Further, repeated use of the phrase “in one implementation” does not necessarily refer to the same implementation, although it may. 15    311769024v1 Attorney Docket No. 243735.000430  Methods of using cfDNA to diagnose or determine xenograft damage or rejection

[0098] The present invention provides methods that leverage cell-free DNA (cfDNA) to diagnose or assess xenograft damage or rejection. Most cfDNA fragments exhibit a peak around 167 base pairs (bp), which corresponds to the length of DNA wrapped around a nucleosome (147 bp) plus an additional linker fragment (20 bp). This nucleosomal footprint in cfDNA may indicate degradation by nucleases as a by-product of cell death (Heitzer et al., 2020).

[0099] In one aspect, the present invention provides a method of diagnosing a xenograft damage or rejection response in a subject or predicting the risk of the subject developing a xenograft damage or rejection response, wherein the subject has received a xenograft transplantation, said method comprising: a) determining a proportion of xenograft-specific cell-free DNA (cfDNA) to total cfDNA in a sample obtained from the subject; and b) determining that the subject is developing or is at a risk of developing the xenograft damage or rejection response if the proportion of the xenograft-specific cfDNA determined in step (a) is more than 1%.

[0100] In some embodiments, step (b) of the method comprises determining that the subject is developing or is at a risk of developing the xenograft damage or rejection response if the proportion of the xenograft-specific cfDNA determined in step (a) is more than 1%, 1.1%, 1.2%, 1.3%, 1.4%, 1.5%, 1.6%, 1.7%, 1.8%, 1.9%, 2%, 2.1%, 2.2%, 2.3%, 2.4%, 2.5%, 2.6%, 2.7%, 2.8%, 2.9%, or 3%.

[0101] In one aspect, the present invention provides a method of diagnosing a xenograft damage or rejection response in a subject or predicting the risk of the subject developing a xenograft damage or rejection response, wherein the subject has received a xenograft transplantation, said method comprising: a) determining a level of xenograft-specific cell-free DNA (cfDNA) or a proportion of xenograft-specific cfDNA to total cfDNA in a sample obtained from the subject; b) comparing the level or the proportion of the xenograft-specific cfDNA determined in step (a) to a corresponding control; and c) determining that the subject is developing or is at a risk of developing the xenograft damage or rejection response if the level or the proportion of the xenograft-specific cfDNA determined in step (a) is increased by 20% or more as compared to the control.

[0102] In some embodiments, step (c) of the method comprises determining that the subject is developing or is at a risk of developing the xenograft damage or rejection response if the level or the proportion of the xenograft-specific cfDNA determined in step (a) is increased 20%, 21%, 22%, 23%, 24%, 25%, 26%, 27%, 28%, 29%, 30%, 31%, 32%, 33%, 34%, 35%, 36%, 37%, 38%, 39%, 40%, 41%, 42%, 43%, 44%, 45%, 46%, 47%, 48%, 49%, 50%, 55%, 60%, 65%, 70%, 75%, 80%, 85%, 90%, 95%. 16    311769024v1 Attorney Docket No. 243735.000430  100%, 110%, 120%, 130%, 140%, 150%, 160%, 170%, 180%, 190%, 200%, 250%, or 300% or more as compared to the control.

[0103] In some embodiments, the control is a predetermined standard. In some embodiments, the control is a level or proportion of the xenograft-specific cfDNA determined in a sample obtained from the subject at an earlier time point. In some embodiments, the control the predetermined standard is generated in one or more subjects that did not undergo xenograft transplantation.

[0104] In further embodiments, the level or the proportion of xenograft-specific cfDNA in a sample obtained from a xenotransplant recipient may be expressed in a variety of ways. In some embodiments, the level or the proportion of donor-derived cell-free nucleic acids is determined as a percentage of the total cell-free nucleic acids in the sample. In some embodiments, the amount of donor-derived cell-free nucleic acids is determined as absolute copies of donor-derived cell-free nucleic acids in the sample. In some embodiments, the amount of donor-derived cell-free nucleic acids is determined as a ratio of donor- derived cell-free nucleic acids to the total cell-free nucleic acids (e.g., total donor-derived and recipient- derived cell-free nucleic acids) in the sample. In some embodiments, the amount of donor-derived cell- free nucleic acids is determined as a ratio of donor-derived cell-free nucleic acids to recipient-derived cell-free nucleic acids in the sample. In some embodiments, the amount of donor-derived cell-free nucleic acids is determined as a ratio or percentage compared to one or more reference nucleic acid molecules in the sample.

[0105] In various embodiments, the methods described herein comprise isolating cfDNA from a sample prior to step (a). In some embodiments, the sample is a bodily fluid. In some embodiments, the sample is whole blood, plasma serum, or urine.

[0106] cfDNA may be obtained by centrifuging the biological fluid (e.g., whole blood, plasma, serum) to remove all cells, and then isolating the cfDNA from the plasma or serum remaining after the cells are removed. Such methods are known (e.g., Lo, et al., 1998). cfDNA can be double-stranded or single- stranded DNA.

[0107] In some embodiments, the isolated cfDNA is about 20-800 bp long. In some embodiments, the isolated cfDNA is about 40-700 bp, about 50-600 bp, about 60-500 bp, about 70-400 bp, about 80-300bp, about 90-250 bp, about 100-250 bp, or about 120-220 bp long. In some embodiments, the isolated cfDNA is 120–220 base pairs (bp) long, with a peak at approximately 167 bp long.

[0108] In various embodiments, the methods described herein comprise sequencing the cfDNA. The cfDNA may include xenograft-specific cfDNA or recipient-specific cfDNA. 17    311769024v1 Attorney Docket No. 243735.000430

[0109] Analysis of cfDNA in samples obtained from a xenotransplant recipient can be performed using nucleic acid sequencing-based methods, such as high-throughput sequencing and next-generation sequencing methods. Additionally, PCR-based methods for quantifying nucleic acids, including quantitative PCR (qPCR) and digital PCR (dPCR), may also be utilized. Sequencing methods may include parallelized sequencing-by-synthesis or sequencing-by-ligation platforms currently used by companies like Life Technologies, Illumina, and Roche. They may also include nanopore sequencing methods, such as those commercialized by Oxford Nanopore Technologies, electronic-detection-based methods like Ion Torrent technology commercialized by Life Technologies, or single-molecule fluorescence-based methods commercialized by Pacific Biosciences.

[0110] In various embodiments, the methods described herein further comprise determining methylation patterns of the cfDNA. The cfDNA may include xenograft-specific cfDNA or recipient- specific cfDNA.

[0111] DNA methylation generally involves the covalent addition of a methyl group to the 5-carbon of cytosine. The human genome contains approximately 28 million CpG sites, where a cytosine nucleotide is followed by a guanine nucleotide (Greenberg and Bourc'his, 2019; Michalak et al., 2019). Stable, cell-type specific patterns of DNA methylation are maintained during DNA replication, providing a primary mechanism for inherited cellular memory during cell growth (Kim & Costello, 2017; Dor & Cedar, 2018). Changes in DNA methylation associated with disease and physiological aging occur at various locations throughout the epigenome, distinct from regions critical to cell-type identity. This makes methylated cfDNA a robust cell-type specific indicator across diverse patient populations (Michalak et al., 2019; Dor & Cedar, 2018).

[0112] The use of patterns of differential methylation of xenograft-specific cfDNA can be applied to methods of determining the type of xenograft damage or rejection response in the subject.

[0113] Methods for quantifying cfDNA are known in the art and may include but are not limited to, fluorescence-based quantification methods, PCR, chromatography techniques (e.g., gas chromatography, supercritical fluid chromatography, ion exchange chromatography, liquid chromatography, partition chromatography, adsorption chromatography, affinity chromatography, thin- layer chromatography, size exclusion chromatography), electrophoresis techniques (e.g., capillary isoelectric focusing, capillary electrophoresis, capillary gel electrophoresis, capillary zone electrophoresis, capillary electrochromatography, isotachophoresis, micellar electrokinetic capillary 18    311769024v1 Attorney Docket No. 243735.000430  chromatography, transient isotachophoresis), comparative genomic hybridization, bead arrays, and microarrays.

[0114] Various DNA methylation detection technologies may be utilized herein. Examples include but are not limited to, restriction enzyme digestion involving DNA cleavage at enzyme-specific CpG sites; affinity-enrichment method (e.g., methyl-CpG-binding domain sequencing (MBD-seq) or methylated DNA immunoprecipitation sequencing (MeDIP-seq)); bisulfite conversion methods (e.g., reduced representation bisulfite sequencing (RRBS), whole genome bisulfite sequencing (WGBS), methylation arrays, and methylated CpG tandem amplification and sequencing (MCTA-seq)); enzymatic approaches (e.g., ten-eleven translocation (TET)-assisted pyridine borane sequencing (TAPS) or enzymatic methyl- sequencing (EM-seq)); and other methods not requiring treatment of DNA (e.g., nanopore-sequencing from Oxford Nanopore Technologies (ONT) and single molecule real-time (SMRT) sequencing from Pacific Biosciences). In some embodiments, the methylation detection is performed via nanopore sequencing. In some embodiments, the methylation detection is performed via bisulfite whole genome sequencing.

[0115] The methylation pattern may comprise one or more segments of nucleotide sequences containing one or more CpG dinucleotides, two or more CpG dinucleotides, or three or more CpG dinucleotides. In some embodiments, the methylation pattern may comprise one or more segments of nucleotide sequences containing four or more CpG dinucleotides, five or more CpG dinucleotides, six or more CpG dinucleotides, seven or more CpG dinucleotides, eight or more CpG dinucleotides, nine or more CpG dinucleotides, or ten or more CpG dinucleotides.

[0116] Genetic differences between the donor and the subject can be used to classify donor-derived cfDNA (dd-cfDNA) molecules with origins in tissue from the donor. The dd-cfDNA molecules exhibiting cell-specific methylation patterns can be used to track the donor-derived cells.

[0117] To determine the source origin of the cfDNA, the cfDNA is genotyped to obtain its genotype profile. This profile can then be compared with the genotype profile of the donor and / or the subject. By comparing these profiles, it can be determined whether the cfDNA originates from biological material from the donor or from the subject.

[0118] In certain embodiments, genotyping involves the detection, quantification, or both detection and quantification of polymorphic markers. Examples of these polymorphic markers include, but are not limited to, restriction fragment length polymorphisms (RFLPs), single nucleotide polymorphisms (SNPs), short tandem repeats (STRs), variable number of tandem repeats (VNTRs), minisatellites, 19    311769024v1 Attorney Docket No. 243735.000430  hypervariable regions, dinucleotide repeats, trinucleotide repeats, tetranucleotide repeats, simple sequence repeats, and insertion elements.

[0119] Examples of methods that can be used in genotyping include, but are not limited to, sequencing of a sufficient number of regions of the genome, whole genome sequencing, and polymorphism arrays. In some embodiments, genotyping the donor and / or the subject may involve sequencing at least about 50, 75, 100, 125, 150, 175, 200, 225, 250, 275, 300, 325, 350, 375, or 400 regions of the genome (e.g., by amplicon sequencing or hybridization capture sequencing). Other methods include, but are not limited to, the use of arrays (e.g., SNP arrays) or polymerase chain reaction (PCR) techniques (e.g., multiplex fluorescent PCR, quantitative PCR, quantitative fluorescent PCR, single-cell PCR, restriction fragment length polymorphism PCR, real-time PCR).

[0120] The xenograft transplant may comprise any cell, tissue, or organ from a donor of one species to a recipient of a different species. In some embodiments, the donor may be a non-human primate, cat, cow, dog, goat, horse, pig, rodent, sheep, reptile, or another non-human animal. In some embodiments, the rodent may be a beaver, guinea pig, hamster, mouse, porcupine, prairie dog, rat, squirrel, or another rodent. In some embodiments, the non-human primate may be an ape or a monkey. In some embodiments, the non-human primate may be an African green monkey, baboon, bonobo, capuchin monkey, chimpanzee, cynomolgus monkey, gorilla, marmoset, orangutan, owl monkey, pig-tailed monkey, rhesus monkey, spider monkey, squirrel monkey, vervet monkey, or another non-human primate. In some embodiments, the donor is a pig.

[0121] In some embodiments, the donor is a genetically modified animal. In one embodiment, the donor is a genetically modified pig.

[0122] In some embodiments, the xenotransplant recipient may be a human, and the xenotransplant donor may be a pig. In some embodiments, the pig may be a Sus scrofa, Sus scrofa domesticus, Phacochoerus aethiopicus, Potamochoerus porcus, or Babirousa babyrussa species. In some embodiments, the pig may be a Hanford pig, mini- or micro-pig, Yucatan pig, Yucatan micro pig, Sinclair pig, Gottingen pig, Duroc pig, Yorkshire pig, Landrace pig, or a combination, hybrid, or cross- species thereof.

[0123] The transplant may be any biological material comprising cells that have DNA and that can be transplanted from a donor to a subject. The cells may be organized as a tissue or portion thereof, an organ or portion thereof, or a population of cells not organized as a tissue or organ. The population of cells may be a population of the same cell type or of different cell types. 20    311769024v1 Attorney Docket No. 243735.000430

[0124] In some embodiments, the xenograft transplant may comprise an organ or portion thereof. Examples include, but are not limited to, kidney, blood vessel, liver, heart, pancreas, lung, colon, skin, bone, prostate, and muscle. In one embodiment, the xenograft transplant comprises a kidney. In one embodiment, the xenograft transplant comprises a heart. In one embodiment, the xenograft transplant comprises a liver.

[0125] In some embodiments, the xenograft transplant comprises a tissue or portion thereof. Examples include, but are not limited to, cardiac tissue, liver tissue, pancreatic tissue, vascular tissue, esophageal tissue, splenic tissue, intestinal tissue, gastric tissue, colon tissue, tracheal tissue, lung tissue, skin tissue, subcutaneous tissue, kidney tissue, hair tissue, connective tissue, muscular tissue, cartilage tissue, skeletal tissue, prostate tissue, bladder tissue, uterine tissue, penile tissue, gonadal tissue, neural tissue, ophthalmologic tissue, corneal tissue, and bone marrow tissue.

[0126] In some embodiments, the xenograft transplant comprises a population of cells. Examples include, but are not limited to, natural killer cells, granulocytes, mature B-cells, naïve B-cells, mature T- cells, macrophages, monocytes, and stem cells.

[0127] In some embodiments, the method further comprises administering to the subject a treatment for the xenograft damage or rejection response based on the type of xenograft damage or rejection response. In some embodiments, the method comprises administering a treatment to the subject when xenograft damage or rejection response is detected. Detection of xenograft damage or rejection response may be in accordance with the methods described herein.

[0128] In some embodiments, the method comprises determining cell type(s) from which the xenograft-specific cfDNA is derived based at least in part on the methylation patterns. Cellular origins are associated with different types of tissue, for example, mature B-cells can be associated with blood or bone marrow; naïve B-cells can be associated with blood or bone marrow; biliary epithelial cells can be associated with liver tissue; breast basal cells can be associated with breast tissue; breast luminal cells can be associated with breast tissue; bulk endothelial cells can be associated with blood vessels; bulk epithelial cells can be associated with any epithelia; bulk immune cells can be associated with immune organs; cardiomyocytes can be associated with heart tissue; cardiopulmonary endothelial cells can be associated with heart or lung tissues; colon epithelial cells can be associated with colon tissue; dermal epithelial cells can be associated with skin tissue; granulocytes can be associated with blood or bone marrow; hepatocytes can be associated with liver tissue; keratinocytes can be associated with skin tissue; kidney epithelial cells can be associated with kidney tissue; liver endothelial cells can be associated with 21    311769024v1 Attorney Docket No. 243735.000430  liver tissue; liver stromal cells can be associated with liver tissue; liver resident immune cells can be associated with liver tissue; lung epithelial cells can be associated with lung tissue; megakaryocytes can be associated with bone marrow; monocytes and macrophages can be associated with blood; neurons can be associated with neural tissue; natural killer cells can be associated with blood; pancreatic cells can be associated with pancreas tissue; prostate epithelial cells can be associated with prostate tissue; skeletal muscular cells can be associated with skeletal muscle tissue; and mature T-cells can be associated with blood.

[0129] In some embodiments, the method comprises comparing the methylation patterns of the xenograft-specific cfDNA to a single-cell methylation atlas (such as those described herein) to determine the cell type(s).

[0130] In one aspect, provided herein is a method of determining a type of xenograft damage or rejection response in a subject, wherein the subject has received a xenograft transplantation, said method comprising: a) determining methylation patterns of xenograft-specific cfDNA identified in a sample obtained from the subject; b) determining cell type(s) from which the xenograft-specific cfDNA is derived based at least in part on the methylation patterns; and c) determining the type of xenograft damage or rejection response in the subject based at least in part on the cell type(s) of the xenograft- specific cfDNA determined at step (b). In some embodiments, step (b) comprises comparing the methylation patterns of the xenograft-specific cfDNA to a single-cell methylation atlas (such as those described herein) to determine the cell type(s).

[0131] In some embodiments, when the xenotransplant comprises a kidney, the cell type(s) of the xenograft-specific cfDNA are determined based on the methylation patterns of one or more marker genes selected from Table 2.2. The presence of a same methylation pattern between the sequence of the cfDNA and the marker genes set forth in Table 2.2 indicates the cell type from which the cfDNA may originate.

[0132] In some embodiments, the cell type(s) of the xenograft-specific cfDNA are determined based on the methylation patterns of two or more, three or more, four or more, five or more, six or more, seven or more, eight or more, nine or more, ten or more, twenty or more, thirty or more, forty or more, fifty or more, sixty or more, seventy or more, eighty or more, ninety or more, one hundred or more, one hundred ten or more, one hundred twenty or more, one hundred thirty or more, one hundred forty or more, one hundred fifty or more, one hundred sixty or more, one hundred seventy or more, or one hundred seventy- six or more marker genes selected from Table 2.2. 22    311769024v1 Attorney Docket No. 243735.000430

[0133] In some embodiments, the cell type(s) of the xenograft-specific cfDNA are determined based on the methylation patterns of about 2, about 3, about 4, about 5, about 6, about 7, about 8, about 9, about 10, about 12, about 15, about 18, about 20, about 25, about 30, about 35, about 40, about 45, about 50, about 55, about 60, about 65, about 70, about 75, about 80, about 85, about 90, about 95, about 100, about 110, about 120, about 130, about 140, about 150, about 160, or about 176 marker genes selected from Table 2.2.

[0134] In various embodiments, the methods described herein further comprise determining methylation patterns of recipient-specific cfDNA. In some embodiments, the methods further comprise determining cell type(s) from which the recipient-specific cfDNA is derived based at least in part on the methylation patterns. In some embodiments, the methods further comprise determining a type of xenograft rejection response in the subject based at least in part on the cell type(s) of the recipient- specific cfDNA.

[0135] In some embodiments, the type of xenograft damage or rejection response described herein is an antibody-mediated rejection, acute cellular rejection (e.g., T-cell mediated rejection), podocyte damage, ischemia damage, and any other cellular damage.

[0136] In some embodiments, the cell type(s) identified may be indicative of the type of xenograft damage or rejection response. For example, the method may involve determining the type of xenograft damage or rejection response is an antibody-mediated rejection when at least a portion of the xenograft- specific cfDNA is determined to be derived from endothelial cells.

[0137] In some embodiments, the response of the subject to the xenograft transplantation is monitored. The monitoring comprises determining a proportion or level of xenograft-specific cfDNA in a sample obtained from the subject at one or more time points after receiving the xenograft transplantation. In some embodiments, the method further comprises determining a proportion or level of xenograft-specific cfDNA in a sample obtained from the subject at multiple time points after receiving the xenograft transplantation.

[0138] The time points may be the same day or one or more days between and including postoperative day (POD) 0 (day of receiving the xenograft transplantation) through POD 150 or greater, such as POD 0, POD 1, POD 2, POD 3, POD 4, POD 5, POD 6, POD 7, POD 8 POD 9, POD 10, POD 11, POD 12, POD 13, POD 14, POD 15, POD 16, POD 17, POD 18, POD 19, POD 20, POD 21, POD 22, POD 23, POD 24, POD 25, POD 26, POD 27, POD 28, POD 29, POD 30, POD 31, POD 32, POD 33, POD 34, POD 35, POD 36, POD 37, POD 38, POD 39, POD 40, POD 41, POD 42, POD 43, POD 44, POD 45, 23    311769024v1 Attorney Docket No. 243735.000430  POD 46, POD 47, POD 48, POD 49, POD 50, POD 51, POD 52, POD 53, POD 54, POD 55, POD 56, POD 57, POD 58, POD 59, POD 60, POD 61, POD 62, POD 63, POD 64, POD 65, POD 66, POD 67, POD 68, POD 69, POD 70, POD 71, POD 72, POD 73, POD 74, POD 75, POD 76, POD 77, POD 78, POD 79, POD 80, POD 81, POD 82, POD 83, POD 84, POD 85, POD 86, POD 87, POD 88, POD 89, POD 90, POD 91, POD 92, POD 93, POD 94, POD 95, POD 96, POD 97, POD 98, POD 99, POD 100, POD 101, POD 102, POD 103, POD 104, POD 105, POD 106, POD 107, POD 108, POD 109, POD 110, POD 111, POD 112, POD 113, POD 114, POD 115, POD 116, POD 117, POD 118, POD 119, POD 120, POD 121, POD 122, POD 123, POD 124, POD 125, POD 126, POD 127, POD 128, POD 129, POD 130, POD 131, POD 132, POD 133, POD 134, POD 135, POD 136, POD 137, POD 138, POD 139, POD 140, POD 141, POD 142,143, POD 144, POD 145, POD 146, POD 147, POD 148, POD 149, or POD 150, or greater. In certain embodiments, the subject may be monitored at time points later than POD 150.

[0139] In some embodiments, the samples are collected from the subject at least one time a week. In some embodiments, the samples are collected from the subject at least two times a week. In some embodiments, the samples are collected from the subject at least three times a week. In some embodiments, the samples are collected from the subject at least four times a week. In some embodiments, the samples are collected from the subject at least five times a week. In some embodiments, the samples are collected from the subject at least six times a week. In some embodiments, the samples are collected from the subject at least seven times a week.

[0140] In some embodiments, the method for treating the xenograft damage or rejection response based on the type of xenograft damage or rejection response comprises administering a treatment for xenograft damage or rejection response. In some embodiments, the methods described herein may further comprise monitoring the efficacy of the treatment. The monitoring may comprise determining a proportion or level of xenograft-specific cfDNA in a sample obtained from the subject.

[0141] In some embodiments, the treatment may comprise an anti-inflammatory agent, including, but not limited to a steroidal anti-inflammatory agent or a non-steroidal anti-inflammatory agent (e.g., naproxen, ketorolac, diclofenac, meloxicam, etodolac, esomeprazole, misoprostol, ibuprofen, famotidine, nabumetone, mefenamic acid, indomethacin, piroxicam, sulindac, ketoprofen, flurbiprofen, diflunisal, oxaprozin, nabumetone, tolmetin). In some embodiments, the treatment may comprise an antibacterial or antiviral therapy. 24    311769024v1 Attorney Docket No. 243735.000430

[0142] In some embodiments, the treatment may comprise an immunosuppressive agent, including, but not limited to a Janus kinase inhibitor, a corticosteroid, a mTOR inhibitor, a calcineurin inhibitor, an inosine-5′-monophosphate dehydrogenase (IMPDH) inhibitor, a biologic (e.g., infliximab, adalimumab, abatacept, certolizumab, anakinra, etanercept, golimumab, natalizumab, tocilizumab, ustekinumab, rituximab, secukinumab, vedolizumab, ixekizumab), or a monoclonal antibody (e.g., daclizumab, basilivimab).   Methods of creating an atlas and software methods

[0143] Figure 38 is a flow diagram illustrating an exemplary method of creating a single-cell methylation atlas characterizing an organ of a donor. The donor may be suitable for allotransplantation and / or xenotransplantation. In some embodiments, the donor is a transgenic animal.

[0144] At block 102, methylated single-nuclei whole genome sequences (m-snWGS), which are obtained from a biopsy of the organ, are clustered based at least in part on differentially methylated patterns of the m-snWGS. The m-snWGS may be obtained via methods described elsewhere herein and otherwise understood by a person skilled in the pertinent art informed by the present disclosure. The m- snWGS may be computationally represented in formats understood by persons skilled in the pertinent art informed by the present disclosure. The m-snWGS may be clustered via methods described elsewhere herein or by using bioinformatics clustering techniques as otherwise understood by persons skilled in the pertinent art informed by the present disclosure. In some embodiments, the biopsy is extracted from the organ pre-transplant. Alternatively, the biopsy is extracted from the organ post-transplant.

[0145] At block 104, a cell type is assigned for each cluster based at least in part on correlation of known cell type markers of the organ with the differentially methylated patterns of the m-snWGS. In some embodiments, the known cell type markers include methylated marker genes of the differentially methylated patterns of the m-snWGS. In some embodiments, the cell type may be assigned by cross- correlating differentially methylated marker genes with known cell type markers derived from public single cell resources and / or known conserved markers in the human genome. The known cell type markers may otherwise include additional or alternative markers as described elsewhere herein or otherwise understood by a person skilled in the pertinent art informed by the present disclosure. The correlation may be performed by methods described elsewhere herein or otherwise understood by a person skilled in the pertinent art informed by the present disclosure. 25    311769024v1 Attorney Docket No. 243735.000430

[0146] At block 106, the atlas is provided to include a database including: genomic sequences, methylation patterns of the genomic sequences, and cell type designation for the genomic sequences. The genomic sequences are based at least in part on the m-snWGS. The methylation patterns of the genomic sequences are based at least in part on the m-snWGS. The cell type designation for the genomic sequences is based at least in part on the cell type assigned for each cluster.

[0147] In some embodiments, blocks 102 and 104 may be executed using biopsies from a plurality of organs from a plurality of donors. In such embodiments, the atlas database provided at block 106 includes genomic sequences, methylation patterns, and cell type designations from the plurality of organs. The plurality of organs are preferably of the same organ type. The plurality of donors are preferably of the same species. The plurality of donors may be of the same breed of animal species or from different breeds of the same animal species.

[0148] Figure 39 illustrates a block diagram of an exemplary embodiment of a database 200 of a single-cell methylation atlas which may be constructed according to the method 100 illustrated in Figure 38, methods for constructing a single-cell methylation atlas as described in examples presented herein, or alternative method as understood by a person skilled in the pertinent art and informed by the present disclosure. The database 200 includes records associated with one or more donor organ. Each record includes a genomic sequence, corresponding methylation pattern, and corresponding cell type designation. The database 200 may include additional fields such as similarity coordinates based on mapping from a clustering algorithm, methylation score, etc.

[0149] Figure 40 is a flow diagram illustrating an exemplary software method for indicating a xenograft damage or rejection response in a subject having received a xenograft transplantation.

[0150] At block 302, methylation patterns of xenograft-specific cfDNA are received. The methylation patterns of xenograft-specific cfDNA are determined from a sample obtained from the subject. The methylation patterns may be computationally represented in formats understood by persons skilled in the pertinent art informed by the present disclosure. The xenograft-specific cfDNA may be determined as described elsewhere herein or otherwise determined by methods understood by persons skilled in the pertinent art informed by the present disclosure.

[0151] At block 304, cell type from which the xenograft-specific cfDNA is derived is determined based at least in part on the methylation patterns. In some embodiments, the cell type is determined based on a comparison to a database of a single-cell methylation atlas such as database 200 illustrated in Figure 39, a database generated according to method 100, a single-cell methylation atlas database described 26    311769024v1 Attorney Docket No. 243735.000430  elsewhere herein, or alternatives, combinations, or variations thereof as understood by persons skilled in the pertinent art informed by the present disclosure.

[0152] At block 306, the xenograft damage or rejection response in the subject is determined based at least in part on the cell type of the cfDNA.

[0153] At block 308, an indication of the xenograft damage or rejection response is provided as an output. In some embodiments, the xenograft damage or rejection response is an antibody-mediated rejection, acute cellular rejection, podocyte damage, ischemia damage, or any other cellular damage.

[0154] The method 300 may further include some or all of the steps of method 400 illustrated in Figure 41 such that the output further includes an indication that the subject is developing or is at a risk of developing the xenograft damage or rejection response.

[0155] The method 300 may further include compatible steps described in other methods of the present disclosure as understood by persons skilled in the pertinent art informed by the present disclosure.

[0156] Figure 41 is a flow diagram illustrating an exemplary software method for indicating a xenograft damage or rejection response in a subject or predicting the risk of the subject developing a xenograft damage or rejection response.

[0157] At block 402, methylation patterns of xenograft-specific cfDNA are received. The methylation patterns of xenograft-specific cfDNA are determined from a sample obtained from the subject.

[0158] At block 404, a level of xenograft-specific cfDNA or the proportion of xenograft-specific cfDNA to a total cfDNA in the sample is determined based at least in part on the methylation patterns of xenograft-specific cfDNA.

[0159] At block 406, the level or the proportion of the xenograft-specific cfDNA is compared to a corresponding control. In some embodiments, the control is a predetermined standard or a level or proportion of the xenograft-specific cfDNA determined in a sample obtained from the subject at an earlier time point.

[0160] At block 408, it can be determined that the subject is developing or is at a risk of developing the xenograft damage or rejection response if the level or the proportion of the xenograft-specific cfDNA is increased by 20% or more as compared to the control.

[0161] At block 410, an indication that the subject is developing or is at a risk of developing the xenograft damage or rejection response is provided as an output.

[0162] The method 400 may further include some or all of the steps of method 300 illustrated in Figure 40 such that the output further includes an indication of the xenograft damage or rejection response. 27    311769024v1 Attorney Docket No. 243735.000430

[0163] The method 400 may further include compatible steps described in other methods of the present disclosure as understood by persons skilled in the pertinent art informed by the present disclosure.

[0164] Figure 42 illustrates a block diagram of an embodiment of a computing device 500 which may be configured to execute methods or method steps disclosed herein. As shown, computing device 500 may include one or more processor(s) 510, an I / O device 520, a memory 530 containing an operating system (“OS”) 540, a database 550, and a program 560. In some embodiments, instructions are stored in the memory 530 that are executable by the processor 510 to perform steps of the computational methods disclosed herein. The computing device can include one or more modules or engines for carrying out computational methods disclosed herein. In some embodiments, the I / O device 520 is configured to communicate with the respective ancillary features such as databased or software computational tools to carry out the functions and computational steps disclosed herein.

[0165] Computing device 500 may be a single server or may be configured as a distributed computer system including multiple servers or computers that interoperate to perform one or more of the processes and functionalities associated with the disclosed embodiments. In some embodiments, computing device 500 may further include a peripheral interface, a transceiver, a mobile network interface in communication with processor 510, a bus configured to facilitate communication between the various components of computing device 500, and a power source configured to power one or more components of computing device 500. A peripheral interface may include the hardware, firmware and / or software that enables communication with various peripheral devices, such as media drives (e.g., magnetic disk, solid state, or optical disk drives), other processing devices, or any other input source used in connection with the instant techniques. In some embodiments, a peripheral interface may include a serial port, a parallel port, a general-purpose input and output (GPIO) port, a game port, a universal serial bus (USB), a micro-USB port, a high definition multimedia (HDMI) port, a video port, an audio port, a BluetoothTMport, an NFC port, another like communication interface, or any combination thereof.

[0166] In some embodiments, a transceiver may be configured to communicate with compatible devices and ID tags when they are within a predetermined range. A transceiver may be compatible with one or more of: RFID, NFC, BluetoothTM, low-energy Bluetooth™ (BLE), WiFi™, ZigBee™, ABC protocols or similar technologies.

[0167] A mobile network interface may provide access to a cellular network, the Internet, or another wide-area network. In some embodiments, a mobile network interface may include hardware, firmware, and / or software that allows processor 510 to communicate with other devices via wired or wireless 28    311769024v1 Attorney Docket No. 243735.000430  networks, whether local or wide area, private or public, as known in the art. A power source may be configured to provide an appropriate alternating current (AC) or direct current (DC) to power components.

