Immunoinformatics approach to discover novel diagnostics in Lyme Arthritis: can T cells unlock the status quo?

Current serology-based testing methods to support Lyme disease (LD) diagnosis are critically flawed: they lack sensitivity (25–50%) and specificity for diagnosis in early LD, and are unable to differentiate active from past infections in the burdensome late LD stages such as Lyme arthritis (LA). In contrast to Borrelia-specific antibodies, T cells have been shown to be consistently recruited in early LD. Furthermore, different T cell subsets are thought to play a key role in the development of later postinfectious (autoimmune-driven) LA. With this FWO-SB project, we will investigate the potential of T cells as a new avenue for diagnosis. Building upon the recent advances in single-cell immune profiling, this project will deliver a novel framework for characterizing disease-associated T-cell signatures in unprecedented detail, integrating the T cell phenotype with its receptor specificity on a single-cell level. We will address the critical need for post-analysis tools for such complex datasets by developing novel immunoinformatic workflows, allowing efficient extraction of clinically actionable insights and extremely specific biomarkers. Employing the developed methodology and tools across various types of LD, LA, and other relevant autoimmune-driven arthritides will then empower us in addressing the imperative diagnostic need and in shedding light on the elusive pathogenic mechanisms behind postinfectious LA.

Keywords:T HELPER LYMPHOCYTE, T CYTOTOXIC LYMPHOCYTE, INFECTIONS

Disciplines:Data mining, Autoimmunity, Single-cell data analysis, Molecular diagnostics, Adaptive immunology