The pathogen exposure history of an individual is recorded in their T-cell repertoire and can be accessed through the study of T-cell receptors (TCRs). For each T-cell, the TCR loci undergoes genetic rearrangement that creates a unique DNA sequence. In theory these unique sequences can be used as biomarkers for tracking T-cell responses and cataloging immunological history. High-throughput sequencing and analyses of immune cell receptor sequences presents a unique opportunity to inform our understanding of immunological responses to infections. Following the body’s recovery, pathogen-specific immune cells and their receptor sequences remain present at higher frequencies, with their increase in frequency preventing secondary infections. As a result of their persistence in the body, T-cells are a useful tool for diagnosing infections and evaluating vaccine efficacy as a stable biomarker. However, this process requires thorough analysis of massive datasets at an accuracy beyond the capabilities of traditional statistical tests. Here we utilize a Deep Neural Network to identify specific viral infections or vaccination statuses using publicly available mouse (monkeypox infection and smallpox vaccination) and human (cytomegalovirus serostatus) T-cell receptor sequenced datasets. The success of our intensive experiments holds the potential for the creation of effective infection assays, given a suitable sampling of infected blood.

Dr. Tae-Hyuk (Ted) Ahn has served as my close mentor and friend for the majority of my time at SLU. Dr. Ahn has always provided me the opportunity to explore collaborations, ideas, and techniques through his many interdisciplinary connections while staying involved in both my growth as a student and a researcher. Dr. Ahn has served both as a guiding educator during my struggles to pursue difficult high-performance computing and a partner in crime in the many late night zoom calls necessary to push our collaborative projects over the finish line.