[0168] Processor 510 may include one or more of a microprocessor, microcontroller, digital signal processor, co-processor or the like or combinations thereof capable of executing stored instructions and operating upon stored data. Memory 530 may include, in some implementations, one or more suitable types of memory (e.g., volatile or non-volatile memory, random access memory (RAM), read only memory (ROM), programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM), magnetic disks, optical disks, floppy disks, hard disks, removable cartridges, flash memory, a redundant array of independent disks (RAID), and the like) for storing files, including an operating system, application programs (including, e.g., a web browser application, a widget or gadget engine, or other applications, as necessary), executable instructions, and data. In one embodiment, the processing techniques described herein are implemented as a combination of executable instructions and data within memory 530.

[0169] Processor 510 may be one or more known processing devices, such as a microprocessor from the PentiumTMfamily manufactured by IntelTMor the TurionTMfamily manufactured by AMDTM. Processor 510 may constitute a single core or multiple core processor that executes parallel processes simultaneously. For example, processor 510 may be a single core processor that is configured with virtual processing technologies. In certain embodiments, processor 510 may use logical processors to simultaneously execute and control multiple processes. Processor 510 may implement virtual machine technologies, or other similar known technologies to provide the ability to execute, control, run, manipulate, store, etc. multiple software processes, applications, programs, etc. Other types of processor arrangements could be implemented that provide for the capabilities disclosed herein as understood by a person skilled in the pertinent art.

[0170] Computing device 500 may include one or more storage devices configured to store information used by processor 510 (or other components) to perform certain functions related to the disclosed embodiments. In one example, computing device 500 may include memory 530 that includes instructions to enable processor 510 to execute one or more applications, such as server applications, network communication processes, and any other type of application or software known to be available on computer systems. Alternatively, the instructions, application programs, etc., may be stored in an external storage or available from a memory over a network. The one or more storage devices may be a 29    311769024v1 Attorney Docket No. 243735.000430  volatile or non-volatile, magnetic, semiconductor, tape, optical, removable, non-removable, or other type of storage device or tangible computer-readable medium.

[0171] In one embodiment, computing device 500 may include memory 530 that includes instructions that, when executed by processor 510, perform one or more processes consistent with the functionalities disclosed herein. Methods, systems, and articles of manufacture consistent with disclosed embodiments are not limited to separate programs or computers configured to perform dedicated tasks. For example, computing device 500 may include memory 530 that may include one or more programs 560 to perform one or more functions of the disclosed embodiments. Moreover, processor 510 may execute one or more programs 560 located remotely from computing device 500. For example, computing device 500 may access one or more remote programs 560, that, when executed, perform functions related to disclosed embodiments.

[0172] Memory 530 may include one or more memory devices that store data and instructions used to perform one or more features of the disclosed embodiments. Memory 530 may also include any combination of one or more databases controlled by memory controller devices (e.g., server(s), etc.) or software, such as document management systems, MicrosoftTMSQL databases, SharePointTMdatabases, OracleTMdatabases, SybaseTMdatabases, or other relational databases. Memory 530 may include software components that, when executed by processor 510, perform one or more processes consistent with the disclosed embodiments. In some embodiments, memory 530 may include database 550 for storing related data to enable computing device 500 to perform one or more of the processes and functionalities associated with the disclosed embodiments.

[0173] Computing device 500 may also be communicatively connected to one or more memory devices (e.g., databases (not shown)) locally or through a network. The remote memory devices may be configured to store information and may be accessed and / or managed by computing device 500. By way of example, the remote memory devices may be document management systems, MicrosoftTMSQL database, SharePointTMdatabases, OracleTMdatabases, SybaseTMdatabases, or other relational databases. Systems and methods consistent with disclosed embodiments, however, are not limited to separate databases or even to the use of a database.

[0174] Computing device 500 may also include one or more I / O devices 520 that may include one or more interfaces for receiving signals or input from devices and providing signals or output to one or more devices that allow data to be received and / or transmitted by computing device 500. For example, computing device 500 may include interface components, which may provide interfaces to one or more 30    311769024v1 Attorney Docket No. 243735.000430  input devices, such as one or more keyboards, mouse devices, touch screens, track pads, trackballs, scroll wheels, digital cameras, microphones, sensors, and the like, that enable computing device 500 to receive data from one or more users (such as via user device 130).

[0175] In example embodiments of the disclosed technology, computing device 500 may include any number of hardware and / or software applications that are executed to facilitate any of the operations. The one or more I / O interfaces may be utilized to receive or collect data and / or user instructions from a wide variety of input devices. Received data may be processed by one or more computer processors as desired in various implementations of the disclosed technology and / or stored in one or more memory devices.

[0176] While computing device 500 has been described as one form for implementing the techniques described herein, other, functionally equivalent techniques may be employed as understood by a person skilled in the pertinent art. For example, as known in the art, some or all of the functionality implemented via executable instructions may also be implemented using firmware and / or hardware devices such as application specific integrated circuits (ASICs), programmable logic arrays, state machines, etc. Furthermore, other implementations may include a greater or lesser number of components than those illustrated.

[0177] Figure 43 illustrates a block diagram of an embodiment of a computing network 600 including computing device(s) 610, server(s) 620, memory store(s) 640, and a network 630 facilitating communication between each. In some embodiments, the network 600 is configured to execute steps of computational methods as disclosed herein. In some embodiments, one or more computing device(s) 610 include various engines and / or modules, which may be distributed across the computing device(s). The computing device(s) are configured to communicate with ancillary databases and / or software services to carry out steps of computational methods disclosed herein.

[0178] Network 630 may be of any suitable type, including individual connections via the internet such as cellular or WiFiTMnetworks. In some embodiments, network 630 may connect terminals, services, and mobile devices using direct connections such as radio-frequency identification (RFID), near-field communication (NFC), BluetoothTM, low-energy BluetoothTM(BLE), WiFiTM, ZigBeeTM, ambient backscatter communications (ABC) protocols, USB, WAN, or LAN. Because the information transmitted may be personal or confidential, security concerns may dictate one or more of these types of connections be encrypted or otherwise secured. In some embodiments, however, the information being 31    311769024v1 Attorney Docket No. 243735.000430  transmitted may be less personal, and therefore the network connections may be selected for convenience over security.

[0179] ASPECTS OF THE INVENTION

[0180] 1. A first aspect of the invention is a method of creating a single-cell methylation atlas characterizing an organ of a donor, the method comprising a) clustering methylated single-nuclei whole genome sequences (m-snWGS) from a biopsy of the organ based at least in part on differentially methylated patterns of the m-snWGS; b) assigning a cell type for each cluster based at least in part on correlation of known cell type markers of the organ with the differentially methylated patterns of the m- snWGS; and c) providing the atlas to include a database including: genomic sequences based at least in part on the m-snWGS, methylation patterns of the genomic sequences based at least in part on the m- snWGS, and cell type designation for the genomic sequences based at least in part on the cell type assigned for each cluster.

[0181] 2. The method of the first aspect, wherein the donor is a genetically modified animal.

[0182] 3. The method of aspect 2, wherein the genetically modified animal is a pig.

[0183] 4. The method of any one of the above aspects, wherein the biopsy is extracted from the organ pre-transplant.

[0184] 5. The method of any one of aspects 1-3, wherein the biopsy is extracted from the organ post- transplant.

[0185] 6. The method of any one of the above aspects, comprising performing steps (a)-(b) on a plurality of organs from a plurality of donors, wherein the database of the atlas provided at step (c) comprises genomic sequences, methylation patterns, and cell type designation derived from the plurality of organs.

[0186] 7. The method of aspect 6, wherein the plurality of organs are of the same organ type.

[0187] 8. The method of aspects 6 or 7, wherein the plurality of donors are of the same breed of animal species.

[0188] 9. The method of aspects 6 or 7, wherein the plurality of donors are from different breeds of the same animal species.

[0189] 10. The method of any one of the above aspects, wherein the known cell type markers comprise known cell type-specific genes and / or known cell type-specific methylation markers.

[0190] 11. An eleventh aspect is a software method for indicating a type of xenograft damage or rejection response in a subject, wherein the subject has received a xenograft transplantation, the method 32    311769024v1 Attorney Docket No. 243735.000430  comprising a) receiving methylation patterns of xenograft-specific cell-free DNA (cfDNA) determined from a sample obtained from the subject; b) determining cell type(s) from which the xenograft-specific cfDNA is derived based at least in part on the methylation patterns; c) determining the type of xenograft damage or rejection response in the subject based at least in part on the cell type(s) of the cfDNA determined at step (b); and d) providing, as an output, an indication of the type of xenograft damage or rejection response.

[0191] 12. The software method of aspect 11, wherein step (b) comprises comparing methylation patterns of the xenograft-specific cfDNA to the atlas created according to any one of aspects 1-10 to determine cell type(s) of the cfDNA.

[0192] 13. The software method of aspect 11 or 12, comprising prior to step a), receiving a proportion of xenograft-specific cell-free DNA (cfDNA) to total cfDNA in a sample obtained from the subject; and determining that the subject is developing or is at a risk of developing the xenograft damage or rejection response if the proportion of the xenograft-specific cfDNA is more than 1%, wherein the output provided at step (d) further comprises an indication that the subject is developing or is at a risk of developing the xenograft damage or rejection response.

[0193] 14. The software method of aspect 11 or 12, comprising prior to step a), receiving a level of xenograft-specific cfDNA or a proportion of xenograft-specific cfDNA to total cfDNA in the sample; comparing the level or proportion of the xenograft-specific cfDNA to a corresponding control; and determining that the subject is developing or is at a risk of developing the xenograft damage or rejection response if the level or the proportion of the xenograft-specific cfDNA is increased by 20% or more as compared to the control, wherein the output provided at step (d) further comprises an indication that the subject is developing or is at a risk of developing the xenograft damage or rejection response.

[0194] 15. A fifteenth aspect of the invention is a software method for indicating a type of xenograft damage or rejection response in a subject or predicting the risk of the subject developing a xenograft damage or rejection response, the method comprising a) receiving a proportion of xenograft-specific cell-free DNA (cfDNA) to total cfDNA in a sample obtained from the subject; b) determining that the subject is developing or is at a risk of developing the xenograft damage or rejection response if the proportion of the xenograft-specific cfDNA is more than 1%; where when the proportion of the xenograft-specific cfDNA is more than 1%, the method further comprises c) receiving methylation patterns of the xenograft-specific cfDNA received in step (a); d) determining cell type(s) from which the xenograft-specific cfDNA is derived based at least in part on the methylation patterns; e) determining 33    311769024v1 Attorney Docket No. 243735.000430  the type of xenograft damage or rejection response in the subject based at least in part on the cell type(s) of the cfDNA determined at step (d); and f) providing, as an output, an indication that the subject is developing or is at a risk of developing the xenograft damage or rejection response; and / or an indication of the type of xenograft damage or rejection response.

[0195] 16. The software method of aspect 15, wherein step (d) comprises comparing methylation patterns of the xenograft-specific cfDNA to the atlas created according to any one of aspects 1-10 to determine cell type(s) of the cfDNA.

[0196] 17. The software method of aspect 15 or 16, wherein the control is a predetermined standard or a level or proportion of the xenograft-specific cfDNA determined in a sample obtained from the subject at an earlier time point.

[0197] 18. An eighteenth aspect of the invention can be a software method for indicating a type of xenograft damage or rejection response in a subject or predicting the risk of the subject developing a xenograft damage or rejection response, the method comprising a) receiving a level of xenograft-specific cfDNA or a proportion of xenograft-specific cfDNA to total cfDNA in a sample obtained from the subject; b) comparing the level or the proportion of the xenograft-specific cfDNA received in step (a) to a corresponding control; c) determining that the subject is developing or is at a risk of developing the xenograft damage or rejection response if the level or the proportion of the xenograft-specific cfDNA determined in step (a) is increased by 20% or more as compared to the control; wherein when the level or the proportion of the xenograft-specific cfDNA determined in step (a) is increased by 20% or more as compared to the control, the method further comprises d) receiving methylation patterns of the xenograft-specific cfDNA received in step (a); e) determining cell type(s) from which the xenograft- specific cfDNA is derived based at least in part on the methylation patterns; f) determining the type of xenograft damage or rejection response in the subject based at least in part on the cell type(s) of the cfDNA determined at step (e); and g) providing, as an output, an indication that the subject is developing or is at a risk of developing the xenograft damage or rejection response; and / or an indication of the type of xenograft damage or rejection response.

[0198] 19. The software method of aspect 18, wherein step (e) comprises comparing methylation patterns of the xenograft-specific cfDNA to the atlas created according to any one of aspects 1-10 to determine cell type(s) of the cfDNA.

[0199] 20. The software method of any one of aspects 11-19, wherein the method further comprises receiving methylation patterns of recipient-specific cfDNA. 34    311769024v1 Attorney Docket No. 243735.000430

[0200] 21. The software method of aspect 20, wherein the method further comprises determining cell type(s) from which the recipient-specific cfDNA is derived based at least in part on the methylation patterns.

[0201] 22. The software method of aspect 21, wherein the method further comprises determining a type of xenograft rejection response in the subject based at least in part on the cell type(s) of the recipient- specific cfDNA.

[0202] 23. The software method of any one of aspects 11-22, wherein the type of xenograft damage or rejection response is selected from an antibody-mediated rejection, acute cellular rejection, podocyte damage, ischemia damage, and any other cellular damage.

[0203] 24. The software method of any one of aspects 11-23, wherein the subject has received the xenograft transplantation from a porcine donor and the xenograft-specific cfDNA is porcine cfDNA.

[0204] 25. The software method of aspect 24, wherein the subject has received a kidney, heart, lung, liver, bone marrow, or pancreas transplantation from a porcine donor.

[0205] 26. The software method of aspect 25, wherein the cell type(s) of the xenograft-specific cfDNA are determined based on the methylation patterns of one or more marker genes selected from Table 2.2.

[0206] 27. The software method of any one of aspects 11-26, wherein the sample is a bodily fluid.

[0207] 28. The software method of any one of aspects 11-27, wherein the sample is whole blood, plasma or serum.

[0208] 29. The software method of any one of aspects 11-28, wherein the cfDNA is about 20-800 bp long.

[0209] 30. The software method of aspect 29, wherein the cfDNA is 120-220 base pairs (bp) long, with a peak at approximately 167 bp long. EXAMPLES

[0210] The following examples are provided to further describe some of the embodiments disclosed herein. The examples are intended to illustrate, not to limit, the disclosed embodiments. Example 1. The use of cell-specific cell-free DNA (cfDNA) to prognosticate cellular damage in humans receiving genetically modified pig organs

[0211] Conventional donor-derived cfDNA (dd-cf-DNA) in human allotransplants are the underlying technology of a number of clinical diagnostics of acute allograft rejection setting, with significant value 35    311769024v1 Attorney Docket No. 243735.000430  shown in the heart transplant setting (Henricksen, et al., 2023; Agbor-Enoh, et al., 2017). dd-cf-DNA relies on a clinically meaningful increase in absolute or relative levels of dd-cf-DNA from damaged donor allograft tissue. Methylation of DNA nucleotides generate epigenetic marks that play significant roles in control of gene expression and chromatin organization, as well as give fundamental insights into cellular identity and developmental processes (Dor, et al., 2018). Many human methylation maps are based either on cell lines that have undergone excessive tissue culture cell-passaging or are from mixed cell types. Single-cell whole-genome bisulfite sequencing allows fragment-level analysis across tens of thousands of unique methylation markers. Recent whole genome methylation human atlases have been generated for 39 cell types, generated from over 200 healthy tissue samples including kidney and heart cell-types (Loyfer, et al., 2023). Unsupervised clustering of these atlases reveals key elements of tissue ontogeny and shows methylation patterns that are retained all the way from embryonic development.

[0212] Methylated cell-free DNA (m-dd-cfDNA) gives an additional layer of specificity as the unique methylation marks for each specific cell-type allow the proportions and absolute levels of cell-specific dd-cf-DNA to be derived. This is particularly important in the transplant setting where specific cell-types such as endothelial cells are typically first to be damaged in the first waves of cell damage from antibody mediated rejection (AbMR) immune responses (Al-Lamki, et al., 2008).

[0213] Cell-specific cell-free DNA has been described in the human allotransplant setting primarily through studies using array based methylated genotyping (Lehmann-Werman, et al., 2018). Methylation atlases in pigs are limited to adipose and muscle tissues (Li, et al., 2012).

[0214] In the present disclosure, prognostic and diagnostic markers of pig kidney xenograft rejection were developed using a combined approach of: (a) generating whole genome sequencing methylation maps of the pig kidney xenograft to create an atlas of methylated marks in each of the major pig kidney cell-types; (b) generating m-dd-cf-DNA patterns from the blood of decedent of both the pig xenograft and the human decedent to assess the peripheral composition of specific cell-types originating from the pig kidney xenograft. It is also possible to map pig and human m-cf-dd-DNA back to their respective ‘personal’ whole genome sequences (WGSs) to increase on-target mapping. Long-read whole genome sequencing data is used for this m-cf-dd-DNA mapping back to the respective ‘personal genome’ for the human and pig genomes from the long term (61 day) xenotransplant procedure.

[0215] The portion of cell-free DNA at Day 33 relative to periods where the transplant is not exhibiting rejection (such as Day 14 and 61) is significantly elevated and is diagnostic of rejection (Figure 8). The pig cfDNA proportion at the biopsy confirmed rejection timepoint on Day 33 is 4.66 and 5.73 times 36    311769024v1 Attorney Docket No. 243735.000430  greater in proportion versus Days 14 and 61 when rejection was not evident. In the initial period of the first few days post-transplant, it can be expected to see an increase in pig cfDNA due to the trauma of the transplant procedure.

[0216] The Day 33 biopsy was performed ‘for-cause’ due to a subtle decrease in kidney function. While it is not currently evident when exactly the molecular rejection event started ahead of this Day 33 timepoint, the Day 25 and 28 timepoints are significantly less than the Day 33 timepoint (2.64 and 2.75 times respectively). This shows a prognostic capability of the assay in these datasets, and it is expected that this would be the case in other pig to human xenotransplant procedures.

[0217] These datasets are consistent with orthogonal molecular datasets from this procedure that indicate significant damage to the pig xenograft on Day 33, and indicate molecular signatures of such damage ahead of the Day 33 timepoint (Figure 3).

[0218] Mapping the methylated pig-specific cell-free DNA fragments back to specific pig kidney cell- type using the methylation cell-atlas generated from the ScaleBio datasets will allow an investigator to determine which pig cell-types are being damaged, and thus will allow for increased specificity to determine what ‘insult’ has occurred to the pig xenograft. These insults may be antibody mediated rejection (which is expected to have an increase in endothelial cells), acute cellular rejection, podocyte damage, and / or ischemia damage, etc.

[0219] Methylated-cf-DNA profiles were generated from blood samples preparation using Oxford Nanopore Technology (ONT) Promethion sequencing on plasma dd-cfDNA on 7 timepoints to date (Days 1, 14, 25, 28, 33, 40 and 61) over the course of the 61-day long-term pig kidney to human decedent xenotransplant procedure. As an example, Days 14, 28 and 61 yielded 253 ng, 794 ng, and 261 ng, respectively. Cf-DNA from the Day 28 timepoint was then run on 2 ONT PromethION Flow Cells with ~5,300 total nanopore channels which yielded ~572.89 million reads of sequence data generated after 46 hours of sequencing. In the Day 14, 28 and 61 samples, 2.00 million, 5.56 million and 2.44 million reads, respectively, were uniquely mapped back to the pig genome and represent 0.59%, 1.04% and 0.48% of the total cfDNA material, respectively. This Day 28 cfDNA timepoint, with an approximate doubling of pig cfDNA, was 5 days before the biopsy confirmed AbMR. If the true baseline of healthy pig kidney xenograft is assumed to be similar to that on Day 10 or 61 (where there were no clinical issues related to the pig kidney xenograft evident) then a clinically meaningful increase in pig cfDNA transplanted is likely. An even greater increase in the proportion of pig to human cfDNA can be expected in the Day 33 and, potentially, in the Day 40 timepoints because while the rejection event was 37    311769024v1 Attorney Docket No. 243735.000430  successfully treated, damage may have continued to occur to the pig xenograft during the antibody- mediated rejection for several days after confirmation of the event at Day 33 and subsequent treatment.

[0220] Methylated bases were recalled on the ONT methylation bases assigners to generate calls for both 5-Methylcytosine (5mC) and 5-Hydroxymethylcytosine (5hmC) bases, and these were mapped to the pig reference genome. CpG methylated sites covered in the human (stranded) and pig were 85.1M and 9.32M, with percent 5mC and 5hmC 52.2% and 3.6% for human and 66.2% and 2.5% for pig. Day 10 and Day 61 cfDNA mappings are shown on Table 8, and an additional 30 cfDNA timepoints will be generated across the 61 day procedure to map the longitudinal cfDNA and m-cfDNA profiles across the AbMR episode in this unique experiment. The relative and absolute levels of conventional pig xenograft versus human decedent cfDNA against the m-cfDNA outputs can be compared in the equivalent timepoints across the 61 day procedure. This can allow an investigator to assess the value of this m- cfDNA assay across different timepoints of a given time course. As an example, the biopsy confirmed rejection timepoint at Day 33 is 4.66 and 5.73 times greater in proportion versus Days 14 and 61 when rejection was not evident. In the initial period of the first few days post-transplant, it can be expected to see an increase in pig cfDNA due to the trauma of the transplant procedure.

[0221] Regarding the mapping of human cfDNA, 20+ kidney cell-types were expected based on human kidney methylome maps (Cirio, et al., 2014).

[0222] Direct nanopore sequencing allows 5mC and 5hMC methylated bases to be detected and allows methylation bases to be directly captured through changes in current when each bases passes through a nanopore, as opposed to inferred by bisulfite-based sequencing.

[0223] This study is the longest pig to human decedent transplant performed to date. The ONT sequencing, performed on a promethION instrument allows methylation bases to be directly captured through changes in current when each base passes through a nanopore, as opposed to inferred by bisulfite-based sequencing. Example 2: Multi-omic profiling in a 61-day pig kidney to human-decedent xenotransplant reveals a concerted immune response spanning a successfully treated rejection episode

[0224] To assess the molecular immune responses and the effectiveness of immunosuppressive regimen, an extended pig kidney to a human recipient xenotransplantation was performed over 61-days and deep tissue and blood omic profiling was performed across dense longitudinal timepoints. 38    311769024v1 Attorney Docket No. 243735.000430

[0225] In situ geospatial and single-nuclei RNA-sequencing transcriptomics of 9 xenograft biopsies, single-cell RNA-sequencing of 30 PBMC time points, B- and T- cell repertoire (BCR / TCR) analysis of 27 time points, and ultra-deep proteomic profiling on 63 blood plasma time points were performed

[0226] The analysis revealed an increase of NK and pDCs in the blood beginning at postoperative day (POD)12, and which was concordant with invasion of these cells in the tissue, and BCR clonotype expansion. CD4+ and CD8+ T cells expanded similarly, but remained high at later time points, with evidence for tissue invasion. A TCR expansion and high clonotype concordance was observed around the antibody mediated rejection (AbMR) episode. This was accompanied by kidney tissue injury observed at POD21, involving pro-fibrotic epithelial cells and interstitial cells, with high expression of CXCL14 and SPP1, in addition to resident macrophage activation, which spatially overlapped with IFTA. These patterns continued into POD33, with CCL19 expression, and a widespread invasion of human immune cells, specifically expressing CXCL9, CXCL10 and CXCL11. Finally, pig and human complement activation proteins were observed in the blood, indicative of potential cross-species activation. All of the observed patterns widely recess after POD33.

[0227] Integrative omic profiling deeply characterized the temporal immune and transplant response of a pig kidney xenograft, including a rejection event, the events leading up to this, and its subsequent successful treatment.

[0228] Transplanting pig xenografts into brain-dead humans (decedents) offers a unique opportunity to test xenografts in a high-fidelity recipient model, and to obtain extensive longitudinally collected blood and tissue samples in a ‘fail-safe’ manner (Montgomery, et al., 2024). In 2021, a single pig thymus- kidney (“thymokidney”) xenograft transplants was successfully performed in two decedents (Montgomery, et al., 2022). In 2023, a longer-term xenotransplant of a pig thymo-kidney into a brain- dead decedent for 61 days was approved. This included a biopsy-confirmed rejection episode at POD33 which was successfully treated.

[0229] In this study, the decedent response to a 61 day pig-to-human thymo-kidney xenotransplant was assessed and the transplant tissue dynamics was evaluated. To this end a deep multi-omics characterization was performed of the recipient blood and the transplanted tissue (Figure 1). These experiments included high resolution tissue spatial transcriptomics (n = 9 time points) and bulk RNA- seq (n = 9 time points). Recipient PBMCs were characterized through bulk and single cell RNA-seq, in addition to BCR / TCR sequencing. Furthermore, sera from 63 time points were subjected to Seer Proteograph proteomic analysis. 39    311769024v1 Attorney Docket No. 243735.000430

[0230] Spatial transcriptomics analysis allowed the superimposition of histological structures such as glomeruli to localized gene expression. Transcript expression levels were associated with cells identified from DAPI fluorescence-based segmentation (Figure 2A). Pig cells in the xenograft could therefore be separated from human cells, based on their transcriptional profiles (Figure 2B), and their respective transcriptional profiles could be separately mapped to identify cell-types (Figures 2C-2D). The infiltrating human immune cells were mainly macrophages, with strong expression of HLA-DRA, HLA- DRB1, and LYZ observed (Figure 2E). Smaller populations of CD8+ T cells (CD8T) and CD4+ T cells (CD4T) were also identified. The CD8T population appeared to be of a cytotoxic phenotype, due to the expression of GZMA and KLRK1. CD4+ T cells also contained 70% of those expressing IL7R. NK cells (GNLY+, PRF1+) were also identified, in addition to plasmacytoid dendritic cells (pDC), expressing JCHAIN, MZB1 and GZMB (Figures 2C, 2E). B or Plasma cells / blasts were not detected in the tissue. Identified pig cells recapitulated all expected cell types of the kidney. Of note, a cluster of resident pig immune cells was observed as well as another cluster containing cells presenting high levels of SPP1 expression. Cell type information was mapped back to the slides to identify and confirm cell type composition of the histological structure of the renal cortex, such as the glomeruli, tubules, ducts and loops (Figure 2G). Podocytes, mesangial and endothelial cells were isolated from glomeruli, with respective specific expressions of SEMA3G, PECAM1, and PDGFRB (Figure 2H). Interestingly, at POD45, infiltrating human NK cells and macrophages were confirmed in and around a glomerulus. In the renal cortex, the proximal convoluted tubules, Henle loops, distal tubules, and the primary and intercalated parts of distal tubules were very distinctly identified by specific marker genes (Figure 2I). Human macrophages and NK cells were also found to be present in the interstitium in between different ducts and tubules. Recipient PBMCs were accurately identified and labeled based on their scRNA-seq transcriptional signature (Figures 2J-2K), and subtypes of T and NK cells were annotated. In addition, B cells, dendritic cells, plasmablasts and pDCs were characterized (Figure 2L), whose transcriptional profile confirmed the pDC cell annotation from spatial transcriptomics.

[0231] Spatial transcriptomics enables the integration of histological architecture, such as glomeruli, with localized gene expression analysis. By correlating transcript expression levels with cells identified via DAPI fluorescence-based segmentation (Figure 2A), pig and human cells were distinguished within the xenograft based on their unique transcriptional signatures (Figure 2B). This differentiation allowed for detailed mapping of their respective transcriptional landscapes, facilitating cell-type identification (Figures 2C-2D). Subsequently, this cell type information was projected onto histological slides to 40    311769024v1 Attorney Docket No. 243735.000430  ascertain and verify the cellular composition within key renal cortex structures, including glomeruli, tubules, ducts, loops, and podocytes, employing specific marker genes (Figures 2G-I).

[0232] Moreover, the present approach pinpointed infiltrating human immune cells within the xenograft, predominantly macrophages and natural killer (NK) cells, localized at the interfaces of various ducts and tubules (Figure 2E). Although less prevalent, subsets of CD8+ T cells (CD8T), CD4+ T cells (CD4T), and plasmacytoid dendritic cells (pDC) were identified (Figures 2C, 2E), whereas B cells or plasmablasts / plasma cells were absent. Interestingly, a resident pig immune cell population was also observed, clustering together with cells presenting high levels of SPP1 expression (Figure 2D).

[0233] The amount of invading immune cells observed in the tissue from the spatial transcriptomics assay varied across time points (Figure 3A). Immediately post-transplantation, almost no human cells are present, while the number increases at POD (post-operative day) 10 to 2-3%, before peaking at POD33, representing >20% of the cells. It then lowers to 3-7% between POD45-56. In the blood, PBMC RNA seq shows an increase of NK cells after POD10 up to 30%, followed by a decrease after D37, returning to low levels after POD49; this trend is confirmed by flow cytometry(Figures 3B-3C). Interestingly, the human NK cells in the transplanted tissue increased after POD10, before declining after POD45, matching the PBMC trend (Figure 3D). The pDC population follows a similar pattern in both PBMC and tissue (Figures 3E-3F). This suggests proliferation of these cells in the recipient blood, before infiltrating into the tissue. On the other hand, B and plasmablast cells reach their peak before POD7 in the peripheral blood, and are absent in the tissue spatial transcriptomics. They are still present, albeit at lower levels, in the blood after POD7, especially with higher levels for the plasmablasts (~ 0.3% of all PBMC) at POD17-D25 (Figures 3G-3H). The relevance of B and plasma cells is confirmed by BCR-seq, which shows a peak of clonotype diversity (Figure 3I) matching that of the plasmablast peak, in addition to another peak around POD40. A high percentage of shared clonotypes at POD17-D25 (Figure 3J) was observed, which matched the diversity peak) and progressive class switching from IgG to IgA and IgM (Figure 3K). These human immune cells seem to be most activated at POD33, with gradual increases of human CXCL9, CXCL10 and CXCL11 expression in the tissue, observed in both the bulkRNA seq (Figure 3L) and by spatial transcriptomics (Figure 3M). These data suggest an innate and B-mediated response of the recipient immune system before the rejection episode, followed by infiltration of these cells into the transplanted tissue, for which the number and activity peak at the rejection time-point, POD33. 41    311769024v1 Attorney Docket No. 243735.000430

[0234] After an initial decrease following rATG induction therapy, there is a progressive increase in T cells in the blood (Figure 4A), beginning of POD14, accelerating after POD33 with the peak at POD42. These levels fall again following rATG re-induction on POD49, to reach near 0 levels at POD56. In the tissue, human CD8T and CD4T cells are notably absent until POD21, reaching maximum around POD33 (Figures 4B-C). While their level decreases by POD45-49, they remain present at low levels. Subtypes of peripheral blood T cells display very similar trends, albeit with minor variations (Figure 4D). CD38, a marker previously established as implicated in transplant rejection (Doberer, et al., 2021; Mancebo, et al., 2016) is expressed at higher levels in CD8T cells around the rejection event (POD33), both in PBMC and human infiltrating cells in the tissue (Figures 4E-4F). A similar trend is seen from NK cells (Figure 4E). Recipient PBMC TCR seq reveals a very high level of clonotype sharing, specifically around the rejection event (POD31,33,42,47,49) (Figure 4G). In addition, specific usage of V2-J1 in TCR sequence accompanied with increases in TCR clonal diversity is observed after POD28, suggesting specificity to a new antigen around the rejection event. These data suggest a T mediated response of the host immune system, followed by their invasion in the transplanted tissue during the rejection event (at POD33). Their activity and number peak during and after POD33, but not before.

