Immune Response to Gene-Edited Pig Kidney in Human Recipient

Kidney xenotransplantation and sample collection

An EGEN-2784 gene-edited pig kidney was transplanted into a 62-year-old male⁤ patient with end-stage kidney disease, recently described in⁣ ref. ⁵. A Yucatan miniature pig was genetically engineered with 69 genomic modifications. ⁢These included the‌ deletion of​ three major glycan antigens, the inactivation of porcine endogenous retroviruses and the insertion of seven human transgenes (TNFAIP3, HMOX1, CD47, CD46, CD55, THBD and EPCR). The immunosuppressive protocol included antithymocyte ⁣globulin (ATG, 1.5 mg kg⁻¹ on days −2 and −1), rituximab (anti-CD20, 1,000 mg on day −3), Fc-modified anti-CD154 monoclonal⁤ antibody (tegoprubart, 20 mg kg⁻¹ on days −3, −2,⁤ −1, 1, 3, 7 and weekly thereafter) and anti-C5⁢ antibody (ravulizumab, 3,330 mg on days −1, 7 and 28). ‍This regimen was combined with standard maintainance immunosuppression consisting of tacrolimus, mycophenolic acid ​(540 mg twice⁣ daily) and glucocorticoids starting on⁣ day 0. On ⁣post-transplant day 8, ​a biopsy revealed Banff grade 2A ⁢TCMR without signs of thrombotic microangiopathy⁣ or antibody-mediated rejection. Treatment included a 500-mg pulse of​ methylprednisolone and anti-IL-6 receptor monoclonal antibody (tocilizumab, 8 mg kg⁻¹). Additional 500-mg methylprednisolone pulses were given on days 9 ⁣and⁣ 10, along with ⁢ATG (1.5 mg kg⁻¹).Tacrolimus and mycophenolic acid doses were⁤ increased.Due to C3 deposition seen in the biopsy, pegcetacoplan, a targeted C3 and C3b ​inhibitor, was administered. However,as⁣ ther was ⁤no ​evidence of antibody-mediated rejection,no further doses of tocilizumab were⁤ given.

Peripheral blood was collected for serum and plasma⁢ at⁤ pretransplantation (day −3), ⁤post-xenotransplantation (days 13, 20, 26, 33 and 51) and during suspected rejection (day 7 post-transplantation). Blood was collected in heparinized tubes for PBMCs isolation at pretransplantation (day −3) and post-xenotransplantation (days 13, 20, 26 and​ 33). blood was also collected in⁣ Paxgene tubes for RNA profiling (days −3 and 7) and⁣ post-xenotransplantation ​(day 26). The sample collection schedule is shown ⁢in Fig. 1a. The patient provided written informed consent, as approved by the MGH Human⁤ Research Committee (

Before integration, each dataset underwent independent quality ⁢control filtering followed by normalization using the Seurat SCTransform ‍function. based on ⁣an empirical evaluation ⁣of the distributions of the number of Unique Molecular identifiers (UMIs) (nCount_RNA), number of detected genes (nFeature_RNA), log-transformed gene-to-UMI ratio ⁢(log10GenesPerUMI) and ‌mitochondrial gene expression ratio (mitoRatio), the following filters were applied: GSE165080: nCount_RNA ≥ 1,800; nFeature_RNA ≥ 900 and <2,500; log10GenesPerUMI ≥ 0.84 ⁢and mitoRatio < 22%. GSE192391: nCount_RNA ≥ 1,800; nFeature_RNA ≥ 1,000 ‍and <2,500;⁣ log10GenesPerUMI ≥ 0.84 and mitoRatio < 10%.GSE171555: nCount_RNA ≥ 1,800; nFeature_RNA ≥ 900 and <2,500; log10GenesPerUMI ≥ 0.84 and mitoRatio < 8%. Recipient samples: nCount_RNA ≥ 350; nFeature_RNA ≥ 300 ⁣and <2,500; log10GenesPerUMI ≥ 0.84 and​ mitoRatio < 10%. An additional gene filtering step was applied to each dataset, retaining only genes expressed (that is, nonzero counts) in at least 10 cells. After integration, marker ⁣genes for each cluster ⁤were identified using the ​FindAllMarkers ‌function following PrepSCTFindMarkers. Clusters were manually annotated into cell types based on the expression of the marker genes (Extended Data Fig. 3).A frist round of annotation ⁤was ⁣performed at clustering resolutions of 0.2 and 0.4, resulting in 13 annotated cell types: CD4⁺ T cells, CD8⁺ T cells, erythrocytes, B cells, CD16⁺ monocytes, megakaryocytes/platelets, naive-like CD8⁺ T ⁣cells, plasmacytoid dendritic cells, conventional dendritic cells, two subsets of CD14⁺ ​ monocytes and two subsets of NK cells.⁣ A second round of annotation was performed after sub-clustering aggregated monocyte and T cell subsets, followed ⁤by marker gene identification. This round annotated a third subset ⁤of CD14⁺ monocytes, naive-like CD4⁺ T ⁤cells, CD8⁺ MAIT cells and T_reg cells. Single cells expressing canonical marker genes⁢ of at least two major cell types were assumed to be doublets and excluded from the Seurat⁢ object.

