Abstract
Organ shortage remains a major challenge in transplantation, and gene-edited pig organs offer a promising solution1,2,3. Despite gene editing, the immune reactions following xenotransplantation can still cause transplant failure4. To understand the immunological response of a pig-to-human kidney xenotransplantation, we conducted large-scale multi-omics profiling of the xenograft and the host’s blood over a 61-day procedure in a brain-dead human (decedent) recipient. Blood plasmablasts, natural killer cells and dendritic cells increased between postoperative day (POD) 10 and 28, concordant with an expansion of IgG and IgA B cell clonotypes and subsequent biopsy-confirmed antibody-mediated rejection (AMR) at POD33. Human T cell frequencies increased from POD14 and peaked between POD33 and POD49 in the blood and xenograft, which coincided with T cell receptor diversification, expansion of a restricted TRBV2 and TRBJ1 clonotype and histological evidence of combined AMR and cell-mediated rejection at POD49. At POD33, the most abundant human immune population in the graft was CXCL9+ macrophages, which aligned with interferon-γ-driven inflammation and a T helper 1-type immune response. There was also evidence of interactions between activated pig-resident macrophages and infiltrating human immune cells. Xenograft tissue showed pro-fibrotic tubular and interstitial injury marked by S100A6 (ref. 5), SPP1 (also known as osteopontin)6 and COLEC11 (ref. 7) expression at POD21–POD33. Proteomic profiling revealed activation of human and pig complement, with a decreased human component after AMR therapy, in which complement was inhibited. Collectively, these data delineate the molecular orchestration of human immune responses to a porcine kidney and reveal potential immunomodulatory targets for improving xenograft survival.
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Data availability
Raw count matrices, in addition to processed and annotated data, can be accessed through Zenodo (https://doi.org/10.5281/zenodo.17390399)86.
Code availability
Scripts used for the analyses presented in this paper are available from Zenodo (https://doi.org/10.5281/zenodo.17390464)87.
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Acknowledgements
We sincerely thank the family of the decedent for their donation to science. We also thank C. Dahn, E. Fitzgerald, J. De Biasio, N. Mammadova, M. Nunnally, L. Angel, H. Neumann, F. Zervou, N. Narula, M. Maloney, R. Keller, H. Chandarana, R. Robalino, D. Bamira, K. Abbinante, K. Allen, M. Grovenburg, G. Boulton, J. McBride, A. Eutsay, B. Sullivan, C. Deterville, C. Hickson, S. Bennett, G. Eickel, K. Luo, A. Eutsay, M. McBridge, J. Ciolko, E. Duggan, L. Chiriboga, S. Mendoza, J. Osea, E. Gallego, Z. Zayas, M. Nally, S. Wu, M. Carolan, K. Frittola, J. Petersen, J. Morales, T. Easter, M. Sun, J. Klapholz, K. Sangwon, V. Galimberti, A. Spindler, P. Kannabran, D. Maas, S. Viscusi, D. Martinez-Krams, J. Erickson, A. Korenek, V. Li, E. Grin, B. Yang, D. Wolbrom, J. Beagle, A. Dandro, T. K. Adams, L. Sorrells, K. Tokoro and T. Katsarou (both supported by NCI NIH EDRN U01 CA214195) for significant sample and data contributions in this study; the Boeke Laboratory Team, the NYU Langone Health Nursing Leadership, the NYU Transplant Research Team and staff at the NYU Langone Health Center for Biospecimen Research and Development (CBRD); staff at the Histology and Immunohistochemistry Laboratory 22 (RRID:SCR_018304); A. Liang, C. Petzold and J. Sall at NYU Microscopy Laboratory, supported in part by the Laura and Isaac Perlmutter Cancer Center Support Grant (NIH/NCI P30CA016087); the Skolnik Laboratory Team, the Goldfarb Laboratory Team and the Sykes Laboratory Team; staff at the CUIMC Human Immune Monitoring Core; staff at the CCTI Flow Cytometry Core (supported in part by the Office of the Director, NIH awards S10RR027050 and S10OD020056); NYU Surgical Intensive Care Unit Advanced Practice Providers; NYU Surgical Intensive Care Unit nursing staff; the NYU Grossman School of Medicine’s Research on Decedent Oversight Committee; staff at the NYU Langone Donor Care Unit, LiveOnNY; staff at Apellis Pharmaceuticals; and B. Parent, JD, Director of Transplant Ethics and Policy Research at NYU Grossman School of Medicine for his contributions towards these studies. Assay and/or analysis support was provided by grants from NIAID/NIH 1U19AI191396 (to Q.G., M.R., J.B., R.A.M., A.G. and B.J.K.) and Yosemite (to B.J.K.). Development of the multi-omics platform and analyses was supported by NIAID-NIH R01 AI144522 (to A.D., B.D.P. and B.J.K.). The Microscopy Laboratory (RRID: SCR_017934) was supported in part by the Laura and Isaac Perlmutter Cancer Center support grant NIH/NCI P30CA016087. The xenotransplant procedures of this study were supported by Lung Biotechnology, a wholly owned subsidiary of United Therapeutics Corporation. We also thank M. Rothblatt, CEO of United Therapeutics, PBC, for funding support (to R.A.M.). Other funding sources include Office of the Director NIH awards S10RR027050 and S10OD020056 (to J.B), Rawabi Scientific Chair, Imam Abdulrahman bin Faisal University (to A.H.H), Väisälä Fund (to E.S.), Aarne Koskelon Foundation (to E.S.), Antti and Tyyne Soininen Foundation (to E.S.), the Finnish Cultural Foundation (to E.S.) and the Fondation Bettencourt Schueller (to E.S.).