[0235] The global transcriptional signal of the transplanted tissue over time reveals a cluster of genes that display high expression at POD0 (immediately post transplantation), but lower expression in all subsequent timepoints. These are enriched in inflammation signals and are suggestive of immediate, but short lived ischemia / reperfusion injury, which does not return (Figure 5A). This is confirmed by the expression patterns of FOS, FOSB, JUNB in spatial transcriptomics, which are exclusive to POD0 (Figure 5B). Interestingly, spatial transcriptomics reveals a clear transcriptional shift at POD21, involving genes that are suggestive of fibrosis, inflammation, hypoxia and injury (Figure 5B). This was confirmed by higher levels of SPP1+ cells at POD21-33, SPP1 being involved in kidney injury (Yu, et al., 2023), and higher percentage of fibroblasts for POD21 (Figures 5C-5D). SPP1+ cells are not a cell- type per se, but rather, a cell state in response to damaged / inflamed / altered loops / ducts / tubules. This cell population is also characterized by specific COLEC11 expression, which has been shown to activate the lectin pathway of the complement system, contributing to kidney allograft rejection (Nauser, et al., 2017). Within them, subpopulations arise, such as C4A+ (peaking at POD21-33), inflamed CXCL14+, SERPINE1+ (POD21 enriched) or VCAM1+, which can relate to epithelial crosstalk and injury (Seron, et al., 1991) (POD21-33 enriched), (Figures 5E-5F). Interestingly, spatial transcriptomics reveals that these populations are colocalized with fibroblasts, endothelial cells and human cells (for POD33) 42    311769024v1 Attorney Docket No. 243735.000430  (Figure 5G). Resident pig immune cells, seen in the transplanted tissue at all time points, also spark interest, as their proportion spikes at POD21 (Figure 5H). Careful analysis of their expression patterns revealed that, while their overall markers suggest resident macrophages (Figure 2E), POD0 resident immune cells population also contain T cells (Figure 5I), but is the only time point to do so. The other resident cells are macrophages with expression of CD163, MRC1, C1QA, TLR2 and CCR5 (Figure 5J). Interestingly, subpopulation analysis reveals an activated group of pig resident macrophages, specifically expressing C3, LYZ, S100A6 and SPP1 (Figure 5J), and are enriched in POD21 and POD33 (Figure 5K). The pig immune cells colocalize with EC, FB and human cells in POD33 (Figure 5M). In POD21, location aligning with SPP1+ cell reveals interstitial fibrosis and tubular atrophy (IFAT) (Figure 5N), this location aligns with activated pig resident macrophages expressing LYZ, S100A6, S100A, as well as the presence of human immune cells. In POD33, there is a region very rich in infiltrating human immune cells, which express high levels of CXCL9, 10, 11 (as observed in Figures 3L-3M), and colocalize with SPP1+ pig cells (Figure 5O). This region aligns with IFAT,also observed in the sample. These data depict a thorough and widespread molecular response of the transplanted tissue before (POD21) and during (POD33) the rejection event, normalizing afterwards. This response especially involves resident pig immune macrophages and tubular and interstitial cells in a damaged SPP1+ state, which show signs of interaction with endothelial cells and fibroblasts, as well as the invading human immune cells.

[0236] Sera proteomes were enriched across all time points using a novel method for deep sera profiling (Proteograph XT, Seer, CA) and analyzed via LC-MS / MS. Mapping the resulting peptides to human and pig proteome reference databases, a total of 93,712 peptides were observed, of which 41,946 were human-specific and 4,192 were pig specific. These corresponded to a total of 6,228 protein groups for the human decedent, and 1,002 for the pig.

[0237] Given the role of complement pathways in regulating transplant autoimmunity, a targeted analysis of longitudinal patterns of different components was performed of the classical and alternative complement pathways. Across these, early activation of complement was observed via lectin and alternative pathways. This was indicated by an increase in MBL and MASPs in the lectin pathway, as well as C3 and Factor B in the alternative pathway, with the peak around POD20-25 followed by a decrease to the lowest level at POD33. Interestingly, complement factors regulating C3 activation show two distinct trends. Factors I and H, show a similar pattern as C3, whereas CD46 and CD55 begin to rise around POD21, reaching a low level plateau by POD40. This decrease could be due to the product 43    311769024v1 Attorney Docket No. 243735.000430  consumption as a result of AbMR event. The variability in proteomic data between the two analyses of the same component (i.e., XT1 vs. XT2; Figure 7) is likely the result of differences in nanoparticle protein corona composition in each analysis. Having said that, XT2 likely shows cleaved products of complement factor, hence representing pathway activation status. This is better observable in membrane attack complex component, where XT2 demonstrates steady and significantly lower level of these products after administration of Eculizumab while similar trend is being seen in C3 and factor B XT2 plot after first dose of pecetacoplan (Empaveli) (Figure 6). The interpretation of the proteomics is more challenging after the rejection event, as several sessions of plasmapheresis were administered which could interfere with plasma protein levels.

[0238] Xenotransplantation is a promising approach for the treatment of end-stage organ disease, but many hurdles remain to fully understand how the xenograft can be maintained long-term. The human decedent model presents a unique opportunity to interrogate key immune pathways via frequent sampling of blood and tissues in a controlled setting, yielding potential biomarkers as well as mechanistic insights into rejection of the xenograft. In this 61-day study of renal xenotransplantation, human immune infiltration into the renal xenograft was observed as early as two weeks after transplantation, followed by development of a brisk immune response involving multiple arms of the immune system which was successfully treated resulting in maintained xenograft function.

[0239] Host innate immune activity was observed as early as POD10, which was accompanied by significant increases in NK and pDC and B cell clonal diversity in recipient peripheral blood. This is followed by pDCs and NK cells spreading in the tissue as early as POD21, in parallel to increased expression of pig SPP1 (Osteopontin) and fibroblast activation. This tissue remodeling stabilizes while host pDC and NK cells peak in the pig tissue and the peripheral blood T cells acquire a new clonotype around POD33 (Figures 2-5). Notably, immune cell infiltration in the POD33 specimen comprised not only human immune cells but also enrichment of their pig counterparts, showing that a component of the induced immune response includes that of the host, as well as resident pig immune cells in the donor tissue. In addition, the involvement of known rejection patterns was confirmed in the infiltrating immune cells, and subsequent resolution of these patterns after the rejection episode, through the gradual increase and further decrease of human CXCL9, CXCL10 and CXCL11 in the tissue, with the maximum at POD33. Studies have shown that the presence of CXCL9 and / or CXCL10 in the urine or transplanted kidney is associated with graft rejection (Fairchild, et al., 2015;Suthanthiran, et al., 2013). 44    311769024v1 Attorney Docket No. 243735.000430

[0240] The tissue remodeling observed at POD21 and POD33 is accompanied by specific expression of molecular markers previously linked with kidney allograft rejection or injury. SPP1 is used as a predictor of early kidney transplant rejection (Jin, et al., 2013), and upregulated in all tubule segments and the glomeruli in pathological states (Sinha, et al., 2023). STNM1 is associated with renal fibrosis (Liu, et al., 2019), VCAM1 is associated with renal allograft rejection (Hill, et al., 1995), and CCL19 has previously been shown to be overexpressed in acute renal rejection (Lo, et al., 2011).

[0241] Finalizing the transplanted tissue response to the rejection event, a very interesting trend is seen in association with the resident macrophage activation, as shown by the increased proportion of a LYZ+ S100A6+ SPP1+ population, specifically at POD21 and POD33. This suggests potential crosstalk with inflamed / damaged cells in the tissue, but also potential interactions with the human immune system, especially that of complement activation.

[0242] Analysis of the peripheral blood shows both an initial increase in BCR clonal diversity by multiple measures followed by a drop in the diversity and d50 index shortly preceding the POD33 rejection specimen (Figure 3I), likely indicative of rejection-mediated B cell clonal expansion. Indeed, the high degree of shared clonotypes in these later time points (Figure 3J) supports the expansion and prevalence of xeno-targeting BCRs concomitant with tissue immune infiltration.

[0243] Moreover, the appearance of TCR clonal diversity as early as POD10 with the steady rise seen in the peripheral blood, accompanied by transcriptional signals associated with human CD4+ and CD8+ T cells, at POD21 in the peripheral blood and POD33 in the tissue, is a surrogate marker for activation and deployment of human T cells, despite multiple lines of immunosuppression. Considering the decrease in BCR clonal diversity by POD30 with increased class switching to IgA and decreased IgA somatic hypermutation around that time-point, together with the development of a new TCR clone at POD30, one could speculate that this is a human immune system reaction to a novel antigen. Despite the decedent’s complex clinical situation with multiple focal and systemic infections, the early increase in human NK cells and pDCs (innate immunity) in the peripheral blood followed by tissue infiltration of these cells as well as human CD4+ and CD8+ and pig tissue resident macrophage, suggests a pig’s antigen to be responsible for this cascade of events. Certainly, this would be more plausible, if more than one experimental decedent model were involved. Thus, the continuation of this program necessitates that going forward, the xenotransplantation field needs to resolve the shortage of donors.

[0244] This study holds great value through the depth of the longitudinal datasets, as the temporal dynamics surrounding the rejection event hints at an earlier, potentially causative role of pDCs, 45    311769024v1 Attorney Docket No. 243735.000430  plasmablasts and NK, which activity peaks before POD33, thereby confirming the AbMR event. The T cell dynamics, however, depict a later invasion in the tissue (absent at POD21), and later TCR specificity and activity, suggesting a more accompanying and amplifying but not causative role in the rejection event.

[0245] A major component of the molecular study design was the utilization of technologies that allowed for simultaneous profiling and stratification of pig- and human-specific cells and biomolecules so that the nature of interactions could be detailed between the xenograft tissue and human immune responses. This included the development of a novel 480-gene pig / human xenotransplant spatial gene expression panel for Xenium, in addition to pig kidney cell type specific markers (derived from Wang, et al., 2022). Further, a novel and recently-launched nanoparticle-based deep plasma proteomic profiling method was utilized combined with differential identification and mapping of human and pig proteins from Seer.

[0246] The present disclosure presents a high resolution molecular profiling of the longest lasting to date pig to human kidney xenotransplant. It provides an extensive resource for understanding the dynamics and importance of inflammatory and fibrotic states within the tissue, their interaction with resident pig immune cells and invading human immune cells, and the activity of both innate and adaptive immune responses of the recipient. This work complements the associated clinical study confirming the absence of initial renal injury post-transplantation, characterizing the rejection episode, before clinical and histological presentation, and confirming its resolution. Importantly, it offers an in-depth look at molecular pathways, immune cell roles, and early immune and tissue cell activity, demonstrating recipient immune activity and invasion preceding the rejection episode, and characterizing the associated transplant response and damage. Example 3: Combining single-nuclei methylated whole genome sequencing cell atlases with cell- specific cell-free DNA reveals prognostic and diagnostic signatures of cellular damage in pig kidney to human xenotransplantation

[0247] Over 100,000 people are currently on US transplant waiting lists, with only 1 in 4 receiving an allograft due to organ shortages. Xenotransplantation (XTx) of solid organs across species has enormous potential to address this unmet need for life-saving organs. The domestic pig is the most acceptable donor species due to the similarity in organ physiology and size. XTx in the brain-dead human (decedent) model offers opportunities to test new experimental medications and to longitudinally collect and 46    311769024v1 Attorney Docket No. 243735.000430  analyze blood and tissue samples in a ‘fail-safe’ manner. To advance the XTx field in living humans, avoidance of rejection through more suitable immunosuppression regimens, and early detection and treatment of rejection events by measuring biomarkers of xenograft cellular health without needle biopsies are amongst the main advancements needed to ensure xenograft longevity.

[0248] Methylated DNA nucleotides can play significant roles in control of gene expression and chromatin organization conferring cell-type function and identity. Bisulfite methylation whole-genome sequencing (m-WGS) atlases in healthy human tissue samples using tens of thousands of unique cell- specific methylation markers reveals key elements of cell and tissue ontogeny. Conventional donor- derived cell-free DNA (dd-cfDNA), used in several clinical diagnostics for acute allograft rejection, relies on clinically meaningful increases in absolute or relative levels of damaged donor allograft cells in recipient plasma, allowing allograft surveillance without needle biopsies. Methylated-dd-cfDNA (m- dd-cfDNA) gives an additional layer of specificity, as the unique methylated DNA patterns for each cell type in a given tissue allow proportions and absolute levels of cell-specific m-dd-cfDNA to be derived from plasma sample. This may be particularly important in the transplant setting where specific cell- types such as endothelial cells (EC) are damaged in initial waves of antibody-mediated rejection (AbMR). M-dd-cfDNA has been described in human allotransplantation studies using array-based methylated genotyping. Methylated pig cell atlases to date are limited primarily to adipose and muscle tissue for the meat industry. Herein is described a novel approach combining single-cell methylated whole genome sequencing cell atlases with cell-type specific cell-free DNA patterns in attempt to assess prognostic and diagnostic signatures of cellular damage in a gene-edited pig kidney transplanted into a human decedent over a period of 61 days.

[0249] The objective herein was to generate a pig kidney cell atlas from a control pig and assessed contrived cell-free DNA from that animal to assess cell-specific cell-free DNA composition.

[0250] Generation of a pig kidney cell atlas from an American Yorkshire pig: Single nuclei preparation was generated from a kidney from an American Yorkshire pig. Methylated snWGS libraries were generated from 4,322 of these pig kidney nuclei, and they were sequenced to a depth of approximately 40x using an Illumina NovaSeq instrument. Figures 34A-34C illustrate quality controls metrics for these methylated WGS reads as well as a UMAP of the methylation cell-types.

[0251] Contrived cell-free DNA preparations: A series of contrived proportions of pig and human cell free DNA plasma samples were prepared as a proof of concept and to assess sensitivity. The samples comprise pig DNA kidney nuclei from an American Yorkshire pig (no genetic edits) and human DNA 47    311769024v1 Attorney Docket No. 243735.000430  from a human cadaver (DNA from a native kidney biopsy). The experimental overview comprises: 1. DNA was extracted from isolated nuclei from pig kidney and separately from human kidney using Qiagen DNA extraction kit; 2. Mechanical shearing was performed using a Covaris sonicator to generate ~350bp DNA fragments;3. Samples were run on a TapeStation to assess fragment size and concentration; and 4. Preparation of contrived samples was performed as follows and in Table 1: Table 1. Preparation of contrived samples. Tube Input % pig DNA Human DNA (ng) Pig DNA (ng) Actual Human Actual Pig proportion post- proportion run post-run 1 100 0 30 0.03 99.97 2 10 27 3 92.06 7.94 3 3 29.1 0.9 97.7 2.3 4 2 29.4 0.6 98.44 1.56 5 1 29.7 0.3 99.28 0.72 6 0.5 29.85 0.15 99.67 0.33 7 0.05 29.985 0.015 99.94 0.06 8 0 30 0 99.98 0.02

[0252] The contrived proportions were then run on a PromethION P24i instrument (Oxford Nanopore Technologies). Similar to the 61-day xenotransplant cs-cfDNA experiments, a clear delineation of pig specific cell-free DNA was observed in the proportions expected from the 8 contrived samples shown above (columns 5-6).

[0253] Study design: Using a pig kidney to human decent model, m-dd-cfDNA levels originating from the pig xenograft were monitored over a 61-day XTx procedure, and inferred cell-type abundances were aligned to clinical events including a biopsy confirmed AbMR on post-operative day (POD) 33. A cell type atlas was created from the pig xenograft using methylation single-nuclei WGS (m-snWGS), and it was combined with methylated cfDNA analysis from 33 timepoints over the 61 day procedure in order to determine prognostic and diagnostic markers of pig xenograft damage (Figure 28).

[0254] Comprehensive methylation profiling of pig-specific cfDNA in XTx: Recent cfDNA sequencing and analyses pipelines using Oxford Nanopore Technologies (ONT) sequencing platform were developed to assess matched tumors and immune cell status in cancer patients and healthy controls 48    311769024v1 Attorney Docket No. 243735.000430  allowing comprehensive ascertainment of cfDNA methylation at single molecule resolution without bisulfite / enzymatic treatment. These pipelines were adapted for the XTx setting.

[0255] cfDNA and m-cfDNA dynamics were monitored over the course of the 61 day study to assess how plasma-derived cfDNA molecules changed in relation to the extensive clinical xenograft phenotyping that was performed in an intensive care setting. cfDNA was extracted using an automated platform from 4-8 ml of human decedent plasma for each time point, and was then processed into sequencing libraries on the ONT platform. Single-molecule sequencing of dd-cfDNA yielded from ~200 to >550 million reads per time point. Alignment of raw sequencing data to a joint human (GRCh38) and pig (Sus scrofa) reference corresponded to ~18-50-fold human genome coverage and several million reads aligning to the Sus scrofa pig reference genome per time point (Figure 35). While each sequencing run was set to a duration of ~72 hours, the real-time sequencing nature of the instrument yielded usable amounts of data after 8-12 hours.

[0256] Assessing the level of cfDNA reads aligning to the pig genome is an indirect biomarker of cell damage. Across the 61 day study, two distinct peaks of elevated pig-specific cfDNA were observed at Day 21 and Day 33 (Figure 27), where pig-specific cfDNA constituted 2.45% and 3.2% respectively of all aligned cfDNA. Day 33 corresponded to a biopsy-confirmed AbMR event. The corresponding decrease in pig-specific cfDNA coincides with therapeutic intervention in response to the rejection episodes. As a control, a pre-XTx timepoint was also sequenced in order to derive a false positive rate for spurious alignment to the Sus scrofa genome. For this timepoint, it was observed that approximately 1 / 1000 reads aligned to the Sus scrofa genome, which is ~10-100X higher than the observed alignment rates post XTx. After successful treatment of the rejection episode at Day 33 a reduction from 3.2% to approximately 1% pig cfDNA was observed in a 3 day period.

[0257] For pig-derived cfDNA, an aggregate 5mC methylation of ~60-70%, and 5hmC methylation of ~2% were observed. In order to derive possible prognostic and diagnostic biomarkers, the Sus scrofa genome annotations were used to calculate the average promoter methylation of each gene. Afterward, it was determined which pig-specific promoters underwent the highest variation across the entire study (Figure 36).

[0258] Further, nanopore sequencing of 13 timepoints over a 61-day pig to human XTx study revealed B. galdioli, EBV, and HHV7 through microbial cfDNA mapping (Figure 29).

[0259] Building a single-nuclei methylation kidney atlas: In order to align the complex dynamics of m-dd-cfDNA to individual cell types, m-snWGS was performed from a biopsy of the XTx kidney from 49    311769024v1 Attorney Docket No. 243735.000430  a genetically modified pig. Overall, methylated WGS profiles were generated from ~18,400 single nuclei, with ~1.5 million reads per nuclei totaling ~30 billion reads. Biopsy tissue was derived from the end of study timepoint where ample tissue was available for biopsy. Differential methylated regions of each cluster were analyzed and used to annotate cell types using the cell-type-differential genes in the tissue snRNA dataset derived from the same xenotransplantation procedure. Published cell-type-specific expression and methylation markers were then used to determine cell types in pig kidney xenograft.

[0260] m-snWGS library metrics conformed to acceptable outputs with a ScaleBio Methylation Data Analysis pipeline (Figure 23A for general sequencing metrics). The raw sequence data was processed into a single-cell methylation matrix. The fastq files were demultiplexed and trimmed, the trimmed reads were aligned to the Sus scrofa 11.1 genome, methylation calls were extracted from deduplicated alignments, and the CG and CH methylation rate matrices were generated using 10kb window “bins” (Figure 23A). This schematic is shown in Figure 25. Subsequently, the hypo-methylation scores were calculated and binarized for each bin, and the features were filtered with greater than 25% presence in the cells. PCA and UMAP algorithms were then applied (Figure 23B, Figures 26A-26B), and the clusters were derived from the knn graph using the Leiden algorithm (Liu, et al., 2021). Figure 23C shows the percentage of each cell type in m-sn-WGS. Figure 23A and Figure 32 shows a single-cell methylation matrix of methylated WGS for 18,400 single nuclei from a pig kidney xenograft.