Gene sets of interest across time were identified empirically by calculating the average log₂ fold change⁤ at each time point relative to the pretransplant baseline. This analysis‌ was performed separately for CD14⁺ monocyte subsets, CD16⁺ monocytes and NK cell subsets.Genes were then ranked based on the absolute average log₂ ​ fold change ⁣to⁣ prioritize thoseChromatography was conducted at 40 °C (column oven) and 4 °C (autosampler), with a gradient flow rate of 0.4 ml per min as‌ follows: an initial hold at 95% B for 0.75 min, a linear decrease to 30% B from 0.75‍ to 3.00 min, followed ‌by a 1.00 min isocratic hold at 30% B. Mobile phase ⁢B was then returned to 95% over 0.50 min,‌ with re-equilibration under initial conditions. Mass spectrometry was performed in positive mode.Data acquisition was carried out in full-scan mode, with the spray voltage set to ⁣3 kV (negative mode) or ⁤3.5 kV (positive mode). The capillary temperature was maintained at 320 °C, the HESI probe at 300 °C, the sheath gas at 40 U, the auxiliary ​gas ‌at 8 U and the sweep gas​ at 1 U.the resolving power⁢ was set to 120,000. An untargeted metabolite⁢ library was generated using⁣ top-15 DDA acquisitions on a ‍pooled ‍study sample, with MS1 and MS2 resolutions ⁣of 60,000 and 30,000, respectively.

Raw data files (.raw) were processed using MZmine 4.5⁴⁴ and emzed⁴⁵. Metabolite MS2 spectra were compared against HMDB, GNPS,‌ MassBank and MoNA‍ databases using spectral library matching (ref to DDA_library). In addition, retention times and ​ m/z values from full-scan acquisitions of a pooled study sample were cross-referenced with an in-house database⁣ containing retention times of authenticated standards (Human Endogenous Metabolite Compound Library ⁢Plus L2501, TargetMol). Internal standards were integrated via emzed, and raw peak areas were normalized by⁢ dividing by sample⁤ biomass ‍and the⁣ mean internal standard area.“`html

We measured 41 soluble factors in⁣ serum using a multiplex ELISA assay (Thermo Fisher), including APRIL, BAFF, CXCL13, CD30, CD40L, CXCL5, CCL11, CCL24, CCL26, fibroblast growth factor-2 (FGF-2), CX3CL1,⁤ G-CSF, ⁣GM-CSF, CXCL1, hepatocyte growth factor, ‍IFNα, ‍IFNγ, IL-1α, IL-1β, IL-10, IL-12p70,​ IL-13, IL-15, IL-16, IL-17A, IL-18, IL-2, IL-20, IL-21, IL-22, IL-23,⁣ IL-27, IL-2R, IL-3,​ IL-31, IL-4, IL-5, IL-6, IL-7, IL-8, IL-9, CXCL10, CXCL11, LIF, CCL2, CCL7, CCL8,​ lymphotoxin-alpha, macrophage colony-stimulating factor, MDC, MIF, CXCL9, CCL3, CCL4, CCL20, MMP-1, nerve growth factor beta (NGF-β), SCF, SDF-1α, TNF, TNF-RII, TRAIL, TSLP, TWEAK and vascular endothelial growth‌ factor A (VEGF-A). We read the assay in a Luminex200 machine,‌ and‍ data ⁤with a bead count <20 were excluded. Samples from the time points from one individual were measured on the same plate to mitigate the ‌effect of potential plate-based technical variation​ on calculated fold‌ changes between time points.

Intragraft tissue bulk‍ transcriptomics

bulk mRNA analysis was performed from formalin-fixed‍ paraffin-embedded xenograft biopsies and a sample​ from the ⁢nontransplanted contralateral donor kidney, using the nCounter instrument (nanostring) combined with the Banff‍ Human Organ Transplant panel, following the methods and data analysis previously documented for human allograft biopsies²⁵. The probe sequences were screened for homology with pig and ⁣NHP transcripts by ⁣a specialist ⁢at ⁤the manufacturer company (NanoString). We used probes from the panel that had >85% homology with porcine sequences for parenchymal cells or‍ endothelium⁤ (n = 235 probes) and all probes for human leukocytes, similar⁢ to ‍those used previously in pig-to-human xenografts^{5,Multiplex‍ immunofluorescence}