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Conceptualization: E.S., B.D.P., R.S.H., J.S., A.G., M.K., J.D.B., R.A.M. and B.J.K. Methodology: E.S., B.D.P., A.K.D., A. Stukalov, F.L.R., R. Bombardi, Q.G., R.S.H., M.K. and B.J.K. Software: E.S., A.K.D., A. Stukalov, R. Bombardi, R.S.H. and B.J.K. Validation: E.S., B.D.P., A.K.D., A. Stukalov, F.L.R., R. Bombardi and B.J.K. Formal analyses: E.S., A.K.D., A. Stukalov, R. Bombardi, R.S.H. and B.J.K. Investigation: E.S., F.L.R., R. Bombardi, F.Z., J.S., A.G., J.D.B., R.A.M. and B.J.K. Resources: R. Bombardi, I.J., K.K., J.A., D. Ayares, R.S.H., J.S., A.G., M.K., J.D.B., R.A.M. and B.J.K. Data curation: E.S., A. Stukalov, F.L.R., R. Bombardi, R.S.H. and B.J.K. Writing original draft: E.S., B.D.P., A.K.D., M. Mohebnasab, A. Stukalov, R. Bombardi and B.J.K. Writing, review and editing: E.S., B.D.P., A.K.D., M. Mohebnasab, S.H.W., A. Stukalov, F.L.R., R. Bombardi, I.J., K.K., J.K., I.A., T.E., D.P.O., M.R., C.W., A.Q.B., F.Z., J.A., D. Andrijevic, B.M., V.M., S.V., D. Argibay, Z.Z., L.W., K.M., B.L., W.Z., L.G., E.W., H.G., L.H., L.K., B.R.C., D.G., R. Bhatt, S.G., R.A.A.-A., A.H.H., A. Chang, S.F., H.M.C., J.D.M., F.A.C., S.C.T., D.S., R.L.F., A.L., A.H., A. Crawford, S.B., M.P.S., A. Siddiqui, M.V.H., A.S.C., M.U.K., S.L.-K., D. Ayares, M.L., A.N., E.Y.S., A.M., V.S.T., R.T., M. Mangiola, Q.G., R.S.H., J.S., A.G., M.K., J.D.B., R.A.M. and B.J.K. Visualization: E.S., B.D.P., A.K.D., A. Stukalov, F.L.R., R. Bombardi, R.S.H. and B.J.K. Supervision: E.S., B.D.P., M. Mohebnasab, R.S.H., J.S., A.G., M.K., J.D.B. and B.J.K. Project administration: I.J., K.K., J.K., I.A., T.E., E.W., H.G., V.S.T., M. Mohebnasab, R.S.H., J.S., A.G., M.K., J.D.B., R.A.M. and B.J.K. Funding acquisition: A.G., M.K., J.D.B., R.A.M. and B.J.K.
