Abstract
In a previous study, heart xenografts from 10-gene-edited pigs transplanted into two human decedents did not show evidence of acute-onset cellular- or antibody-mediated rejection. Here, to better understand the detailed molecular landscape following xenotransplantation, we carried out bulk and single-cell transcriptomics, lipidomics, proteomics and metabolomics on blood samples obtained from the transplanted decedents every 6 h, as well as histological and transcriptomic tissue profiling. We observed substantial early immune responses in peripheral blood mononuclear cells and xenograft tissue obtained from decedent 1 (male), associated with downstream T cell and natural killer cell activity. Longitudinal analyses indicated the presence of ischemia reperfusion injury, exacerbated by inadequate immunosuppression of T cells, consistent with previous findings of perioperative cardiac xenograft dysfunction in pig-to-nonhuman primate studies. Moreover, at 42 h after transplantation, substantial alterations in cellular metabolism and liver-damage pathways occurred, correlating with profound organ-wide physiological dysfunction. By contrast, relatively minor changes in RNA, protein, lipid and metabolism profiles were observed in decedent 2 (female) as compared to decedent 1. Overall, these multi-omics analyses delineate distinct responses to cardiac xenotransplantation in the two human decedents and reveal new insights into early molecular and immune responses after xenotransplantation. These findings may aid in the development of targeted therapeutic approaches to limit ischemia reperfusion injury-related phenotypes and improve outcomes.
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Data availability
Count matrices for the generated data (PBMC scRNA-seq, PBMC bulk RNA-seq, tissue snRNA-seq, tissue spatial transcriptomics, proteomics, lipidomics and metabolomics data) are available on Zenodo at https://doi.org/10.5281/zenodo.10888596 (ref. 61). Owing to commercial sensitivities with pig genome and peptide sequence level data, and for the equivalent data for the human decedents, we can only make gene/protein counts available for the transcriptomic and proteomic datasets. We will endeavor to perform nucleotide or peptide resolution analyses upon reasonable request to B.J.K. within 2 months, with return of composite data results.
Code availability
Custom scripts and code are deposited on Zenodo at https://doi.org/10.5281/zenodo.10887629 (ref. 62).
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Acknowledgements
The authors sincerely thank the families of the two decedents for their generous donation to science. The authors thank S. H. Williams, M. Williams, Q. Guo, D. E. Smith, A. Reyentovich and D. L. Segev for their valuable insight and help in editing the manuscript. Development of the multi-omics platform and analyses was supported by NIAID-NIH R01 AI144522 (to B.J.K., A.K.D. and B.P.). The authors also thank M. Rothblatt, CEO of United Therapeutics Corporation, PBC and United Therapeutics Corporation, PBC, for funding support. For major contributions to the performance of this study the authors thank the Boeke Laboratory Team, NYU Langone Health Nursing Leadership, NYU Transplant Research Team and the NYU Langone Health Center for Biospecimen Research and Development, Histology and Immunohistochemistry Laboratory (RRID SCR_018304) and the Microscopy Laboratory (RRID SCR_017934) supported in part by the Laura and Isaac Perlmutter Cancer Center Support Grant (NIH/NCI P30CA016087). Other funding sources are the Orion Research Foundation (to E.S.), Yrjö Jahnsson Foundation (to E.S.), Vilho, Yrjö and Väisälä Fund (to E.S.), Aarne Koskelon foundation (to E.S.) and the Antti and Tyyne Soininen Foundation (to E.S.). We thank S.E.L. Craig in the office of Science and Research at NYU Grossman School of Medicine for her editorial assistance.
