Link to bioRxiv paper: http://biorxiv.org/cgi/content/short/2020.09.09.289355v1?rss=1 Authors: Meysman, P., Postovskaya, A., De Neuter, N., Ogunjimi, B., Laukens, K. Abstract: Much is still not understood about the human adaptive immune response to SARS-CoV-2, the causative agent of COVID-19. In this paper, we demonstrate the use of machine learning to classify SARS-CoV-2 epitope specific T-cell clonotypes in T-cell receptor (TCR) sequencing data. We apply these models to public TCR data and show how they can be used to study T-cell longitudinal profiles in COVID-19 patients to characterize how the adaptive immune system reacts to the SARS-CoV-2 virus. Our findings confirm prior knowledge that SARS-CoV-2 reactive T-cell diversity increases over the course of disease progression. However our results show a difference between those T cells that react to epitope unique to SARS-CoV-2, which show a more prominent increase, and those T cells that react to epitopes common to other coronaviruses, which begin at a higher baseline. Copy rights belong to original authors. Visit the link for more info