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.

Finance, race, ethnicity, and spoken language impact clinical outcomes for children with acute liver failure.

BACKGROUND: Pediatric acute liver failure (PALF) is an emergency, necessitating prompt referral and management at an experienced liver transplant center. Social determinants of health (SDOH) drive healthcare disparities and can affect many aspects of disease presentation, access to care, and ultimately clinical outcomes. Potential associations between SDOH and PALF outcomes, including spontaneous recovery (SR), liver transplant (LT) or death, are unknown. This study aims to investigate how SDOH may affect PALF and therefore identify areas for intervention to mitigate unrecognized disparities. METHODS: A retrospective, single-center cohort was analyzed and then compared and validated with data from the multicenter National Institutes of Health PALF Study Group. The single-center review included 145 patients admitted with PALF using diagnostic codes. Medical records were reviewed to extract patient demographics, family structure, inpatient social worker assessments, and clinical outcomes. Data were stratified by outcome. RESULTS: This analysis determined that level of family support (p = .02), caretaker employment (p = .02), patient age, race, and language (p = .01) may impact clinical outcomes. Specifically, the cohort of children that died had the largest proportion of non-English speaking patients with limited support systems and parents who worked full-time. Conversely, patients who underwent LT more often belonged to English-speaking families with a homemaker and extensive support systems. CONCLUSION: This study suggests that SDOH impact PALF outcomes and highlights patient populations facing additional challenges during an already complex healthcare emergency. These associations may indicate unconscious biases held by transplant teams when evaluating waitlist candidacy, as well as barriers to healthcare access. Strategies to better understand the broader applicability of our findings and, if confirmed, efforts to mitigate social disparities, may improve clinical outcomes in PALF.

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.

CHALF Score: A Novel Tool to Rapidly Risk Stratify Children in Need of Liver Transplant Evaluation During Acute Liver Failure.

BACKGROUND: Pediatric acute liver failure (PALF) can require emergent liver transplantation (LT, >25%) or lead to death (~15%). Existing models cannot predict clinical trajectory or survival with native liver (SNL). We aimed to create a predictive model for PALF clinical outcomes based on admission variables. METHODS: A retrospective, single-center PALF cohort (April 2003 to January 2022) was identified using International Classification of Disease codes, selected using National Institutes of Health PALF Study Group (PALFSG) criteria, and grouped by clinical outcome (SNL, LT, or death). Significant admission variables were advanced for feature selection using least absolute shrinkage and selection operator regression with bootstrapping (5000×). A predictive model of SNL versus LT or death was created using logistic regression and validated using PALFSG data. RESULTS: Our single-center cohort included 147 patients (58% SNL, 32% LT, 10% expired), while the PALFSG validation cohort included 492 patients (50% SNL, 35% LT, 15% expired). Admission variables associated with SNL included albumin (odds ratio [OR], 16; P < 0.01), ammonia (OR, 2.37; P < 0.01), and total bilirubin (OR, 2.25; P < 0.001). A model using these variables predicted SNL versus LT or death with high accuracy (accuracy [0.75 training, 0.70 validation], area under the curve [0.83 training, 0.78 validation]). A scaled score (CHLA-acute liver failure score) was created that predicted SNL versus LT or death with greater accuracy (C statistic 0.83) than Pediatric End-Stage Liver Disease (C statistic 0.76) and admission liver injury unit (C statistic 0.76) scores. CONCLUSIONS: The CHLA-acute liver failure score predicts SNL versus LT or mortality in PALF using admission laboratories with high accuracy. This novel, externally validated model offers an objective guide for urgent referral to a pediatric LT center.

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. Deconvolution 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 celullar 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.

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

Allograft rejection is a frequent complication following solid organ transplantation, but defining specific immune subsets mediating alloimmunity has been elusive due to the scarcity of tissue in clinical biopsy specimens. Single cell techniques have emerged as valuable tools for studying mechanisms of disease in complex tissue microenvironments. Here, we developed a highly multiplexed imaging mass cytometry panel, single cell analysis pipeline, and semi-supervised immune cell clustering algorithm to study archival biopsy specimens from 79 liver transplant (LT) recipients with histopathological diagnoses of 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 derived from 98 pathologist-selected regions of interest relevant to clinical diagnosis of rejection. We identified 41 distinct cell populations (32 immune and 9 parenchymal cell phenotypes) that defined key elements of the alloimmune microenvironment (AME), identified significant cell-cell interactions, and established higher order cellular neighborhoods. Our analysis revealed that both regulatory (HLA-DR+ Treg) and exhausted T-cell phenotypes (PD1+CD4+ and PD1+CD8+ T-cells), combined with variations in M2 macrophage polarization, were a unique signature of TCMR. TCMR was further characterized by alterations in cell-to-cell interactions among both exhausted immune subsets and inflammatory populations, with expansion of a CD8 enriched cellular neighborhood comprised of Treg, exhausted T-cell subsets, proliferating CD8+ T-cells, and cytotoxic T-cells. These data enabled creation of a predictive model of clinical outcomes using a subset of cell types to differentiate TCMR from NR (AUC = 0.96 ± 0.04) and TCMR from CR (AUC = 0.96 ± 0.06) with high sensitivity and specificity. Collectively, these data provide mechanistic insights into the AME in clinical LT, including a substantial role for immune exhaustion in TCMR with identification of novel targets for more focused immunotherapy in allograft rejection. Our study also offers a conceptual framework for applying spatial proteomics to study immunological diseases in archival clinical specimens.

Examining the Role for Donor-specific Antibody Testing in Simultaneous Liver-kidney Transplantation: A Single-center Analysis of Outcomes.

BACKGROUND: Simultaneous liver-kidney transplantation (SLKT) is increasingly used for patients with concurrent end-stage liver and renal disease. Emerging evidence suggests that simultaneous liver transplant can provide a tolerogenic benefit to multiorgan transplant recipients. Posttransplant donor-specific antibody (DSA) may be associated with worse outcomes; however, the role for testing DSA in SLKT is unclear. METHODS: This study retrospectively assessed the impact of DSA on outcomes following primary SLKT at a large-volume center between 2008 and 2018. Patients were grouped by positive DSA, negative DSA, and DSA not tested, and data were obtained from our institutional database and chart review. RESULTS: The cohort included 138 SLKT recipients with a mean age of 56.1 ± 9.7 y; 61.6% were male, and 55.8% were Hispanic. Overall, 62 patients were tested for DSA posttransplant, and 33 patients (23.9%) had at least 1 DSA detected. A total of 34 patients (24.6%) experienced at least 1 episode of liver rejection, and 23 patients (16.7%) experienced kidney rejection. Over 50% of patients with de novo DSA changed status during their posttransplant course. Rates of both liver and kidney rejection were slightly higher in the DSA + group, but liver allograft, kidney allograft, and patient survival did not differ when grouped by whether DSA testing was performed or DSA positivity. CONCLUSIONS: These data demonstrate that SLKT is associated with excellent long-term patient and allograft survival with a relatively low rate of rejection. In our experience, testing for DSA does not impact SLKT outcomes' and further multicenter analyses are needed to establish standard of care.