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Conceptualization was the responsibility of E.S., B.P., M.M., J.S., M.P.S., R.A.M., J.D.B. and B.J.K. Methodology was the responsibility of E.S., B.P., M.M., C.X., J.S., W.Z., A.K.D., K.K., I.S.J., S.H.W., B.L., N.N., N.M., H.P., R.S.H., M.K., M.P.S., R.A.M., J.D.B. and B.J.K. Software was the responsibility of E.S., B.P., A.K.D., H.G., R.S.H., M.K. and B.J.K. Validation was the responsibility of E.S., B.P., M.M., N.N., R.S.H., R.A.M., J.D.B. and B.J.K. Formal analysis was the responsibility of E.S., B.P., C.Z., A.K.D., M.K., M.P.S., R.A.M., J.D.B. and B.J.K. Investigation was the responsibility of E.S., B.P., M.M., B.X., C.Z., J.S., W.Z., A.K.D., J.I.K., K.K., I.S.J., S.H.W., H.G., N.N., N.M., H.P., R.S.H., A.G., M.P.S., R.A.M., J.D.B. and B.J.K. Resources were the responsibility of E.S., B.P., J.S., D.A., K.K., I.S.J., S.L.K., M.U.K., N.N., N.M., H.P., R.S.H., A.G., M.K., M.P.S., R.A.M., J.D.B. and B.J.K. Data curation was the responsibility of E.S., B.P., C.Z., A.K.D., D.A., D.G., S.H.W., H.G., M.P.S., J.D.B. and B.J.K. Writing of the original draft was the responsibility of E.S., B.P., M.M., J.S., I.S.J., D.P.O. and B.J.K. Review and editing was carried out by all authors. Visualization was the responsibility of E.S., B.P., M.M., C.Z., A.K.D., M.K., M.P.S., R.A.M., J.D.B. and B.J.K. Supervision was the responsibility of B.P., J.S., S.L.K., M.U.K., N.N., R.S.H., A.G., M.K., M.P.S., R.A.M., J.D.B. and B.J.K. Project administration was the responsibility of B.P., J.S., J.I.K., K.K., I.S.J., R.A.M. and B.J.K. Funding acquisition was the responsibility of E.S., B.P., J.S., S.L.K., M.U.K., M.K., M.P.S., R.A.M. and B.J.K.
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J.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: CZ Biohub New York, Logomix, Modern Meadow, Rome Therapeutics, Sangamo, Tessera Therapeutics and the Wyss Institute. M.P.S is a co-founder and member of the scientific advisory board of Personalis, Qbio, January, SensOmics, Protos, Mirvie, NiMo, Onza and Oralome. He is also on the scientific advisory board of Danaher, Genapsys and Jupiter. R.A.M. is on scientific advisory boards for eGenesis, Sanofi, Regeneron, CareDx and Hansa Biopharma, is a consultant to Recombinetics, reports consulting fees from Hansa Medical, Regeneron, Thermo Fisher Scientific, Genentech, CareDx, One Lambda, ITB Med, Sanofi and PPD Development and reports grant support from Hansa Biopharma, all unrelated to the present work. R.A.M. also reports grant support from United Therapeutics Corporation, PBC. D.A. works for Revivicor. M.L. works for United Therapeutics. S.E.B.T. and M.F.O. work for 10x Genomics. All other authors declare no competing interests.
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Extended data
Extended Data Fig. 1 Detailed PBMC scRNA-seq data analysis.
For dotplots, dot size reflects the percentage of cells in each group with gene expression above zero. Dot color displays each group’s mean gene expression, min–max scaled. a. Low dimension embedding representation of the PBMC scRNA-seq data, colored by cell types. HSC-CD34+: Hematopoietic stem cells. b. Expression distribution of selected marker genes of the main cell types, basis for their identification. c. Low dimension embedding representation of the T/NK cells, colored by subtype. NK: Natural Killer cells, CD8 T: CD8+ T cells, CD4 T: CD4+ T cells, Treg: T-regulatory cells. d. Expression distribution of selected marker genes of T subtypes, basis for their identification. e-f. Further dissection CD8 T (e) and CD4 T (f) cells. g. Low dimension embedding representation of the B cells, colored by subtype. h. Expression distribution of selected marker genes of B subtypes, basis for their identification. i. Proportion distribution across time points, for both decedents, in selected cell types and subtypes (labeled on each plot). Percentages are calculated based on the total number of cells (all cell types) in each sample. j-k. Genes selected as highly specific for the B cell (j.) and T/NK (k.) population, used for bulk RNA-seq proportion validation in Fig. 1. l. Summary of DEG analysis from PBMC scRNA-seq. Number of overexpressed genes (left, LFC > 0 and padj < 0.05) and underexpressed genes (right, LFC < 0 and padj < 0.05). The LFC and adjusted p value (Wald test p value, two-tailed, adjusted with the Benjamini–Hochberg method) were obtained directly from the DESeq2 DEA output.
