Acute T-cell mediated rejection (TCMR) is the most frequent complication after liver transplantation (LT), which can affect up to 50% of LT recipients in the first 5-year post-transplant. Little is known about the immunological factors and specific leukocyte subpopulations driving TCMR and its recurrence in LT. The introduction of spatial single-cell technologies such as Imaging Mass Cytometry (IMC), allow an in-depth immune cell identification, including detection of rare cell phenotypes, along with spatial resolution of cell-to-cell interaction. However, the analysis of those high dimensional data requires computational and immunology expertise to meaningfully characterize immune-reach microenvironments. IMC comes with certain images artifacts (e.g. noise, aggregates) that can deteriorate the quality of the data which lead to either discard part of data (in variable percentage) or acceptance of errors in cell classification due to the association of markers belonging to different cell lineage, thus creating non-biological phenotypes. Additionally, segmentation of raw images (training of classifiers) can be time consuming and operator dependent thus introducing additional data variability.  Several toolkits have been developed for images preprocessing (e.g MAUI) and segmentation including Mesmer and Ilastik. By using a multi-steps IMC pipeline comprehensive of images preprocessing and semi-supervised clustering, we analyzed 96 images obtained from liver biopsies of patients with no-rejection, TCMR and chronic rejection. We identified 32 unique immune subpopulations, including three specific PD1⁺ T-cell subclusters, with a peculiar spatial distribution, which are associated with TCMR in LT recipients. The PD1 coinhibitory pathway has emerged as an important mediator and therapeutic target in several diseases including cancer, but its role in TCMR in LT is unknown. Our findings provide new insights in the pathophysiology of TCMR which support further investigations of the role of PD1 in TCMR.

Learning Objectives:

1. Identify the advantage of spatial single-cell technology (IMC), in the study of liver allograft rejection.

2. Use of images preprocessing to enhance data analysis

3. Discuss the identification of biomarker of rejection.