Multi-Omics Analysis of a Pig-to-Human Decedent Kidney Xenotransplant

Organ shortage remains a major problem in transplantation. Due to similarities in organ physiology and size, the domestic pig has been identified as the most promising donor species for xenotransplantation, and advances in gene-editing have helped reduce molecular incompatibilities. Despite gene-editing, the immune reactions following xenotransplantation can still cause transplant failure. Therefore, this study 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 to understand the immunological response of a pig-to-human kidney xenotransplantation. The donor was a 57-year-old male brain-dead decedent with a Stage IV glioblastoma that precluded organ donation and the xeno-kidney was procured from an galactosyltransferase gene-knockout pig 5 months after thymic auto-transplantation.

The initial authorization for the study was obtained for 2 to 4 weeks but was extended to 4 to 8 weeks to study the xenograft response to and effectiveness of tacrolimus maintenance immunosuppression in the setting of rebounding lymphocytes, as well as the sustained effectiveness of post-induction immunosuppression given the potential delay in antibody response expected in the setting of lymphopenia. Biopsies were performed on postoperative day 0, 10, 14, 21, 28, 33, 45, 49, 56, and 61. The experiments for the study include high-resolution spatial transcriptomics, single-nucleus RNA sequencing, and bulk RNA sequencing. In addition, recipient peripheral blood mononuclear cells were characterized by bulk and single-cell RNA sequencing, B-cell receptor (BCR) sequencing, and T-cell receptor (TCR) sequencing. Sera from 63 timepoints underwent deep proteome enrichment and liquid chromatography tandem mass spectrometry proteomic analysis. The dataset contains flow cytometry, single-nucleus RNA sequencing, bulk RNA sequencing, single-cell RNA sequencing, BCR sequencing, TCR sequencing, spatial transcriptomics, and proteomic data.