Computational strategies for dissecting the high-dimensional complexity of adaptive immune repertoires

TL;DR

This paper reviews computational methods for analyzing high-dimensional immune receptor sequencing data, highlighting advances in diversity, clustering, phylogenetics, and machine learning to understand immune system complexity and aid therapeutic development.

Contribution

It provides a comprehensive overview of current computational techniques and proposes future directions for integrating AIRR-seq with immunotherapeutic discovery.

Findings

Summarizes methods for diversity analysis of immune repertoires.

Reviews clustering, network, and phylogenetic approaches.

Discusses machine learning applications in immune repertoire analysis.

Abstract

The adaptive immune system recognizes antigens via an immense array of antigen-binding antibodies and T-cell receptors, the immune repertoire. The interrogation of immune repertoires is of high relevance for understanding the adaptive immune response in disease and infection (e.g., autoimmunity, cancer, HIV). Adaptive immune receptor repertoire sequencing (AIRR-seq) has driven the quantitative and molecular-level profiling of immune repertoires thereby revealing the high-dimensional complexity of the immune receptor sequence landscape. Several methods for the computational and statistical analysis of large-scale AIRR-seq data have been developed to resolve immune repertoire complexity in order to understand the dynamics of adaptive immunity. Here, we review the current research on (i) diversity, (ii) clustering and network, (iii) phylogenetic and (iv) machine learning methods applied to dissect, quantify and compare the architecture, evolution, and specificity of immune repertoires. We summarize outstanding questions in computational immunology and propose future directions for systems immunology towards coupling AIRR-seq with the computational discovery of immunotherapeutics, vaccines, and immunodiagnostics.