[0261] To perform cell type annotation of each cluster, differentially methylated marker genes were cross-correlated with known cell type markers derived from public single cell resources as well as known conserved markers in the human genome. These identified marker genes are summarized in Table 2.1. The “source” column indicates the overlap between the cell type markers between the msnWGS dataset and other datasets. “snRNA” refers to the markers identified in a single nuclei RNA dataset. “PigAtlas” refers to the markers published by ‘PigAtlas’ (dreamapp.biomed.au.dk / pigatlas / ; Wang, F., Ding, P., Liang, X. et al. Endothelial cell heterogeneity and microglia regulons revealed by a pig cell landscape at single-cell level. Nat Commun 13, 3620 (2022). doi.org / 10.1038 / s41467-022-31388-z). “Human methylation cell type marker” refers to the markers identified by a human kidney methylation atlas (Yan, Y., Liu, H., Abedini, A. et al. Unraveling the epigenetic code: human kidney DNA methylation and chromatin dynamics in renal disease development. Nat Commun 15, 873 (2024). doi.org / 10.1038 / s41467-024-45295-y). Table 2.1 Cell type markers overlapped between the msnWGS dataset and the other databases. Cell-type gene source Collecting duct Principal cell ABCA5 Pig kidney snRNA as described herein 50    311769024v1 Attorney Docket No. 243735.000430  Collecting duct Principal cell ABCA6 Pig kidney snRNA as described herein Collecting duct Principal cell ABCC5 Pig kidney snRNA as described herein Collecting duct Principal cell ACOXL Pig kidney snRNA as described herein Collecting duct Principal cell ADAMTS16 Pig kidney snRNA as described herein Collecting duct Principal cell ADGRF1 Pig kidney snRNA as described herein Collecting duct Principal cell AFF1 Pig kidney snRNA as described herein Collecting duct Principal cell ARHGAP28 Pig kidney snRNA as described herein Collecting duct Principal cell ARHGAP6 Pig kidney snRNA as described herein Collecting duct Principal cell ARHGEF10L Pig kidney snRNA as described herein Collecting duct Principal cell ARL15 Pig kidney snRNA as described herein Collecting duct Principal cell ATP1B1 Pig kidney snRNA as described herein Collecting duct Principal cell BICC1 Pig kidney snRNA as described herein Collecting duct Principal cell C9H1orf21 Pig kidney snRNA as described herein Collecting duct Principal cell CCDC148 Pig kidney snRNA as described herein Collecting duct Principal cell CLASP1 Pig kidney snRNA as described herein Collecting duct Principal cell CRYBG1 Pig kidney snRNA as described herein Collecting duct Principal cell CSMD2 Pig kidney snRNA as described herein Collecting duct Principal cell CTNND2 Pig kidney snRNA as described herein Collecting duct Principal cell CYFIP2 Pig kidney snRNA as described herein Collecting duct Principal cell DACH1 Pig kidney snRNA as described herein Collecting duct Principal cell DCDC2 Pig kidney snRNA as described herein Collecting duct Principal cell DMD Pig kidney snRNA as described herein Collecting duct Principal cell DTNB Pig kidney snRNA as described herein Collecting duct Principal cell EFEMP1 Pig kidney snRNA as described herein Collecting duct Principal cell ELOVL7 Pig kidney snRNA as described herein Collecting duct Principal cell ESRRG Pig kidney snRNA as described herein Collecting duct Principal cell FARP1 Pig kidney snRNA as described herein Collecting duct Principal cell FHOD3 Pig kidney snRNA as described herein Collecting duct Principal cell FRAS1 Pig kidney snRNA as described herein Collecting duct Principal cell GLIS3 Pig kidney snRNA as described herein Collecting duct Principal cell GRIA4 Pig kidney snRNA as described herein Collecting duct Principal cell GRIP1 Pig kidney snRNA as described herein Collecting duct Principal cell GUCY1A3 Pig kidney snRNA as described herein Collecting duct Principal cell GULP1 Pig kidney snRNA as described herein Collecting duct Principal cell HDAC9 Pig kidney snRNA as described herein Collecting duct Principal cell HOMER1 Pig kidney snRNA as described herein Collecting duct Principal cell HOXA10 Pig kidney snRNA as described herein Collecting duct Principal cell HS3ST3A1 Pig kidney snRNA as described herein Collecting duct Principal cell ITGAV Pig kidney snRNA as described herein Collecting duct Principal cell KIFC3 Pig kidney snRNA as described herein Collecting duct Principal cell KL Pig kidney snRNA as described herein Collecting duct Principal cell KLHL3 Pig kidney snRNA as described herein Collecting duct Principal cell LAMB1 Pig kidney snRNA as described herein Collecting duct Principal cell LINGO2 Pig kidney snRNA as described herein Collecting duct Principal cell LMO7 Pig kidney snRNA as described herein Collecting duct Principal cell LOC100153543 Pig kidney snRNA as described herein Collecting duct Principal cell LOC100524475 Pig kidney snRNA as described herein Collecting duct Principal cell LOC100626206 Pig kidney snRNA as described herein Collecting duct Principal cell LOC102157778 Pig kidney snRNA as described herein Collecting duct Principal cell LOC102163816 Pig kidney snRNA as described herein Collecting duct Principal cell LOC102165694 Pig kidney snRNA as described herein Collecting duct Principal cell LOC106505633 Pig kidney snRNA as described herein Collecting duct Principal cell LRBA Pig kidney snRNA as described herein Collecting duct Principal cell MAML3 Pig kidney snRNA as described herein 51    311769024v1 Attorney Docket No. 243735.000430  Collecting duct Principal cell MECOM Pig kidney snRNA as described herein Collecting duct Principal cell MKL1 Pig kidney snRNA as described herein Collecting duct Principal cell MPDZ Pig kidney snRNA as described herein Collecting duct Principal cell MPPED2 Pig kidney snRNA as described herein Collecting duct Principal cell MYO16 Pig kidney snRNA as described herein Collecting duct Principal cell MYO1B Pig kidney snRNA as described herein Collecting duct Principal cell NAALADL2 Pig kidney snRNA as described herein Collecting duct Principal cell NEDD4L Pig kidney snRNA as described herein Collecting duct Principal cell NEO1 Pig kidney snRNA as described herein Collecting duct Principal cell NLK Pig kidney snRNA as described herein Collecting duct Principal cell NR3C2 Pig kidney snRNA as described herein Collecting duct Principal cell PAK5 Pig kidney snRNA as described herein Collecting duct Principal cell PCDH15 Pig kidney snRNA as described herein Collecting duct Principal cell PDE3B Pig kidney snRNA as described herein Collecting duct Principal cell PKHD1 Pig kidney snRNA as described herein Collecting duct Principal cell PKP4 Pig kidney snRNA as described herein Collecting duct Principal cell PPFIBP1 Pig kidney snRNA as described herein Collecting duct Principal cell PPP2R3A Pig kidney snRNA as described herein Collecting duct Principal cell PRKAG2 Pig kidney snRNA as described herein Collecting duct Principal cell PTPN21 Pig kidney snRNA as described herein Collecting duct Principal cell QKI Pig kidney snRNA as described herein Collecting duct Principal cell RBBP8 Pig kidney snRNA as described herein Collecting duct Principal cell REPS2 Pig kidney snRNA as described herein Collecting duct Principal cell SAMD5 Pig kidney snRNA as described herein Collecting duct Principal cell SASH1 Pig kidney snRNA as described herein Collecting duct Principal cell SDK1 Pig kidney snRNA as described herein Collecting duct Principal cell SESTD1 Pig kidney snRNA as described herein Collecting duct Principal cell SH3BP4 Pig kidney snRNA as described herein Collecting duct Principal cell SLC16A10 Pig kidney snRNA as described herein Collecting duct Principal cell SLC8A1 Pig kidney snRNA as described herein Collecting duct Principal cell SLIT2 Pig kidney snRNA as described herein Collecting duct Principal cell SSBP2 Pig kidney snRNA as described herein Collecting duct Principal cell TBC1D1 Pig kidney snRNA as described herein Collecting duct Principal cell TMEM52B Pig kidney snRNA as described herein Collecting duct Principal cell TNIK Pig kidney snRNA as described herein Collecting duct Principal cell TRPV5 Pig kidney snRNA as described herein Collecting duct Principal cell UTRN Pig kidney snRNA as described herein Collecting duct Principal cell VEPH1 Pig kidney snRNA as described herein Collecting duct Principal cell WLS Pig kidney snRNA as described herein Collecting duct Principal cell WNK1 Pig kidney snRNA as described herein Collecting duct Principal cell WWC1 Pig kidney snRNA as described herein Distal tubule ABI3BP Pig kidney snRNA as described herein Distal tubule ABLIM1 Pig kidney snRNA as described herein Distal tubule ADAMTS16 Pig kidney snRNA as described herein Distal tubule ANKRD44 Pig kidney snRNA as described herein Distal tubule APP Pig kidney snRNA as described herein Distal tubule ARHGAP6 Pig kidney snRNA as described herein Distal tubule ARNT2 Pig kidney snRNA as described herein Distal tubule ATP1A1 Pig kidney snRNA as described herein Distal tubule ATP1B1 Pig kidney snRNA as described herein Distal tubule CCDC148 Pig kidney snRNA as described herein Distal tubule CDK14 Pig kidney snRNA as described herein Distal tubule CDK8 Pig kidney snRNA as described herein Distal tubule CLCNKA Pig kidney snRNA as described herein 52    311769024v1 Attorney Docket No. 243735.000430  Distal tubule CPEB3 Pig kidney snRNA as described herein Distal tubule CPEB4 Pig kidney snRNA as described herein Distal tubule CRYBG3 Pig kidney snRNA as described herein Distal tubule CTNND2 Pig kidney snRNA as described herein Distal tubule DCDC2 Pig kidney snRNA as described herein Distal tubule DLG2 Pig kidney snRNA as described herein Distal tubule DMD Pig kidney snRNA as described herein Distal tubule DNM3 Pig kidney snRNA as described herein Distal tubule ENOX1 Pig kidney snRNA as described herein Distal tubule ERBB4 Pig kidney snRNA as described herein Distal tubule ESRRG Pig kidney snRNA as described herein Distal tubule FAM160A1 Pig kidney snRNA as described herein Distal tubule FARP1 Pig kidney snRNA as described herein Distal tubule FRY Pig kidney snRNA as described herein Distal tubule FXYD2 Pig kidney snRNA as described herein Distal tubule GHR Pig kidney snRNA as described herein Distal tubule HMCN1 Pig kidney snRNA as described herein Distal tubule IER3 Pig kidney snRNA as described herein Distal tubule KATNAL2 Pig kidney snRNA as described herein Distal tubule KAZN Pig kidney snRNA as described herein Distal tubule KCTD1 Pig kidney snRNA as described herein Distal tubule LAMB1 Pig kidney snRNA as described herein Distal tubule LOC100514494 Pig kidney snRNA as described herein Distal tubule LOC100621701 Pig kidney snRNA as described herein Distal tubule LOC102157778 Pig kidney snRNA as described herein Distal tubule LOC102159645 Pig kidney snRNA as described herein Distal tubule LOC102163816 Pig kidney snRNA as described herein Distal tubule LOC110255436 Pig kidney snRNA as described herein Distal tubule LOC110257722 Pig kidney snRNA as described herein Distal tubule LOC110258970 Pig kidney snRNA as described herein Distal tubule LOC110259328 Pig kidney snRNA as described herein Distal tubule LRBA Pig kidney snRNA as described herein Distal tubule MECOM Pig kidney snRNA as described herein Distal tubule MOB3B Pig kidney snRNA as described herein Distal tubule MPPED2 Pig kidney snRNA as described herein Distal tubule NAALADL2 Pig kidney snRNA as described herein Distal tubule NHSL2 Pig kidney snRNA as described herein Distal tubule NNT Pig kidney snRNA as described herein Distal tubule NR3C2 Pig kidney snRNA as described herein Distal tubule OSBPL3 Pig kidney snRNA as described herein Distal tubule PABPC1 Pig kidney snRNA as described herein Distal tubule PAK5 Pig kidney snRNA as described herein Distal tubule PKHD1 Pig kidney snRNA as described herein Distal tubule PKP4 Pig kidney snRNA as described herein Distal tubule PLCB1 Pig kidney snRNA as described herein Distal tubule PLCL1 Pig kidney snRNA as described herein Distal tubule PPP2R3A Pig kidney snRNA as described herein Distal tubule PRKD1 Pig kidney snRNA as described herein Distal tubule RAI14 Pig kidney snRNA as described herein Distal tubule RBBP8 Pig kidney snRNA as described herein Distal tubule RORA Pig kidney snRNA as described herein Distal tubule RUBCNL Pig kidney snRNA as described herein Distal tubule RYR2 Pig kidney snRNA as described herein Distal tubule SAMD5 Pig kidney snRNA as described herein 53    311769024v1 Attorney Docket No. 243735.000430  Distal tubule SASH1 Pig kidney snRNA as described herein Distal tubule SGSM1 Pig kidney snRNA as described herein Distal tubule SLC16A10 Pig kidney snRNA as described herein Distal tubule SSBP2 Pig kidney snRNA as described herein Distal tubule ST6GALNAC3 Pig kidney snRNA as described herein Distal tubule STK32B Pig kidney snRNA as described herein Distal tubule TMEM72 Pig kidney snRNA as described herein Distal tubule TOX3 Pig kidney snRNA as described herein Distal tubule TSC22D1 Pig kidney snRNA as described herein Distal tubule UBE2E2 Pig kidney snRNA as described herein Distal tubule UMOD Pig kidney snRNA as described herein Distal tubule USP24 Pig kidney snRNA as described herein Distal tubule VEPH1 Pig kidney snRNA as described herein Distal tubule WNK1 Pig kidney snRNA as described herein Distal tubule ZCCHC11 Pig kidney snRNA as described herein Distal tubule ZNF385B Pig kidney snRNA as described herein Endothelial cell ADGRF5 Pig kidney snRNA as described herein Endothelial cell ADGRL4 Pig kidney snRNA as described herein Endothelial cell AKAP2 Pig kidney snRNA as described herein Endothelial cell AKT3 Pig kidney snRNA as described herein Endothelial cell ARAP3 Pig kidney snRNA as described herein Endothelial cell ARHGAP26 Pig kidney snRNA as described herein Endothelial cell ARHGAP31 Pig kidney snRNA as described herein Endothelial cell ARHGEF9 Pig kidney snRNA as described herein Endothelial cell ARL15 Pig kidney snRNA as described herein Endothelial cell ASAP1 Pig kidney snRNA as described herein Endothelial cell CALCRL Pig kidney snRNA as described herein Endothelial cell CD74 Pig kidney snRNA as described herein Endothelial cell CHRM3 Pig kidney snRNA as described herein Endothelial cell CHST15 Pig kidney snRNA as described herein Endothelial cell CLIC5 Pig kidney snRNA as described herein Endothelial cell CMTM8 Pig kidney snRNA as described herein Endothelial cell CSGALNACT1 Pig kidney snRNA as described herein Endothelial cell CYYR1 Pig kidney snRNA as described herein Endothelial cell DGKH Pig kidney snRNA as described herein Endothelial cell DLC1 Pig kidney snRNA as described herein Endothelial cell DNAH11 Pig kidney snRNA as described herein Endothelial cell DOCK4 Pig kidney snRNA as described herein Endothelial cell DOCK9 Pig kidney snRNA as described herein Endothelial cell DYSF Pig kidney snRNA as described herein Endothelial cell EBF1 Pig kidney snRNA as described herein Endothelial cell EGFL7 Pig kidney snRNA as described herein Endothelial cell EHD4 Pig kidney snRNA as described herein Endothelial cell ELMO1 Pig kidney snRNA as described herein Endothelial cell EPAS1 Pig kidney snRNA as described herein Endothelial cell ERG Pig kidney snRNA as described herein Endothelial cell ETS1 Pig kidney snRNA as described herein Endothelial cell FGD5 Pig kidney snRNA as described herein Endothelial cell FLI1 Pig kidney snRNA as described herein Endothelial cell FLT1 Pig kidney snRNA as described herein Endothelial cell FMNL2 Pig kidney snRNA as described herein Endothelial cell FRMD3 Pig kidney snRNA as described herein Endothelial cell FRMD4B Pig kidney snRNA as described herein Endothelial cell FYN Pig kidney snRNA as described herein 54    311769024v1 Attorney Docket No. 243735.000430  Endothelial cell GALNT18 Pig kidney snRNA as described herein Endothelial cell GNAQ Pig kidney snRNA as described herein Endothelial cell GRB10 Pig kidney snRNA as described herein Endothelial cell HEG1 Pig kidney snRNA as described herein Endothelial cell HIP1 Pig kidney snRNA as described herein Endothelial cell INPP4B Pig kidney snRNA as described herein Endothelial cell KALRN Pig kidney snRNA as described herein Endothelial cell LDB2 Pig kidney snRNA as described herein Endothelial cell LIMCH1 Pig kidney snRNA as described herein Endothelial cell LRRC8C Pig kidney snRNA as described herein Endothelial cell LTBP1 Pig kidney snRNA as described herein Endothelial cell MACF1 Pig kidney snRNA as described herein Endothelial cell MCC Pig kidney snRNA as described herein Endothelial cell MCTP1 Pig kidney snRNA as described herein Endothelial cell MEF2C Pig kidney snRNA as described herein Endothelial cell MEIS2 Pig kidney snRNA as described herein Endothelial cell MMRN2 Pig kidney snRNA as described herein Endothelial cell MSN Pig kidney snRNA as described herein Endothelial cell NAV1 Pig kidney snRNA as described herein Endothelial cell NCALD Pig kidney snRNA as described herein Endothelial cell PBX1 Pig kidney snRNA as described herein Endothelial cell PCDH17 Pig kidney snRNA as described herein Endothelial cell PEAK1 Pig kidney snRNA as described herein Endothelial cell PIK3C2B Pig kidney snRNA as described herein Endothelial cell PITPNM2 Pig kidney snRNA as described herein Endothelial cell PLPP1 Pig kidney snRNA as described herein Endothelial cell PLPP3 Pig kidney snRNA as described herein Endothelial cell PPP3CA Pig kidney snRNA as described herein Endothelial cell PREX2 Pig kidney snRNA as described herein Endothelial cell PRKCH Pig kidney snRNA as described herein Endothelial cell PTPRB Pig kidney snRNA as described herein Endothelial cell PTPRM Pig kidney snRNA as described herein Endothelial cell RAPGEF4 Pig kidney snRNA as described herein Endothelial cell RASAL2 Pig kidney snRNA as described herein Endothelial cell RASGRF2 Pig kidney snRNA as described herein Endothelial cell RASGRP3 Pig kidney snRNA as described herein Endothelial cell RBMS3 Pig kidney snRNA as described herein Endothelial cell RUNX1T1 Pig kidney snRNA as described herein Endothelial cell SH3BP5 Pig kidney snRNA as described herein Endothelial cell SH3PXD2A Pig kidney snRNA as described herein Endothelial cell SHANK3 Pig kidney snRNA as described herein Endothelial cell SLA-1 Pig kidney snRNA as described herein Endothelial cell SLA-2 Pig kidney snRNA as described herein Endothelial cell SLA-DQA1 Pig kidney snRNA as described herein Endothelial cell SLC8A1 Pig kidney snRNA as described herein Endothelial cell SRGAP2 Pig kidney snRNA as described herein Endothelial cell SVIL Pig kidney snRNA as described herein Endothelial cell SYNE1 Pig kidney snRNA as described herein Endothelial cell TANC1 Pig kidney snRNA as described herein Endothelial cell TCF4 Pig kidney snRNA as described herein Endothelial cell TEK Pig kidney snRNA as described herein Endothelial cell TGFBR2 Pig kidney snRNA as described herein Endothelial cell TIAM1 Pig kidney snRNA as described herein Endothelial cell TIMP3 Pig kidney snRNA as described herein 55    311769024v1 Attorney Docket No. 243735.000430  Endothelial cell TMTC1 Pig kidney snRNA as described herein Endothelial cell TMTC2 Pig kidney snRNA as described herein Endothelial cell TTC28 Pig kidney snRNA as described herein Endothelial cell UTRN Pig kidney snRNA as described herein Endothelial cell ZEB1 Pig kidney snRNA as described herein Endothelial cell ZNF521 Pig kidney snRNA as described herein Fibroblast ARHGAP24 Pig kidney snRNA as described herein Fibroblast ARHGEF28 Pig kidney snRNA as described herein Fibroblast ATP1B1 Pig kidney snRNA as described herein Fibroblast ATP6V1B1 Pig kidney snRNA as described herein Fibroblast B3GALT1 Pig kidney snRNA as described herein Fibroblast CA12 Pig kidney snRNA as described herein Fibroblast CACNA1C Pig kidney snRNA as described herein Fibroblast CACNA2D1 Pig kidney snRNA as described herein Fibroblast CDH11 Pig kidney snRNA as described herein Fibroblast CDKL1 Pig kidney snRNA as described herein Fibroblast COBLL1 Pig kidney snRNA as described herein Fibroblast COL12A1 Pig kidney snRNA as described herein Fibroblast COLEC12 Pig kidney snRNA as described herein Fibroblast CPEB4 Pig kidney snRNA as described herein Fibroblast CYTH3 Pig kidney snRNA as described herein Fibroblast DDAH1 Pig kidney snRNA as described herein Fibroblast DLC1 Pig kidney snRNA as described herein Fibroblast EGF Pig kidney snRNA as described herein Fibroblast EPAS1 Pig kidney snRNA as described herein Fibroblast EPB41L4A Pig kidney snRNA as described herein Fibroblast ESRRG Pig kidney snRNA as described herein Fibroblast ESYT2 Pig kidney snRNA as described herein Fibroblast FBLN5 Pig kidney snRNA as described herein Fibroblast FGD4 Pig kidney snRNA as described herein Fibroblast FRYL Pig kidney snRNA as described herein Fibroblast GNA14 Pig kidney snRNA as described herein Fibroblast GPM6B Pig kidney snRNA as described herein Fibroblast GPR39 Pig kidney snRNA as described herein Fibroblast HNF1B Pig kidney snRNA as described herein Fibroblast HPSE2 Pig kidney snRNA as described herein Fibroblast IGFBP7 Pig kidney snRNA as described herein Fibroblast IL1R1 Pig kidney snRNA as described herein Fibroblast KCND3 Pig kidney snRNA as described herein Fibroblast KIRREL Pig kidney snRNA as described herein Fibroblast LAMA2 Pig kidney snRNA as described herein Fibroblast LAMA4 Pig kidney snRNA as described herein Fibroblast LOC100738003 Pig kidney snRNA as described herein Fibroblast LOC102157778 Pig kidney snRNA as described herein Fibroblast LOC102160410 Pig kidney snRNA as described herein Fibroblast LOC106506202 Pig kidney snRNA as described herein Fibroblast LTBP1 Pig kidney snRNA as described herein Fibroblast MAP7 Pig kidney snRNA as described herein Fibroblast MEIS1 Pig kidney snRNA as described herein Fibroblast NAV2 Pig kidney snRNA as described herein Fibroblast NFIA Pig kidney snRNA as described herein Fibroblast NR4A3 Pig kidney snRNA as described herein Fibroblast NRP1 Pig kidney snRNA as described herein Fibroblast NTRK2 Pig kidney snRNA as described herein 56    311769024v1 Attorney Docket No. 243735.000430  Fibroblast NXN Pig kidney snRNA as described herein Fibroblast PAX2 Pig kidney snRNA as described herein Fibroblast PCDH7 Pig kidney snRNA as described herein Fibroblast PDE3A Pig kidney snRNA as described herein Fibroblast PHLDB2 Pig kidney snRNA as described herein Fibroblast PID1 Pig kidney snRNA as described herein Fibroblast PKHD1 Pig kidney snRNA as described herein Fibroblast PKP4 Pig kidney snRNA as described herein Fibroblast PLEKHA7 Pig kidney snRNA as described herein Fibroblast PPARGC1A Pig kidney snRNA as described herein Fibroblast PPM1L Pig kidney snRNA as described herein Fibroblast PPP2R3A Pig kidney snRNA as described herein Fibroblast PRKG1 Pig kidney snRNA as described herein Fibroblast RBM47 Pig kidney snRNA as described herein Fibroblast RBMS3 Pig kidney snRNA as described herein Fibroblast ROBO2 Pig kidney snRNA as described herein Fibroblast ROR1 Pig kidney snRNA as described herein Fibroblast SLC16A10 Pig kidney snRNA as described herein Fibroblast SOX5 Pig kidney snRNA as described herein Fibroblast SOX6 Pig kidney snRNA as described herein Fibroblast SPON1 Pig kidney snRNA as described herein Fibroblast SYNE1 Pig kidney snRNA as described herein Fibroblast TFCP2L1 Pig kidney snRNA as described herein Fibroblast THSD7A Pig kidney snRNA as described herein Fibroblast TMTC1 Pig kidney snRNA as described herein Fibroblast TNS3 Pig kidney snRNA as described herein Fibroblast TRPS1 Pig kidney snRNA as described herein Fibroblast TSHZ2 Pig kidney snRNA as described herein Fibroblast TTC28 Pig kidney snRNA as described herein Fibroblast VAV3 Pig kidney snRNA as described herein Fibroblast WDR72 Pig kidney snRNA as described herein Fibroblast WWC1 Pig kidney snRNA as described herein Fibroblast ZEB1 Pig kidney snRNA as described herein Fibroblast ZEB2 Pig kidney snRNA as described herein Fibroblast ZFPM2 Pig kidney snRNA as described herein Henle loop ABHD2 Pig kidney snRNA as described herein Henle loop ABI3BP Pig kidney snRNA as described herein Henle loop ABLIM1 Pig kidney snRNA as described herein Henle loop ACACB Pig kidney snRNA as described herein Henle loop ADAM22 Pig kidney snRNA as described herein Henle loop ADAMTS17 Pig kidney snRNA as described herein Henle loop ADORA2A Pig kidney snRNA as described herein Henle loop AFF2 Pig kidney snRNA as described herein Henle loop ANAPC13 Pig kidney snRNA as described herein Henle loop ANK3 Pig kidney snRNA as described herein Henle loop ANKMY1 Pig kidney snRNA as described herein Henle loop ANKRD23 Pig kidney snRNA as described herein Henle loop APOBEC2 Pig kidney snRNA as described herein Henle loop ARHGAP11A Pig kidney snRNA as described herein Henle loop ARHGAP6 Pig kidney snRNA as described herein Henle loop ARHGEF18 Pig kidney snRNA as described herein Henle loop ARNT2 Pig kidney snRNA as described herein Henle loop ATCAY Pig kidney snRNA as described herein Henle loop ATP1A1 Pig kidney snRNA as described herein 57    311769024v1 Attorney Docket No. 243735.000430  Henle loop ATP1B1 Pig kidney snRNA as described herein Henle loop ATP6V1F Pig kidney snRNA as described herein Henle loop B3GNT6 Pig kidney snRNA as described herein Henle loop B3GNT7 Pig kidney snRNA as described herein Henle loop BEAN1 Pig kidney snRNA as described herein Henle loop BICDL1 Pig kidney snRNA as described herein Henle loop C13H21orf62 Pig kidney snRNA as described herein Henle loop C1H15orf62 Pig kidney snRNA as described herein Henle loop C2H11orf68 Pig kidney snRNA as described herein Henle loop CACNA1H Pig kidney snRNA as described herein Henle loop CASKIN1 Pig kidney snRNA as described herein Henle loop CASR Pig kidney snRNA as described herein Henle loop CCNA2 Pig kidney snRNA as described herein Henle loop CCSER1 Pig kidney snRNA as described herein Henle loop CD5L Pig kidney snRNA as described herein Henle loop CD6 Pig kidney snRNA as described herein Henle loop CGNL1 Pig kidney snRNA as described herein Henle loop CHMP1B Pig kidney snRNA as described herein Henle loop CHPF Pig kidney snRNA as described herein Henle loop CLDN16 Pig kidney snRNA as described herein Henle loop CLEC14A Pig kidney snRNA as described herein Henle loop CNTNAP1 Pig kidney snRNA as described herein Henle loop CPEB3 Pig kidney snRNA as described herein Henle loop CPO Pig kidney snRNA as described herein Henle loop CPTP Pig kidney snRNA as described herein Henle loop CYGB Pig kidney snRNA as described herein Henle loop DCDC2 Pig kidney snRNA as described herein Henle loop DGAT2L6 Pig kidney snRNA as described herein Henle loop DMD Pig kidney snRNA as described herein Henle loop DMWD Pig kidney snRNA as described herein Henle loop DNAAF1 Pig kidney snRNA as described herein Henle loop DNAJC4 Pig kidney snRNA as described herein Henle loop DNM3 Pig kidney snRNA as described herein Henle loop DOK2 Pig kidney snRNA as described herein Henle loop DOK6 Pig kidney snRNA as described herein Henle loop DPM2 Pig kidney snRNA as described herein Henle loop DST Pig kidney snRNA as described herein Henle loop DYNLL1 Pig kidney snRNA as described herein Henle loop EFNA5 Pig kidney snRNA as described herein Henle loop EGF Pig kidney snRNA as described herein Henle loop ENOX1 Pig kidney snRNA as described herein Henle loop EPHA6 Pig kidney snRNA as described herein Henle loop EPYC Pig kidney snRNA as described herein Henle loop ERBB4 Pig kidney snRNA as described herein Henle loop ERN1 Pig kidney snRNA as described herein Henle loop ESRRB Pig kidney snRNA as described herein Henle loop ESRRG Pig kidney snRNA as described herein Henle loop EXOC3L1 Pig kidney snRNA as described herein Henle loop FAM160A1 Pig kidney snRNA as described herein Henle loop FAM26E Pig kidney snRNA as described herein Henle loop FAM89B Pig kidney snRNA as described herein Henle loop FEM1A Pig kidney snRNA as described herein Henle loop FETUB Pig kidney snRNA as described herein Henle loop FMR1NB Pig kidney snRNA as described herein 58    311769024v1 Attorney Docket No. 243735.000430  Henle loop FXYD2 Pig kidney snRNA as described herein Henle loop GPA33 Pig kidney snRNA as described herein Henle loop GPR4 Pig kidney snRNA as described herein Henle loop GRAMD2B Pig kidney snRNA as described herein Henle loop HMGB3 Pig kidney snRNA as described herein Henle loop HOXD11 Pig kidney snRNA as described herein Henle loop IER3 Pig kidney snRNA as described herein Henle loop IGFN1 Pig kidney snRNA as described herein Henle loop IL17RE Pig kidney snRNA as described herein Henle loop ILDR1 Pig kidney snRNA as described herein Henle loop IRF2BP1 Pig kidney snRNA as described herein Henle loop JAK3 Pig kidney snRNA as described herein Henle loop KCNC1 Pig kidney snRNA as described herein Henle loop KCNJ1 Pig kidney snRNA as described herein Henle loop KCNN1 Pig kidney snRNA as described herein Henle loop KCTD1 Pig kidney snRNA as described herein Henle loop KIF22 Pig kidney snRNA as described herein Henle loop KIF2C Pig kidney snRNA as described herein Henle loop KNG1 Pig kidney snRNA as described herein Henle loop KRT7 Pig kidney snRNA as described herein Henle loop LBP Pig kidney snRNA as described herein Henle loop LENG1 Pig kidney snRNA as described herein Henle loop LGI1 Pig kidney snRNA as described herein Henle loop LGR4 Pig kidney snRNA as described herein Henle loop LNX1 Pig kidney snRNA as described herein Henle loop LOC100156167 Pig kidney snRNA as described herein Henle loop LOC100517735 Pig kidney snRNA as described herein Henle loop LOC100521647 Pig kidney snRNA as described herein Henle loop LOC100623720 Pig kidney snRNA as described herein Henle loop LOC100626258 Pig kidney snRNA as described herein Henle loop LOC100736720 Pig kidney snRNA as described herein Henle loop LOC100737180 Pig kidney snRNA as described herein Henle loop LOC102157773 Pig kidney snRNA as described herein Henle loop LOC102158113 Pig kidney snRNA as described herein Henle loop LOC102158334 Pig kidney snRNA as described herein Henle loop LOC102158739 Pig kidney snRNA as described herein Henle loop LOC102160111 Pig kidney snRNA as described herein Henle loop LOC102160197 Pig kidney snRNA as described herein Henle loop LOC102160528 Pig kidney snRNA as described herein Henle loop LOC102161379 Pig kidney snRNA as described herein Henle loop LOC102161909 Pig kidney snRNA as described herein Henle loop LOC102163368 Pig kidney snRNA as described herein Henle loop LOC102165783 Pig kidney snRNA as described herein Henle loop LOC106504267 Pig kidney snRNA as described herein Henle loop LOC106504386 Pig kidney snRNA as described herein Henle loop LOC106504530 Pig kidney snRNA as described herein Henle loop LOC106506219 Pig kidney snRNA as described herein Henle loop LOC106506844 Pig kidney snRNA as described herein Henle loop LOC106506916 Pig kidney snRNA as described herein Henle loop LOC106507591 Pig kidney snRNA as described herein Henle loop LOC106507784 Pig kidney snRNA as described herein Henle loop LOC106508818 Pig kidney snRNA as described herein Henle loop LOC106509013 Pig kidney snRNA as described herein Henle loop LOC106509267 Pig kidney snRNA as described herein 59    311769024v1 Attorney Docket No. 243735.000430  Henle loop LOC106509690 Pig kidney snRNA as described herein Henle loop LOC106510575 Pig kidney snRNA as described herein Henle loop LOC110255436 Pig kidney snRNA as described herein Henle loop LOC110255617 Pig kidney snRNA as described herein Henle loop LOC110255759 Pig kidney snRNA as described herein Henle loop LOC110256308 Pig kidney snRNA as described herein Henle loop LOC110256433 Pig kidney snRNA as described herein Henle loop LOC110256437 Pig kidney snRNA as described herein Henle loop LOC110256625 Pig kidney snRNA as described herein Henle loop LOC110256953 Pig kidney snRNA as described herein Henle loop LOC110257026 Pig kidney snRNA as described herein Henle loop LOC110257073 Pig kidney snRNA as described herein Henle loop LOC110257357 Pig kidney snRNA as described herein Henle loop LOC110257722 Pig kidney snRNA as described herein Henle loop LOC110258091 Pig kidney snRNA as described herein Henle loop LOC110258955 Pig kidney snRNA as described herein Henle loop LOC110259328 Pig kidney snRNA as described herein Henle loop LOC110259659 Pig kidney snRNA as described herein Henle loop LOC110259780 Pig kidney snRNA as described herein Henle loop LOC110259974 Pig kidney snRNA as described herein Henle loop LOC110259986 Pig kidney snRNA as described herein Henle loop LOC110260050 Pig kidney snRNA as described herein Henle loop LOC110260188 Pig kidney snRNA as described herein Henle loop LOC110260320 Pig kidney snRNA as described herein Henle loop LOC110260422 Pig kidney snRNA as described herein Henle loop LOC110260588 Pig kidney snRNA as described herein Henle loop LOC110260745 Pig kidney snRNA as described herein Henle loop LOC110260985 Pig kidney snRNA as described herein Henle loop LOC110261190 Pig kidney snRNA as described herein Henle loop LOC110261244 Pig kidney snRNA as described herein Henle loop LOC110261988 Pig kidney snRNA as described herein Henle loop LOC110262080 Pig kidney snRNA as described herein Henle loop LOC110262141 Pig kidney snRNA as described herein Henle loop LOC110262162 Pig kidney snRNA as described herein Henle loop LRBA Pig kidney snRNA as described herein Henle loop LSM3 Pig kidney snRNA as described herein Henle loop MAP3K5 Pig kidney snRNA as described herein Henle loop MDGA2 Pig kidney snRNA as described herein Henle loop MECOM Pig kidney snRNA as described herein Henle loop MED22 Pig kidney snRNA as described herein Henle loop MIEF2 Pig kidney snRNA as described herein Henle loop MOGAT2 Pig kidney snRNA as described herein Henle loop MSI2 Pig kidney snRNA as described herein Henle loop MYOT Pig kidney snRNA as described herein Henle loop NAALADL2 Pig kidney snRNA as described herein Henle loop NHP2 Pig kidney snRNA as described herein Henle loop NHSL2 Pig kidney snRNA as described herein Henle loop NME8 Pig kidney snRNA as described herein Henle loop NMU Pig kidney snRNA as described herein Henle loop NR3C2 Pig kidney snRNA as described herein Henle loop NRCAM Pig kidney snRNA as described herein Henle loop NRK Pig kidney snRNA as described herein Henle loop NUDT16 Pig kidney snRNA as described herein Henle loop OCLN Pig kidney snRNA as described herein 60    311769024v1 Attorney Docket No. 243735.000430  Henle loop OLAH Pig kidney snRNA as described herein Henle loop OLFML3 Pig kidney snRNA as described herein Henle loop OSBPL3 Pig kidney snRNA as described herein Henle loop P2RX1 Pig kidney snRNA as described herein Henle loop PAQR5 Pig kidney snRNA as described herein Henle loop PATJ Pig kidney snRNA as described herein Henle loop PCDH9 Pig kidney snRNA as described herein Henle loop PCLAF Pig kidney snRNA as described herein Henle loop PEG3 Pig kidney snRNA as described herein Henle loop PHLDB2 Pig kidney snRNA as described herein Henle loop PIWIL3 Pig kidney snRNA as described herein Henle loop PKHD1 Pig kidney snRNA as described herein Henle loop PKP4 Pig kidney snRNA as described herein Henle loop PLCB1 Pig kidney snRNA as described herein Henle loop PLEKHG1 Pig kidney snRNA as described herein Henle loop PPARGC1A Pig kidney snRNA as described herein Henle loop PPFIA4 Pig kidney snRNA as described herein Henle loop PPP1R14B Pig kidney snRNA as described herein Henle loop PPP2R3A Pig kidney snRNA as described herein Henle loop PRKD1 Pig kidney snRNA as described herein Henle loop PRR15 Pig kidney snRNA as described herein Henle loop PTGER3 Pig kidney snRNA as described herein Henle loop PTPRG Pig kidney snRNA as described herein Henle loop RAP1GAP2 Pig kidney snRNA as described herein Henle loop RBBP8 Pig kidney snRNA as described herein Henle loop RBFOX2 Pig kidney snRNA as described herein Henle loop RERGL Pig kidney snRNA as described herein Henle loop RGS13 Pig kidney snRNA as described herein Henle loop RHPN1 Pig kidney snRNA as described herein Henle loop RP1L1 Pig kidney snRNA as described herein Henle loop RPL39 Pig kidney snRNA as described herein Henle loop RPP38 Pig kidney snRNA as described herein Henle loop RYR2 Pig kidney snRNA as described herein Henle loop SAMD10 Pig kidney snRNA as described herein Henle loop SCAND1 Pig kidney snRNA as described herein Henle loop SDCBP2 Pig kidney snRNA as described herein Henle loop SERTAD4 Pig kidney snRNA as described herein Henle loop SIM1 Pig kidney snRNA as described herein Henle loop SLC16A7 Pig kidney snRNA as described herein Henle loop SLC35E4 Pig kidney snRNA as described herein Henle loop SLC43A1 Pig kidney snRNA as described herein Henle loop SLC4A11 Pig kidney snRNA as described herein Henle loop SLC4A7 Pig kidney snRNA as described herein Henle loop SLC50A1 Pig kidney snRNA as described herein Henle loop SLC7A5 Pig kidney snRNA as described herein Henle loop SLCO3A1 Pig kidney snRNA as described herein Henle loop SLIRP Pig kidney snRNA as described herein Henle loop SNRPN Pig kidney snRNA as described herein Henle loop SPAG5 Pig kidney snRNA as described herein Henle loop SSBP2 Pig kidney snRNA as described herein Henle loop ST6GALNAC3 Pig kidney snRNA as described herein Henle loop STAP2 Pig kidney snRNA as described herein Henle loop STARD13 Pig kidney snRNA as described herein Henle loop STK32B Pig kidney snRNA as described herein 61    311769024v1 Attorney Docket No. 243735.000430  Henle loop STK39 Pig kidney snRNA as described herein Henle loop TBC1D9 Pig kidney snRNA as described herein Henle loop TBX20 Pig kidney snRNA as described herein Henle loop TFAP2B Pig kidney snRNA as described herein Henle loop THSD4 Pig kidney snRNA as described herein Henle loop TIGD5 Pig kidney snRNA as described herein Henle loop TMEM101 Pig kidney snRNA as described herein Henle loop TMEM56 Pig kidney snRNA as described herein Henle loop TMEM72 Pig kidney snRNA as described herein Henle loop TREX2 Pig kidney snRNA as described herein Henle loop UMOD Pig kidney snRNA as described herein Henle loop UQCR10 Pig kidney snRNA as described herein Henle loop USP24 Pig kidney snRNA as described herein Henle loop USP53 Pig kidney snRNA as described herein Henle loop VAV3 Pig kidney snRNA as described herein Henle loop VIPR1 Pig kidney snRNA as described herein Henle loop VWA5B1 Pig kidney snRNA as described herein Henle loop XKRX Pig kidney snRNA as described herein Henle loop ZNF385B Pig kidney snRNA as described herein Intercalated cells AKAP2 Pig kidney snRNA as described herein Intercalated cells AKR1C1_1 Pig kidney snRNA as described herein Intercalated cells ALS2 Pig kidney snRNA as described herein Intercalated cells ANKRD42 Pig kidney snRNA as described herein Intercalated cells ARHGAP6 Pig kidney snRNA as described herein Intercalated cells ARID1B Pig kidney snRNA as described herein Intercalated cells ARMC7 Pig kidney snRNA as described herein Intercalated cells ATP8A1 Pig kidney snRNA as described herein Intercalated cells BAZ2B Pig kidney snRNA as described herein Intercalated cells BCAT1 Pig kidney snRNA as described herein Intercalated cells CDC14B Pig kidney snRNA as described herein Intercalated cells CDKL1 Pig kidney snRNA as described herein Intercalated cells CDS1 Pig kidney snRNA as described herein Intercalated cells CEP85L Pig kidney snRNA as described herein Intercalated cells CHL1 Pig kidney snRNA as described herein Intercalated cells CLDN10 Pig kidney snRNA as described herein Intercalated cells COBL Pig kidney snRNA as described herein Intercalated cells COBLL1 Pig kidney snRNA as described herein Intercalated cells CPQ Pig kidney snRNA as described herein Intercalated cells CRIM1 Pig kidney snRNA as described herein Intercalated cells DAB2 Pig kidney snRNA as described herein Intercalated cells DEPTOR Pig kidney snRNA as described herein Intercalated cells DLGAP1 Pig kidney snRNA as described herein Intercalated cells DMD Pig kidney snRNA as described herein Intercalated cells DMGDH Pig kidney snRNA as described herein Intercalated cells DMXL1 Pig kidney snRNA as described herein Intercalated cells DST Pig kidney snRNA as described herein Intercalated cells EGF Pig kidney snRNA as described herein Intercalated cells ELMO1 Pig kidney snRNA as described herein Intercalated cells ENPEP Pig kidney snRNA as described herein Intercalated cells EPB41L3 Pig kidney snRNA as described herein Intercalated cells ESRRG Pig kidney snRNA as described herein Intercalated cells FBXL7 Pig kidney snRNA as described herein Intercalated cells FOXP1 Pig kidney snRNA as described herein Intercalated cells FTCD Pig kidney snRNA as described herein 62    311769024v1 Attorney Docket No. 243735.000430  Intercalated cells GNA14 Pig kidney snRNA as described herein Intercalated cells GRAMD1B Pig kidney snRNA as described herein Intercalated cells GRIN2A Pig kidney snRNA as described herein Intercalated cells GULP1 Pig kidney snRNA as described herein Intercalated cells HDAC9 Pig kidney snRNA as described herein Intercalated cells HRH2 Pig kidney snRNA as described herein Intercalated cells IMMP2L Pig kidney snRNA as described herein Intercalated cells INPP4B Pig kidney snRNA as described herein Intercalated cells ITPR2 Pig kidney snRNA as described herein Intercalated cells KAZN Pig kidney snRNA as described herein Intercalated cells KCNJ16 Pig kidney snRNA as described herein Intercalated cells KCNMA1 Pig kidney snRNA as described herein Intercalated cells KITLG Pig kidney snRNA as described herein Intercalated cells KMO Pig kidney snRNA as described herein Intercalated cells LAMB1 Pig kidney snRNA as described herein Intercalated cells LARGE1 Pig kidney snRNA as described herein Intercalated cells LIMCH1 Pig kidney snRNA as described herein Intercalated cells LMO7 Pig kidney snRNA as described herein Intercalated cells LOC100518109 Pig kidney snRNA as described herein Intercalated cells LOC100519130 Pig kidney snRNA as described herein Intercalated cells LOC100739741 Pig kidney snRNA as described herein Intercalated cells LOC102157778 Pig kidney snRNA as described herein Intercalated cells LOC102166036 Pig kidney snRNA as described herein Intercalated cells LOC106506781 Pig kidney snRNA as described herein Intercalated cells LOC110256651 Pig kidney snRNA as described herein Intercalated cells LOC110257572 Pig kidney snRNA as described herein Intercalated cells LOC110260338 Pig kidney snRNA as described herein Intercalated cells LOC110262153 Pig kidney snRNA as described herein Intercalated cells LRRFIP1 Pig kidney snRNA as described herein Intercalated cells MAGI1 Pig kidney snRNA as described herein Intercalated cells MAML2 Pig kidney snRNA as described herein Intercalated cells MAN1A1 Pig kidney snRNA as described herein Intercalated cells MAN2A1 Pig kidney snRNA as described herein Intercalated cells MAP3K15 Pig kidney snRNA as described herein Intercalated cells MAP4K3 Pig kidney snRNA as described herein Intercalated cells MECOM Pig kidney snRNA as described herein Intercalated cells MEF2A Pig kidney snRNA as described herein Intercalated cells MIPOL1 Pig kidney snRNA as described herein Intercalated cells MYO1D Pig kidney snRNA as described herein Intercalated cells MYO1E Pig kidney snRNA as described herein Intercalated cells MYOM1 Pig kidney snRNA as described herein Intercalated cells NAALADL2 Pig kidney snRNA as described herein Intercalated cells NCALD Pig kidney snRNA as described herein Intercalated cells NEDD4L Pig kidney snRNA as described herein Intercalated cells NFAT5 Pig kidney snRNA as described herein Intercalated cells NFIA Pig kidney snRNA as described herein Intercalated cells NHS Pig kidney snRNA as described herein Intercalated cells NOX4 Pig kidney snRNA as described herein Intercalated cells NPNT Pig kidney snRNA as described herein Intercalated cells NR3C2 Pig kidney snRNA as described herein Intercalated cells NYAP2 Pig kidney snRNA as described herein Intercalated cells PAM Pig kidney snRNA as described herein Intercalated cells PDE1C Pig kidney snRNA as described herein Intercalated cells PDE4D Pig kidney snRNA as described herein 63    311769024v1 Attorney Docket No. 243735.000430  Intercalated cells PDZD2 Pig kidney snRNA as described herein Intercalated cells PHYHIPL Pig kidney snRNA as described herein Intercalated cells PIAS1 Pig kidney snRNA as described herein Intercalated cells PJA2 Pig kidney snRNA as described herein Intercalated cells PKHD1 Pig kidney snRNA as described herein Intercalated cells PLEKHG1 Pig kidney snRNA as described herein Intercalated cells PLXNA2 Pig kidney snRNA as described herein Intercalated cells PM20D1 Pig kidney snRNA as described herein Intercalated cells PPARGC1A Pig kidney snRNA as described herein Intercalated cells PPP1R12B Pig kidney snRNA as described herein Intercalated cells PSD3 Pig kidney snRNA as described herein Intercalated cells PTBP3 Pig kidney snRNA as described herein Intercalated cells PTH1R Pig kidney snRNA as described herein Intercalated cells PTPRD Pig kidney snRNA as described herein Intercalated cells PTPRJ Pig kidney snRNA as described herein Intercalated cells RAPGEF5 Pig kidney snRNA as described herein Intercalated cells REPS2 Pig kidney snRNA as described herein Intercalated cells RFC1 Pig kidney snRNA as described herein Intercalated cells RGL3 Pig kidney snRNA as described herein Intercalated cells RHOBTB2 Pig kidney snRNA as described herein Intercalated cells RNF19A Pig kidney snRNA as described herein Intercalated cells RNF24 Pig kidney snRNA as described herein Intercalated cells SDC2 Pig kidney snRNA as described herein Intercalated cells SETBP1 Pig kidney snRNA as described herein Intercalated cells SKAP1 Pig kidney snRNA as described herein Intercalated cells SLC16A12 Pig kidney snRNA as described herein Intercalated cells SLC16A7 Pig kidney snRNA as described herein Intercalated cells SLC4A4 Pig kidney snRNA as described herein Intercalated cells STRBP Pig kidney snRNA as described herein Intercalated cells SVOPL Pig kidney snRNA as described herein Intercalated cells SYNE2 Pig kidney snRNA as described herein Intercalated cells SYNPO2 Pig kidney snRNA as described herein Intercalated cells TANC1 Pig kidney snRNA as described herein Intercalated cells TANC2 Pig kidney snRNA as described herein Intercalated cells TBC1D1 Pig kidney snRNA as described herein Intercalated cells TBC1D4 Pig kidney snRNA as described herein Intercalated cells TLDC2 Pig kidney snRNA as described herein Intercalated cells TLE4 Pig kidney snRNA as described herein Intercalated cells TLN2 Pig kidney snRNA as described herein Intercalated cells TMEM116 Pig kidney snRNA as described herein Intercalated cells TMEM117 Pig kidney snRNA as described herein Intercalated cells TMTC2 Pig kidney snRNA as described herein Intercalated cells TRPM3 Pig kidney snRNA as described herein Intercalated cells UBE2E2 Pig kidney snRNA as described herein Intercalated cells UNC13C Pig kidney snRNA as described herein Intercalated cells UVRAG Pig kidney snRNA as described herein Intercalated cells VAV3 Pig kidney snRNA as described herein Intercalated cells VWDE Pig kidney snRNA as described herein Intercalated cells WDR72 Pig kidney snRNA as described herein Intercalated cells WDSUB1 Pig kidney snRNA as described herein Intercalated cells ZFHX3 Pig kidney snRNA as described herein Intercalated cells ZNF385D Pig kidney snRNA as described herein Intercalated cells ZSWIM5 Pig kidney snRNA as described herein Podocytes ARHGAP24 Pig kidney snRNA as described herein 64    311769024v1 Attorney Docket No. 243735.000430  Podocytes ARHGEF12 Pig kidney snRNA as described herein Podocytes BICD1 Pig kidney snRNA as described herein Podocytes C10H9orf3 Pig kidney snRNA as described herein Podocytes CADM2 Pig kidney snRNA as described herein Podocytes CDH23 Pig kidney snRNA as described herein Podocytes CDK14 Pig kidney snRNA as described herein Podocytes CEP85L Pig kidney snRNA as described herein Podocytes CHST9 Pig kidney snRNA as described herein Podocytes CNKSR3 Pig kidney snRNA as described herein Podocytes COL4A3 Pig kidney snRNA as described herein Podocytes COL4A4 Pig kidney snRNA as described herein Podocytes DTNA Pig kidney snRNA as described herein Podocytes DYNC1I1 Pig kidney snRNA as described herein Podocytes EPB41L5 Pig kidney snRNA as described herein Podocytes F5 Pig kidney snRNA as described herein Podocytes FAM19A2 Pig kidney snRNA as described herein Podocytes FNBP1L Pig kidney snRNA as described herein Podocytes GOLIM4 Pig kidney snRNA as described herein Podocytes GPC6 Pig kidney snRNA as described herein Podocytes HDAC9 Pig kidney snRNA as described herein Podocytes IMMP2L Pig kidney snRNA as described herein Podocytes ITGAV Pig kidney snRNA as described herein Podocytes ITGB5 Pig kidney snRNA as described herein Podocytes ITGB8 Pig kidney snRNA as described herein Podocytes KANK1 Pig kidney snRNA as described herein Podocytes KIRREL Pig kidney snRNA as described herein Podocytes LARGE1 Pig kidney snRNA as described herein Podocytes LGR5 Pig kidney snRNA as described herein Podocytes LOC100737186 Pig kidney snRNA as described herein Podocytes LOC102161978 Pig kidney snRNA as described herein Podocytes LOC106505633 Pig kidney snRNA as described herein Podocytes LOC110260827 Pig kidney snRNA as described herein Podocytes LRCH1 Pig kidney snRNA as described herein Podocytes MAGI2 Pig kidney snRNA as described herein Podocytes MGAT5 Pig kidney snRNA as described herein Podocytes MME Pig kidney snRNA as described herein Podocytes MPP5 Pig kidney snRNA as described herein Podocytes MTSS1 Pig kidney snRNA as described herein Podocytes MYO1D Pig kidney snRNA as described herein Podocytes MYO1E Pig kidney snRNA as described herein Podocytes NES Pig kidney snRNA as described herein Podocytes NPAS3 Pig kidney snRNA as described herein Podocytes NPNT Pig kidney snRNA as described herein Podocytes P3H2 Pig kidney snRNA as described herein Podocytes PAMR1 Pig kidney snRNA as described herein Podocytes PARD3B Pig kidney snRNA as described herein Podocytes PDE3A Pig kidney snRNA as described herein Podocytes PEX5L Pig kidney snRNA as described herein Podocytes PHACTR4 Pig kidney snRNA as described herein Podocytes PLEKHG1 Pig kidney snRNA as described herein Podocytes PODXL Pig kidney snRNA as described herein Podocytes PRKCI Pig kidney snRNA as described herein Podocytes PTH1R Pig kidney snRNA as described herein Podocytes PTPN13 Pig kidney snRNA as described herein 65    311769024v1 Attorney Docket No. 243735.000430  Podocytes PTPRD Pig kidney snRNA as described herein Podocytes RAB3C Pig kidney snRNA as described herein Podocytes