Computational identification of responding T cell clones from longitudinal repertoire sequencing

Abstract

T cells activate and expand upon interaction with cognate antigen, derived from pathogens or mutated proteins. T cell clones can be identified by their T cell receptor (TCR) which acts as a unique barcode to track their expansion. Longitudinal TCR sequencing can be used to track T cell responses to a large array of stimuli. However, experimental identification of T cell clones of interest is challenging, especially when information about the driving antigen is lacking. Computational identification based on clonal dynamics is an antigen-agnostic alternative. However, it is subject to sequencing noise and biological variability, and relies on the choice of particular timepoints that are compared to find expanding and contracting clones. We review existing methods for identification of responding T cell clones from TCR sequencing, and the strategies that have been used to account for noise. We highlight that most methods look for clones at specific timepoints, and discuss the need for unbiased methods to shed light on new dynamics. We showcase how to address these needs by proposing CloneSearch, a method to identify expanding and contracting T cell clones from longitudinal TCR sequencing which is agnostic to the time of stimulus and can account for the noise these clones are subject to. CloneSearch can recapitulate previously identified responses from published data, and expand the analysis to show identification of previously undetected responses from these same datasets.