Extended Data Fig. 2 Multi-omics DE integration.
Comparative temporal DEA of individual omics analyses. Bulk transcriptomics, proteomics, lipidomics and metabolomics for blood samples of two pig heart to human xenotransplantations spanning 26 time points are illustrated. The early phase encompasses the 6 hr and 12 hr post-transplant time points, the mid-phase includes 18 hr to 36 hr post-transplant, while the late phase comprises all time points beyond 36 hr. For each panel, the x-axis represents the fold change in log2 scale, and the y-axis depicts the -log10 transformed unadjusted p value. These analyses were run individually for decedent 1 (left) and decedent 2 (right). The individual omics are as follows: (a) Transcriptomics: DEA analysis using DESeq2 is limited to genes whose average expression exceeds 4 counts across samples. Genes with significant expression changes (FDR<0.05) are highlighted in red (absolute log2FC > 1) or blue (log2FC < 1). Only the top 10 most significant genes were labeled due to space limitation. The LFC and adjusted p value (Wald test p value, two-tailed, adjusted with the Benjamini–Hochberg method) were obtained directly from the DESeq2 DEA output. (b) Proteomics: proteins present in more than half of the samples, yielding a total of 895 proteins. Proteins with significant changes (FDR < 0.05) are highlighted in red (absolute log2FC > 1) or blue (log2FC < 1). (c) Metabolomics: DEA was performed for 459 metabolites. Metabolites with significant changes (FDR < 0.05) are marked in red (absolute log2FC > 1) or blue (log2FC < 1). (d) Lipidomics: The analysis included 720 lipids. Lipids showing significant changes (FDR < 0.05) are denoted in red (absolute log2FC > 1) or blue (log2FC < 1). In the metabolomics and lipidomics panels, only the top features are highlighted with the molecule names. In all panels, molecules without significant changes are represented in gray. Corrected p-values (FDR, from two-tailed moderated t-tests, adjusted using the Benjamini–Hochberg method) and log fold changes (LFC) were derived from limma outputs for the proteomics, metabolomics and lipidomics analyses.
Extended Data Fig. 3 Intersection between clinical measurements and multi-omics integration.
a. Overlap of clinical blood markers (Lactate, Troponin, Fibrinogen, Ferritin, SBP, BNP, DBP, IL-2R) and multi-omics integration-based cluster of analytes upregulated in D1. The blood marker level is shown in bold green. For each marker, decedent 1 measures are shown on the top graph, decedent 2 on the bottom. b. Cardiac output, cardiac index measurements and vasopressor dose (bottom graphs) put in perspective with the multi-omics integration-based cluster of analytes upregulated in D1 (top graph). Decedent 1 is shown on the left and decedent 2 on the right.
Extended Data Fig. 4 Xenograft snRNA-seq based gene expression dissection.
a-b. Top marker genes (Wilcoxon rank-sum) for human nuclei (a.) and pig nuclei (b.) in the Xenograft snRNA-seq data. Dot size reflects the percentage of nuclei in each group with gene expression above zero. Dot color displays each group’s mean gene expression, min–max scaled. c. Cell-type proportions for pig nuclei. d. Cell-type proportion of human nuclei. e. Human T/NK subtypes markers expression within Xenograft T/NK human population, across samples. f. T/NK cell activity markers expression across samples. g-i. Expression distribution of lead genes in D1 which contributed to the Reactome Interleukin Signaling enrichment in CM (g), NIK to Non-canonical NF-kB signaling in CM (h), and Bioplanet 2019 DNA replication in FB (i) Dot size reflects the -log10(FDR) from DESeq result, color reflect log2FC. Significance (padj < 0.05) is denoted by a black star. The LFC and adjusted p value (Wald test p value, two-tailed, adjusted with the Benjamini–Hochberg method) were obtained directly from the DESeq2 DEA output.