RAPGEF4 Pig kidney snRNA as described herein Podocytes RBMS3 Pig kidney snRNA as described herein Podocytes RIPOR1 Pig kidney snRNA as described herein Podocytes ROBO2 Pig kidney snRNA as described herein Podocytes SAMD12 Pig kidney snRNA as described herein Podocytes SDC2 Pig kidney snRNA as described herein Podocytes SEMA3E Pig kidney snRNA as described herein Podocytes SEMA3G Pig kidney snRNA as described herein Podocytes SGIP1 Pig kidney snRNA as described herein Podocytes SPATA13 Pig kidney snRNA as described herein Podocytes SPATS2L Pig kidney snRNA as described herein Podocytes SPOCK1 Pig kidney snRNA as described herein Podocytes SRGAP2 Pig kidney snRNA as described herein Podocytes ST6GALNAC3 Pig kidney snRNA as described herein Podocytes THSD7A Pig kidney snRNA as described herein Podocytes TMCC3 Pig kidney snRNA as described herein Podocytes TSPAN5 Pig kidney snRNA as described herein Podocytes UACA Pig kidney snRNA as described herein Podocytes VEGFA Pig kidney snRNA as described herein Proximal tubule ABCC2 Pig kidney snRNA as described herein Proximal tubule ABCC4 Pig kidney snRNA as described herein Proximal tubule ABLIM3 Pig kidney snRNA as described herein Proximal tubule ACSM2B Pig kidney snRNA as described herein Proximal tubule ACSS3 Pig kidney snRNA as described herein Proximal tubule ADGRL3 Pig kidney snRNA as described herein Proximal tubule ADGRV1 Pig kidney snRNA as described herein Proximal tubule AGXT2 Pig kidney snRNA as described herein Proximal tubule AK4 Pig kidney snRNA as described herein Proximal tubule AKR1C1_1 Pig kidney snRNA as described herein Proximal tubule ALDH1L1 Pig kidney snRNA as described herein Proximal tubule ALDOB Pig kidney snRNA as described herein Proximal tubule AMN Pig kidney snRNA as described herein Proximal tubule ANK2 Pig kidney snRNA as described herein Proximal tubule ANKRD33B Pig kidney snRNA as described herein Proximal tubule ANKS1A Pig kidney snRNA as described herein Proximal tubule APBB2 Pig kidney snRNA as described herein Proximal tubule AQP7 Pig kidney snRNA as described herein Proximal tubule ARHGAP10 Pig kidney snRNA as described herein Proximal tubule ARSB Pig kidney snRNA as described herein Proximal tubule ASS1 Pig kidney snRNA as described herein Proximal tubule ATP6V0D2 Pig kidney snRNA as described herein Proximal tubule ATP6V1C2 Pig kidney snRNA as described herein Proximal tubule ATRNL1 Pig kidney snRNA as described herein Proximal tubule BEND7 Pig kidney snRNA as described herein Proximal tubule BHMT2 Pig kidney snRNA as described herein Proximal tubule BNC2 Pig kidney snRNA as described herein Proximal tubule BTBD11 Pig kidney snRNA as described herein Proximal tubule C9H11orf70 Pig kidney snRNA as described herein Proximal tubule C9H11orf87 Pig kidney snRNA as described herein Proximal tubule CAMK1D Pig kidney snRNA as described herein Proximal tubule CAT Pig kidney snRNA as described herein Proximal tubule CDKL1 Pig kidney snRNA as described herein 66    311769024v1 Attorney Docket No. 243735.000430  Proximal tubule CLDN10 Pig kidney snRNA as described herein Proximal tubule CLIC4 Pig kidney snRNA as described herein Proximal tubule CNDP1 Pig kidney snRNA as described herein Proximal tubule COBL Pig kidney snRNA as described herein Proximal tubule CPXM2 Pig kidney snRNA as described herein Proximal tubule CRYL1 Pig kidney snRNA as described herein Proximal tubule CTTNBP2 Pig kidney snRNA as described herein Proximal tubule CYP2C33 Pig kidney snRNA as described herein Proximal tubule CYP4A21 Pig kidney snRNA as described herein Proximal tubule CYP4A24 Pig kidney snRNA as described herein Proximal tubule DAB2 Pig kidney snRNA as described herein Proximal tubule DDC Pig kidney snRNA as described herein Proximal tubule DLGAP1 Pig kidney snRNA as described herein Proximal tubule DMGDH Pig kidney snRNA as described herein Proximal tubule DPYD Pig kidney snRNA as described herein Proximal tubule ENPP1 Pig kidney snRNA as described herein Proximal tubule EPB41L3 Pig kidney snRNA as described herein Proximal tubule FBP1 Pig kidney snRNA as described herein Proximal tubule FGGY Pig kidney snRNA as described herein Proximal tubule FREM1 Pig kidney snRNA as described herein Proximal tubule FTCD Pig kidney snRNA as described herein Proximal tubule GAB1 Pig kidney snRNA as described herein Proximal tubule GGT5 Pig kidney snRNA as described herein Proximal tubule GK Pig kidney snRNA as described herein Proximal tubule GPT2 Pig kidney snRNA as described herein Proximal tubule GRAMD1B Pig kidney snRNA as described herein Proximal tubule HAO2 Pig kidney snRNA as described herein Proximal tubule HDAC6 Pig kidney snRNA as described herein Proximal tubule HECW1 Pig kidney snRNA as described herein Proximal tubule HHLA2 Pig kidney snRNA as described herein Proximal tubule HNF4A Pig kidney snRNA as described herein Proximal tubule HOGA1 Pig kidney snRNA as described herein Proximal tubule HSD17B4 Pig kidney snRNA as described herein Proximal tubule IDO2 Pig kidney snRNA as described herein Proximal tubule IGF1R Pig kidney snRNA as described herein Proximal tubule KALRN Pig kidney snRNA as described herein Proximal tubule KCNJ16 Pig kidney snRNA as described herein Proximal tubule LAMA2 Pig kidney snRNA as described herein Proximal tubule LOC100153543 Pig kidney snRNA as described herein Proximal tubule LOC100513133 Pig kidney snRNA as described herein Proximal tubule LOC100514700 Pig kidney snRNA as described herein Proximal tubule LOC100518109 Pig kidney snRNA as described herein Proximal tubule LOC100519130 Pig kidney snRNA as described herein Proximal tubule LOC100522735 Pig kidney snRNA as described herein Proximal tubule LOC100525483 Pig kidney snRNA as described herein Proximal tubule LOC100739741 Pig kidney snRNA as described herein Proximal tubule LOC102159476 Pig kidney snRNA as described herein Proximal tubule LOC102164585 Pig kidney snRNA as described herein Proximal tubule LOC106507559 Pig kidney snRNA as described herein Proximal tubule LOC106509513 Pig kidney snRNA as described herein Proximal tubule LOC106510465 Pig kidney snRNA as described herein Proximal tubule LOC110255682 Pig kidney snRNA as described herein Proximal tubule LOC110255689 Pig kidney snRNA as described herein Proximal tubule LOC110255839 Pig kidney snRNA as described herein 67    311769024v1 Attorney Docket No. 243735.000430  Proximal tubule LOC110256117 Pig kidney snRNA as described herein Proximal tubule LOC110256746 Pig kidney snRNA as described herein Proximal tubule LOC110259242 Pig kidney snRNA as described herein Proximal tubule LOC110260099 Pig kidney snRNA as described herein Proximal tubule LOC110260265 Pig kidney snRNA as described herein Proximal tubule LOC110260439 Pig kidney snRNA as described herein Proximal tubule LRIT3 Pig kidney snRNA as described herein Proximal tubule LRMDA Pig kidney snRNA as described herein Proximal tubule MME Pig kidney snRNA as described herein Proximal tubule MSRA Pig kidney snRNA as described herein Proximal tubule MTSS1 Pig kidney snRNA as described herein Proximal tubule MYO15A Pig kidney snRNA as described herein Proximal tubule NCALD Pig kidney snRNA as described herein Proximal tubule NFIA Pig kidney snRNA as described herein Proximal tubule NGEF Pig kidney snRNA as described herein Proximal tubule NHS Pig kidney snRNA as described herein Proximal tubule NKAIN3 Pig kidney snRNA as described herein Proximal tubule NOX4 Pig kidney snRNA as described herein Proximal tubule NRG3 Pig kidney snRNA as described herein Proximal tubule NUAK1 Pig kidney snRNA as described herein Proximal tubule OAT Pig kidney snRNA as described herein Proximal tubule OXR1 Pig kidney snRNA as described herein Proximal tubule PARM1 Pig kidney snRNA as described herein Proximal tubule PCK1 Pig kidney snRNA as described herein Proximal tubule PDE1A Pig kidney snRNA as described herein Proximal tubule PDE4B Pig kidney snRNA as described herein Proximal tubule PDZD2 Pig kidney snRNA as described herein Proximal tubule PDZK1 Pig kidney snRNA as described herein Proximal tubule PELI2 Pig kidney snRNA as described herein Proximal tubule PKHD1 Pig kidney snRNA as described herein Proximal tubule PLEKHA7 Pig kidney snRNA as described herein Proximal tubule PLPPR1 Pig kidney snRNA as described herein Proximal tubule PM20D1 Pig kidney snRNA as described herein Proximal tubule PON1 Pig kidney snRNA as described herein Proximal tubule POU2F3 Pig kidney snRNA as described herein Proximal tubule PRODH2 Pig kidney snRNA as described herein Proximal tubule PTPRK Pig kidney snRNA as described herein Proximal tubule RHOBTB1 Pig kidney snRNA as described herein Proximal tubule SATB2 Pig kidney snRNA as described herein Proximal tubule SGK2 Pig kidney snRNA as described herein Proximal tubule SGPP2 Pig kidney snRNA as described herein Proximal tubule SLC13A2 Pig kidney snRNA as described herein Proximal tubule SLC16A12 Pig kidney snRNA as described herein Proximal tubule SLC17A1 Pig kidney snRNA as described herein Proximal tubule SLC22A1 Pig kidney snRNA as described herein Proximal tubule SLC22A12 Pig kidney snRNA as described herein Proximal tubule SLC22A2 Pig kidney snRNA as described herein Proximal tubule SLC23A3 Pig kidney snRNA as described herein Proximal tubule SLC25A21 Pig kidney snRNA as described herein Proximal tubule SLC4A4 Pig kidney snRNA as described herein Proximal tubule SLC5A10 Pig kidney snRNA as described herein Proximal tubule SLC5A11 Pig kidney snRNA as described herein Proximal tubule SLC5A12 Pig kidney snRNA as described herein Proximal tubule SLC6A18 Pig kidney snRNA as described herein 68    311769024v1 Attorney Docket No. 243735.000430  Proximal tubule SLC7A13 Pig kidney snRNA as described herein Proximal tubule SPPL3 Pig kidney snRNA as described herein Proximal tubule STS Pig kidney snRNA as described herein Proximal tubule SUGCT Pig kidney snRNA as described herein Proximal tubule TINAG Pig kidney snRNA as described herein Proximal tubule TRHDE Pig kidney snRNA as described herein Proximal tubule TRPM3 Pig kidney snRNA as described herein Proximal tubule TRPS1 Pig kidney snRNA as described herein Proximal tubule TSPAN5 Pig kidney snRNA as described herein Proximal tubule UPB1 Pig kidney snRNA as described herein Proximal tubule UPP2 Pig kidney snRNA as described herein Proximal tubule XPR1 Pig kidney snRNA as described herein Proximal tubule YTHDC2 Pig kidney snRNA as described herein Proximal tubule ZNF385D Pig kidney snRNA as described herein Proximal tubule ZNF704 Pig kidney snRNA as described herein Endothelial cell EMCN "PigAtlas,snRNA" Endothelial cell PECAM1 "PigAtlas,snRNA" Fibroblast SLIT3 "PigAtlas,snRNA" Henle loop SLC12A1 "PigAtlas,snRNA" Proximal tubule CUBN "PigAtlas,snRNA" Proximal tubule LRP2 "PigAtlas,snRNA" Proximal tubule SLC13A3 "PigAtlas,snRNA" Proximal tubule SLC22A8 "PigAtlas,snRNA" Proximal tubule SLC34A1 "PigAtlas,snRNA" Collecting duct Principal cell AQP3 PigAtlas Collecting duct Principal cell CLDN8 PigAtlas Collecting duct Principal cell PVALB PigAtlas Collecting duct Principal cell TMEM213 PigAtlas Distal convoluted tubule TMEM213 PigAtlas Distal convoluted tubule TMEM52B PigAtlas Distal convoluted tubule TMEM72 PigAtlas Endothelial cell BST2 PigAtlas Endothelial cell ENG PigAtlas Endothelial cell KDR PigAtlas Endothelial cell NRP1 PigAtlas Fibroblast COL6A1 PigAtlas Fibroblast EMILIN1 PigAtlas Collecting duct Principal cell BMPR1B "Human methylation cell type marker,snRNA" Collecting duct Principal cell PAPPA "Human methylation cell type marker,snRNA" Collecting duct Principal cell SCNN1B "Human methylation cell type marker,snRNA" Collecting duct Principal cell SCNN1G "Human methylation cell type marker,snRNA" Distal tubule CADM1 "Human methylation cell type marker,snRNA" Distal tubule CNNM2 "Human methylation cell type marker,snRNA" Distal tubule DACH1 "Human methylation cell type marker,snRNA" Distal tubule EFNA5 "Human methylation cell type marker,snRNA" Distal tubule EGF "Human methylation cell type marker,snRNA" Distal tubule ESRRB "Human methylation cell type marker,snRNA" Distal tubule KLHL3 "Human methylation cell type marker,snRNA" Distal tubule KNG1 "Human methylation cell type marker,snRNA" Distal tubule LNX1 "Human methylation cell type marker,snRNA" Distal tubule MSI2 "Human methylation cell type marker,snRNA" Distal tubule RAP1GAP2 "Human methylation cell type marker,snRNA" Distal tubule SLC12A3 "Human methylation cell type marker,snRNA" Distal tubule STK39 "Human methylation cell type marker,snRNA" 69    311769024v1 Attorney Docket No. 243735.000430  Distal tubule TBC1D9 "Human methylation cell type marker,snRNA" Distal tubule TMEM52B "Human methylation cell type marker,snRNA" Distal tubule TRPM7 "Human methylation cell type marker,snRNA" Distal tubule WNK4 "Human methylation cell type marker,snRNA" Fibroblast AKAP12 "Human methylation cell type marker,snRNA" Fibroblast ANTXR1 "Human methylation cell type marker,snRNA" Fibroblast C7 "Human methylation cell type marker,snRNA" Fibroblast CCBE1 "Human methylation cell type marker,snRNA" Fibroblast COL4A1 "Human methylation cell type marker,snRNA" Fibroblast COL4A2 "Human methylation cell type marker,snRNA" Fibroblast CPED1 "Human methylation cell type marker,snRNA" Fibroblast EBF1 "Human methylation cell type marker,snRNA" Fibroblast GRK5 "Human methylation cell type marker,snRNA" Fibroblast GUCY1A2 "Human methylation cell type marker,snRNA" Fibroblast LDB2 "Human methylation cell type marker,snRNA" Fibroblast MEIS2 "Human methylation cell type marker,snRNA" Fibroblast PDGFRB "Human methylation cell type marker,snRNA" Fibroblast RFTN1 "Human methylation cell type marker,snRNA" Fibroblast SLC2A3 "Human methylation cell type marker,snRNA" Intercalated cells ADGRF1 "Human methylation cell type marker,snRNA" Intercalated cells ADGRF5 "Human methylation cell type marker,snRNA" Intercalated cells ARL15 "Human methylation cell type marker,snRNA" Intercalated cells ATP6V0A4 "Human methylation cell type marker,snRNA" Intercalated cells ATP6V0D2 "Human methylation cell type marker,snRNA" Intercalated cells ATP6V1C2 "Human methylation cell type marker,snRNA" Intercalated cells ATP6V1G3 "Human methylation cell type marker,snRNA" Intercalated cells BMPR1B "Human methylation cell type marker,snRNA" Intercalated cells CA12 "Human methylation cell type marker,snRNA" Intercalated cells CAMK1D "Human methylation cell type marker,snRNA" Intercalated cells DMRT2 "Human methylation cell type marker,snRNA" Intercalated cells EPS8 "Human methylation cell type marker,snRNA" Intercalated cells FOXI1 "Human methylation cell type marker,snRNA" Intercalated cells HEPACAM2 "Human methylation cell type marker,snRNA" Intercalated cells INSRR "Human methylation cell type marker,snRNA" Intercalated cells KIT "Human methylation cell type marker,snRNA" Intercalated cells MPPED2 "Human methylation cell type marker,snRNA" Intercalated cells PLCG2 "Human methylation cell type marker,snRNA" Intercalated cells RHBG "Human methylation cell type marker,snRNA" Intercalated cells RNF152 "Human methylation cell type marker,snRNA" Intercalated cells SGPP2 "Human methylation cell type marker,snRNA" Intercalated cells SHROOM3 "Human methylation cell type marker,snRNA" Intercalated cells SLC26A4 "Human methylation cell type marker,snRNA" Intercalated cells SLC26A7 "Human methylation cell type marker,snRNA" Intercalated cells SLC4A9 "Human methylation cell type marker,snRNA" Intercalated cells SNTB1 "Human methylation cell type marker,snRNA" Intercalated cells SPIRE1 "Human methylation cell type marker,snRNA" Intercalated cells STAP1 "Human methylation cell type marker,snRNA" Intercalated cells TFCP2L1 "Human methylation cell type marker,snRNA" Podocytes ARHGAP28 "Human methylation cell type marker,snRNA" Podocytes ARHGEF3 "Human methylation cell type marker,snRNA" Podocytes ATP10A "Human methylation cell type marker,snRNA" Podocytes CDC14A "Human methylation cell type marker,snRNA" Podocytes CRIM1 "Human methylation cell type marker,snRNA" Podocytes DACH1 "Human methylation cell type marker,snRNA" 70    311769024v1 Attorney Docket No. 243735.000430  Podocytes FYN "Human methylation cell type marker,snRNA" Podocytes GRK5 "Human methylation cell type marker,snRNA" Podocytes HTRA1 "Human methylation cell type marker,snRNA" Podocytes IQGAP2 "Human methylation cell type marker,snRNA" Podocytes KIAA1211 "Human methylation cell type marker,snRNA" Podocytes PLA2R1 "Human methylation cell type marker,snRNA" Podocytes PLCE1 "Human methylation cell type marker,snRNA" Podocytes PTPRO "Human methylation cell type marker,snRNA" Podocytes PTPRQ "Human methylation cell type marker,snRNA" Podocytes SPOCK2 "Human methylation cell type marker,snRNA" Podocytes SRGAP1 "Human methylation cell type marker,snRNA" Podocytes TJP1 "Human methylation cell type marker,snRNA" Podocytes UNC5C "Human methylation cell type marker,snRNA" Podocytes ZBTB7C "Human methylation cell type marker,snRNA" Proximal tubule CDH2 "Human methylation cell type marker,snRNA" Fibroblast CALD1 "Human methylation cell type marker,PigAtlas,snRNA" Podocytes CLIC5 "Human methylation cell type marker,PigAtlas,snRNA" Podocytes NPHS1 "Human methylation cell type marker,PigAtlas,snRNA" Podocytes NPHS2 "Human methylation cell type marker,PigAtlas,snRNA" Podocytes WT1 "Human methylation cell type marker,PigAtlas,snRNA" Collecting duct Principal cell AQP2 "Human methylation cell type marker,PigAtlas" Collecting duct Principal cell GATA2 "Human methylation cell type marker,PigAtlas" Fibroblast DCN "Human methylation cell type marker,PigAtlas" Collecting duct Principal cell 10-Mar Human methylation cell type marker Collecting duct Principal cell ABTB2 Human methylation cell type marker Collecting duct Principal cell ADAP1 Human methylation cell type marker Collecting duct Principal cell ALDH3B2 Human methylation cell type marker Collecting duct Principal cell ANGPT4 Human methylation cell type marker Collecting duct Principal cell AQP5 Human methylation cell type marker Collecting duct Principal cell AVPR2 Human methylation cell type marker Collecting duct Principal cell B3GNT8 Human methylation cell type marker Collecting duct Principal cell BCL2L15 Human methylation cell type marker Collecting duct Principal cell BPIFB1 Human methylation cell type marker Collecting duct Principal cell C11orf80 Human methylation cell type marker Collecting duct Principal cell C19orf81 Human methylation cell type marker Collecting duct Principal cell C4orf54 Human methylation cell type marker Collecting duct Principal cell CASP14 Human methylation cell type marker Collecting duct Principal cell CASZ1 Human methylation cell type marker Collecting duct Principal cell CCNO Human methylation cell type marker Collecting duct Principal cell CLIC6 Human methylation cell type marker Collecting duct Principal cell COG7 Human methylation cell type marker Collecting duct Principal cell COL9A2 Human methylation cell type marker Collecting duct Principal cell CPA5 Human methylation cell type marker Collecting duct Principal cell CYSLTR2 Human methylation cell type marker Collecting duct Principal cell DOC2B Human methylation cell type marker Collecting duct Principal cell EHF Human methylation cell type marker Collecting duct Principal cell ELF5 Human methylation cell type marker Collecting duct Principal cell EYA4 Human methylation cell type marker Collecting duct Principal cell FAIM2 Human methylation cell type marker 71    311769024v1 Attorney Docket No. 243735.000430  Collecting duct Principal cell FXYD4 Human methylation cell type marker Collecting duct Principal cell GATA3 Human methylation cell type marker Collecting duct Principal cell GPR148 Human methylation cell type marker Collecting duct Principal cell GPRC5A Human methylation cell type marker Collecting duct Principal cell GRHL2 Human methylation cell type marker Collecting duct Principal cell HDAC4 Human methylation cell type marker Collecting duct Principal cell HOXB3 Human methylation cell type marker Collecting duct Principal cell HOXB7 Human methylation cell type marker Collecting duct Principal cell HOXB9 Human methylation cell type marker Collecting duct Principal cell HOXD10 Human methylation cell type marker Collecting duct Principal cell HOXD11 Human methylation cell type marker Collecting duct Principal cell IL1RN Human methylation cell type marker Collecting duct Principal cell INPP5J Human methylation cell type marker Collecting duct Principal cell IRX6 Human methylation cell type marker Collecting duct Principal cell KLHL30 Human methylation cell type marker Collecting duct Principal cell KRT15 Human methylation cell type marker Collecting duct Principal cell KRT19 Human methylation cell type marker Collecting duct Principal cell L1CAM Human methylation cell type marker Collecting duct Principal cell LMO3 Human methylation cell type marker Collecting duct Principal cell LTBP4 Human methylation cell type marker Collecting duct Principal cell MCIDAS Human methylation cell type marker Collecting duct Principal cell MFSD6L Human methylation cell type marker Collecting duct Principal cell MSLNL Human methylation cell type marker Collecting duct Principal cell MYH7 Human methylation cell type marker Collecting duct Principal cell NECTIN4 Human methylation cell type marker Collecting duct Principal cell NHLRC4 Human methylation cell type marker Collecting duct Principal cell NOTCH3 Human methylation cell type marker Collecting duct Principal cell NR0B2 Human methylation cell type marker Collecting duct Principal cell PAK6 Human methylation cell type marker Collecting duct Principal cell PDE6G Human methylation cell type marker Collecting duct Principal cell PIGQ Human methylation cell type marker Collecting duct Principal cell PKN1 Human methylation cell type marker Collecting duct Principal cell PLA2G4F Human methylation cell type marker Collecting duct Principal cell POU2F3 Human methylation cell type marker Collecting duct Principal cell PRDM16 Human methylation cell type marker Collecting duct Principal cell PRR35 Human methylation cell type marker Collecting duct Principal cell PRSS22 Human methylation cell type marker Collecting duct Principal cell PTGER1 Human methylation cell type marker Collecting duct Principal cell RASAL1 Human methylation cell type marker Collecting duct Principal cell RBP2 Human methylation cell type marker Collecting duct Principal cell RECQL5 Human methylation cell type marker Collecting duct Principal cell RNF223 Human methylation cell type marker Collecting duct Principal cell RPH3AL Human methylation cell type marker Collecting duct Principal cell SAMD11 Human methylation cell type marker Collecting duct Principal cell SLC29A2 Human methylation cell type marker Collecting duct Principal cell SLC38A1 Human methylation cell type marker Collecting duct Principal cell SLC45A4 Human methylation cell type marker Collecting duct Principal cell SLC52A3 Human methylation cell type marker Collecting duct Principal cell SLC7A1 Human methylation cell type marker Collecting duct Principal cell SLC9A4 Human methylation cell type marker Collecting duct Principal cell SMIM5 Human methylation cell type marker Collecting duct Principal cell SPINK4 Human methylation cell type marker Collecting duct Principal cell SPTBN2 Human methylation cell type marker Collecting duct Principal cell ST6GAL1 Human methylation cell type marker 72    311769024v1 Attorney Docket No. 243735.000430  Collecting duct Principal cell SYK Human methylation cell type marker Collecting duct Principal cell TAF3 Human methylation cell type marker Collecting duct Principal cell TFAP2A Human methylation cell type marker Collecting duct Principal cell TFAP2C Human methylation cell type marker Collecting duct Principal cell TMEM238L Human methylation cell type marker Collecting duct Principal cell TPM4 Human methylation cell type marker Collecting duct Principal cell TRIB3 Human methylation cell type marker Collecting duct Principal cell TRPV3 Human methylation cell type marker Collecting duct Principal cell TSPAN5 Human methylation cell type marker Collecting duct Principal cell VAV2 Human methylation cell type marker Collecting duct Principal cell VGLL3 Human methylation cell type marker Collecting duct Principal cell WFDC2 Human methylation cell type marker Collecting duct Principal cell ZNF98 Human methylation cell type marker Collecting duct Principal cell ZSCAN1 Human methylation cell type marker Connecting Tubule ABTB2 Human methylation cell type marker Connecting Tubule ANXA11 Human methylation cell type marker Connecting Tubule APCDD1L Human methylation cell type marker Connecting Tubule ARHGEF3 Human methylation cell type marker Connecting Tubule ATP6V0A4 Human methylation cell type marker Connecting Tubule ATP8B1 Human methylation cell type marker Connecting Tubule B4GALNT3 Human methylation cell type marker Connecting Tubule BARX2 Human methylation cell type marker Connecting Tubule BFSP2 Human methylation cell type marker Connecting Tubule BMPR1B Human methylation cell type marker Connecting Tubule BSND Human methylation cell type marker Connecting Tubule C11orf80 Human methylation cell type marker Connecting Tubule C6orf223 Human methylation cell type marker Connecting Tubule CACNB4 Human methylation cell type marker Connecting Tubule CALB1 Human methylation cell type marker Connecting Tubule CASZ1 Human methylation cell type marker Connecting Tubule CCDC12 Human methylation cell type marker Connecting Tubule CDCP1 Human methylation cell type marker Connecting Tubule CDH1 Human methylation cell type marker Connecting Tubule COG7 Human methylation cell type marker Connecting Tubule CPA5 Human methylation cell type marker Connecting Tubule CSGALNACT1 Human methylation cell type marker Connecting Tubule CTSD Human methylation cell type marker Connecting Tubule DACH1 Human methylation cell type marker Connecting Tubule DEFB1 Human methylation cell type marker Connecting Tubule EFNA5 Human methylation cell type marker Connecting Tubule EHF Human methylation cell type marker Connecting Tubule EMX1 Human methylation cell type marker Connecting Tubule FAM110B Human methylation cell type marker Connecting Tubule FAM83B Human methylation cell type marker Connecting Tubule GATA2 Human methylation cell type marker Connecting Tubule GATA3 Human methylation cell type marker Connecting Tubule GRHL2 Human methylation cell type marker Connecting Tubule HOXD3 Human methylation cell type marker Connecting Tubule IFITM10 Human methylation cell type marker Connecting Tubule INPP5J Human methylation cell type marker Connecting Tubule IQCK Human methylation cell type marker Connecting Tubule KCNJ1 Human methylation cell type marker Connecting Tubule KLHL3 Human methylation cell type marker Connecting Tubule KLK15 Human methylation cell type marker 73    311769024v1 Attorney Docket No. 243735.000430  Connecting Tubule KRT19 Human methylation cell type marker Connecting Tubule L1CAM Human methylation cell type marker Connecting Tubule LGALS3 Human methylation cell type marker Connecting Tubule MAL Human methylation cell type marker Connecting Tubule MAPK4 Human methylation cell type marker Connecting Tubule MSI2 Human methylation cell type marker Connecting Tubule MYO10 Human methylation cell type marker Connecting Tubule MYO1B Human methylation cell type marker Connecting Tubule MYO1E Human methylation cell type marker Connecting Tubule MYO5A Human methylation cell type marker Connecting Tubule MYO5C Human methylation cell type marker Connecting Tubule NECTIN4 Human methylation cell type marker Connecting Tubule NHLRC4 Human methylation cell type marker Connecting Tubule NOS1AP Human methylation cell type marker Connecting Tubule NOTCH3 Human methylation cell type marker Connecting Tubule NR0B2 Human methylation cell type marker Connecting Tubule NT5DC3 Human methylation cell type marker Connecting Tubule NUDC Human methylation cell type marker Connecting Tubule OLFM4 Human methylation cell type marker Connecting Tubule OSBPL3 Human methylation cell type marker Connecting Tubule PAK6 Human methylation cell type marker Connecting Tubule PAQR5 Human methylation cell type marker Connecting Tubule PFKP Human methylation cell type marker Connecting Tubule PI16 Human methylation cell type marker Connecting Tubule PLA2G3 Human methylation cell type marker Connecting Tubule PLA2G4F Human methylation cell type marker Connecting Tubule PLB1 Human methylation cell type marker Connecting Tubule PRDM16 Human methylation cell type marker Connecting Tubule PRKAG2 Human methylation cell type marker Connecting Tubule RAB11FIP1 Human methylation cell type marker Connecting Tubule RAI1 Human methylation cell type marker Connecting Tubule RASSF9 Human methylation cell type marker Connecting Tubule RHBG Human methylation cell type marker Connecting Tubule RHCG Human methylation cell type marker Connecting Tubule S100A2 Human methylation cell type marker Connecting Tubule SALL3 Human methylation cell type marker Connecting Tubule SCIN Human methylation cell type marker Connecting Tubule SCNN1B Human methylation cell type marker Connecting Tubule SCNN1G Human methylation cell type marker Connecting Tubule SFXN5 Human methylation cell type marker Connecting Tubule SIM1 Human methylation cell type marker Connecting Tubule SLC2A1 Human methylation cell type marker Connecting Tubule SLC38A1 Human methylation cell type marker Connecting Tubule SLC52A3 Human methylation cell type marker Connecting Tubule SLC7A1 Human methylation cell type marker Connecting Tubule SLC9A2 Human methylation cell type marker Connecting Tubule SLC9A4 Human methylation cell type marker Connecting Tubule SORL1 Human methylation cell type marker Connecting Tubule SPIRE1 Human methylation cell type marker Connecting Tubule SPTBN2 Human methylation cell type marker Connecting Tubule SUSD1 Human methylation cell type marker Connecting Tubule TBC1D9 Human methylation cell type marker Connecting Tubule TFAP2A Human methylation cell type marker Connecting Tubule TFEB Human methylation cell type marker 74    311769024v1 Attorney Docket No. 243735.000430  Connecting Tubule TMEM52B Human methylation cell type marker Connecting Tubule TMPRSS2 Human methylation cell type marker Connecting Tubule TRPV5 Human methylation cell type marker Connecting Tubule VWA5B1 Human methylation cell type marker Connecting Tubule ZC3H12A Human methylation cell type marker Connecting Tubule ZNF98 Human methylation cell type marker Cortical thick ascending Henle loop ABHD17C Human methylation cell type marker Cortical thick ascending Henle loop ACPP Human methylation cell type marker Cortical thick ascending Henle loop ADAMTS15 Human methylation cell type marker Cortical thick ascending Henle loop ADCY5 Human methylation cell type marker Cortical thick ascending Henle loop AGPAT4 Human methylation cell type marker Cortical thick ascending Henle loop ATP1A1 Human methylation cell type marker Cortical thick ascending Henle loop ATP1B1 Human methylation cell type marker Cortical thick ascending Henle loop BACE2 Human methylation cell type marker Cortical thick ascending Henle loop BSND Human methylation cell type marker Cortical thick ascending Henle loop BTBD11 Human methylation cell type marker Cortical thick ascending Henle loop C10orf55 Human methylation cell type marker Cortical thick ascending Henle loop C5orf38 Human methylation cell type marker Cortical thick ascending Henle loop CADM1 Human methylation cell type marker Cortical thick ascending Henle loop CASR Human methylation cell type marker Cortical thick ascending Henle loop CASZ1 Human methylation cell type marker Cortical thick ascending Henle loop CLCNKA Human methylation cell type marker Cortical thick ascending Henle loop CLCNKB Human methylation cell type marker Cortical thick ascending Henle loop CLDN14 Human methylation cell type marker Cortical thick ascending Henle loop CNTD1 Human methylation cell type marker Cortical thick ascending Henle loop CPEB3 Human methylation cell type marker Cortical thick ascending Henle loop CRTC1 Human methylation cell type marker Cortical thick ascending Henle loop CYFIP2 Human methylation cell type marker Cortical thick ascending Henle loop DENND2A Human methylation cell type marker Cortical thick ascending Henle loop DTX1 Human methylation cell type marker Cortical thick ascending Henle loop DUSP9 Human methylation cell type marker Cortical thick ascending Henle loop EFHD1 Human methylation cell type marker Cortical thick ascending Henle loop EFNA5 Human methylation cell type marker Cortical thick ascending Henle loop EGF Human methylation cell type marker Cortical thick ascending Henle loop EHF Human methylation cell type marker Cortical thick ascending Henle loop ESRRB Human methylation cell type marker Cortical thick ascending Henle loop FAM131C Human methylation cell type marker Cortical thick ascending Henle loop FAM167A Human methylation cell type marker Cortical thick ascending Henle loop FAM171A1 Human methylation cell type marker Cortical thick ascending Henle loop FAM234B Human methylation cell type marker Cortical thick ascending Henle loop FAM83B Human methylation cell type marker Cortical thick ascending Henle loop FGF9 Human methylation cell type marker Cortical thick ascending Henle loop GP2 Human methylation cell type marker Cortical thick ascending Henle loop GRHL2 Human methylation cell type marker Cortical thick ascending Henle loop HIP1 Human methylation cell type marker Cortical thick ascending Henle loop HLCS Human methylation cell type marker Cortical thick ascending Henle loop HOXB3 Human methylation cell type marker Cortical thick ascending Henle loop HOXB7 Human methylation cell type marker Cortical thick ascending Henle loop HRG Human methylation cell type marker Cortical thick ascending Henle loop HS6ST2 Human methylation cell type marker Cortical thick ascending Henle loop HSPB7 Human methylation cell type marker Cortical thick ascending Henle loop IRX1 Human methylation cell type marker Cortical thick ascending Henle loop IRX2 Human methylation cell type marker Cortical thick ascending Henle loop KCNIP3 Human methylation cell type marker 75    311769024v1 Attorney Docket No. 243735.000430  Cortical thick ascending Henle loop KCNJ1 Human methylation cell type marker Cortical thick ascending Henle loop KCTD1 Human methylation cell type marker Cortical thick ascending Henle loop KNG1 Human methylation cell type marker Cortical thick ascending Henle loop LNX1 Human methylation cell type marker Cortical thick ascending Henle loop LRIT3 Human methylation cell type marker Cortical thick ascending Henle loop MACROD1 Human methylation cell type marker Cortical thick ascending Henle loop MAL Human methylation cell type marker Cortical thick ascending Henle loop MFSD4A Human methylation cell type marker Cortical thick ascending Henle loop MGAT5 Human methylation cell type marker Cortical thick ascending Henle loop MSI2 Human methylation cell type marker Cortical thick ascending Henle loop MTURN Human methylation cell type marker Cortical thick ascending Henle loop MYO10 Human methylation cell type marker Cortical thick ascending Henle loop MYO3B Human methylation cell type marker Cortical thick ascending Henle loop NAT8L Human methylation cell type marker Cortical thick ascending Henle loop NTRK2 Human methylation cell type marker Cortical thick ascending Henle loop OSBPL3 Human methylation cell type marker Cortical thick ascending Henle loop OXR1 Human methylation cell type marker Cortical thick ascending Henle loop PADI2 Human methylation cell type marker Cortical thick ascending Henle loop PDE1A Human methylation cell type marker Cortical thick ascending Henle loop PPP1R36 Human methylation cell type marker Cortical thick ascending Henle loop PRDM16 Human methylation cell type marker Cortical thick ascending Henle loop PRKCQ Human methylation cell type marker Cortical thick ascending Henle loop PROM2 Human methylation cell type marker Cortical thick ascending Henle loop PROX1 Human methylation cell type marker Cortical thick ascending Henle loop PTGER3 Human methylation cell type marker Cortical thick ascending Henle loop PXDNL Human methylation cell type marker Cortical thick ascending Henle loop RANBP3L Human methylation cell type marker Cortical thick ascending Henle loop RAP1GAP2 Human methylation cell type marker Cortical thick ascending Henle loop RNF150 Human methylation cell type marker Cortical thick ascending Henle loop RNF165 Human methylation cell type marker Cortical thick ascending Henle loop SFRP1 Human methylation cell type marker Cortical thick ascending Henle loop SGIP1 Human methylation cell type marker Cortical thick ascending Henle loop SGPP2 Human methylation cell type marker Cortical thick ascending Henle loop SH3BP4 Human methylation cell type marker Cortical thick ascending Henle loop SIAH3 Human methylation cell type marker Cortical thick ascending Henle loop SIM1 Human methylation cell type marker Cortical thick ascending Henle loop SIM2 Human methylation cell type marker Cortical thick ascending Henle loop SLC12A1 Human methylation cell type marker Cortical thick ascending Henle loop SLC16A7 Human methylation cell type marker Cortical thick ascending Henle loop SLC7A5 Human methylation cell type marker Cortical thick ascending Henle loop SMUG1 Human methylation cell type marker Cortical thick ascending Henle loop STK32B Human methylation cell type marker Cortical thick ascending Henle loop SUSD4 Human methylation cell type marker Cortical thick ascending Henle loop TDGF1 Human methylation cell type marker Cortical thick ascending Henle loop TFAP2B Human methylation cell type marker Cortical thick ascending Henle loop TLE1 Human methylation cell type marker Cortical thick ascending Henle loop TMEM8A Human methylation cell type marker Cortical thick ascending Henle loop TRIM2 Human methylation cell type marker Cortical thick ascending Henle loop UGT8 Human methylation cell type marker Cortical thick ascending Henle loop UMOD Human methylation cell type marker Cortical thick ascending Henle loop WNK4 Human methylation cell type marker Cortical thick ascending Henle loop ZFPM1 Human methylation cell type marker Distal tubule ADAMTS17 Human methylation cell type marker Distal tubule APBB2 Human methylation cell type marker 76    311769024v1 Attorney Docket No. 243735.000430  Distal tubule ARHGEF3 Human methylation cell type marker Distal tubule ATP6V0A4 Human methylation cell type marker Distal tubule ATP6V1B1 Human methylation cell type marker Distal tubule ATP8A1 Human methylation cell type marker Distal tubule B4GALNT3 Human methylation cell type marker Distal tubule BACE2 Human methylation cell type marker Distal tubule BFSP2 Human methylation cell type marker Distal tubule BSND Human methylation cell type marker Distal tubule BTBD11 Human methylation cell type marker Distal tubule C3orf52 Human methylation cell type marker Distal tubule CACNA1D Human methylation cell type marker Distal tubule CACNB4 Human methylation cell type marker Distal tubule CALB1 Human methylation cell type marker Distal tubule CASZ1 Human methylation cell type marker Distal tubule CCDC192 Human methylation cell type marker Distal tubule CDCP1 Human methylation cell type marker Distal tubule CLCNKB Human methylation cell type marker Distal tubule CNTD1 Human methylation cell type marker Distal tubule CPXM2 Human methylation cell type marker Distal tubule DEFB1 Human methylation cell type marker Distal tubule EXPH5 Human methylation cell type marker Distal tubule FAM167A Human methylation cell type marker Distal tubule FAM171A1 Human methylation cell type marker Distal tubule FMN1 Human methylation cell type marker Distal tubule GATA3 Human methylation cell type marker Distal tubule GPRIN3 Human methylation cell type marker Distal tubule GRAMD1B Human methylation cell type marker Distal tubule HOXB3 Human methylation cell type marker Distal tubule HOXD3 Human methylation cell type marker Distal tubule HRG Human methylation cell type marker Distal tubule HS6ST2 Human methylation cell type marker Distal tubule INPP5J Human methylation cell type marker Distal tubule ITPKB Human methylation cell type marker Distal tubule ITPR1 Human methylation cell type marker Distal tubule KCNJ1 Human methylation cell type marker Distal tubule KCNJ10 Human methylation cell type marker Distal tubule KCNN3 Human methylation cell type marker Distal tubule LRRC52 Human methylation cell type marker Distal tubule MAL Human methylation cell type marker Distal tubule MAP2K6 Human methylation cell type marker Distal tubule MAPK4 Human methylation cell type marker Distal tubule MYO10 Human methylation cell type marker Distal tubule MYO3B Human methylation cell type marker Distal tubule NOS1AP Human methylation cell type marker Distal tubule NR0B2 Human methylation cell type marker Distal tubule NRG2 Human methylation cell type marker Distal tubule OGDHL Human methylation cell type marker Distal tubule OLR1 Human methylation cell type marker Distal tubule PEX5L Human methylation cell type marker Distal tubule PIP5K1B Human methylation cell type marker Distal tubule PLA2G3 Human methylation cell type marker Distal tubule PRDM16 Human methylation cell type marker Distal tubule PROM2 Human methylation cell type marker Distal tubule PTGER3 Human methylation cell type marker 77    311769024v1 Attorney Docket No. 243735.000430  Distal tubule RAB31 Human methylation cell type marker Distal tubule RHCG Human methylation cell type marker Distal tubule SALL3 Human methylation cell type marker Distal tubule SCTR Human methylation cell type marker Distal tubule SERPINA5 Human methylation cell type marker Distal tubule SFRP1 Human methylation cell type marker Distal tubule SFXN5 Human methylation cell type marker Distal tubule SH3BP4 Human methylation cell type marker Distal tubule SHROOM3 Human methylation cell type marker Distal tubule SIAH3 Human methylation cell type marker Distal tubule SIM1 Human methylation cell type marker Distal tubule SLC16A7 Human methylation cell type marker Distal tubule SORL1 Human methylation cell type marker Distal tubule SPERT Human methylation cell type marker Distal tubule SPSB4 Human methylation cell type marker Distal tubule SUSD4 Human methylation cell type marker Distal tubule TCF24 Human methylation cell type marker Distal tubule TFAP2A Human methylation cell type marker Distal tubule TFAP2B Human methylation cell type marker Distal tubule TMEM108 Human methylation cell type marker Distal tubule TMEM213 Human methylation cell type marker Distal tubule TMPRSS2 Human methylation cell type marker Distal tubule TPST2 Human methylation cell type marker Distal tubule TRIM50 Human methylation cell type marker Distal tubule TRPM6 Human methylation cell type marker Distal tubule UNC5C Human methylation cell type marker Distal tubule ZMIZ1 Human methylation cell type marker Endothelial (glomerular capillary tuft) ABCG1 Human methylation cell type marker Endothelial (glomerular capillary tuft) ABR Human methylation cell type marker Endothelial (glomerular capillary tuft) ACVRL1 Human methylation cell type marker Endothelial (glomerular capillary tuft) ADGRF5 Human methylation cell type marker Endothelial (glomerular capillary tuft) AFAP1L1 Human methylation cell type marker Endothelial (glomerular capillary tuft) ARAP3 Human methylation cell type marker Endothelial (glomerular capillary tuft) ARHGEF15 Human methylation cell type marker Endothelial (glomerular capillary tuft) BCL6B Human methylation cell type marker Endothelial (glomerular capillary tuft) BTNL9 Human methylation cell type marker Endothelial (glomerular capillary tuft) CAVIN2 Human methylation cell type marker Endothelial (glomerular capillary tuft) CD34 Human methylation cell type marker Endothelial (glomerular capillary tuft) CD93 Human methylation cell type marker Endothelial (glomerular capillary tuft) CDA Human methylation cell type marker Endothelial (glomerular capillary tuft) CDH5 Human methylation cell type marker Endothelial (glomerular capillary tuft) CLDN5 Human methylation cell type marker Endothelial (glomerular capillary tuft) CLEC14A Human methylation cell type marker Endothelial (glomerular capillary tuft) CMKLR1 Human methylation cell type marker Endothelial (glomerular capillary tuft) DOCK9 Human methylation cell type marker Endothelial (glomerular capillary tuft) EBF1 Human methylation cell type marker Endothelial (glomerular capillary tuft) EGFL7 Human methylation cell type marker Endothelial (glomerular capillary tuft) ELK3 Human methylation cell type marker Endothelial (glomerular capillary tuft) ENG Human methylation cell type marker Endothelial (glomerular capillary tuft) ENTPD1 Human methylation cell type marker Endothelial (glomerular capillary tuft) EPAS1 Human methylation cell type marker Endothelial (glomerular capillary tuft) ERG Human methylation cell type marker Endothelial (glomerular capillary tuft) ETS1 Human methylation cell type marker Endothelial (glomerular capillary tuft) EXOC3L2 Human methylation cell type marker 78    311769024v1 Attorney Docket No. 243735.000430  Endothelial (glomerular capillary tuft) F2RL3 Human methylation cell type marker Endothelial (glomerular capillary tuft) FGD5 Human methylation cell type marker Endothelial (glomerular capillary tuft) FGR Human methylation cell type marker Endothelial (glomerular capillary tuft) FLI1 Human methylation cell type marker Endothelial (glomerular capillary tuft) FLT1 Human methylation cell type marker Endothelial (glomerular capillary tuft) GATA2 Human methylation cell type marker Endothelial (glomerular capillary tuft) GIMAP1 Human methylation cell type marker Endothelial (glomerular capillary tuft) GIMAP5 Human methylation cell type marker Endothelial (glomerular capillary tuft) GIMAP6 Human methylation cell type marker Endothelial (glomerular capillary tuft) GIMAP8 Human methylation cell type marker Endothelial (glomerular capillary tuft) GPIHBP1 Human methylation cell type marker Endothelial (glomerular capillary tuft) GRASP Human methylation cell type marker Endothelial (glomerular capillary tuft) GSN Human methylation cell type marker Endothelial (glomerular capillary tuft) HHEX Human methylation cell type marker Endothelial (glomerular capillary tuft) HLX Human methylation cell type marker Endothelial (glomerular capillary tuft) HSPG2 Human methylation cell type marker Endothelial (glomerular capillary tuft) ICAM2 Human methylation cell type marker Endothelial (glomerular capillary tuft) IFI27 Human methylation cell type marker Endothelial (glomerular capillary tuft) IFT140 Human methylation cell type marker Endothelial (glomerular capillary tuft) INPP5D Human methylation cell type marker Endothelial (glomerular capillary tuft) ITGA8 Human methylation cell type marker Endothelial (glomerular capillary tuft) ITPRIP Human methylation cell type marker Endothelial (glomerular capillary tuft) JCAD Human methylation cell type marker Endothelial (glomerular capillary tuft) KDR Human methylation cell type marker Endothelial (glomerular capillary tuft) KLF2 Human methylation cell type marker Endothelial (glomerular capillary tuft) LDB2 Human methylation cell type marker Endothelial (glomerular capillary tuft) LIMS2 Human methylation cell type marker Endothelial (glomerular capillary tuft) LRRC32 Human methylation cell type marker Endothelial (glomerular capillary tuft) LRRC74A Human methylation cell type marker Endothelial (glomerular capillary tuft) LRRC8C Human methylation cell type marker Endothelial (glomerular capillary tuft) LYL1 Human methylation cell type marker Endothelial (glomerular capillary tuft) MEF2C Human methylation cell type marker Endothelial (glomerular capillary tuft) MEIS2 Human methylation cell type marker Endothelial (glomerular capillary tuft) MYCT1 Human methylation cell type marker Endothelial (glomerular capillary tuft) NOS3 Human methylation cell type marker Endothelial (glomerular capillary tuft) NOVA2 Human methylation cell type marker Endothelial (glomerular capillary tuft) NRN1 Human methylation cell type marker Endothelial (glomerular capillary tuft) PALD1 Human methylation cell type marker Endothelial (glomerular capillary tuft) PDE2A Human methylation cell type marker Endothelial (glomerular capillary tuft) PECAM1 Human methylation cell type marker Endothelial (glomerular capillary tuft) PGS1 Human methylation cell type marker Endothelial (glomerular capillary tuft) PLXND1 Human methylation cell type marker Endothelial (glomerular capillary tuft) PODXL Human methylation cell type marker Endothelial (glomerular capillary tuft) POPDC2 Human methylation cell type marker Endothelial (glomerular capillary tuft) PPP1R14A Human methylation cell type marker Endothelial (glomerular capillary tuft) PREX1 Human methylation cell type marker Endothelial (glomerular capillary tuft) PRR29 Human methylation cell type marker Endothelial (glomerular capillary tuft) PTPRB Human methylation cell type marker Endothelial (glomerular capillary tuft) PTPRR Human methylation cell type marker Endothelial (glomerular capillary tuft) RAPGEF4 Human methylation cell type marker Endothelial (glomerular capillary tuft) RFPL3 Human methylation cell type marker Endothelial (glomerular capillary tuft) ROBO4 Human methylation cell type marker Endothelial (glomerular capillary tuft) S1PR1 Human methylation cell type marker Endothelial (glomerular capillary tuft) SEC14L1 Human methylation cell type marker 79    311769024v1 Attorney Docket No. 243735.000430  Endothelial (glomerular capillary tuft) SH2D3C Human methylation cell type marker Endothelial (glomerular capillary tuft) SH3RF3 Human methylation cell type marker Endothelial (glomerular capillary tuft) SLC9A3R2 Human methylation cell type marker Endothelial (glomerular capillary tuft) SOX17 Human methylation cell type marker Endothelial (glomerular capillary tuft) SOX18 Human methylation cell type marker Endothelial (glomerular capillary tuft) SPARC Human methylation cell type marker Endothelial (glomerular capillary tuft) ST8SIA4 Human methylation cell type marker Endothelial (glomerular capillary tuft) TAL1 Human methylation cell type marker Endothelial (glomerular capillary tuft) TBXA2R Human methylation cell type marker Endothelial (glomerular capillary tuft) TCEA2 Human methylation cell type marker Endothelial (glomerular capillary tuft) TCF15 Human methylation cell type marker Endothelial (glomerular capillary tuft) TCF4 Human methylation cell type marker