Revisiting transplant immunology through the lens of single-cell technologies

Abstract

Solid organ transplantation (SOT) is the standard of care for end-stage organ disease. The most frequent complication of SOT involves allograft rejection, which may occur via T cell- and/or antibody-mediated mechanisms. Diagnosis of rejection in the clinical setting requires an invasive biopsy as there are currently no reliable biomarkers to detect rejection episodes. Likewise, it is virtually impossible to identify patients who exhibit operational tolerance and may be candidates for reduced or complete withdrawal of immunosuppression. Emerging single-cell technologies, including cytometry by time-of-flight (CyTOF), imaging mass cytometry, and single-cell RNA sequencing, represent a new opportunity for deep characterization of pathogenic immune populations involved in both allograft rejection and tolerance in clinical samples. These techniques enable examination of both individual cellular phenotypes and cell-to-cell interactions, ultimately providing new insights into the complex pathophysiology of allograft rejection. However, working with these large, highly dimensional datasets requires expertise in advanced data processing and analysis using computational biology techniques. Machine learning algorithms represent an optimal strategy to analyze and create predictive models using these complex datasets and will likely be essential for future clinical application of patient level results based on single-cell data. Herein, we review the existing literature on single-cell techniques in the context of SOT.

Keywords: Mass cytometry; Multiomics; Single cell; Solid organ transplantation; Transplant immunology.

Fig. 1

a National cumulative numbers of transplants by year for all organs and all donors. b Number of transplants by organ type (all donors) in the 2021

Fig. 2

Schematic illustrations of single-cell technologies. Peripheral blood mononuclear cells (PBMC) and cells in suspension can be analyzed by either platforms protein based (CyTOF) or nucleic acid based (sc-RNAseq, sn-RNAseq). FFPE or frozen tissue samples obtained from biopsy or explant can be analyzed using protein-based assays including IMC, CODEX, MIBI, or CycIF. Whole transcriptome can be obtained via meFISH, seqFISH, Visium 10X, and CosMx. Data from single-cell characterization are used for phenotyping and cluster identification using dimensionality reduction analysis such as PhenoGraph, t-SNE, as well as neighborhood and differential gene expression analyses. (Figure created with Biorender.com)

Fig. 3

Schematic representation of IMC workflow for investigation of chronic rejection in liver transplantation recipients. Liver tissue sections were obtained from patients who underwent re-transplantation for chronic rejection (CR) of the primary graft. Pre-implantation liver biopsies from donors were used as control for liver with no rejection (NR). Staining was performed using a cocktail of 11 antibodies coupled with metal tags targeting immune cells. Tissue ablation and measurement of the metal ion abundance by time-of-flight mass spectrometry was performed using Hyperion Imaging System. Multidimensional images were segmented using Ilastik and Cell Profiler. t-SNE plots were used for dimensionality reduction to visualize level of expression of individual markers for each cell. PhenoGraph was used to establish immune meta-cluster identification. Cell subpopulation proportions were compared between chronic CR and NR and difference in macrophage and neutrophil proportions were observed between the two cohorts. Principal component and regression analyses were performed and revealed that PC1 had a high accuracy in rejection prediction. (Created with Biorender.com)