Performance Evaluation of Viral Infection Diagnosis using T-Cell Receptor Sequence and Artificial Intelligence

Abstract

The adaptive immune system expresses millions of different receptors that detect and fight pathogens encountered throughout life. These receptors are encoded by unique DNA sequences that allow immune cells to express millions of different receptors. High-throughput sequencing and analyses of immune cell receptor sequences present a unique opportunity to inform our understanding of immunological responses to infections and to evaluate vaccine efficacy. Even after the infection is eliminated, pathogen-specific immune cells and their receptor sequences are present at higher frequencies than prior to infection, and their increase in frequency prevents secondary infections. As a result of their persistence in the body, they may be useful for diagnosing infections and evaluating vaccine efficacy as a stable biomarker. However, this process requires thorough analysis of massive datasets at an accuracy beyond traditional statistical tests to diagnose infectious statuses based on sequence analyses. Here we evaluate various machine learning and deep learning algorithms to measure the performance of the identification and diagnosis of specific viral infections or vaccination statuses using the publicly available mouse (monkeypox infection and smallpox vaccination) and human (cytomegalovirus serostatus) T-cell receptor sequenced datasets. Our intensive experiments hold the potential for effective screening of disease status, including recently encountered strains like the ongoing SARS-CoV-2 pandemic.