Inferring T-cells repertoire dynamics for healthy individuals.

The adaptive immune system is a very diverse ecosystem built to respond to pathogens by selecting clones of cells with specific receptors. While clonal expansion in response to a particular antigen has been studied, we still do not know the neutral dynamics that drive the immune system to develop in absence of strong pathogenic threats. We investigate the forces shaping T-cells clonal dynamics in healthy individuals.
Using a model that has shown good consistency with stationary data and longitudinal data from different Repertoire Sequencing (RepSeq) techniques, we use Bayesian Inference to learn parameters underlying the healthy T-cells population dynamics for individuals of various ages and both sexes. Quantifying the experimental noise accurately for each RepSeq technique enables us to robustly infer the dynamics parameters and extract biological information from it. We find that the majority of clones follow a first order non-Markovian dynamic and the inferred turn-over time-scales of the repertoire seem to be strongly correlated with the age of the patients. As an application of our method we developed a classifier that discriminates different individuals based on their immune samples and show the discrimination task is stable over time.