Progress in kidney transplantation: The role for systems immunology

Abstract

The development of systems biology represents an immense breakthrough in our ability to perform translational research and deliver personalized and precision medicine. A multidisciplinary approach in combination with use of novel techniques allows for the extraction and analysis of vast quantities of data even from the volume and source limited samples that can be obtained from human subjects. Continued advances in microfluidics, scalability and affordability of sequencing technologies, and development of data analysis tools have made the application of a multi-omics, or systems, approach more accessible for use outside of specialized centers. The study of alloimmune and protective immune responses after solid organ transplant offers innumerable opportunities for a multi-omics approach, however, transplant immunology labs are only just beginning to adopt the systems methodology. In this review, we focus on advances in biological techniques and how they are improving our understanding of the immune system and its interactions, highlighting potential applications in transplant immunology. First, we describe the techniques that are available, with emphasis on major advances that allow for increased scalability. Then, we review initial applications in the field of transplantation with a focus on topics that are nearing clinical integration. Finally, we examine major barriers to adapting these methods and discuss potential future developments.

Keywords: eplet; graft failure; immunopeptidome; kidney transplantation; multi-omics; rejection; systems biology.

FIGURE 1

Complex factors play a role in kidney transplant recipient outcomes. Baseline factors at the time of transplantation for both donor and recipient determine the immune micro-environment in the transplant recipient. Modulating factors such as immunosuppression and antiviral prophylaxis moderate the recipient response to this disturbance. The interplay between the fixed baseline characteristics of donor and recipient and modifiable features (immunosuppression) determines the outcome for each transplant recipient.

FIGURE 2

Application of systems immunology to transplantation. In kidney transplantation, samples can be obtained from 3 primary sources: whole blood, urine, and allograft tissue. High dimensional measurement of each sample type provides a unique source of information, which can be integrated using bioinformatics to build a more complete understanding of the biologic system.

FIGURE 3

Machine learning–fundamental concepts. In supervised methods, input data is already classified, and machine learning is used to determine associations. In unsupervised methods, input features are used to determine classification.

FIGURE 4

Dimensionality reduction (144). An example of dimensionality reduction use to display the results of unsupervised clustering. Heatmaps (C) display the representation of input features in each cluster. While TSNE graphs allow visualization of the distribution of clusters or cell types (A), input feature (B), and detection of differences in cluster frequency between sample types (D).

FIGURE 5

T-cell receptor (TCR) similarity analysis (217). TCRs can be grouped in terms of sequence similarity (A). Connected components can be identified by network analysis (B). Known specificities can be overlaid on similarity networks (C), and motifs inferred (D).