Endothelial (glomerular capillary tuft) TEK Human methylation cell type marker Endothelial (glomerular capillary tuft) TIMP3 Human methylation cell type marker Endothelial (glomerular capillary tuft) TM4SF1 Human methylation cell type marker Endothelial (glomerular capillary tuft) TM6SF1 Human methylation cell type marker Endothelial (glomerular capillary tuft) TMEM204 Human methylation cell type marker Endothelial (glomerular capillary tuft) TMTC1 Human methylation cell type marker Endothelial (glomerular capillary tuft) VASH1 Human methylation cell type marker Endothelial (peritubular) ABCG1 Human methylation cell type marker Endothelial (peritubular) ADGRF5 Human methylation cell type marker Endothelial (peritubular) ADGRL4 Human methylation cell type marker Endothelial (peritubular) AFAP1L1 Human methylation cell type marker Endothelial (peritubular) BCL6B Human methylation cell type marker Endothelial (peritubular) BMERB1 Human methylation cell type marker Endothelial (peritubular) CAVIN2 Human methylation cell type marker Endothelial (peritubular) CD34 Human methylation cell type marker Endothelial (peritubular) CD93 Human methylation cell type marker Endothelial (peritubular) CDA Human methylation cell type marker Endothelial (peritubular) CDH5 Human methylation cell type marker Endothelial (peritubular) CLEC14A Human methylation cell type marker Endothelial (peritubular) CLEC1A Human methylation cell type marker Endothelial (peritubular) CMKLR1 Human methylation cell type marker Endothelial (peritubular) DCHS1 Human methylation cell type marker Endothelial (peritubular) DNASE1L3 Human methylation cell type marker Endothelial (peritubular) DYSF Human methylation cell type marker Endothelial (peritubular) EBF1 Human methylation cell type marker Endothelial (peritubular) EGFL7 Human methylation cell type marker Endothelial (peritubular) EHD4 Human methylation cell type marker Endothelial (peritubular) ELK3 Human methylation cell type marker Endothelial (peritubular) ENG Human methylation cell type marker Endothelial (peritubular) ENTPD1 Human methylation cell type marker Endothelial (peritubular) ERG Human methylation cell type marker Endothelial (peritubular) EXOC3L2 Human methylation cell type marker Endothelial (peritubular) F2RL3 Human methylation cell type marker Endothelial (peritubular) FAM167B Human methylation cell type marker Endothelial (peritubular) FGR Human methylation cell type marker Endothelial (peritubular) FLRT2 Human methylation cell type marker Endothelial (peritubular) FLT1 Human methylation cell type marker Endothelial (peritubular) FLT4 Human methylation cell type marker Endothelial (peritubular) GALNT15 Human methylation cell type marker Endothelial (peritubular) GIMAP5 Human methylation cell type marker Endothelial (peritubular) GIMAP6 Human methylation cell type marker Endothelial (peritubular) GIMAP8 Human methylation cell type marker 80    311769024v1 Attorney Docket No. 243735.000430  Endothelial (peritubular) GNA14 Human methylation cell type marker Endothelial (peritubular) GPR182 Human methylation cell type marker Endothelial (peritubular) GSN Human methylation cell type marker Endothelial (peritubular) GYPC Human methylation cell type marker Endothelial (peritubular) HS3ST3A1 Human methylation cell type marker Endothelial (peritubular) ICAM2 Human methylation cell type marker Endothelial (peritubular) IFT140 Human methylation cell type marker Endothelial (peritubular) INPP5D Human methylation cell type marker Endothelial (peritubular) ITGA8 Human methylation cell type marker Endothelial (peritubular) ITPRIP Human methylation cell type marker Endothelial (peritubular) JCAD Human methylation cell type marker Endothelial (peritubular) KDR Human methylation cell type marker Endothelial (peritubular) LDB2 Human methylation cell type marker Endothelial (peritubular) LDLRAD4 Human methylation cell type marker Endothelial (peritubular) LIMS2 Human methylation cell type marker Endothelial (peritubular) LRRC32 Human methylation cell type marker Endothelial (peritubular) LYL1 Human methylation cell type marker Endothelial (peritubular) LYVE1 Human methylation cell type marker Endothelial (peritubular) MEF2C Human methylation cell type marker Endothelial (peritubular) MEIS2 Human methylation cell type marker Endothelial (peritubular) MYCT1 Human methylation cell type marker Endothelial (peritubular) NOS3 Human methylation cell type marker Endothelial (peritubular) NOVA2 Human methylation cell type marker Endothelial (peritubular) NRN1 Human methylation cell type marker Endothelial (peritubular) NXN Human methylation cell type marker Endothelial (peritubular) P2RY8 Human methylation cell type marker Endothelial (peritubular) PALD1 Human methylation cell type marker Endothelial (peritubular) PDE2A Human methylation cell type marker Endothelial (peritubular) PECAM1 Human methylation cell type marker Endothelial (peritubular) PLAT Human methylation cell type marker Endothelial (peritubular) PLVAP Human methylation cell type marker Endothelial (peritubular) PLXND1 Human methylation cell type marker Endothelial (peritubular) PODXL Human methylation cell type marker Endothelial (peritubular) POPDC2 Human methylation cell type marker Endothelial (peritubular) PREX2 Human methylation cell type marker Endothelial (peritubular) PRKCH Human methylation cell type marker Endothelial (peritubular) PTPRB Human methylation cell type marker Endothelial (peritubular) PTPRR Human methylation cell type marker Endothelial (peritubular) RAMP3 Human methylation cell type marker Endothelial (peritubular) RAPGEF4 Human methylation cell type marker Endothelial (peritubular) RARB Human methylation cell type marker Endothelial (peritubular) RGCC Human methylation cell type marker Endothelial (peritubular) ROBO4 Human methylation cell type marker Endothelial (peritubular) S1PR1 Human methylation cell type marker Endothelial (peritubular) SASH1 Human methylation cell type marker Endothelial (peritubular) SEC14L1 Human methylation cell type marker Endothelial (peritubular) SELE Human methylation cell type marker Endothelial (peritubular) SELP Human methylation cell type marker Endothelial (peritubular) SH2D3C Human methylation cell type marker Endothelial (peritubular) SH3RF3 Human methylation cell type marker Endothelial (peritubular) SIN3B Human methylation cell type marker Endothelial (peritubular) SLCO2A1 Human methylation cell type marker Endothelial (peritubular) SOX18 Human methylation cell type marker Endothelial (peritubular) SPARC Human methylation cell type marker 81    311769024v1 Attorney Docket No. 243735.000430  Endothelial (peritubular) TAL1 Human methylation cell type marker Endothelial (peritubular) TCF15 Human methylation cell type marker Endothelial (peritubular) TCF4 Human methylation cell type marker Endothelial (peritubular) TEAD2 Human methylation cell type marker Endothelial (peritubular) TEK Human methylation cell type marker Endothelial (peritubular) THBD Human methylation cell type marker Endothelial (peritubular) TM4SF1 Human methylation cell type marker Endothelial (peritubular) TMEM204 Human methylation cell type marker Endothelial (peritubular) TMTC1 Human methylation cell type marker Endothelial (peritubular) TRIML1 Human methylation cell type marker Endothelial (peritubular) XXYLT1 Human methylation cell type marker Fibroblast ABCC9 Human methylation cell type marker Fibroblast ACTA2 Human methylation cell type marker Fibroblast ACVRL1 Human methylation cell type marker Fibroblast ADAMTS2 Human methylation cell type marker Fibroblast ADAMTSL3 Human methylation cell type marker Fibroblast ADCY3 Human methylation cell type marker Fibroblast ADGRA2 Human methylation cell type marker Fibroblast ADGRD1 Human methylation cell type marker Fibroblast ADH1B Human methylation cell type marker Fibroblast ADRA1A Human methylation cell type marker Fibroblast ADRA1D Human methylation cell type marker Fibroblast ANGPTL2 Human methylation cell type marker Fibroblast ARHGEF25 Human methylation cell type marker Fibroblast ATP10A Human methylation cell type marker Fibroblast BMPER Human methylation cell type marker Fibroblast BOC Human methylation cell type marker Fibroblast C11orf96 Human methylation cell type marker Fibroblast C1QTNF2 Human methylation cell type marker Fibroblast C1orf21 Human methylation cell type marker Fibroblast C9orf152 Human methylation cell type marker Fibroblast CALHM2 Human methylation cell type marker Fibroblast CD248 Human methylation cell type marker Fibroblast CD44 Human methylation cell type marker Fibroblast CHST15 Human methylation cell type marker Fibroblast CLDN11 Human methylation cell type marker Fibroblast CNN1 Human methylation cell type marker Fibroblast COL1A2 Human methylation cell type marker Fibroblast COL3A1 Human methylation cell type marker Fibroblast COL5A1 Human methylation cell type marker Fibroblast COL6A3 Human methylation cell type marker Fibroblast CRISPLD2 Human methylation cell type marker Fibroblast CRYGN Human methylation cell type marker Fibroblast CSRP1 Human methylation cell type marker Fibroblast DPYSL3 Human methylation cell type marker Fibroblast EBF2 Human methylation cell type marker Fibroblast EDNRB Human methylation cell type marker Fibroblast EHD2 Human methylation cell type marker Fibroblast FAM180A Human methylation cell type marker Fibroblast FBLN1 Human methylation cell type marker Fibroblast FBN1 Human methylation cell type marker Fibroblast FGF7 Human methylation cell type marker Fibroblast FHL5 Human methylation cell type marker Fibroblast FILIP1 Human methylation cell type marker 82    311769024v1 Attorney Docket No. 243735.000430  Fibroblast FLRT2 Human methylation cell type marker Fibroblast FOXC2 Human methylation cell type marker Fibroblast FOXL1 Human methylation cell type marker Fibroblast FXYD1 Human methylation cell type marker Fibroblast GAS7 Human methylation cell type marker Fibroblast GATA6 Human methylation cell type marker Fibroblast GJC2 Human methylation cell type marker Fibroblast GLI2 Human methylation cell type marker Fibroblast GREB1L Human methylation cell type marker Fibroblast HEYL Human methylation cell type marker Fibroblast HGF Human methylation cell type marker Fibroblast ICAM4 Human methylation cell type marker Fibroblast IDO2 Human methylation cell type marker Fibroblast IGF1 Human methylation cell type marker Fibroblast IL16 Human methylation cell type marker Fibroblast IL17B Human methylation cell type marker Fibroblast ISLR Human methylation cell type marker Fibroblast KCNE4 Human methylation cell type marker Fibroblast KCNJ8 Human methylation cell type marker Fibroblast KLHL29 Human methylation cell type marker Fibroblast LCP2 Human methylation cell type marker Fibroblast LMOD1 Human methylation cell type marker Fibroblast LONRF2 Human methylation cell type marker Fibroblast LPAR1 Human methylation cell type marker Fibroblast LRRC17 Human methylation cell type marker Fibroblast LUM Human methylation cell type marker Fibroblast MAMDC2 Human methylation cell type marker Fibroblast MATN2 Human methylation cell type marker Fibroblast MEDAG Human methylation cell type marker Fibroblast MFAP4 Human methylation cell type marker Fibroblast MMP23B Human methylation cell type marker Fibroblast MN1 Human methylation cell type marker Fibroblast MRVI1 Human methylation cell type marker Fibroblast MTHFD1L Human methylation cell type marker Fibroblast MYOZ3 Human methylation cell type marker Fibroblast NCALD Human methylation cell type marker Fibroblast NGF Human methylation cell type marker Fibroblast NID1 Human methylation cell type marker Fibroblast NNMT Human methylation cell type marker Fibroblast NTF3 Human methylation cell type marker Fibroblast NTN1 Human methylation cell type marker Fibroblast NXPE2 Human methylation cell type marker Fibroblast PALLD Human methylation cell type marker Fibroblast PCDH18 Human methylation cell type marker Fibroblast PCSK7 Human methylation cell type marker Fibroblast PDLIM1 Human methylation cell type marker Fibroblast PDPN Human methylation cell type marker Fibroblast PEAR1 Human methylation cell type marker Fibroblast PLA2G4A Human methylation cell type marker Fibroblast PLXND1 Human methylation cell type marker Fibroblast PODN Human methylation cell type marker Fibroblast PPP1R14A Human methylation cell type marker Fibroblast PRELP Human methylation cell type marker Fibroblast PRRX1 Human methylation cell type marker 83    311769024v1 Attorney Docket No. 243735.000430  Fibroblast PTGDR Human methylation cell type marker Fibroblast PTGIR Human methylation cell type marker Fibroblast PTGIS Human methylation cell type marker Fibroblast PXDN Human methylation cell type marker Fibroblast RAB33A Human methylation cell type marker Fibroblast RAMP1 Human methylation cell type marker Fibroblast RAPGEF5 Human methylation cell type marker Fibroblast RARB Human methylation cell type marker Fibroblast RASGRP2 Human methylation cell type marker Fibroblast RUNX1T1 Human methylation cell type marker Fibroblast S1PR2 Human methylation cell type marker Fibroblast SAMHD1 Human methylation cell type marker Fibroblast SCARA5 Human methylation cell type marker Fibroblast SERPINE1 Human methylation cell type marker Fibroblast SERTM1 Human methylation cell type marker Fibroblast SH2D2A Human methylation cell type marker Fibroblast SH3PXD2B Human methylation cell type marker Fibroblast SHISAL1 Human methylation cell type marker Fibroblast SLC14A2 Human methylation cell type marker Fibroblast SMG6 Human methylation cell type marker Fibroblast SPARC Human methylation cell type marker Fibroblast SPECC1 Human methylation cell type marker Fibroblast SRGN Human methylation cell type marker Fibroblast SRPX2 Human methylation cell type marker Fibroblast STAMBPL1 Human methylation cell type marker Fibroblast SVEP1 Human methylation cell type marker Fibroblast SYNPO2 Human methylation cell type marker Fibroblast TAGLN Human methylation cell type marker Fibroblast TBX3 Human methylation cell type marker Fibroblast TCF21 Human methylation cell type marker Fibroblast TCF23 Human methylation cell type marker Fibroblast TG Human methylation cell type marker Fibroblast THBS2 Human methylation cell type marker Fibroblast TIMP3 Human methylation cell type marker Fibroblast TMEM204 Human methylation cell type marker Fibroblast TNFAIP8L3 Human methylation cell type marker Fibroblast TNNT3 Human methylation cell type marker Fibroblast TNXB Human methylation cell type marker Fibroblast TPM2 Human methylation cell type marker Fibroblast TPM4 Human methylation cell type marker Fibroblast TRERF1 Human methylation cell type marker Fibroblast TRPC4 Human methylation cell type marker Fibroblast TWIST2 Human methylation cell type marker Fibroblast VSIG10L2 Human methylation cell type marker Fibroblast WNT9A Human methylation cell type marker Fibroblast WT1 Human methylation cell type marker Fibroblast XYLT1 Human methylation cell type marker Intercalated cells ABCC4 Human methylation cell type marker Intercalated cells ADAMTSL3 Human methylation cell type marker Intercalated cells ADRB1 Human methylation cell type marker Intercalated cells AKNAD1 Human methylation cell type marker Intercalated cells AMPD3 Human methylation cell type marker Intercalated cells AQP6 Human methylation cell type marker Intercalated cells ASB5 Human methylation cell type marker 84    311769024v1 Attorney Docket No. 243735.000430  Intercalated cells ATP6V1B1 Human methylation cell type marker Intercalated cells ATP9A Human methylation cell type marker Intercalated cells AVPR1A Human methylation cell type marker Intercalated cells BACE2 Human methylation cell type marker Intercalated cells C10orf71 Human methylation cell type marker Intercalated cells C2orf91 Human methylation cell type marker Intercalated cells CA8 Human methylation cell type marker Intercalated cells CAB39 Human methylation cell type marker Intercalated cells CALCA Human methylation cell type marker Intercalated cells CASZ1 Human methylation cell type marker Intercalated cells CCBE1 Human methylation cell type marker Intercalated cells CELF5 Human methylation cell type marker Intercalated cells CLEC3B Human methylation cell type marker Intercalated cells CLNK Human methylation cell type marker Intercalated cells CLPB Human methylation cell type marker Intercalated cells CSGALNACT1 Human methylation cell type marker Intercalated cells DAP Human methylation cell type marker Intercalated cells DES Human methylation cell type marker Intercalated cells DGKI Human methylation cell type marker Intercalated cells DLL1 Human methylation cell type marker Intercalated cells DNAH11 Human methylation cell type marker Intercalated cells DRAM1 Human methylation cell type marker Intercalated cells EDARADD Human methylation cell type marker Intercalated cells EFNA5 Human methylation cell type marker Intercalated cells EIPR1 Human methylation cell type marker Intercalated cells ELP3 Human methylation cell type marker Intercalated cells EXOSC7 Human methylation cell type marker Intercalated cells FAM110B Human methylation cell type marker Intercalated cells FAM120B Human methylation cell type marker Intercalated cells FAM13C Human methylation cell type marker Intercalated cells FAM184B Human methylation cell type marker Intercalated cells FAM24B Human methylation cell type marker Intercalated cells FOXI2 Human methylation cell type marker Intercalated cells FOXN3 Human methylation cell type marker Intercalated cells FOXO1 Human methylation cell type marker Intercalated cells FYB2 Human methylation cell type marker Intercalated cells GATA2 Human methylation cell type marker Intercalated cells GCNT1 Human methylation cell type marker Intercalated cells HCAR1 Human methylation cell type marker Intercalated cells HTR3B Human methylation cell type marker Intercalated cells IFITM10 Human methylation cell type marker Intercalated cells IGFBP5 Human methylation cell type marker Intercalated cells INPP5J Human methylation cell type marker Intercalated cells IQCK Human methylation cell type marker Intercalated cells IQGAP2 Human methylation cell type marker Intercalated cells ITGA6 Human methylation cell type marker Intercalated cells KBTBD12 Human methylation cell type marker Intercalated cells KLHL3 Human methylation cell type marker Intercalated cells LGALS3 Human methylation cell type marker Intercalated cells LIMS1 Human methylation cell type marker Intercalated cells LRRC61 Human methylation cell type marker Intercalated cells LRRC69 Human methylation cell type marker Intercalated cells MAG Human methylation cell type marker Intercalated cells MBOAT1 Human methylation cell type marker 85    311769024v1 Attorney Docket No. 243735.000430  Intercalated cells MPP7 Human methylation cell type marker Intercalated cells MR1 Human methylation cell type marker Intercalated cells MVB12B Human methylation cell type marker Intercalated cells MYMX Human methylation cell type marker Intercalated cells MYO10 Human methylation cell type marker Intercalated cells NEURL1 Human methylation cell type marker Intercalated cells NHLRC4 Human methylation cell type marker Intercalated cells NLRC5 Human methylation cell type marker Intercalated cells NOS1AP Human methylation cell type marker Intercalated cells NTN1 Human methylation cell type marker Intercalated cells NTRK1 Human methylation cell type marker Intercalated cells NXPH2 Human methylation cell type marker Intercalated cells OXGR1 Human methylation cell type marker Intercalated cells OXR1 Human methylation cell type marker Intercalated cells PARVB Human methylation cell type marker Intercalated cells PKD2L1 Human methylation cell type marker Intercalated cells PLEK2 Human methylation cell type marker Intercalated cells PRDM16 Human methylation cell type marker Intercalated cells PRKCD Human methylation cell type marker Intercalated cells PSKH2 Human methylation cell type marker Intercalated cells PTGER3 Human methylation cell type marker Intercalated cells PYY Human methylation cell type marker Intercalated cells RCAN2 Human methylation cell type marker Intercalated cells RGS8 Human methylation cell type marker Intercalated cells RHCG Human methylation cell type marker Intercalated cells RIOX2 Human methylation cell type marker Intercalated cells SCIN Human methylation cell type marker Intercalated cells SEMA3C Human methylation cell type marker Intercalated cells SH2D1B Human methylation cell type marker Intercalated cells SLC22A23 Human methylation cell type marker Intercalated cells SLC35F3 Human methylation cell type marker Intercalated cells SLC38A4 Human methylation cell type marker Intercalated cells SLC4A1 Human methylation cell type marker Intercalated cells SLC52A3 Human methylation cell type marker Intercalated cells SPARCL1 Human methylation cell type marker Intercalated cells SPSB4 Human methylation cell type marker Intercalated cells ST6GAL1 Human methylation cell type marker Intercalated cells STAC2 Human methylation cell type marker Intercalated cells STIM1 Human methylation cell type marker Intercalated cells SYT17 Human methylation cell type marker Intercalated cells TBC1D14 Human methylation cell type marker Intercalated cells TECTB Human methylation cell type marker Intercalated cells TFEB Human methylation cell type marker Intercalated cells THSD7A Human methylation cell type marker Intercalated cells TMEM101 Human methylation cell type marker Intercalated cells TMEM61 Human methylation cell type marker Intercalated cells TMPRSS2 Human methylation cell type marker Intercalated cells TNFAIP8L3 Human methylation cell type marker Intercalated cells TRIM2 Human methylation cell type marker Intercalated cells TSPAN5 Human methylation cell type marker Intercalated cells U2SURP Human methylation cell type marker Intercalated cells UBE2QL1 Human methylation cell type marker Intercalated cells UMAD1 Human methylation cell type marker Intercalated cells UMODL1 Human methylation cell type marker 86    311769024v1 Attorney Docket No. 243735.000430  Intercalated cells UST Human methylation cell type marker Intercalated cells VAT1L Human methylation cell type marker Intercalated cells VAX2 Human methylation cell type marker Intercalated cells VWA5B1 Human methylation cell type marker Intercalated cells VWF Human methylation cell type marker Intercalated cells WNT3A Human methylation cell type marker Intercalated cells ZFP57 Human methylation cell type marker Podocytes ABLIM2 Human methylation cell type marker Podocytes ADAMTS19 Human methylation cell type marker Podocytes ADORA1 Human methylation cell type marker Podocytes ARHGAP23 Human methylation cell type marker Podocytes ARHGEF26 Human methylation cell type marker Podocytes ATP6V0D2 Human methylation cell type marker Podocytes CALHM5 Human methylation cell type marker Podocytes CBLB Human methylation cell type marker Podocytes CCBE1 Human methylation cell type marker Podocytes CERS6 Human methylation cell type marker Podocytes CLDN5 Human methylation cell type marker Podocytes CORO2B Human methylation cell type marker Podocytes CRB2 Human methylation cell type marker Podocytes DOCK5 Human methylation cell type marker Podocytes F2R Human methylation cell type marker Podocytes FAM240A Human methylation cell type marker Podocytes FGF1 Human methylation cell type marker Podocytes FLT1 Human methylation cell type marker Podocytes FMN2 Human methylation cell type marker Podocytes FOXL1 Human methylation cell type marker Podocytes FRMD1 Human methylation cell type marker Podocytes FRY Human methylation cell type marker Podocytes FXYD1 Human methylation cell type marker Podocytes GJA1 Human methylation cell type marker Podocytes GPSM1 Human methylation cell type marker Podocytes HS3ST3A1 Human methylation cell type marker Podocytes IQCJ Human methylation cell type marker Podocytes KCNH2 Human methylation cell type marker Podocytes KIRREL1 Human methylation cell type marker Podocytes KLHL29 Human methylation cell type marker Podocytes LINGO1 Human methylation cell type marker Podocytes LMX1B Human methylation cell type marker Podocytes LRRC20 Human methylation cell type marker Podocytes MCC Human methylation cell type marker Podocytes MED27 Human methylation cell type marker Podocytes MYBPH Human methylation cell type marker Podocytes NDNF Human methylation cell type marker Podocytes NEBL Human methylation cell type marker Podocytes NFASC Human methylation cell type marker Podocytes NGF Human methylation cell type marker Podocytes NKD1 Human methylation cell type marker Podocytes NTNG1 Human methylation cell type marker Podocytes PALLD Human methylation cell type marker Podocytes PARD6G Human methylation cell type marker Podocytes PCARE Human methylation cell type marker Podocytes PCED1B Human methylation cell type marker Podocytes PDGFRB Human methylation cell type marker 87    311769024v1 Attorney Docket No. 243735.000430  Podocytes PGLYRP2 Human methylation cell type marker Podocytes POLA2 Human methylation cell type marker Podocytes PPFIA4 Human methylation cell type marker Podocytes PRDM5 Human methylation cell type marker Podocytes PSKH2 Human methylation cell type marker Podocytes RAMP1 Human methylation cell type marker Podocytes SFMBT2 Human methylation cell type marker Podocytes SIX2 Human methylation cell type marker Podocytes SLAMF8 Human methylation cell type marker Podocytes SLC45A1 Human methylation cell type marker Podocytes SNCA Human methylation cell type marker Podocytes SPECC1 Human methylation cell type marker Podocytes SPSB1 Human methylation cell type marker Podocytes SPTB Human methylation cell type marker Podocytes SRGAP2B Human methylation cell type marker Podocytes SULF1 Human methylation cell type marker Podocytes SULF2 Human methylation cell type marker Podocytes TACR3 Human methylation cell type marker Podocytes TANC1 Human methylation cell type marker Podocytes TCF21 Human methylation cell type marker Podocytes TET2 Human methylation cell type marker Podocytes TGFBR3 Human methylation cell type marker Podocytes TMEM266 Human methylation cell type marker Podocytes TMOD2 Human methylation cell type marker Podocytes TRPC6 Human methylation cell type marker Podocytes TSPAN2 Human methylation cell type marker Podocytes TTC33 Human methylation cell type marker Podocytes TYRO3 Human methylation cell type marker Podocytes VTI1A Human methylation cell type marker Proximal tubule ABCC3 Human methylation cell type marker Proximal tubule ACTN1 Human methylation cell type marker Proximal tubule ADAMTS10 Human methylation cell type marker Proximal tubule ADGRB2 Human methylation cell type marker Proximal tubule ADGRG6 Human methylation cell type marker Proximal tubule ADM2 Human methylation cell type marker Proximal tubule AKAP12 Human methylation cell type marker Proximal tubule ALDH1A3 Human methylation cell type marker Proximal tubule ALPK2 Human methylation cell type marker Proximal tubule ANK1 Human methylation cell type marker Proximal tubule APBB1IP Human methylation cell type marker Proximal tubule ARHGAP42 Human methylation cell type marker Proximal tubule ATP1A2 Human methylation cell type marker Proximal tubule BCL11A Human methylation cell type marker Proximal tubule BIN1 Human methylation cell type marker Proximal tubule BMP7 Human methylation cell type marker Proximal tubule C1QTNF1 Human methylation cell type marker Proximal tubule CD200 Human methylation cell type marker Proximal tubule CHST13 Human methylation cell type marker Proximal tubule CLCF1 Human methylation cell type marker Proximal tubule CLDN4 Human methylation cell type marker Proximal tubule COL16A1 Human methylation cell type marker Proximal tubule COL23A1 Human methylation cell type marker Proximal tubule COL27A1 Human methylation cell type marker Proximal tubule CRISPLD2 Human methylation cell type marker 88    311769024v1 Attorney Docket No. 243735.000430  Proximal tubule CRYAB Human methylation cell type marker Proximal tubule CUX1 Human methylation cell type marker Proximal tubule DUSP8 Human methylation cell type marker Proximal tubule EDAR Human methylation cell type marker Proximal tubule ETV7 Human methylation cell type marker Proximal tubule FAM110B Human methylation cell type marker Proximal tubule FAM3D Human methylation cell type marker Proximal tubule GCNT3 Human methylation cell type marker Proximal tubule GFRA1 Human methylation cell type marker Proximal tubule HAVCR1 Human methylation cell type marker Proximal tubule HS1BP3 Human methylation cell type marker Proximal tubule HSPB2 Human methylation cell type marker Proximal tubule HSPB8 Human methylation cell type marker Proximal tubule IL32 Human methylation cell type marker Proximal tubule IRF8 Human methylation cell type marker Proximal tubule ITGB3 Human methylation cell type marker Proximal tubule ITGB8 Human methylation cell type marker Proximal tubule JPH2 Human methylation cell type marker Proximal tubule KCNB1 Human methylation cell type marker Proximal tubule KLRG2 Human methylation cell type marker Proximal tubule KRT7 Human methylation cell type marker Proximal tubule KRT80 Human methylation cell type marker Proximal tubule KRT86 Human methylation cell type marker Proximal tubule LRRN4 Human methylation cell type marker Proximal tubule MACC1 Human methylation cell type marker Proximal tubule MB21D2 Human methylation cell type marker Proximal tubule MSC Human methylation cell type marker Proximal tubule MYC Human methylation cell type marker Proximal tubule NECAB1 Human methylation cell type marker Proximal tubule NEK6 Human methylation cell type marker Proximal tubule NXNL2 Human methylation cell type marker Proximal tubule PDZK1IP1 Human methylation cell type marker Proximal tubule PFKP Human methylation cell type marker Proximal tubule PLPP4 Human methylation cell type marker Proximal tubule PRUNE2 Human methylation cell type marker Proximal tubule PSORS1C1 Human methylation cell type marker Proximal tubule PXDN Human methylation cell type marker Proximal tubule QRFPR Human methylation cell type marker Proximal tubule RASA3 Human methylation cell type marker Proximal tubule RASEF Human methylation cell type marker Proximal tubule RASGEF1A Human methylation cell type marker Proximal tubule RDH5 Human methylation cell type marker Proximal tubule RETREG1 Human methylation cell type marker Proximal tubule RHEX Human methylation cell type marker Proximal tubule RHPN2 Human methylation cell type marker Proximal tubule SEL1L3 Human methylation cell type marker Proximal tubule SERPINA1 Human methylation cell type marker Proximal tubule SH3TC2 Human methylation cell type marker Proximal tubule SLC28A1 Human methylation cell type marker Proximal tubule SLC34A2 Human methylation cell type marker Proximal tubule SMOX Human methylation cell type marker Proximal tubule SNTG2 Human methylation cell type marker Proximal tubule SOGA3 Human methylation cell type marker Proximal tubule SPNS3 Human methylation cell type marker 89    311769024v1 Attorney Docket No. 243735.000430  Proximal tubule SPON2 Human methylation cell type marker Proximal tubule SSPN Human methylation cell type marker Proximal tubule STOX2 Human methylation cell type marker Proximal tubule SULT1C4 Human methylation cell type marker Proximal tubule TGFB2 Human methylation cell type marker Proximal tubule TGM3 Human methylation cell type marker Proximal tubule TMEM200A Human methylation cell type marker Proximal tubule TMEM98 Human methylation cell type marker Proximal tubule TNC Human methylation cell type marker Proximal tubule TNFAIP2 Human methylation cell type marker Proximal tubule TNIK Human methylation cell type marker Proximal tubule TTC9 Human methylation cell type marker Proximal tubule TUNAR Human methylation cell type marker Proximal tubule UGT2A3 Human methylation cell type marker Proximal tubule VASH2 Human methylation cell type marker Proximal tubule VCAM1 Human methylation cell type marker Proximal tubule VCAN Human methylation cell type marker Proximal tubule VIM Human methylation cell type marker Proximal tubule VXN Human methylation cell type marker Proximal tubule ZEB2 Human methylation cell type marker Proximal tubule S1 ABLIM3 Human methylation cell type marker Proximal tubule S1 ACO1 Human methylation cell type marker Proximal tubule S1 ACSF2 Human methylation cell type marker Proximal tubule S1 ACSM2A Human methylation cell type marker Proximal tubule S1 ACSM2B Human methylation cell type marker Proximal tubule S1 AGXT2 Human methylation cell type marker Proximal tubule S1 AK4 Human methylation cell type marker Proximal tubule S1 ALDOB Human methylation cell type marker Proximal tubule S1 ANPEP Human methylation cell type marker Proximal tubule S1 ASS1 Human methylation cell type marker Proximal tubule S1 BNC2 Human methylation cell type marker Proximal tubule S1 CAPN3 Human methylation cell type marker Proximal tubule S1 CCDC158 Human methylation cell type marker Proximal tubule S1 CCR1 Human methylation cell type marker Proximal tubule S1 CDH11 Human methylation cell type marker Proximal tubule S1 CDH2 Human methylation cell type marker Proximal tubule S1 CDH6 Human methylation cell type marker Proximal tubule S1 CRYL1 Human methylation cell type marker Proximal tubule S1 CTTNBP2 Human methylation cell type marker Proximal tubule S1 CUBN Human methylation cell type marker Proximal tubule S1 CYP27B1 Human methylation cell type marker Proximal tubule S1 CYP3A5 Human methylation cell type marker Proximal tubule S1 CYP4A11 Human methylation cell type marker Proximal tubule S1 DDC Human methylation cell type marker Proximal tubule S1 DPEP1 Human methylation cell type marker Proximal tubule S1 DPYS Human methylation cell type marker Proximal tubule S1 ENPEP Human methylation cell type marker Proximal tubule S1 EPB41L3 Human methylation cell type marker Proximal tubule S1 ETNK2 Human methylation cell type marker Proximal tubule S1 FAM151A Human methylation cell type marker Proximal tubule S1 FBXO21 Human methylation cell type marker Proximal tubule S1 FRMD5 Human methylation cell type marker Proximal tubule S1 GDA Human methylation cell type marker Proximal tubule S1 GLG1 Human methylation cell type marker 90    311769024v1 Attorney Docket No. 243735.000430  Proximal tubule S1 GLYAT Human methylation cell type marker Proximal tubule S1 GPR137B Human methylation cell type marker Proximal tubule S1 GPX3 Human methylation cell type marker Proximal tubule S1 GRB10 Human methylation cell type marker Proximal tubule S1 HNF4A Human methylation cell type marker Proximal tubule S1 IGSF11 Human methylation cell type marker Proximal tubule S1 IL6R Human methylation cell type marker Proximal tubule S1 IQSEC3 Human methylation cell type marker Proximal tubule S1 L3MBTL4 Human methylation cell type marker Proximal tubule S1 LHX4 Human methylation cell type marker Proximal tubule S1 LRP2 Human methylation cell type marker Proximal tubule S1 MAPT Human methylation cell type marker Proximal tubule S1 MEIS1 Human methylation cell type marker Proximal tubule S1 MME Human methylation cell type marker Proximal tubule S1 MRLN Human methylation cell type marker Proximal tubule S1 MSH3 Human methylation cell type marker Proximal tubule S1 MSRA Human methylation cell type marker Proximal tubule S1 NOX4 Human methylation cell type marker Proximal tubule S1 PAH Human methylation cell type marker Proximal tubule S1 PCSK5 Human methylation cell type marker Proximal tubule S1 PDGFC Human methylation cell type marker Proximal tubule S1 PDZK1IP1 Human methylation cell type marker Proximal tubule S1 PEPD Human methylation cell type marker Proximal tubule S1 PLG Human methylation cell type marker Proximal tubule S1 PPP1R16B Human methylation cell type marker Proximal tubule S1 PSAT1 Human methylation cell type marker Proximal tubule S1 RHOBTB1 Human methylation cell type marker Proximal tubule S1 RNF212B Human methylation cell type marker Proximal tubule S1 SCN8A Human methylation cell type marker Proximal tubule S1 SH3GL2 Human methylation cell type marker Proximal tubule S1 SIMC1 Human methylation cell type marker Proximal tubule S1 SLC13A1 Human methylation cell type marker Proximal tubule S1 SLC13A2 Human methylation cell type marker Proximal tubule S1 SLC13A3 Human methylation cell type marker Proximal tubule S1 SLC16A12 Human methylation cell type marker Proximal tubule S1 SLC16A9 Human methylation cell type marker Proximal tubule S1 SLC17A1 Human methylation cell type marker Proximal tubule S1 SLC17A3 Human methylation cell type marker Proximal tubule S1 SLC1A1 Human methylation cell type marker Proximal tubule S1 SLC22A23 Human methylation cell type marker Proximal tubule S1 SLC22A6 Human methylation cell type marker Proximal tubule S1 SLC22A8 Human methylation cell type marker Proximal tubule S1 SLC36A2 Human methylation cell type marker Proximal tubule S1 SLC47A1 Human methylation cell type marker Proximal tubule S1 SLC5A12 Human methylation cell type marker Proximal tubule S1 SLC66A2 Human methylation cell type marker Proximal tubule S1 SLC6A13 Human methylation cell type marker Proximal tubule S1 SLC6A19 Human methylation cell type marker Proximal tubule S1 SLC7A7 Human methylation cell type marker Proximal tubule S1 SLIT2 Human methylation cell type marker Proximal tubule S1 ST7 Human methylation cell type marker Proximal tubule S1 TBX2 Human methylation cell type marker Proximal tubule S1 TNIK Human methylation cell type marker Proximal tubule S1 TRIM71 Human methylation cell type marker 91    311769024v1 Attorney Docket No. 243735.000430  Proximal tubule S1 UGT1A10 Human methylation cell type marker Proximal tubule S1 UGT1A3 Human methylation cell type marker Proximal tubule S1 UGT1A4 Human methylation cell type marker Proximal tubule S1 UGT1A5 Human methylation cell type marker Proximal tubule S1 UGT1A6 Human methylation cell type marker Proximal tubule S1 UGT1A7 Human methylation cell type marker Proximal tubule S1 UGT1A8 Human methylation cell type marker Proximal tubule S1 UGT1A9 Human methylation cell type marker Proximal tubule S1 UGT2B7 Human methylation cell type marker Proximal tubule S1 UGT3A1 Human methylation cell type marker Proximal tubule S1 UPB1 Human methylation cell type marker Proximal tubule S1 WDR72 Human methylation cell type marker Proximal tubule S2 ABLIM3 Human methylation cell type marker Proximal tubule S2 ACO1 Human methylation cell type marker Proximal tubule S2 ACSF2 Human methylation cell type marker Proximal tubule S2 ACSM2A Human methylation cell type marker Proximal tubule S2 AGXT2 Human methylation cell type marker Proximal tubule S2 AK4 Human methylation cell type marker Proximal tubule S2 ALDOB Human methylation cell type marker Proximal tubule S2 ANPEP Human methylation cell type marker Proximal tubule S2 AQP1 Human methylation cell type marker Proximal tubule S2 ASS1 Human methylation cell type marker Proximal tubule S2 BAIAP2 Human methylation cell type marker Proximal tubule S2 BHMT Human methylation cell type marker Proximal tubule S2 BNC2 Human methylation cell type marker Proximal tubule S2 BNIP3L Human methylation cell type marker Proximal tubule S2 CAPN3 Human methylation cell type marker Proximal tubule S2 CDH2 Human methylation cell type marker Proximal tubule S2 CDH6 Human methylation cell type marker Proximal tubule S2 CES3 Human methylation cell type marker Proximal tubule S2 CHRNA4 Human methylation cell type marker Proximal tubule S2 CRYL1 Human methylation cell type marker Proximal tubule S2 CUBN Human methylation cell type marker Proximal tubule S2 CYP27B1 Human methylation cell type marker Proximal tubule S2 CYP4A11 Human methylation cell type marker Proximal tubule S2 DDC Human methylation cell type marker Proximal tubule S2 DPEP1 Human methylation cell type marker Proximal tubule S2 DPYS Human methylation cell type marker Proximal tubule S2 ENPEP Human methylation cell type marker Proximal tubule S2 ETNK2 Human methylation cell type marker Proximal tubule S2 FAM151A Human methylation cell type marker Proximal tubule S2 FRMD5 Human methylation cell type marker Proximal tubule S2 FUT6 Human methylation cell type marker Proximal tubule S2 GDA Human methylation cell type marker Proximal tubule S2 GLYAT Human methylation cell type marker Proximal tubule S2 GLYATL1 Human methylation cell type marker Proximal tubule S2 GPR137B Human methylation cell type marker Proximal tubule S2 GRB10 Human methylation cell type marker Proximal tubule S2 HNF4A Human methylation cell type marker Proximal tubule S2 HNF4G Human methylation cell type marker Proximal tubule S2 IQSEC3 Human methylation cell type marker Proximal tubule S2 KCNG2 Human methylation cell type marker Proximal tubule S2 KCNJ15 Human methylation cell type marker Proximal tubule S2 KCNK5 Human methylation cell type marker 92    311769024v1 Attorney Docket No. 243735.000430  Proximal tubule S2 KIAA1549 Human methylation cell type marker Proximal tubule S2 L3MBTL4 Human methylation cell type marker Proximal tubule S2 LRP2 Human methylation cell type marker Proximal tubule S2 MAPT Human methylation cell type marker Proximal tubule S2 MGAM Human methylation cell type marker Proximal tubule S2 MME Human methylation cell type marker Proximal tubule S2 MSRA Human methylation cell type marker Proximal tubule S2 MT1G Human methylation cell type marker Proximal tubule S2 MT1H Human methylation cell type marker Proximal tubule S2 NUGGC Human methylation cell type marker Proximal tubule S2 PAH Human methylation cell type marker Proximal tubule S2 PDZK1IP1 Human methylation cell type marker Proximal tubule S2 PEPD Human methylation cell type marker Proximal tubule S2 PLG Human methylation cell type marker Proximal tubule S2 POR Human methylation cell type marker Proximal tubule S2 PPP1R14C Human methylation cell type marker Proximal tubule S2 PPP1R16B Human methylation cell type marker Proximal tubule S2 RAB11FIP3 Human methylation cell type marker Proximal tubule S2 RHOBTB1 Human methylation cell type marker Proximal tubule S2 RNF152 Human methylation cell type marker Proximal tubule S2 SCAMP2 Human methylation cell type marker Proximal tubule S2 SLC13A2 Human methylation cell type marker Proximal tubule S2 SLC13A3 Human methylation cell type marker Proximal tubule S2 SLC16A12 Human methylation cell type marker Proximal tubule S2 SLC16A9 Human methylation cell type marker Proximal tubule S2 SLC17A1 Human methylation cell type marker Proximal tubule S2 SLC17A3 Human methylation cell type marker Proximal tubule S2 SLC1A1 Human methylation cell type marker Proximal tubule S2 SLC22A6 Human methylation cell type marker Proximal tubule S2 SLC22A8 Human methylation cell type marker Proximal tubule S2 SLC25A42 Human methylation cell type marker Proximal tubule S2 SLC25A48 Human methylation cell type marker Proximal tubule S2 SLC27A2 Human methylation cell type marker Proximal tubule S2 SLC2A9 Human methylation cell type marker Proximal tubule S2 SLC34A1 Human methylation cell type marker Proximal tubule S2 SLC36A2 Human methylation cell type marker Proximal tubule S2 SLC47A1 Human methylation cell type marker Proximal tubule S2 SLC47A2 Human methylation cell type marker Proximal tubule S2 SLC5A12 Human methylation cell type marker Proximal tubule S2 SLC66A2 Human methylation cell type marker Proximal tubule S2 SLC6A13 Human methylation cell type marker Proximal tubule S2 SLC6A19 Human methylation cell type marker Proximal tubule S2 SLC7A7 Human methylation cell type marker Proximal tubule S2 SLIT2 Human methylation cell type marker Proximal tubule S2 ST7 Human methylation cell type marker Proximal tubule S2 TMEM132E Human methylation cell type marker Proximal tubule S2 TRIM71 Human methylation cell type marker Proximal tubule S2 TUBGCP3 Human methylation cell type marker Proximal tubule S2 UCN3 Human methylation cell type marker Proximal tubule S2 UGT1A10 Human methylation cell type marker Proximal tubule S2 UGT1A3 Human methylation cell type marker Proximal tubule S2 UGT1A4 Human methylation cell type marker Proximal tubule S2 UGT1A5 Human methylation cell type marker Proximal tubule S2 UGT1A6 Human methylation cell type marker 93    311769024v1 Attorney Docket No. 243735.000430  Proximal tubule S2 UGT1A7 Human methylation cell type marker Proximal tubule S2 UGT1A8 Human methylation cell type marker Proximal tubule S2 UGT1A9 Human methylation cell type marker Proximal tubule S2 WDR72 Human methylation cell type marker Proximal tubule S3 AC005726.1 Human methylation cell type marker Proximal tubule S3 ACE Human methylation cell type marker Proximal tubule S3 ACSF2 Human methylation cell type marker Proximal tubule S3 AGT Human methylation cell type marker Proximal tubule S3 AGXT Human methylation cell type marker Proximal tubule S3 AGXT2 Human methylation cell type marker Proximal tubule S3 AK4 Human methylation cell type marker Proximal tubule S3 AL845331.2 Human methylation cell type marker Proximal tubule S3 ALDOB Human methylation cell type marker Proximal tubule S3 ANO1 Human methylation cell type marker Proximal tubule S3 ANPEP Human methylation cell type marker Proximal tubule S3 AOX1 Human methylation cell type marker Proximal tubule S3 AQP1 Human methylation cell type marker Proximal tubule S3 AZGP1 Human methylation cell type marker Proximal tubule S3 BIN1 Human methylation cell type marker Proximal tubule S3 BNIP3L Human methylation cell type marker Proximal tubule S3 CAPN3 Human methylation cell type marker Proximal tubule S3 CDH9 Human methylation cell type marker Proximal tubule S3 CHRNA4 Human methylation cell type marker Proximal tubule S3 CRIP3 Human methylation cell type marker Proximal tubule S3 CUBN Human methylation cell type marker Proximal tubule S3 CYP4A11 Human methylation cell type marker Proximal tubule S3 DDC Human methylation cell type marker Proximal tubule S3 DENND1C Human methylation cell type marker Proximal tubule S3 DPF3 Human methylation cell type marker Proximal tubule S3 ETNPPL Human methylation cell type marker Proximal tubule S3 FBLN5 Human methylation cell type marker Proximal tubule S3 FFAR4 Human methylation cell type marker Proximal tubule S3 FRMD4B Human methylation cell type marker Proximal tubule S3 FTO Human methylation cell type marker Proximal tubule S3 FUT6 Human methylation cell type marker Proximal tubule S3 GGACT Human methylation cell type marker Proximal tubule S3 GGT1 Human methylation cell type marker Proximal tubule S3 GGT1.1 Human methylation cell type marker Proximal tubule S3 GLYAT Human methylation cell type marker Proximal tubule S3 GLYATL1 Human methylation cell type marker Proximal tubule S3 GPR153 Human methylation cell type marker Proximal tubule S3 GPT Human methylation cell type marker Proximal tubule S3 GREB1 Human methylation cell type marker Proximal tubule S3 HAAO Human methylation cell type marker Proximal tubule S3 HNF4A Human methylation cell type marker Proximal tubule S3 HNF4G Human methylation cell type marker Proximal tubule S3 IQSEC3 Human methylation cell type marker Proximal tubule S3 IRX5 Human methylation cell type marker Proximal tubule S3 KCNH6 Human methylation cell type marker Proximal tubule S3 KCNK10 Human methylation cell type marker Proximal tubule S3 KIAA1549 Human methylation cell type marker Proximal tubule S3 KIAA1755 Human methylation cell type marker Proximal tubule S3 LBP Human methylation cell type marker Proximal tubule S3 LIME1 Human methylation cell type marker 94    311769024v1 Attorney Docket No. 243735.000430  Proximal tubule S3 LRP2 Human methylation cell type marker Proximal tubule S3 MOGAT3 Human methylation cell type marker Proximal tubule S3 MSRA Human methylation cell type marker Proximal tubule S3 MT1G Human methylation cell type marker Proximal tubule S3 MT1H Human methylation cell type marker Proximal tubule S3 MYOM3 Human methylation cell type marker Proximal tubule S3 NEU4 Human methylation cell type marker Proximal tubule S3 NIT2 Human methylation cell type marker Proximal tubule S3 NUGGC Human methylation cell type marker Proximal tubule S3 PCK1 Human methylation cell type marker Proximal tubule S3 PDZK1IP1 Human methylation cell type marker Proximal tubule S3 PEPD Human methylation cell type marker Proximal tubule S3 PLCH2 Human methylation cell type marker Proximal tubule S3 PLG Human methylation cell type marker Proximal tubule S3 PPP1R14C Human methylation cell type marker Proximal tubule S3 PPP1R16B Human methylation cell type marker Proximal tubule S3 PPP1R3G Human methylation cell type marker Proximal tubule S3 RBP4 Human methylation cell type marker Proximal tubule S3 RETREG1 Human methylation cell type marker Proximal tubule S3 RNF152 Human methylation cell type marker Proximal tubule S3 RSKR Human methylation cell type marker Proximal tubule S3 SDHC Human methylation cell type marker Proximal tubule S3 SEL1L3 Human methylation cell type marker Proximal tubule S3 SERPINE2 Human methylation cell type marker Proximal tubule S3 SLC13A2 Human methylation cell type marker Proximal tubule S3 SLC13A3 Human methylation cell type marker Proximal tubule S3 SLC16A9 Human methylation cell type marker Proximal tubule S3 SLC22A11 Human methylation cell type marker Proximal tubule S3 SLC22A7 Human methylation cell type marker Proximal tubule S3 SLC23A3 Human methylation cell type marker Proximal tubule S3 SLC27A2 Human methylation cell type marker Proximal tubule S3 SLC28A1 Human methylation cell type marker Proximal tubule S3 SLC38A8 Human methylation cell type marker Proximal tubule S3 SLC47A1 Human methylation cell type marker Proximal tubule S3 SLC5A1 Human methylation cell type marker Proximal tubule S3 SLC5A11 Human methylation cell type marker Proximal tubule S3 SLC5A12 Human methylation cell type marker Proximal tubule S3 SLC6A13 Human methylation cell type marker Proximal tubule S3 SLC6A18 Human methylation cell type marker Proximal tubule S3 SLC6A19 Human methylation cell type marker Proximal tubule S3 SLC6A20 Human methylation cell type marker Proximal tubule S3 SLC7A13 Human methylation cell type marker Proximal tubule S3 SMIM32 Human methylation cell type marker Proximal tubule S3 SSTR1 Human methylation cell type marker Proximal tubule S3 TGFBR3L Human methylation cell type marker Proximal tubule S3 TNNI3K Human methylation cell type marker Proximal tubule S3 TTBK1 Human methylation cell type marker Proximal tubule S3 TUB Human methylation cell type marker Proximal tubule S3 UCN3 Human methylation cell type marker Proximal tubule S3 UPK1B Human methylation cell type marker 95    311769024v1 Attorney Docket No. 243735.000430

