Big Data Science on T Cell Receptor-mediated Immune Regulation.

Abstract

T cell receptors (TCRs) have a highly diverse sequence pattern resulting from the random recombination of gene components in the thymus. This diversity enables TCRs to distinguish between a wide range of self and non-self-antigens, thereby shaping the reactivity of the acquired immune system. Self-responsiveness arising from impaired TCR-based self-discrimination is a crucial trigger for the development of autoimmune diseases. The immunological importance of TCR research is evident, yet traditional experimental and analytical techniques have not fully captured the vast information contained within the TCR repertoire. However, recent advancements in massive parallel sequencing, efficient library preparation pipelines, single-cell experiment platforms, and genome engineering are poised to transform our understanding of TCR diversity, sparking interest in the field. These advancements have made it possible to "read through" the entire TCR repertoire and partially identify their cognate antigens. In parallel, methods for efficiently analyzing large datasets of comprehensive TCR sequences have also progressed. These innovations in experimental and analytical techniques are leading TCR research in new directions, such as using TCR as a real-time biomarker, exploring the link between TCR and T cell differentiation, and investigating TCR genetic regulation. This review will cover recent updates on big data science related to TCR-mediated immune regulation.