Characterizing the Renal Immune Microenvironment in Antibody-Mediated Rejection to Predict Treatment Response using Imaging Mass Cytometry

The immune response of solid organ transplant rejection is a complex process that can lead to the loss of the transplanted organ. Transplant rejection is broadly characterized as antibody-mediated rejection (ABMR) and T cell-mediated rejection (TCMR) with evidence showing that ABMR confers a higher risk for graft loss. A significant number of renal biopsies exhibit a mixed TCMR/ABMR phenotype, suggesting there is more disease overlap than previously known. Unfortunately, irreversible graft damage often predates the diagnosis of ABMR, and treatment options are costly with debatable efficacy, underscoring the need for improved earlier diagnostics and prognostics. Evidence supports that local renal immune microenvironment dysregulation may play a significant role in renal rejection pathobiology. This proposal aims to address the limitations of current histopathological assessments in predicting post-transplant outcomes by providing a granular atlas of the immune cell interactions and spatial distributions in the renal microenvironment. The central hypothesis of this proposal posits that higher abundance, interactions, and activity of cytotoxic immune cell subsets correlate with more severe ABMR disease and worse treatment responses. To test this hypothesis, we will utilize advanced imaging mass cytometry to comprehensively profile the immune cells and their functional states within kidney tissues. Experiments proposed in Aim 1 will establish an immune atlas that differentiates between ABMR and TCMR by examining immune cell phenotypes, functions, and spatial patterns in renal biopsies. Aim 2 will integrate this immune data with clinical variables to develop predictive models for treatment response, using a novel machine learning algorithm specifically designed for high-dimensional data analysis. This work will be completed at Stanford University School of Medicine in Dr. Brice Gaudilliere’s laboratory (primary sponsor) with guidance from Dr. Minnie Sarwal (co-sponsor, UCSF). The proposed rigorous training plan harnesses cutting-edge high-dimensional single-cell spatial immune profiling with sparse machine learning methods. The training, approach, and results generated will offer a unique framework for future research on cytotoxic mechanisms of renal transplant rejection, aiding my career development and enabling me to develop the expertise in transplant immunology and computational biology necessary for becoming an independent academic translational physician-scientist. Importantly, these findings will enhance our understanding of the immune dynamics in kidney transplantation and pave the way for innovative diagnostic tools and therapeutic interventions, ultimately improving patient outcomes and graft survival rates. This research holds significant promise for advancing personalized medicine in the field of organ transplantation. Project Number: 1F32AI194514-01 | Fiscal Year: 2025 | NIH Institute/Center: National Institute of Allergy and Infectious Diseases (NIAID) | Principal Investigator: Amy Tsai | Institution: STANFORD UNIVERSITY, STANFORD, CA | Award Amount: $76,756 | Activity Code: F32 | Study Section: Special Emphasis Panel[ZRG1 F07B-G (20)] View on NIH RePORTER: https://reporter.nih.gov/project-details/1F32AI19451401