[0262] Table 2.2 provides a list of selected cell type marker genes and methylation status at these marker genes that are indicative of different cell types. Cell type specific markers were identified by comparison of hypomethylation status of each gene across all the cell types. The four columns, z-score, p-value, p-val adjusted, lg2(fold change), can be used to quantify the hypomethylation status of each gene in each cell type versus other cell types. Table 2.2 Porcine kidney methylation cell type markers generated from the msnWGS dataset Gene name Cell type z-score p-value p-val lg2 (fold Methylation ENTREZ GenBank adjusted change) Status ID Accession LINGO2 Collecting 39.65658 0 0 0.492548 Hypomethylated 10062287 XM_02106418 duct 6 35 8 7 Principal cell NELL1 Collecting 39.37554 0 0 0.579919 Hypomethylated 10052071 XM_02108529 duct 64 8 7 Principal cell LOC10216004 Collecting 35.95017 5.03E- 2.71E- 0.570055 Hypomethylated 10216004 XR_00233855 3 duct 6 283 279 54 3 2 Principal cell LOC11025726 Collecting 34.56966 7.22E- 3.24E- 0.573309 Hypomethylated 11025726 XR_00233965 6 duct 4 262 258 06 6 8 Principal cell LOC11025929 Collecting 33.73440 1.81E- 6.96E- 0.428091 Hypomethylated 11025929 XR_00234154 0 duct 6 249 246 47 0 4 Principal cell CNBD1 Collecting 33.20166 1.02E- 2.74E- 0.493279 Hypomethylated 10015323 XM_02109080 duct 241 238 96 1 4 Principal cell CADM2 Collecting 33.15520 4.77E- 1.17E- 0.318706 Hypomethylated 10062629 XM_02107135 duct 5 241 237 33 4 8 Principal cell SGCZ Collecting 32.99091 1.10E- 2.46E- 0.390045 Hypomethylated 10015783 NM_00114484 duct 238 235 08 2 4 Principal cell MYO16 Collecting 32.07596 9.54E- 1.83E- 0.299073 Hypomethylated 10216167 XM_02106590 duct 6 226 222 5 9 5 Principal cell GRID1 Collecting 30.78805 3.79E- 5.66E- 0.382031 Hypomethylated 10015311 XM_01398338 duct 2 208 205 6 6 9 Principal cell 96    311769024v1 Attorney Docket No. 243735.000430  CDH18 Collecting 30.47453 5.67E- 8.03E- 0.301016 Hypomethylated 10062567 XM_02107696 duct 204 201 45 4 3 Principal cell TFAP2B Collecting 29.87311 4.40E- 5.92E- 1.620029 Hypomethylated 399526 NM_00124452 duct 196 193 2 0 Principal cell LOC10650593 Collecting 29.73779 2.49E- 3.20E- 0.367006 Hypomethylated 10650593 XR_00233878 4 duct 3 194 191 87 4 4 Principal cell LHFPL3 Collecting 29.53237 1.11E- 1.35E- 0.340970 Hypomethylated 10073857 XM_02106349 duct 3 191 188 43 6 7 Principal cell LOC11025645 Collecting 29.11613 2.24E- 2.51E- 0.436221 Hypomethylated 11025645 XR_00233796 7 duct 8 186 183 93 7 9 Principal cell CTNND2 Collecting 29.04823 1.62E- 1.74E- 0.247319 Hypomethylated 10051797 XM_02107684 duct 9 185 182 8 1 Principal cell PCDH15 Collecting 25.48963 2.57E- 1.57E- 0.205419 Hypomethylated 10015199 XM_02107353 duct 4 143 140 8 1 5 Principal cell SLIT2 Collecting 24.39413 1.97E- 9.65E- 0.322166 Hypomethylated 10062057 XM_02110105 duct 6 131 129 9 7 9 Principal cell ACOXL Collecting 22.18619 4.67E- 1.65E- 0.249546 Hypomethylated 10052055 XM_02108704 duct 5 109 106 02 4 2 Principal cell SLC8A1 Collecting 21.66638 4.26E- 1.33E- 0.208379 Hypomethylated 10062202 XM_02108830 duct 6 104 101 95 0 5 Principal cell PAK5 Collecting 21.04019 2.81E-98 7.88E-96 0.260772 Hypomethylated 10015231 XM_02107740 duct 32 6 4 Principal cell NAALADL2 Collecting 20.43503 8.16E-93 2.05E-90 0.149941 Hypomethylated 10052227 XM_02107118 duct 4 1 9 5 Principal cell MYO16 Connecting 49.45188 0 0 0.280693 Hypomethylated 10216167 XM_02106590 tubule 5 47 9 5 LINGO2 Connecting 46.74756 0 0 0.299579 Hypomethylated 10062287 XM_02106418 tubule 2 83 8 7 LOC11025726 Connecting 46.25500 0 0 0.446449 Hypomethylated 11025726 XR_00233965 6 tubule 5 58 6 8 MDGA2 Connecting 38.5682 0 0 0.255150 Hypomethylated 10062108 XM_02107407 tubule 17 2 4 97    311769024v1 Attorney Docket No. 243735.000430  TFAP2B Connecting 37.79140 0 0 1.203737 Hypomethylated 399526 NM_00124452 tubule 5 1 0 NAALADL2 Connecting 36.75802 8.66E- 2.59E- 0.168731 Hypomethylated 10052227 XM_02107118 tubule 2 296 292 29 9 5 ERBB4 Connecting 36.63454 8.07E- 2.17E- 0.227482 Hypomethylated 10052578 XM_02107596 tubule 4 294 290 83 9 8 LOC11025547 Connecting 36.41068 2.88E- 6.47E- 0.234405 Hypomethylated 11025547 XR_00233583 7 tubule 3 290 287 16 7 8 NELL1 Connecting 36.31263 1.02E- 2.12E- 0.272392 Hypomethylated 10052071 XM_02108529 tubule 288 285 42 8 7 PCDH15 Connecting 35.41280 1.08E- 2.08E- 0.176117 Hypomethylated 10015199 XM_02107353 tubule 4 274 271 61 1 5 NLGN1 Connecting 34.24735 4.78E- 8.57E- 0.200840 Hypomethylated 10052104 XM_01399013 tubule 3 257 254 52 3 9 PAK5 Connecting 33.82836 7.55E- 1.20E- 0.261917 Hypomethylated 10015231 XM_02107740 tubule 5 251 247 56 6 4 NOL4 Connecting 33.19433 1.30E- 1.84E- 0.296928 Hypomethylated 10051884 XM_01399928 tubule 241 238 76 7 1 SLIT2 Connecting 32.79812 6.26E- 7.66E- 0.268947 Hypomethylated 10062057 XM_02110105 tubule 236 233 72 7 9 DLG2 Distal 39.12446 0 0 0.189767 Hypomethylated 10051638 XM_00335722 tubule 88 4 8 CNTNAP5 Distal 35.54821 8.85E- 5.91E- 0.254437 Hypomethylated 10062210 XM_02107583 tubule 4 277 273 03 3 0 DCC Distal 35.54217 1.10E- 5.91E- 0.220509 Hypomethylated 10015210 XM_01399285 tubule 276 273 34 2 6 STK32B Distal 33.07601 6.58E- 2.53E- 0.259437 Hypomethylated 10062815 XM_02110099 tubule 240 236 05 2 3 KCTD16 Distal 32.85136 1.09E- 3.26E- 0.288794 Hypomethylated 11025952 XM_02108495 tubule 4 236 233 34 8 0 NRXN3 Distal 32.29193 9.08E- 2.44E- 0.184274 Hypomethylated 10062018 XM_02109995 tubule 5 229 225 2 9 8 GRM7 Distal 32.2536 3.13E- 7.66E- 0.237996 Hypomethylated 10062111 XM_02107105 tubule 228 225 62 9 0 CSMD1 Distal 32.17537 3.90E- 8.75E- 0.224771 Hypomethylated 10062533 XM_02107582 tubule 3 227 224 5 5 6 SPOCK1 Distal 31.22728 4.54E- 8.15E- 0.215244 Hypomethylated 10062062 XM_00335428 tubule 3 214 211 64 3 7 HS6ST3 Distal 30.44793 1.28E- 2.02E- 0.200784 Hypomethylated 10062074 XM_02106537 tubule 9 203 200 55 4 3 ZMAT4 Distal 29.91171 1.39E- 2.07E- 0.220651 Hypomethylated 10052158 XM_02107759 tubule 5 196 193 02 9 9 SLIT2 Distal 29.66881 1.94E- 2.75E- 0.290894 Hypomethylated 10062057 XM_02110105 tubule 6 193 190 7 7 9 LOC11025744 Distal 28.85793 4.03E- 4.52E- 0.253049 Hypomethylated 11025744 XR_00234003 6 tubule 183 180 05 6 4 LOC10650606 Distal 28.81974 1.21E- 1.31E- 0.272993 Hypomethylated 10650606 XR_00130104 4 tubule 6 182 179 53 4 7 CTNND2 Distal 28.56937 1.61E- 1.67E- 0.178259 Hypomethylated 10051797 XM_02107684 tubule 179 176 76 8 1 RYR2 Distal 27.68726 9.94E- 7.64E- 0.207959 Hypomethylated 396856 XM_02107268 tubule 5 169 166 18 3 ERBB4 Distal 25.24013 1.45E- 6.52E- 0.192495 Hypomethylated 10052578 XM_02107596 tubule 3 140 138 08 9 8 98    311769024v1 Attorney Docket No. 243735.000430  NAALADL2 Distal 21.65639 5.29E- 9.37E- 0.122846 Hypomethylated 10052227 XM_02107118 tubule 1 104 102 09 9 5 PAK5 Distal 21.38165 1.98E- 3.37E-99 0.200912 Hypomethylated 10015231 XM_02107740 tubule 5 101 12 6 4 CHRM3 Endothelial 54.63313 0 0 0.575665 Hypomethylated 10014447 NM_00112309 35 8 8 LOC11025992 Endothelial 53.99230 0 0 0.506408 Hypomethylated 11025992 XR_00234240 2 6 93 2 2 PRKCB Endothelial 53.30912 0 0 0.435550 Hypomethylated 10052279 XM_02108645 87 2 9 LOC11025679 Endothelial 51.45950 0 0 0.503025 Hypomethylated 11025679 XR_00233877 0 7 77 0 8 FARP1 Endothelial 51.41879 0 0 0.413315 Hypomethylated 10015818 XM_02106537 7 8 3 4 DNAH11 Endothelial 47.92064 0 0 0.388702 Hypomethylated 10062054 XM_02106342 7 33 3 9 VAV3 Endothelial 47.61941 0 0 0.331578 Hypomethylated 10015182 XM_02109011 5 4 6 0 LOC10650599 Endothelial 47.10390 0 0 0.513583 Hypomethylated 10650599 XR_00233863 3 5 8 3 3 NAV3 Endothelial 47.05153 0 0 0.227582 Hypomethylated 10062288 XM_02109307 3 04 3 3 ZNF423 Endothelial 46.50818 0 0 0.381316 Hypomethylated 10052...