Extended Data Fig. 5 Spatial transcriptomics composition.
a. Spatial transcriptomic slide mapped with each Spatial cluster, in all samples. Scale bar: 1mm. b-d. Spatial transcriptomics cell type deconvolution (Cell2location) signal, min–max scaled, across Spatial clusters (b), Visium samples (c), and their combination (d). e. Cluster proportion distribution across samples. f. Spatial distribution of IL1RL1 expression, represented by the color scale (log norm expression). Scale bar: 1mm. g. Spatial distribution of IL33 expression, represented by the color scale (log norm expression). Scale bar: 1mm. h. snRNA-seq based cell–cell communication inference results for IL33 signaling.
Extended Data Fig. 6 Xenograft tissue imaging.
a-d. H&E staining of Pig Xenograft Endomyocardial Biopsies from decedent 1 (a-b) and decedent 2 (c-d). Scale bar: 1mm. a. Contraction band necrosis, with interstitial edema and endothelial swelling of small interstitial capillary. b. Endothelial swelling of intramyocardial muscular artery and detached endothelial cells with perivascular edema. c. Endothelial swelling in the intramyocardial muscular artery. d. Endothelial swelling and lifting of small interstitial capillary. e-k. Electron Microscopy (EM) images of the Xenograft samples. e. Electron Microscopy image of D2 transplanted Xenograft. Scale bar: 5 µm. f. EM image of D1 transplanted Xenograft. Ultrastructural changes in the capillary endothelial cells are characterized by endothelial swelling with narrowing of the capillary lumen and enlarged vacuoles in the cytoplasm. There is perivascular edema resulting in separation of the collagen fibrils. The myocytes have an intact sarcolemma; however, the cytoplasm shows decrease to loss of myofibrils under the sarcolemma. Scale bar: 5 µm. g: D1. Accumulation of mitochondria (red arrow) as a result of degenerated myofibrils. Widened disorganized Z-lines (blue arrow) in degenerating myofibrils. Swollen interstitial capillary endothelial cell (white arrow). Scale bar: 10 µm. h. D1. Degenerating myofibrils (blue arrow), swollen endothelial cells in intramyocardial capillary (red arrow), sarcolemma disruption (white arrow). Scale bar: 5 µm. i. D1. Endothelial cell swelling with near collapse in an intramyocardial capillary lumen. Scale bar: 5 µm. j. D2. Endothelial swelling (white arrow). Scale bar: 5 µm.
Extended Data Fig. 7 Graft PCXD and hypoxia signal.
a. Expression of genes of interest and hypoxia related genes across Visium samples (within spots from clusters 7 and 8). b. Top enrichments for Visium cluster 7 markers (from GSEA on Wilcoxon rank-sum based marker discovery). Color depicts the NES, with blue indicating negative and red positive scores. A white star denotes a false discovery rate (FDR) below 0.05. c. Top enrichments for D1-LV-A sample, from Visium data (from GSEA on Wilcoxon rank-sum based DE, at sample level, each sample vs. the others together). d-e. Hypoxia and damage-associated genes expression changes in snRNA-seq. With DEA (pseudobulk) signals in dividing cells, LEC, L2, MP and SMC and VEC (d), and across cell types (e). f-j. Expression of genes associated with PCXD in the spatial transcriptomics and snRNA-seq data. Dot size reflects the percentage of spots in each group with gene expression above zero. Dot color displays each group’s mean gene expression, min–max scaled. Genes labeled as PCXDplus were previously described24 as being overexpressed in PCXD, while PCXDminus as underexpressed. Expression distribution across spatial clusters (f). Within all spots, between samples (g). Within vascular spatial clusters (only spots of cluster 8 and 7 are included), between samples (h). Across cell types (snRNA-seq, i). Across conditions (snRNA-seq, j).
