Kidney allograft inflammation, mostly attributed to rejection and infection, is an important cause of graft injury and loss. Standard histopathological assessment of allograft inflammation provides limited insights into biological processes and immune landscape. Here, using Imaging Mass Cytometry (IMC) with a panel of 28 validated biomarkers, we explored the single-cell landscape of kidney allograft inflammation in 32 kidney transplant biopsies and 247 high-dimensional histopathology images of various phenotypes of allograft inflammation (antibody-mediated rejection, T cell-mediated rejection, BK nephropathy and chronic pyelonephritis). Using novel analytical tools including Universal StarDist for QuPath (USDQP) for cell segmentation, we segmented over 900,000 cells and developed a tissue-based classifier using over 3,000 manually annotated renal microstructures (glomeruli, tubules, interstitium and arteries). Using PhenoGraph, we identified 11 immune and 9 non-immune clusters, and found high prevalence of memory T cell and macrophage-enriched immune populations across phenotypes. Additionally, we trained a machine learning classifier to identify spatial biomarkers that could discriminate the different allograft inflammatory phenotypes. Further validation of IMC in larger cohorts and more biomarkers will likely help interrogate kidney allograft inflammation in more depth than has been possible to date.
Copyright © 2023. Published by Elsevier Inc.