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R.A.M. has received research funds from Lung Biotechnology, a wholly owned subsidiary of United Therapeutics, PBC. He serves on the advisory board of eGenesis and has been a strategic advisor for Recombinetics. J.D.B. is a Founder and Director of CDI Labs, a Founder of and consultant to Opentrons LabWorks/Neochromosome, and serves or served on the scientific advisory board of the following companies: CZ Biohub New York, Logomix, Modern Meadow, Rome Therapeutics, Tessera Therapeutics and the Wyss Institute. M.P.S. is cofounder and a member of the scientific advisory board of Personalis, Qbio, January, SensOmics, Protos, Mirvie and Oralome. He is on the scientific advisory board of Danaher, GenapSys and Jupiter. The other co-authors have no conflicts of interest.
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Extended data figures and tables
Extended Data Fig. 1 Further dissection of human macrophages, NK cells, B cells and dendritic cells.
A-B. Dimensionality reduction graph (A) and percentage across all cells by timepoint (B) for human macrophage, monocytes and dendritic subtypes found in the 5.1k panel ST data colored by cell-type (11205 cells). C-D. Dimensionality reduction graph (C) and percentage across all cells by timepoint (D) for B cells, plasma cells, mast cells and neutrophils found in the 5.1k panel ST data (1652 cells). E-G. Marker gene expression of the populations in the 5.1k panel ST data. H. Percentage levels for additional cell-type populations not shown in the main figure. I-J. Cell-type markers (I) and associated percentage (J) of B, plasma, and dendritic cell populations found in the PBMC scRNAseq data. K. Percentage of human macrophages, pDC and NK cells found in the 478 panel ST data. L-M. pDC activation marker genes and CXCL9 – CXCL11 expression in pDC-cDC1 populations in 5.1k ST data (L) and PBMC scRNAseq data (M).
Extended Data Fig. 2 Early response from human macrophages and B cells as evidenced by ST and snRNAseq in addition to BCR-seq.
A. Expression of immunoglobulin genes in PBMC bulk RNAseq. B. CDR3 length (nt) distribution in bulk BCRseq. C. Somatic hypermutation frequency in bulk BCRseq divided by isotype. D. Shared overlapping clonotypes across bulk BCRseq postoperative timepoints. E. Tracking top BCR IgH clones in bulk BCRseq postoperative time course. F-I. 478 panel ST data highlighting human subpopulations of macrophages and NK cells expressing CXCL9, CXCL10, and CXCL11 as shown in a dimension reduction map (F), distribution of these markers across those subpopulations (G) and across time in the NK - MP populations (H) and percentage distribution of these subpopulations across time (I). J-K. Similar populations of interest in snRNAseq data, as shown in expression of markers corresponding to the 5.1k panel data subtypes markers (J) and their distribution over time (K). L. Expression of human CXCL9, CXCL10, CXCL11 observed in the tissue bulk RNAseq.
Extended Data Fig. 3 Further human T cell response dissection.
A-C. Human T cell subtypes found in 5.1k ST data (2595 cells), as shown via dimension reduction plot (A), percentage across all cells in each timepoint (B), and marker genes defining these cells (C). D. Cell-type markers of PBMC NK and T cells populations. E. Proportion of proliferative human NK cells found in the 5.1k ST data. F-G. Proportions (F) and marker gene expression (G) of human CD8T and CD4T from the 478 panel ST data. H. Distribution of T and NK subtypes found in the PBMC scRNAseq data, as percentage of all cells across timepoints (for subtypes not shown in Fig. 4). (H). I. Percentage of human Dividing and T cells found in snRNAseq data. J. Expression levels of CD8T markers (CD8A, CD8B, top) and Tregs markers (RTKN2, CTLA4, bottom), from PBMC bulk RNAseq. Treatment with rATG is annotated with red marks. K. Flow cytometry T cells distributions, CD8+ T cells (middle) and CD4+ T cells (bottom), separated by central memory and effector memory cells.
Extended Data Fig. 4 Distribution of marker genes delineating resident and circulating T and NK cells.
A. NK markers, 5.1k panel ST data. B. T cell markers, 5.1k panel ST data. C. NK markers, PBMC scRNAseq data. D. T cell markers, PBMC scRNAseq data. E-F. Dimension reduction map showing NK subtypes and circulatory and residency markers in 5.1k ST data (E, 2595 cells) and in 478 ST data (F, 606 cells). G-H. Expression of circulatory and residency markers across NK subtypes in 5.1k ST data (G) and in 478 ST data (H).
Extended Data Fig. 5 Porcine transcriptional response from tissue and porcine resident immune cells dissection.