Frozen slides from the donor collateral kidney biopsy and xenografts ⁢at days 8 and 34 were processed for ⁣multiplex imaging on the Orion platform (RareCyte), following Lin et⁢ al.⁵². An ​Orion multiplex antibody panel was used‍ to profile immune cell subsets (Supplementary Table 2). In brief, slides were thawed at room temperature and then fixed in 4% paraformaldehyde in⁤ PBS for 45 min.They were washed in Tris‑buffered saline, permeabilized in ⁤0.5% Triton X‑100 in Tris‑buffered⁤ saline for ‍5 min at room temperature, washed‍ once in Tris‑buffered saline and once ⁣in PBS, and​ treated with 4.5% H₂O₂/24 mM NaOH in PBS (hydrogen peroxide solution) to⁣ reduce autofluorescence. After⁣ a surfactant wash and enhancer treatment, slides ⁤were incubated overnight at 4 °C ‌with ArgoFluor‑conjugated antibodies and Hoechst⁣ 33342.The next day, slides were washed extensively ⁤in PBS, mounted in ArgoFluor mounting medium (RareCyte 42‑1214‑000), and imaged on an Orion microscope. Fluorophores were ‌then quenched​ with hydrogen peroxide solution, and a second round of overnight 4 °C staining⁤ was performed with a different set of​ ArgoFluor‑conjugated ‍antibodies (Supplementary Table 2) plus Hoechst 33342, followed by mounting and imaging.

We normalized all the data series to z-score with scikit-learn 1.6.156. To analyze the data​ in time, we used scikit-fda 0.9.1⁵⁷. With this package,we excluded outliers using the MS-Plot Outlier Detector method. We then used Fuzzy C-Means⁣ with l2-distance to​ cluster. We defined the optimal number of three clusters, calculating the silhouette score with scikit-learn 1.6.1. To plot the time series, we calculated ⁣the‍ median ⁤with NumPy 2.2.0 and plotted the line ⁢and the violins with Matplotlib 3.8.0.

Network analysis

For the three clusters defined by functional data‍ analysis,we used​ the z-score normalized⁣ proteomics data to calculate‌ the nearest ⁣neighbors with scikit-fda 0.9.1, calculated the ‌Louvain communities with NetworkX 3.4.2⁵⁸ based on the connectivity of the nearest neighbour’s graph,followed by⁤ an enrichment analysis⁢ for each communit

The Role of Chord Diagrams in Data ⁣Visualization

Chord diagrams are a powerful method for visualizing relationships between entities,particularly in complex ⁤datasets,and⁣ have seen increasing use in fields like genomics and​ network analysis. They effectively illustrate interconnections and flows, offering a visually intuitive portrayal of data that traditional tables or charts may struggle to convey.

Definition and Function

A chord diagram displays⁤ relationships ‍between data points using arcs arranged in a circle; the connections between these arcs represent the ‌strength or⁢ quantity of‍ the relationship.‌ The width of ⁤the⁢ connecting chord is proportional to the value ⁣of the relationship. This ​visualization ⁤technique excels at showing many-to-many relationships, making it ideal‍ for datasets where multiple entities interact with each other.

For example, a chord diagram can illustrate gene co-expression ‌patterns,‍ migration⁤ patterns between countries, or financial flows between sectors. The circular layout minimizes visual clutter and allows for a clear understanding ‍of the⁣ overall network structure.

Technical Implementation and Software

Chord diagrams are typically created using ​specialized software‌ packages. The circlize package in R is a popular choice, offering extensive customization‌ options and integration with other data analysis tools. Version 0.4.16 of the ‍circlize package, as of January ‌8, 2026, remains ‌a ⁢widely used version for generating​ these visualizations. Other tools include D3.js, a ⁢JavaScript library,​ allowing for ​interactive chord ‌diagrams within web browsers.

The creation process generally involves preparing the data in ⁢a matrix format,⁢ where rows ‌and columns ⁢represent the entities, and the cells contain the values representing the relationships between them.The software then ⁢translates this​ matrix⁣ into ​the visual representation of the chord diagram.

Applications in Genomics

In genomics,⁣ chord diagrams are frequently used to visualize relationships between genes, proteins, and ⁣other biological entities. They can effectively represent gene co-expression networks, identifying groups of genes⁤ that are consistently activated ​or deactivated ⁤together. This data is crucial for understanding biological pathways and disease mechanisms.

As an⁤ example,‍ researchers at the National Center for Biotechnology Information (NCBI) have utilized ‌chord diagrams to analyze RNA sequencing data, revealing complex regulatory networks within cancer cells. these visualizations ‌help identify potential drug targets‍ and biomarkers.

Applications in Network Analysis

Beyond genomics, chord diagrams are valuable in network analysis across various domains. They can illustrate relationships in social networks, transportation networks, and financial networks. ‍ The ‌ability to visualize many-to-many relationships makes them particularly useful for understanding complex systems.

A‍ 2024 report by the U.S. Department⁣ of Transportation used chord diagrams to visualize passenger flow between major airports, identifying key hubs and potential bottlenecks in the national air transportation system.​ This data informed infrastructure⁣ planning and resource‍ allocation.

Limitations and ‍Considerations

While powerful, chord diagrams ⁣have limitations. They can become visually cluttered with a large number of entities, making it arduous to discern individual relationships.Careful design and data filtering are essential⁤ to maintain clarity. Furthermore, interpreting the diagram requires understanding the underlying data and the chosen visualization parameters.

Researchers have explored ‍techniques to address these limitations, such as hierarchical chord diagrams and interactive visualizations that allow users ‌to explore⁢ the data in more detail. The choice of‍ visualization ‌technique should always be guided by the specific research question and the characteristics of the dataset.