Abstract
Severe asthma in children is notoriously difficult to treat, and its immunopathogenesis is complex. In particular, the contribution of T cells and relationships to anti-viral immunity, remain enigmatic. Here, we coupled deep phenotyping with machine learning methods to resolve the dynamics of T cells in the diseased lower airways, and examined rhinovirus (RV) as a driver. Our strategy revealed a T-cell landscape dominated by type 1 and type 17 CD8+ signatures. Interrogation of phenotypic relationships coupled with trajectory mapping identified T-cell migratory and differentiation pathways spanning the blood and airways that culminated in tissue residency, and included transitions between type 1 and type 17 tissue-resident types. These T-cell dynamics were reflected in cytokine polyfunctionality in situ . Use of machine learning to cross-compare T-cell populations that were enriched in the airways of RV-positive children with those induced in the blood after RV challenge in an experimental infection model, precisely pinpointed RV-responsive signatures that mapped to T-cell differentiation pathways. Despite their rarity, these signatures were detected in the airways of uninfected children. Together, our results underscore the aberrant nature of type 1 immunity in the airways of children with severe asthma, and implicate an important viral trigger as a driver.