Claims

Attorney Docket No. 243735.000430  CLAIMS   1. A method of creating a single-cell methylation atlas characterizing an organ of a donor, the method comprising: a) clustering methylated single-nuclei whole genome sequences (m-snWGS) from a biopsy of the organ based at least in part on differentially methylated patterns of the m-snWGS; b) assigning a cell type for each cluster based at least in part on correlation of known cell type markers of the organ with the differentially methylated patterns of the m-snWGS; and c) providing the atlas to include a database including: genomic sequences based at least in part on the m-snWGS, methylation patterns of the genomic sequences based at least in part on the m-snWGS, and cell type designation for the genomic sequences based at least in part on the cell type assigned for each cluster.

2. The method of claim 1, wherein the donor is a genetically modified animal.

3. The method of claim 2, wherein the genetically modified animal is a pig.

4. The method of any one of claims 1-3, wherein the biopsy is extracted from the organ pre- transplant.

5. The method of any one of claims 1-3, wherein the biopsy is extracted from the organ post- transplant.

6. The method of any one of claims 1-5, comprising: performing steps (a)-(b) on a plurality of organs from a plurality of donors, wherein the database of the atlas provided at step (c) comprises genomic sequences, methylation patterns, and cell type designation derived from the plurality of organs.

7. The method of claim 6, wherein the plurality of organs are of the same organ type. 128  311769024v1 Attorney Docket No. 243735.000430  8. The method of claim 6 or 7, wherein the plurality of donors are of the same breed of animal species.

9. The method of claim 6 or 7, wherein the plurality of donors are from different breeds of the same animal species.

10. The method of any one of claims 1-9, wherein the known cell type markers comprise known cell type-specific genes and / or known cell type-specific methylation markers.

11. A software method for indicating a type of xenograft damage or rejection response in a subject, wherein the subject has received a xenograft transplantation, the method comprising: a) receiving methylation patterns of xenograft-specific cell-free DNA (cfDNA) determined from a sample obtained from the subject; b) determining cell type(s) from which the xenograft-specific cfDNA is derived based at least in part on the methylation patterns; c) determining the type of xenograft damage or rejection response in the subject based at least in part on the cell type(s) of the cfDNA determined at step (b); and d) providing, as an output, an indication of the type of xenograft damage or rejection response.

12. The software method of claim 11, wherein step (b) comprises: comparing methylation patterns of the xenograft-specific cfDNA to the atlas created according to any one of claims 1-10 to determine cell type(s) of the cfDNA.

13. The software method of claim 11 or 12, comprising prior to step a): receiving a proportion of xenograft-specific cell-free DNA (cfDNA) to total cfDNA in a sample obtained from the subject; and determining that the subject is developing or is at a risk of developing the xenograft damage or rejection response if the proportion of the xenograft-specific cfDNA is more than 1%, wherein the output provided at step (d) further comprises an indication that the subject is developing or is at a risk of developing the xenograft damage or rejection response. 129  311769024v1 Attorney Docket No. 243735.000430  14. The software method of claim 11 or 12, comprising prior to step a): receiving a level of xenograft-specific cfDNA or a proportion of xenograft-specific cfDNA to total cfDNA in the sample; comparing the level or proportion of the xenograft-specific cfDNA to a corresponding control; and determining that the subject is developing or is at a risk of developing the xenograft damage or rejection response if the level or the proportion of the xenograft-specific cfDNA is increased by 20% or more as compared to the control, wherein the output provided at step (d) further comprises an indication that the subject is developing or is at a risk of developing the xenograft damage or rejection response.

15. A software method for indicating a type of xenograft damage or rejection response in a subject or predicting the risk of the subject developing a xenograft damage or rejection response, the method comprising: a) receiving a proportion of xenograft-specific cell-free DNA (cfDNA) to total cfDNA in a sample obtained from the subject; b) determining that the subject is developing or is at a risk of developing the xenograft damage or rejection response if the proportion of the xenograft-specific cfDNA is more than 1%; wherein when the proportion of the xenograft-specific cfDNA is more than 1%, the method further comprises: c) receiving methylation patterns of the xenograft-specific cfDNA received in step (a); d) determining cell type(s) from which the xenograft-specific cfDNA is derived based at least in part on the methylation patterns; e) determining the type of xenograft damage or rejection response in the subject based at least in part on the cell type(s) of the cfDNA determined at step (d); and f) providing, as an output, an indication that the subject is developing or is at a risk of developing the xenograft damage or rejection response; and / or an indication of the type of xenograft damage or rejection response.

16. The software method of claim 15, wherein step (d) comprises: 130  311769024v1 Attorney Docket No. 243735.000430  comparing methylation patterns of the xenograft-specific cfDNA to the atlas created according to any one of claims 1-10 to determine cell type(s) of the cfDNA.

17. The software method of claim 15 or 16, wherein the control is a predetermined standard or a level or proportion of the xenograft-specific cfDNA determined in a sample obtained from the subject at an earlier time point.

18. A software method for indicating a type of xenograft damage or rejection response in a subject or predicting the risk of the subject developing a xenograft damage or rejection response, the method comprising: a) receiving a level of xenograft-specific cfDNA or a proportion of xenograft-specific cfDNA to total cfDNA in a sample obtained from the subject; b) comparing the level or the proportion of the xenograft-specific cfDNA received in step (a) to a corresponding control; c) determining that the subject is developing or is at a risk of developing the xenograft damage or rejection response if the level or the proportion of the xenograft-specific cfDNA determined in step (a) is increased by 20% or more as compared to the control; wherein when the level or the proportion of the xenograft-specific cfDNA determined in step (a) is increased by 20% or more as compared to the control, the method further comprises: d) receiving methylation patterns of the xenograft-specific cfDNA received in step (a); e) determining cell type(s) from which the xenograft-specific cfDNA is derived based at least in part on the methylation patterns; f) determining the type of xenograft damage or rejection response in the subject based at least in part on the cell type(s) of the cfDNA determined at step (e); and g) providing, as an output, an indication that the subject is developing or is at a risk of developing the xenograft damage or rejection response; and / or an indication of the type of xenograft damage or rejection response.

19. The software method of claim 18, wherein step (e) comprises: 131  311769024v1 Attorney Docket No. 243735.000430  comparing methylation patterns of the xenograft-specific cfDNA to the atlas created according to any one of claims 1-10 to determine cell type(s) of the cfDNA.

20. The software method of any one of claims 11-19, wherein the method further comprises receiving methylation patterns of recipient-specific cfDNA.

21. The software method of claim 20, wherein the method further comprises determining cell type(s) from which the recipient-specific cfDNA is derived based at least in part on the methylation patterns.

22. The software method of claim 21, wherein the method further comprises determining a type of xenograft rejection response in the subject based at least in part on the cell type(s) of the recipient- specific cfDNA.

23. The software method of any one of claims 11-22, wherein the type of xenograft damage or rejection response is selected from an antibody-mediated rejection, acute cellular rejection, podocyte damage, ischemia damage, and any other cellular damage.

24. The software method of any one of claims 11-23, wherein the subject has received the xenograft transplantation from a porcine donor and the xenograft-specific cfDNA is porcine cfDNA.

25. The software method of claim 24, wherein the subject has received a kidney, heart, lung, liver, bone marrow, or pancreas transplantation from a porcine donor.

26. The software method of claim 25, wherein the cell type(s) of the xenograft-specific cfDNA are determined based on the methylation patterns of one or more marker genes selected from Table 2.

2.

27. The software method of any one of claims 11-26, wherein the sample is a bodily fluid.

28. The software method of any one of claims 11-27, wherein the sample is whole blood, plasma, serum, or urine. 132  311769024v1 Attorney Docket No. 243735.000430  29. The software method of any one of claims 11-28, wherein the cfDNA is about 20-800 bp long.

30. The software method of claim 29, wherein the cfDNA is 120–220 base pairs (bp) long, with a peak at approximately 167 bp long. 133  311769024v1

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