High throughput T cell repertoire sequencing-based analysis of T cell subsets as a viral diagnostic tool

The acquired immune system fights pathogens by remembering previous infections, in this way it can respond quickly if the same pathogens return. T cells are a crucial part of the acquired immunity. They are coated with receptors (also called TCRs) that recognise potential disease-related or pathogen-derived peptides presented by infected host cells or cells from the innate immune system. In theory, the entire collection of one’s TCRs contains imprints of past, recent and currently active infections. This TCR repertoire can be mapped using high throughput TCR sequencing. In this PhD project the first objective is to deliver a proof of concept that TCR sequencing on recently activated T cell populations can be used to diagnose an acute viral infection (e.g., SARS-CoV-2). Hence COVID-19 patients and healthy individuals (i.e., controls) from the IMSEQ study (NCT04368143) will be selected for TCR sequencing. The resulting data will be analysed employing various machine learning and immuno-informatic methods at the ADReM Data Lab (UA). The objective of the analysis is to identify SARS-CoV-2 specific TCR imprints in infected IMSEQ individuals. The controls will be used to calculate thresholds for background reactivities. In later phases we will expand this project either towards evaluation of the methodology as a vaccination biomarker or expand to other infectious pathogens. Additionally, besides the active T cell repertoire, the memory T cell population could be analysed in the same manner. Ultimately TCR sequencing (on activated and memory T cell populations) could allow the mapping of all patient’s current and past exposures from one single sample. In time this could reshuffle state-of-the-art diagnostics (e.g., RT-qPCR and serology) by screening the T cell repertoire for pathogen (and disease) specific TCRs.