Extended Data Fig. 8 Further dissection of vascular remodeling in Xenografts.
a. Top enrichments in D1 for PER from pseudobulk based DE analysis. b. Top enrichments for SMC in D1 left ventricle. Here, GSEA was performed from Wilcoxon rank-sum test DE analysis at the nuclei level (each condition vs. non-ischemic in vivo pig cardiac tissue, within snRNA-seq, selecting for SMC). Color depicts the NES, with blue indicating negative and red positive scores. A white star denotes a false discovery rate (FDR) is below 0.05, while dot size denotes the negative logarithm of the FDR. c. Expression distribution of lead genes in D1 which contributed to the MSigDB Hallmark 2020 EMT enrichment (up), MSigDB Myc Targets V1 enrichment (middle) and MSigDB TNF-alpha Signaling via NF-kB, all in VEC. Dot size reflects the -log10(FDR) from DESEQ result, color reflects log2FC. Significance (padj < 0.05) is denoted by a black star. The LFC and adjusted p value (Wald test p value, two-tailed, adjusted with the Benjamini–Hochberg method) were obtained directly from the DESeq2 DEA output.
Extended Data Fig. 9 Lymphatic endothelial cells (LECs) dissection.
a. Top enrichments in D1 for LECs from pseudobulk based DE analysis. b-g. LEC subclustering analysis. UMAP of LECs colored by subclusters (b). Correlation of subcluster expression signal (c). Cell cycle score distribution (d). Proportion distribution of subclusters LEC-3 and LEC-6 across samples (e). Subcluster LEC-3 marker genes expression distribution (f). GSEA analysis of LEC-3 DEA results (g).
Extended Data Fig. 10 Dividing cells dissection.
a. Subtyping and re-embedding of the dividing cells population, Low dimensional embedding representation of the dividing cells colored by their associated cell types. Cells belonging to the dividing group in Fig. 3a were isolated and re-clustered. Marker gene expression was used to label them b-c. Top marker based GSEA results in DIV-PER (b.) and DIV-VEC2 (c.). Color depicts the NES, with blue indicating negative and red positive scores. A white star denotes a false discovery rate (FDR) is below 0.05, while dot size denotes the negative logarithm of the FDR d. Proportion distribution of dividing cells subtypes between samples and conditions. Percentages are calculated based on the total number of cells, from all cell types. e. Top marker genes of the DIV-VEC2 cells population. f-i. Expression of ICAM1, P-selectin (SELP), E-selectin (SELE); across all cell types (f.) Pseudobulk level DE results in VEC population (g.), dividing cells subtypes (h.), across conditions in DIV-VEC1 and DIV-VEC2 (i.).
Supplementary information
Supplementary Information
Supplementary Figs. 1–16
Supplementary Table 1
Clinical parameters and blood laboratory measurements of the two decedents during transplantation. Hemodynamics: incorporates mean arterial pressure (MAP), central venous pressure (CVP), cardiac output (CO) in l min−1, cardiac index (CI) in l min−1 m−2 and systemic vascular resistance (SVR) in dyne × s/cm-5. CO/CI measurements commence approximately 24 h post-reperfusion. Inotropes: dosages for dobutamine, epinephrine and other inotropes, quantified in μg kg−1 min−1. Vasopressors: quantifies the administration of levophed (levo gtt) in μg kg−1 min−1 and vasopressin (vaso) in U min−1. Hematology: includes white blood cell count (WBC) in 103 per μl and platelets (plt). Liver function: assesses liver health through aspartate aminotransferase (AST) and alanine aminotransferase (ALT) levels, alongside lactate measurements in mmol l−1. Inflammatory markers: D-dimer, ferritin, CRP levels. Cardiac function: enhanced by Swan Ganz catheter data, offering quantitative insights into cardiac performance beyond the basic CO/CI metrics, including troponin (Trop) in ng l−1 and brain natriuretic peptide (BNP) levels, underscoring the extent of cardiac stress or damage. Coagulation: INR is provided to monitor coagulation status. Cytokines: profiles cytokine levels including IL-2R, IL-6, IL-8, IL-10 and IL-13 in pg ml−1.
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Schmauch, E., Piening, B., Mohebnasab, M. et al. Integrative multi-omics profiling in human decedents receiving pig heart xenografts. Nat Med 30, 1448–1460 (2024). https://doi.org/10.1038/s41591-024-02972-1
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DOI: https://doi.org/10.1038/s41591-024-02972-1