A. An inflammatory gene cluster identified by bulk RNAseq longitudinal analysis. Expression over time (left) and top pathway enrichment (right). B. Expression distribution of selected time-point specific marker genes (pig probes) in the 478 ST panel. C-D. Macrophage and T-cell marker expression across cell-types in the 5.1k panel (C) and 478 panel (D) ST data. E. T-cell marker expression across timepoints, shown in pig immune cells in the 5.1k panel (top) and 478 panel (bottom) ST data. F. Top 80 marker genes for POD33 pig immune cells, identified by Wilcoxon rank-sum analysis comparing their transcriptomic profile to all other time points. G-H. Expression of marker genes of interest across timepoints in specific pig cell populations, in snRNAseq samples of the xenograft (G) and in 5.1k ST data (H).
Extended Data Fig. 6 B-HOT AMR signature revealed by ST.
A–D: B-HOT AMR (also known as AbMR) signature expression in the 478-gene ST panel, using genes identified as differentially expressed (DEG post- vs. pre-transplantation) in xenografts from a previous study18,75 E–G: B-HOT AMR signature expression in the 5.1k-gene ST panel, using the entire B-HOT panel, regardless of overlap18,75 results. A. Spatial distribution of the B-HOT AMR signature in POD33 tissue. B. B-HOT signature enrichment in UMAP, overlaid with cell-type composition. C. AMR signature enrichment across time points. D. Marker expression of B-HOT AMR genes. E. Expression of genes (human probes) belonging to the B-HOT AMR signature across timepoints in all human cells. F. Expression of pig genes whose human orthologues are part of the B-HOT panel, across timepoints in all pig cells. G. Spatial distribution in POD33 of the AMR gene set score, pig MX1, and pig SERPINE1 expression.
Extended Data Fig. 7 Neighborhood enrichment analysis of spatial transcriptomics data.
A. Analysis based on the 478-gene ST panel. B. based on the 5.1k-gene ST panel. Each subplot corresponds to a selected reference cell-type (indicated above each heatmap). Heatmaps display neighborhood enrichment z-scores, quantifying the spatial colocalization between the reference cell-type (per subplot) and all other cell-types (columns), across all samples (rows). Enrichment scores were computed using a permutation test (1,000 permutations) via Squidpy. The legend scale ranges from –10 to +10. White stars denote z-score ≥ 1.96 or ≤ –1.96.
Extended Data Fig. 8 Spatial Niche Profiling in the 478 ST Data.
A. Cell-type annotations from 478-gene ST panel on POD33 biopsy. B. Depiction of spatial niches from neighborhood analyses on cell-types from (A). C. Relative cellular contribution to spatial niches by timepoint from which cells belong. D. Distribution of cells within spatial niches at each timepoint. E. Niche residency of indicated cell-type, represented as frequency of total cells of indicated type. F. Heatmap with clustering of cell-types and spatial niches at POD33.
Extended Data Fig. 9 Spatial Niche Profiling in the 5.1k ST Data.
A. Cell-type annotations from 5.1k gene panel on POD33 biopsy. B. Depiction of spatial niches from neighborhood analyses on cell-types from (A). C. Relative cellular contribution to spatial niches by timepoint from which cells belong. D. Distribution of cells within spatial niches at each timepoint. E. Niche residency of indicated cell-type, represented as frequency of total cells of indicated type. F. Heatmap with clustering of cell-types and spatial niches at POD33.
Supplementary information
Supplementary Information (download PDF )
This file contains Supplementary Figs. 1–18.
Supplementary Table 1 (download XLSX )
478 ST Xenium panel design. List of the 478 genes constituting the panel, with the genome name (GRCh38 for human, Sscrofa11 for pig) their Symbol, and Ensembl IDs.
Supplementary Table 2 (download XLSX )
478 ST Xenium panel design gene annotations.
Supplementary Table 3 (download XLSX )
5.1k ST panel design. List of genes and annotations.
Supplementary Table 4 (download XLSX )
Detailed quality-control metrics from BCR-seq.
Supplementary Table 5 (download XLSX )
Detailed quality-control metrics from TCR-seq.
Supplementary Table 6 (download XLSX )
List of antibodies used for the flow cytometry.
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Schmauch, E., Piening, B.D., Dowdell, A.K. et al. Multi-omics analysis of a pig-to-human decedent kidney xenotransplant. Nature 650, 205–217 (2026). https://doi.org/10.1038/s41586-025-09846-7
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DOI: https://doi.org/10.1038/s41586-025-09846-7
