A statistical ensemble approach to understanding adaptive immunity: using sequence data to quantify the extraordinary diversity, in both sequence and protective specifictiy, of the T- and B-cells cells that make up an individual human's immune system.*

Individual T cells of the adaptive immune system recognize specific pathogens via a certain protein expressed on the surface of the cell. The gene for this protein undergoes random editing each time a new immune cell is created. As a result, any individual’s immune system is an ensemble of distinct T cell types (in fact about 10^9 in number) created by a stochastic process. I will explain how high-throughput DNA sequencing has made it possible to develop a precise quantitative understanding of this stochastic process. I will then explain some consequences of this understanding: a) that the stochastic gene editing process is nearly universal across the human species, b) that the diversity of the process is so large that the overlap between T cell repertoires of two individuals is small, c) that the one-shot generation probabilities of specific T cell types range over nearly twenty orders of magnitude, and, as a corollary, d) that certain specific T cell types will be found in all individuals, while others have negligible sharing likelihood. I will then discuss how these developments bring into sharp focus the central biophysical question of adaptive immunity, namely: what is the diversity of pathogenic molecules that a single T cell can recognize, and is the diversity of the T cells present in one individual large enough so that the entire space of pathogenic molecules can be recognized? Implications of these developments for cancer immunotherapy will be briefly discussed. References for this talk are:
1. Statistical inference of the generation probability of T-cell receptors from sequence repertoires, with A.Murugan, T.Mora, and A.Walczak, Proc. Natl. Acad. Sci. USA, 109 (2012) 16161-16166.
2. OLGA: fast computation of generation probability of B- and T-cell receptor amino-acid sequences and motifs, with Y.Elhanati, Z.Sethna, T.Mora, and A.M.Walczak, Bioinformatics, 35 (2019) 2974-2981.