Kidney Transplantation in ≤15 kg Children: Outcomes and Prognostic Indicators-A Review of the Organ Procurement and Transplantation Database.

BACKGROUND: Approximately 2500 pediatric patients are awaiting kidney transplantation in the United States, with <5% comprising those ≤15 kg. Transplant in this cohort is often delayed by center-based growth parameters, often necessitating transplantation after the initiation of dialysis. Furthermore, prognostication remains somewhat ambiguous. In this report, we scrutinize the Organ Procurement and Transplantation Network (OPTN) data from 2001 to 2021 to help better understand specific variables impacting graft and patient outcomes in these children. METHODS: The OPTN kidney transplant dataset from 2001 to 2021 was analyzed. Inclusion criteria included age <18 years, weight ≤15 kg, and recipient of primary living donor kidney transplantation (LDKT) or deceased donor kidney transplantation (DDKT). Patient and graft survival probabilities were calculated using the Kaplan-Meier method. The Cox proportional hazards model was used to calculate hazard ratio (HR) and identify variables significantly associated with patient and graft survival. RESULTS: Two thousand one hundred sixty-eight pediatric transplant recipients met inclusion criteria. Patient survival at 1 and 3 years was 98% and 97%, respectively. Graft survival at 1 and 3 years was 95% and 92%, respectively. Dialysis was the sole significant variable impacting both patient and graft survival. Graft survival was further impacted by transplant era, recipient gender and ethnicity, and donor type. Infants transplanted at Age 1 had better graft survival compared with older children, and nephrotic syndrome was likewise associated with a better prognosis. CONCLUSION: Pediatric kidney transplantation is highly successful. The balance between preemptive transplantation, medical optimization, and satisfactory technical parameters seems to suggest a "Goldilocks zone" for many children, favoring transplantation between 1 and 2 years of age.

Spatially resolved immune exhaustion within the alloreactive microenvironment predicts liver transplant rejection.

Allograft rejection is common following clinical organ transplantation, but defining specific immune subsets mediating alloimmunity has been elusive. Calcineurin inhibitor dose escalation, corticosteroids, and/or lymphocyte depleting antibodies have remained the primary options for treatment of clinical rejection episodes. Here, we developed a highly multiplexed imaging mass cytometry panel to study the immune response in archival biopsies from 79 liver transplant (LT) recipients with either no rejection (NR), acute T cell-mediated rejection (TCMR), or chronic rejection (CR). This approach generated a spatially resolved proteomic atlas of 461,816 cells (42 phenotypes) derived from 96 pathologist-selected regions of interest. Our analysis revealed that regulatory (HLADR+ Treg) and PD1+ T cell phenotypes (CD4+ and CD8+ subsets), combined with variations in M2 macrophage polarization, were a unique signature of active TCMR. These data provide insights into the alloimmune microenvironment in clinical LT, including identification of potential targets for focused immunotherapy during rejection episodes and suggestion of a substantial role for immune exhaustion in TCMR.

Technical optimization of spatially resolved single-cell transcriptomic datasets to study clinical liver disease.

Single cell and spatially resolved 'omic' techniques have enabled deep characterization of clinical pathologies that remain poorly understood, providing unprecedented insights into molecular mechanisms of disease. However, transcriptomic platforms are costly, limiting sample size, which increases the possibility of pre-analytical variables such as tissue processing and storage procedures impacting RNA quality and downstream analyses. Furthermore, spatial transcriptomics have not yet reached single cell resolution, leading to the development of multiple deconvolution methods to predict individual cell types within each transcriptome 'spot' on tissue sections. In this study, we performed spatial transcriptomics and single nucleus RNA sequencing (snRNAseq) on matched specimens from patients with either histologically normal or advanced fibrosis to establish important aspects of tissue handling, data processing, and downstream analyses of biobanked liver samples. We observed that tissue preservation technique impacts transcriptomic data, especially in fibrotic liver. Single cell mapping of the spatial transcriptome using paired snRNAseq data generated a spatially resolved, single cell dataset with 24 unique liver cell phenotypes. We determined that cell-cell interactions predicted using ligand-receptor analysis of snRNAseq data poorly correlated with cellular relationships identified using spatial transcriptomics. Our study provides a framework for generating spatially resolved, single cell datasets to study gene expression and cell-cell interactions in biobanked clinical samples with advanced liver disease.